Change Management In Sony Pictures Management Essay

Change Management In Sony Pictures Management Essay In 2008 Amy Pascal (Co-Chairman, Sony Pictures Entertainment, Chairman, Motion Picture Group, Sony Pictures Entertainment) and Michael Lynton (Chairman CEO, Sony Pictures Entertainment) engaged The Energy Project as a part of an effort to create a culture in which employees felt energetic and excited about coming to work every day. Beginning with the senior team, our facilitators delivered our curriculum to some 500 managers and leaders. More than 90% said it has helped them bring more energy to work every day. Almost 88% felt that it has made them more focused and productive. We trained more than a dozen Sony internal facilitators to deliver our work, and by April 2010, some version of our curriculum will have been delivered to all 5500 Sony Pictures employees around the world.   In the midst of a severe recession, and a dramatic industry-wide decline in DVD sales, Sony expects to record one of its most profitable years ever in the fiscal year ending March 2009.   TABLE OF CONTENTS INDEX PAGE NO. Introductionà ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦. Literature review Change Why change management Discussion of Change Management Theoriesà ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦. Plan Do Check Act Lewins Freeze Phases Issues to Changeà ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦.. Implementation of Change Managementà ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦. Transformation in Sony pictures Overcoming resistance in employeeà ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦ Sustaining changeà ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦ Conclusionà ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦ Referencesà ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦. Appendixà ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦Ãƒ ¢Ã¢â€š ¬Ã‚ ¦. INTRODUCTION Sony Pictures Entertainment (SPE) was formerly known as Columbia pictures entertainment, headquartered in Culver City, CALIFORNIA. The company was founded in 1987 and it was renamed Sony pictures entertainment in 1991. It is a subsidiary of Sony Corporation of America (SCA), a subsidiary of Tokyo-based Sony Corporation. SPEs global operations encompass motion picture production and distribution; television production and distribution; digital content creation and distribution; worldwide channel investments; home entertainment acquisition and distribution; operation of studio facilities; development of new entertainment products, services and technologies; and distribution of entertainment in more than 140 countries. The companys slogan is Sony like no other. SPE recorded total sales of $7.6 billion for fiscal year ended March 31, 2010. Key people of SONY PICTURES are Howard Stringer (Chairman, President and CEO of  Sony Corporation),Michael Lynton (Chairman CEO, Sony Pictures Ente rtainment),Amy Pascal (Co-Chairman, Sony Pictures Entertainment, Chairman, Motion Picture Group, Sony Pictures Entertainment), Jeff Blake (Vice Chairman, Sony Pictures Entertainment). Sony Pictures Plaza in Culver City, California LITERATURE REVIEW CHANGE: Change is all around us in different types and categories; it can be brought by us or can come in any way to us. Change is the way through which future enters your life. Future is coming fast; we cannot predict but only react when we face it. Steven Kerr Why change management? Changes can come yourself or it can come in ways that give you little choice about its what, when, and how. Fighting against change slows it down or diverts it, but it wont stop it however. If you wish to succeed in this rapidly changing new world you must learn to look on change as a friend one who presents you with an opportunity for growth and improvement. The rate of change in  todays world  is constantly increasing. Everything that exists is getting old, wearing out and should be replaced. Revolutionary technologies, consolidation, well-funded new competition, unpredictable customers, and a quickening in the pace of change hurled unfamiliar conditions at management. Realities of Todays  World   The magnitude of todays environmental, competitive, and global market change is unprecedented. Its a very interesting and exciting world, but its also volatile and chaotic: Volatility  describes the economys rate of change: extremely fast, with explosive upsurges and sudden downturns. Chaos  describes the direction of the economys changes: were not sure exactly where were headed, but we are swinging between the various alternatives at a very high speed. To cope with an unpredictable world you must build an enormous amount of flexibility into your organization. While you cannot predict the future, you can get a handle on  trends, which is a way to take advantage of change and convert risks into  opportunities. DISCUSSION OF CHANGE MANAGEMENT THEORIES Plan Do Check Act   Plan, Do, Check, Act is a cycle of activities designed to drive continuous improvement. Initially implemented in manufacturing, it has broad applicability in business. First developed by Walter Shewhart, it was popularized by Edwards Deming. It originated in the 1920s with the eminent statistics expert Mr. Walter A. Shewhart, who introduced the concept of PLAN, DO and SEE. The late Total Quality Management (TQM) guru and renowned statistician W. Edwards Deming modified the Shewhart cycle as: PLAN, DO, STUDY, and ACT.PDCA  (plan-do-check-act) is a four-step problem-solving process typically used in  business process improvement. It is also called as Shewhart cycle, Deming cycle, PDSA (PLAN DO STUDY ACT),PDCA (PLAN DO CHECK ACT). It reduced error rate during implementation the Plan, Do, Check, Act cycle in manufacturing.This Act is useful for change management. The PDCA cycle should be repeated again and again for continuous improvement. PLAN: Establish the objectives and processes necessary to deliver results in accordance with the expected output. By making the expected output the focus, it differs from other techniques in that the completeness and accuracy of the  specification is also part of the improvement. PROCEDURE- Recognize an opportunity and plan a change. DO: Implement the new processes. Often on a small scale if possible. PROCEDURE- Executes the plan, taking small steps in controlled circumstances. CHECK: Measure the new processes and compare the results against the expected results to ascertain any differences. PROCEDURE- Review the test, analyze the results and identify what youve learned. ACT: Analyse the differences to determine their cause. Each will be part of either one or more of the P-D-C-A steps. Determine where to apply changes that will include improvement. When a pass through these four steps does not result in the need to improve, refine the scope to which PDCA is applied until there is a plan that involves improvement. PROCEDURE- Take action to standardize or improve the process. Benefits of the PDCA cycle: daily routine management-for the individual and/or the team, problem-solving process, project management, continuous development, vendor development, human resources development, new product development, and process trials Lewins Freeze Phases- In the early 20th century, the psychologist Kurt Lewin developed the model known as Lewins Freeze Phases and which still forms the underlying basis of many change management theories models and strategies for managing change. His model suggests that change involves a move from one static state via a state of activity to another static status quo -and all this via a three-stage process of managing change: unfreezing, changing and re-freezing. Unfreezing: Faced with a dilemma or disconfirmation, the individual or group becomes aware of a need to change. Changing: The situation is diagnosed and new models of behaviour are explored and tested. Refreezing: Application of new behaviour is evaluated, and if reinforcing, adopted in figure 1 summarizes the steps and processes involved in planned change through action research. Action research is depicted as a cyclical process of change. Figure Kurt Lewins change model recognizes that people derive a strong sense of identity to from their environment. It also recognizes that they like the safety, comfort and feeling of control within their environment. ISSUE TO CHANGE Our CEO, Tony Schwartz first met with Sony Pictures Entertainment (SPE) co-CEOs, Michael Lynton and Amy Pascal, in the summer of 2007. Pascal and Lynton saw the work of the Energy Project as a way to bring to life their vision of making Sony the most desirable studio to work for and of building a culture of high engagement. Initially, Tony worked with Lynton and Pascal and their team of 17 direct reports. The initial focus was on how they managed their own energy individually, and as an intact team. This senior group found our curriculum sufficiently valuable that they asked to brong it to the top 500 executives, all vice president or above.   A 2007 Towers Perrin survey of nearly 90,000 employees worldwide, for instance, found that only 21% felt fully engaged at work and nearly 40% were disenchanted or disengaged. That negativity has a direct impact on the bottom line. Towers Perrin found that companies with low levels of employee engagement had a 33% annual decline in operating income and an 11% annual decline in earnings growth. Those with high engagement, on the other hand, reported a 19% increase in operating income and 28% growth in earnings per share. Nearly a decade ago, the Energy Project, the company I head, began to address work performance and the problem of employee disengagement. We still believe that enduring organizational change is possible only if individuals alter their attitudes and behaviors first.  Weve come to understand that its not possible to generate lasting cultural change without deeply involving an organizations senior leadership. IMPLEMENTATION OF CHANGE MANAGEMENT Once people understand how their supply of available energy is influenced by the choices they make, they can learn new strategies that increase the fuel in their tanks and boost their productivity.      They include practices such as shutting down your e-mail for a couple of hours during the day, so you can tackle important or complex tasks without distracting interruptions, or taking a daily 3  PM  walk to get an emotional and mental breather. Two fundamental shifts-   We encouraged Sony to make two fundamental shifts in the way it manages employees. We also created a three -day version of a new way of working that included a renewal day that provided participants with specific techniques to improve the quality, quantity and focus of their energy. This day featured individual consults with a nutritionist, exercise physiologist and massage therapist as well as group circuit training, yoga and meditation. Group coaching was offered during the 3 day sessions and then on twice more two and four weeks after the end of the session. The purpose of the coaching was to support the participants in successfully launching and sustaining the rituals they built once they had returned to the challenges of their daily life. Tony continued to work with Pascal and Lynton a senior team on a quarterly basis throughout 2008 to help them model the behaviors they learned and to drive the work down through their own teams. TRANSFORMATION IN SONY PICTURES Sony pictures went through a transformation in order to embrace energy building and renewing rituals at all levels. Out of the 3000 employees of the 6300 employees of Sony have gone through the energy management program. This summer 1700 more will be covered from Europe, Singapore, and Latin America. OVERCOMING RESISTANCE IN EMPLOYEE The reaction of the program has been overwhelmingly positive. 88% of the participants say, it has made them more focused and productive. Some 90% of them reported that as a result of the work, they began bringing higher levels of energy to work every day. 84% say they feel better and are able to manage their jobs demands and are more engaged at work. Sonys leaders believe that these changes have helped boost the companys performance. E.g. in spite of recession also Sony pictures had its most profitable year ever in 2008 and one of its highest revenue years in 2009. SUSTAINING CHANGE For sustaining change of the Sony Pictures, there are some important points which must keep in mind. These are as under: Employee should be highly engaged. Employee should be friendly. High performance culture. As a leader, you have myriad opportunities to set the right context for your employees to replenish their energy. Its all about providing examples for others and creating a safe environment. DOS AND DONTS FOR IMPLEMENTATION OF CULTURAL CHANGE Management is doing things right; Leadership is doing the right things. -Peter Drucker STRATEGIES OF A POSITIVE CHANGE So that Sony pictures can progress. Conclusion CHANGE STARTS AT THE TOP AND BEGINS ON DAY 1. REAL CHANGE HAPPENS AT THE BOTTOM. IN ORDER TO CHANGE OTHERS FIRST YOU SHOULD YOURSELF AS LYNTON AND PASCAL DID. E.Q IS ONE OF THE KEY ELEMENTS TOWARDS POSITIVE BEHAVIOURAL CHANGE WHICH ULTIMATELY LEADS TO THE GOAL OF A SUCCESSFUL CHANGE MANAGEMENT. STRONG CULTURAL VALUES ACTS AS A MAGNET IN THE SUCCESS OF A COMPANY WHICH BINDS AN EMPLOYEE IRRESPECTIVE OF THE EXTERNAL FACTORS LIKE RECESSION OR ECONOMY.

Zora Neale Hurston, Alice Walker, and Paule Marshall :: essays papers

Zora Neale Hurston, Alice Walker, and Paule Marshall Alice Walker, through her essay "In Search of Our Mothers' Gardens", and Paule Marshall, in "Poets In The Kitchen", both write about the African-American women of the past and how these women have had an impact on their writing. Walker and Marshall write about an identity they have found with these women because of their exposure to the African culture. These women were searching for independence and freedom. Walker expresses independence as found in the creative spirit, and Marshall finds it through the spoken word. Walker and Marshall celebrate these women's lives and they see them as inspirations to become black women writers. Zora Neale Hurston's "Sweat" embodies some aspects that are found in Walker's and Marshall's essays. Delia, the main character, has an identity that is found through her hard work and spirituality. She also finds her freedom and independence in her home. It is essential to first analyze Walker's and Marshall's essays through each of the themes of identity, independence, and inspiration, respectively. Then these themes will be drawn out of Hurston's work to show the similarity between each of these writers' works. Walker and Marshall write about an identity that they have found with African-American women of the past. They both refer to great writers such as Zora Neale Hurston or Phillis Wheatley. But more importantly, they connect themselves to their ancestors. The see that their writings can be identified with what the unknown African-American women of the past longed to say but they did not have the freedom to do so. They both admire many literary greats such as Charles Dickens, Virginia Woolf, and Jane Austen, but they appreciate these authors' works more than they can identify with them. Walker's and Marshall's identification is related to the African-American culture that they have been exposed to throughout their lives. Walker states that: Therefore we must pull out of ourselves and look at and identify with our lives the living creativity some of our great-grandmothers were not allowed to know. I stress some of them because it is well-known that the majority of our great-grandmothers knew without even "knowing" it, the reality of their spirituality, even if they didn't recognize it beyond what happened in the singing at church (Walker, 1996: 2318-2319). Walker delves into the subconscious and ever-present spirituality that is found in African-American women and she believes that it is important to identify with this.

Wireless Sensor Networks

1. Introduction The increasing interest in wireless sensor networks can be promptly understood simply by thinking about what they essentially are: a large number of small sensing self-powered nodes which gather information or detect special events and communicate in a wireless fashion, with the end goal of handing their processed data to a base station. Sensing, processing and communication are three key elements whose combination in one tiny device gives rise to a vast number of applications [A1], [A2]. Sensor networks provide endless opportunities, but at the same time pose formidable challenges, uch as the fact that energy is a scarce and usually non-renewable resource. However, recent advances in low power VLSI, embedded computing, communication hardware, and in general, the convergence of computing and communications, are making this emerging technology a reality [A3]. Likewise, advances in nanotechnology and Micro Electro-Mechanical Systems (MEMS) are pushing toward networks of tiny distributed sensors and actuators. 2. Applications of Sensor Networks Possible applications of sensor networks are of interest to the most diverse fields. Environmental monitoring, warfare, child education, surveillance, micro-surgery, and griculture are only a few examples [A4]. Through joint efforts of the University of California at Berkeley and the College of the Atlantic, environmental monitoring is carried out off the coast of Maine on Great Duck Island by means of a network of Berkeley motes equipped with various sensors [B6]. The nodes send their data to a base station which makes them available on the Internet. Since habitat monitoring is rather sensitive to human presence, the deployment of a sensor network provides a noninvasive approach and a remarkable degree of granularity in data acquisition [B7]. The same idea lies behind thePods project at the University of Hawaii at Manoa [B8], where environmental data (air temperature, light, wind, relative humidity and rain fall) are gathered by a network of weather sensors embedded in the communication units deployed in the South-West Rift Zone in Volcanoes National Park on the Big Island of Hawaii. A major concern of the researchers was in this case camouflaging the sensors to make them invisible to curious tourists. In Princeton’s Zebranet Project [B9], a dynamic sensor network has been created by attaching special collars equipped with a low-power GPS system to the necks of zebras to onitor their moves and their behavior. Since the network is designed to operate in an infrastructure-free environment, peer-to-peer swaps of information are used to produce redundant databases so that researchers only have to encounter a few zebras in order to collect the data. Sensor networks can also be used to monitor and study natural phenomena which intrinsically discourage human presence, such as hurricanes and forest fires. Joint efforts between Harvard University, the University of New Hampshire, and the University of North Carolina have recently led to the deployment of a wireless sensor etwork to monitor eruptions at Volcan Tungurahua, an active volcano in central Ecuador. A network of Berkeley motes monitored infrasonic signals during eruptions, and data were transmitted over a 9 km wireless link to a base station at the volcano observatory [B10]. Intel’s Wireless Vineyard [B11] is an example of using ubiquitous computing for agricultural monitoring. In this application, the network is expected not only to collect and interpret data, but also to use such data to make decisions aimed at detecting the presence of parasites and enabling the use of the appropriate kind of insecticide.Data collection relies on data mules, small devices carried by people (or dogs) that communicate with the nodes and collect data. In this project, the attention is shifted from reliable information collection to active decisionmaking based on acquired data. Just as they can be used to monitor nat ure, sensor networks can likewise be used to monitor human behavior. In the Smart Kindergarten project at UCLA [B12], wirelessly-networked, sensor-enhanced toys and other classroom objects supervise the learning process of children and allow unobtrusive monitoring by the teacher. Medical research and healthcare can greatly benefit rom sensor networks: vital sign monitoring and accident recognition are the most natural applications. An important issue is the care of the elderly, especially if they are affected by cognitive decline: a network of sensors and actuators could monitor them and even assist them in their daily routine. Smart appliances could help them organize their lives by reminding them of their meals and medications. Sensors can be used to capture vital signs from patients in real-time and relay the data to handheld computers carried by medical personnel, and wearable sensor nodes can store patient data such as identification, history, and treatments.With these ideas in mind, Harvard University is cooperating with the School of Medicine at Boston University to develop CodeBlue, an infrastructure designed to support wireless medical sensors, PDAs, PCs, and other devices that may be used to monitor and treat patients in various medical scenarios [B13]. On the hardware side, the research team has Martin Haenggi is with the Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556; Fax +1 574 631 4393; [email  protected]@nd. edu. Daniele Puccinelli is also with the Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556. reated Vital Dust, a set of devices based on the MICA21 sensor node platform (one of the most popular members of the Berkeley motes family), which collect heart rate, oxygen saturation, and EKG data and relay them over a medium-range (100 m) wireless network to a PDA [B14]. Interactions between sensor networks and humans are already judged controversial. The US has recently app roved the use of a radio-frequency implantable device (VeriChip) on humans, whose intended application is accessing the medical records of a patient in an emergency. Potential future repercussions of this decision have been discussed in the media.An interesting application to civil engineering is the idea of Smart Buildings: wireless sensor and actuator networks integrated within buildings could allow distributed monitoring and control, improving living conditions and reducing the energy consumption, for instance by controlling temperature and air flow. Military applications are plentiful. An intriguing example is DARPA’s self-healing minefield [B15], a selforganizing sensor network where peer-to-peer communication between anti-tank mines is used to respond to attacks and redistribute the mines in order to heal breaches, complicating the progress of enemy troops.Urban warfare is another application that distributed sensing lends itself to. An ensemble of nodes could be deploy ed in a urban landscape to detect chemical attacks, or track enemy movements. PinPtr is an ad hoc acoustic sensor network for sniper localization developed at Vanderbilt University [B16]. The network detects the muzzle blast and the acoustic shock wave that originate from the sound of gunfire. The arrival times of the acoustic events at different sensor nodes are used to estimate the position of the sniper and send it to the base station with a special data aggregation and routing service.Going back to peaceful applications, efforts are underway at Carnegie Mellon University and Intel for the design of IrisNet (Internet-scale Resource-Intensive Sensor Network Services) [B17], an architecture for a worldwide sensor web based on common computing hardware such as Internet-connected PCs and low-cost sensing hardware such as webcams. The network interface of a PC indeed senses the virtual environment of a LAN or the Internet rather than a physical environment; with an architecture based on the concept of a distributed database [B18], this hardware can be orchestrated into a global sensor system hat responds to queries from users. 3. Characteristic Features of Sensor Networks In ad hoc networks, wireless nodes self-organize into an infrastructureless network with a dynamic topology. Sensor networks (such as the one in Figure 1) share these traits, but also have several distinguishing features. The number of nodes in a typical sensor network is much higher than in a typical ad hoc network, and dense deployments are often desired to ensure coverage and connectivity; for these reasons, sensor network hardware must be cheap. Nodes typically have stringent energy limitations, which make them more failure-prone. They are enerally assumed to be stationary, but their relatively frequent breakdowns and the volatile nature of the wireless channel nonetheless result in a variable network topology. Ideally, sensor network hardware should be power-efficient, small, inexpensive, and reliable in order to maximize network lifetime, add flexibility, facilitate data collection and minimize the need for maintenance. Lifetime Lifetime is extremely critical for most applications, and its primary limiting factor is the energy consumption of the nodes, which need to be self-powering. Although it is often assumed that the transmit power associated with acket transmission accounts for the lion’s share of power consumption, sensing, signal processing and even hardware operation in standby mode consume a consistent amount of power as well [C19], [C20]. In some applications, extra power is needed for macro-scale actuation. Many researchers suggest that energy consumption could be reduced by considering the existing interdependencies between individual layers in the network protocol stack. Routing and channel access protocols, for instance, could greatly benefit from an information exchange with the physical layer. At the physical layer, benefits can be obtained wi th ower radio duty cycles and dynamic modulation scaling (varying the constellation size to minimize energy expenditure THIRD QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE 21 External Infrastructure Gateway Base Station Sensing Nodes Figure 1. A generic sensor network with a two-tiered archi1 tecture. See Section 5 for a hardware overview. [D35]). Using low-power modi for the processor or disabling the radio is generally advantageous, even though periodically turning a subsystem on and off may be more costly than always keeping it on. Techniques aimed at reducing the idle mode leakage current in CMOS-based rocessors are also noteworthy [D36]. Medium Access Control (MAC) solutions have a direct impact on energy consumption, as some of the primary causes of energy waste are found at the MAC layer: collisions, control packet overhead and idle listening. Powersaving forward error control techniques are not easy to implement due to the high amount of computing power that they require a nd the fact that long packets are normally not practical. Energy-efficient routing should avoid the loss of a node due to battery depletion. Many proposed protocols tend to minimize energy consumption on forwarding aths, but if some nodes happen to be located on most forwarding paths (e. g. , close to the base station), their lifetime will be reduced. Flexibility Sensor networks should be scalable, and they should be able to dynamically adapt to changes in node density and topology, like in the case of the self-healing minefields. In surveillance applications, most nodes may remain quiescent as long as nothing interesting happens. However, they must be able to respond to special events that the network intends to study with some degree of granularity. In a self-healing minefield, a number of sensing mines ay sleep as long as none of their peers explodes, but need to quickly become operational in the case of an enemy attack. Response time is also very critical in control applications (sensor/actuator networks) in which the network is to provide a delay-guaranteed service. Untethered systems need to self-configure and adapt to different conditions. Sensor networks should also be robust to changes in their topology, for instance due to the failure of individual nodes. In particular, connectivity and coverage should always be guaranteed. Connectivity is achieved if the base station can be reached from any node.Coverage can be seen as a measure of quality of service in a sensor network [C23], as it defines how well a particular area can be observed by a network and characterizes the probability of detection of geographically constrained phenomena or events. Complete coverage is particularly important for surveillance applications. Maintenance The only desired form of maintenance in a sensor network is the complete or partial update of the program code in the sensor nodes over the wireless channel. All sensor nodes should be updated, and the restrictions on the size of the new code should be the same as in the case of wired programming.Packet loss must be accounted for and should not impede correct reprogramming. The portion of code always running in the node to guarantee reprogramming support should have a small footprint, and updating procedures should only cause a brief interruption of the normal operation of the node [C24]. The functioning of the network as a whole should not be endangered by unavoidable failures of single nodes, which may occur for a number of reasons, from battery depletion to unpredictable external events, and may either be independent or spatially correlated [C25]. Faulttolerance is particularly crucial as ongoing maintenance s rarely an option in sensor network applications. Self-configuring nodes are necessary to allow the deployment process to run smoothly without human interaction, which should in principle be limited to placing nodes into a given geographical area. It is not desirable to have humans configure node s for habitat monitoring and destructively interfere with wildlife in the process, or configure nodes for urban warfare monitoring in a hostile environment. The nodes should be able to assess the quality of the network deployment and indicate any problems that may arise, as well as adjust to hanging environmental conditions by automatic reconfiguration. Location awareness is important for selfconfiguration and has definite advantages in terms of routing [C26] and security. Time synchronization [C27] is advantageous in promoting cooperation among nodes, such as data fusion, channel access, coordination of sleep modi, or security-related interaction. Data Collection Data collection is related to network connectivity and coverage. An interesting solution is the use of ubiquitous mobile agents that randomly move around to gather data bridging sensor nodes and access points, whimsically named dataMULEs (Mobile Ubiquitous LAN Extensions) in [C28]. The predictable mobility of the data sink can be used to save power [C29], as nodes can learn its schedule. A similar concept has been implemented in Intel’s Wireless Vineyard. It is often the case that all data are relayed to a base station, but this form of centralized data collection may shorten network lifetime. Relaying data to a data sink causes non-uniform power consumption patterns that may overburden forwarding nodes [C21]. This is particularly harsh on nodes providing end links to base stations, which may end up relaying traffic coming from all ther nodes, thus forming a critical bottleneck for network throughput [A4], [C22], as shown in Figure 2. An interesting technique is clustering [C30]: nodes team up to form clusters and transmit their information to their cluster heads, which fuse the data and forward it to a 22 IEEE CIRCUITS AND SYSTEMS MAGAZINE THIRD QUARTER 2005 sink. Fewer packets are transmitted, and a uniform energy consumption pattern may be achieved by periodic re-clustering. Data redundancy is minimized, as the aggregation process fuses strongly correlated measurements. Many applications require that queries be sent to sensing nodes.This is true, for example, whenever the goal is gathering data regarding a particular area where various sensors have been deployed. This is the rationale behind looking at a sensor network as a database [C31]. A sensor network should be able to protect itself and its data from external attacks, but the severe limitations of lower-end sensor node hardware make security a true challenge. Typical encryption schemes, for instance, require large amounts of memory that are unavailable in sensor nodes. Data confidentiality should be preserved by encrypting data with a secret key shared with the intended receiver. Data integrity should be ensured to revent unauthorized data alteration. An authenticated broadcast must allow the verification of the legitimacy of data and their sender. In a number of commercial applications, a serious disservice to the user of a sensor network is compromising data availability (denial of service), which can be achieved by sleep-deprivation torture [C33]: batteries may be drained by continuous service requests or demands for legitimate but intensive tasks [C34], preventing the node from entering sleep modi. 4. Hardware Design Issues In a generic sensor node (Figure 3), we can identify a power module, a communication block, a processing unit ith internal and/or external memory, and a module for sensing and actuation. Power Using stored energy or harvesting energy from the outside world are the two options for the power module. Energy storage may be achieved with the use of batteries or alternative devices such as fuel cells or miniaturized heat engines, whereas energy-scavenging opportunities [D37] are provided by solar power, vibrations, acoustic noise, and piezoelectric effects [D38]. The vast majority of the existing commercial and research platforms relies on batteries, which dominate the no de size. Primary (nonrechargeable) batteries are often chosen, predominantlyAA, AAA and coin-type. Alkaline batteries offer a high energy density at a cheap price, offset by a non-flat discharge, a large physical size with respect to a typical sensor node, and a shelf life of only 5 years. Voltage regulation could in principle be employed, but its high inefficiency and large quiescent current consumption call for the use of components that can deal with large variations in the supply voltage [A5]. Lithium cells are very compact and boast a flat discharge curve. Secondary (rechargeable) batteries are typically not desirable, as they offer a lower energy density and a higher cost, not to mention the fact that in most pplications recharging is simply not practical. Fuel cells [D39] are rechargeable electrochemical energy- conversion devices where electricity and heat are produced as long as hydrogen is supplied to react with oxygen. Pollution is minimal, as water is the main byproduct of the reaction. The potential of fuel cells for energy storage and power delivery is much higher than the one of traditional battery technologies, but the fact that they require hydrogen complicates their application. Using renewable energy and scavenging techniques is an interesting alternative. Communication Most sensor networks use radio communication, even if lternative solutions are offered by laser and infrared. Nearly all radio-based platforms use COTS (Commercial Off-The-Shelf) components. Popular choices include the TR1000 from RFM (used in the MICA motes) and the CC1000 from Chipcon (chosen for the MICA2 platform). More recent solutions use industry standards like IEEE 802. 15. 4 (MICAz and Telos motes with CC2420 from Chipcon) or pseudo-standards like Bluetooth. Typically, the transmit power ranges between ? 25 dBm and 10 dBm, while the receiver sensitivity can be as good as ? 110 dBm. THIRD QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE 23 Base Station Critical Nodes F igure 2.A uniform energy consumption pattern should avoid the depletion of the resources of nodes located in the vicinities of the base station. Communication Hardware Power Sensors (? Actuators) ADC Memory Processor Figure 3. Anatomy of a generic sensor node. Spread spectrum techniques increase the channel reliability and the noise tolerance by spreading the signal over a wide range of frequencies. Frequency hopping (FH) is a spread spectrum technique used by Bluetooth: the carrier frequency changes 1600 times per second on the basis of a pseudo-random algorithm. However, channel synchronization, hopping sequence search, and the high data rate ncrease power consumption; this is one of the strongest caveats when using Bluetooth in sensor network nodes. In Direct Sequence Spread Spectrum (DSSS), communication is carried out on a single carrier frequency. The signal is multiplied by a higher rate pseudo-random sequence and thus spread over a wide frequency range (typical DSSS radios h ave spreading factors between 15 and 100). Ultra Wide Band (UWB) is of great interest for sensor networks since it meets some of their main requirements. UWB is a particular carrier-free spread spectrum technique where the RF signal is spread over a spectrum as large as several GHz.This implies that UWB signals look like noise to conventional radios. Such signals are produced using baseband pulses (for instance, Gaussian monopulses) whose length ranges from 100 ps to 1 ns, and baseband transmission is generally carried out by means of pulse position modulation (PPM). Modulation and demodulation are indeed extremely cheap. UWB provides built-in ranging capabilities (a wideband signal allows a good time resolution and therefore a good location accuracy) [D40], allows a very low power consumption, and performs well in the presence of multipath fading. Radios with relatively low bit-rates (up to 100 kbps) re advantageous in terms of power consumption. In most sensor networks, high data rates are not needed, even though they allow shorter transmission times thus permitting lower duty cycles and alleviating channel access contention. It is also desirable for a radio to quickly switch from a sleep mode to an operational mode. Optical transceivers such as lasers offer a strong power advantage, mainly due to their high directionality and the fact that only baseband processing is required. Also, security is intrinsically guaranteed (intercepted signals are altered). However, the need for a line of sight and recise localization makes this option impractical for most applications. Processing and Computing Although low-power FPGAs might become a viable option in the near future [D41], microcontrollers (MCUs) are now the primary choice for processing in sensor nodes. The key metric in the selection of an MCU is power consumption. Sleep modi deserve special attention, as in many applications low duty cycles are essential for lifetime extension. Just as in the case of the rad io module, a fast wake-up time is important. Most CPUs used in lower-end sensor nodes have clock speeds of a few MHz. The memory requirements depend on the pplication and the network topology: data storage is not critical if data are often relayed to a base station. Berkeley motes, UCLA’s Medusa MK-2 and ETHZ’s BTnodes use low-cost Atmel AVR 8-bit RISC microcontrollers which consume about 1500 pJ/instruction. More sophisticated platforms, such as the Intel iMote and Rockwell WINS nodes, use Intel StrongArm/XScale 32-bit processors. Sensing The high sampling rates of modern digital sensors are usually not needed in sensor networks. The power efficiency of sensors and their turn-on and turn-off time are much more important. Additional issues are the physical ize of the sensing hardware, fabrication, and assembly compatibility with other components of the system. Packaging requirements come into play, for instance, with chemical sensors which require contact with the envi ronment [D42]. Using a microcontroller with an onchip analog comparator is another energy-saving technique which allows the node to avoid sampling values falling outside a certain range [D43]. The ADC which complements analog sensors is particularly critical, as its resolution has a direct impact on energy consumption. Fortunately, typical sensor network applications do not have stringent resolution requirements.Micromachining techniques have allowed the miniaturization of many types of sensors. Performance does decrease with sensor size, but for many sensor network applications size matters much more than accuracy. Standard integrated circuits may also be used as temperature sensors (e. g. , using the temperaturedependence of subthreshold MOSFETs and pn junctions) or light intensity transducers (e. g. , using photodiodes or phototransistors) [D44]. Nanosensors can offer promising solutions for biological and chemical sensors while concurrently meeting the most ambitious miniaturiza tion needs. 5. Existing Hardware PlatformsBerkeley motes, made commercially available by Crossbow, are by all means the best known sensor node hardware implementation, used by more than 100 research organizations. They consist of an embedded microcontroller, low-power radio, and a small memory, and they are powered by two AA batteries. MICA and MICA2 are the most successful families of Berkeley motes. The MICA2 platform, whose layout is shown in Figure 4, is equipped with an Atmel ATmega128L and has a CC1000 transceiver. A 51-pin expansion connector is available to interface sensors (commercial sensor boards designed for this specific platform are available).Since the MCU is to handle 24 IEEE CIRCUITS AND SYSTEMS MAGAZINE THIRD QUARTER 2005 medium access and baseband processing, a fine-grained event-driven real-time operating system (TinyOS) has been implemented to specifically address the concurrency and resource management needs of sensor nodes. For applications that require a bet ter form factor, the circular MICA2Dot can be used: it has most of the resources of MICA2, but is only 2. 5 cm in diameter. Berkeley motes up to the MICA2 generation cannot interface with other wireless- enabled devices [E47]. However, the newer generations MICAz and Telos support IEEE 802. 15. , which is part of the 802. 15 Wireless Personal Area Network (WPAN) standard being developed by IEEE. At this point, these devices represent a very good solution for generic sensing nodes, even though their unit cost is still relatively high (about $100–$200). The proliferation of different lowerend hardware platforms within the Berkeley mote family has recently led to the development of a new version of TinyOS which introduces a flexible hardware abstraction architecture to simplify multi-platform support [E48]. Tables 1 and 2 show an overview of the radio transceivers and the microcontrollers most commonly used in xisting hardware platforms; an overview of the key features of the pl atforms is provided in Table 3. Intel has designed its own iMote [E49] to implement various improvements over available mote designs, such as increased CPU processing power, increased main memory size for on-board computing and improved radio reliability. In the iMote, a powerful ARM7TDMI core is complemented by a large main memory and non-volatile storage area; on the radio side, Bluetooth has been chosen. Various platforms have been developed for the use of Berkeley motes in mobile sensor networks to enable investigations into controlled mobility, which facilitates eployment and network repair and provides possibilities for the implementation of energy-harvesting. UCLA’s RoboMote [E50], Notre Dame’s MicaBot [E51] and UC Berkeley’s CotsBots [E52] are examples of efforts in this direction. UCLA’s Medusa MK-2 sensor nodes [E53], developed for the Smart Kindergarten project, expand Berkeley motes with a second microcontroller. An on-board power management a nd tracking unit monitors power consumption within the different subsystems and selectively powers down unused parts of the node. UCLA has also developed iBadge [E54], a wearable sensor node with sufficient computational power to process the sensed data.Built around an ATMega128L and a DSP, it features a Localization Unit designed to estimate the position of iBadge in a room based on the presence of special nodes of known location attached to the ceilings. In the context of the EYES project (a joint effort among several European institutions) custom nodes [E55], [C24] have been developed to test and demonstrate energy-efficient networking algorithms. On the software side, a proprietary operating system, PEEROS (Preemptive EYES Real Time Operating System), has been implemented. The Smart-Its project has investigated the possibility of embedding computational power into objects, leading o the creation of three hardware platforms: DIY Smartits, Particle Computers and BTnodes. The DIY S mart-its [E56] have been developed in the UK at Lancaster University; their modular design is based on a core board that provides processing and communication and can be extended with add-on boards. A typical setup of Smart-its consists of one or more sensing nodes that broadcast their data to a base station which consists of a standard core board connected to the serial port of a PC. Simplicity and extensibility are the key features of this platform, which has been developed for the creation of Smart Objects.An interesting application is the Weight Table: four load cells placed underneath a coffee table form a Wheatstone bridge and are connected to a DIY node that observes load changes, determines event types like placement and removal of objects or a person moving a finger across the surface, and also retrieves the position of an object by correlating the values of the individual load cells after the event type (removed or placed) has been recognized [E57]. Particle Computers have been developed at the University of Karlsruhe, Germany. Similarly to the DIY platform, the Particle Smart-its are based on a core board quipped with a Microchip PIC; they are optimized for energy efficiency, scalable communication and small scale (17 mm ? 30 mm). Particles communicate in an ad hoc fashion: as two Particles come close to one another, THIRD QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE 25 Oscillator 7. 3728-MHz DS2401P Silicon Serial No. Antenna Connector Connector LEDs Battery Connection 32. 768-kHz Oscillator 14. 7456-MHz Oscillator ATMEL ATMega 128L CPU CC1000 Transceiver ATMEL AT45DB041 Data Flash Figure 4. Layout of the MICA2 platform. they are able to talk. Additionally, if Particles come near a gateway device, they can be connected to Internet-enabled evices and access services and information on the Internet as well as provide information [E58]. The BTnode hardware from ETHZ [E47] is based on an Atmel ATmega128L microcontroller and a Bluetooth module. Altho ugh advertised as a low-power technology, Bluetooth has a relatively high power consumption, as discussed before. It also has long connection setup times and a lower degree of freedom with respect to possible network topologies. On the other hand, it ensures interoperability between different devices, enables application development through a standardized interface, and offers a significantly higher bandwidth (about 1 Mbps) ompared to many low-power radios (about 50 Kbps). Moreover, Bluetooth support means that COTS hardware can be used to create a gateway between a sensor network and an external network (e. g. , the Internet), as opposed to more costly proprietary solutions [E59]. MIT is working on the ? AMPS (? -Adaptive Multidomain Power-aware Sensors) project, which explores energy-efficiency constraints and key issues such as selfconfiguration, reconfigurability, and flexibility. A first prototype has been designed with COTS components: three stackable boards (processing, radio and power) and an ptional extension module. The energy dissipation of this microsensor node is reduced through a variety of poweraware design techniques [D45] including fine-grain shutdown of inactive components, dynamic voltage and frequency scaling of the processor core, and adjustable radio transmission power based on the required range. Dynamic voltage scaling is a technique used for active power management where the supply voltage and clock frequency of the processor are regulated depending on the computational load, which can vary significantly based on the operational mode [D36], [C20]. The main oal of second generation ? AMPS is clearly stated in [D46] as breaking the 100 ? W average power barrier. Another interesting MIT project is the Pushpin computing system [E60], whose goal is the modelling, testing, and deployment of distributed peer-to-peer sensor networks consisting of many identical nodes. The pushpins are 18 mm ? 18 mm modular devices with a power substrate, an in frared communication module, a processing module (Cygnal C8051F016) and an expansion module (e. g. , for sensors); they are powered by direct contact between the power substrate and layered conductive sheets. 26 MCU Max.Freq. [MHz] Memory Data Size [bits] ADC [bits] Architecture AT90LS8535 (Atmel) 4 8 kB Flash, 512B EEPROM, 512B SRAM 8 10 AVR ATMega128L (Atmel) 8 128 kB Flash, 4 kB EEPROM, 4 kB SRAM 8 10 AVR AT91FR4081 (Atmel) 33 136 kB On-Chip SRAM, 8 Mb Flash 32 — Based on ARM core (ARM7TDMI) MSP430F149 (TI) 8 60 kB + 256B Flash, 2 kB RAM 16 12 Von Neumann C8051F016 (Cygnal) 25 2304B RAM, 32 kB Flash 8 10 Harvard 8051 PIC18F6720 (Microchip) 25 128 kB Flash, 3840B SRAM, 1 kB EEPROM 8 10 Harvard PIC18F252 (Microchip) 40 32 K Flash, 1536B RAM, 256B EEPROM 8 10 Harvard StrongARM SA-1110 (Intel) 133 — 32 — ARM v. 4PXA255 (Intel) 400 32 kB Instruction Cache, 32 kB Data 32 — ARM v. 5TE Cache, 2 kB Mini Data Cache Table 2. Microcontrollers used in sensor node p latforms. Radio (Manufacturer) Band [MHz] Max. Data Rate [kbps] Sensit. [dBm] Notes TR1000 (RFM) 916. 5 115. 2 ? 106 OOK/ASK TR1001 (RFM) 868. 35 115. 2 ? 106 OOK/ASK CC1000 (Chipcon) 300–1,000 76. 8 ? 110 FSK, ? 20 to 10 dBm CC2420 (Chipcon) 2,400 250 ? 94 OQPSK, ? 24 to 0 dBm, IEEE 802. 15. 4, DSSS BiM2 (Radiometrix) 433. 92 64 ? 93 9XStream (MaxStream) 902–928 20 ? 114 FHSS Table 1. Radios used in sensor node platforms. IEEE CIRCUITS AND SYSTEMS MAGAZINE THIRD QUARTER 2005MIT has also built Tribble (Tactile reactive interface built by linked elements), a spherical robot wrapped by a wired skinlike sensor network designed to emulate the functionalities of biological skin [E61]. Tribble’s surface is divided into 32 patches with a Pushpin processing module and an array of sensors and actuators. At Lancaster University, surfaces provide power and network connectivity in the Pin&Play project. Network nodes come in different form factors, but all share the Pin&Play connector, a custom component that allows physical connection and networking through conductive sheets which re embedded in surfaces such as a wall or a bulletin board [E62]. Pin&Play falls in between wired and wireless technologies as it provides network access and power across 2D surfaces. Wall-mounted objects are especially suited to be augmented to become Pin&Play objects. In a demonstration, a wall switch was augmented and freely placed anywhere on a wall with a Pin&Play surface as wallpaper. For applications which do not call for the minimization of power consumption, high-end nodes are available. Rockwellis WINS nodes and Sensoria’s WINS 3. 0 Wireless Sensing Platform are equipped with more powerful rocessors and radio systems. The embedded PC modules based on widely supported standards PC/104 and PC/104-plus feature Pentium processors; moreover, PC/104 peripherals include digital I/O devices, sensors and actuators, and PC-104 products support almost all PC software. PFU Systems’ Plug-N-Run products, which feature Pentium processors, also belong to this category. They offer the capabilities of PCs and the size of a sensor node, but lack built-in communication hardware. COTS components or lower-end nodes may be used in this sense [C32]. Research is underway toward the creation of sensor nodes that are more capable than the motes, yet maller and more power-efficient than higher-end nodes. Simple yet effective gateway devices are the MIB programming boards from Crossbow, which bridge networks of Berkeley motes with a PC (to which they interface using the serial port or Ethernet). In the case of Telos motes, any generic node (i. e. , any Telos mote) can act as a gateway, as it may be connected to the USB port of a PC and bridge it to the network. Of course, more powerful gateway devices are also available. Crossbow’s Stargate is a powerful embedded computing platform (running Linux) with enhanced communication and sensor signal process ing capabilities based n Intel PXA255, the same X-Scale processor that forms the core of Sensoria WINS 3. 0 nodes. Stargate has a connector for Berkeley motes, may be bridged to a PC via Ethernet or 802. 11, and includes built-in Bluetooth support. 6. Closing Remarks Sensor networks offer countless challenges, but their versatility and their broad range of applications are eliciting more and more interest from the research community as well as from industry. Sensor networks have the potential of triggering the next revolution in information technology. The challenges in terms of circuits and systems re numerous: the development of low-power communication hardware, low-power microcontrollers, MEMSbased sensors and actuators, efficient AD conversion, and energy-scavenging devices is necessary to enhance the potential and the performance of sensor networks. System integration is another major challenge that sensor networks offer to the circuits and systems research community. We believ e that CAS can and should have a significant impact in this emerging, exciting area. 27 Platform CPU Comm. External Memory Power Supply WesC (UCB) AT90LS8535 TR1000 32 kB Flash Lithium Battery MICA (UCB, Xbow) ATMega128L TR1000 512 kB Flash AAMICA2 (UCB, Xbow) ATMega128L CC1000 512 kB Flash AA MICA2Dot (UCB, Xbow) ATMega128L CC1000 512 kB Flash Lithium Battery MICAz (UCB, Xbow) ATMega128L CC2420 512 kB Flash AA Telos (Moteiv) MSP430F149 CC2420 512 kB Flash AA iMote (Intel) ARM7TDMI Core Bluetooth 64 kB SRAM, 512 kB Flash AA Medusa MK-2 (UCLA) ATMega103L TR1000 4 Mb Flash Rechargeable Lithium Ion AT91FR4081 iBadge (UCLA) ATMega128L Bluetooth, TR1000 4 Mb Flash Rechargeable Lithium Ion DIY (Lancaster University) PIC18F252 BiM2 64 Kb FRAM AAA, Lithium, Rechargeable Particle (TH) PIC18F6720 RFM TR1001 32 kB EEPROM AAA or Lithium Coin Battery or RechargeableBT Nodes (ETHZ) ATMega128L Bluetooth, CC1000 244 kB SRAM AA ZebraNet (Princeton) MSP430F149 9XStream 4 Mb Flash Lithium Ion Pushpin (MIT) C8051F016 Infrared — Power Substrate WINS 3. 0 (Sensoria) PXA255 802. 11b 64 MB SDRAM, 32 MB + 1 GB Flash Batteries Table 3. Hardware features of various platforms. THIRD QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE Acknowledgments The support of NSF (grants ECS 03-29766 and CAREER CNS 04-47869) is gratefully acknowledged. References General References [A1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, â€Å"A survey on sensor networks,† in IEEE Communications Magazine, pp. 02–114, Aug. 2002. [A2] L. B. Ruiz, L. H. A. Correia, L. F. M. Vieira, D. F. Macedo, E. F. Nakamura, C. M. S. Figueiredo, M. A. M. Vieira, E. H. B. Maia, D. Camara, A. A. F. Loureiro, J. M. S. Nogueira, D. C. da Silva Jr. , and A. O. Fernandes, â€Å"Architectures for wireless sensor networks (In Portuguese),† in Proceedings of the 22nd Brazilian Symposium on Computer Networks (SBRC’04), Gramado, Brazil, pp. 167–218, May 2004. Tutorial. ISBN: 85-8 8442-82-5. [A3] C. Y. Chong and S. P. Kumar, â€Å"Sensor networks: Evolution, opportunities, and challenges,† in IEEE Proceedings, pp. 1247–1254, Aug. 003. [A4] M. Haenggi, â€Å"Opportunities and Challenges in Wireless Sensor Networks,† in Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, M. Ilyas and I. Mahgoub, eds. , Boca Raton, FL, pp. 1. 1–1. 14, CRC Press, 2004. [A5] J. Hill, System Architecture for Wireless Sensor Networks. Ph. D. thesis, University of California at Berkeley, Spring 2003. Applications [B6] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson, â€Å"Wireless sensor networks for habitat monitoring,† in First ACM Workshop on Wireless Sensor Networks and Applications, Atlanta, GA, Sept. 002. [B7] A. Cerpa, J. Elson, D. Estrin, L. Girod, M. Hamilton, and J. Zhao, â€Å"Habitat monitoring: Application driver for wireless communications technology,† in ACM SIGCOMM Workshop on Data Comm unications in Latin America and the Caribbean, San Jose, Costa Rica, Apr. 2001. [B8] E. Biagioni and K. Bridges, â€Å"The application of remote sensor technology to assist the recovery of rare and endangered species,† International Journal of High Performance Computing Applications, vol. 16, pp. 315–324, Aug. 2002. [B9] P. Juang, H. Oki, Y. Wang, M. Martonosi, L. Peh, and D.Rubenstein, â€Å"Energy-efficient computing for wildlife tracking: Design tradeoffs and early experiences with ZebraNet,† in Proceedings of the 10th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-X), San Jose, CA, Oct. 2002. [B10] G. Werner-Allen, J. Johnson, M. Ruiz, J. Lees, and M. Welsh, â€Å"Monitoring volcanic eruptions with a wireless sensor network,† in Proceedings of the Second European Workshop on Wireless Sensor Networks (EWSN’05), Jan. 2005. [B11] J. Burrell, T. Brooke, and R. Beckwith, â€Å"Vineyard comp uting: Sensor networks in agricultural production,† IEEE Pervasive Computing, vol. , no. 1, pp. 38–45, 2004. [B12] M. Srivastava, R. Muntz, and M. Potkonjak, â€Å"Smart kindergarten: Sensor- based wireless networks for smart developmental problem-solving enviroments,† in Proceedings of the 7th Annual International Conference on Mobile Computing and Networking (MobiCom’01), Rome, Italy, pp. 132–138, 2001. [B13] T. Fulford-Jones, D. Malan, M. Welsh, and S. Moulton, â€Å"CodeBlue: An ad hoc sensor network infrastructure for emergency medical care,† in International Workshop on Wearable and Implantable Body Sensor Networks, London, UK, 2004. [B14] D. Myung, B. Duncan, D. Malan, M. Welsh, M.Gaynor, and S. Moulton, â€Å"Vital dust: Wireless sensors and a sensor network for realtime patient monitoring,† in 8th Annual New England Regional Trauma Conference, Burlington, MA, 2002. [B15] â€Å"Self-healing Mines† http://www. darpa. mil/ ato/programs/SHM/. [B16] M. Maroti, G. Simon, A. Ledeczi, and J. Sztipanovits, â€Å"Shooter localization in urban terrain,† IEEE Computer, vol. 37, pp. 60–61, Aug. 2004. [B17] P. Gibbons, B. Karp, Y. Ke, S. Nath, and S. Seshan, â€Å"IrisNet: An architecture for a worldwide sensor web,† IEEE Pervasive Computing, vol. 2, no. 4, pp. 22–33, 2003. [B18] P. Gibbons, B. Karp, Y. Ke, S.Nath, and S. Seshan, â€Å"IrisNet: An Architecture for Enabling Sensor-Enriched Internet Service,† Tech. Rep. IRP-TR-03-04, Intel Research, Pittsburgh, PA, June 2003. Characteristic Features Lifetime [C19] A. Goldsmith and S. Wicker, â€Å"Design challenges for energy-constrained ad hoc wireless networks,† IEEE Wireless Communications Magazine, vol. 9, pp. 8–27, Aug. 2002. [C20] L. Yuan and G. Qu, â€Å"Energy-efficient Design of Distributed Sensor Networks,† in Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, M. Ilyas and I. M ahgoub, eds. , Boca Raton, FL, pp. 38. 1–38. 19, CRC Press, 2004. [C21] M.Haenggi, â€Å"Twelve Reasons not to Route over Many Short Hops,† in IEEE Vehicular Technology Conference (VTC’04 Fall), Los Angeles, CA, Sept. 2004. [C22] M. Haenggi, â€Å"Energy-Balancing Strategies for Wireless Sensor Networks,† in IEEE International Symposium on Circuits and Systems (ISCAS’03), Bangkok, Thailand, May 2003. Coverage [C23] S. Meguerdichian, F. Koushanfar, M. Potkonjak, and M. Srivastava, â€Å"Coverage problems in wireless ad-hoc sensor networks,† in Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’01), vol. 3, Anchorage, AK, pp. 1380–1387, Apr. 001. Maintenance [C24] N. Reijers and K. Loangendoen, â€Å"Efficient code distribution in wireless sensor networks,† in Second ACM International Workshop on Wireless Sensor Networks and Applications, San Diego, CA, Sept. 2003. [C 25] D. Ganesan, R. Govindan, S. Shenker, and D. Estrin, â€Å"Highly resilient, energy efficient multipath routing in wireless sensor networks,† in Proceedings of the 2nd ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’01), Long Beach, CA, pp. 251–254, 2001. Localization and Synchronization [C26] M. Mauve, H. Hartenstein, H. Fuessler, J. Widmer, and W.Effelsberg, â€Å"Positionsbasiertes Routing fuer die Kommunikation zwischen Fahrzeugen,† it—Information Technology (formerly it + ti)—Methoden und innovative Anwendungen der Informatik und Informationstechnik, vol. 44, pp. 278–286, Oct. 2002. [C27] F. Sivrikaya and B. Yener, â€Å"Time synchronization in sensor networks: A survey,† IEEE Network, vol. 18, pp. 45–50, July–Aug. 2004. Data Collection, Routing, and Architectures [C28] R. C. Shah, S. Roy, S. Jain, and W. Brunette, â€Å"Data MULEs: Modeling and analysis of a three-tier arch itecture for sparse sensor networks,† in Ad Hoc Networks Journal, vol. 1, pp. 215–233, Elsevier, Sept. 2003. [C29] A.Chakrabarti, A. Sabharwal, and B. Aazhang, â€Å"Using predictable observer mobility for power efficient design of sensor networks,† in Information Processing in Sensor Networks (IPSN’03), Palo Alto, CA, Apr. 2003. [C30] O. Younis and S. Fahmy, â€Å"HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad-hoc Sensor Networks,† in IEEE Transactions on Mobile Computing, vol. 3, pp. 366–379, 2004. [C31] R. Govindan, J. Hellerstein, W. Hong, S. Madden, M. Franklin, and S. Shenker, â€Å"The Sensor Network as a Database,† Tech. Rep. 02–771, University of Southern California, 2002. ftp://ftp. usc. edu/pub/csinfo/ tech-reports/papers/02-771. df. [C32] M. Yarvis and W. Ye, â€Å"Tiered Architectures in Sensor Networks,† in Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, M. Ilyas and I. Mahgoub, eds. , Boca Raton, FL, pp. 13. 1–13. 22, CRC Press, 2004. Security [C33] F. Stajano and R. Anderson, â€Å"The resurrecting duckling: Security issues for ad-hoc wireless networks,† in 7th International Workshop on Security Protocols, Cambridge, UK, Apr. 1999. [C34] T. Martin, M. Hsiao, D. Ha, and J. Krishnaswami, â€Å"Denial-of-service attacks on battery-powered mobile computers,† in Proceedings of the 2nd IEEE Pervasive Computing Conference, Orlando, FL, pp. 09–318, Mar. 2004. Hardware [D35] C. Schurgers, O. Aberthorne, and M. Srivastava, â€Å"Modulation scaling for energy aware communication systems,† in Proceedings of the 2001 International Symposium on Low Power Electronics and Design, 28 IEEE CIRCUITS AND SYSTEMS MAGAZINE THIRD QUARTER 2005 Huntington Beach, CA, pp. 96–99, Aug. 2001. [D36] A. P. Chandrakasan, R. Min, M. Bhardwaj, S. Cho, and A. Wang, â€Å"Power aware wireless microsensor systems,† in 28th European Solid- State Circuits Conference (ESSCIRC’02), Florence, Italy, 2002. [D37] S. Roundy, P. Wright, and J. Rabaey, â€Å"A study of low level vibrations as a power source for ireless sensor nodes,† Computer Communications, vol. 26, pp. 1131–1144, July 2003. [D38] J. Kymissis, C. Kendall, J. Paradiso, and N. Gershenfeld, â€Å"Parasitic power harvesting in shoes,† in Proceedings of the 2nd IEEE International Symposium on Wearable Computers (ISWC’04), Pittsburgh, PA, Oct. 1998. [D39] A. J. Appleby, Fuel Cell Handbook, New York, NY: Van Reinhold Co. , 1989. [D40] W. C. Chung and D. S. Ha, â€Å"An Accurate Ultra WideBand (UWB) Ranging for precision asset location,† in International Conference on UWB Systems and Technologies, Reston, VA, Nov. 2002. [D41] M. Vieira, D. da Silva Jr. C. C. Jr. , and J. da Mata, â€Å"Survey on wireless sensor network devices,† in Proceedings of the 9th IEEE International Conference on Emerging Techno logies and Factory Automation (ETFA’03), Lisbon, Portugal, Sept. 2003. [D42] B. A. Warneke and K. S. J. Pister, â€Å"MEMS for distributed wireless sensor networks,† in Proceedings of the 9th International Conference on Electronics, Circuits and Systems (ICECS’02), vol. 1, Dubrovnik, Croatia, pp. 291–294, 2002. [D43] Z. Karakehayov, â€Å"Low-Power Design for Smart Dust Networks,† in Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, M.Ilyas and I. Mahgoub, eds. , Boca Raton, FL, pp. 37. 1–37. 12, CRC Press, 2004. [D44] B. Warneke, â€Å"Miniaturizing Sensor Networks with MEMS,† in Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, M. Ilyas and I. Mahgoub, eds. , Boca Raton, FL, pp. 5. 1–5. 19, CRC Press, 2004. [D45] R. Min, M. Bhardwaj, S. Cho, A. Sinha, E. Shih, A. Wang, and A. P. Chandrakasan, â€Å"An Architecture for a Power-Aware Distributed Microsensor Node,† in IEEE Wor kshop on Signal Processing Systems (SiPS’00), Lafayette, LA, Oct. 2000. [D46] D. D. Wentzloff, B. H.Calhoun, R. Min, A. Wang, N. Ickes, and A. P. Chandrakasan, â€Å"Design considerations for next generation wireless power-aware microsensor nodes,† in Proceedings of the 17th International Conference on VLSI Design, Mumbai, India, pp. 361–367, 2004. Existing Platforms [E47] J. Beutel, O. Kasten, M. Ringwald, F. Siegemund, and L. Thiele, â€Å"Poster abstract: Btnodes—a distributed platform for sensor nodes,† in Proceedings of the First International Conference on Embedded Networked Sensor Systems (SenSys-03), Los Angeles, CA, Nov. 2003. [E48] V. Handziski, J. Polastre, J. H. Hauer, C. Sharp, A. Wolisz, and D. Culler, â€Å"Flexible hardware abstraction for wireless sensor networks,† in Proceedings of the 2nd International Workshop on Wireless Sensor Networks (EWSN 2005), Istanbul, Turkey, Jan. 2005. [E49] R. M. Kling, â€Å"Intel Mote: An En hanced Sensor Network Node,† in International Workshop on Advanced Sensors, Structural Health Monitoring and Smart Structures at Keio University, Tokyo, Japan, Nov. 2003. [E50] K. Dantu, M. Rahimi, H. Shah, S. Babel, A. Dhariwal, and G. Sukhatme, â€Å"Robomote: Enabling Mobility In Sensor Networks,† Tech. Rep.CRES-04-006, University of Southern California. [E51] M. B. McMickell, B. Goodwine, and L. A. Montestruque, â€Å"MICAbot: A robotic platform for large-scale distributed robotics,† in Proceedings of International Conference on Intelligent Robots and Systems (ICRA’03), vol. 2, Taipei, Taiwan, pp. 1600–1605, 2003. [E52] S. Bergbreiter and K. S. J. Pister, â€Å"CotsBots: An Off-the-Shelf Platform for Distributed Robotics,† in Proceedings of the 2003 IEEE International Conference on Intelligent Robots and Systems (ICRA’03), Las Vegas, NV, Oct. 2003. [E53] A. Savvides and M. B.Srivastava, â€Å"A distributed computation platform for wireless embedded sensing,† in 20th International Conference on Computer Design (ICCD’02), Freiburg, Germany, Sept. 2002. [E54] S. Park, I. Locher, and M. Srivastava, â€Å"Design of a wearable sensor badge for smart kindergarten,† in 6th International Symposium on Wearable Computers (ISWC2002), Seattle, WA, pp. 13. 1–13. 22, Oct. 2002. [E55] L. F. W. van Hoesel, S. O. Dulman, P. J. M. Havinga, and H. J. Kip, â€Å"Design of a low-power testbed for Wireless Sensor Networks and verification,† Tech. Rep. R-CTIT-03-45, University of Twente, Sept. 2003. [E56] M.Strohbach, â€Å"The smart-its platform for embedded contextaware systems,† in Proceedings of the First International Workshop on Wearable and Implantable Body Sensor Networks, London, UK, Apr. 2004. [E57] A. Schmidt, M. Strohbach, K. V. Laerhoven, and H. -W. Gellersen, â€Å"Ubiquitous interaction—Using surfaces in everyday environments as pointing devices,† in 7th ERCIM Wo rkshop â€Å"User Interfaces For All,† Chantilly, France, 2002. [E58] M. Beigl, A. Krohn, T. Zimmer, C. Decker, and P. Robinson, â€Å"Aware- Con: Situation aware context communication,† in The Fifth International Conference on Ubiquitous Computing (Ubicomp’03), Seattle, WA, Oct. 003. [E59] J. Beutel, O. Kasten, F. Mattern, K. Roemer, F. Siegemund, and L. Thiele, â€Å"Prototyping sensor network applications with BTnodes,† in IEEE European Workshop on Wireless Sensor Networks (EWSN’04), Berlin, Germany, Jan. 2004. [E60] J. Lifton, D. Seetharam, M. Broxton, and J. Paradiso, â€Å"Pushpin computing system overview: A platform for distributed, embedded, ubiquitous sensor networks,† in Proceedings of the Pervasive Computing Conference, Zurich, Switzerland, Aug. 2002. [E61] J. A. Paradiso, J. Lifton, and M. Broxton, â€Å"Sensate media—multimodal electronic skins as dense sensor networks,† BT Technology Journal, vol. 2, pp. 32â€⠀œ44, Oct. 2004. [E62] K. V. Laerhoven, N. Villar, and H. -W. Gellersen, â€Å"Pin&Mix: When Pins Become Interaction Components. . . ,† in Physical Interaction (PI03)— Workshop on Real World User Interfaces†Ã¢â‚¬â€Mobile HCI Conference, Udine, Italy, Sept. 2003. Daniele Puccinelli received a Laurea degree in Electrical Engineering from the University of Pisa, Italy, in 2001. After spending two years in industry, he joined the graduate program in Electrical Engineering at the University of Notre Dame, and received an M. S. Degree in 2005. He is currently working toward his Ph. D. degree.His research has focused on cross-layer approaches to wireless sensor network protocol design, with an emphasis on the interaction between the physical and the network layer. Martin Haenggi received the Dipl. Ing. (M. Sc. ) degree in electrical engineering from the Swiss Federal Institute of Technology in Zurich (ETHZ) in 1995. In 1995, he joined the Signal and Information Process ing Laboratory at ETHZ as a Teaching and Research Assistant. In 1996 he earned the Dipl. NDS ETH (post-diploma) degree in information technology, and in 1999, he completed his Ph. D. thesis on the analysis, design, and optimization of ellular neural networks. After a postdoctoral year at the Electronics Research Laboratory at the University of California in Berkeley, he joined the Department of Electrical Engineering at the University of Notre Dame as an assistant professor in January 2001. For both his M. Sc. and his Ph. D. theses, he was awarded the ETH medal, and he received an NSF CAREER award in 2005. For 2005/06, he is a CAS Distinguished Lecturer. His scientific interests include networking and wireless communications, with an emphasis on ad hoc and sensor networks. THIRD QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE 29

Avoiding Ice and Snow Damage to Trees

Avoiding Ice and Snow Damage to Trees Brittle tree species that retain dead, persistent winter leaves normally take the brunt of heavy icing after a winter storm. Knowing and managing your brittle trees and you can make it through a normal ice storm. Many of the elms, most true poplars (not yellow poplar), silver maples, birches, willows, and hackberry are tree species that simply cant handle the weight of the ice slurry coating their limbs, persistent leaves, and needles. They do well with the snows of the north but have problems in areas that have regular ice storms. Cold climate conifers like fir, spruce and hemlock can handle moderate icing. Southern yellow pines usually take a beating during major icing events that occur on the edge of their natural range. Brittle trees tend to be fast growers. Because of their desirable growth potential and the prospect of making quick shade, weak trees are sought out and planted by homeowners in late winter ice zones. Planting these trees will only exacerbate the problem of limb breakage during heavy icing. Fast-growing trees often develop weak, V-shaped crotches that easily split apart under the added weight of ice. Because these trees usually take some damage from storms throughout the year, internal rot, decay and included bark (some of which you cannot readily see) lead to weakened trunks and limbs (some callery pears). Multiple leader, upright evergreens, such as arborvitae and juniper, and multiple leader or clump trees, such as birch, are most subject to snow and ice damage. Smaller trees need to be wrapped and larger trees with wide-spreading leaders should be cabled in ice-prone areas. Here are things you can do in the yard or landscape to prevent ice damage: Plant Only Strong Trees in Your Landscape Certain trees are popular year in and year out for a reason - they show well and live well. Prefer these trees but eliminate those I have mentioned that door poorly in ice-prone regions.   Brittle Species Should Not Be Planted These species will not do well on sites where heavy ice and snow is a problem. Brittle species include elm, willow, box-elder, hackberry, true poplar and silver maple. Avoid Planting Species With Persistent Leaves Species  that hold their persistent leaves into late fall and early winter where early ice storms are common isnt a great idea. These trees are quickly damaged and removed where the ice storm is common. Wrap Small Multi-Leader Trees So you have a valuable, small specimen you want to preserve. If ice is predicted, secure the tree with strips of carpet, strong cloth or nylon stockings two-thirds of the way above the weak crotches. Always remove any wrapping during spring to avoid binding new growth and girdling limbs and trunk. Begin an Annual Pruning Program When Trees Are Young There is not much you can do with a weak crotch so use tip 4. Prune dead or weakened limbs and excessive branches from trunk and crowns. This reduces ice weight that can rapidly destroy the trees form. Hire a Professional Arborist The expense is worth it for particularly valuable susceptible or wide-spreading large trees. An arborist can strengthen a tree by installing cabling or bracing on weak limbs and split crotches. Favor Conical Formed Trees Trees like conifers, sweetgum or yellow poplar will be robust additions to your landscape. Species with less branch surface area, such as black walnut, sweetgum, ginkgo, Kentucky coffeetree, white oak, and northern red oak are preferred.

Vectors

Vectors Free Online Research Papers A vector is a mathematical object possessing, and fully described by, a magnitude and a direction. It’s possible to talk about vectors simply in terms of numbers, but it’s often a lot easier to represent them graphically as arrows. The vector’s magnitude is equal to the length of the arrow, and its direction corresponds to where the arrow is pointing. Physicists commonly refer to the point of a vector as its tip and the base as its tail. There are a number of ways to label vectors. You may have seen vectors labeled or A. This book will follow the convention you’ll find on SAT II Physics: vectors are written in boldface and vector magnitudes in plain script. For example, vector A has magnitude A. Vectors vs. Scalars In contrast to a vector quantity, a scalar quantity does not have a direction; it is fully described by just a magnitude. Examples of scalar quantities include the number of words in this sentence and the mass of the Hubble Space Telescope. Vector quantities you’ll likely come across quite frequently in physics include displacement, s; velocity, v; acceleration, a; force, F; momentum, p; electric field, E; and magnetic field, B. When in doubt, ask yourself if a certain quantity comes with a direction. If it does, it’s a vector. If it doesn’t, it’s a scalar. Research Papers on VectorsBionic Assembly System: A New Concept of SelfGenetic EngineeringRelationship between Media Coverage and Social andUnreasonable Searches and SeizuresHarry Potter and the Deathly Hallows EssayThe Hockey GameResearch Process Part OneCapital PunishmentOpen Architechture a white paperEffects of Television Violence on Children

Economics & Public Policies Essay Example | Topics and Well Written Essays - 2500 words

Economics & Public Policies - Essay Example The paper tells that the production possibilities curve represents the total output of the combination of two products in an economy provided the inputs and technology are available. There are 3 basic assumptions underlying the production possibilities curve: quantity and quality of all resources as inputs fixed; unchanged technology and fully employed resources. If the federal state and local governments in the U.S. engage in increased health-related expenditure, they would require increasing taxes to meet the increased costs. Assuming the fixed quantity of resources, i.e. public taxes and technology over a given time period, the U.S. is capable of providing limited amount of public health care services. This also depends upon how much the taxpayers are willing to sacrifice other goods and services for increased public health service. The production possibilities curve shows that increase in health care services will lead to less of other government public services due to its limite d resources. Therefore, the production of other goods and services will be less. As the quantity of health care services is reduced, the government will be able to provide more of other goods and services. The probable production possibilities curve is provided in the study. When each member of the community makes a voluntary contribution towards per unit of a pure public good, that contribution equals to his/her marginal benefit derived from the public good at efficient level of output. This equilibrium contribution per unit is known as Lindahl price. The Lindahl prices are assigned in such a manner that no budget deficit or surplus arise at the efficient output of the good. Lindahl equilibrium requires that the total contribution by the community towards the public good is equal to the Marginal Social Benefit and total cost of producing that public good (Hyman, 2010, p.165). This means that ?Ti*Q = MC*Q = AC*Q Where Ti is the voluntary contribution by each individual; Q is the eff icient output; MC is the Marginal Cost of pure public good; AC is the Average Cost of pure public good; Therefore, the total contribution or revenue collected will be (?Ti*Q) and it should be equal to the total cost of the production, i.e. AC*Q. MC*Q equal to AC*Q implies there is no budget surplus or deficit. However, assuming that marginal cost of pure public good increases if more is purchased by the community, i.e. MC>AC then ?MBi>AC because MC = ?MBi. Therefore, the sum of per unit voluntary contribution becomes more than the average costs of production and so this will result in budget surplus at the efficient annual output. Answer 3: Pollution Abatement There are very few human activities that do not pollute the environment, and it has become imperative to address the global concerns over the environmental degradation. The objective of a hundred percent pollution abatement cannot be achieved because the regulators do not have the information of the pollution’s marginal external costs and marginal costs of abatement (Grafton, 2004, p.63). Therefore, most of the pollution abatement policies aim to ensure that the pollution control methods are cost-effective. The costs of pollution abatement not only consider the marginal social costs, but also the opportunity costs associated with cleaner environment.

Change and Continuity in Contemporary Business Essay - 1

Change and Continuity in Contemporary Business - Essay Example A smart enterprise would keep a close eye on the changes in competitive advantages of the organization and realign their strategies & policies such that the business revenues and market share can be sustained. Such enterprises grow globally by carefully choosing their countries/regions of expansion after analysis of the external & internal factors that can drive the competitive advantages of the company in those geographies. Ford Motors is one such smart enterprise that has been changing their strategies & policies to sustain the challenges posed by the local factors in a country by not only globalizing rapidly but also changing their strategies pertaining to their regions of operations. This report presents the internal and external factors and the corresponding responses by Ford Motors to sustain as well as grow their business globally. Balanced Score Card System developed by Kaplan and Norton via their book "Balanced Score-Card - Translating Strategy into Action" published in 1996 (Source: http://www.balancedscorecard.org/BSCResources/AbouttheBalancedScorecard/tabid/55/Default.aspx) Ford Motors was incorporated by Henry Ford in 1903 in Dearborn, Michigan, USA. Henry Ford is known to have adapted practices that were not popular in those days - like, doubling per day wages and reducing the shifts from 9 hours to 8 hours. It was the worker friendly policies that boosted productivity of Ford much ahead of their competition. Ford is known for their methods of large scale car manufacturing and management of huge workforces globally. In 1911, Henry Ford established the first production unit outside USA in the UK by converting a tram works at Trafford Park that is in south of Manchester. In 1920, after the Second World War, the famous Dagenham facility was established that formed the base for launch of Ford Motor Company Limited (UK) in 1929 that was the hub of the European Ford organization (till the time Ford Motor Company Europe was established much later in 1967). In 1971 Ford combined the US, Canadian and Mexican operations together and established the North Ameri can Automotive Operations. The Dagenham facility was one of the most productive in assembly plant in entire Europe which, however, was closed in 2001 amidst some local factors that reduced the economy advantages of Ford manufacturing in Britain while the manufacturing in Germany and other parts of Europe was much more economical. The primary reasons for Ford manufacturing closure in Dagenham was the insurgence of shop-floor militants that developed a powerbase disrupting production by launching almost continuous Guerrilla warfare while Germany offered much more peaceful and strike free industrialization proposition. Moreover, Ford Motor Company