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An average human body burns about 10.5 MJ of energy per day. (This corresponds to an average power dissipation of 121 W.) Starner has proposed tapping into some of this energy to power wearable electronics [46]. For example, wristwatches are powered using both the kinetic energy of a swinging arm and the heat ow away from the surface of the skin [47]. The conclusion of studies undertaken at MIT suggests that the most energy-rich and most easily exploitable source occurs at the foot during heel strike and in the bending of the ball of the foot [48]. This research has led to the development of piezoelectric shoe inserts capable of producing an average of 330 mW/cm2 while a person is walking. The shoe inserts have been used to power a low-power wireless transceiver mounted to the shoes. While this power source is of great use for a wireless node worn on a person s foot, the problem of how to get the power from the shoe to any other point of interest still remains. The sources of power mentioned earlier are passive power sources in that the human does not need to do anything other than what he or she would normally do to generate power. There is also a class of power generators that could be classi ed as active human power in that they require the human to perform an action that they would not normally perform. For example, Freeplay [49] markets a line of products that are powered by a constant-force spring that the user must wind up. While these types of products are extremely useful, they are not very applicable to wireless sensor networks, because it would be impractical and not cost ef cient to individually wind up every node. 3.5.4 Wind/Air ow

20 log10 (ZN + ZL )/ (ZN ZL ).

, Burke, K A, et al (1997) Activity wheel running reduces escape latency and alters brain monoamine levels after footshock Brain Research Bulletin, 42, 399 406 Dishman, R K, Washburn, R A, & Heath, G W (2004) Physical activity epidemiology Champaign, IL: Human Kinetics Draganski, B, Gaser, C, Busch, V, Schuierer, G, Bogdahn, U, & May, A (2004) Changes in grey matter induced by training Nature, 427, 311 312 Driver, S, & O Connor, J (2003) Exercise participation, selfesteem, and affective experiences of people with a brain injury Journal of Cognitive Rehabilitation, 21, 26 33 Dunn, A L, Trivedi, M H, Kampert, J B, Clark, C G, & Chambliss, H O (2005) Exercise treatment for depression: Efficacy and dose response American Journal of Preventive Medicine, 28, 1 8 Ekeland, E, Heian, F, Hagen, K B.

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, Abbott, J, & Nordheim, L (2004) Exercise to improve self-esteem in children and young people Cochrane Database of Systematic Reviews, 1, CD003683..

and seek answers to questions on the causes and consequences of human social behavior rather than a theory in and of itself (Tetlock & Manstead, 1985, p. 62). One example of a self-presentational framework is Jones and Pittman s (1982) taxonomy of strategic selfpresentation strategies. Their taxonomy delineates five classes of self-presentational strategies, each linked with the type of impression desired by the person using the strategy (see Table 6.3). Jones and Pittman s objective was to provide a conceptual framework that would organize and relate self-presentational phenomena and provide a basis for theory-building research in the area. Their taxonomy was not conceived as a theory in and of itself. Likewise, the goal of Leary and Kowalski s (1990) twocomponent model was to bring coherence to the selfpresentation area by reducing myriad self-presentation variables to a small set of conceptually meaningful factors. As shown in Figure 6.1, their model delineates factors contributing to the underlying self-presentation processes of impression motivation and construction. The two-component model elegantly captures and organizes the many processes involved in impression-relevant behavior and is an excellent conceptual framework to use as a basis for self-presentation research. However, it does not yet provide (nor does it claim to provide) the predictive and explanatory powers associated with more fully developed and delineated theories. As an alternative to self-presentational frameworks, some physical activity researchers have cast their studies in theories and models that are frequently used in sport and

Figure 3.11 Reliable invoke message with no result message (taken from the WAP Forum, 2000).

Wireless sensor networks usually employ performance metrics that are different from those of more conventional data networks, emphasizing low power consumption and low cost rather than data throughput or channel ef ciency. Since power is consumed every time a networked device accesses the channel (either transmitting or receiving), the method by which the device accesses the channel can have a large effect on its power consumption, and therefore the valuation of the wireless sensor network as a whole. Should the channel-access method require specialized or additional hardware (e.g., a second transceiver, or more processor memory), the cost incurred can similarly affect network valuation. The open systems interconnection (OSI) stack places the responsibility for channel access in the medium-access control (MAC) sublayer of the data link layer, the second layer of the stack. This chapter will discuss the function of the MAC sublayer, identify some of the problems that are addressed (or avoided) in a good MAC design, and review the relationships between the performance goals of the network and tradeoffs that can be made in selecting and designing a MAC protocol. The chapter will then review the major categories of MAC protocols used in data networks of all types, describe a selection of MAC protocols designed for wireless sensor networks, and conclude with a look at directions for future research. 8.1 INTRODUCTION

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