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Percentage Cost Change -022.7% 12.0% 4.9% 2.8% 0.9%

Energy ef ciency and network capacity are two of the most important issues in wireless sensor networks. Topology-control algorithms have been proposed to maintain network connectivity while reducing energy consumption and improving network capacity. Several studies have also been performed to investigate critical conditions on several network parameters in order to ensure network k-connectivity (in the asymptotic sense). In this chapter, several problems (and their corresponding solutions) related to topology construction, maintenance, and connectivity in wireless sensor networks are discussed. Speci cally, topics discussed include (1) various communication models and generation of random network topologies; (2) neighbor discovery and maintenance; (3) basic connectivity properties of wireless sensor networks (with the random unit graph model as the underlying model); (4) localized topology construction algorithms, along with their associated geometric structures in both homogeneous and heterogeneous networks; and (5) how to enhance fault tolerance in topology construction and connectivity. 10.1 INTRODUCTION

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THE SOCIAL-COGNITIVE PERSPECTIVE In this section, the primary variables studied using the social-cognitive approach in psychology are reviewed. The social-cognitive approach, in particular, self-efficacy theory (Bandura, 1977, 1982, 1986, 1997), when applied to exercise behavior, emphasizes the role that task-specific states have on perceiving and coping with exercise stimuli (e.g., physical effort). Goal Orientation Goal orientations reflect individual differences in assigning subjective meaning to outcomes (Ames, 1984; Maehr & Braskamp, 1986). The subjective meaning given to success and failure is linked to either a differentiated or an undifferentiated concept of ability. A differentiated concept of ability is determined by comparing one s performance and outcome to others . Ability can be evidenced by performing better than others, by surpassing normative-based standards, or by achieving success with little effort (Covington, 1984). This goal orientation is termed ego orientation. An undifferentiated concept of ability is utilized when subjective achievements are compared to self-referenced standards. This orientation is termed task orientation and is evoked by the need to meet and improve personal standards (Nicholls, 1984, 1989). Task goal orientation is associated with behaviors such as skill improvement, task mastery, working hard, and persistence. In contrast, ego goal orientation is associated with maladaptive or inhibitive behaviors when social comparisons are avoided; as a consequence, effort and confidence decrease (Jagacinski & Nicholls, 1990). Task and ego goal orientations are independent of each other and important determinants of motivation (see Duda & Hall, 2001, for a review). It is assumed that

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is called the cochannel interference reduction factor or reuse factor and is the measure of cochannel interference. The Q factor determines spectral efficiency within a cell and is related to the number of cells in the cluster K. Assuming that all the cells transmit the same power, the frequency reuse distance D can be increased by increasing K. One could expect that by making K as large as possible, all problems concerning cochannel interference could be solved. An advantage of large clusters is the fact that the interference from cochannel cells decreases because the distance between the cochannel cells also increases with the increase in cluster size. On the other hand, the available bandwidth and therefore the available number of channels is fixed. When K is large, the number of channels per cell is small. That causes spectrum inefficiency. 3.3.2 Cosite and Adjacent Channel Interference In addition to cochannel interference, a second source of noise is the interference between two adjacent channels of the same (cosite interference) or adjacent cells (adjacent channel

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PROBLEMS 5.1 Uniquely decodable and instantaneous codes. Let L = m pi li100 be the expected value of the 100th power i=1 of the word lengths associated with an encoding of the random variable X. Let L1 = min L over all instantaneous codes; and let L2 = min L over all uniquely decodable codes. What inequality relationship exists between L1 and L2

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