Figure 14.1. Three sensor network problems. in .NET framework

Generating Denso QR Bar Code in .NET framework Figure 14.1. Three sensor network problems.
Figure 14.1. Three sensor network problems.
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Now, however, that a very large number of nodes will be excited at similar energy levels; and so to avoid unnecessary expenditure of energy, means must be found to limit the number of clusters that are involved in fusing information. Individual clusters might, for example, perform beamforming to establish bearing angles and enhance the SNR, with the longer-range transport being limited to likelihoods of target identity and bearing angle, thus permitting fusion and localization. In all of these cases, there is also potentially longer-range multihop transport of data to the end user, along with distribution of queries from users to the network that set policies for searching for and storing (fused and compressed) data. Latency in both processes should be within the constraints set by the end user. The transport of both data and queries should be reliable, and in many situations there will also be concerns for the security of the transport. Additionally, there is little point in collecting sensor data unless the sensors are calibrated and metadata relevant to the data interpretation is also included. A variety of propagation models can be considered in these problems, including distance loss laws and discontinuities caused by obstructions, while source models can range from the very simple (low-order differential equations for elds, Gaussian sources) to the more complicated, with varying levels of uncertainty in the models. The remainder of the chapter is as follows. In Section 14.2 we discuss some background topics in network information theory relevant to the ef cient collection, compression, and reliable communication of sensor data. We then discuss how a QoS perspective enables scalability in classical at sensor networks, and we explore a number of practical approaches for high- delity data extraction in large-scale networks. In Section 14.3 we discuss some of the implications of introducing mobile elements and other forms of heterogeneity. In Section 14.4 we describe some of the data integrity concerns in sensor networks, including both calibration and security issues, for both at and heterogeneous networks. In Section 14.5 we provide our conclusions.
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14.2 FIDELITY AND RESOURCE TRADEOFFS IN FLAT NETWORKS 14.2.1 Network Information Theory The sensor network problem is at a fundamental level governed by network information theory: It is a network rate distortion problem, subject to a number of practical constraints. Sharp information theoretic results could therefore provide guidance on design tradeoffs. Unfortunately, information theory has had relatively little impact in multihop networking compared to its success in point-to-point communications. There are several reasons, among them: It is dif cult to compute the capacity of large networks (or even some networks with as few as three nodes), there is no sourcechannel coding separation theorem for general networks, and the number of possible interactions presents combinatorial dif culties. Additionally, the optimizations posed by Shannon are not as complete a match to the practical issues in networking because they are in point-to-point communications. Latency is less of a concern in single
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links than when it gets aggregated in multiple links across a network. With multiple users sharing a network, it is possible to assign different QoS values to ows, vastly expanding the number of possible acceptable solutions. Consequently, the usual mode of networking research is to (a) create a hierarchy of abstractions to manage the complexity and (b) then propose (and analyze) protocols within each layer to deal with different QoS, physical resource, and traf c model scenarios. Corresponding to these abstractions are particular physical components and algorithms [12]. This structure enables many of the components to be reused even when the conditions or objectives change, minimizing the effort involved in redesigning the system. Therefore, after reviewing progress in network information theory, we will consider how a QoS perspective for the theory of sensor networks can enable fresh progress. Information Theory for 1-Hop Neighborhoods. Neglecting cryptography, there are two basic questions asked in classical information theory: r Channel Capacity: For communications between a transmitter and receiver, what is the maximum rate at which information can be conveyed given the power, bandwidth and error rate constraints, and the noise in the channel r Source Coding: What is the rate of the minimum description of some random process (discrete or continuous) such that we can perfectly reproduce the process from our description (noiseless source coding) or can reproduce it with some bounded distortion (rate distortion coding) Enormous progress has been made since Shannon rst posed these questions in the context of point-to-point communications systems [13, 14], and subsequently for many-to-one or one-to-many systems. Channel coding systems have been devised for the Gaussian channel and a number of its variants (e.g., the Rayleigh fading channel) that get within a fraction of a decibel of the SNR corresponding to channel capacity. The capacities of multiple access, broadcast, and multi-input multiple-output (MIMO) channels have been characterized [15, 16]. In multiple cell communication systems, users communicate directly with one or more base stations over a common set of channel resources (bandwidth/time) while the base stations use an independent (and low-cost) set of resources to communicate among themselves. One can assume varying degrees of cooperation among the base stations in coordinating their own communications and in assisting in separating the communications of the users, with, for example, cellular capacity more than doubling in going from no cooperation to having all base stations share information [17, 18]. There is also a broad set of practical techniques for managing interference, assuming differing levels of ability to estimate the (dynamic) channel [e.g., 19 21]. In the domain of source coding, practical lossless schemes have been devised that get very close to the entropy limits for a single source. Additionally, Slepian Wolf coding enables separate lossless coding of multiple sources to the entropy limit assuming modest statistical knowledge has been distributed [22]. In rate distortion (lossy) coding, considerable progress has been made for Gaussian sources. In
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