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framework that allows for fully trusted nodes to be included (e.g., a trusted central or mobile authority), and for fusion of information collected by very different types of sensor. It is also amenable to using results from decision theory [143 144] to deal with various malicious attacks [96]. 14.4.6 Ef cient Query Systems Sensor networks are fundamentally a means to actively answer queries about the physical world. They differ from traditional databases in some important respects. For example, streaming data from sensors presents a problem of scaling. Even without energy and communication bandwidth constraints, a network of thousands of sensors will quickly overrun whatever xed storage media are supplied (and especially if the bandwidth is large). For scalability it is required that queries that direct the collection, storage, and transport of data should specify not only geographic and temporal scope, but also spatial, temporal, and accuracy delity requirements. An expression of priority or interest is also needed because network resources are nite. On the other hand, problems of concurrency, integrity, security, and ef cient access to perform logical inferences remain, and so it is desirable to re-use as much of the scaffolding from traditional databases as possible, particularly when multiple applications are intended to use the same system. A useful abstraction for such problems is diffusion of interests. A user indicates interests in data with particular attributes, with multiple such interests interacting within the network to determine the ow of information. At the bottom (physical) layer they might include sensor type, spatial resolution (or location), and temporal resolution (or time). Higher-level queries such as a request for a map of the temperature gradients in some particular region must then be translated into a set of requests for information from particular nodes, a strategy for communicating the information, and selection of the means for performing the signal processing, all consonant with resource constraints. Interests interact in increasing the likelihood of particular information being stored, processed, and communicated, but also in creating possibilities for congestion and other resource contention. These interests thus compete with each other and against the inhibition built into the network for usage of its resources. Examples of protocols that fairly directly implement this abstraction to establish routes in a resource-ef cient fashion for both queries and responses are push-directed diffusion [145] (oriented toward situations where there are many data sources and sinks) and two-phase pull diffusion [146] (few sinks and many data sources). In general, which routing mechanism to employ depends on (a) how data are to be stored and used and (b) constraints such as latency for query dissemination and network reply. Very different traf c volumes result, depending upon the choices made. In external storage systems, data are automatically sent to a sink whenever some standing conditions have been met (generically, an event has occurred); data transport clearly dominates the traf c in this case. In local storage systems, data are only sent to a sink in response to a particular request. In this case, there can be many queries. In rendezvous systems, data regarding events and queries meet at intermediate points, which may shift in order to balance the load, as for example in data-centric
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storage (DCS) [147]. This allows trading storage requirements against query response time, as well as trading query against data traf c, at the cost of requiring geographic information for routing. Clearly, establishment of routes is but part of the problem. There are possibilities for data fusion and aggregation along the way [148, 149], data resolution may be reduced if resources are de cient [e.g., 150], and consideration must be given to the timeliness of response to queries and the reliability of the data storage in the network (e.g., replication to deal with node failure) [147]. An example of a system that enables network operators to balance some of this concerns is TinyDB, a SQL-like utility developed for operation on networks of (resource-constrained) sensor nodes running TinyOS [151]. It has a number of features that allow datadriven applications to be developed and deployed with far less effort than attempting to use fully custom code, including meta-data management, high-level query support, network topology management (including routing tables), support for multiple queries with different delity criteria, and adaptation to nodes entering/leaving the network. Hierarchy can be used to considerable bene t. For example, in the aggregration system proposed in reference 148, a base station with additional resources is required for computing the routing trees and aggregation strategy. Nodes with longer-range communication links and additional storage are natural locations for rendevous points, allowing for faster response to queries and greater delity in the data records. More generally, with powerful higher-level nodes, standard communication and database might be re-used, with novel algorithms required only for control of the information ows to and from the lower-level nodes. On the other hand, rapid network topology changes will, as for routing in MANETs, result in considerably more overhead if response times are to be kept reasonable.
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14.5 CONCLUSIONS Sensor networks come in a wide variety of forms, from at networks of highly resource-constrained nodes to hierarchical networks with mobile elements and powerful communications, processing, and storage capability. The networks themselves may be designed for one special purpose, or as a tool to answer a broad variety of questions about the physical world. Which resource constraints apply and the scope of the network objectives lead to vastly different optimization problems; and thus in considering QoS requirements for sensor networks, the assumptions concerning the technology, physical model, and usage must be made explicit. In this chapter, we have attempted to show that while classical network information theory problems are at best a partial t to problems of resource optimization in sensor networks, a QoS perspective at once makes many of the problems both more relevant and tractable. Mobility in network elements at the same time expands the richness of the problem set and gives rise to simple solutions to many otherwise dif cult problems in the domains of data communication and integrity. This is a yet another manifestation of the truism that for robust/simple design there must be at
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