MODELING SENSOR NETWORKS in Visual Studio .NET

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the distribution of node identi ers. Because many algorithms are based on node IDs, their performance can depend on how IDs are distributed among the nodes (and thus also in space). Model 4.4.13 (Node Identi ers). Typically, nodes can be assumed to have unique identi ers. IDs could, for example, be generated during deployment using a random number generator. Moreover, because RFID tags already have IDs, we believe that it is reasonable to assume that sensor nodes obtain a unique ID during the production process. Finally, also note that certain tasks cannot be solved by any distributed algorithm if there are no identi ers, because there is no way to break symmetries among the nodes. Similarly to the node distribution in space, the most common models for ID distributions are random distributions and worst-case distributions. Sometimes, it also matters from which range the identi ers are chosen. Again, many variations are possible. For example, each of the n nodes can have a unique 128-bit identi er (range 0, ..., 2128 1). Or, in a more restrictive case, the nodes may have consecutive numbers (e.g., range 1, ..., n). Alternatively or in addition! node IDs can contain location information for example, if the nodes are equipped with a Global Positioning System (GPS) or a Galileo device. Location information can boost the performance of certain operations [30]: for example, a routing algorithm can exploit geographic information to forward the message to a neighbor which lies in the direction of the message s destination (greedy routing). Model 4.4.14 (Location Information). Sensor nodes can have access to various forms of (absolute or relative) geographic information about other nodes. For example, a node u might sense its distance to another node v, or sense in which direction (angle of arrival) u lies, or even know v s exact position. Distributed algorithms for sensor networks are usually evaluated with respect to their time complexity, their space complexity, and their message complexity. However, in order to be successful in a real sensor network, an algorithm has to pursue additional objectives. For instance, if sensor nodes are deployed in large numbers, recharging their batteries seems out of question, in particular in adversarial territory. A node s energy supply must suf ce for the whole operational phase. Therefore, the conservation of energy is of utmost importance. Basically, there are two approaches to capture the energy consumption of a node. Historically, since during the transmission of data much energy is consumed, a model has been studied which only takes the transmission energy into account [31]. Model 4.4.15 (Transmission Energy). The energy consumed by a node is calculated by the sum over all its transmissions. Thus, the energy needed to transmit one message is of the form c d , where d is the distance between sender and receiver, is the path-loss exponent (usually > 2), and c is a constant.
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FINAL REMARKS
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Although transmitting data is a costly operation, sensor nodes with short-range radios available today spend as much energy receiving or waiting for data. Therefore, techniques have been developed which allow nodes to change to a parsimonious sleep mode [32]. During the time periods a node is sleeping, it cannot receive any data. The idea is that if all nodes can somehow be synchronized to wake up at the same moment of time to exchange data (e.g., every minute), much energy is saved. This motivates the following model. Model 4.4.16 (Sleeping Time). The energy consumed by a node is given by the accumulated time in which it is not in sleep mode. If there are no external disturbances, a node is assumed to live as long as it has some energy left. The lifetime of the entire network is modeled in different ways. Model 4.4.17 (Network Lifetime). In applications that depend on every single node, the lifetime of a network can be de ned as the time until the rst node runs out of battery power [33]. Alternatively, a network might be able to tolerate certain node failures; for example, the network might live as long as all live nodes are still connected to each other. 4.5 FINAL REMARKS This chapter has given an overview and discussion of many sensor network models in use today. It has been shown how the models are related to each other. Therefore, we have assumed an algorithmic point of view and have concentrated on models of higher levels of abstraction. Of course, it does not make sense to argue about which model is better and which is worse. For example, a large warehouse has different physical characteristics and signal propagation paths than an of ce building; or GPS might not work indoors and hence algorithms based on coordinates are not be feasible; and so on. A good model also depends on the question studied. A media access study might need a detailed model capturing several low-level aspects; for example, it has to be taken into account that a message might not be received correctly due to a nearby concurrent transmission. Hence, it is crucial that the model appropriately incorporates interference aspects. For a transport layer study, however, a much simpler model that assumes random transmission errors might be suf cient. This chapter helps to compare the different options. Clearly, it is always desirable to have algorithms for sensor networks which can be proved correct in the most general possible model that covers all possible characteristics of a real environment. Only then can we be sure that the algorithm will actually work in practice. However, for ef ciency considerations, a more idealistic model that does not yield overly conservative results might be ne. Moreover, we believe that when developing algorithms for sensor networks, it is often useful to study idealistic models rst, because these models are simpler and may provide helpful insights into the given problem. After having found algorithms for these models, it is still possible to tackle the more general cases.
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