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Figure 2.6. Simple command-and-control console interface.
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tracking, (d) the investigation of alternate backbone hardware, and (e) the investigation of long-range ex ltration schemes.
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2.3 SCALABILITY AND SYSTEM LIFETIME It is well known that homogeneous ad hoc networks, which include homogeneous WSNs, suffer poor capacity. Gupta and Kumar [11] were the rst to show that the throughput of a node is (1/ n log n), where n is the number of nodes in the network a very pessimistic result! In addition, as paths between nodes become longer, the probability of packets being lost becomes higher. As the number of nodes in a network grows, the successful end-to-end transmission rate drops signi cantly [12]. Experimentation in simulation [13] and in testbeds [14] has con rmed that the performance of homogeneous ad hoc networks does not scale with increasing n. A primary difference between WSNs and ad hoc networks is that the traf c pattern is many-to-one, from the sensor nodes to the base station. Figure 2.7 shows a homogeneous WSN with a base station at the center. A transmission from any sensor node to the base station goes through one of the nodes within a distance of r of the base station. These critical nodes have the highest burden of relaying traf c. As a result, they are likely to exhaust their energy before other sensor nodes [15]. When the critical nodes die, connectivity of the network is lost. Hence the energy drainage rate of the critical nodes determine the system lifetime. Indeed, Du and Xiao [16] found that when connectivity is lost, more than half of the nodes still have more than 50% of their energy left. This energy is wasted since communication with the sink is no longer viable.
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Figure 2.7. The energy of critical nodes (within the circle) drain nonuniformly.
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Adding structure to the network can help improve scalability and also alleviate the problem of nonuniform drainage of energy. Clustering is one way to add structure in a homogeneous WSN. Heinzelman et al. propose LEACH, a clustered network in which each cluster head aggregates data and transmits it directly to the base station [17]. The cluster heads are periodically rotated for ef cient load balancing and a consequent lengthening of network lifetime. In order to minimize the total energy, the number of cluster heads must scale as the square root of the total number of sensor nodes [17]. The LRS [18] and power-aware chessboard-based adaptive routing (PCAR) [19] protocols also aim to balance the energy consumption in a homogeneous sensor network. All of these protocols suffer from overhead associated with frequent cluster-head rotation. While the problem of routing in homogeneous WSNs has been considered in at architectures (see Directed Diffusion [20] as an example), when the architecture is hierarchical, the routing protocol makes use of the hierarchy (see TTDD [21] and LEACH [17] as examples). Hierarchy is shown to help a homogeneous WSN achieve higher total throughput and increase the network lifetime. In heterogeneous WSNs, it is often natural to organize the nodes into a hierarchy. In this section we consider algorithms for heterogeneous WSNs to form hierarchical topologies, and address the related problem of routing, in order to tackle the problems of scalability and nonuniform energy drainage. 2.3.1 A Resource-Oriented Protocol: Topology Formation and Routing In WSN applications in which the sensor nodes are inherently heterogeneous in energy resources, these differences should be considered in order to improve the network capacity and extend the system lifetime.
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Ma et al. [22, 23] consider a wireless in-home heterogeneous sensor network. This may include devices embedded into everyday objects such as appliances, devices for climate monitoring and environmental control, and medical devices integrated into the home for monitoring medical conditions. Even mobile sensor nodes, such as those carried by people or on mobile toys, are considered. In-home sensor nodes are heterogeneous in their power units. Some nodes are directly connected to the alternating current power supply and have, essentially, unlimited energy. Others are powered by batteries with varying capacities. To exploit the heterogeneity in power units, a resource-oriented protocol (ROP) is proposed. The goal is to achieve the longest system lifetime. To be precise, the system lifetime for a sensor network is the shortest lifetime of any participating node in the network. The node lifetime for a sensor is the time at which the sensor exhausts all its energy. ROP exploits the existence of sensor nodes with unlimited resources. A topology is formed to minimize the consumption of resources of energy-constrained sensor nodes. Sensor nodes with unlimited energy serve as relays, since they can afford higher transmission power and hence longer transmission range. This saves energy in the energy-constrained sensor nodes and, as a result, extends the functional lifetime of the network. Figure 2.8 shows an example of ROP; node U has unlimited energy resources, while the others are energy-constrained sensor nodes. Of these energy-constrained nodes, nodes E1 and E2 are battery-powered local cluster heads with medium energy capacity, and the rest of the nodes only have small energy capacity. When there is a message that needs to be sent, for example, from a source node 1 to a destination node 7, a traditional multihop routing protocol might route the packets from node 1 through nodes 2, 3, 4, 5, and 6 to 7. However, because of the existence of the unlimited energy node U and the medium energy capacity node E1 , ROP would route the packets from
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