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where source rates x = {xs } are the optimization variables, link capacities {cl } are the constant parameters, L(s) denotes the set of links l in the path originated from source s, and the utility Us for each source can be any increasing, concave function. However, in this standard form of network utility maximization framework, the link capacities {cl } are assumed to be constant, which is not true in wireless ad hoc networks where the transmission power control can change the attainable data rates on the links. Intuitively, a proper power-level algorithm would allocate the right amount of power at the right nodes to alleviate the bandwidth bottlenecks by increasing capacity on the appropriate links, which will then induce an increase in end-to-end TCP throughput. What complicates this approach is that changing the transmit power on one link would affect the data rates available on other links, due to interference in wireless networks. With this intuition, the network utility maximization framework can be modi ed to t into the wireless ad hoc network with elastic link capacities:

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If several routes are received, it chooses the one with the largest resources; and if two routes have the same resources, the one with fewer children is selected The details of ROP and the reactive routing scheme are described in Ma et al [23] The performance of ROP is evaluated in simulation In ROP, energy ef ciency cannot always result in longer system lifetime Rather, balancing resources among sensors and saving energy for those more resource-constrained sensor nodes contributes to lengthening system lifetime 232 Chessboard Clustering and Routing Protocol Du and Xiao [16] propose a chessboard clustering and routing protocol for heterogeneous WSNs to overcome the performance bottleneck and poor scalability of.

Us (xs ) xs cl (P)

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homogeneous WSNs and to address, at same time, the problem of nonuniform energy consumption A good observation is made that clustering alone does not solve the problem of nonuniform energy drainage; indeed, the center node in Figure 27 can as well be a cluster head rather than a base station Two types of nodes are assumed: a small number of high-end sensor nodes and a large number of low-end sensor nodes Each node is assumed to be aware of its location A cluster is formed around each high-end sensor node which serves as a cluster head Low-end sensor nodes perform the basic sensing as well as the relaying of packets within the cluster Given its powerful energy reserve and communication ability, each high-end node performs data fusion within its cluster, and it transmits the aggregated data to the sink via a single-hop link or a multihop path.

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l L(s)

In this way, the network is divided into multiple regions, with each region assuming a smaller burden of the communication due to the smaller number of sensor nodes within the cluster The network lifetime is therefore increased by transmitting fewer packets at lowend sensor nodes and utilizing the less power-constrained or non-power-constrained nodes as much as possible Figure 210 shows the sensor eld divided into equal-sized cells with adjacent cells colored with different colors, resembling a chessboard These nodes are assumed to be uniformly and randomly distributed in the sensor eld Since each node knows its location, it can determine if it is in a white cell or a black cell The basic idea is to use the underlying chessboard to de ne two clustered topologies, with only one clustering in use at a given time.

In a white clustering, all highend sensor nodes in white cells are active while all high-end sensor nodes in black cells are inactive In a black clustering, all high-end sensor nodes in black cells are active while all high-end sensor nodes in white cells are inactive Low-end sensor nodes are all active, forming multihop clusters around the currently active high-end sensor nodes The motivation for switching colors is as follows: sensor nodes that are critical nodes in a white clustering are likely to become non-critical nodes in a black clustering and vice versa Since critical nodes consume more energy in packet.

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java ean 13 reader

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java ean 13 reader

java ean 13 reader

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java ean 13 reader

java ean 13 reader