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The sensed event can be either dynamic or static depending on the application For instance, in a target detection/ tracking application, the event (phenomenon) is dynamic, whereas forest monitoring for early re prevention is an example of static events Monitoring static events allows the network to work in a reactive mode, simply generating traf c when reporting Dynamic events in most applications require periodic reporting, and consequently generate signi cant traf c to be routed to the sink An important design consideration is the topological deployment of nodes This is usually application-dependent and affects the performance of the communication protocol The deployment is either deterministic or self-organizing In deterministic situations, the sensors are manually placed and data are routed through predetermined paths In addition, collision among the transmissions of the different nodes can be minimized through the prescheduling of medium access.



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where the inequality follows from Jensen s inequality and the concavity of the logarithm. Now since the yi are distinct, we have i:|yi |=l,si =s P (yi |si ) 1. Thus, log Qk (x1 , x2 , . . . , xn |s1 )





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Tables 8.4 and 8.5 show the calculated values of coef cients Q i0j and Q i1j , respectively. In the second iteration, the updated values of coef cients Q i0j and Q i1j allow us to determine the updated values of coef cients Ri0j and Ri1j . This iteration is different from the rst iteration in the sense that coef cients Q i0j and Q i1j now contain updated information, rather than simply channel information. In this second iteration the calculation of values of coef cients Ri0j and Ri1j leads to a new estimated decoded vector d, which again does not satisfy the syndrome condition, and so the decoding process continues. In the example under analysis, the decoder is able to nd the correct code vector after three iterations, and is also able to correct the two errors that the hard-decision received vector contained. In this particular case the errors are in the message part of the code vector, that is, in two of the four bits that are nally taken as the message bits, after truncating the redundancy. The iterative decoding algorithm is able to correct these two errors. Tables 8.6 and 8.7 illustrate the evolution of the decoding algorithm by presenting the values of the coef cients involved, until arriving at the nal solution. These tables show values that are truncated to four decimal places, though actual values were determined in a more accurate way by using MATLAB R Program 5.3. The updated values of coef cients Ri0j and Ri1j allow us to determine a new estimated decoded vector. This second estimate of the decoded vector is shown in Table 8.8.

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(FIFO) queueing strategy is used and infinite queue size at the BS is assumed. For a finite queue size, see the discussion in the next secton. The duration of a MS in the handoff area depends on system parameters such as the moving speed, the direction of the MS, and the cell size. We define this as the dwell time of a mobile in the handoff area and denote it by random variable Th dwell. For simplicity of analysis, we assume that this dwell time is exponentially distributed with mean E[Th dwell] (= 1/ h dwell). Let us define the state i (i = 0, 1, , ) of a cell as the sum of channels being used in the cell and the number of handoff requests in the queue. It is apparent from the above assumptions that i is a one-dimensional Markov chain. The state transition diagram of the cell is given in Figure 1.8. The equilibrium probabilities P(i) are related to each other through the following state balance equations: i P(i) = ( i P(i) =

However, in selforganizing systems, the sensor nodes are scattered randomly, creating an infrastructure in an ad hoc manner In that infrastructure, the position of the sink or the CH is also crucial in terms of energy ef ciency and performance When the distribution of nodes is not uniform, optimal clustering becomes a pressing issue to enable energyef cient network operation During the creation of an infrastructure, the process of setting up the network topology is greatly in uenced by energy considerations Since the transmission power of a wireless radio is proportional to distance squared or even higher order in the presence of obstacles, multihop routing will consume less energy than direct communication However, multihop routing introduces signi cant overhead for topology management and MAC Direct routing would perform.

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