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First they get the minimum power levels and data rates required to support the ow rates However, this does not guarantee that we will nd a link schedule to satisfy the obtained solution Thus, a scale factor is computed to scale-up the data rates, in order to nd a schedule that support the given ow rate Therefore, the algorithm is a near-optimal approximation algorithm that consumes more energy, and the authors prove that they can approximate the optimal solution by a factor of 3 By appropriate transformation of the original optimization problem, the original problem can be transformed into a Quadratically Constrained Programming problem [56] with only ow rates as the optimization variables The transformed problem can be solved in polynomial time for example, using the interior point method.

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Input:

The authors also propose an iterative algorithm based on the Frank Wolfe method [57], which is ef cient in practice and has the nice property that it can be computed in distributed way In each iteration, the optimal routing policy is shortest path routing..

Action: 4: if ni B then {If this node is a beacon node.} 5: (xi , yi ) := getGpsPosition(); 6: Send position(xi , yi ) to all nj Neigi . 7: end if Input:

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11.4.2 Power Control: Other Approaches As mentioned in Section 11.3.1, PCDC considers the interplay between power control, MAC, and routing. The scheme can ef ciently save energy and increase the throughput at the same time. Li and Ephermides [58] propose a heuristic solution to the joint scheduling, power control, and routing algorithm for wireless ad hoc networks. They assume a TDMA-based wireless ad hoc network where each node has one transceiver. They consider the joint scheduling, routing, and power control problem as two subproblems: (a) joint scheduling and power control subproblem and (b) joint scheduling and routing subproblem. The joint scheduling and power control algorithm is similar to the one in reference 40, and the main idea is to iteratively update the power level of links while making sure that all the scheduled links satisfy their SIR requirement. The link metrics are based on queue size of links, and the link with lower link metric has higher priority in the scheduling to occupy the timeslot and update the power level as follows: Pi

8: msgi = position(xk , yk ) such that distk = distanceEstimation(msgi ).

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For example, the mini-sized Dow trades about the same dollar amount as the e-mini S&P does If the Chicago Board of Trade system is operating and you are in a couple of e-mini S&P contracts when Globex goes down, you could balance the risk by entering the opposite side of the trade in a like product that has a fairly close monetary value on a tick per tick basis This situation, though rare, has happened, leaving traders at the CME with no access until the next day s trading session while the problem was being repaired If you trade actively, you always need to have an emergency backup plan for every step of the trading process Smaller, less frequent traders may want to consult with a firm that offers different trading platforms to determine whether the costs warrant the access of such online order entry systems.

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Action: 9: if ni U then {If this node is an unknown node.} positionsi := positionsi {(xk , yk , distk )}; 10: Re Start waitTimer. 12: end if Input:

as acoustic or temperature, the authors had to account for the restricted capabilities of the range nders, which provide lower and upper bounds only, and for the reliability of detecting objects at grazing angles The problem is formulated as an Art-Gallery model, for which the fewest guards are to be placed to monitor a gallery Similarly, the work of Navarro et al [23] addresses the orientation of video cameras so that high quality images of target objects are captured Randomized sensor placement often becomes the only option For example, in application of WSNs in reconnaissance missions during combat, disaster recovery, and forest re detection, deterministic deployment of sensors is very risky and/or infeasible It is widely expected that sensors will be dropped by helicopter, grenade launchers, or clustered bombs Such means of deployment lead to random spreading of sensors, although the node density can be controlled to some extend.

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Although it is somewhat unrealistic, many research projects (eg, reference 24), have assumed uniform node distribution when evaluating the network performance The rationale is that with the continual decrease in cost and size of microsensors, a large population of nodes is expected and thus a uniform distribution becomes a reasonable approximation Ishizuka and Aida [25] have investigated random node distribution functions by trying to capture the fault-tolerance properties of stochastic placement They compared three deployment patterns (Figure 93, taken from reference 25); namely simple diffusion (two-dimensional normal distribution), uniform, and R-random where the nodes are uniformly scattered with respect to the radial and angular directions from the base-station The R-random node distribution pattern resembles the effect of an exploded shell The experiments tracked coverage and node reachability as well as data loss in a target tracking application.

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