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Therefore, energy consumption and network lifetime are not impacted by the energy needed to move them In the case where the sensor nodes move, a great deal of the nodes energy is spent on the movement itself, thus having a detrimental impact on the lifetime of the nodes The nal part of the chapter concerns a quite thorough discussion on how to compare different approaches to mobility in WSNs, focusing on sink and on relay mobility In particular, we show how, by using simulations, one can effectively assess pros and cons of different solutions and gain useful insights for de ning and testing new ones..

10.3 TCP OVER MANETS TCP was originally designed to facilitate a reliable, connection-oriented service over IP networks. The bulk of its application scope consisted of deployment in wired networks, and a number of the design decisions made at its inception re ect that fact [19]. In a mobile, multihop, wireless environment, such as a MANET, a number of the assumptions inherent in the design of TCP are inapplicable or false, which leads to suboptimal performance in that setting. This section outlines the causes of this discrepancy and offers some insight in concert with recent research ndings in the literature. 10.3.1 Congestion Avoidance and Packet Loss As mentioned in Section 10.2.2, TCP reduces its sending rate to facilitate congestion control when a congestion indication event occurs (i.e., an RTO expiration is triggered or a number of duplicate ACKs are received). Essentially, congestion indication events denote a reasonable degree of certainty that segment loss has occurred in the network. In wired networks, such losses are mostly the result of over owing buffers in the routers along the segments path; hence, to ensure that the network is not overwhelmed, TCP reduces its sending rate. This allows the routers to process and forward the backlog of segments in their buffers so that subsequent segment arrivals may enter the router s queue rather than be rejected [10].

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10.2 SENSOR NODES MOBILITY Mobility of the sensor nodes has been exploited for improving, or enabling altogether, sensing and communication coverage [20 22]. The idea presented in reference 20 is to have the sensors move into positions that minimize the energy cost of reporting streams of data to the sink, which is statically placed. The protocols proposed by Wang et al. [21] aim at moving mobile sensors from densely deployed areas to areas with coverage holes, where for some reasons a limited number of sensors have been deployed. The three protocols are proven by simulations to be effective in terms of coverage, deployment time, and moving distance. Minimization of the energy consumption of moving nodes has been subsequently addressed by the authors in reference 22 by letting the node move logically that is, only after they can decide

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26 D Cavin, Y Sasson, and A Schiper On the accuracy of MANET simulators In POMC 02: Proceedings of the Second ACM International Workshop on Principles of Mobile Computing, ACM Press, New York, pp 38 43 27 M D Yarvis, W S Conner, L Krishnamurthy, J Chhabra, B Elliott, and A Mainwaring Real-world experiences with an interactive ad hoc sensor network ICPP Workshop, 2002, pp 143 147 28 R Draves, J Padhye, and B Zill Routing in multi-radio, multi-hop wireless mesh networks In MobiCom 04: Proceedings of the 10th Annual International Conference on Mobile Computing and Networking, ACM Press, New York, 2004, pp 114 128 29 A Adya, P Bahl, J Padhye, A Wolman, and L Zhou A multi-radio uni cation protocol for ieee 80211 wireless networks.

whether their moving maximizes the coverage or not. The idea of using mobile sensors has also been explored within the robotic community, where mobile robots are (also) equipped with sensors. Typical examples are the works by Howard et al. [23, 24]. In reference 23 an algorithm for the deployment of the members of a robotic team into an unknown environment is given. The aim of this algorithm is the maximization of the coverage area, while maintaining line-of-sight contact among the robots. In reference 24 the same authors draw from the theory of potential elds to distribute the mobile sensors throughout a given area. The elds are constructed in such a way that each node is repelled by obstacles and other nodes, thereby forcing the node to spread throughout the area. Finally, distributed algorithms for the mobility of sensor nodes have been investigated in reference 25. In this work, mobility algorithms are proposed that move the nodes to positions that reduce the transmission power needed to send the data to the (static) sink. The positions for the moving sensors are determined via distributed simulated annealing, as opposed to a greedy strategy that could lead to a suboptimal placement. By using distributed simulated annealing, a node based only on information on its current neighbors accepts a bad move with a positive probability. This move could be locally nonoptimal, but could bene t the network globally in the longer run. Works that consider mobile sensors and robots are mostly concerned with sensor deployment time and with sensing coverage. The costs associated with sensor movements as well as the cost of transmitting sensed data are often not considered, and network lifetime is rarely a metric of interest.

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