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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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10.3 MOBILE RELAYS Chatzigiannakis and Nikoletseas [26] explore the possibility of using the coordinated motion of a small number of users in the network for ef cient communication between any pair of other mobile nodes. Some of the nodes act as mobile relays in that they carry packets for other nodes. Packets are exchanged when the source node and the relay are neighbors (namely, they are in the radio vicinity of each other), and they are then delivered to the intended destination when the relay passes by it. This is basically what has been introduced to WSNs by Shah et al. in their works on data MULEs [15, 27]. The MULEs are mobile nodes roaming among the sensors to act as forwarding agents. Energy conservation is possible because of single-hop communications (from a sensor to the MULE that is passing by) rather than in a multihop way (from the sensor to the sink). The packet now reaches the sink when the MULE eventually passes by it and transfers all collected sensed data to it. MULEs are effective for energy conservation in the so-called delay tolerant networks [28]. Energy is traded off for latency; that is, the energy needed to communicate a packet to the sink is decreased at the cost of waiting for a MULE to pass nearby and at the cost of waiting for the MULE to move to the vicinity of a sink. Other problems, such as scheduling sensor-to-sink transmissions within this model, have been studied in reference 29.
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Unmanned vehicles for data collection have been further investigated in reference 30. Nodes send their packets to nearby cluster heads via multihop routing. The moving relays then pass by the cluster heads to collect the data according to some prede ned schedule. Therefore, the collector has to visit only the cluster heads and not all the nodes. Furthermore, multihop routing is reduced to a smaller number of hops since the data are sent from a sensor node to a clusterhead who is nearby. Data delivery to the static sink occurs periodically, when the collectors return to the sink to drop their packets and recharge. This concept and architecture has been further explored in reference 31. In this case the authors consider different classes of nodes, where the collectors roam (controllably) among the nodes, grouped into clusters, and can be (uncontrollably) mobile. The goal here is that of determining the schedules of the collectors visits to the nodes that minimize transmission energy consumption, data latency, and nodal buffer requirements. A rst discussion on how to include controllable mobile relays into the network has been presented in reference 32. In the paper, an implementation of a sensor network with an autonomous mobile relay (a robot) is presented. The robot visits the (static) sensors, collects their data (single-hop exchange), and delivers them to the sink, similarly to what happens for the MULEs. However, in this case the movements of the robot adapt to the network application priorities, which dictate data collection performance parameters. In other words, the robot is part of the system, and it is the system that controls its mobility. The testbed-based experimental results concern the evaluation of methods for controlling the speed of the robot for optimizing data collection. The robot traverses networks with different densities following a straight trail and collects the data. The data are then delivered to the sink. Methods are de ned for routing the sensed data to nodes that are one hop from the robot route when the robot itself does not pass suf ciently close to some sensors. Further development of this work with multiple controlled mobile elements (here called explicitly data MULEs) has been presented in reference 33. This work considers the two cases where nodes are deployed uniformly and randomly in a given geographic area and when, more realistically, they are distributed differently. In the rst case, criteria are given for the choice of the number of MULEs and for dealing with nodes that can be served by multiple mules. In the case of nonuniform nodal distribution, a load balancing algorithm is introduced for distributing the number of sensor nodes to the various MULEs so that each MULE serves approximately the same number of sensors. MULEs roam through the network in straight lines and gather information about the nodes they can reach. Then the MULEs, which can wirelessly talk to each other directly, elect a leader and send the information they gather to it. Based on this information, the leader executes the load balancing algorithm and associate nodes to MULEs. Data collection is nally performed by the MULEs that travel through the network (in straight lines) and explicitly poll the assigned sensor nodes for collecting their data. The problem of scheduling the visit to the sensor nodes of a single relay (called ME, for Mobile Element) so that there is no data loss (due to buffer over ow) is tackled in reference 34. The corresponding Mobile Element Scheduling (MES) problem is proven to be NP-complete, and centralized analytical model (ILP) and algorithms are given for solving the problem. In particular, given as input the data generation patterns
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