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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.

Morningstar Category Small Blend World Stock Conservative Allocation Moderate Allocation Large Blend Large Blend Large Growth Intermediate-Term Bond Mid-Cap Blend Large Value Large Growth Large Blend Mid-cap Growth Foreign Large Blend Small Growth 089 091 080 091 084 080 -016 081 082 084 082 092 087 088 097 084 097 094 089 -013 084 092 089 092 093 096 085 086 099 098 094 -013 091 095 094 096 096 097 086 092 083 075 037 073 080 074 078 076 084 074 096 091 -001 088 094 090 094 093 0.

[ Team LiB ]

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.

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.

[ Team LiB ]

97 085 097 -014 089 098 095 099 093 095 079 -020 087 096 097 099 093 089 078 -015 -017 -023 -019 -026 -013 -013 086 087 089 089 085 077 093 098 092 095 073 096 094 087 085 Degree of Correlation 70 to 10 High 11 to 69 Moderate 10 to -10 None -11 to -69 Moderate Negative -70 to -10 Highly Negative (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) 093 094 076 090 089 0.