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, the estimated density function becomes sharper), they broadcast this information to other nodes A similar approach is applied in reference 28 to probabilistic localization using AOA measurements As usual, the error for AOA measurement is modeled probabilistically from of ine experiments The nodes utilize this information to re ne position estimates obtained from beacons In reference [26], the authors proposed a method applied to mobile sensors that is based on Monte Carlo localization, which uses particle lter combined with probabilistic modeling The basic idea is to rst obtain a posterior distribution of possible locations using a set of weighted samples Nodes use a mobility model to re ne the probability distribution obtained at an initial step In a second step, the nodes use ltering to eliminate impossible locations based on new observations This approach has been found to work better with higher node mobility.

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12.3 PROTOCOL POWER SAVING IN SOLAR-POWERED WLAN MESH NETWORKS From our previous discussion, it is clear that reducing mesh node power consumption can lead to signi cant cost savings. For this reason, SMAPs/SMPs should be designed so that power consumption is as low as possible. Unfortunately, there are limits to what can be achieved without any protocol-based mechanism for access point power saving. Using protocol-based power saving, SMAPs/SMPs could reduce their power consumption signi cantly, and in this case the achievable levels would be constrained only by satisfying user QoS requirements. Unfortunately, the current IEEE 802.11 standard only provides for mobile station power saving. In IEEE 802.11 power saving the stations can be in one of two states: awake and doze. When the station s radio components are fully powered, this is de ned as the awake state. In this state the station is capable of transmitting and receiving frames, as well as listening on the channel. In the doze state, however, the station is free to perform power saving (PS). In this case, the station is operating at reduced functionality and is not able to perform normal radio functions. When a station is in doze mode the AP is responsible for buffering incoming frames for that station. The frames are then transmitted when the station is available in the awake state. A power-saving station listens to the AP beacons; and if the AP is holding frames for a given station, it indicates this in the traf c indication map (TIM), which is transmitted with the AP beacons. The station can then poll the AP (using PS-poll

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An algorithm that applies probabilistic modeling for localization using RSSI based ranging from a mobile node is proposed in reference 29 Here, it is assumed that all sensor nodes are static while a mobile beacon node travels through the network area broadcasting beacon packets Beacon packets carry updated location of the beacon node As the beacon packets are received at a sensor node, it obtains new position estimates and combines them probabilistically to re ne its position estimate 127 CONCLUSION Localization is a challenging problem to solve for successful implementation in small, low-cost wireless sensor nodes Although a signi cant amount of research has been devoted to this problem, a single practical solution is hard to nd It is likely that solutions would have to be application-speci c, because sensors are used in a large number of application scenarios and environments.

(queuing) at any node at any time Traf c Demand Oracle contains information about the present or future traf c demand Based on the assumption of which oracles are available, the authors present corresponding routing algorithms For example, if all the oracles are known, a linear programming is formulated to nd the best route If only the Contacts Summary Oracle is available, Dijkstra with time-invariant edge costs based on average waiting time is used to nd the best route If only the Contact Oracle is available, modi ed Dijkstra with time-varying cost function based on waiting time is used to nd the route All of the algorithms developed (except the zero-knowledge case) in reference 16 are for the deterministic case Assuming that the characteristic pro le is known over an in nite time horizon may not be realistic in ad hoc networks.

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