In mobile wireless sensor networks (MWSNs), each sensor has the ability not only to sense and transmit data but also to move to some specific location. Because the movement of sensors consumes much more power than that in sensing and communication, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance of mobile sensors is minimized has received a great deal of attention. However, in reality, due to a limited budget or numerous targets, mobile sensors may be not enough to cover all targets or form a connected network. Therefore, targets must be weighted by their importance. The more important a target, the higher the weight of the target. A more general problem for target coverage and network connectivity, termed the Maximum Weighted Target Coverage and Sensor Connectivity with Limited Mobile Sensors (MWTCSCLMS) problem, is studied. In this paper, an approximation algorithm, termed the weighted-maximum-coverage-based algorithm (WMCBA), is proposed for the subproblem of the MWTCSCLMS problem. Based on the WMCBA, the Steiner-tree-based algorithm (STBA) is proposed for the MWTCSCLMS problem. Simulation results demonstrate that the STBA provides better performance than the other methods.
 Sensors are used to monitor and control the physical environment. In mobile sensor networks, nodes can self-propel via springs, wheels, or they can be attached to transporters, such as vehicles. Sensors have limited energy supply and the sensor network is expected to be functional for a long time, so optimizing the energy consumption to prolong the network lifetime becomes an important issue. In static sensor networks, if sensors are uniformly deployed, sensors near the sinks die first. This is because besides sending their own sensed data, they also participate in forwarding data on behalf of other sensors located farther away from the sink. This uneven energy consumption results in network partitioning and limitation of the network lifetime. In this paper, we survey mechanisms that utilize nodes' mobility to extend the network lifetime. We divide these mechanisms into three groups: mechanisms using mobile sinks, mechanisms using mobile sensors redeployment, and mechanisms using mobile relays. Using mobile sinks, energy is saved by using shorter multi-hop data delivery paths and the set of sensors located near a sink changes over time, thus the energy consumption is balanced in the whole network. Using mobile sensors, the initial deployment can be improved through sensor relocation such that to balance energy consumption and to extend network lifetime. Mobile nodes can also be used as relays, which can inherit the responsibilities of the co-locating static sensors or they can carry data to the sink to reduce the cost of long distance communication. We provide overviews and comparisons among different mechanisms. One of the research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we solve the k-coverage sensor deployment problem to achieve multi-level coverage of an area I. We consider two sub-problems: k-coverage placement and distributed dispatch problems. The placement problem asks how to determine the minimum number of sensors required and their locations in I to guarantee that I is k-covered and the network is connected; the dispatch problem asks how to schedule mobile sensors to move to the designated locations according to the result computed by the placement strategy such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models, and allow an arbitrary relationship between the communication distance and sensing distance of sensors. For the dispatch problem, we propose a competition-based and a pattern-based schemes. The former allows mobile sensors to bid for their closest locations, while the latter allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness. One of the main operations in wireless sensor networks is the surveillance of a set of events (targets) that occur in the field. In practice, a node monitors an event accurately when it is located closer to it, while the opposite happens when the node is moving away from the target. This detection accuracy can be represented by a probabilistic distribution. Since the network nodes are usually randomly deployed, some of the events are monitored by a few nodes and others by many nodes. In applications where there is a need of a full coverage and of a minimum allowed detection accuracy, a single node may not be able to sufficiently cover an event by itself. In this case, two or more nodes are needed to collaborate and to cover a single target. Moreover, all the nodes must be connected with a base station that collects the monitoring data. In this paper we describe the problem of the minimum sampling quality, where an event must be sufficiently detected by the maximum possible amount of time. Since the probability of detecting a single target using randomly deployed static nodes is quite low, we present a localized algorithm based on mobile nodes. Our algorithm sacrifices a part of the energy of the nodes by moving them to a new location in order to satisfy the desired detection accuracy. It divides the monitoring process in rounds to extend the network lifetime, while it ensures connectivity with the base station. Furthermore, since the network lifetime is strongly related to the number of rounds, we propose two redeployment schemes that enhance the performance of our approach by balancing the number of sensors between densely covered areas and areas that are poorly covered. Finally, our evaluation results show an over 10 times improvement on the network lifetime compared to the case where the sensors are static. Our approaches, also, outperform a virtual forces algorithm when connectivity with the base station is required. The redeployment schemes present a good balance between network lifetime and convergence time. We propose a sensor node relocation approach in wireless sensor networks to maintain connectivity between a Region Of Interest (ROI) where the sensor nodes are initially deployed and a Center Of Interest (COI) outside the ROI where a particular event happens. Our proposed approach, called Chain Based Relocation Approach (CBRA), aims to relocate a minimum number of redundant sensors from their initial positions within the ROI towards the COI to maintain the connectivity between the ROI and the COI. CBRA uses steps which determine the redundant nodes' set, the propagation of the COI coordinates within the ROI and then the selection and the relocation of the redundant nodes towards the COI. The selection of the redundant nodes is based on an average energy consumption model to balance the energy consumption among the sensor nodes when they are relocated depending on their initial and final positions. We evaluate the performance of CBRA using performance metrics such as energy consumption, the number of relocated nodes, relocation time and number of transmitted messages. Sensor nodes are relocated using a chain-based method between the ROI and the COI. In addition, if one relocated sensor node fails, the connectivity between the COI and the ROI is affected. To address this possible failure, we propose a fault tolerant recovery procedure to repair the route between the COI and the ROI. Finally, we compare the performance of CBRA with two other approaches.