We formalize the distance-sensitive service discovery problem in wireless sensor and actor networks, and propose a novel localized algorithm, iMesh. Unlike existing solutions, iMesh uses no global computation and generates constant per node storage load. In iMesh, new service providers (i.e., actors) publish their location information in four directions, updating an information mesh. Information propagation for relatively remote services is restricted by a blocking rule, which also updates the mesh structure. Based on an extension rule, nodes along mesh edges may further advertise newly arrived relatively near service by backward distancelimited transmissions, replacing previously closer service location. The final information mesh is a planar structure constituted by the information propagation paths. It stores locations of all the service providers and serves as service directory. Service consumers (i.e., sensors) conduct a lookup process restricted within their home mesh cells to discover nearby services. We analytically study the properties of iMesh including construction cost and distance sensitivity over a static network model. We evaluate its performance in static/dynamic network scenarios through extensive simulation. Simulation results verify our theoretical findings and show that iMesh guarantees nearby (closest) service selection with very high probability> 99 % (resp.,> 95%).

Abstract—Mobile sensors are useful in many environments because they can move to increase the sensing coverage. In this paper, we present a mobile sensor prototype in which the Mica2 sensor node is used to control the movement of the robot built with commercial off-the-shelf (COTS) components. We use a sensor relocation application to demonstrate the feasibility of our design. In the sensor relocation application, after a sensor node failure creates a coverage hole, a mobile sensor node is relocated to cover the hole in a timely and energy-efficient way. We present a distributed sensor relocation algorithm and provide novel solutions to implement this algorithm in our mobile sensor platform. Experimental results show that our relocation algorithm can reduce the sensor relocation time and balance the energy consumption of the mobile nodes.

Abstract—Sensor relocation is to repair coverage holes caused by node failures. One way to repair coverage holes is to find redundant nodes to replace faulty nodes. Most researches took a long time to find redundant nodes since they randomly scattered redundant nodes around the sensing field. To record the precise position of sensor nodes, most researches assumed that GPS was installed in sensor nodes. However, high costs and power-consumptions of GPS are heavy burdens for sensor nodes. Thus, we propose a fast sensor relocation algorithm to arrange redundant nodes to form redundant walls without GPS. Redundant walls are constructed in the position where the average distance to each sensor node is the shortest. Redundant walls can guide sensor nodes to find redundant nodes in the minimum time. Simulation results show that our algorithm can find the proper redundant node in the minimum time and reduce the relocation time with low message complexity. Keywords—Coverage, distributed algorithm, sensor relocation, wireless sensor networks. I.

Abstract—More and more researches focus on the development of mobile wireless sensor networks (MWSNs) due to the favorable advantages and applications of MWSNs. However, there is not a comprehensive survey about the research issues on MWSNS for the state of art. In this paper, we survey the communication and data management issues on MWSNs and provide extensible research directions of MWSNs. Keywords—Mobile wireless sensor networks, Survey, Overview,

Abstract—Wireless sensor networks (WSNs) which is proposed in the late of 1990s have received unprecedented attention because of their exciting potential applications in military, industrial and civilian areas (e.g, environmental and habitat monitoring). Although WSNs have become more and more prospective in human life with the development of hardware and communications technologies, there are some natural limitations of WSNs (e.g., network connectivity, network lifetime) due to the static network style in WSNs. Moreover, more and more application scenarios require the sensors in WSNs to be mobile rather than static so as to make traditional applications in WSNs become smarter and enable some new applications. All this induce the mobile wireless sensor networks (MWSNs) which can greatly promote the development and application of WSNs. However, to the best of our knowledge, there is not a comprehensive survey about research issues in MWSNs. In this paper, we research the communication issues and data management issues in MWSNs, discuss different research methods in MWSNs and propose some further research areas in MWSNs. We hope that our work provide some guidance about research in MWSNs. Index Terms—Wireless sensor networks (WSNs), Mobile wireless sensor networks (MWSNs), survey, communication, data management. I.

Abstract—Network coverage is one of the most decisive factors for determining the efficiency of a wireless sensor network. However, in dangerous or hostile environments such as battle fields or active volcano areas, we can neither deterministically or purposely deploy sensors as desired, thus the emergence of coverage holes (the unmonitored areas) is unavoidable. In addition, the introduction of new coverage holes during network operation due to sensor failures due to energy depletion shall significantly reduce coverage efficacy. Therefore, we need to either remotely control or set up a protocol to heal them as soon as possible in an automated fashion. In this paper, we focus on how to schedule mobile sensors in order to cope with coverage hole issues in a hybrid sensor network containing both static and mobile sensors. To this end, we introduce a new metric, namely to maximize the minimum remaining energy of all moved sensor since the more energy remains, the longer the network can operate. Based on this metric, we propose an efficient coverage healing algorithm that always determines an optimal location for each mobile sensor in order to heal all coverage holes, after all mobile sensors locations and coverage holes are located. Simulation results confirm the efficiency and utilization of our proposed method. Index Terms—hybrid sensor network; coverage hole, movement schedule, mobile sensor, coverage hole healing I.

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