Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> A. Post Deployment Repositioning <s> This paper describes two novel techniques, information-driven sensor querying IDSQ and constrained anisotropic diffusion routing CADR, for energy-efficient data querying and routing in ad hoc sensor networks for a range of collaborative signal processing tasks. The key idea is to introduce an information utility measure to select which sensors to query and to dynamically guide data routing. This allows us to maximize information gain while minimizing detection latency and bandwidth consumption for tasks such as localization and tracking. Our simulation results have demonstrated that the information-driven querying and routing techniques are more energy efficient, have lower detection latency, and provide anytime algorithms to mitigate risks of link/node failures. <s> BIB001 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> A. Post Deployment Repositioning <s> To provide satisfactory coverage is very important in many sensor network applications such as military surveillance. In order to obtain the required coverage in harsh environments, mobile sensors are helpful since they can move to cover the area not reachable by static sensors. Previous work on mobile sensor deployment is based on a round by round process, where sensors move iteratively until the maximum coverage is reached. Although these solutions can deploy mobile sensors in a distributed way, the mobile sensors may move in a zig-zag way and waste a lot of energy compared to moving directly to the final location. To address this problem, we propose a proxy-based sensor deployment protocol. Instead of moving iteratively, sensors calculate their target locations based on a distributed iterative algorithm, move logically, and exchange new logical locations with their new logical neighbors. Actual movement only occurs when sensors determine their final locations. Simulation results show that the proposed protocol can significantly reduce the energy consumption compared to previous work, while maintaining similar coverage. <s> BIB002 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> A. Post Deployment Repositioning <s> Recently there has been a great deal of research on using mobility in sensor networks to assist in the initial deployment of nodes. Mobile sensors are useful in this environment because they can move to locations that meet sensing coverage requirements. This paper explores the motion capability to relocate sensors to deal with sensor failure or respond to new events. We define the problem of sensor relocation and propose a two-phase sensor relocation solution: redundant sensors are first identified and then relocated to the target location. We propose a Grid-Quorum solution to quickly locate the closest redundant sensor with low message complexity, and propose to use cascaded movement to relocate the redundant sensor in a timely, efficient and balanced way. Simulation results verify that the proposed solution outperforms others in terms of relocation time, total energy consumption, and minimum remaining energy. <s> BIB003 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> A. Post Deployment Repositioning <s> Constructing a sensor network with a mix of mobile and static sensors can achieve a balance between sensor coverage and sensor cost. In this paper, we design two bidding protocols to guide the movement of mobile sensors in such sensor networks to increase the coverage to a desirable level. In the protocols, static sensors detect coverage holes locally by using Voronoi diagrams and bid mobile sensors to move. Mobile sensors accept the highest bids and heal the largest holes. Simulation results show that our protocols achieve suitable trade-off between coverage and sensor cost <s> BIB004 </s> Node Relocation Techniques for Wireless Sensor Networks: A Short Survey <s> A. Post Deployment Repositioning <s> Abstract In this paper we study an opportunistic geographically constrained information sharing paradigm known under the name Floating Content (FC), considering two different mobility models that describe the behavior of pedestrians. We assume that users carrying their smartphones walk from one location to another and then stop for a while. Information transfers take place in the periods when users pause between movements. We develop analytical models to compute the performance metrics that characterize FC in this case and we validate analytical results with data collected during an experiment performed in a university campus. The comparison proves the accuracy of our analytical models. Moreover, results unveil the key relevance for FC performance of group dynamics in user movements. <s> BIB005
Generally, the deployment of WSN in a conventional manner involves locating the sensor nodes either randomly or by the ad-hoc procedure for the appropriate and convenient outdoor application BIB001 . However, a sensor node or piece of equipment in an industrial setup is designed to be positioned at the same point for several years. Therefore, it is not possible to contemplate the luxury of relocating nodes in a random way. Additionally, connectivity and coverage are essential for sending information to the required destination effectively. To overcome such difficulties, systematic and well-planned design and arrangement is required. This arrangement and planning helps in the reduction of cost and is ultimately time-saving for designing a WSN. Hence, it is necessary to organize and simulate the WSN before deploying it. VECtor-centered technique (VEC), Minimax technique, and VORonoi-centered technique (VOR) BIB003 involves the conceptualization of BIB003 BIB002 associated with this type. There is a firm bond of all these three techniques to sensor node's Voronoi polygon or position in which the sensor node or the point that is in proximity rather than to the sensory border. In this scenario, the VECtor-based technique can be handled by applying Coulomb's law as the corresponding procedure so as to demonstrate deterrence amongst electrostatic components. The sensor node that is meant to dispossess a constituent of Voronoi polygon of the node subsequently is aimed to get eliminated from other sensors close by via vigour analogous to interlude that it acquires probably from sensors or polygon's angular points. Knowingly, for reducing the covered area for the route, BIB002 has suggested a technique depending on the proxy that lacks the physical movement of the nodes, apart from the computed destination. At this juncture, it is found that the author emphasis to a great extent on the methodology of authentication of movable or stationary sensors. In this regard, the movable nodes are responsible for occupying the level of that point where sensors are missing in distributed pattern evaluated by stationary sensors, indicating that movable sensors are in the consistent mobility and locate stationary sensors in the form of an agent. Following this frame of reference, there will be a major depletion in distance whether from the totality or the calculated set of numbers in which the mobile sensors BIB005 direct alongside in retaining the coverage at equal rate BIB004 . By implementing it using a methodology www.ijacsa.thesai.org approved from a widely accepted design for the load balance amongst the sensors in conformations number on parallel computing through the process is separated into multiple parts, concurrently carried out various processors. This strategy, successively, is used in multi-cluster WSN in which the square cell that forms 2-D mesh will specify each single cluster and several nodes added on the single cell shall categorize cluster's load. The position in an inter-connection either in vertical or horizontal indices and the sensor node quantity in its cluster signify that the only communication the cluster-head could have it is by using its same location in other nearby cells. Consequently, the corresponding attained coverage is relevant to the problem of balancing the level of energy to stabilize the distribution of nodes among clusters. Fig. 4 shows the post deployment repositioning techniques. 