Corona based deployment strategies in wireless sensor network: A survey

Rahman, Atiq Ur; Alharby, Abdulrahman; Hasbullah, Halabi; Almuzaini, Khalid K.

doi:10.1016/j.jnca.2016.02.003

Cited by 43 publications

(30 citation statements)

References 78 publications

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“…By contrast, heuristic solvers cannot generally guarantee the optimal solution but are known to quickly find a near-optimal solution [18]. In [10], the authors developed a classification scheme for sensor node deployment strategies. The primary classification of static deployments is based on non-corona-based and corona-based deployments.…”

Section: Preliminary Definitionsmentioning

confidence: 99%

“…For simplicity, and as considered in many related works such as [7,[9][10][11], we assume omni-directional sensing ability of the sensor, and a binary detection model. The choice of the sensor detection model does not limit the applicability of the proposed approach.…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned in [10], each and every approach has its limitations and considers the coverage and connectivity problem to be solved under certain circumstances. Only a few have considered solving the issue of the optimal number of nodes while optimizing coverage and connectivity.…”

Section: Related Workmentioning

confidence: 99%

“…These algorithms are adequate only for off-line applications where the sensor nodes need no adaptation after deployment. Based on [10], only the authors in [28], have presented a Corona-based deployment approach using optimum sensor nodes to achieve balanced energy consumption, and to maximize the area coverage and the network lifetime. As mentioned in [10], each and every approach has its limitations and considers the coverage and connectivity problem to be solved under certain circumstances.…”

Section: Related Workmentioning

confidence: 99%

“…WSN applications can only operate for a given period of time or as long as possible, due to sensor nodes' limited supply of energy from batteries. Hence, the network lifetime becomes a very important issue [10]. In the phase of deployment, if nodes are too close to each other, coverage overlapping and redundant nodes could occur, which generates unnecessary data transfer and more traffic load, and if the distance between nodes is too large, nodes will consume more energy to distribute data through the network.…”

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confidence: 99%

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A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

et al. 2017

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Abstract:Wireless sensor network (WSN) applications are rapidly growing and are widely used in various disciplines. Deployment is one of the key issues to be solved in WSNs, since the sensor nodes' positioning affects highly the system performance. An optimal WSN deployment should maximize the collection of the desired interest phenomena, guarantee the required coverage and connectivity, extend the network lifetime, and minimize the network cost in terms of energy consumption. Most of the research effort in this area aims to solve the deployment issue, without minimizing the network cost by reducing unnecessary working nodes in the network. In this paper, we propose a deployment approach based on the gradient method and the Simulated Annealing algorithm to solve the sensor deployment problem with the minimum number of sensor nodes. The proposed algorithm is able to heuristically optimize the number of sensors and their positions in order to achieve the desired application requirements.

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Section: Preliminary Definitionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

et al. 2017

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A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks

Goyal

Dave

Verma

2017

Int J Communication

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The performance of underwater wireless sensor network gets affected by the working of a cluster in the network. The cluster head (CH) or cluster member (CM) fails because of energy depletion or hardware errors that increase delay and message overhead of the network. To recover the affected cluster, a technique is required to identify the failed CH or CM. We propose a fault detection and recovery technique (FDRT) for a cluster-based network in this paper. Primarily, while selecting the CH, a backup cluster head (BCH) is selected using fuzzy logic technique based on parameters such as node density, residual energy, load, distance to sink, and link quality. Then, failure of CH, BCH, and CM is detected. If fault is detected at CH, then the BCH will start performing the task of failed CH. Simultaneously, when BCH failed, any other CM will be elected as BCH. If any of the CM appears to be nonperforming, then CH will detect the communication failure and request BCH to transfer the data from the failed CM to CH. The comparison of proposed FDRT is performed with existing FDRTs EDETA, RCH, and SDMCGC on the basis of packet drop, end-to-end delay, energy consumption, and delivery ratio of data packets. By simulation results, it is shown that FDRT for cluster-based underwater wireless sensor network results in quicker detection of failures and recovery of the network along with the reduction in energy consumption, thereby increasing the lifespan of the network.KEYWORDS backup cluster head, cluster head, cluster member, fault detection, fault recovery, UWSN

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A novel scheme for mitigation of energy hole problem in wireless sensor network for military application

Behera

Mohapatra

2021

Int J Communication

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Wireless Sensor Networks (WSNs) have been widely considered as one of the most important technologies with unique characteristics designed for various applications. However, one of the major problems in WSNs is a limited lifetime. The creation of an energy hole near the sink has been a chief concern for researchers working to mitigate the lifetime problem. The data transmission in a multi-hop network, as used for military purposes, follows a many-to-one pattern resulting in a heavier traffic load for nodes near to sink. Hence, these nodes deplete energy very soon resulting in a vacuum of energy and breakage of the network. This article aims to mitigate the energy hole problem (EHP) by implementing a corona-based model in a clustered network. Here the cluster head selection is regularized by preferring the outer corona nodes over the inner ones. As a result, the energy balances evenly in the network with fewer loads to the nodes nearer to the sink. The distance between the sending node and the sink is evaluated to estimate the required energy for transmission. Only when a node has energy more than the required value, it is allowed to transmit, or else it turns off the radio to save energy. The proposed model is implemented in both homogeneous and heterogeneous networks and better network performance is observed.

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Corona based deployment strategies in wireless sensor network: A survey

Cited by 43 publications

References 78 publications

A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks

A novel scheme for mitigation of energy hole problem in wireless sensor network for military application

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