CHHP: coverage optimization and hole healing protocol using sleep and wake-up concept for wireless sensor network

2022 ◽  
Author(s):  
Vipul Narayan ◽  
A. K. Daniel
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Coverage Optimization ◽  
Hole Healing

scholarly journals Hole Detection and Healing in Hybrid Wireless Sensor Network

2016 ◽  
Vol 1 (2) ◽  
pp. 41-45
Author(s):  
Samidha N. Kalwaghe ◽  
Atul V. Dusane
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Healing Process ◽  
Residual Energy ◽  
Maximum Energy ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Hole Healing ◽  
Cost Efficient ◽  
Hole Detection

The emerging technology of wireless sensor network (WSN) is expected to provide a broad range of applications, such as battlefield surveillance, environmental monitoring, smart spaces and so on. The coverage problem is a fundamental issue in WSN, which may cause due to low residual energy of nodes or poor installment. But in order to get full coverage of sensing area Coverage problem must be avoided. If the problem is unavoidable the coverage hole must be healed. Current hole healing algorithms uses complicated hole detection strategies like TENT rule.  This project seeks to address the problem of hole detection and healing in mobile WSNs by deploying mobile sensors in the network, which is called hybrid sensor network. An enhanced hole detection and healing method (MHEAL) is proposed. MHEAL is a distributed and localized algorithm that operates in two distinct phases. First, is Distributed Hole Detection (DHD) proposed to identify the boundary nodes and discover holes. Second, is hole healing which uses a virtual forces based hole healing approach where only the nodes located at an appropriate distance from the hole and the nodes having maximum energy  will be involved in the healing process. Unlike existing algorithms, proposed algorithm uses QURD based node detection and energy efficient Hole healing and thus solves the problem of hole with 100% coverage, minimum node movements and minimum node distance travelled thus giving a cost efficient solution.

scholarly journals Coverage Optimization Algorithm of Wireless Sensor Network Based on Mobile Nodes

2016 ◽  
Vol 12 (08) ◽  
pp. 45 ◽  
Author(s):  
Li Zhu ◽  
Chunxiao Fan ◽  
Huarui Wu ◽  
Zhigang Wen
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Optimization Algorithm ◽  
Quantitative Relationship ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Mobile Nodes ◽  
Coverage Optimization ◽  
Blind Zone

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; -ms-layout-grid-mode: line; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">To reduce the blind zone in network coverage, we propose a coverage optimization algorithm of wireless sensor network based on mobile nodes. This algorithm calculates the irregularity of blind zone in network coverage and obtains the minimum approximate numerical solution by utilizing the quantitative relationship between energy consumption of related nodes and the position of the mobile nodes. After determining the optimal relative position of the mobile nodes, the problem of blind zone between the static nodes is addressed. Simulation result shows that the proposed algorithm has high dynamic adaptability and can address the problem of blind zone maximally. Besides increasing the network coverage, the algorithm also reduces the network energy consumption, optimizes network coverage control and exhibits high convergence. </span>

scholarly journals Wireless Sensor Network Coverage Optimization Based on Fruit Fly Algorithm

2018 ◽  
Vol 14 (06) ◽  
pp. 58 ◽  
Author(s):  
Ren Song ◽  
Zhichao Xu ◽  
Yang Liu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Fruit Fly ◽  
Effective Radius ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Improved Algorithm

<p class="0abstract"><span lang="EN-US">To solve the defect of traditional node deployment strategy, the improved <a name="_Hlk502130691"></a>fruit fly algorithm was combined with wireless sensor network. The optimization of network coverage was implemented. </span><span lang="EN-US">Based on a new type of intelligent algorithm, the change step of fruit fly optimization algorithm (CSFOA)</span><span lang="EN-US">was proposed. At the same time, the mathematical modeling of two network models was carried out respectively. The grid coverage model was used. The network coverage and redundancy were transformed into corresponding mathematical variables by means of grid partition.</span><span lang="EN-US">Among them, the maximum effective radius of sensor nodes was fixed in mobile node wireless sensor network. The location of nodes was randomly cast. The location of sensor nodes was placed in fixed position nodes. The effective radius of nodes can be changed dynamically.</span><span lang="EN-US">Finally, combined with the corresponding network model, the improved algorithm was applied to wireless sensor network.</span><span lang="EN-US">The combination of the optimal solution of the node position and the perceptual radius was found through the algorithm. The maximum network coverage was achieved.</span><span lang="EN-US">The two models were simulated and verified. The results showed that the improved algorithm was effective and superior to the coverage optimization of wireless sensor networks.</span></p>

Hole Detection and Healing in Hybrid Wireless Sensor Network

2016 ◽  
Vol 1 (2) ◽  
pp. 41-45
Author(s):  
Samidha N Kalwaghe ◽  
Atul Vasudev Dusane
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Healing Process ◽  
Residual Energy ◽  
Maximum Energy ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Hole Healing ◽  
Cost Efficient ◽  
Hole Detection

The emerging technology of wireless sensor network (WSN) is expected to provide a broad range of applications, such as battlefield surveillance, environmental monitoring, smart spaces and so on. The coverage problem is a fundamental issue in WSN, which may cause due to low residual energy of nodes or poor installment. But in order to get full coverage of sensing area Coverage problem must be avoided. If the problem is unavoidable the coverage hole must be healed. Current hole healing algorithms uses complicated hole detection strategies like TENT rule. This project seeks to address the problem of hole detection and healing in mobile WSNs by deploying mobile sensors in the network, which is called hybrid sensor network. An enhanced hole detection and healing method (MHEAL) is proposed. MHEAL is a distributed and localized algorithm that operates in two distinct phases. First, is Distributed Hole Detection (DHD) proposed to identify the boundary nodes and discover holes. Second, is hole healing which uses a virtual forces based hole healing approach where only the nodes located at an appropriate distance from the hole and the nodes having maximum energy will be involved in the healing process. Unlike existing algorithms, proposed algorithm uses QURD based node detection and energy efficient Hole healing and thus solves the problem of hole with 100% coverage, minimum node movements and minimum node distance travelled thus giving a cost efficient solution.

scholarly journals Wireless sensor network coverage optimization based on whale group algorithm

2018 ◽  
Vol 15 (3) ◽  
pp. 569-583 ◽  
Author(s):  
Lei Wang ◽  
Weihua Wu ◽  
Junyan Qi ◽  
Zongpu Jia
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Network Performance ◽  
Mathematic Model ◽  
Initial Distribution ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization

For all of types of applications in wireless sensor networks (WSNs), coverage is a fundamental and hot topic research issue. To monitor the interest field and obtain the valid data, the paper proposes a wireless sensor network coverage optimization model based on improved whale algorithm. The mathematic model of node coverage in wireless sensor networks is developed to achieve full coverage for the interest area. For the model, the idea of reverse learning is introduced into the original whale swarm optimization algorithm to optimize the initial distribution of the population. This method enhances the node search capability and speeds up the global search. The experiment shows that this algorithm can effectively improve the coverage of nodes in wireless sensor networks and optimize the network performance.

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