Deployment strategy of WSN based on minimizing cost per unit area

Fan, Tie-Gang; Teng, Guifa; Hou, Limin

doi:10.1016/j.comcom.2013.10.002

Cited by 22 publications

(3 citation statements)

References 19 publications

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“…Nonlinear methods give better accuracy and reliable results which is not possible by linear optimization algorithms for the same application Harrag et al (2018). Nodes are deployed in hexagonal cell architecture model to minimize the cot per unit are in network by Tiegang et al (2015) and Yang et al (2014). Integer programming model and energy allocation algorithm help in finding the cost per unit area in network and thereby help in proper deployment of sensors, sinks to reduce energy wastage in the network.…”

Section: Multilayer In Vehicular Ad Hoc Network and 5g Networkmentioning

confidence: 99%

Multilayer DS-MAC with game theory optimization

Radha

Bala

Nagabushanam

2021

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Purpose Energy is the major concern in wireless sensor networks (WSNs) for most of the applications. There exist many factors for higher energy consumption in WSNs. The purpose of this work is to increase the coverage area maintaining the minimum possible nodes or sensors. Design/methodology/approach This paper has proposed multilayer (ML) nodes deployment with distributed MAC (DS-MAC) in which nodes listen time is controlled based on communication of neighbors. Game theory optimization helps in addressing path loss constraints while selecting path toward base stations (BS). Findings The simulation is carried out using NS-2.35, and it shows better performance in ML DS-MAC compared to random topology in DS-MAC with same number of BS. The proposed method improves performance of network in terms of energy consumption, network lifetime and better throughput. Research limitations/implications Energy consumption is the major problem in WSNs and for which there exist many reasons, and many approaches are being proposed by researchers based on application in which WSN is used. Node mobility, topology, multitier and ML deployment and path loss constraints are some of the concerns in WSNs. Practical implications Game theory is used in different situations like countries whose army race, business firms that are competing, animals generally fighting for prey, political parties competing for vote, penalty kicks for the players in football and so on. Social implications WSNs find applications in surveillance, monitoring, inspections for wild life, sea life, underground pipes and so on. Originality/value Game theory optimization helps in addressing path loss constraints while selecting path toward BS.

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Section: Multilayer In Vehicular Ad Hoc Network and 5g Networkmentioning

confidence: 99%

Multilayer DS-MAC with game theory optimization

Radha

Bala

Nagabushanam

2021

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“…More recently, Fan et al focused on calculating the most energy efficient deployment strategy for WSNs using an integral programming model [35]. They used a regular hexagonal cell architecture [36,37] to model the location of sensor nodes in the plane, which, in fact, is similar to a ring model with C = 6.…”

Section: Related Workmentioning

confidence: 99%

The impact of dual prediction schemes on the reduction of the number of transmissions in sensor networks

Dias¹,

Bellalta²,

Oechsner³

2017

Computer Communications

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Future Internet of Things (IoT) applications will require that billions of wireless devices transmit data to the cloud frequently. However, the wireless medium access is pointed as a problem for the next generations of wireless networks; hence, the number of data transmissions in Wireless Sensor Networks (WSNs) can quickly become a bottleneck, disrupting the exponential growth in the number of interconnected devices, sensors, and amount of produced data. Therefore, keeping a low number of data transmissions is critical to incorporate new sensor nodes and measure a great variety of parameters in future generations of WSNs. Thanks to the high accuracy and low complexity of state-of-the-art forecasting algorithms, Dual Prediction Schemes (DPSs) are potential candidates to optimize the data transmissions in WSNs at the finest level because they facilitate for sensor nodes to avoid unnecessary transmissions without affecting the quality of their measurements. In this work, we present a sensor network model that uses statistical theorems to describe the expected impact of DPSs and data aggregation in WSNs. We aim to provide a foundation for future works by characterizing the theoretical gains of processing data in sensors and conditioning its transmission to the predictions' accuracy. Our simulation results show that the number of transmissions can be reduced by almost 98% in the sensor nodes with the highest workload. We also detail the impact of predicting and aggregating transmissions according to the parameters that can be observed in common scenarios, such as sensor nodes' transmission ranges, the correlation between measurements of different sensors, and the period between two consecutive measurements in a sensor.

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“…Node deployment is mainly related to features such as coverage, connectivity, cost, and network lifetime [37,38,39,40,41,42], wherein coverage and connectivity are the most important indicators in the assessment of the performance of WSN information collection systems [43]. The goal of full coverage node deployment is to access network target area information without missing any relevant data.…”

Section: Introductionmentioning

confidence: 99%

Node Deployment with k-Connectivity in Sensor Networks for Crop Information Full Coverage Monitoring

Liu

Cao

Zhu

et al. 2016

Sensors

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Wireless sensor networks (WSNs) are suitable for the continuous monitoring of crop information in large-scale farmland. The information obtained is great for regulation of crop growth and achieving high yields in precision agriculture (PA). In order to realize full coverage and k-connectivity WSN deployment for monitoring crop growth information of farmland on a large scale and to ensure the accuracy of the monitored data, a new WSN deployment method using a genetic algorithm (GA) is here proposed. The fitness function of GA was constructed based on the following WSN deployment criteria: (1) nodes must be located in the corresponding plots; (2) WSN must have k-connectivity; (3) WSN must have no communication silos; (4) the minimum distance between node and plot boundary must be greater than a specific value to prevent each node from being affected by the farmland edge effect. The deployment experiments were performed on natural farmland and on irregular farmland divided based on spatial differences of soil nutrients. Results showed that both WSNs gave full coverage, there were no communication silos, and the minimum connectivity of nodes was equal to k. The deployment was tested for different values of k and transmission distance (d) to the node. The results showed that, when d was set to 200 m, as k increased from 2 to 4 the minimum connectivity of nodes increases and is equal to k. When k was set to 2, the average connectivity of all nodes increased in a linear manner with the increase of d from 140 m to 250 m, and the minimum connectivity does not change.

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Deployment strategy of WSN based on minimizing cost per unit area

Cited by 22 publications

References 19 publications

Multilayer DS-MAC with game theory optimization

Multilayer DS-MAC with game theory optimization

The impact of dual prediction schemes on the reduction of the number of transmissions in sensor networks

Node Deployment with k-Connectivity in Sensor Networks for Crop Information Full Coverage Monitoring

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