LEACH Protocol Analysis and Optimization of Wireless Sensor Networks Based on PSO and AC

Jiang, Shuyu

doi:10.1109/ihmsc.2018.10163

Cited by 24 publications

(8 citation statements)

References 2 publications

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“…Particle Swarm Optimization (PSO) is a stochastic population-based optimization method proposed in [9]. Communications and networking is an area where PSO has been applied widely to solve network performance optimization problems [10] [11] [12]. PSO is also used for wireless sensor networks to effectively create clusters of nodes where data collection, energy consumptions and network lifetime is optimized, see [12] and [13].…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

PSO and Kalman Filter-Based Node Motion Prediction for Data Collection from Ocean Wireless Sensors Network with UAV

Grøtli

Johansen

2021

2021 IEEE International Conference on Consumer Electronics (ICCE)

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In this paper, we consider a wireless sensor network of nodes at the sea surface drifting due to wind and sea currents. In our scenario an Unmanned Aerial Vehicle (UAV) will be used to gather data from the sensor nodes. The goal is to find a flyable path which is optimal in terms of sensor node energy consumption, total channel throughput between the UAV and sensor nodes, flight time for the UAV and frequency of the node visits by the UAV. Finally, the path should also be optimal concerning node position estimation uncertainty. A Kalman Filter (KF) is used to estimate the nodes motions and Particle Swarm Optimization (PSO) is the method used to calculate the UAV path taking all of these objectives into account. The proposed node tracking aware path planning solution is compared to two other scenarios: One where the path planning is based on full knowledge of the node positions at all times, and one where path planning is based on the last known positions of the nodes.

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

PSO and Kalman Filter-Based Node Motion Prediction for Data Collection from Ocean Wireless Sensors Network with UAV

Grøtli

Johansen

2021

2021 IEEE International Conference on Consumer Electronics (ICCE)

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“…• In a similar idea of research done by [1], [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], we also used random techniques in the cluster formation phase, but we added the remaining energy constraint to the CH selection.…”

Section: Figure 1 Cluster-based Data Transmissionmentioning

confidence: 99%

“…For a decade, LEACH has become prevalent, and many of the previous works have demonstrated another approach in cluster formation by introducing the remaining energy parameters, RSSI, etc. [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] also in data gathering [56][57][58].…”

Section: Introductionmentioning

confidence: 99%

A New Bio-Inspired for Cooperative Data Transmission of IoT

et al. 2020

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The original queen honey bee migration (QHBM) was developed for independent action on solving efficient mobile routing in WSN. In this paper, we enhanced the original QHBM using Binary testing injection on the cooperative node's selection on IoT system. We also added a new cost function for making nodes' coalition, implementing the threshold value for modified QHBM (mQHBM for short), and demonstrating the mQHBM-CMIMO in fair comparison with another previous algorithms. Our research portrayed that mQHBM can perform better than its competitors such as Fuzzy-BT, Neuro Fuzzy and PSO in terms of network lifetime and the end to end delay.

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“…The transmission distance and energy consumption are also considered. Jiang [7] optimize a WSN routing protocol, i.e. LEACH protocol, using ant colony algorithm and particle swarm algorithm.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Optimization of Data Access and Hybrid Transmission in Wireless Sensor Network

et al. 2020

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In recent years, the rapid advancements in Internet of Things boost the utilization of Wireless Sensor Network (WSN). Through WSN, a huge amount of various kinds of data can be acquired from different environments, which further facilitate us to harness the world we live. However, the error-prone and unpredictable natures of wireless links significantly impair the quality of data transmission and the utility of acquired data in WSN. Therefore, to solve this crucial problem, this paper focuses on data access control and hybrid transmission control of WSN. Based on the mathematical models of the two control operations, an intractable optimization problem is first formulated with numerous considerations, including data utility, energy consumption, network stability and data loss rate. Because of the complexity and intractability of the originally formulated problem, a Lyapunov function-based network optimization theory is utilized to transform and decompose it into three relatively simple subproblems. Stochastic Data Scheduling Mechanism (SDSM) is designed based on the solutions to the three subproblmes. The optimality and implementation of SDSM are also analyzed. Finally, the performance of SDSM is demonstrated through extensive evaluations. INDEX TERMS Wireless sensor network, data access, hybrid transmission, stochastic scheduling.

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LEACH Protocol Analysis and Optimization of Wireless Sensor Networks Based on PSO and AC

Cited by 24 publications

References 2 publications

PSO and Kalman Filter-Based Node Motion Prediction for Data Collection from Ocean Wireless Sensors Network with UAV

PSO and Kalman Filter-Based Node Motion Prediction for Data Collection from Ocean Wireless Sensors Network with UAV

A New Bio-Inspired for Cooperative Data Transmission of IoT

Stochastic Optimization of Data Access and Hybrid Transmission in Wireless Sensor Network

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