Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization

Raj, P. V. Pravija; Khedr, Ahmed M.; Aghbari, Zaher Al

doi:10.1007/s11276-020-02254-x

Cited by 44 publications

(22 citation statements)

References 57 publications

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“…Where E can is the residual energy of the next-hop cluster head to be selected, and E can is the average residual en- The traditional ant colony optimization algorithm only considers the length of the path when updating pheromone, but ignores some other factors which have great influence on the path [45]. Therefore, we optimized the pheromone update method.…”

Section: Multi-hop Transmission Path Based On Ant Colony Optimizationmentioning

confidence: 99%

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

et al. 2020

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Clustering sensor nodes is an effective method in designing routing algorithms for Wireless Sensor Networks (WSNs), which improves network lifetime and energy efficiency. In clustered WSNs, cluster heads are the key nodes, they need to perform more tasks, so they consume more energy. Therefore, it is an important problem to select the optimal cluster heads. In this paper, we propose a clustering algorithm that selects cluster heads using an improved artificial bee colony (ABC) algorithm. Based on the standard ABC algorithm, an efficient improved ABC algorithm is proposed, and then the network cluster head energy, cluster head density, cluster head location and other similar factors are introduced into the improved ABC algorithm theory to solve the clustering problem in WSNs. In the network initialization period, all nodes have the same energy level, the improved ABC algorithm is used to optimize fuzzy C-means clustering to find the optimal clustering method. We also propose an energy-efficient routing algorithm based on an improved ant colony optimization for routing between the cluster heads and the base station. In order to improve energy efficiency and further improve network throughput, in the stable transmission phase, we introduce a polling control mechanism based on busy/idle nodes into intra-cluster communication. The performance of the proposed protocol is evaluated in several different scenarios. The simulation results show that the proposed protocol has a better performance compared to a number of recent similar protocols. INDEX TERMS WSN, clustering, energy efficiency, network lifetime, high throughput, polling, routing algorithm, artificial bee colony

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Section: Multi-hop Transmission Path Based On Ant Colony Optimizationmentioning

confidence: 99%

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

et al. 2020

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“…Raj et al 28 presented data gathering in WSNs via MS using the technique named game theory and ant colony data gathering (GTAC‐DG). This approach has combined the game theory and ant colony for choosing the best rendezvous points (RPs) and optimal path for MS.…”

Section: Related Workmentioning

confidence: 99%

Energy efficient hotspot problem mitigation techniques using multiple mobile sink in heterogeneous wireless sensor network

Gupta

Tripathi

Singh

2020

Int J Communication

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Summary Wireless sensor networks (WSNs) are application‐oriented and mainly used in the following fields, such as battleground surveillance, border security, and disaster management. The sensor nodes should be extra reliable, and also, high consistency is needed to perform these tasks. Therefore, heterogeneous wireless sensor networks (HWSNs) are introduced to enhance the network lifetime and stability period by minimizing the node's communicative distance from mobile sinks (MSs). The maximum amount of energy is consumed while communicating the data from nodes to a single sink. In this work, multiple mobile sinks (MMSs) are used to transfer the data from nodes to sink with less energy consumption and a reduced number of hops. Also, higher connectivity is ensured as the sinks are placed close to the nodes, and data can be successfully delivered to sinks. This work reveals the importance of multiple MSs in HWSNs. The use of MMSs and cluster‐based routing approaches improves the efficiency of data collection in HWSNs. K‐medoid with adaptive sunflower optimization algorithm is used for the selection of cluster head (CH). To achieve energy efficiency (EE), detecting an optimal route for MS is a critical task. Then, the hybrid Harris Hawk and ant colony optimization (HANT) algorithm‐based sink moving strategy is used for finding an optimal traversal path. The proposed heterogeneous network is implemented in the network simulator (version 2) (NS2) platform. Simulation results show enhancement in EE in terms of network lifetime, stability period, several dead and alive nodes, and network's remaining energy.

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“…There are numerous studies to formulate an energy-efficient mobile sink trajectory in the WSN [27][28][29][30][31][32][33][34]. In [27], an obstacle in the communication link between a sensor and a mobile sink is considered.…”

Section: Mobile Sink Trajectorymentioning

confidence: 99%

Iterative Sensor Clustering and Mobile Sink Trajectory Optimization for Wireless Sensor Network with Nonuniform Density

Park

Kim

Youn

et al. 2020

Wireless Communications and Mobile Computing

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Sensor clustering and trajectory optimization are a hot topic for last decade to improve energy efficiency of wireless sensor network (WSN). Most of existing studies assume that the sensor is uniformly deployed or all regions in the WSN coverage have the same level of interest. However, even in the same WSN, areas with high probability of disaster will have to form a “hotspot” with more sensors densely placed in order to be sensitive to environmental changes. The energy hole can be serious if sensor clustering and trajectory optimization are formulated without considering the hotspot. Therefore, we need to devise a sensor clustering and trajectory optimization algorithm considering the hotspots of WSN. In this paper, we propose an iterative algorithm to minimize the amount of energy consumed by components of WSN named ISCTO. The ISCTO algorithm consists of two phases. The first phase is a sensor clustering phase used to find the suitable number of clusters and cluster headers by considering the density of sensor and residual battery of sensors. The second phase is a trajectory optimization phase used to formulate suitable trajectory of multiple mobile sinks to minimize the amount of energy consumed by mobile sinks. The ISCTO algorithm performs two phases repeatedly until the amount of energy consumed by the WSN is not reduced. In addition, we show the performance of the proposed algorithm in terms of the total amount of energy consumed by sensors and mobile sinks.

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Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization

Cited by 44 publications

References 57 publications

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

Energy efficient hotspot problem mitigation techniques using multiple mobile sink in heterogeneous wireless sensor network

Iterative Sensor Clustering and Mobile Sink Trajectory Optimization for Wireless Sensor Network with Nonuniform Density

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