Optimized hierarchical routing technique for wireless sensors networks

El-Said, Shaimaa Ahmed; Osamaa, Asmaa; Hassanien, Aboul Ella

doi:10.1007/s00500-015-1762-x

Cited by 21 publications

(6 citation statements)

References 35 publications

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“…In this technique, artificial fish swarm optimization is used for the best possible cluster head location. The experimental results show that the proposed AFSA technique is stable and more energy efficient compared to existing protocols [11].…”

Section: Optimal Cluster Head Selection Andmentioning

confidence: 93%

Optimal Cluster Head Selection and Clustering for WSN Using PSO

Shinde¹,

Bichkar²

2020

IJITEE

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Nowadays, Wireless Sensor Network is the promising and booming technology used in a variety of applications like disaster monitoring, health care, environmental monitoring, agriculture, industrial automation, etc. However the main drawback of the wireless sensor network is the limited energy source of the sensor nodes. Consequently, efficient utilization of the energy becomes essential for increasing the lifetime of network. Clustering protocol is one of the best energy efficient approach for saving the energy and maximizing the network lifetime. But the improper selection of cluster heads (CHs) may lead to the death of the CHs which deteriorate the performance of the network. Therefore the proper selection of cluster head becomes important for the energy conservation of sensor nodes and to maximize the lifetime of network. In this paper, we have presented PSO based optimal cluster head selection algorithm, in which the best possible CHs are chosen on the basis of parameters like residual energy, intra-cluster distance, and inter-cluster distance of the sensor node. With the effective scheme of particle encoding and fitness function, the proposed PSO algorithm is implemented for reducing the energy consumption and improving lifetime of network. The proposed algorithm also ensures the uniform distribution of the energy over network, by changing the role of CHs after each round. We extend our research to cluster formation approach where the sensor nodes are joined to the CH on the basis distance and energy of cluster head. The proposed algorithm is simulated extensively under various conditions like number of sensor nodes in the field, number of CHs, the position of the base station, constant energy and random energy, etc. and the simulation results are analyzed with the extant algorithms. Under all the circumstances the proposed algorithm outperforms the existing LEACH and SEP protocols in terms of average residual energy, the network lifetime and number of data packets received by the base station. Because of the improvement in the lifetime of the network, the proposed algorithm can be used in the applications like environmental monitoring, agriculture etc.

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Section: Optimal Cluster Head Selection Andmentioning

confidence: 93%

Optimal Cluster Head Selection and Clustering for WSN Using PSO

Shinde¹,

Bichkar²

2020

IJITEE

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“…This method provides better results when compared to Q-MST and handoff algorithms by using end-to-end delay, throughput, and power consumption. (El-Said et al, 2016) have presented an optimal hierarchical routing technique to minimize energy consumption and extend the network lifetime. The optimal location of the cluster heads is selected by using the Artificial Fish Swarming Algorithm (AFSA).…”

Section: Problem Definition and Scopementioning

confidence: 99%

Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

Bose

2022

International Journal of Cognitive Informatics and Natural Intelligence

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Network lifetime and energy constraint are the main issues for the applications of Wireless Sensor Network. Sensor nodes spend more energy in the communication process and affect the network lifetime. Clustering is the technique for choosing the optimal cluster head from the clusters. LEACH-C is the clustering protocol in WSN. BFA is applied in the LEACH-C protocol to form the optimal clusters. This optimization obtains more number of steps in the tumbling process and reaches the global optimum solution very slowly. This method directly affects the network lifetime. The above limitations are overcome by introducing the hybrid approach of Bacteria foraging algorithm by integrating the PSO and DE is applied in LEACH-C algorithm for finding the optimal cluster head. The best foraging solution utilized in the chemotactic behavior of the bacterium by using PSO and DE algorithm. The proposed methodology increases by 66% and 77% of the alive nodes when compared to FA and BFPSO.

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“…Several studies have used fish as an inspiration in approaching problems in complex optimization domains [40], such as networks [41][42][43][44][45], image processing [46][47][48], robotics, motion control [49][50][51], machine learning [52][53][54], path planning [55,56], industries [57], automation [58], and many other fields. However, few fish-inspired heuristics have been proposed, particularly targeting the task allocation problem.…”

Section: Related Workmentioning

confidence: 99%

Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions

Alhaqbani

Kurdi

2020

Remote Sensing

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The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.

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Optimized hierarchical routing technique for wireless sensors networks

Cited by 21 publications

References 35 publications

Optimal Cluster Head Selection and Clustering for WSN Using PSO

Optimal Cluster Head Selection and Clustering for WSN Using PSO

Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions

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