SOA-EACR: Seagull optimization algorithm based energy aware cluster routing protocol for wireless sensor networks in the livestock industry

Sankar, S.; Ramasubbareddy, Somula; Parvathala, Balakesavareddy; Kolli, Srinivas; Pulipati, Srilatha; T., Aditya Sai Srinivas

doi:10.1016/j.suscom.2021.100645

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…The service-oriented SOA architecture [9,10] has been proposed later. How to apply formal methods to improve the quality of the distributed system has aroused widespread concern in the society.…”

Section: Related Workmentioning

confidence: 99%

Traffic Carrying and Delay Response Scheduling Algorithm for Distributed E- commerce Platforms

Gao,

Yang,

Chen

et al. 2024

Preprint

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The popularization of digitalization, informatization and the Internet has given birth to the rapid development of e-commerce. Faced with the rapidly expanding user traffic, there are still technical bottlenecks in how e-commerce platforms can carry more user traffic and improve server response performance. This article conducts system optimization performance analysis from both hardware and software aspects, and constructs a high-performance distributed AR-AFSA system. (1) The AR (Application Router, AR) architecture is configured with three JobManager server nodes, each receiving three types of user access requests. A traffic allocation mechanism is used to distribute the system's traffic carrying pressure, and user requests are divided into four traffic queues for scheduling according to different access methods. (2) Improve AFSA for container scheduling, re plan the execution order of various behaviors of artificial fish, reduce ineffective search steps, and influence the direction of artificial fish's movement through the global optimal solution, increasing the possibility of finding the optimal solution and accelerating local convergence speed. (3) Using the CPU, memory performance, and load balancing parameters of the container as the parameters and evaluation indicators for artificial fish, matching sufficient resource containers for user requests while ensuring container resource conservation and system load balancing. Finally, the traffic carrying capacity of the AR system and the single JobManager system was validated using the Taobao user behavior dataset and multiple control experiments. The AR system can withstand three times the traffic pressure of traditional servers. The improved AFSA algorithm can converge to a more optimal solution compared to the control algorithm, and in more complex server resource sizes, it consumes lower latency, reduces iteration times, schedules and uses more reasonable resources, demonstrating greater advantages.

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Section: Related Workmentioning

confidence: 99%

Traffic Carrying and Delay Response Scheduling Algorithm for Distributed E- commerce Platforms

Gao,

Yang,

Chen

et al. 2024

Preprint

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“…With a mean packet delivery rate of 23.21 and 22.83 percent, respectively, it was discovered to be superior to LEACH and GA in terms of improving the network lifetime.Longevity and throughput improvements in WSNs can be achieved with the help of an Energy Aware Cluster Routing Protocol (SOA-EACR) based on the Seagull Optimization Algorithm. During CH selection, the SOA-EACR ensures a healthy equilibrium between exploration and exploitation [17]. Methods based Fuzzy [18], Gaussian Regression [19] and Cyclic Grey Wolf Optimization [20] were developed.…”

Section: Related Studymentioning

confidence: 99%

“…The performance evaluation of proposed method HF-GSO is carried out in MATLAB, and it is compared with the various existing algorithms that are currently available, Adaptive MCFL [11], SIWODE [14], HFFGSOCP [15], HMABCFA [16] and SOA-EACC [17]. Table 1 contains a description of the parameters that were taken into account when carrying out the simulations.…”

Section: Experimental Analysismentioning

confidence: 99%

Energy Efficient Cluster Head Selection and Routing Algorithm using Hybrid Firefly Glow-Worm Swarm Optimization in WSN

2023

KSII TIIS

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The Wireless Sensor Network (WSN), is constructed out of teeny-tiny sensor nodes that are very low-cost, have a low impact on the environment in terms of the amount of power they consume, and are able to successfully transmit data to the base station. The primary challenges that are presented by WSN are those that are posed by the distance between nodes, the amount of energy that is consumed, and the delay in time. The sensor node's source of power supply is a battery, and this particular battery is not capable of being recharged. In this scenario, the amount of energy that is consumed rises in direct proportion to the distance that separates the nodes. Here, we present a Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) guided routing strategy for preserving WSNs' low power footprint. An efficient fitness function based on firefly optimization is used to select the Cluster Head (CH) in this procedure. It aids in minimising power consumption and the occurrence of dead sensor nodes. After a cluster head (CH) has been chosen, the Glow-Worm Swarm Optimization (GSO) algorithm is used to figure out the best path for sending data to the sink node. Power consumption, throughput, packet delivery ratio, and network lifetime are just some of the metrics measured and compared between the proposed method and methods that are conceptually similar to those already in use. Simulation results showed that the proposed method significantly reduced energy consumption compared to the state-of-the-art methods, while simultaneously increasing the number of functioning sensor nodes by 2.4%. Proposed method produces superior outcomes compared to alternative optimization-based methods.

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“…The cluster head selection process introduces node residual energy and node distance, effectively improving routing efficiency. The work in [30] proposed an energy-aware cluster routing protocol based on a seagull optimization algorithm for animal husbandry WSN, which can prolong the life of the network and reduce the end-to-end delay. The work in [31] initializes the population by selecting nodes in the monitoring area to increase the diversity of the population and uses the proposed optimization algorithm to choose a better cluster head set, which balances the energy consumption of sensor nodes, thereby prolonging the life cycle of the network.…”

Section: Related Workmentioning

confidence: 99%

Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm

Qiu,

Zhao,

Zhang

et al. 2023

Sensors

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Sensor nodes are widely distributed in the Internet of Things and communicate with each other to form a wireless sensor network (WSN), which plays a vital role in people’s productivity and life. However, the energy of WSN nodes is limited, so this paper proposes a two-layer WSN system based on edge computing to solve the problems of high energy consumption and short life cycle of WSN data transmission and establishes wireless energy consumption and distance optimization models for sensor networks. Specifically, we propose the optimization objective of balancing load and distance factors. We adopt an improved sparrow search algorithm to evenly distribute sensor nodes in the system to reduce resource consumption, consumption, and network life. Through the simulation experiment, our method is illustrated, effectively reducing the network’s energy consumption by 26.8% and prolonging the network’s life cycle.

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SOA-EACR: Seagull optimization algorithm based energy aware cluster routing protocol for wireless sensor networks in the livestock industry

Cited by 9 publications

References 21 publications

Traffic Carrying and Delay Response Scheduling Algorithm for Distributed E- commerce Platforms

Traffic Carrying and Delay Response Scheduling Algorithm for Distributed E- commerce Platforms

Energy Efficient Cluster Head Selection and Routing Algorithm using Hybrid Firefly Glow-Worm Swarm Optimization in WSN

Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm

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