Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

Jia, Wenxian; Liu, Menghan; Zhou, Jie

doi:10.1155/2021/5051863

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…H a,a+1 is heuristic function information; D1 is the reciprocal of the distance from the current node to the next node; D2 is the reciprocal of the distance from the next node to the target point; i a , j a , k a is the current node coordinate; ði a+1 , j a+1 , k a+1 Þ is the coordinate of the next node; i G , j G , k G is the target point coordinate; N is the total number of nodes in the exploration area of the current node; N a+1 is the number of infeasible nodes in the exploration area of the current node; w 1 , w 2 , w 3 is the corresponding weight. By introducing the obstacle avoidance strategy and improving the heuristic information function, the global search ability of the algorithm can be effectively improved [18].…”

Section: Routementioning

confidence: 99%

Path Planning of Energy Robot Based on Improved Ant Colony Algorithm

Zhu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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In order to optimize robot road planning, automation will increase production efficiency and reduce production costs. An improved ant colony algorithm based on a two-dimensional flat path planning study divided the three-dimensional space into planes, rasterized each plane, and replaced the original shape by storing a pheromone at the intersection. The pheromone storage space along the route will be reduced, and a three-dimensional space route planning study will be gradually conducted. As complex 3D environments and landscape features change, obstacle avoidance strategies have increased, road heuristics have improved, and new heuristic features have emerged. The initial value of the road node pheromone increases the efficiency of early ant search because of the uneven distribution of starting point, target point location information, and forward direction. After each iteration, the stuck ants that did not reach the target point are discarded according to the high-quality ant renewal rules, the iteration threshold is set, and the pheromone fluctuation coefficient is adjusted as the algorithm tends to merge. Compared with the basic ant colony algorithm, the convergence iteration times of the improved ant colony algorithm in this paper are reduced by about 40%, and the optimal path length is shortened by about 10. The duration of the algorithm increases. In terms of algorithm performance, it takes some time to improve the ant colony algorithm. Because of the complexity of the algorithm, some search strategies are added to the algorithm. The contribution of this article is the basis for the mobile robot to walk accurately from the initial position to the working position and perform various tasks independently.

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Section: Routementioning

confidence: 99%

Path Planning of Energy Robot Based on Improved Ant Colony Algorithm

Zhu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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“…The MAC protocol used for the execution of the proposed system was IEEE 802.11. To evaluate the overall performance, the proposed TMERP was compared with the EEMR, 17 ACACO, 18 PSOFCA 19 and APSA 24 . The evaluation metrics used for the performance evaluation were the routing overhead, energy consumption, hop count, average reliability, average link connectivity, and maximum round of networks 31,32 .…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Jia et al 18 proposed adaptive chaotic ant colony optimisation (ACACO) to optimize energy in smart sensor networks. The proposed system is designed to balance the node's energy dissipation and maximize the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

An optimized ticket manager based energy‐aware multipath routing protocol design for IoT based wireless sensor networks

Roberts¹,

Thangavel²

2022

Concurrency and Computation

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Recently, wireless sensor networks (WSNs) and Internet of Things (IoTs) have emanated as an indispensable assets that play a critical role in revolutionizing the field of data communication. Owing to the evolution of communication standards, research trends in IoT based wireless sensor networks have been rapidly progressing toward achieving effective data routing with a prolonged network lifetime and minimized energy consumption. In this article, an optimized ticket manager based energy-aware multipath routing protocol (TMERP) is proposed. The proposed protocol design comprises three important functional entities: ticket manager (TM), routing planner (RP), and backup node (BN). The TM is responsible for controlling and monitoring all the constraints related to networking. Then, the RP minimizes the overall complexity of the optimal resource allocation by avoiding an end-to-end delay. Finally, the BN facilitates efficient data routing through the optimal selection of routing paths using the node trust evaluation and backup process to minimize data loss. Hence, the proposed multipath routing system has a distinct advantage in enhancing the network lifetime constraint with minimal energy consumption owing to the collective performance of its functional entities. The simulation results of the experimental studies show that the proposed protocol design achieved an improved performance in terms of network energy, throughput, and network operational lifetime by 39.3%, 47.9%, and 10.5%, respectively when compared with similar existing protocols.

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“…After considering the different arguments made to explain the phenomena, the shortcomings should not impede the use of ant colony optimization in agriculture, given that significant cost savings in machine optimization were achieved despite sub-optimal stagnation phase, exploitation, and exploration phases [16,19,140]. The positive outlook is further reinforced by the utility of the ant colony algorithm in energy optimization in smart sensor networks [148]. On the downside, the worldviews advanced by the researcher were not supported by market data in Europe, Asia, the Americas, Africa, and Oceania.…”

Section: Ant Colony Optimization Algorithm (Acoa)mentioning

confidence: 99%

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Maraveas

Arvanitis

Bartzanas

et al. 2022

Arch Computat Methods Eng

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The article reviewed the four major Bioinspired intelligent algorithms for agricultural applications, namely ecological, swarm-intelligence-based, ecology-based, and multi-objective algorithms. The key emphasis was placed on the variants of the swarm intelligence algorithms, namely the artificial bee colony (ABC), genetic algorithm, flower pollination algorithm (FPA), particle swarm, the ant colony, firefly algorithm, artificial fish swarm, and Krill herd algorithm because they had been widely employed in the agricultural sector. There was a broad consensus among scholars that certain BIAs' variants were more effective than others. For example, the Ant Colony Optimization Algorithm and genetic algorithm were best suited for farm machinery path optimization and pest detection, among other applications. On the contrary, the particle swarm algorithm was useful in determining the plant evapotranspiration rates, which predicted the water requirements and optimization of the irrigation process. Despite the promising applications, the adoption of hyper-heuristic algorithms in agriculture remained low. No universal algorithm could perform multiple functions in farms; different algorithms were designed to perform specific functions. Secondary concerns relate to data integrity and cyber security, considering the history of cyber-attacks on smart farms. Despite the concerns, the benefits associated with the BIAs outweighed the risks. On average, farmers can save 647–1866 L on fuel which is equivalent to US$734-851, with the use of GPS-guided systems. The accuracy of the BIAs mitigated the risk of errors in applying pesticides, fertilizers, irrigation, and crop monitoring for better yields.

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Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

Cited by 9 publications

References 18 publications

Path Planning of Energy Robot Based on Improved Ant Colony Algorithm

Path Planning of Energy Robot Based on Improved Ant Colony Algorithm

An optimized ticket manager based energy‐aware multipath routing protocol design for IoT based wireless sensor networks

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

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