A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs

Gao, Yu; Wang, Jin; Wu, Wenbing; Sangaiah, Arun Kumar; Lim, Se-Jung

doi:10.3390/s19030575

Cited by 43 publications

(25 citation statements)

References 38 publications

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“…For example in [146] the authors studied the problem of controlling sink mobility in event-driven applications to achieve maximum network lifetime, and proposed a convex optimization model inspired by the support vector regression technique to determine an optimal trajectory of an MS without considering predefined structures. Gao et al [147] adopted a hybrid method called HM-ACOPSO which combines ant colony optimization (ACO) and particle swarm optimization (PSO) to schedule an efficient moving path for the mobile agent, and the presented method outperforms some similar works in terms of energy consumption and data gathering efficiency. A fishswarm based algorithm is designed requiring local information at each fish node and maximizing the joint detection probabilities of distress signals.…”

Section: B Data Collectionmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people's requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

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Section: B Data Collectionmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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“…In large scale WSNs, due to the limit of the mobility of the mobile sink, the network is divided into several parts and each part introduces a mobile sink. Wang et al [29] presented a distance-aware routing algorithm using multiple mobile sinks to decrease the energy consumption of the network. Wang et al [30] introduced multiple mobile sinks to move along the predetermined paths to gather raw data.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks

Wang

Gao

Liu

et al. 2019

Sensors

Self Cite

190

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Recently, wireless sensor network (WSN) has drawn wide attention. It can be viewed as a network with lots of sensors that are autonomously organized and cooperate with each other to collect, process, and transmit data around targets to some remote administrative center. As such, sensors may be deployed in harsh environments where it is impossible for battery replacement. Therefore, energy efficient routing is crucial for applications that introduce WSNs. In this paper, we present an energy efficient routing schema combined with clustering and sink mobility technology. We first divide the whole sensor field into sectors and each sector elects a Cluster Head (CH) by calculating its members’ weight. Member nodes calculate energy consumption of different routing paths to choose the optimal scenario. Then CHs are connected into a chain using the greedy algorithm for intercluster communication. Simulation results prove the presented schema outperforms some similar work such as Cluster-Chain Mobile Agent Routing (CCMAR) and Energy-efficient Cluster-based Dynamic Routing Algorithm (ECDRA). Additionally, we explore the influence of different network parameters on the performance of the network and further enhance its performance.

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“…At present, there exist some process optimization methods, including ant colony optimization (ACO) [20], particle swarm optimization (PCO) [21] and model predictive control (MPC) [22]. These methods can optimize the motion path of each agent in the MAS.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Multi-Agent Dynamic Area Coverage Algorithm Based on Reinforcement Learning

et al. 2020

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Dynamic area coverage is widely used in military and civil fields. Improving coverage efficiency is an important research direction for multi-agent dynamic area coverage. In this paper, we focus on the non-optimal coverage problem of free dynamic area coverage algorithms. We propose a distributed dynamic area coverage algorithm based on reinforcement learning and a γ-information map. The γ-information map can transform the continuous dynamic coverage process into a discrete γ point traversal process, while ensuring no-hole coverage. When agent communication covers the whole target area, agents can obtain the global optimal coverage strategy by learning the whole dynamic coverage process. In the event that communication does not cover the whole target area, agents can obtain a local optimal coverage strategy; in addition, agents can use the proposed algorithm to obtain a global optimal coverage path through off-line planning. Simulation results demonstrate that the required time for area coverage with the proposed algorithm is close to the optimal value, and the performance of the proposed algorithm is significantly better than the distributed anti-flocking Algorithms for dynamic area coverage. INDEX TERMS Dynamic area coverage, multi-agent, reinforcement learning, optimal coverage.

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A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs

Cited by 43 publications

References 38 publications

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks

A Distributed Multi-Agent Dynamic Area Coverage Algorithm Based on Reinforcement Learning

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