A Quantum Ant Colony Multi-Objective Routing Algorithm in WSN and Its Application in a Manufacturing Environment

Li, Fei; Liu, Min; Xu, Gaowei

doi:10.3390/s19153334

Cited by 28 publications

(12 citation statements)

References 29 publications

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“…The performance of the proposed algorithm is evaluated and compared with other quantum-based algorithms such as Quantum Ant Colony Optimization (QACO) [34] and Quantum Particle Swarm Optimization (QPSO) [35]. We implemented these algorithms in MATLAB to obtain their results with the same settings for comparison as we used for our results.…”

Section: Results and Performance Evaluationmentioning

confidence: 99%

“…Rebai et al [32] have proposed a combination of local search genetic algorithm to decrease the number of positioned sensor nodes that attain maximum coverage for a 2D sensing area and forms a connected network [33]. Li et al [34] have developed quantum ant colony multiobjective routing for monitoring complex manufacturing environments by considering the nodes' energy consumption, transmission delay, and network loadbalancing degree. A range-free localization algorithm based on quantum particle swarm optimization (QPSO) is proposed to estimate the distance among the nodes for the random and uniform deployment of nodes in heterogeneous wireless sensor networks [35].…”

Section: Related Workmentioning

confidence: 99%

“…In the dynamic environment, the degree of network connectivity varies subject to changes in the topology. The parameter designed in (33) and (34) describes dynamic objective functions with reference to lower and upper limits for preferred solution space to decide the optimal pattern of sensor positioning in the target region. With the evolution in the optimization process, the established topology's connectivity degree is varied for reducing energy consumption.…”

Section: A) Energy Consumptionmentioning

confidence: 99%

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A Novel Enhanced Quantum PSO for Optimal Network Configuration in Heterogeneous Industrial IoT

et al. 2021

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A novel enhanced quantum particle swarm optimization algorithm for IIoT deployments is proposed. It provides enhanced connectivity, reduced energy consumption, and optimized delay. We consider heterogeneous scenarios of network topologies for optimal path configuration by exploring and exploiting the hunts. It uses multiple inputs from heterogeneous IIoT into quantum and bio-inspired optimization techniques. The differential evolution operator and crossover operations are used for information interchange among the nodes to avoid trapping into local minima. The different topology scenarios are simulated to study the impact of p-degrees of connectivity concerning objective functions' evaluation and compared with existing techniques. The results demonstrate that our algorithm consumes a minimum of 30.3% lesser energy. Furthermore, it offers improved searching precision and convergence swiftness in the possible search space for p-disjoint paths and reduces the delay by a minimum of 26.7%. Our algorithm also improves the throughput by a minimum of 29.87% since the quantum swarm inclines to generate additional diverse paths from multiple source nodes to the gateway.

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Section: Results and Performance Evaluationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A) Energy Consumptionmentioning

confidence: 99%

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A Novel Enhanced Quantum PSO for Optimal Network Configuration in Heterogeneous Industrial IoT

et al. 2021

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“…But this bird flocking optimization has frequent path change and high overhead, and has no way to find the optimal route. Another better optimization algorithm is Ant Colony Optimization (ACO) [19], [20], which is a heuristic stochastic optimal algorithm with excellent performance. The positive feedback mechanism is adapted in the ACO.…”

Section: Related Workmentioning

confidence: 99%

Game Theory Based Congestion Control for Routing in Wireless Sensor Networks

2021

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The Congestion in wireless sensor networks can lead to the loss and retransmissions of packets, and excessive energy consumption. Since network congestion can deteriorate the throughput, the quality of service (QoS) in data transmission, and network lifetime, congestion control is a critical problem. In this paper, the data transmission process is firstly presented based on the data type. The cooperation game theory is used to model the transfer behavior of different type data. Further, a cooperation game based congestion control (CGCC) is proposed. The different type data cooperates with each other to use link capacity. And then Ant colony Routing algorithm combined with CGCC (ARCGCC) is designed. According to the congestion degree and QoS requirement of each type data, the routing path is built in the routing algorithm. When congestion is detected, the congestion information is exchange. In the light of the data traffic and priority, the nodes adjust the sending rate to the next hop node to mitigate the congestion. Experimental results show that our mechanism and algorithm have better performance than some other mechanism and algorithms in terms of the throughput, reliability, delay, and energy consumption.INDEX TERMS Wireless senor networks, congestion control, game theory, routing algorithm, QoS.

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“…The paper “A Quantum Ant Colony Multi-Objective Routing Algorithm in WSN and Its Application in a Manufacturing Environment” [ 5 ] highlights some limitations in the traditional routing algorithms for WSNs. In particular, the focus is on ant-based routing algorithms that, in their basic formulations, can easily fall into a “local optimum”, thus leading to premature stagnation of the algorithms.…”

mentioning

confidence: 99%