Ant colony optimization algorithm based on mobile sink data collection in industrial wireless sensor networks

Zhang, Hong; Li, Zhanming; Shu, Wanneng; Chou, Jarong

doi:10.1186/s13638-019-1472-7

Cited by 28 publications

(14 citation statements)

References 26 publications

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“…They constructed a multi-constrained and multi-objective exam scheduling model and verified that the improved ant colony algorithm can effectively solve the problem of exam scheduling optimization. Zhang et al [20] proposed a data collection strategy based on the ACO with mobile sink to study industrial wireless sensor network problems. In their research, on the one hand, they cited the selection of rendezvous nodes based on entropy weight method; on the other hand, they used ant colony algorithm to obtain the optimal access path of mobile sink, and the simulation results proved that the strategy could achieve the purpose of reducing network delay and extending network life.…”

Section: Ant Colony Optimization Based Methods a Introduction To mentioning

confidence: 99%

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Scheduling Problem of Movie Scenes Based on Three Meta-Heuristic Algorithms

Long

Zhao

2020

IEEE Access

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Movie scenes scheduling problem (MSSP) is the NP-hard. It refers to the process of film shooting through the reasonable sequence of film scenes to minimize the total cost of film shooting. Studying scheduling problem of this kind is based on the location of the scene shooting, the cost of the scene transfer, the different remuneration of actors, and the different duration of the film scene shooting. The actors' waiting time and transfer cost during the shooting process are reduced as much as possible to make the total film shooting cost smaller. In this paper, an ILP(integer linear programming) model is established and a TABU search based method (TSBM), a particle swarm optimization based method (PSOBM) and an ant colony based method (ACOBM) are used to study the movie scenes scheduling problem. The objective is to compare and analyze relation performances of the three methods in the problem. By compared the experiment, the results show that TSBM, PSOBM and ACOBM can effectively reduce the total cost of film shooting. Comparison with the experiments show that the optimization result of ACOBM is better, the running time of TSBM is shorter, and the optimization result of TSBM is better than that of PSOBM. In addition, the running time of ACOBM is faster than that of PSOBM by setting the number of ants and the number of particles. The potential application of this study has great relevance for the optimization and ILP.

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Section: Ant Colony Optimization Based Methods a Introduction To mentioning

confidence: 99%

“…Constraint (18) explain p make sort according to θ mnp . Constraints (19), (20), and (21) describe the relationship between the two variables θ mnp and t m . All four of them represent the constraint relations between the total shooting time and the number of shooting days.…”

Section: Model Formulas and Constraintsmentioning

confidence: 99%

Scheduling Problem of Movie Scenes Based on Three Meta-Heuristic Algorithms

Long

Zhao

2020

IEEE Access

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“…The energy consumption of each node can be computed as function of the transmitted and received bits: in fact, it is assumed that the sensing energy is negligible with respect to the communication energy [20]. Thus, the total energy consumption is composed by two terms:…”

Section: Network Energy Modelmentioning

confidence: 99%

“…In [19], an algorithm based on ACO is exploited to find the optimal information path from a source node to a sink node in a multi-hop WSN. ACO is also applied in [20] for the path design of a mobile data collection node.…”

Section: Introductionmentioning

confidence: 99%

Social Network Optimization for WSN Routing: Analysis on Problem Codification Techniques

et al. 2020

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The correct design of a Wireless Sensor Network (WSN) is a very important task because it can highly influence its installation and operational costs. An important aspect that should be addressed with WSN is the routing definition in multi-hop networks. This problem is faced with different methods in the literature, and here it is managed with a recently developed swarm intelligence algorithm called Social Network Optimization (SNO). In this paper, the routing definition in WSN is approached with two different problem codifications and solved with SNO and Particle Swarm Optimization. The first codification allows the optimization algorithm more degrees of freedom, resulting in a slower and in many cases sub-optimal solution. The second codification reduces the degrees of freedom, speeding significantly the optimization process and blocking in some cases the convergence toward the real best network configuration.

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“…The fitness function for CH selection involves the integration of different factors. Although, there are numerous meta‐heuristic optimization techniques, namely, ant colony optimization, 28 differential evolution, 29 and genetic algorithm (GA), 30 that could be considered for selecting the optimal CH 31 . It is discovered that PSO has convenience of execution, has improved quality solution, and possesses the ability to evade from the optimal local point 32 .…”

Section: Introductionmentioning

confidence: 99%

A novel approach for particle swarm optimization‐based clustering with dual sink mobility in wireless sensor network

Kaur

Grewal

2020

Int J Communication

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SummaryThe proliferation in the sensing technology in wireless sensor network has left an everlasting impact to revolutionize every sector of human's lives. The limited battery of sensor nodes (SNs) has generated a tremendous challenge for the researchers to ameliorate the network's life span. To pacify this concern, in this paper, we have proposed a particle swarm optimization (PSO)‐based dual sink mobility (PSODSM) technique to reduce the energy expenditure of the SNs. PSODSM has the paramount focus on the cluster head (CH) selection based on integration of crucial factors: “ratio of remaining energy to the initial energy,” node degree, node centrality, separation factor between the SN and the sink, CH number, and energy consumption rate. As soon as the CHs are selected, two sinks placed opposite to each other are made to move towards the selected CHs for data collection. The most prominent fact is the movement of sinks which is done outside the periphery of the network to collect data rather than the reported conventional research works that employ sink mobility inside the network. Extensive simulations are performed to examine the empirical evaluation of PSODSM based on the benchmark of different performance metrics. The performance validation of PSODSM is done against the other meta‐heuristic algorithms, and findings show that the PSODSM outperforms the competitive algorithms and is also found to be scalable pertaining to real time implementation.

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Ant colony optimization algorithm based on mobile sink data collection in industrial wireless sensor networks

Cited by 28 publications

References 26 publications

Scheduling Problem of Movie Scenes Based on Three Meta-Heuristic Algorithms

Scheduling Problem of Movie Scenes Based on Three Meta-Heuristic Algorithms

Social Network Optimization for WSN Routing: Analysis on Problem Codification Techniques

A novel approach for particle swarm optimization‐based clustering with dual sink mobility in wireless sensor network

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