A Task Allocation In IoT Using Ant Colony Optimization

Zannou, Abderrahim; Boulaaam, Abdelhak; Nfaoui, El Habib

doi:10.1109/isacs48493.2019.9068889

Cited by 14 publications

(2 citation statements)

References 13 publications

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“…The second ACO-based approach was proposed by Zannou et al [ 36 ]. Their approach seeks to minimize energy consumption by assigning the most capable nodes for a task and minimizing both the length and hop-counts of the transmission paths.…”

Section: State Of the Art On The Task Allocation Problem For Iotmentioning

confidence: 99%

Survey on Multi-Objective Task Allocation Algorithms for IoT Networks

Weikert

Steup

Mostaghim

2022

Sensors

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The Internet of Things (IoT) has been an area of growing research interest for decades. Task allocation is an important problem for the optimized operation of Internet-of-Things networks. This paper provides an overview of recent research in the field of Internet-of-Things task allocation optimization. First, the task allocation problem for the IoT itself is analyzed and divided into distinct sub-problem categories, such as deployment optimization, static or dynamic optimization as well as single- or multi-objective optimization. Following that, the commonly used optimization objectives are explained. Various recent works in the field of task allocation optimization are then summarized and catalogued according to the problem categories. Finally, the paper concludes with a qualitative analysis of the categorized approaches and a description of open problems and highlights promising directions for future research.

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Section: State Of the Art On The Task Allocation Problem For Iotmentioning

confidence: 99%

Survey on Multi-Objective Task Allocation Algorithms for IoT Networks

Weikert

Steup

Mostaghim

2022

Sensors

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“…According to different constraints, the traditional task allocation problem can be subdivided into the vehicle routing problem (VRP) [14][15][16] , the multi-travel salesman problem (MTSP) [17][18][19] , and the multi-processor resource allocation problem (MPRA) 20,21 . As the number of UAV swarms increases on a large scale and the amount of computation increases, swarm intelligent optimization algorithms, such as particle swarm optimization algorithm (PSO) [22][23][24] , genetic algorithm (GA) [25][26][27][28] , grey wolf algorithm (GWO) [29][30][31] , ant colony optimization (ACO) [32][33][34] etc., are being used to increase the optimization speed, which mitigates the disadvantage of excessive computation caused by the increase in swarms. For the task allocation problem of a UAV swarm under an intentional attack, however, the classic task allocation optimization method struggles to account for the influence of the dynamic change of the optimization index before and after the attack.…”

Section: Introductionmentioning

confidence: 99%

A Resilient Task Allocation Method for UAV Swarm Based on PSO-ILP Bilevel Nested Optimization

Zeng¹,

Ren²,

Sun³

et al. 2023

Preprint

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Traditional task allocation methods are threatened by the complexity and adversarial nature of modern battlefields. This work focuses on the modeling, optimization, and simulation verification of UAV swarm multi-domain fighting under the constraint of task resilience in order to address the issues created by various ways of deliberate enemy attack. Initially, a novel idea of equivalent load is proposed, considering it as the fundamental unit of reconnaissance, assault, communication, and other activities, in order to construct the capability load matrix of our single UAV and the needed load matrix of attacking each fighting unit in each battle region. Then, by integrating the strike probability and task completion degree, the task resilience capability index was developed, which improved the current UAV swarm task resilience measurement process. Due to the difficulty of traditional task allocation optimization methods in dealing with dynamic changes of optimization indexes before and after attacks, a resilience compensation load relaxation variable was added to the traditional Integral Linear Programming (ILP) problem description model of a UAV swarm. On the basis of a bilevel nested structure, a task allocation optimization method is created. Before an assault, the lower layer's ILP optimizer uses the swarm load cost as the target. The uppermost layer is comprised of Particle Swarm Optimization (PSO), which targets the comprehensive indices of UAV swarm load cost and task resilience after attack. It effectively resolves the multi-objective optimization problem of UAV swarms taking task difficulty into account. Ultimately, the test scenarios of three conflict domains, five basic battle units, and five load kinds were constructed, and the Ranchester battle model was used to simulate and validate the rationale and efficacy of the bilevel nested optimization method based on PSO-ILP.

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