Ant Algorithm for Optimal Sensor Deployment

Fidanova, Stefka; Marinov, Pencho; Alba, Enrique

doi:10.1007/978-3-642-27534-0_2

Cited by 29 publications

(21 citation statements)

References 10 publications

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“…Frat et al [45] devise approximation algorithms to tackle this problem with obstacles. Fidanova et al [50] propose an ant colony algorithm. Rotar et al [106] use an evolutionary algorithm with a hyperplane (points in space that have better performance than any other individual within the current population).…”

Section: Optimal Sensor Deployment and Coveragementioning

confidence: 99%

Constraint programming for wireless sensor networks

Bijarbooneh

2015

Constraints

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In recent years, wireless sensor networks (WSNs) have grown rapidly and have had a substantial impact in many applications. A WSN is a network that consists of interconnected autonomous nodes that monitor physical and environmental conditions, such as temperature, humidity, pollution, etc. If required, nodes in a WSN can perform actions to affect the environment.WSNs present an interesting and challenging field of research due to the distributed nature of the network and the limited resources of the nodes. It is necessary for a node in a WSN to be small to enable easy deployment in an environment and consume as little energy as possible to prolong its battery lifetime. There are many challenges in WSNs, such as programming a large number of nodes, designing communication protocols, achieving energy efficiency, respecting limited bandwidth, and operating with limited memory. WSNs are further constrained due to the deployment of the nodes in indoor and outdoor environments and obstacles in the environment.In this dissertation, we study some of the fundamental optimisation problems related to the programming, coverage, mobility, data collection, and data loss of WSNs, modelled as standalone optimisation problems or as optimisation problems integrated with protocol design. Our proposed solution methods come from various fields of research including constraint programming, integer linear programming, heuristic-based algorithms, and data inference techniques. I would like to say many thanks to Roland Bol and Ivan Christoff for accepting me to MSc studies at Uppsala University, which led to this PhD. Your decision brought me on a very long and joyful journey: without it, I would not have been here today. KeywordsI had the special opportunity to see the ASTRA group on constraint programming grow larger with new PhD students and post-doc fellows throughout my PhD. I enjoyed working at the ASTRA group. Especially my appreciation goes to Jun He, Joseph Scott, Maria Andreina Francisco Rodriguez, and JeanNoël Monette, who always supported me with their thoughts and constructive discussions, and have been great friends for me. I have also always looked forward to your seminars with delicious cakes and cookies. Many thanks to Prof. Christian Schulte, Guido Tack, Mats Carlsson for their discussions and many constructive comments during my seminars and on my publications.I would like to specially thank all members of my PhD parent project (ProFuN). Thank you for your constructive suggestions and discussions during the seminars.I have made many wonderful friends during my PhD, friends who always stood by my side and supported me. There are many names to mention and I wish I could name everyone here, but I believe you know who you are and you know how much I appreciate your precious friendship. Thank you so much for being my friends and helping me to improve. There has been a very difficult time during my PhD when my cat Niuniu had an extremely harsh accident. I must specifically thank Edith Ngai, Palle Raabjerg, Ryoko Asai, and V...

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Section: Optimal Sensor Deployment and Coveragementioning

confidence: 99%

Constraint programming for wireless sensor networks

Bijarbooneh

2015

Constraints

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“…presented a PSO‐based work that has the ability to achieve optimal solution of coverage problem with minimum number of sensors in wireless sensor networks. In addition, the Glowworm swarm optimization algorithm , artificial bee colony algorithm , monkey algorithm (MA) , and ant colony optimization algorithm were also applied successfully to identify the optimal sensor network. Far too many intelligent algorithms exist to mention them all in this paper.…”

Section: Introductionmentioning

confidence: 99%

Health monitoring sensor placement optimization for Canton Tower using immune monkey algorithm

Zhang

2014

Struct. Control Health Monit.

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SUMMARYSensor placement is an integral component in the design of an effective and reliable structural health monitoring system for large-scale civil engineering structures. In this paper, a novel hybrid optimization algorithm, that is, immune monkey algorithm (IMA), by combining the monkey algorithm with the immune algorithm, is proposed to solve the optimal sensor placement problem. In the proposed algorithm, the dual-structure coding method is designed and adopted for the representation of design variables and a chaos-based approach is presented to initialize the monkey population. In addition, some improved strategies such as the advanced climb process, immune selection, clonal proliferation, and immune renewal are defined to maintain population diversity and inhibit degeneracy during evolution. The effectiveness and reliability of the proposed IMA is demonstrated by designing the sensor arrays of the Canton Tower, the second highest TV tower in the world. Numerical investigations presented clearly suggest that the proposed hybrid IMA outperforms the original monkey algorithm implementations both in terms of generating optimal solutions as well as faster convergence. The proposed algorithm can easily be extended to other multiple global optimization problems.

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“…To determine one location, the minMAC method needs to compute the MAC matrices of the variable combinations until all candidate locations have been selected. In order to improve the computational efficiency of the minMAC method, computational intelligence technology has been used, such as simulated annealing algorithm [16], ant colony optimization algorithm [17], particle swarm optimization algorithm [18], and monkey algorithm [19]. Due to easy coding method and quick evolution velocity, genetic algorithm (GA) has become a hot research direction.…”

Section: Introductionmentioning

confidence: 99%

A New Optimal Sensor Placement Strategy Based on Modified Modal Assurance Criterion and Improved Adaptive Genetic Algorithm for Structural Health Monitoring

Xing

Li³

et al. 2015

Mathematical Problems in Engineering

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Optimal sensor placement (OSP) is an important part in the structural health monitoring. Due to the ability of ensuring the linear independence of the tested modal vectors, the minimum modal assurance criterion (minMAC) is considered as an effective method and is used widely. However, some defects are present in this method, such as the low modal energy and the long computation time. A new OSP method named IAGA-MMAC is presented in this study to settle the issue. First, a modified modal assurance criterion (MMAC) is proposed to improve the modal energy of the selected locations. Then, an improved adaptive genetic algorithm (IAGA), which uses the root mean square of off-diagonal elements in the MMAC matrix as the fitness function, is proposed to enhance computation efficiency. A case study of sensor placement on a numerically simulated wharf structure is provided to verify the effectiveness of the IAGA-MMAC strategy, and two different methods are used as contrast experiments. A comparison of these strategies shows that the optimal results obtained by the IAGA-MMAC method have a high modal strain energy, a quick computational speed, and small off-diagonal elements in the MMAC matrix.

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Ant Algorithm for Optimal Sensor Deployment

Cited by 29 publications

References 10 publications

Constraint programming for wireless sensor networks

Constraint programming for wireless sensor networks

Health monitoring sensor placement optimization for Canton Tower using immune monkey algorithm

A New Optimal Sensor Placement Strategy Based on Modified Modal Assurance Criterion and Improved Adaptive Genetic Algorithm for Structural Health Monitoring

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