Bio-inspired topology control for knowledge sharing mobile agents

Urrea, Elkin; Şahin, Cem; Hökelek, İbrahim; Uyar, M. í.; Conner, Michael; Bertoli, Giorgio; Pizzo, Christian

doi:10.1016/j.adhoc.2008.03.005

Cited by 27 publications

(27 citation statements)

References 9 publications

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“…The fga is run by each mobile node as a standalone topology control application to uniformly distribute mobile nodes in an unknown terrain [14,16,17]. Compared to other techniques, our ga-based approach presents encouraging results by converging towards a uniform node distribution as shown in [15]. In our research, we used discrete-time walk model adapted from [18].…”

Section: Literature Reviewmentioning

confidence: 65%

“…The mean node degree is the expected number of node degree to maximize the coverage. σ i , which depends on the geographical area size, the number of nodes, and the communication range, is analytically derived in [15]. The fitness function f i is given as the sum of all the partial forces that node N i exerts on its k neighboring nodes:…”

Section: Genetic Operators and Fitness Function In The Fgamentioning

confidence: 99%

“…We have introduced different ga-based approaches for topology control problems in manets [14,15,16]. In this framework, a force-based ga (fga) [17] is constructed as a decentralized topology control mechanism among active running software agents to achieve a uniform deployment of autonomous nodes over an unknown geographical area.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bio-inspired Approach to Self-organization of Mobile Nodes in Real-Time Mobile Ad Hoc Network Applications

Şahin

Urrea

Gundry

2012

Variants of Evolutionary Algorithms for Real-World Applications

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Abstract. In this chapter, we study the applicability and effectiveness of an evolutionary computation approach to a topology control problem in the domain of mobile ad hoc networks (manets). We present formal and practical aspects of convergence properties of our force-based genetic algorithm, called fga, which is run by each mobile node to achieve a uniform spread. Our fga is suitable for manet environments since mobile nodes, while running the fga, only use local neighborhood information. An inhomogeneous Markov chain is used to analyze the convergence speed of our bio-inspired algorithm. To demonstrate our topology control algorithm's applicability to real-life problems and to evaluate its effectiveness, we have implemented a simulation software system and two testbed platforms. The simulation and testbed experiment results indicate that, for important performance metrics such as the normalized area coverage and convergence rate, the fga can be an effective mechanism to deploy mobile nodes with restrained communication capabilities in manets operating in unknown areas. Since the fga adapts to the local environment rapidly and does not require global network knowledge, it can be used as a real-time topology controller for realistic military and civilian applications.

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Section: Literature Reviewmentioning

confidence: 65%

Section: Genetic Operators and Fitness Function In The Fgamentioning

confidence: 99%

See 1 more Smart Citation

A Bio-inspired Approach to Self-organization of Mobile Nodes in Real-Time Mobile Ad Hoc Network Applications

Şahin

Urrea

Gundry

2012

Variants of Evolutionary Algorithms for Real-World Applications

Self Cite

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“…Zou et al introduced a three-dimensional genetic algorithm-based topology control mechanism (3D-GA) as a solution for the dispersion of AUVs operating in Underwater Sensor Networks [4]. The average neighborhood degree or mean node degree is considered in different papers [31], [32], [33]. Urrea et al [32] introduced different GA applications for knowledge sharing bio-inspired mobile agents to obtain a uniform distribution of the nodes over an area.…”

Section: Related Workmentioning

confidence: 99%

“…The average neighborhood degree or mean node degree is considered in different papers [31], [32], [33]. Urrea et al [32] introduced different GA applications for knowledge sharing bio-inspired mobile agents to obtain a uniform distribution of the nodes over an area. The main objectives are to obtain a uniform distribution over the given area, to provide the agents with a balanced number of neighbors, and to improve NAC.…”

Section: Related Workmentioning

confidence: 99%

A topology control algorithm for autonomous underwater robots in three-dimensional space using PSO

Amiri

Pouyan

Mashayekhi

2015

JAIDM

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Data collection from seabed by means of underwater wireless sensor networks (UWSN) has recently attracted considerable attention. Autonomous underwater vehicles (AUVs) are increasingly used as UWSNs in underwater missions. Events and environmental parameters in underwater regions have a stochastic nature. Sensors to observe and report events must cover the target area. A 'topology control algorithm' characterizes how well a sensing field is monitored and how well pairs of sensors are mutually connected in UWSNs. It is prohibitive to use a central controller to guide AUVs' behavior due to ever changing, unknown environmental conditions, limited bandwidth and lossy communication media. In this research, a completely decentralized three-dimensional topology control algorithm for AUVs is proposed. It is aimed at achieving maximal coverage of the target area. The algorithm enables AUVs to autonomously decide on and adjust their speed and direction based on the information collected from their neighbors. Each AUV selects the best movement at each step by independently executing a Particle Swarm Optimization (PSO) algorithm. In the fitness function, the global average neighborhood degree is used as the upper limit of the number of neighbors of each AUV. Experimental results show that limiting number of neighbors for each AUV can lead to more uniform network topologies with larger coverage. It is further shown that the proposed algorithm is more efficient in terms of major network parameters such as target area coverage, deployment time, and average travelled distance by the AUVs.

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