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Mobile ad hoc networks (MANETs) can be useful for providing network coverage in harsh and adversarial environments where nodes may become randomly or systematically disabled in disaster recovery sites or border protection zones. For these networks to be reliable and scalable, robust topology control algorithms must be developed to guide the deployment and geometric configuration of mobile nodes without a priori knowledge of the terrain. This paper studies a fault tolerant differential evolution based topology control mechanism, called TCM-Y, that directs the movements of autonomous vehicles to efficiently and dynamically deploy themselves into a uniformly dispersed configuration. TCM-Y uses a Yao graph inspired fitness function to maintain a minimum desired number of connections for a node with its neighbors while uniformly dispersing autonomous vehicles over an unknown terrain. We evaluate the performance of mobile nodes running TCM-Y in hostile environments where nodes systematically or randomly experience hostility and become disabled. Experimental results obtained from our simulation software show that mobile nodes running TCM-Y perform well in the face of neighbor losses with respect to normalized area coverage, average distance traveled, and average connectivity, while limiting the number of network partitions that may occur as a result of node failures. [ABSTRACT FROM PUBLISHER]

Autonomous underwater vehicles (AUVs) are increasingly used in maritime applications to acquire underwater information. With the guidance of intelligent topology control algorithms, AUVs adjust their positions efficiently such that they achieve desired underwater wireless sensor network (UWSN) configurations. A suitable application can be using AUVs in surveillance tasks, where they uniformly spread throughout a harbor entry or around a large civilian or military maritime vessel (e.g., an aircraft carrier) to detect any hostile or unexpected underwater intrusions. Since geographical characteristics are different for each port (shape, depth, etc.) and underwater currents constantly change, manually controlling underwater vehicles for these tasks is not feasible. In this paper, we present a particle swarm optimization (PSO) based topology control mechanism, called 3D-PSO, for AUVs operating in unknown 3D underwater spaces. 3D-PSO uses limited information collected from an AUV's neighborhood to make movement decisions over an unknown 3D space. Each AUV, using our 3D-PSO, is able to adjust its movement speed and direction to achieve a better location towards obtaining a uniform distribution. Simulation experiments demonstrate that, despite the significant variance of topographies of harbor entrances and the undersides of ships, 3D-PSO is an efficient tool to guide AUVs in creating a protection area for a harbor or a maritime vessel. [ABSTRACT FROM PUBLISHER]

We introduce two new self-positioning techniques for autonomous nodes in a mobile ad hoc network to spread over unknown two-dimensional deployment terrains. In our first node self-spreading algorithm, called NSVA, each node moves according to the Voronoi tessellation of its sensing area. Our second self-positioning technique, called NSVGA, is based on a genetic algorithm that utilizes the area of moving node's Voronoi cell as a fitness function. To establish a basis for our comparisons, we also include the results for nodes moving to the next positions by means of the distributed self-spreading algorithm, called DSSA. We present formal analysis of NSVA, NSVGA, and DSSA to evaluate the area covered by all nodes (NAC) and the average distance traveled (ADT) by nodes until a desired network topology is reached. Simulation experiments demonstrate that both NSVA and NSVGA perform well with respect to NAC, ADT, and convergence speed. Our NSVGA is able to improve NAC considerably faster in the initial steps of the experiments than NSVA and DSSA. On the other hand, a node running NSVA travels a shorter distance on the average than a NSVGA node before reaching a desired network topology. We show that our NSVA and NSVGA are good candidates for self-spreading autonomous nodes that provide power-efficient solutions for many military and civilian applications. [ABSTRACT FROM PUBLISHER]

We introduce a fault tolerant bio-inspired topolog-ical control mechanism (TCM-Y) for the evolutionary decision making process of autonomous mobile nodes that adaptively adjust their spatial configuration in MANETs. TCM-Y is based on differential evolution and maintains a user-defined minimum connectivity for each node with its near neighbors. TCM-Y, therefore, provides a topology control mechanism which is fault tolerant with regards to network connectivity that each mobile node is required to maintain. In its fitness calculations, TCM-Y uses the Yao graph structure to enforce a user-defined minimum number of neighbors while obtaining uniform network topology. The effectiveness of TCM-Y is evaluated by comparing it with our differential evolution based topology mechanism (TCM-DE) that uses virtual forces from neighbors in its fitness function. Experimental results obtained from simulation software show that TCM-Y performs well with respect to normalized area coverage, the average connectivity, and the minimum connectivity achieved by mobile nodes. Simulation experiments demonstrate that TCM-Y generates encouraging results for uniform distribution of mobile nodes over unknown terrains while maintaining a user-defined minimum connectivity between neighboring nodes. [ABSTRACT FROM PUBLISHER]

We present a differential evolution based topology control mechanism, called TCM-DE, for the decision making process of evolutionary and autonomous systems that adaptively reconfigures spatial configuration in MANETs. We introduce quantitative metrics to evaluate performance of our TCM-DE with respect to uniform distribution, total terrain covered by communication areas of all nodes, and distance traveled by each node until a desired network topology is reached. Voronoi tessellation for configurations of mobile nodes is used to create two uniformity metrics. Physical relocation of mobile nodes is a power consuming task. Therefore, minimizing the average distance each node travels (ADT) until the network reaches a desired distribution is an important indicator for the performance of MANET nodes. Another important performance metric is the network area coverage (NAC) achieved by all nodes. NAC is used to measure the speed of network convergence and the efficiency of node deployment. Experimental results from our simulation software shows that TCM-DE performs well with respect to NAC, ADT, and Voronoi-based uniformity evaluation techniques. [ABSTRACT FROM PUBLISHER]

Mobile Ad hoc Networks (MANETs) are used for many strategic commercial and military applications where it is not feasible to use a centralized controller or manually deploy assets. They have proved useful for many practical applications, such as search and rescue, clearing mine fields, and transportation systems. We introduce a differential evolution based topological control mechanism for the decision making process of evolutionary and autonomous systems that adaptively reconfigures spatial configuration in MANETs. We present a formal analysis of the effectiveness of our topology control mechanism and introduce an inhomogeneous Markov chain model to prove its convergence. The experiment results from our simulation software show that our biologically-inspired algorithm produces encouraging results for uniform distribution of mobile nodes over unknown terrains. [ABSTRACT FROM PUBLISHER]

Wireless sensor networks (WSNs) are increasingly being used to acquire information in harsh and inaccessible environments including underwater and aerial theatres. Self-deployment of autonomous mobile assets in three-dimensional (3D) environments is a critical task to efficiently create WSNs. In this paper, we present a topology control mechanism called 3D-PSO, based on particle swarm optimization algorithm (PSO) running in holonomic unmanned vehicles (HUVs) operating in 3D space. Each HUV is able to adjust its speed and movement direction to achieve better fitness location using our 3D-PSO. We demonstrate that 3D-PSO uses limited information collected from each HUVs local neighbors to make movement decisions over an unknown 3D space to obtain a uniform spatial distribution while maintaining network connectivity. Simulation experiments demonstrate that parameters such as the number of iterations used in our 3D-PSO, and acceleration coefficients influence the efficiency of 3D-PSO. [ABSTRACT FROM PUBLISHER]

We present new quantitative techniques to assess performance of mobile ad hoc network MANET nodes with respect to uniform distribution, total terrain covered by communication areas of all nodes, and distance traveled by each node before a desired network topology is reached. Our uniformity metrics exploit information of Voronoi tessellation of a deployment territory generated by nodes. Since node movement is a power consuming task, average distance that each node travels (ADT) before the network reaches its final distribution is an important indicator for the performance of MANET nodes. Another performance metric is the network area coverage (NAC) achieved by all nodes showing how efficiently the MANET nodes perform. For evaluation of these metrics we use our node-spreading bio-inspired game (BioGame) that combines force-based genetic algorithm (FGA) and game theory to guide autonomous mobile agents in selecting new improved locations. We formally define BioGame, FGA, Voronoi based node uniformity measures, ADT, and NAC. Our simulation experiments demonstrate that these performance evaluation techniques are good indicators for assessing node distribution methods. [ABSTRACT FROM PUBLISHER]