Janusz Kusyk

Analysis of Emergent Behavior for GA-based Topology Control Mechanism for Self-Spreading Nodes in MANETs

We introduce a genetic algorithm based MANET topology control mechanism to be used in decision making process of adaptive and autonomic systems at run time. A mobile node adapts its speed and direction using limited information collected from local neighbors operating in an unknown geographical terrain. We represent the genetic operators (i.e., selection, crossover and mutation) as a dynamical system model to describe the behavior of a single node’s decision mechanism. In this dynamical system model each mobile node is viewed as a stochastic variable. We build a homogeneous Markov chain to study the convergent nature of multiple mobile nodes running our algorithm, called FGA. Each state in our chain represents a configuration of the nodes in a MANET for a given instant. The homogeneous Markov chain model of our FGA is shown to be ergodic; its convergence is demonstrated using Dobrushin’s contraction coefficients. We also observe that the nodes with longer communication ranges utilize more information about their neighborhood to make better decisions, require less movement and converge faster, whereas smaller communication ranges utilize limited information, take more time to escape local optima, and, hence, consume more energy.

Markov chain model for differential evolution based topology control in MANETs

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.

Resilient node self-positioning methods for MANETS based on game theory and genetic algorithms

We present a distributed and scalable game participated by autonomous MANET nodes to place themselves uniformly over a dynamically changing environment. A node spreading potential game, called Rel-NSPG, run at each node, autonomously makes movement decisions based on localized data while the best next location to move is selected by a genetic algorithm (GA). Since it requires only a limited synchronization among the closest neighbors of a player, and does not require a priori knowledge of the environment, Rel-NSPG is a good candidate for node spreading class of applications used in military tasks. The performance of Rel-NSPG degrades gracefully when the number of MANET nodes decrease either due to equipment malfunction or hostile activities. We show that this resilience to loss of nodes is inherent in Rel-NSPG. Simulation experiments demonstrate that, after a subset of the MANET nodes arbitrarily become unavailable, the remaining nodes recover and offset lost nodes. Similarly, when there are losses concentrated in a given region, remaining nodes reconfigure their positions to compensate for the missing area coverage. The simulation experiments with arbitrarily placed obstacles, in addition to lost assests, produce promising results.

Efficient node distribution techniques in mobile ad hoc networks using game theory

Self deployment of nodes in mobile ad hoc networks (MANETs) is a challenging task due to the characteristics of a MANET such as dynamically changing topology, lack of centralized authority, decentralized architecture and heterogeneous nodes. In military applications where the deployed nodes, due to their limited communication ranges and hostile environment, may act selfishly with conflicting individual interests among their neighbors, game-theoretic approaches become relevant. Using our distributed game (NSPG-G1) for MANET nodes to position themselves in an unknown geographical terrain to maximize the area coverage, we show that, combined with a distributed genetic algorithm (GA) to determine the next best location to move, NSPG-G1 can provide a near uniform node spreading. In this distributed and scalable game participated by the neighboring nodes autonomously, the decisions about node movements are solely based on localized data about the neighboring nodes while requiring a limited synchronization among a players closest neighbors.

Applications of game theory to mobile ad hoc networks: Node spreading potential game

Sustaining a complete and accurate information about MANET nodes is often impractical due to dynamic topology, lack of centralized authority, decentralized architecture and heterogeneous nodes in MANETs. Main concerns for MANET performance are power consumption, topology control, spectrum sharing, and localization, all of which are intensified by node mobility. Another inherent characteristic of mobile nodes in MANETs is that they have limited or no cooperation among themselves, and their motivations are often selfish with conflicting individual interests. We present a distributed game for obtaining a uniform node distribution among the MANET nodes over a given geographical territory. We show that our potential game can be an effective mechanism for distributed tasks such as uniform node distribution.

Fault tolerant bio-inspired topology control mechanism for autonomous mobile node distribution in MANETs

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.

Performance evaluation of differential evolution based topology control method for autonomous MANET nodes

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.

Differential Evolution Based Fault Tolerant Topology Control in MANETs

We study a fault tolerant differential evolution based topology control mechanism, called TCM-Y, to direct the movements of autonomous vehicles that dynamically adjust their speed and directions in MANETs. TCM-Y uses a Yao graph inspired fitness function to preserve a nodes minimum desired number of connections with its neighbors while uniformly dispersing mobile nodes in an unknown terrain. We present a formal analysis of TCM-Y to show that it provides a fault tolerant node spreading mechanism since any node will have at least k neighbors at all times. The effectiveness of TCM-Y is evaluated by comparing it with a popular deterministic node spreading mechanism called Constrained Coverage for Mobile Sensor Nodes (CC-MSN) that has similar objectives as TCM-Y. Experimental results obtained from our simulation software show that TCM-Y performs significantly better than CC-MSN with respect to normalized area coverage, average distance traveled, average connectivity, and the minimum connectivity achieved by mobile nodes.

Bio-inspired and Voronoi-based algorithms for self-positioning autonomous mobile nodes

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 nodes 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.

Metrics for performance evaluation of self-positioning autonomous MANET nodes

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.