Vladimir Marbukh

Distributed Deployment Algorithms for Improved Coverage in a Network of Wireless Mobile Sensors

In this paper, efficient sensor deployment strategies are developed to increase coverage in wireless mobile sensor networks. The sensors find coverage holes within their Voronoi polygons and then move in an appropriate direction to minimize them. Novel edge-based and vertex-based strategies are introduced, and their performances are compared with existing techniques. The proposed movement strategies are based on the distances of each sensor and the points inside its Voronoi polygon from the edges or vertices of the polygon. Simulations confirm the effectiveness of the proposed deployment algorithms and their superiority to the techniques reported in the literature.

Self-Deployment Algorithms for Coverage Problem in a Network of Mobile Sensors with Unidentical Sensing Ranges

In this paper, efficient sensor deployment algorithms are proposed to improve the coverage area in the target field. The proposed algorithms calculate the position of the sensors iteratively, based on the existing coverage holes in the target field. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is used to discover the coverage holes corresponding to different sensors with different sensing ranges. Under the proposed procedures, the sensors move in such a way that the coverage holes in the target field are reduced. Simulation results are provided to demonstrate the effectiveness of the deployment schemes proposed in this paper.

An Efficient Target Monitoring Scheme With Controlled Node Mobility for Sensor Networks

This paper is concerned with target monitoring using a network of collaborative mobile sensors. The objective is to compute (online) the desired sensing and communication radii of sensors as well as their location at each time instant, such that a set of prescribed specifications are met. These specifications include end-to-end connectivity preservation from the target to a fixed destination, while durability of sensors is maximized and the overall energy consumption is minimized. The problem is formulated as a constrained optimization, and a procedure is presented to solve it. Simulation results demonstrate the effectiveness of the proposed techniques.

Demand Pricing & Resource Allocation in Market-Based Compute Grids: A Model and Initial Results

Market-based compute grids encompass service providers offering limited resources to potential users with varying quality of service demands and willingness to pay. Providers face problems of pricing and allocating resources to maximize revenue. Previous research proposed and analyzed a method for allocating resources based on joint optimization of access control and scheduling strategies. This paper proposes a tractable analytical model for joint optimization of job pricing and scheduling strategies with the objective of maximizing provider revenue. The paper provides initial results for the case of a single provider serving price-sensitive users whose utilities decay linearly with increasing service delay. The paper also shows that providers must combine both pricing and admission control to achieve maximum revenue.

Towards a Metric for Communication Network Vulnerability to Attacks: A Game Theoretic Approach

In this paper, we propose a quantification of the vulnerability of a communication network where links are subject to failures due to the actions of a strategic adversary. We model the adversarial nature of the problem as a 2-player game between a network manager who chooses a spanning tree of the network as communication infrastructure and an attacker who is trying to disrupt the communication by attacking a link. We use previously proposed models for the value of a network to derive payoffs of the players and propose the network’s expected loss-in-value as a metric for vulnerability. In the process, we generalize the notion of betweenness centrality: a metric largely used in Graph Theory to measure the relative importance of a link within a network. Furthermore, by computing and analyzing the Nash equilibria of the game, we determine the actions of both the attacker and the defender. The analysis reveals the existence of subsets of links that are more critical than the others. We characterize these critical subsets of links and compare them for the different network value models. The comparison shows that critical subsets depend both on the value model and on the connectivity of the network.

Minimum Regret Approach to Network Management under Uncertainty with Application to Connection Admission Control and Routing

This paper proposes a framework for network management intended to balance network performance under normal steady operational conditions with robustness under non-steady, and/or adverse conditions. Working in conjunction with anomaly and/or intrusion detection, the proposed framework allows the network to develop a set of measured responses to possible anomalies and external threats by minimizing the average maximum network losses, i.e., regrets or risks, due to uncertainty. Loss maximization guards against uncertainty within each scenario. Averaging of the maximum losses reflects an available information on the likelihood of different scenarios. The proposed framework includes Bayesian and minimax approaches as particular cases. The paper demonstrates how the proposed framework can alleviate high sensitivity of a cost-based admission and routing scheme to uncertain resource costs and possible presence of excessive and/or adversarial traffic. Specific examples include competitive and minimum interference admission and routing schemes.

A Game-Theoretic Framework for Network Security Vulnerability Assessment and Mitigation

In this paper we propose and discuss a game-theoretic framework for (a) evaluating security vulnerability, (b) quantifying the corresponding Pareto optimal vulnerability/cost tradeoff, and (c) identifying the optimal operating point on this Pareto optimal frontier. We discuss our framework in the context of a flow-level model of Supply-Demand (S-D) network where we assume a sophisticated attacker attempting to disrupt the network flow. The vulnerability metric is determined by the Nash equilibrium payoff of the corresponding game. The vulnerability/cost tradeoff is derived by assuming that “the network” can reduce the security vulnerability at the cost of using more expensive flows and the optimal operating point is determined by “the network” preferences with respect to vulnerability and cost. We illustrate the proposed framework on examples through numerical investigations.

Optimal target tracking strategy with controlled node mobility in mobile sensor networks

This paper is concerned with target tracking using a network of collaborative sensors. The objective is to compute (online) the desired sensing and communication radii of sensors as well as their location at each time instant, such that a set of prescribed specifications are achieved. These specifications include end-to-end connectivity preservation from the target to a fixed destination, while durability of sensors is maximized and the overall energy consumption is minimized. The problem is formulated as a constrained optimization, and a procedure is presented to solve it. Simulation results demonstrate the effectiveness of the proposed techniques.

Distributed deployment algorithms for improved coverage in mobile sensor networks

In this paper, sensor deployment strategies are studied for effective coverage in wireless sensor networks. In the proposed algorithms, each sensor discovers the coverage holes within its Voronoi polygons, and then moves in a proper direction to minimize them. Novel edge-based and vertex-based strategies are proposed for efficient sensor deployment, and their features are compared with existing techniques. The algorithms proposed in this paper consider the distances of each sensor and the points inside its corresponding Voronoi polygon from the edges or vertices of the polygon. It is shown that the methods introduced in this work outperform existing strategies. Simulations confirm the effectiveness of the proposed deployment algorithms, and their superiority over the techniques reported in the literature.

Link layer adaptation in body area networks: Balancing reliability and longevity

A wireless Body Area Network (BAN) consists of multiple radio-enabled wearable and implantable sensor nodes for use inside or in proximity to the human body. BANs are expected to balance requirement for reliable communication of their nodes with a need for battery energy conservation to ensure their longevity. The main challenge in managing this tradeoff is the inability of interfering BANs to explicitly coordinate their transmissions. This may result in unacceptably high battery energy draining rates to overcome cross-interference by other BANs in the vicinity. Here, we propose a utility-based, link-layer adaptation framework for balancing these tradeoffs for each BAN as well as across different BANs. The framework accounts for the utility of the BANs data rates and penalties associated with high transmission powers. Our analysis indicates that link-layer adaptation is beneficial for mitigating the interference from multiple adjacent BANs. Simulation results support our mathematical framework, indicating a much better spectral efficiency obtainable by using link layer adaptation techniques.