Renita Machado

A survey of game-theoretic approaches in wireless sensor networks

Wireless sensor networks (WSNs) comprising of tiny, power-constrained nodes are gaining popularity due to their potential for use in a wide variety of environments like monitoring of environmental attributes, intrusion detection, and various military and civilian applications. While the sensing objectives of these environments are unique and application-dependent, a common performance criteria for wireless sensor networks is prolonging network lifetime while satisfying coverage and connectivity in the deployment region. Security is another important performance parameter in wireless sensor networks, where adverse and remote environments pose various kinds of threats to reliable network operation. In this paper, we look at the problems of security and energy efficiency and different formulations of these problems based on the approach of game theory. The potential applicability of WSNs to intruder detection environments also lends itself to game-theoretic formulation of these environments, where pursuit-evasion games provide a relevant framework to model detection, tracking and surveillance applications. The suitability of using game theory to study security and energy efficiency problems and pursuit-evasion scenarios using WSNs stems from the nature of strategic interactions between nodes. Approaches from game theory can be used to optimize node-level as well as network-wide performance by exploiting the distributed decision-making capabilities of WSNs. The use of game theory has proliferated, with a wide range of applications in wireless sensor networking. In the wake of this proliferation, we survey the use of game-theoretic approaches to formulate problems related to security and energy efficiency in wireless sensor networks.

Adaptive density control in heterogeneous wireless sensor networks with and without power management

The authors study the design of heterogeneous two-tier wireless sensor networks (WSNs), where one tier of nodes is more robust and computationally intensive than the other tier. The authors find the ratios of densities of nodes in each tier to maximise coverage and network lifetime. By employing coverage processes and optimisation theory, the authors show that any topology of WSN derived from random deployments can result in maximum coverage for the given node density and power constraints by satisfying a set of conditions. The authors show that network design in heterogeneous WSNs plays a key role in determining key network performance parameters such as network lifetime. The authors discover a functional relationship between the redundancy, density of nodes in each tier for active coverage and the network lifetime. This relationship is much less pronounced in the absence of heterogeneity. The results of this work can be applied to network design of multi-tier networks and for studying the optimal duty cycles for power saving states for nodes in each tier.

Diffusion-based approach to deploying wireless sensor networks

An important objective of Wireless Sensor Networks (WSNs) is to reliably sense data about the environment in which they are deployed. Reliability in WSNs has been widely studied in terms of providing reliable routing protocols for message dissemination and reliability of communication from sink to sensors. In this work, we define a reliability metric by the amount of data sensed by the network. In order to satisfy this reliability constraint, we propose a diffusion-based approach for a deployment pattern for the sensor nodes. We show that this deployment pattern achieves sufficient coverage and connectivity and requires lesser number of sensors than popular regular deployment patterns. We further obtain the bounds on establishing connectivity between nodes in the WSN and extend this analysis for heterogeneous WSNs.

Coverage properties of clustered wireless sensor networks

This article studies clustered wireless sensor networks (WSNs), a realistic topology resulting from common deployment methods. We study coverage in naturally clustered networks of wireless sensor nodes, as opposed to WSNs where clustering is facilitated by selection. We show that along with increasing the vacancy in random placement of nodes in a WSN, it also alters the connectivity properties in the network. We analyze varying levels of redundancy to determine the probability of coverage in the network. The phenomenon of clustering in networks of wireless sensor nodes raises interesting questions for future research and development. The article provides a foundation for the design to optimize network performance with the constraint of sensing coverage.

Network Planning for Heterogeneous Wireless Sensor Networks in Environmental Survivability

To deal with the problem of hostile environments, we proposed to construct heterogeneous sensor networks composed of both regular nodes and robust nodes, where robust nodes are better equipped for hostile environments and hence are more expensive than regular nodes. We study the problem of network design in heterogeneous wireless sensor networks that involves optimization of network costs associated with different classes of nodes versus maximizing coverage and network lifetime. We consider the design of heterogeneous networks with the objectives of minimizing costs and maximizing network lifetime. The association we present in heterogeneous sensor network design between optimizing the number of nodes in each class with cost constraints and network lifetime for corresponding network composition maybe of independent interest in the design of networks in general.

Bounds on the Error in Estimating Redundancy in Randomly Deployed Wireless Sensor Networks

A challenging requirement in wireless sensor networks is the deployment of nodes in a wireless sensor network to satisfy continuous sensing with extended network lifetime while maintaining uniform coverage in the deployment region. While dense random deployments satisfy coverage and sensing requirements, constructing dense networks of sensor nodes poses economical constraints as well as the problem of redundancy. We provide an analytical framework for estimating the redundancy in a random deployment of nodes without the need of location information of nodes. We use an information theoretic approach to estimate the redundancy in a randomly deployed wireless sensor network and provide the Cramer-Rao bound on the error in estimating the redundancy in a wireless sensor network. We illustrate this redundancy estimation approach and calculate the bounds on the error in estimating the redundancy for a wireless sensor network with 1-redundancy. We also analytically show the interdependence between redundancy and network lifetime for random deployment.

Neural Network-Based Approach for Adaptive Density Control and Reliability in Wireless Sensor Networks

A primary constraint in wireless sensor networks (WSNs) is obtaining reliable and prolonged network operation with power-limited sensor nodes. Most of the approaches to the energy constraint problem focus mainly on the WSN and its architecture without analyzing the underlying process for the depletion of battery levels of individual nodes and consequent reduction in network lifetime: the variation of sensing environment in the deployment region. We study the energy model of a WSN as interdependence between the environmental variation and its impact on the energy consumption at individual nodes. This paper motivates the need for modeling energy variation in WSNs along with the environment in the deployment region. Defining network energy as the sum of residual battery energy at nodes, we provide an analytical framework for the dependence of node energy and sensitivity of network energy as a function of environmental variation and reliability parameters. Using a neural network based approach, we perform adaptive density control and show how reliability requirements and environment variation influences the rate of change of network energy.