Formal Probabilistic Analysis of a Wireless Sensor Network for Forest Fire Detection

Wireless Sensor Networks (WSNs) have been widely explored for forest fire detection, which is considered a fatal threat throughout the world. Energy conservation of sensor nodes is one of the biggest challenges in this context and random scheduling is frequently applied to overcome that. The performance analysis of these random scheduling approaches is traditionally done by paper-and-pencil proof methods or simulation. These traditional techniques cannot ascertain 100% accuracy, and thus are not suitable for analyzing a safety-critical application like forest fire detection using WSNs. In this paper, we propose to overcome this limitation by applying formal probabilistic analysis using theorem proving to verify scheduling performance of a real-world WSN for forest fire detection using a k-set randomized algorithm as an energy saving mechanism. In particular, we formally verify the expected values of coverage intensity, the upper bound on the total number of disjoint subsets, for a given coverage intensity, and the lower bound on the total number of nodes.Comment: In Proceedings SCSS 2012, arXiv:1307.802

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Heterogeneity-aware and energy-aware scheduling and routing in wireless sensor networks

A Wireless Sensor Network (WSN) is a group of specialized transducers, called sensor nodes, with a communication infrastructure intended to monitor and record conditions at diverse locations. Since WSN applications are usually deployed in an open environment, the network is exposed to rough weather conditions, such as rain and snow. Another problem that WSN applications need to deal with is the energy constraints of sensor nodes. Both problems adversely affect the lifetime of WSN applications. A lot of research has been conducted to prolong the lifetime of WSN applications considering energy constraints of sensor nodes, but not much research has gone into tackling both the environmental effects and energy constraints. The goal of this research is to efficiently deal with these two problems and provide a solution for scheduling and routing in a heterogeneous sensor network. The research has been divided into two phases - Scheduling and Routing. In the scheduling phase, only some sensor nodes are scheduled to run for a particular timeslot and during that timeslot other sensor nodes are kept in sleep mode. A set of sensor nodes for a timeslot is chosen based on their positional information. In the routing phase, a least cost route from a sensor to the sink is dynamically determined to prolong the lifetime of the sensor network