An Energy Driven Architecture for Wireless Sensor Networks

Most wireless sensor networks operate with very limited energy sources-their batteries, and hence their usefulness in real life applications is severely constrained. The challenging issues are how to optimize the use of their energy or to harvest their own energy in order to lengthen their lives for wider classes of application. Tackling these important issues requires a robust architecture that takes into account the energy consumption level of functional constituents and their interdependency. Without such architecture, it would be difficult to formulate and optimize the overall energy consumption of a wireless sensor network. Unlike most current researches that focus on a single energy constituent of WSNs independent from and regardless of other constituents, this paper presents an Energy Driven Architecture (EDA) as a new architecture and indicates a novel approach for minimising the total energy consumption of a WS

Analysis of Energy Consumption Performance towards Optimal Radioplanning of Wireless Sensor Networks in Heterogeneous Indoor Environments

In this paper the impact of complex indoor environment in the deployment and energy consumption of a wireless sensor network infrastructure is analyzed. The variable nature of the radio channel is analyzed by means of deterministic in-house 3D ray launching simulation of an indoor scenario, in which wireless sensors, based on an in-house CyFi implementation, typically used for environmental monitoring, are located. Received signal power and current consumption measurement results of the in-house designed wireless motes have been obtained, stating that adequate consideration of the network topology and morphology lead to optimal performance and power consumption reduction. The use of radioplanning techniques therefore aid in the deployment of more energy efficient elements, optimizing the overall performance of the variety of deployed wireless systems within the indoor scenario

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

Statistical Analysis to Extract Effective Parameters on Overall Energy Consumption of Wireless Sensor Network (WSN)

In this paper, we use statistical tools to analysis dependency between Wireless Sensor Network (WSN) parameters and overall Energy Consumption (EC). Our approach has two main phases: profiling, and effective parameter extraction. In former, a sensor network simulator is re-run 800 times with different values for eight WSN parameters to profile consumed energy in nodes; then in latter, three statistical analyses (p-value, linear and non-linear correlation) are applied to the outcome of profiling phase to extract the most effective parameters on WSN overall energy consumption.Comment: 5-pages. This paper has been accepted in PDCAT-2012 conference (http://www.pdcat2012.org/