Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks

RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs; however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes’ energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes

An MDP-based application oriented optimal policy for wireless sensor networks

Technological advancements due to Moore’s law have led to the proliferation of complex wireless sensor network (WSN) domains. One commonality across all WSN domains is the need to meet application requirements (i.e. lifetime, respon-siveness, etc.) through domain specific sensor node design. Techniques such as sensor node parameter tuning enable WSN designers to specialize tunable parameters (i.e. proces-sor voltage and frequency, sensing frequency, etc.) to meet these application requirements. However, given WSN do-main diversity, varying environmental situations (stimuli), and sensor node complexity, sensor node parameter tuning is a very challenging task. In this paper, we propose an auto-mated Markov Decision Process (MDP)-based methodology to prescribe optimal sensor node operation (selection of val-ues for tunable parameters such as processor voltage, pro-cessor frequency, and sensing frequency) to meet application requirements and adapt to changing environmental stimuli. Numerical results confirm the optimality of our proposed methodology and reveal that our methodology more closely meets application requirements compared to other feasible policies