Spectrum Sensing Scheduling in Cognitive Radio Networks

In cognitive radio (CR) networks, spectrum sensing has gained great importance for opportunistic spectrum access. There are many factors that affect the efficiency of spectrum sensing. High sensing accuracy can help reduce the chance of interference to primary user and improve the spectrum utility. However, high sensing accuracy requires a large amount of sensing resources including multiple collaborative CRs and the sensing duration. We propose a cost based framework for spectrum sensing scheduling, in which all these factors are modeled by certain cost or gain of the system. A sequential energy detector is used for accumulating all energy measurements for sensing. Depending on the decision made, the CRs decide whether to wait as the channel is occupied or to start data transmission as there is a spectral hole. The optimal number of CRs, the sensing accuracy levels and the waiting/transmission time are obtained such that the average gain per unit time including both sensing and wait/data transmission stages are maximized. We provide various experimental results to show the effectiveness of the proposed design and the effects of various parameters on the performance are analyzed. The idea is then extended to a multiple channel CR network. The channel profile generated from a single channel design is utilized for CR assignment to channels that request for sensing. Two approaches, viz., greedy approach and non-greedy approach are designed for scheduling. Then the two approaches are compared on the basis of total average gain obtained from each approaches. The non-greedy approach outperforms the greedy approach with respect to the total average gain.School of Electrical & Computer Engineerin

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