Adaptive Channel Selection and Data Traffic Scheduling using Mutliobjective Optimization for Cognitive Radio based Smart Grid Networks

Abstract

Smart grid (SG) uses bi-directional communication among the components of power system. The basic architecture of SG is comprised of multi-layered network with applications that have diverse quality-of-service (QoS) requirements. Integrating cognitive radio (CR) in SG network results in efficient handling of differential data amounts and latency, while meeting stringent reliability requirements through cognition in the system parameters and bandwidth. Meeting data rate, reliability, and latency demands of various smart grid applications pose greater challenge in the presence of uncertainty factors e.g. spectrum sensing errors, channel unavailability with desired parameters and signal-to-noise ratio (SNR) etc. Existing channel selection algorithms do not consider exact SG communication requirements simultaneously to allocate a suitable channel in accordance with some wireless standards. Furthermore, efficient traffic scheduling and optimization techniques to prioritize data from various SG applications are required. Recently, focus has only been on the latency and criticality based priority-aware policies for same channel bandwidth. Traffic scheduling and subsequent channel allocation are required to support both the differential throughput and latency requirements simultaneously for channels having different bandwidths and SNRs. The objective of this thesis is two-fold; (1) Adaptive channel selection, (2) SG data traffic scheduling and optimization. Firstly, we propose a technique which selects the optimum channel for a particular application, from a pool of available channels which best meets the QoS requirements. For the optimum channel selection, fuzzy inference system (FIS) optimization technique is used. The physical layer is based on mode-4 of IEEE 802.22 Standard, wireless regional area network (WRAN). A novel approach is also proposed for allocation of channel when desired channel in not available. This approach is based on selecting an alternate channel, in event of unavailability of desired channel, with parameters that closely match with the desired requirements in order to reduce re-transmission probability. The proposed technique outperforms existing algorithms in terms of achieved latency iv by a minimum of 200%, and throughput by approximately 50%. Secondly, a QoS-aware framework for data traffic scheduling in cognitive radio based SG communication network is proposed. The channels available to smart grid communication nodes (SCNs) are categorized as low and high bandwidths. For each bandwidth, all SG applications are categorized into several priority-classes comprised of latency and throughput sub-classes. The proposed SG traffic scheduling framework takes into account various types of SCNs’ data (i.e. emergent, interrupted and recurrent) to guarantee differential QoS requirements. For both channel bandwidths, the scheduler maintains and updates two sets of priority queues based on weights associated with each class and its data type. The complete scheduling framework is formulated as a multi-objective optimization problem. The overall objective function is the weighted sum of individual utility functions of latency and throughput. A novel usage of Adam optimizer is proposed to minimize the latency and maximize the throughput by obtaining optimal system cost, resulting in optimal decision policy. Simulation results show that the proposed algorithm provides lower latency and higher throughput for most critical and high data rate demanding applications, respectively, while achieving the desired QoS requirements of all SG applications at the same time