Energy Aware Channel Allocation with Spectrum Sensing in Pilot Contamination Analysis for Cognitive Radio Networks

Cognitive radio (CR) is an innovative and contemporary technology that has been making an effort to overcome the problems of bandwidth reduction by rising the usage of mobile cellular bandwidth connections. The reallocation and distribution of channels is a fundamental characteristic of cellular mobile networks (CMN) to exploit the consumption of CMS. Meanwhile, throughput maximization might lead to higher power utilization, the spectrum sensing system must tackle the energy throughput tradeoff. The spectrum sensing time should be defined by the residual battery energy of secondary user (SU). In that context, energy effective algorithm for spectrum sensing should be developed for meeting the energy constraint of CRN. This study designs a new quantum particle swarm optimization-based energy aware spectrum sensing scheme (QPSO-EASSS) for CRNs. Here, the presented QPSO-EASSS technique dynamically estimates the sensing time depending upon the battery energy level of SUs and the transmission power can be computed based on the battery energy level and PU signal of the SUs. In addition, in this work, the QPSO-EASSS technique applies the QPSO algorithm for throughput maximization with energy constraints in the CRN. The detailed set of experimentations take place and reported the improvements of the QPSO-EASSS technique compared to existing models

Efficient energy management for the internet of things in smart cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities

Cross-layer Balanced and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks

For improving the efficiency and the reliability of the opportunistic routing algorithm, in this paper, we propose the cross-layer and reliable opportunistic routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the improved efficiency fuzzy logic and humoral regulation inspired topology control into the opportunistic routing algorithm. In CBRT, the inputs of the fuzzy logic system are the relative variance (rv) of the metrics rather than the values of the metrics, which reduces the number of fuzzy rules dramatically. Moreover, the number of fuzzy rules does not increase when the number of inputs increases. For reducing the control cost, in CBRT, the node degree in the candidate relays set is a range rather than a constant number. The nodes are divided into different categories based on their node degree in the candidate relays set. The nodes adjust their transmission range based on which categories that they belong to. Additionally, for investigating the effection of the node mobility on routing performance, we propose a link lifetime prediction algorithm which takes both the moving speed and moving direction into account. In CBRT, the source node determines the relaying priorities of the relaying nodes based on their utilities. The relaying node which the utility is large will have high priority to relay the data packet. By these innovations, the network performance in CBRT is much better than that in ExOR, however, the computation complexity is not increased in CBRT.Comment: 14 pages, 17 figures, 31 formulas, IEEE Sensors Journal, 201