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In cognitive radio communication, spectrum sensing plays a vital role in sensing the existence of the primary user (PU). The sensing performance is badly affected by fading and shadowing in case of single secondary user(SU). To overcome this issue, cooperative spectrum sensing (CSS) is proposed. Although the reliability of the system is improved with cooperation but existence of malicious user (MU) in the CSS deteriorates the performance. In this work, we consider the Kullback-Leibler (KL) divergence method for minimizing spectrum sensing data falsification (SSDF) attack. In the proposed CSS scheme, each SU reports the fusion center(FC) about the availability of PU and also keeps the same evidence in its local database. Based on the KL divergence value, if the FC acknowledges the user as normal, then the user will send unified energy information to the FC based on its current and previous sensed results. This method keeps the probability of detection high and energy optimum, thus providing an improvement in performance of the system. Simulation results show that the proposed KL divergence method has performed better than the existing equal gain combination (EGC), maximum gain combination (MGC) and simple KL divergence schemes in the presence of MUs.

Abstract-Underwater sensor networks (UWSNs) face specific challenges due to the transmission properties in the underwater environment. Radio waves propagate only for short distances under water, and acoustic transmissions have limited data rate and relatively high latency. One of the possible solutions to these challenges involves the use of autonomous underwater vehicles (AUVs) to visit and offload data from the individual sensor nodes. We consider an underwater sensor network visually monitoring an offshore oil platform for hazards such as oil spills from pipes and blowups. To each observation chunk (image or video) we attach a numerical value of information (VoI). This value monotonically decreases in time with a speeed which depends on the urgency of the captured data. An AUV visits different nodes along a specific path and collects data to be transmitted to the customer. Our objective is to develop path planners for the movement of the AUV which maximizes the total VoI collected. We consider three different path planners: the lawn mower path planner (LPP), the greedy planner (GPP) and the random planner (RPP). In a simulation study we compare the total VoI collected by these algorithms and show that the GPP outperforms the other two proposed algorithms on the studied scenarios.

Wireless Power Transfer (WPT) is an innovative technology employed for enhancing the energy sustainability of wireless devices with a limited life span. The idea of integrating WPT in wireless communication leads to the idea of Simultaneous Wireless Information and Power Transfer (SWIPT) that transfers information and power to wireless devices simultaneously, thereby resulting in a drastic increase in spectral efficiency of the network. SWIPT aided Cooperative Relaying (CoR) has emerged as a new trend for Fifth Generation (5G) and Beyond 5G (B5G) systems owing to the rapidly increasing challenges faced by these networks. Cooperative relaying combined with SWIPT can be helpful in overcoming the rising demands of next generation wireless networks by providing an enhanced date rate, low latency, shorter coverage, wide spread connectivity of massive number of devices along with energy-efficiency. This article provides a comprehensive review of SWIPT technology that enables the use of CoR networks for 5G and B5G mobile networks including the significance, technologies, and protocols which can be applied. This article also examines the deployment of cooperative SWIPT involving a single relay, multiple relays and optimal relay selection, multi antenna systems and optimal beamforming .SWIPT under the influence of Hardware Impairments (HI), imperfect Channel State Information (CSI), non-linear energy harvesting models, Intelligent Reconfigurable Surface (IRS), massive MIMO, massive access for the Internet of Things (IoT) has been discussed in detail. Meanwhile, this study discusses key challenges being faced in the implementation of SWIPT for future wireless networks that need to be addressed efficiently.