Broadcasting with Controlled Redundancy and Improved Localization in Wireless Sensor Networks

Wireless sensor networks （WSNs） consist of sensor nodes that broadcast a message within a network. Efficient broadcasting is a key requirement in sensor networks and has been a focal point of research over the last few years. There are many challenging tasks in the network, including redundancy control and sensor node localization that mainly depend on broadcasting. In this paper, we propose a broadcasting algorithm to control redundancy and improve localization （BACRIL） in WSNs. The proposed algorithm incorporates the benefits of the gossip protocol for optimizing message broadcasting within the network. Simulation results show a controlled level of redundancy, which is up to 57.6% if the number of sensor nodes deployed in a 500 m×500 m area are increased from 50 to 500.

Performance evaluation of DHRR-RIS based HP design using machine learning algorithms

Reconfigurable Intelligent Surfaces(RIS)have emerged as a promising technology for improving the reliability of massive MIMO communication networks.However,conventional RIS suffer from poor Spectral Efficiency(SE)and high energy consumption,leading to complex Hybrid Precoding(HP)designs.To address these issues,we propose a new low-complexity HP model,named Dynamic Hybrid Relay Reflecting RIS based Hybrid Precoding(DHRR-RIS-HP).Our approach combines active and passive elements to cancel out the downsides of both conventional designs.We first design a DHRR-RIS and optimize the pilot and Channel State Information(CSI)estimation using an adaptive threshold method and Adaptive Back Propagation Neural Network(ABPNN)algorithm,respectively,to reduce the Bit Error Rate(BER)and energy consumption.To optimize the data stream,we cluster them into private and public streams using Enhanced Fuzzy C-Means(EFCM)algorithm,and schedule them based on priority and emergency level.To maximize the sum rate and SE,we perform digital precoder optimization at the Base Station(BS)side using Deep Deterministic Policy Gradient(DDPG)algorithm and analog precoder optimization at the DHRR-RIS using Fire Hawk Optimization(FHO)algorithm.We implement our proposed work using MATLAB R2020a and compare it with existing works using several validation metrics.Our results show that our proposed work outperforms existing works in terms of SE,Weighted Sum Rate(WSR),and BER.

Relay-assisted wireless energy harvesting for multihop clustered IoT network

In large-scale networks such as the Internet of Things(IoT),devices seek multihop communication for longdistance communications,which considerably impacts their power exhaustion.Hence,this study proposes an energy harvesting-enabled,relay-based communication in multihop clustered IoT networks in a bid to conserve the battery power in multihop IoT networks.Initially,this study proposes an efficient,hierarchical clustering mechanism in which entire IoT devices are clustered into two types:the closest cluster(CC)and remote clusters(RCs).Additionally,Euclidean distance is employed for the CC and fuzzy c-means for the RCs.Next,for cluster head(CH)selection,this study models a fitness function based on two metrics,namely residual energy and distance(device-to-device distance and device-to-sink distance).After CH selection,the entire clustered network is partitioned into several layers,after which a relay selection mechanism is applied.For every CH of the upper layer,we assign a few lower-layer CHs to function as relays.The relay selection mechanism is applied only for the devices in the RCs,while for devices in the CC,the CH functions as a relay.Finally,several simulation experiments are conducted to validate the proposed method’s performance.The results show the method’s superiority in terms of energy efficiency and optimal number of relays in comparison with the state-of-the-art methods.