Inter-cluster Communication Research Articles

Consequence-based Clustered Architecture

We recognize that the execution of many dynamic instructions have no consequence on the overall execution of the program. For example, the execution of a correctly predicted conditional branch instruction, as well as all the instructions leading up to it, are inconsequential. We propose a clustered architecture that steers consequential instructions to the primary cluster, and inconsequential ones to the secondary one called the I-Pipe that is less capable and thereby, more area and power efficient. The proposed architecture also entails minimal inter-cluster communication, thereby greatly reducing the complexities of inter-cluster result buses. Such a steering policy also helps increase the performance as the consequential instructions do not face any interference from the inconsequential ones. We demonstrate a \(42\% \) area reduction as compared to a baseline single cluster (Tigerlake-based) architecture, a \(18.5\% \) power reduction in the SPEC CPU2017 suite ( \(13.7\% \) power reduction in GAPBS), and a \(5.15\% \) performance uplift in the SPEC CPU2017 suite ( \(10.22\% \) in the GAPBS suite).

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring.

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.

Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs).

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

Event-Triggered Cluster Consensus of Multi-Agent Systems via a Modified Genetic Algorithm.

This article is concerned with the event-triggered output feedback cluster consensus of leader-following multi-agent systems (MASs) under limited communication resources. Specifically, the distributed agents are divided into several clusters to accomplish different collective tasks under diverse intracluster and intercluster communications. First, to alleviate excessive communication resource consumption, two sampled-data-based event-triggered schemes are developed to distinguish agent-to-agent communications within clusters and between clusters. Based on these schemes, an event-based cluster consensus control protocol is proposed to solve the problem. Then, sufficient criteria on asymptotic stability of the resulting closed-loop system are derived and expressed in terms of matrix inequalities. It is noteworthy that the derived criteria for controller design are nonlinear and nonconvex with respect to the output feedback control gains and triggering parameters. To handle this issue, a modified genetic algorithm (MGA) with multiple subpopulations is proposed, where the subpopulations are independent of each other. The key feature of the designed MGA lies in that the fitness value is described as an accumulation of initial value and weighing value of each matrix inequality. Finally, an application of satellite formation flying is exemplified to demonstrate the effectiveness of the derived theoretical results.

Energy Efficient for Data Aggregation in Wireless Sensor Networks

Abstract: The Wireless Sensor Networks (WSNs), known for their decentralized and self-configuring properties, utilize sensor nodes to collect and transmit data to a base station. However, due to their sprawling nature and the substantial distances involved, energy management remains a critical challenge in WSNs. To mitigate this, hierarchical clustering has been identified as an effective strategy. This approach involves dividing the network into clusters of a predetermined size, each led by a cluster head selected based on their energy levels and proximity to the base station. The chosen head is typically the node with the least distance to the base station and the highest energy reserve. These heads facilitate inter-cluster communication and data transfer to the base station. This research presents the implementation and enhancement of the WEMER protocol, aimed at prolonging the lifespan of WSNs. The WEMER protocol initially segments the network into clusters with respective heads and designates leader nodes to relay data from the cluster heads to the base station. Enhancements to this protocol include the deployment of gateway nodes close to the base station. These nodes receive data from the leader nodes, which in turn acquire data from the cluster heads, thus boosting network longevity. Implemented and tested in MATLAB, the enhanced WEMER protocol demonstrates significant improvements over its predecessor. The results indicate a reduction in the number of inactive (dead) nodes, an increase in active (alive) nodes, a higher data packet transmission rate, and improved overall energy efficiency.

Enhanced ACO in clustering algorithm for QoS in WSN enabled IoT

Various sensor nodes whose deployment is done in a random way are included in Wireless sensor networks (WSN) within a region for gathering the information out of the respective atmospheres as well as forwards it towards the Base Station (BS). Constrained sources of energy are allotted to the WSN nodes. The consumption of energy of nodes must be reduced for obtaining the enhanced lifetime of the network. Clustering is referred as a technique that reduced the consumption of energy in WSNs. By transmitting the gathered data to the sink through the Cluster Head (CH) nodes located within the clustered networks, the energy of the node can be saved. In WSNs, fault tolerance is a significant issue to consider. The entire data communication system can be rendered inoperable by the failure of just one cluster head. Within the scope of this research, a cluster-based routing method with fault tolerance is provided. The purpose of this study is to present an innovative technique for increasing tolerance of failure and data accumulation in clustered WSN by making use of backup CHs and improving the relay node selection mechanism by modifying ACO. The selection of the backup cluster head is what allows for fault tolerance in this case (BKCH). The method can be broken down into two distinct stages. In the first step, the network is categorized into clusters; in the second step, CHs and BCHs are chosen from the pool of candidates. Communication within the cluster takes place between the member nodes and the CH nodes. Aggregator nodes (AG) are utilised for the purpose of inter-cluster communication, and the modified ACO is utilised to determine which node serves as the most effective relay between CH and AG. When compared with other energy-conserving protocols, this technique uses less energy while increasing fault resilience and PDR.

A Design of Improved Trust-based Clustering Approach to Enhance the Energy Computation in Secured Internet of Things (IoT)

The Internet of Things (IoT) is the trending area that occupies maximum of the applications of intelligent communication. The nodes in the IoT system are located in the monitoring environment to attain stable and reliable communication. In between the nodes and the sink, similar data is sensed and transmitted at maximum of the period and that leads to high energy consumption. On the other hand, it increases the security issues of the network. So the main objective is reducing power utilization and increasing the IoT network security. In this paper, we suggest an improved trust-based clustering approach to enhance energy computation and security (ITCES-IoT) in IoT networks which can able to reduce energy consumption and increase the security among the nodes in the network. We first introduced an effective system model that can able to reduce the transmitting and receiving power during communication. Secondly, the initial cluster setup phase, LEACH-based cluster head (CH) selection phase, and re-clustering principles are discussed. Then, the trust calculation is established for both the cluster members (CM) and the CH through direct and indirect trust calculation processes followed by the best solution of path selection is performed between the CM and the sink node. We constructed Bad mouthing attacks, Ballot stuffing attacks, and detection mechanisms with a Fast Entropy algorithm. Finally, intra-cluster and inter-cluster communication are elaborated in the communication phase. Through this process, the nodes and CHs can able to perform effective communication with minimum power utilization, and the nodes with the transmitted information are secured from the external environment. At the end stage, we validate our proposed ITCES-IoT approach in certain scenarios in network simulator NS2 and their performance analysis includes the parameters of packet delivery ratio, network throughput, and packet loss and energy consumption. Then the final outcomes are compared with the base methods such as ECET-IoT, DRTP-IoT, and RCDA-IoT. From the results it is understood that the proposed ITCES-IoT outperforms the baseline methods in terms of delivery ratio and throughput and as well it consumes less power at the time of data transmission.

Output synchronization of wide-area multi-agent systems via an event-triggered hybrid control approach

This study considers an output synchronization problem for a clustered multiagent system (CMAS) in a wide-area communication network. Motivated by energy and communication constraints, a wide-area communication network was modeled via a directed graph consisting of clusters, each having a spanning tree with a leader as the root. To avoid continuous updates to the controller, the communications among agents inside each cluster were implemented in an event-triggered manner, and inter-cluster communications were executed by leaders only at the event-triggered time instants. Hybrid reduced-order observers were designed for agents and leaders when only output information was available. To drive all agents to achieve output synchronization, a hybrid event-triggered control strategy was then suggested to each agent. Several sufficient conditions were obtained using the Lyapunov function method for analyzing stability of the hybrid closed-loop CMASs. Simultaneously, any manifestation of Zeno behavior was analyzed and simulation results were shown to illustrate the feasibility of the proposed event-triggered hybrid control strategy.

Fuzzy Enhanced Black Widow Spider with Secure Encryption Random Permutation Pseudo Algorithm for Energy Efficient Cluster Communication in WSN

Devices connected to the Internet of Things (IoT) are increasingly being used. Modern mobile apps may be incorporated onto low-cost, low-power devices with the help of these extensions. This integration is made possible by the utilization of low-cost, low-power sensor nodes. All sensor nodes transmit data either directly or via a multi-hop path to other receivers or nodes. In order to enable effective inter-cluster communication through ideal cluster head selection, we build a fuzzy-enhanced black widow spider based on the Secure Encrypted Random Permutation Pseudo Algorithm (FEBWS-SERPPA) in this study. By taking energy, latency, and distance characteristics into account, the suggested FEBWS-SERPPA algorithm enhances the black widow spider optimization method by choosing the optimal cluster head in the cluster. To ensure its effectiveness, the suggested FEBWS-SERPPA algorithm's performance is compared to the state-of-the-art. The suggested FEBWS-SERPPA algorithm delivers enhanced performance speed with very low energy consumption and extended network lifetime when compared to existing cutting-edge methods.

PIRAP: Intelligent Hybrid Approach for Secure Data Transmission in Wireless Sensor Networks

The wireless sensor network (WSN) is a mobile adhoc network which has no support of infrastructure. The complexities in network management are critical to resolve even when choosing the cluster head is more efficient. From clustering procedure, appropriate cluster heads are selected with attention to energy saving among member nodes. When it comes to WSN security, trust-based cluster head selection is critical, assuming the cooperation of all sensor nodes. In light of this assumption, existing approaches were unable to assist in identifying the network’s ideal cluster head. Due to the dynamic topology and mobility of nodes in WSNs, security is a challenge. However, secrecy is often accomplished end-to-end via symmetric keys between two corresponding applications. The symmetric key is incompatible with the WSN environment. Additionally, the WSN nodes vary in their qualification for which portion of the data is accessible in which context. This work proposes an elliptic curve cryptography-enabled ciphertext policy attribute-based encryption (ECC-CP-ABE) algorithm for secure data transmission during intra-cluster communication and inter-cluster communication. In this work, we select a reliable node-based trust value mechanism and this reliable node acts as an attribute authority that inter-cluster provides a decryption private key to cluster members who are involved in intra- and inter-cluster communications. In ECC-CP-ABE, the cluster head node (CHN) and cluster member node (CMN) utilize CP-ABE for the encryption of network messages using an access policy matrix A that is computed from an AND–OR operation-based monotonic tree access structure which is defined over a various set of attributes. To ensure authenticity and integrity, the cluster member and CHN sign each ciphertext using an ECC algorithm. For performance evaluation, we use packet delivery ratio, energy consumption, encryption time, network lifetime, and decryption time as metric measures and compared the results of proposed ECC-CP-ABE with two benchmark methods, Secured WSN and Taylor-based Cat Salp Swarm Algorithm. From the results, we analyze that the proposed ECC-CP-ABE reduces energy consumption by 53.2%; increases packet delivery ratio by 98.6%; increases network lifespan by 97.5%, and reduces encryption time by 20[Formula: see text]s and decryption time by 15[Formula: see text]s.

A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme

Clustering methods are promising tools for ensuring the network scalability and maintainability of large-scale flying ad hoc networks (FANETs). However, due to the high mobility and limited energy resources of unmanned aerial vehicles (UAVs), it is difficult to maintain the network reliability and extend the network life of FANETs. In this paper, a new K-means algorithm is developed, and a dynamic transmission power of the cluster heads based clustering (DTPCH-C) scheme is proposed. The goal of this scheme is presented for FANETs to improve the reliability and lifetime of FANETs. Firstly, the optimal number of clusters is calculated and the initial UAV clusters are set up by a K-means algorithm. Then, using a weighted clustering algorithm, the adaptive node degree, the node energy and the distance from the cluster head are weighted and summed for the cluster head election. In the process of inter-cluster communication, the cluster head adjusts its transmit power in real-time through meshing and mobile prediction, thus saving the energy consumption and improving the network lifetime. The proposed DTPCH-C simultaneously optimizes the cluster number, the cluster head energy consumption, the selected cluster head, and the cluster maintenance process. The simulation results show that compared with traditional clustering methods, the proposed DTPCH-C has obvious advantages in terms of the network reliability, network life, and energy consumption.

Metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks

Recently, vehicular ad hoc networks (VANETs) finds applicability in different domains such as security, rescue operations, intelligent transportation systems (ITS), etc. VANET has unique features like high mobility, limited mobility patterns, adequate topology modifications, and wireless communication. Despite the benefits of VANET, scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques. It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes. The main drawback of VANET network is the network unsteadiness that results in minimum lifetime. In order to avoid reduced network lifetime in VANET, this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization (EMCMHR-LM) in VANET. The presented EMCMHR-LM model involves the procedure of arranging clusters, cluster head (CH) selection, and route selection appropriate for VANETs. The presented EMCMHR-LM model uses slime mold optimization based clustering (SMO-C) technique to group the vehicles into clusters. Besides, an enhanced wild horse optimization based multihop routing (EWHO-MHR) protocol by the optimization of network parameters. The presented EMCMHR-LM model is simulated using Network Simulator (NS3) tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.

A Novel Approach for Dynamic Stable Clustering in VANET Using Deep Learning (LSTM) Model

Clustering in VANETs, which dynamically evolve into wireless networks, is difficult due to the networks' frequent disconnection and fast changing topology. The stability of the cluster head (CH) has a huge impact on the network's robustness and scalability. The overhead is decreased. The stable CH assures that intra- and inter-cluster communication is minimal. Because of these difficulties, the authors seek a CH selection technique based on a weighted combination of four variables: community neighborhood, quirkiness, befit factor, and trust. The stability of CH is influenced by the vehicle's speed, distance, velocity, and change in acceleration. These are considered for in the befit factor. Also, when changing the model, the precise location of the vehicle is critical. Thus, the predicted location is used to evaluate CH stability with the help of the Kalman filter. The results showed that the befit factor performed better than the latest developments. Because of the high speed of the vehicle, dynamic changes and frequent communication link breaks are unavoidable. In order to fully perceive issue, a graphing approach employed to assess the eccentricity then the communal neighborhood. Using Eigen gap heuristic, the link dependability is determined. Trust is the final important parameter that has not yet been taken into account in the weighted method. The trust levels are specifically being evaluated for the primary users using an adaptive spectrum sensing. Long short-term memory (LSTM), a deep recurrent learning network, used to train the likelihood of detection under diverse signal and noise situations. By using LSTM model, significantly decreased the false rate. The cluster head stability has improved for high traffic density, significantly improved according to the comparative analysis with the weighted and individual metrics. The efficiency of the network has also greatly increased in terms of throughput, packet delay, packet delay ratio, and energy consumption.

Performance Analysis of Hybridization of [PIO-GSO] Algorithms in Wireless Sensor Networks

In wireless sensor networks (WSN), clustering is treated as an energy efficient technique employed to achieve augmenting network lifetime. But, the process of cluster head (CH) selection for stabilized network operation and prolonged network lifetime remains a challenging issue in WSN. In this research, presents a novel Hybridization of Pigeon Inspired with Glowworm Swarm Optimization (HPIGSO) algorithm based clustering innovation in WSN. This innovative HPIGSO algorithm integrates the good characteristics of Pigeon Inspired Optimization (PIO) algorithm and Glowworm Swarm Optimization (GSO) algorithm. The proposed algorithm operates on three major stages namely initialization, cluster head selection and cluster construction. Once the nodes are deployed, the initialization process takes place. Followed by, Base Station (BS) executes the HPIGSO algorithm and selects the cluster heads effectively. Subsequently, nearby nodes joins the cluster head and becomes cluster members, thereby cluster construction takes place. Finally, the cluster members send the data to cluster heads which is then forwarded to the base station via inter-cluster communication. The performance of the proposed HPIGSO method has been evaluated and compared with QOGSO, PIOA-DS, ALO, GOA and FFOA. Finally the proposed HPIGSO algorithm provides prolonged the lifetime of WSN over the existing clustering techniques.

Applying Quantum Search Algorithm to Select Energy-Efficient Cluster Heads in Wireless Sensor Networks

Clustering is an effective topology control approach that evenly distributes loads across sensor nodes, enhances network scalability, and increases the lifetime in wireless sensor networks. In this paper, we propose a novel energy-efficient weighted cluster head (CH) selection approach that improves the overall performance of the network and increases energy efficiency. An optimization strategy is proposed that emphasizes adjusting the transmission range with the appropriate node density, which increases energy efficiency for intra- and inter-cluster communications to 86% and 97%, respectively. In addition, the implementation of a quantum search algorithm for choosing the CH is explained. Compared to the classical method such as EECS and HEED, the proposed quantum search algorithm has a quadratic speed-up advantage. The classical search algorithm requires N steps to find a specific element in an array of N elements, but instead of using a classical algorithm, Grover’s quantum search algorithm minimizes the complexity to O (N). In this work, an energy-efficient cluster head selection approach is illustrated through a classical weighted clustering algorithm, and its implementation is also extended through a quantum weighted search algorithm which is demonstrated by the simulation results.

A Secure Long-Range Transceiver for Monitoring and Storing IoT Data in the Cloud: Design and Performance Study.

Due to the high demand for Internet of Things (IoT) and real-time data monitoring and control applications in recent years, the long-range (LoRa) communication protocols leverage technology to provide inter-cluster communications in an effective manner. A secure LoRa system is required to monitor and store IoT data in the cloud. This paper aims to report on the design, analysis, and performance evaluation of a low-cost LoRa transceiver interface unit (433 MHz band) for the real-time monitoring and storing of IoT sensor data in the cloud. We designed and analyzed a low-cost LoRa transceiver interface unit consisting of a LoRa communication module and Wi-Fi module in the laboratory. The system was built (prototype) using radially available hardware devices from the local electronics shops at about USD 150. The transmitter can securely exchange IoT sensor data to the receiver node at about 10 km using a LoRa Wi-Fi module. The receiver node accumulates the sensor data and stores it in the cloud for processing. The performance of the proposed LoRa transceiver was evaluated by field experiments in which two transmitter nodes were deployed on the rooftop of Auckland University of Technology's Tower building on city campus (New Zealand), and the receiver node was deployed in Liston Park, which was located 10 km away from the University Tower building. The manual incident field tests examined the accuracy of the sensor data, and the system achieved a data accuracy of about 99%. The reaction time of the transmitter nodes was determined by the data accumulation of sensor nodes within 2-20 s. Results show that the system is robust and can be used to effectively link city and suburban park communities.

D2CRP: A Novel Distributed 2-Hop Cluster Routing Protocol for Wireless Sensor Networks

Typically, wireless sensor networks (WSNs) use limited-capacity batteries that cannot be recharged or replaced. In general, designing an energy-efficient routing protocol has a significant impact on prolonging the network lifetime. In this article, a novel distributed 2-hop cluster-routing protocol (D2CRP) is introduced to achieve energy efficiency in WSNs. In the cluster formation phase, each node obtains the information of its neighbor nodes within the 2-hop range to form the 2-hop cluster in a fully distributed manner. The transmission distance and residual energy are jointly considered to determine the energy-efficient cluster head (CH) in each 2-hop cluster. After the CH is generated, each member node can transmit packets to its 1-hop neighbor or the CH directly. To reduce the overall transmission distance for intercluster communication, multiple chains can be formed among CHs via their adjacent CHs that are closer to the base station (BS). As a result, energy-efficient intracluster and intercluster routing can be achieved for packet transmission. In addition, the optimal cluster number of 2-hop clustering is formulated and derived to minimize the energy consumption for both intracluster and intercluster communications. Simulation results show that the optimal cluster number of D2CRP can be achieved and D2CRP is effectively improved on the performances of network lifetime, energy consumption, and packet transmission than the other four state-of-the-art competitive routing protocols, including low-energy adaptive clustering hierarchy (LEACH), R-LEACH, power-efficient gathering in sensor information system (PEGASIS), and two-tier distributed fuzzy logic-based protocol (TTDFP).

TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks

On account of the highly dynamic topology of vehicular networks, network congestion and energy utilization are greatly increased, which directly affects the performance of VANETs. So, managing traffic and reducing energy consumption in the network becomes a challenging task in such huge mobility-based VANET networks. Thus, in this paper a new traffic and cluster-based network method is introduced, namely, Traffic-Aware Clustering based Routing Protocol (TACRP). The main aim of the approach is to improve traffic management in the network as well as to reduce energy consumption in it. In the constructed network, a Traffic Management Unit (TMU) is introduced to control the entire network traffic with the help of RSUs. Vehicles with similar speed and direction are grouped into a cluster to increase the network stability and help to reduce the energy consumption of the network. The clustering model provides principles associated with vehicles leaving the clusters, joining the clusters, cluster updates and inter-cluster communication, which makes the network more stable and reliable. For instance, in the proposed work the CH selection is based on centralization, weight, distance, and energy calculation. Such network settings facilitate successfully clustering of vehicles on the road. Simulation experimental analysis showed that the proposed TACRP routing protocol achieved better results in terms of energy efficiency, throughput, packet delivery ratio, and end to end delay of the network when compared with earlier methods, such as ECHS and NRHCS.

A fixed clustering protocol based on random relay strategy for EHWSN

In Energy Harvesting Wireless Sensor Networks (EHWSN), the communication protocol will directly affect the final performance of the network, so it is necessary to study the communication protocol based on EHWSN. In this paper, for the low-cost fixed clustering problem, a fixed clustering protocol RRCEH is based on random relaying. Our proposed RRCEH abandons the inefficient inter-cluster communication method of the traditional fixed clustering protocol. To coordinate the data upload of the cluster head, RRCEH allocates different random relay vectors to each ring area of the network, and combines all the random relay vectors into a random relay matrix of RRCEH. In each communication round, the cluster head node randomly selects its relay target node to send data according to the probability distribution in the random relay vector in the area. For two different cluster head configuration scenarios, by optimizing the random relay matrix, RRCEH can effectively reduce the network's configuration requirements for cluster head energy harvesting capability, thus reducing the deployment cost of EHWSN.

Transmission Dynamics and Genomic Epidemiology of Emerging Variants of SARS-CoV-2 in Bangladesh.

With the progression of the global SARS-CoV-2 pandemic, the new variants have become more infectious and continue spreading at a higher rate than pre-existing ones. Thus, we conducted a study to explore the epidemiology of emerging variants of SARS-CoV-2 that circulated in Bangladesh from December 2020 to September 2021, representing the 2nd and 3rd waves. We collected new cases and deaths per million daily data with the reproduction rate. We retrieved 928 SARS-CoV-2 sequences from GISAID and performed phylogenetic tree construction and mutation analysis. Case counts were lower initially at the end of 2020, during January–February and April–May 2021, whereas the death toll reached the highest value of 1.587 per million on the first week of August and then started to decline. All the variants (α, β, δ, η) were prevalent in the capital city, Dhaka, with dispersion to large cities, such as Sylhet and Chattogram. The B.1.1.25 lineage was prevalent during December 2020, but the B.1.617.2/δ variant was later followed by the B.1.351/β variant. The phylogeny revealed that the various strains found in Bangladesh could be from numerous countries. The intra-cluster and inter-cluster communication began in Bangladesh soon after the virus arrived. The prominent amino acid substitution was D614G from December 2020 to July 2021 (93.5 to 100%). From February–April, one of the VOC’s important mutations, N501Y substitution, was also estimated at 51.8%, 76.1%, and 65.1% for the α, β and γ variants, respectively. The γ variant’s unique mutation K417T was detected only at 1.8% in February. Another frequent mutation was P681R, a salient feature of the δ variant, detected in June (88.2%) and July (100%). Furthermore, only one γ variant was detected during the entire second and third wave, whereas no η variant was observed in this period. This rapid growth in the number of variants identified across Bangladesh shows virus adaptation and a lack of strict quarantine, prompting periodic genomic surveillance to foresee the spread of new variants, if any, and to take preventive measures as soon as possible.