Energy-efficient Communication Research Articles

UAVs Revolutionizing Efficiency: Integrating DRL and Random Walrus for Enhanced Energy and Spectral Resource Management

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems.

Unlocking innovation and sustainability: lessons from Dubai Expo 2020 for business transformation

Research methodology The data used in the case are collected through both primary and secondary sources. The interview method was used to collect data on the experience of the protagonist on visiting the expo and details about the Expo was collected using secondary sources. Case overview/synopsis The case study examines the success factors of initiatives implemented by the Dubai Expo 2020 team, with a focus on sustainable transportation, energy efficiency, people-centric spaces, biodiversity preservation, water efficiency, waste management, green building and communication. Even though not all objectives were met, the overall progress demonstrates a strong commitment to sustainability and positive effects on environmental, social and economic aspects. In addition, the case study demonstrates how businesses can incorporate social and environmental factors into their decision-making processes, supply chain management and responsible procurement practices. It highlights the significance of sustainability in business operations, stakeholder collaboration and continuous improvement. In addition, the case study provides innovative business models and practices that promote circular economy principles, waste reduction, resource efficiency and inclusivity. This case provides business students with valuable insights into successful sustainability initiatives and strategies for creating a more inclusive and equitable economy. Complexity academic level This case study is appropriate for intermediate undergraduate students in their third year or postgraduate students in their first year, particularly those enrolled in courses on sustainability, operations management, strategic management, supply chain management and corporate social responsibility. The case study is designed to enhance cognitive skills by analyzing and evaluating real-world examples of successful initiatives in sustainability, energy efficiency, people-centric spaces, biodiversity preservation, water efficiency, waste management, green building and open communication. It also aims to develop affective skills by fostering a commitment to sustainable practices and psychomotor skills through practical applications and projects. By analyzing these initiatives, students can comprehend how businesses can integrate social and environmental factors into their decision-making processes, supply chain management and responsible procurement practices to create a more inclusive and equitable economy. In addition, the case study introduces innovative business models and practices that promote circular economy principles, waste reduction and resource efficiency while fostering economic inclusion and equity. Overall, the case study equips students with the knowledge and motivation necessary to drive sustainable change within organizations and contribute to a more sustainable and equitable future.

Fuzzy based Energy Efficient Rider Remora Routing protocol for secured communication in WSN network

Due to the resource constraint nature of WSN, ensuring secured communication in WSN is a challenging problem. Moreover, enhancing the network lifetime is one of the major issues faced by the existing studies. So, in order to secure the communication between WSNs and achieve improved network lifetime, a novel trust enabled routing protocol is proposed in this study. Initially, the clusters are constructed using Direct, Indirect, and Total Trust evaluations, which helps to identify the faulty nodes. After, an Improved Fuzzy-based Balanced Cost Cluster Head Selection (IFBECS) method is used to choose the cluster head (CH). Finally, to determine the best path from source to destination, a hybrid bionic energy-efficient routing model known as an Energy Efficient Rider Remora Routing (EERRR) protocol is introduced. To improve the network lifetime and throughput, the parameters like remaining energy of the CH, sensor node space, CH, etc., are considered by the utilized protocol. The proposed mechanism is implemented in NS-2 programming tool. The simulation results show that the proposed routing protocol has attained improved PDR of 97.92 % at the time period of 50 ms, reduced energy consumption of 3.336 at the time period of 100 ms, higher throughput of 86262.7 at the time period of 250 ms, and enhanced network lifetime of 1028.08 rounds in 200 nodes. Therefore, by attaining better results as compared with other existing protocols, it is clearly revealed that the proposed routing protocol is highly suitable for secured energy efficient WSN communication.

Optimized DBSCAN with RLA-ODV routing and encryption based on homomartino algorithm for energy efficient and secure V2X communication

Optimized DBSCAN with RLA-ODV routing and encryption based on homomartino algorithm for energy efficient and secure V2X communication

Reliable and Energy-Efficient Communications via Collaborative Beamforming for UAV Networks

Reliable and Energy-Efficient Communications via Collaborative Beamforming for UAV Networks

Communication Technology for Renewable Energy Access Grid System Based on Multi-source Heterogeneous Data Fusion

Abstract Incorporating renewable energy solutions into the power grid is essential for facilitating a shift in energy sources. Ensuring the effectiveness and dependability of communication technologies within smart distribution grids is vital, as these grids serve as the link between end users and the central station, thereby contributing to the grid’s stable functioning and the effective dissemination of energy. This study introduces a framework for communication that tackles the challenges associated with the integration of multi-source, heterogeneous data in smart distribution grids. The framework leverages multi-source heterogeneous data fusion and capitalizes on edge computing to efficiently process and consolidate data from diverse sensors and devices. An optimal weight allocation algorithm is proposed to mitigate data redundancy and to improve the energy efficiency of communication. The paper demonstrates, through theoretical examination and empirical tests, the proposed system’s enhanced communication capabilities, especially in terms of data transmission effectiveness and energy usage optimization. The discussion highlights the advantages of smart distribution networks over conventional communication methods and offers insights for further advancements in communication technologies within this sector.

Reliable and Energy-Efficient Communications in Mobile Robotic Networks by Collaborative Beamforming

For mobile robotic networks in industrial scenarios, reliable and energy-efficient communications are crucial yet challenging. Fortunately, collaborative beamforming (CB) emerges as a promising solution, which can increase the transmission gain and reduce the transmit power of robots by constructing a mobile robot-enabled virtual antenna array (MRVAA). The performance of CB is tightly related to robot positions, necessitating proper robot selection. However, robot selection may expose the network to the risk of unbalanced energy distribution, reducing network lifetime. Additionally, the mobility and variable numbers of robots require flexible and scalable robot selection algorithms. To tackle these challenges, we first formulate a multi-objective optimization problem to reduce the maximum sidelobe level (MSLL) of MRVAA while minimizing the standard deviation of the network energy distribution (SDNED) by selecting robots for CB. Then, based on distributed multi-agent learning (MARL), we propose an effective and scalable robot selection algorithm with energy considered (RoSE) to solve the problem, where difference-rewards function (DRF) and policy sharing are designed for enhancing convergence rate and policy stability. Simulation results show that the RoSE has the scalability to positions and numbers of robots. Furthermore, RoSE surpasses existing selection algorithms in network lifetime and time efficiency, while still maintaining comparable MSLL.

Optimizing Energy Efficiency in 6G Communication Networks Based on Data Transmission Rate Allocation

To progress communication and sensing in recent years, the Sixth-Generation (6G) network was developed. To transform the technological landscape of model transmission networks we use the 6G network, and it also used to enhance the speed, decrease the latency and improve the connectivity of network. Yet, in the 6G infrastructure with these developments it has severe challenge, that is the 6G network does not negate the benefits of energy consumption associated with network. In 6G communication the sensing performance of the smart device only relay on the available power of the network, and in the 6G communication energy efficiency becomes crucial. To progress the energy efficiency of data transmission in 6G communication networks, we deployed a Data Transmission Rate Allocation (DTRA) method. Moreover, to progress the data transmission of efficient nodes we deploy a Residual Energy Cluster Head (RECH) method. In addition, to improve the reliability and speed in the 6G network we use Dynamic Multipath Routing Protocol (DMRP) method, and it also mitigate the channel defects. Atlast, in the 6G communication networks we certify QoS through optimizing the transmission rate of the user equipment through low energy consumption, and quality of channel by the use of DTRA method. Finally, in this paper we evaluate the metrices performance of the existing methods and proposed method, for instance: efficiency, energy consumption, network lifetime, and transmission speed. After evaluate the performance metrices the DTRA method enhance network lifetime and energy efficiency of 95.3% based on 6G communication networks.

Energy and throughput aware adequate routing for wireless sensor networks using integrated game theory method

A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR.

Enhanced channel estimation with atomic norm minimization and reconfigurable intelligent surfaces in mmWave MIMO systems

SummaryThe performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems.

Unlocking the impact of international financial support to infrastructure, energy efficiency, and ICT on CO2 emissions in India

Financial resource constraints prevent developing nations from investing in the infrastructure needed for the green transition. For this reason, it is well known that numerous financial aid programs are offered globally, mainly to wealthy developing nations whose economies are predicated on high energy consumption. It hasn't been looked into if these resources are allocated to worthwhile projects. This study examines how foreign financial assistance in this area affects India's infrastructure emissions. The effects of natural resources, economic growth, information and communication technology, energy efficiency (oil and gas), and natural resources on the environment are being investigated as well. The novel multivariate quantile on-quantile regression technique is used in this context to examine a time-series data set spanning 2000–2021. According to the findings, there is a correlation between reduced carbon emissions and increased foreign financial aid. Likewise, advancements in energy efficiency and information and communication technologies also benefit the ecosystem. However, India's emissions are rising due to its abundant natural resources and economic expansion. Policy recommendations were made regarding the need for India to use international financial aid primarily to strengthen environmentally friendly infrastructure rather than solving problems such as current account deficit or budget deficit.

Energy efficient data communication for WSN based resource constrained IoT devices

In the Internet of Things (IoTs) and wireless sensor networks (WSNs), improving security and energy efficiency are key concerns. Clustering, which involves managing cluster heads, plays a pivotal role in extending network lifetime. The selection of a cluster head, responsible for data transfer between nodes, is a key aspect of network management. This paper proposes two variants of a novel algorithm designed for energy efficient communication in a resource constrained IoT environments. One variant considers remaining energy, distance, and node degree for cluster head selection, while the other focuses on remaining energy and distance only. Including node degree ensures cluster heads do not waste energy by remaining idle or performing unnecessary tasks such as the cluster head selection process in every round. The authors tested these variants against several well known algorithms using MATLAB simulation environment, evaluating factors such as operating nodes, number of clusters, transmission energy, and remaining energy. The proposed algorithm extends network lifetime by maintaining more operating nodes for longer, not changing clusters or cluster heads frequently, minimizing energy consumption for transmission, and conserving more remaining energy. Consequently, the proposed algorithm outperforms existing approaches by addressing issues like zero cluster head selection, compulsory cluster head selection in every round, avoiding cluster heads that connect to no nodes, and preventing network destabilization due to unnecessary re-elections.

A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC.

Exploring GreenIoT's technical landscape: Sustainable development, deployment, use case and research challenges

The Internet of Things (IoT) has experienced exponential expansion, raising questions about its sustainability and environmental impact. In response, the developing discipline of Green Internet of Things(GreenIoT) seeks to address these problems by encouraging energy-saving and environmentally friendly IoT deployment practices. The technological environment of the GreenIoT is thoroughly explored in this article, including enabling technologies, simulators and platforms, smart applications, open projects, information on certificates and compliance, and open research challenges. The article focuses on intelligent applications that use GreenIoT concepts to meet sustainability objectives in a variety of industries, including smart buildings, transportation, agriculture, and healthcare. It demonstrates the revolutionary potential of GreenIoT in creating sustainable and environmentally friendly urban settings by outlining the fundamental characteristics, advantages, and actual implementations of these applications. The paper also highlights the value of open projects and initiatives in the GreenIoT space, looking at teamwork and research projects that promote innovation and advance energy-efficient IoT deployments, particularly in the context of India. The paper also discusses the significance of certificate and compliance information in ensuring that environmental standards and laws are followed in GreenIoT implementations. The study concludes by outlining the open research challenges in the GreenIoT, including creating specific GreenIoT architectures, controlling infrastructure complexity, resolving environmental security issues, and investigating energy-efficient communication protocols. These difficulties offer chances for more research, invention, and development in the area. It serves as a valuable resource for researchers, practitioners, and policymakers curious about the crosswords of IoT and sustainability.

Joint Spectrum Partitioning and Power Allocation for Energy Efficient Semi-Integrated Sensing and Communications

Joint Spectrum Partitioning and Power Allocation for Energy Efficient Semi-Integrated Sensing and Communications

Distance- and Angle-Based Hybrid Localization Integrated in the IEEE 802.15.4 TSCH Communication Protocol

Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol’s operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space.

A game theoretic approach for spectral and energy efficient D2D communication in 5G-IoT networks

In the context of fifth generation (5G) technology, Device-to-Device (D2D) communication plays a pivotal role, requiring swift and intelligent decision-making in mode selection and device discovery. This study addresses the challenge of rapid mode selection and device discovery within 5G communication networks, focusing specifically on enhancing spectral and energy efficiency for Internet of Things (IoT) applications. A novel self-centered game theory-based algorithm is introduced to optimize spectral efficiency and support intelligent mode selection. Additionally, the utilization of the support vector machine (SVM) expedites mode selection decisions. For D2D discovery, the Frank-Wolfe method is adopted, significantly improving the differentiation between D2D and Cellular users based on signal strength and interference, thereby enhancing spectral efficiency. The proposed approach maximizes spectral efficiency while adhering to strict power and interference constraints, intelligently partitioning bandwidth into two subparts using game theoretic principles to amplify spectral efficiency. Furthermore, the emphasis on energy efficiency is underscored through iterative calculations to achieve maximum energy-efficient spectral allocation. Numerical analyses validate the efficacy of the proposed technique, revealing substantial improvements in accurately predicted labels. As the number of devices increases, precision and recall rates experience noteworthy enhancements, ultimately leading to superior bandwidth utilization. This research presents a significant contribution to the field of 5G communication, particularly concerning energy efficiency, which is paramount for IoT applications. By accelerating D2D connectivity and optimizing energy and spectrum resources, it advances the goals of energy-efficient D2D communication within 5G-IoT networks.

Adaptive joint source coding LDPC for energy efficient communication in wireless network on chip

Adaptive joint source coding LDPC for energy efficient communication in wireless network on chip

Optimisation of energy efficiency of ambient backscatter communication and reconfigurable intelligent surfaces in non‐orthogonal multiple access downlink

AbstractThe authors study the energy efficiency (EE) of ambient backscatter communication (AmBC) device‐assisted and reconfigurable intelligent surfaces (RIS)‐assisted non‐orthogonal multiple access (NOMA) downlinks. The authors establish two optimisation problems based on the two collaborative devices (AmBC devices, RIS) with the objective of maximising the EE of the system, taking into account the requirements of power limitation and rate limitation, etc. and also obtain the solutions of two problems by optimising the relevant performance metrics based on the alternating optimisation algorithm. For the backscatter device (BD)‐aided downlink NOMA network, the problem is first decoupled into three subproblems, where the power allocation optimisation subproblem is solved by using the quadratic transformation method and the subgradient algorithm. The maximum EE is obtained by iterating according to the Dinkelbach's algorithm. For the RIS‐aided downlink NOMA network, the power allocation problem is solved by the same method as above and the phase optimisation problem is solved by the successive convex approximation method. Numerical results show that the proposed algorithm can achieve convergence after several iterations, and the EE of systems with BD‐assisted and RIS‐assisted have different levels of sensitivity to different influencing factors.

XplAInable: Explainable AI Smoke Detection at the Edge

Wild and forest fires pose a threat to forests and thereby, in extension, to wild life and humanity. Recent history shows an increase in devastating damages caused by fires. Traditional fire detection systems, such as video surveillance, fail in the early stages of a rural forest fire. Such systems would see the fire only when the damage is immense. Novel low-power smoke detection units based on gas sensors can detect smoke fumes in the early development stages of fires. The required proximity is only achieved using a distributed network of sensors interconnected via 5G. In the context of battery-powered sensor nodes, energy efficiency becomes a key metric. Using AI classification combined with XAI enables improved confidence regarding measurements. In this work, we present both a low-power gas sensor for smoke detection and a system elaboration regarding energy-efficient communication schemes and XAI-based evaluation. We show that leveraging edge processing in a smart way combined with buffered data samples in a 5G communication network yields optimal energy efficiency and rating results.