Communication Energy Research Articles

Deep contextual bandits-based energy-efficient beamforming for integrated sensing and communication

In this paper, a transmit beamforming design is considered to jointly improve the communication and sensing functionalities in an integrated sensing and communication (ISAC) system, where a dual-functional base station (BS) serves a set of communication users (UEs) while sensing some potential targets (TGs). To this end, a multi-objective beamforming optimization problem is formulated to maximize the weighted linear combination of the communication energy efficiency (EE) and sensing beampattern gains. Using the normalized weighted sum method, the multi-objective problem is relaxed to a single-objective problem. For dealing with the non-convexity nature of the considered problem and the continuity of the beamforming space, a novel deep contextual bandits (DCB) scheme inspired by the soft actor-critic (S-DCB) method is proposed in which the channel state information of UEs is used as the context. Both the reward and policy functions are approximated to solve the problem. Simulation results indicate that the multi-objective function enables an adjustable trade-off between the communication and sensing functionalities. Besides, the effectiveness of the proposed S-DCB algorithm is verified through a comprehensive comparison with the state-of-the-art DCB-DDPG and the upper bound benchmarks.

Adaptive communication power control for enhancing attack resilience in UAV networks

A swarm of Unmanned Aerial Vehicles (UAVs) comprises multiple UAVs that are capable of completing tasks beyond the capabilities of a single UAV. Due to the unique challenges of UAV missions, these vehicles often operate far from base stations, making network connectivity crucial for the successful completion of UAV swarm missions. However, existing methods do not account for strategies to maintain the overall connectivity of the UAV network when nodes are under attack. To address this issue, we propose a method named Adaptive Communication Power Control (ACPC) that dynamically adjusts UAV communication power to mitigate potential connectivity losses caused by node failures or malicious attacks. This adjustment ensures that the network can maintain information exchange among the remaining UAVs even in the event of disruptions. Additionally, we introduce a novel evaluation method to assess the overall connectivity of the network and validate ACPC through simulations of UAV swarm missions where some UAVs experience failures. Using this evaluation method, we measured the network’s state before and after attacks and recovery and calculated the additional energy consumption required by the UAVs. The results indicate that our method can increase the resilience of the UAV network by up to 5.36 times, while only raising the total communication energy consumption to 1.53 times.

Office Furniture Partition Space Design Based on Intelligent Domain Perception and Digital Twins

With the increasing focus on sustainable development in society, intelligent domain perception and digital twin technology can be used to evaluate and optimize the design of office furniture. This study analyzed sensor data through machine-learning and data-mining techniques to identify patterns and trends in the office environment. Simultaneously, a digital twin model of office furniture partition space was established to simulate the usage of furniture partition space throughout its full lifecycle. When 50% of nodes fail, the minimum transmission energy mode was significantly better than the maximum greedy forwarding mode in terms of cumulative throughput. The distributed, event-based, unsupervised clustering algorithm successfully reduced communication energy consumption, and the lightweight gradient boosting machine algorithm achieved the best design optimization rate, with an improvement of 0.53%. The ratio of value-added time to non-value-added time increased by 56.3%. The study aimed to provide innovative ideas for the development of intelligent office environments, promote the design of office furniture toward intelligence and sustainability, and improve the adaptability and efficiency of the work environment.

Maximizing energy efficiency and system throughput using a self-adaptive penalty function with a whale optimization algorithm in social-aware device-to-device communications

Device-to-device (D2D) communication is an emerging approach for 5 G networks. It allows for high capacity, low latency, and increased bandwidth. These features have prompted several research studies to focus on meeting customers’ expectations. This research concentrates on obtaining optimal resource and power allocation, better quality of service, and reliable connectivity between D2D clients through a whale optimization algorithm (WOA). The WOA possesses greater stability, faster integration speeds, a powerful global search ability, and greater accuracy than traditional methods. The WOA utilizes an unconstrained objective function obtained through a self-adaptive penalty function. This unconstrained objective function yields optimal solutions for the resource as well as power allocation, higher throughputs, and energy and spectral efficiencies in D2D communications. For D2D communications, the energy efficiency of the WOA is 47.12 % higher than a genetic algorithm (GA) and 70.33 % higher than particle swarm optimization (PSO), and it yields high spectral efficiency and system throughput. Obviously, the WOA in this proposal is more efficient than a GA or PSO and shows the advantages of combining physical and social parameters for improved wireless network performance.

Distributed service caching with deep reinforcement learning for sustainable edge computing in large-scale AI

Increasing reliance on large-scale AI models has led to rising demand for intelligent services. The centralized cloud computing approach has limitations in terms of data transfer efficiency and response time, and as a result many service providers have begun to deploy edge servers to cache intelligent services in order to reduce transmission delay and communication energy consumption. However, finding the optimal service caching strategy remains a significant challenge due to the stochastic nature of service requests and the bulky nature of intelligent services. To deal with this we propose a distributed service caching scheme integrating deep reinforcement learning (DRL) with mobility prediction, which we refer to as DSDM. Specifically, we employ the D3QN (Deep Double Dueling Q-Network) framework to integrate Long Short-Term Memory (LSTM) predicted mobile device locations into the service caching replacement algorithm and adopt the distributed multi-agent approach for learning and training. Experimental results demonstrate that DSDM achieves significant performance improvements in reducing communication energy consumption compared to traditional methods across various scenarios.

Data-sampled time-varying formation for singular multi-agent systems with multiple leaders

The time-varying formation problem of singular multi-agent systems under sampled data with multiple leaders is investigated in this paper. Firstly, a data-sampled time-varying formation control protocol is proposed in the current study where the communication among followers merely occurred at sampling instants, which can save the controller communication energy significantly. Secondly, necessary and sufficient conditions for the feasibility of the formation function are provided. In addition, an approach is presented to design the formation tracking control under sampled data with multiple leaders. Finally, numerical simulations validate the efficacy of the theoretical results.

An one-time pad cryptographic algorithm with Huffman Source Coding based energy aware sensor node design

Recently, the security-based algorithms for energy-constrained sensor nodes are being developed to consume less energy for computation as well as communication. For the mission critical wireless sensor network (WSN) applications, continuous and secure data collection from WSN nodes is an essential task on the deployed field. Therefore, in this manuscript, One-Time Pad Cryptographic Algorithm with Huffman Source Coding Based Energy Aware sensor node Design is proposed (EA-SND-OTPCA-HSC). Before transmission, the distance among transmitter and receiver is rated available in mission critical WSN for lessen communication energy consume of sensor node. For the mission critical WSN applications, continuous and secure data collection from WSN nodes is an essential task on the deployed field. The periodic sleep/wake up scheme with Huffman source coding algorithm is used to save energy at the node level. Then, one-time pad cryptographic algorithm in each sensor node, the vernam cipher encryption technique is applied to the compact payload. The proposed technique is executed and efficacy of proposed method is assessed using Payload Vs Energy consume for one sensor node, communication energy consume for one sensor node with different distances, energy consume for one sensor node under various methods, Throughput, delay and Jitter are analyzed. Then the proposed method provides 90.12 %, 89.78 % and 91.78 % lower delay and 88.25 %, 95.34 % and 94.12 % lesser energy consumption comparing to the existing EA-SND-Hyb-MG-CUF, EA-SND-PVEH and EA-SND-PIA techniques respectively.

Multi-Buoy Deployment Method Based on an Improved Tuna Swarm Optimizer Enhanced with Fractional-Order Calculus Method for Marine Observation

Ocean buoys play a critical role in marine hydrological, water quality, and meteorological monitoring, with applications in navigation, environmental observation, and communication. However, accurately modeling and deploying a multi-buoy system in the complex marine environment presents significant challenges. To address these challenges, this study proposes an enhanced deployment strategy using the tuna swarm optimizer enhanced with the fractional-order calculus method for marine observation. The proposed method first introduces a detailed observation model that precisely captures the performance of buoys in terms of coverage and communication efficiency. By integrating the observation coverage ratio and communication energy consumption, we establish an optimal multi-buoy deployment model. The proposed method leverages tent chaotic mapping to improve the diversity of initial solution generation and incorporates fractional-order calculus to strengthen its search capabilities. Simulation experiments and statistical analysis verify the effectiveness of the proposed deployment model, with the proposed method achieving the best performance in deploying the multi-buoy system, reaching a final fitness value of 0.190052 at iteration 449, outperforming TSA, PSO, GWO, and WOA. These results highlight the potential of the proposed method in optimizing multi-buoy system deployment in marine observation.

Event-triggered cascade predictor for a class of nonlinear MIMO systems with large communication delays

The event-triggered predictor design problem for a class of nonlinear MIMO systems with large time delays is investigated in this paper. A periodic event-triggered mechanism is designed to avoid unnecessary data transmission and save communication energy. The triggering condition is only determined by sampled outputs of the system, such that it is applicable in case of the communication delay. Then a novel observer-based cascade predictor is proposed to reconstruct the original system state based on the delayed and event-triggered measurements, which is composed of a continuous-discrete observer and several sub-predictors in a chain structure. The stability of proposed predictor is analyzed through the Lyapunov approach. By using a sufficient number of sub-predictors and applying appropriate parameters, the prediction errors converge to bounded regions exponentially under large time delays. Finally, simulations are performed to verify the effectiveness of the proposed predictor and the event-triggered communication mechanism.

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.

Hybrid Control Strategy for 5G Base Station Virtual Battery-Assisted Power Grid Peak Shaving

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load.

An Energy Efficient Transmission Mechanism based on Optimal Relay Distance for Underwater Acoustic Sensor Networks

Abstract Due to the limited resources and energy of communication in underwater acoustic sensor network (UASN), there are many challenges in designing energy-efficient and reliable media access control (MAC) protocols for UASN. This paper proposes an energy-efficient media access control (MAC) protocol with dynamic transmission range control (TRC), referred to as TRA-MAC. The protocol provides an adaptive, multi-hop and energy-saving solution for underwater sensor network communication. The protocol dynamically adjusts the transmission power of the node according to the distance between the source node and the destination node, and has the ability to adapt to network conditions. Simulation results show that compared with Slotted Aloha, the proposed adaptive relay node selection mechanism increases the energy efficiency by 43.07%, the network throughput by 46.6%, and the end-to-end delay is greatly reduced. Compared with Slotted FAMA, it has more significant advantages.

EBPT-CRA: A clustering and routing algorithm based on energy-balanced path tree for wireless sensor networks

With the rapid development of internet of things (IoT), the scale of wireless sensor networks (WSNs) is gradually expanding, and it is necessary to study the energy saving and balancing algorithm for WSNs in multi-sensor nodes and large deployment range scenarios. A clustering and routing algorithm based on an energy-balanced path tree (EBPT-CRA) is proposed for large-scale WSNs. First, the energy-balanced path tree (EBPT) is constructed, and then the aggregation betweenness of the nodes are calculated to obtain the cluster head competition coefficients of the nodes. Second, an iterative process is executed to select the cluster heads and build the cluster trees to complete the clustering and intra-cluster routing. Finally, the inter-cluster routing is determined based on the node residual energy and the communication energy. The simulation results demonstrate that the EBPT-CRA can better save and balance the node energy, while improve the network lifetime, throughput and service capability in large-scale WSNs.

Three-dimensional deployment strategy for a multi-BWBUG cooperative system at deep depths

ABSTRACT This study investigates the issue of the three-dimensional deployment for blended-wing-body underwater gliders (BWBUGs) at deep depths. Initially, an underwater three-dimensional deployment optimisation model is formulated, considering factors such as the three-dimensional coverage ratio and the balance of node communication energy consumption. Subsequently, a sine-logistic chaos strategy with excellent ergodicity is proposed. Thereafter, the dynamic decision-assisted heuristic method (DDHM) is proposed based on the dynamic decision framework, sine-logistic chaos strategy, and opposition-based learning. The well-designed dynamic decision framework effectively avoids the premature convergence and local optimum of the algorithm. Additionally, the multi-objective variant of DDHM, referred to as MDDHM, is established utilising the archival mechanism. Lastly, the performance of the DDHM and MDDHM is evaluated by simulation experiments. Statistical analysis demonstrates that DDHM is highly effective for the underwater three-dimensional deployment of the multi-BWBUG cooperative system. Furthermore, its overall performance surpasses that of other comparative methods.

Synchronization of Coupled Neural Networks With Constant Time-Delay Using Sampled-Data Information.

In this article, a synchronization control method is studied for coupled neural networks (CNNs) with constant time delay using sampled-data information. A distributed control protocol relying on the sampled-data information of neighboring nodes is proposed. Lyapunov functional is constructed to analyze the synchronization of CNNs with constant time delay. Using Park's integral inequality and improved free-weight matrix integral inequality, sufficient conditions are provided for CNNs to achieve synchronization with less conservatism. In addition, the maximum sampling interval is determined by transforming the sufficient conditions into an optimization problem, and an aperiodic sampling control technique is implemented to reduce the communication energy load. Finally, numerical simulations are provided to demonstrate that the proposed method is capable of achieving synchronization.

Optimized Resource Management and Dynamic Routing Protocol for Wireless Sensor Networks Through Load Balancing, Packet Scheduling, and Intelligent Clustering

Heterogeneous Wireless Sensor Networks (HWSNs) are pivotal for providing weather-related event data, enabling universal location access, and facilitating remote monitoring through multi-hop transmission. Efficient energy utilization is critical in ensuring the optimal functioning of HWSNs. Previously, Compressive Sensing (CS) technology was established to enhance communication efficiency within HWSNs. While previous methods were effective in managing energy consumption and reducing transmission delays across network devices, the increased number of devices has impacted their efficacy. Consequently, energy becomes a vital limitation in constructing HWSNs. In order to address these challenges, this study introduces Load Balancing and Packet Scheduling with Intelligent Clustering based Improved Routing Protocol (LPICR). This integrates load balancing, packet scheduling, intelligent clustering, and enhanced routing techniques. The protocol is structured into three main categories: intelligent route selection, load balancing-based Cluster Head (CH) selection, and path scheduling. Initially, an efficient opportunistic routing is conducted by the intelligent route selection process. This routing method minimizes data forwarding during communication and significantly decreases energy consumption in the HWSN. Furthermore, by using a load balancing-oriented procedure for selecting cluster heads, the system achieves efficient determination of cluster heads and construction of clusters, resulting in the most efficient use of energy in communication. Path scheduling reduces the probability of delays by facilitating effective data flow between the source and destination in the HWSN. The NS2 platform is used to implement the proposed LPICR-HWSN protocol. The calculation of the result and comparison analysis is considered for the parameters are Data loss rate, communication time, packet success rate, malicious detection ratio, throughput, Routing overhead and energy efficiency. The results are thoroughly investigated by accounting for factors like the quantity of nodes and the varying speed of the network. To assess the efficacy of this proposed protocol, we conduct a comparative analysis using established methodologies such as CDAS-WSN, EEPC-WSN, TCCS-WSN, and MTODS-HWSN. The results suggest that the proffered LPICR-HWSN model demonstrates superior performance compared to previous methods.

Joint Optimization of Age of Information and Energy Consumption in NR-V2X System Based on Deep Reinforcement Learning.

As autonomous driving may be the most important application scenario of the next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancements in cellular V2X (C-V2X) with the improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur and thus degrade the age of information (AOI). Therefore, an interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation intervals (RRIs), higher-frequency transmissions use more energy to reduce AoI. Hence, it is important to jointly considerAoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations demonstrate the performance of our proposed algorithm.

Study on the performance of lithium iron phosphate battery based on its application in transportation field

Abstract The technology of lithium iron phosphate batteries is increasingly becoming developed and stable as a result of the new energy sector’s quick and steady development. With its unique performance advantages and characteristics, lithium iron phosphate battery occupies a large market share and has a wide range of application scenarios. This paper analyzes the specific application scenarios of lithium iron phosphate batteries in the field of transportation and derives the specific performance advantages of lithium iron phosphate batteries. Lithium iron phosphate battery has high energy density, high safety, high cycle life, high-temperature adaptability and other performance advantages. In addition, Lithium iron phosphate batteries are also inexpensive and kind to the environment. Therefore, lithium iron phosphate batteries can better meet the demand for battery applications in the field of transportation. At the same time, these advantages also make the lithium iron phosphate battery in other areas such as communication energy storage and peak energy storage have a high application value. By specifically studying the advantages of lithium iron phosphate, I believe that we can further promote the market application of lithium iron phosphate batteries and provide a method of reference for other batteries.

The Distributed Multi Constrained Channel Access Mechanism By Using Framework For Mobile Adhoc Network

The Mobile Ad hoc Network (MANET) is an autonomous wireless communication system, and does not have any fixed infrastructure. The Network loads which arise due to application evolving using MANET environment has to balance using routing protocol. Networks are increasingly resource intensive to efficiently manage the bandwidth & Energy in the group communication on basis of addressing the routing issues to provide the enhanced functionality of the network on basis of channel utilization and channel conditioning against load balancing. In this research, proposed a novel unified framework for Distributed multi constrained Channel Access Mechanism for mobile Adhoc Network on inclusion of Scheduled Throughput Discriminant Multi-Data synchronization Strategies for Channel Allocation in Mobile Adhoc Network on various traffic patterns, Multi Trajectory Particle Swarm Optimization towards Optimal Velocity Control against load balancing and band width controlling and finally Self Adaptive Redundancy Elimination Clustering and Distributed Load Bandwidth Management Constraints for Mobile Ad Hoc Network.

Improving Quality of Service (QoS) in Wireless Multimedia Sensor Networks using Epsilon Greedy Strategy

Abstract Wireless Multimedia Sensor Networks (WMSNs) are networks consisting of sensors that have limitations in terms of memory, computational power, bandwidth and battery life. Multimedia transmission using Wireless Sensor Network (WSN) is a difficult task because certain Quality of Service (QoS) guarantees are required. These guarantees include a large quantity of bandwidth, rigorous latency requirements, improved packet delivery and lower loss ratio. The main area of research would be to investigate the process of greedy techniques that could be modified to guarantee QoS provisioning for multimedia traffic in WSNs. This could include optimization of routing decisions, dynamic allocation of resources and effective congestion management. This study introduces a framework called Epsilon Greedy Strategy based Routing Protocol (EGS-RP) for multimedia content transmission over WSN. The framework focuses on energy efficiency and QoS by using reinforcement learning to optimize rewards. These incentives are determined by a number of variables, including node residual energy, communication energy and the effectiveness of sensor type-dependent data collection. Experimental analysis was conducted to evaluate the effectiveness of the proposed routing strategy and compare it with the performance of standard energy-aware routing algorithms. The proposed EGS-RP achieves a throughput of 217 kbps, a bandwidth of 985 bps, a packet delivery ratio of 94.45% and an energy consumption of 32%.