Communication Energy Consumption Research Articles

Intelligent multi-agent model for energy-efficient communication in wireless sensor networks

The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.

Lc‐Stream: An elastic scheduling strategy with latency constraints in geo‐distributed stream computing environments

SummaryAn effective scheduling strategy is critical for achieving better performance in real‐time stream processing systems. How to quickly and efficiently process real‐time data stream is always challenging, especially when clusters are collaborating in a Geo‐Distributed computing environment. To address these challenges, we propose an elastic scheduling strategy with Latency Constraints in Geo‐Distributed stream computing environments called Lc‐Stream. This article discusses our work from the following aspects: (1) An optimized data stream redirection method that is proposed based on queuing network algorithm, along with a computing resource model, a latency constrained scheduling model and a communication energy consumption model. (2) An updated node selection method based on the inter‐layer task correlation, to reduce the communication latency between groups at the executor granularity. (3) A network cluster distribution for Geo‐Distributed computing environment to ensure energy saving under low transmission latency. Experimental results show that compared to R‐Storm, Lc‐Stream reduces total latency by over 19% and increases throughput by over 37% in typical cross‐domain multi‐task topologies. Compared to Ts‐Stream, Lc‐Stream also reduces total latency by over 15% and increases throughput by over 21%. At the same time, it helps to balance the load among the systems and avoid overuse of compute nodes.

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks.

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.

Optimal design and performance analysis of secure estimator for cyber‐physical systems against deception attacks

AbstractCyber‐physical systems (CPSs) are increasingly vulnerable to the threat from malicious adversaries owing to the deep integration of networks and facilities; therefore, CPSs' security against cyber attacks has attracted extensive attention. The deception attack is one of the typical cyber attacks targeting CPSs, and a deception attack mode is established to describe the simultaneous occurrence of actuator and sensor attacks in this paper. Compared with single‐target deception attacks with constant value, the designed attack mode that can describe deception attacks against multiple components and embedded random characteristics has higher applicability in practical engineering processes. To counter such attack behavior, a secure estimator is proposed based on the ideology of augmented state to achieve the optimal design of secure state estimation and deception attack identification. Taking the energy shortage problem of CPSs into account, a novel event‐triggered communication mechanism is introduced on the basis of measurement residuals to improve resource efficiency by reducing low‐value information transmission. The communication scheme and the augmented state estimator are co‐designed by adopting the implicit information of the event‐triggered mechanism. Then, the performance of the co‐designed estimator is analyzed by the upper bound of the estimation error covariance matrix. Besides, the reconciliation between estimation performance and communication energy consumption is achieved by the derivation of Pareto equilibrium. Finally, applications of our approaches to a 3D target tracking system are provided to illustrate the effectiveness of the proposed method.

Research on decentralized resource operation optimization of virtual power plant with 5G base station

Abstract The extensive construction and promotion of 5G base stations (5GBSs) have led to a surge in communication energy consumption, as 5G energy consumption is about three to five times that of 4G. To reduce the energy consumption of 5GBS, this article incorporates 5GBS into power demand side management and proposes a flexible resource collaborative optimization method that integrates 5GBS with virtual power plants (VPPs). Firstly, starting from the basic structure of 5GBS, the adjustable potential of 5GBS and the paths for participating in VPP optimization were analyzed. Secondly, with the minimum operating cost as the optimization objective and considering the constraints of 5GBS and various decentralized resources, a demand side decentralized resource collaborative optimization model for VPP integrating 5GBS was established. This model can be solved using the Non-dominated sorting genetic algorithm II (NSGA-II) algorithm. The results indicate that the participation of 5GBS in power demand side management can reduce their comprehensive energy consumption and operating costs.

Event-Triggered Distributed Fusion Estimator for Asynchronous Markov Jump Systems with Correlated Noises and Fading Measurements.

In this study, we investigate event-triggered distributed fusion estimation for asynchronous Markov jump systems subject to correlated noises and fading measurements. The measurement noises are interrelated, and they are simultaneously coupled with the system noise. The sensor samples measurements uniformly, and the sampling rates of the sensors are different. First, the asynchronous system is synchronized at state update points; then, the local filter is obtained. Furthermore, a variance-based event-triggered strategy is introduced between the local estimator and the fusion center to decrease the energy consumption of network communication. Then, a distributed fusion estimation algorithm is proposed using a matrix-weighted fusion criterion. Finally, the effectiveness of the algorithm is verified using computer simulations.

Physical layer security‐oriented energy‐efficient resource allocation and trajectory design for UAV jammer over 5G millimeter wave cognitive relay system

AbstractThis work focuses on the optimal trajectory of a rotary‐wing unmanned aerial vehicle (UAV) jammer and the system power allocation with the objective of maximizing the total secure energy efficiency (EE) of a slotted relay‐assisted millimeter wave (mm‐Wave) cognitive radio system. The UAV flies from starting location to final one in a round of communication mission that is overheard by a malicious eavesdropper. The network terminals are equipped with multiple‐input multiple‐output (MIMO) antenna array. This work first develops a practical approximation propagation model by addressing three key challenges: path loss, small‐scale Nakagami‐m fading channel gains, and transceiver antenna beamforming gains. By using the approximate model, the upper and lower bounds of the ergodic rate of each communication link are derived. Second, with the objective of maximizing secure EE, the optimal UAV trajectory and system resource allocation framework is established by considering the constraints of interference at primary receiver (PR), information‐causality relationship, rotary‐wing UAV propulsion energy and so forth. The optimal framework is solved with a tractable CVX tool by using the derived upper and lower bounds and successive convex approximation. Finally, an iterative optimizing algorithm is established by using Dinkelbach's method. In the simulation, the work presents the comparison of UAV trajectories, secure rates, and secure EEs of three schemes, that is, the proposed EE scheme, the non‐EE scheme where only the optimal secure rate is considered, and the EE scheme with the consideration of all communication‐related energy consumption. The simulations show that the EE schemes achieve smoother UAV trajectory than non‐EE, but the non‐EE scheme has higher secure rate. The impact of communication energy consumption is negligible. In addition, the effect of MIMO antenna array and mm‐Wave on system performance is exploited.

Fixed-time event-triggered control for multi-agent systems with input delay.

An in-depth study on the fixed-time event-triggered obstacle avoidance consensus control in heterogeneous USV-AUV systems with input delay and uncertain disturbances are conducted in this paper. When initial state of the system fails to achieve consensus, the desired heterogeneous USV-AUV formation can be achieved by fixed-time consensus control, within a fixed predetermined time, regardless of the initial states. Besides, an event-triggered communication strategy among the agents is introduced in the system, significantly reducing communication energy consumption. By employing the proposed control strategy, the Zeno behavior also can be avoided. Additionally, an obstacle avoidance control algorithm for the heterogeneous USV-AUV system based on improved artificial potential fields (IAPF) is designed, which helps in avoiding both static and dynamic obstacles. Compared to existing research, this algorithm reduces control input jitter, resulting in smoother obstacle avoidance paths. Through extensive simulation experiments and comparisons with other methods, effectiveness and superiority of the proposed algorithm is validated.

Bounded Antisynchronization of Multiple Neural Networks via Multilevel Hybrid Control.

The bounded antisynchronization (AS) problem of multiple discrete-time neural networks (NNs) based on the fuzzy model is studied, in consideration of the differences in quantity and communication among different NN groups, the variabilities of dynamics, and communication topological affected by environments. To reduce the energy consumption of communication, a cluster pinning communication mechanism is proposed, and an impulsive observer is designed to estimate the state of target NN. Then, a multilevel hybrid controller based on the impulsive observer is built including the AS controller and the bounded synchronization (BS) controller. Sufficient conditions for bounded AS are obtained by analyzing the stability of the BS augmented error (BSAE) and the AS augmented error (ASAE) based on the fuzzy-based Lyapunov functional (FBLF). Finally, a numerical example and an application example are given to verify the validity of the obtained results.

Air-Ground Cooperative Access Algorithm for UAV-assisted Wireless Networks in Transmission Lines

To reduce the energy consumption of wireless network communication in the transmission line scenario and improve the service life of the network, this paper proposes an air-ground cooperative access algorithm. The algorithm aims to minimize the communication energy consumption of the base station serving transmission line nodes. Firstly, the user rate contribution weight provided by the air-ground base station is optimized, and then the air-ground coordinated power allocation is realized according to the optimal weight. The simulation results show that the proposed air-ground cooperative access algorithm achieves a reduction in energy consumption of over 50% compared to the traditional single base station access method.

UAV-Assisted Wideband Terahertz Wireless Communications with Time-Delay Phased UPA under Beam Squint

Future Unmanned Aerial Vehicle (UAV)-assisted wireless communication systems are expected to utilize wide bandwidths available at terahertz (THz) frequencies to enhance system throughput. To compensate for the severe path loss in the THz band, it is essential to have a multitude of antennas in the UAV to generate narrow beams for directional transmission. However, narrow beams severely limit its spatial coverage, which greatly affects the efficiency of large-scale access UAV-assisted THz systems. Moreover, the combination of massive antennas and large bandwidth at THz makes the misalignment of the beams caused by beam squint non-negligible and also high energy consumption. UAV-assisted communication technology can effectively increase spatial coverage and provide reliable LoS communication links. In addition, reducing the number of radio frequency (RF) chains while ensuring the number of transmitted data streams and space division multiplexing capability is also an effective way to reduce energy consumption in the UAV communication. In this paper, a single RF chain uniform planar array (UPA) with true-time-delays (TTDs) is equipped on the UAV to achieve two dimensional (2D) beams and split spatial beams to improve transmission efficiency. We analyze the 2D beam squint of the UPA and design a time-delay phased UPA for UAV-assisted THz communication systems. By introducing TTDs between the single RF chain and phase shifters, the beam squint can be controlled flexibly by introducing the delay between each antenna. When TTDs are arranged in both the horizontal and vertical dimensions, the coverage of the beams becomes more complicated compared to uniform linear arrays (ULA). Simulation results show that the proposed time-delay phased UPA can achieve better performance in both single-beam and multi-beam modes for single user and multi-user scenarios compared with conventional phased UPA, respectively. In addition, we propose frequency division beam multiple access (FDBMA) multi access technology, which achieves more efficient multi access by reusing resources from different frequency beam pairs. Finally, the results also show that the enlargement of the beamwidth through the proposed FDBMA strategy can also increase the performance in multi-user scenarios.

Chaotic heuristic assisted method for the search path planning of the multi-BWBUG cooperative system

Blended-wing-body underwater gliders (BWBUGs) are low cost and have high endurance. As such, they are highly suitable for underwater search missions. However, the search path planning of BWBUGs presents significant challenges due to the complexities in designing the planning model and solution algorithm. In this study, we propose a novel chaotic heuristic assisted method (CHAM) for search missions involving multi-BWBUG cooperative systems. First, our approach addresses the challenges by introducing a probabilistic sensing model that accurately describes the detection performance of the BWBUG. Then, a search path planning model of the multi-BWBUG cooperative system is developed on the basis of the cumulative detection probability and communication energy consumption ratio. Second, we propose the sine-cosine chaos strategy, which exhibits earlier chaotic state entry and higher ergodicity compared to the sine chaos strategy. Leveraging this, CHAM integrates the sine-cosine chaos strategy with a well-designed hierarchical collaboration mechanism. The proposed algorithm adapts different position update strategies at various stages to enhance convergence speed and avoid local optima. Finally, the simulation experiments and statistical analysis results provide compelling evidence that CHAM excels in performing search path planning for the multi-BWBUG cooperative system, and the comprehensive performance of CHAM is better than that of comparison algorithms.

Binary Addressable Optical Multiplexing Waveguides via Electrochromic Switching

Photonic circuits attract much attention as promising candidates to overcome the drawbacks of their electronic counterparts. By utilizing the broad bandwidth and low energy consumption of optical communication, hybrid circuits can provide a comprehensive platform for the era beyond Moore's law. In particular, parallel matrix operations, the heavy lifting behind neural networks, remain challenging for traditional electronics due to high heat dissipation. To enable these parallel computations optically, (de‐)multiplexing is crucial to address the different channels. Previously this has been accomplished with complex spectral or time encodings in wave division or time division methods. However, herein, a simple method to address parallel optical channels exclusively with 2‐bit signals is presented. By using PEDOT:PSS as electrochromic material for intensity modulation, light transmission or absorption is controlled by oxidation and reduction with an electrolyte. Y‐branch structures are used to design the multiplexing layout and to assign the 2‐bit states to the channels. This binary addressable optical multiplexer, therefore, combines optical communication with electronic signals into a hybrid circuit.

Unmanned Aerial Vehicle Assisted Post-Disaster Communication Coverage Optimization Based on Internet of Things Big Data Analysis.

The rapid development of Internet of Things (IoT) communication devices has brought about significant convenience. However, simultaneously, the destruction of communication infrastructure in emergency situations often leads to communication disruptions and challenges in information dissemination, severely impacting rescue operations and the safety of the affected individuals. To address this challenge, IoT big data analytics and unmanned aerial vehicle (UAV) technologies have emerged as key elements in the solution. By analyzing large-scale sensor data, user behavior, and communication traffic, IoT big data analytics can provide real-time communication demand prediction and network optimization strategies, offering decision support for post-disaster communication reconstruction. Given the unique characteristics of post-disaster scenarios, this paper proposes a UAV-assisted communication coverage strategy based on IoT big data analytics. This strategy employs UAVs in a cruising manner to assist in communication by partitioning the target area into multiple cells, each satisfying the minimum data requirements for user communication. Depending on the distribution characteristics of users, flight-communication or hover-communication protocols are selectively employed to support communication. By optimizing the UAV's flight speed and considering the coverage index, fairness index, and average energy efficiency of the mission's target area, the Inner Spiral Cruise Communication Coverage (IS-CCC) algorithm is proposed to plan the UAV's cruising trajectory and achieve UAV-based communication coverage. Simulation results demonstrate that this strategy can achieve energy-efficient cruising communication coverage in regions with complex user distributions, thereby reducing energy consumption in UAV-based communication.

Monitoring and control of smart urban drainage systems using NB-IoT cellular sensor networks.

Narrow-Band-Internet of Things (NB-IoT) is a promising wireless communication technology for harsh signal propagation conditions, including underground connectivity. The purpose of this study is to evaluate the real-life signal quality of an NB-IoT cellular sensor network for the monitoring and control of urban drainage systems. The implementation of the control systems toward smart infrastructures increase the need for low-cost and low-energy-consumption underground sensing devices. A measurement campaign was conducted in Rakvere, Estonia, to analyze the NB-IoT network's coverage and underground signal quality. The results were used to analyze the flood risk reduction potential in the pilot catchment, considering the quality of NB-IoT cellular network coverage. The signal measurements showed that in the case of plastic manholes, the best cellular transmitter placement is 0.5 m below the manhole cover, and at the ground level NB-RSRP reference signal strength of -104 dBm or better must be ensured to have a link budget with optimal energy consumption for communication. The results of flood risk reduction showed that excluding control locations with insufficient signal strength increased the number of major flooding nodes by 3.9% and mean flooding duration by 4.2% which is a relatively small change and characterizes the flexibility of the developed control system.

Energy-Efficient Data Transmission for Underwater Wireless Sensor Networks: A Novel Hierarchical Underwater Wireless Sensor Transmission Framework

The complexity of the underwater environment enables significant energy consumption of sensor nodes for communication with base stations in underwater wireless sensor networks (UWSNs), and the energy consumption of nodes in different water depths is unbalanced. How to improve the energy efficiency of sensor nodes and meanwhile balance the energy consumption of nodes in different water depths in UWSNs are thus urgent concerns. Therefore, in this paper, we first propose a novel hierarchical underwater wireless sensor transmission (HUWST) framework. We then propose a game-based, energy-efficient underwater communication mechanism in the presented HUWST. It improves the energy efficiency of the underwater sensors personalized according to the various water depth layers of sensor locations. In particular, we integrate the economic game theory in our mechanism to trade off variations in communication energy consumption due to sensors in different water depth layers. Mathematically, the optimal mechanism is formulated as a complex nonlinear integer programming (NIP) problem. A new energy-efficient distributed data transmission mode decision algorithm (E-DDTMD) based on the alternating direction method of multipliers (ADMM) is thus further proposed to tackle this sophisticated NIP problem. The systematic simulation results demonstrate the effectiveness of our mechanism in improving the energy efficiency of UWSNs. Moreover, our presented E-DDTMD algorithm achieves significantly superior performance to the baseline schemes.

TaskPOI Priority-Based Energy Balanced Multi-UAVs Cooperative Trajectory Planning Algorithm in 6G Networks

In the emergency rescue scenario where the communication infrastructure is damaged, Unmanned Air Vehicle(UAV) networking communication, as a part of the 6G air-space-ground integrated network system, plays a huge role in the establishment of the emergency communication network. In this paper, we fully consider the distribution of rescue areas, the type of tasks and the characteristics of UAVs, and design the TaskPOI-based multi-UAVs collaborative trajectory planning algorithm(TMCTP) based on regional tasks, aiming to improve the completed efficiency of rescue tasks and the utilization rate of energy consumption in the UAV backbone network. Firstly, we filter the population-intensive points of interest(PiPOI) region based on the location heat map of population density distribution and use the K-means clustering algorithm to generate task points of interest(TaskPOI) and use the neural collaborative filtering algorithm to determine the task priority and different priorities correspond to different task types. Then, we design a single UAV flight model and a multi-UAVs cooperative flight model. At the same time, considering the flight energy consumption, communication energy consumption and data acquisition energy consumption of UAV, the energy consumption equilibrium distribution model is designed to ensure that multi-UAVs have low energy consumption and balance the residual energy distribution while completing various levels of tasks. Finally, based on UAV flight model, energy consumption equilibrium distribution model, and combined with task priority of TaskPOI, TMCTP algorithm for the trajectory planning of the UAV group is designed by using multi-agent reinforcement learning algorithm. The experimental results show that in the TMCTP algorithm, the success rate of the UAV arriving at the goal and the completed rate of the action are converged to 1, the times of collisions is converged to 0, and the average reward, the loss of algorithm are all converged to a good degree, and the PiPOI coverage rate in the area exceeds 60%. In terms of energy consumption, the difference between the energy consumption of each UAV is small, and compared with the average residual energy using GP3, the difference is not large, indicating that the algorithm can improve the PiPOI coverage of the entire rescue area without generating a large energy consumption. In this way, the UAV group after arriving at the goal will provide communication services for the rescue area and improve the connectivity and life cycle of the 6G air network.

Dynamic event-triggered fixed-time consensus control of multi-agent systems with unknown bounded disturbances

This paper investigates the fixed-time consensus control for a class of multi-agent systems subject to unknown bounded disturbances under undirected topology. Firstly, the nonlinear controllers and the measurement errors are designed based on the hyperbolic tangent function to avoid the non-differential problem. Secondly, a distributed fixed-time control protocol is designed, which can realise the fixed-time convergence and improve the system convergence rate independent of the system’s initial state. Thirdly, the dynamic event-triggered mechanism is proposed, such that the triggering frequency of events is reduced and communication energy consumption can be saved. The stability analysis is presented by employing the Lyapunov theory and the Zeno phenomenon is excluded. Finally, the simulation examples are presented to show the effectiveness and advantages of the proposed method in this paper.

COP: A Combinational Optimization Power Budgeting Method for Manycore Systems in Dark Silicon

Dark silicon is a phenomenon of under-utilization in today's manycore systems due to power and thermal limitations. In order to improve the performance of dark silicon systems, it is necessary to adopt dynamic power constraints for different core mapping decisions. However, existing power budgeting methods are generally over pessimistic, e.g. Thermal Safe Power (TSP), or over optimistic, e.g. Greedy based Dynamic Power (GDP). This paper proposes a practical power budgeting method, called Combinational Optimization Power (COP). Different from existing methods, which ignore some actual factors, such as communication overhead and lifetime reliability, COP formulates the power budgeting problem as a thermal-constrained combinational optimization power problem. For the steady-state case, COP achieves the target fusion of optimized temperature and communication energy consumption by applying task priority ranking and task-to-core mapping. For the transient case, COP uses the rainflow counting algorithm to construct the reliability framework based on the thermal cycling failure mechanism, and then establishes a linear time-invariant transient temperature model to obtain the core mapping selection and the corresponding dynamic power budget. Experimental results demonstrate that COP is capable of providing an optimized core mapping decision, which can maximize power budget while ensuring the system performance.

A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System

In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy (IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy. The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.