Energy Consumption Minimization Problem Research Articles

Energy Consumption Minimization with SNR Constraint for Wireless Powered Communication Networks.

The article addresses the energy consumption minimization problem in wireless powered communication networks (WPCNs) and proposes a time allocation scheme, named DaTA, which is based on the Different Target Simultaneous Wireless Information and Power Transfer (DT-SWIPT) scheme such that the wireless station can share the remaining energy after transmission to the Hybrid Access Point (HAP) to those who have not transmitted to the HAP to minimize the energy consumption of the WPCN. In addition to proposing a new frame structure, the article also considers the Signal-to-Noise (SNR) constraint to guarantee that the HAP can successfully receive data from wireless stations. In the article, the problem of minimization of energy consumption is formulated as a nonlinear programming model. We employ the SQP (Sequential Quadratic Programming) algorithm to figure out the optimal solution. Moreover, a heuristic method is proposed as well to obtain a near-optimal solution in a shorter time. The simulation results showed that the proposed scheme outperforms the related work in terms of energy consumption and energy efficiency.

Multi-Agent-Deep-Reinforcement-Learning-Enabled Offloading Scheme for Energy Minimization in Vehicle-to-Everything Communication Systems

Offloading computation-intensive tasks to mobile edge computing (MEC) servers, such as road-side units (RSUs) and a base station (BS), can enhance the computation capacities of the vehicle-to-everything (V2X) communication system. In this work, we study an MEC-assisted multi-vehicle V2X communication system in which multi-antenna RSUs with liner receivers and a multi-antenna BS with a zero-forcing (ZF) receiver work as MEC servers jointly to offload the tasks of the vehicles. To control the energy consumption and ensure the delay requirement of the V2X communication system, an energy consumption minimization problem under a delay constraint is formulated. The multi-agent deep reinforcement learning (MADRL) algorithm is proposed to solve the non-convex energy optimization problem, which can train vehicles to select the beneficial server association, transmit power and offloading ratio intelligently according to the reward function related to the delay and energy consumption. The improved K-nearest neighbors (KNN) algorithm is proposed to assign vehicles to the specific RSU, which can reduce the action space dimensions and the complexity of the MADRL algorithm. Numerical simulation results show that the proposed scheme can decrease energy consumption while satisfying the delay constraint. When the RSUs adopt the indirect transmission mode and are equipped with matched-filter (MF) receivers, the proposed joint optimization scheme can decrease the energy consumption by 56.90% and 65.52% compared to the maximum transmit power and full offloading schemes, respectively. When the RSUs are equipped with ZF receivers, the proposed scheme can decrease the energy consumption by 36.8% compared to the MF receivers.

UAV Relay Energy Consumption Minimization in an MEC-Assisted Marine Data Collection System

Recently, unmanned aerial vehicle (UAV)-assisted maritime communication systems have drawn considerable attention due to their potential for broadband maritime communication applications. However, their limited energy resources remains a critical issue in providing long-term data transmission support for maritime applications. In this study, an integrated sea–air–terrestrial communication system was constructed for marine data collection, where several unmanned surface vessels (USVs) are deployed to collect marine data from underwater sensors (UWSs), and a UAV hovers above these USVs as a relay node, transmitting marine data from USVs to an onshore base station (BS). To prolong the lifetime of the UAV relay, mobile edge computing technology is applied in USVs for partial data computing, which reduces the to-be-relayed data volume from UAVs to USVs to onshore BS as well as the relay energy consumption of UAV. A parallel data computing and transmission scheme was developed for simultaneous local data computing and relaying in the proposed system. Accordingly, a UAV energy consumption minimization problem was formulated with constraints on the USV’s computational ability, the USV’s transmission power budget, the UAV transmission power budget, and the maximum system latency. To effectively solve this nonconvex optimal problem, an energy optimal partial data computing and relaying strategy was constructed by successively optimizing the data partial computational offloading ratio, USV transmit power allocation, and UAV transmit power. Numerical simulations were used to verify the effectiveness of the proposed strategy.

Energy Consumption Minimization in Secure Multi-Antenna UAV-Assisted MEC Networks With Channel Uncertainty

This paper investigates the robust and secure task transmission and computation scheme in multi-antenna unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where the UAV is dual-function, i.e., aerial MEC and aerial relay. The channel uncertainty is considered during information offloading and downloading. An energy consumption minimization problem is formulated under some constraints including users’ quality of service and information security requirements and the UAV’s trajectory’s causality, by jointly optimizing the CPU frequency, the offloading time, the beamforming vectors, the artificial noise and the trajectory of the UAV, as well as the CPU frequency, the offloading time and the transmit power of each user. To solve the non-convex problem, a reformulated problem is first derived by a series of convex reformation methods, i.e., semi-definite relaxation, S-Procedure and first-order approximation, and then, solved by a proposed successive convex approximation (SCA)-based algorithm. The convergence performance and computational complexity of the proposed algorithm are analyzed. Numerical results demonstrate that the proposed scheme outperforms existing benchmark schemes. Besides, the proposed SCA-based algorithm is superior to traditional alternative optimization-based algorithm.

Joint optimization of trajectory and resource allocation in cellular-connected multi-UAV MEC networks

Unmanned aerial vehicles (UAVs)-based mobile edge computing (MEC) has been introduced as a promising model for enhanced edge communications in the future of integrated air-sky-earth and sea communications. However, in UAV-assisted mobile edge computing systems, the energy consumption problem of UAVs during computational offloading becomes a serious challenge. Due to UAVs constrained energy and processing capabilities, the majority of current research does not adequately take into the task of offloading UAVs as end users and the energy consumption problem. Considering this, this paper proposes a novel cellular-connected multi-UAV MEC network in which the UAV can offload its computational tasks to a ground-based station (GBS) and the UAV can harvest energy from the GBS. Total UAV energy consumption is minimized by jointly optimizing the UAV-GBS association, UAV transmit power, UAV trajectory, and energy harvesting time. The formulated energy consumption minimization problem is a difficult non-convex optimization problem. To solve this issue, the optimization problem is first broken down into three sub-optimization problems and resolved using successive convex optimization techniques. Then a joint block coordinate descent (BCD) based resource allocation and UAV trajectory algorithm is proposed to solve the overall optimization problem. Finally, the effectiveness of the suggested scheme is verified by simulation. The simulation results demonstrate that the suggested strategy is more efficient and performs better.

Study on a Boat-Assisted Drone Inspection Scheme for the Modern Large-Scale Offshore Wind Farm

This study proposes a novel and comprehensive boat-assisted drone inspection scheme for offshore wind farms. Specifically, we develop a unique wireless communication model, a detailed wind turbine blade detection model, and a route planning scheme for the drone and boat to formulate the minimum energy consumption problem for drone inspection. Then, we demonstrate that this problem is a combinatorial optimization mixed-integer nonlinear programming problem, which we solve by decomposing the optimization problem into four subproblems. Moreover, an improved heuristic algorithm is constructed to solve the optimum energy consumption problem, exploiting these subproblems. The simulation results indicate that the proposed scheme achieves optimal energy consumption for boat-assisted drone inspection. Furthermore, compared with the existing methods, the proposed algorithm obtains the lowest total energy consumption and provides a higher route planning performance for the unmanned aerial vehicle and boat when utilized for offshore wind farm inspection.

FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing

Integrating unmanned aerial vehicles (UAVs) into vehicular networks have shown high potentials in affording intensive computing tasks. In this paper, we study the digital twin driven vehicular edge computing networks for adaptively computing resource management where an unmanned aerial vehicle (UAV) named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FlexEdge</i> acts as a flying server. In particular, we first formulate an energy consumption minimization problem by jointly optimizing UAV trajectory and computation resource under the practical constraints. To address such a challenging problem, we then build the computation offloading process as a Markov decision process and propose a deep reinforcement learning-based proximal policy optimization algorithm to dynamically learn the computation offloading strategy and trajectory design policy. Numerical results indicate that our proposed algorithm can achieve quick convergence rate and significantly reduce the system energy consumption.

Joint Task Offloading and Resource Allocation for Space–Air–Ground Collaborative Network

The space–air–ground collaborative network can provide computing service for ground users in remote areas by deploying edge servers on satellites and high-altitude platform (HAP) drones. However, with the growing number of ground devices required to be severed, it becomes imperative to address the issue of spectrum demand for the HAP drone to meet the access of a large number of users. In addition, the long propagation distance between devices and the HAP drone, and between the HAP drone and LEO satellites, will lead to high data transmission energy consumption. Motivated by these factors, we introduce a space–air–ground collaborative network that employs the non-orthogonal multiple access (NOMA) technique, enabling all ground devices to access the HAP drone. Therefore, all devices can share the same communication spectrum. Furthermore, the HAP drone can process part of the ground devices’ tasks locally, and offload the rest to satellites within the visible range for processing. Based on this system, we formulate a weighted energy consumption minimization problem considering power control, computing frequency allocation, and task-offloading decision. The problem is solved by the proposed low-complexity iterative algorithm. Specifically, the original problem is decomposed into interconnected coupled subproblems using the block coordinate descent (BCD) method. The first subproblem is to optimize power control and computing frequency allocation, which is solved by a convex algorithm after a series of transformations. The second subproblem is to make an optimal task-offloading strategy, and we solve it using the concave–convex procedure (CCP)-based algorithm after penalty-based transformation on binary variables. Simulation results verify the convergence and performance of the proposed iterative algorithm compared with the two benchmark algorithms.

Unmanned Aerial Vehicle-Assisted Sparse Sensing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) suffer from enormous energy consumption when sensor nodes transmit data over long distances. Hence, this paper develops an Unmanned Aerial Vehicle (UAV)-assisted sparse sensing algorithm to address this issue. Specifically, the data from the sensor nodes are locally stored and are sparsely sampled through Compressive Sensing (CS), while a UAV is utilized as a mobile base station to collect data from the partial sensor nodes. In the proposed UAV and WSN system, the energy consumption minimization problem is formulated as a combinatorial optimization problem, which is solved utilizing a joint sparse sensing and Greedy algorithm based on a Neighborhood Search Strategy (GNSS). Extensive experiments demonstrate that the proposed algorithm achieves a sparse measurement with a smaller error and decreases energy consumption.

A renewable-energy-driven energy-harvesting-based task scheduling and energy management framework

Aiming to provide low-cost MEC services for mobile devices in areas without/lacking infrastructure, we propose a renewable-energy-driven energy-harvesting-based task scheduling and energy management framework. First, for mobile devices, the constructed energy consumption minimization problem is solved by an alternating-optimization-based algorithm. Then, we design an energy management algorithm based on sampling average approximation to derive the optimal charging/discharging strategies, number of energy storage units, and renewable energy utilization. The simulation results show that the proposed framework can significantly reduce the cost of MEC servers and prolong the working hours of mobile devices.

Dynamic Transmission and Computation Resource Optimization for Dense LEO Satellite Assisted Mobile-Edge Computing

A dense satellite-terrestrial integrated mobile-edge computing network (SATIMECN) architecture is developed to meet the computing demands for next generation networks. We formulate an average weighted sum energy consumption minimization problem by jointly considering task ratio allocation of computing or offloading at local and the gateway (GW), ground user terminal (GUT)-satellite association relation, GUT multiple-input and multiple-output (MIMO) precoding, and computation resource allocation at local and the GW. Due to the stochastic property of the optimization problem, we adopt Lyapunov optimization theory to transform it into a deterministic one. Then, we decompose the optimization problem into four subproblems and solve each one iteratively. Specifically, task ratio allocation of computing or offloading at local and the GW is obtained in a closed-form expression using the delay constraint. Then, the binary GUT-satellite association subproblem is solved by the weighted minimum mean-squared error and quadratic transform based fractional programming (QTFP) methods. Moreover, the MIMO precoding subproblem is solved by QTFP and interior point methods. Finally, the computation resource allocation subproblem for local and edge computing is derived in closed-form expressions. Simulation results demonstrate that the tradeoff between the average weighted sum energy consumption and the average queue length can be realized by adjusting the Lyapunov control parameter. Moreover, the proposed MIMO communication and frequency reuse schemes for dense satellite network can realize efficient computation offloading with relative low cost.

Reinforcement Learning-Based Approach for Minimizing Energy Loss of Driving Platoon Decisions.

Reinforcement learning (RL) methods for energy saving and greening have recently appeared in the field of autonomous driving. In inter-vehicle communication (IVC), a feasible and increasingly popular research direction of RL is to obtain the optimal action decision of agents in a special environment. This paper presents the application of reinforcement learning in the vehicle communication simulation framework (Veins). In this research, we explore the application of reinforcement learning algorithms in a green cooperative adaptive cruise control (CACC) platoon. Our aim is to train member vehicles to react appropriately in the event of a severe collision involving the leading vehicle. We seek to reduce collision damage and optimize energy consumption by encouraging behavior that conforms to the platoon's environmentally friendly aim. Our study provides insight into the potential benefits of using reinforcement learning algorithms to improve the safety and efficiency of CACC platoons while promoting sustainable transportation. The policy gradient algorithm used in this paper has good convergence in the calculation of the minimum energy consumption problem and the optimal solution of vehicle behavior. In terms of energy consumption metrics, the policy gradient algorithm is used first in the IVC field for training the proposed platoon problem. It is a feasible training decision-planning algorithm for solving the minimization of energy consumption caused by decision making in platoon avoidance behavior.

Application-aware computation offloading in edge computing networks

Edge computing involves distributive computation resources deployed at the network edge, unlike cloud computing, which has central computation resources in data centers. Edge computing is a complement of cloud computing because edge computing effectively reduces the computing response delay by processing computation tasks and data near terminals. Considering the dramatic increase of terminals connected to networks and data generated by terminals, computation tasks from different applications may require significantly different services with different computation requirements, storage requirements, and response delay requirements. Application-aware computation offloading and resource allocation in edge computation can provide efficient and guaranteed computation services to terminals. In this paper, an application-aware computation offloading and resource allocation problem is investigated in edge computing networks, where computation tasks from different applications have different requirements. A non-convex optimization problem of energy consumption minimization is formulated, where terminals, edge nodes, and a cloud are considered. We convert the original non-convex optimization problem into a lower-bound convex problem and an upper-bound convex problem. Then, an algorithm based on the branch-and-bound method is proposed to force the lower- and upper-bound solutions to approach the optimal solution. Finally, the performance of the algorithm is analyzed where the gap to the optimal solution is provided. Numerical results show that the proposed algorithm can provide guaranteed services for tasks of different application types, with improvements over application-unaware algorithms.

Motion Planning in UAV-Aided Data Collection with Dynamic Jamming

Unmanned-aerial-vehicle (UAV)-aided data collection for Internet of Things applications has attracted increasing attention. This paper investigates motion planning for UAV collecting low-power ground sensor node (SN) data in a dynamic jamming environment. We targeted minimizing the flight energy consumption via optimization of the UAV trajectory while considering the indispensable constraints which cover the collection data demodulation threshold, obstacle avoidance, data collection volume, and motion principle. Firstly, we formulate the UAV-aided data collection problem as an energy consumption minimization problem. To solve this nonconvex optimization problem, we rewrite the original problem by introducing relaxation variables and constructing equivalence constraints to obtain a new relaxation convex problem, which can be solved iteratively using the successive convex approximation (SCA) method. However, SCA is susceptible to initial values, especially in dynamic environments where fixed initial values may lead to a wide range of results, making it difficult to obtain a truly optimal solution to the optimization problem. To solve the initial value problem in dynamic environments, we further propose a communication-flight-corridor(CFC)-based initial path generation method to improve the reliability and convergence speed of the SCA method by constructing reliable communication regions and resilient secure paths in real time. Finally, simulation results validate the performance of the proposed algorithm compared to the benchmark algorithms under different parameter configurations.

Joint Communication and Computation Design in Transmissive RMS Transceiver Enabled Multi-Tier Computing Networks

In this paper, a novel transmissive reconfigurable meta-surface (RMS) transceiver enabled multi-tier computing network architecture is proposed for improving computing capability, decreasing computing delay and reducing base station (BS) deployment cost, in which transmissive RMS equipped with a feed antenna can be regarded as a new type of multi-antenna system. We formulate a total energy consumption minimization problem by a joint optimization of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient while taking into account the constraints of communication resources and computing resources. This formulated problem is a non-convex optimization problem due to the high coupling of optimization variables, which is NP-hard to obtain its optimal solution. To address the above challenging problems, block coordinate descent (BCD) technique is employed to decouple the optimization variables to solve the problem. Specifically, the joint optimization problem of subcarrier allocation, task input bits, time slot allocation, transmit power allocation and RMS transmissive coefficient is divided into three subproblems to solve by applying BCD. Then, the decoupled three subproblems are optimized alternately by using successive convex approximation (SCA) and difference-convex (DC) programming until the convergence is achieved. Numerical results verify that our proposed algorithm is superior in reducing total energy consumption compared to other benchmarks.

Joint computation offloading and resource allocation strategy for D2D-assisted and NOMA-empowered MEC systems

Multi-access edge computing (MEC) emerged as a promising network paradigm that provides computation, storage and networking features within the edge of the pervasive mobile radio access network. This paper jointly considers computation offloading and resource allocation problem in device-to-device (D2D)-assisted and non-orthogonal multiple access (NOMA)-empowered MEC systems, where each mobile device (MD) is allowed to execute its task in one of the three ways, i.e., local computing, MEC offloading or D2D offloading. We invoke orthogonal multiple access (OMA) and NOMA schemes for MDs that select D2D offloading mode, allowing them to assign tasks to their peers using OMA or NOMA. The original problem is formulated as an overall energy consumption minimization problem, which proves to be NP-hard, making it intractable to solve optimally. We start from a simple case, OMA case and transform the original problem into two sub-problems, i.e., resource allocation sub-problem and computation offloading sub-problem and propose two heuristic algorithms to obtain the sub-optimal solutions of both sub-problems. Then, for the MDs selecting D2D offloading mode, we conduct user pairing and apply the NOMA scheme. Finally, simulation results demonstrate the efficiency of the proposed scheme when compared with the related schemes.

Multiuser Co-Inference With Batch Processing Capable Edge Server

Graphics processing units (GPUs) can improve deep neural network inference throughput via batch processing, where multiple tasks are concurrently processed. We focus on novel scenarios that the energy-constrained mobile devices offload inference tasks to an edge server with GPU. The inference task is partitioned into sub-tasks for a finer granularity of offloading and scheduling, and the user energy consumption minimization problem under inference latency constraints is investigated. To deal with the coupled offloading and scheduling introduced by concurrent batch processing, we first consider an offline problem with a constant edge inference latency and the same latency constraint. It is proven that optimizing the offloading policy of each user independently and aggregating all the same sub-tasks in one batch is optimal, and thus the independent partitioning and same sub-task aggregating (IP-SSA) algorithm is inspired. Further, the optimal grouping (OG) algorithm is proposed to optimally group tasks when the latency constraints are different. Finally, when future task arrivals cannot be precisely predicted, a deep deterministic policy gradient (DDPG) agent is trained to call OG. Experiments show that IP-SSA reduces up to 94.9% user energy consumption in the offline setting, while DDPG-OG outperforms DDPG-IP-SSA by up to 8.92% in the online setting.

Energy-Aware Coded Transmission Strategy for Hierarchical Cooperative Caching Networks

With the rapid development of millions more edge base stations (EBSs) and billions of user devices in the future networks, the desire for low-energy system design and transmission strategy will be even more compelling. In this letter, we investigate the energy consumption issue of hierarchical cooperative caching networks (HCCNs) in which both EBSs and users are capable of storing content data in their local caches. A three-stage coded transmission strategy in HCCNs is proposed, and the closed expression of energy consumption of each stage is derived. In addition, we formulate a total energy consumption minimization problem with the constraint of EBSs’ cache sizes, and provide an algorithm to obtain the optimal cache placement matrix. We demonstrate by numerical results that, our optimized three-stage coded transmission strategy can achieve lower energy consumption than various multicast transmission strategies under the different users’ cache sizes.

Energy Consumption Minimization for Data Collection From Wirelessly-Powered IoT Sensors: Session-Specific Optimal Design With DRL

Reliable and energy-efficient data collection from resource-limited sensors is essential to the success of future Internet of Things (IoT). In this article, we study the energy consumption minimization problem during the data collection from a generic wirelessly-powered sensor. Specifically, we determine the optimal data collection parameters, in terms of charging duration and charging power as well as sensor transmission rate, in real time according to the instantaneous channel condition while satisfying a certain latency constraint. For the scenario of ideal rate-adaptive transmission with linear energy harvesting, we derive closed-form expressions for optimal transmission parameters. We also establish the condition on channel quality for successful data collection under a latency constraint. For the more practical case of finite block-length transmission with nonlinear energy harvesting, we develop a deep reinforcement learning (DRL) solution for efficient online implementation. We also propose an online tuning scheme to cater for model inaccuracy and environment variation. The accuracy and effectiveness of our proposed approaches are verified by comparing with benchmark schemes. Our DRL-based approach has broad applicability and can solve other real-time optimal design problems in wireless communications.

Service Duration Maximization for Continuous Coverage in UAV-Assisted Communication System

This letter studies a UAV-assisted communication design where a rotary-wing UAV is launched to provide continuous coverage to mobile nodes. We aim to maximize the service time duration of the UAV by optimizing its three-dimensional (3D) trajectory while simultaneously ensuring adequate coverage to mobile nodes. Considering the limited energy available with the UAV, we frame an equivalent energy consumption minimization problem. Though the formulated problem is non-convex, it is shown that it is pseudoconvex under horizontal and vertical UAV coordinates. Based on this observation, we use alternating optimization to solve the problem. Numerical results provide trajectory insight and compare the service duration to the mobile nodes with different schemes.