Sum Of Energy Consumption Research Articles

Distributed and Collective Intelligence for Computation Offloading in Aerial Edge Networks

Unmanned aerial vehicles (UAVs) with integrated computing platforms can be used to provide computing offloading services for ground user equipments (UEs) with limited local computing capabilities, especially in remote areas. In this paper, we focus on the task offloading in an aerial edge network (AEN) assisted by a UAV. We aim at minimizing the sum energy consumption of all UEs by the joint optimization of the task offloading decisions and the UAV position under the constraints of the latency and the total energy of UAV. The formulated optimization problem is a mixed-integer nonconvex problem and involves coupling of many optimization variables. To address this challenge, we first transform the original optimization problem into a linear convex optimization problem via reformulation linearization technology, and then the alternating direction method of multipliers (ADMM) algorithm is proposed to achieve the approximate optimal solution. Numerical results confirm that the proposed ADMM algorithm can effectively reduce the total of energy consumption of UEs and ensure the continuous operation of the UEs.

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.

Resource Allocation for Energy Efficient STAR-RIS Aided MEC Systems

In this letter, we investigate a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided mobile edge computing (MEC) system. Our object is to minimize the sum energy consumption of all users by jointly optimizing the transmission and reflection time and coefficients of the STAR-RIS, as well as the transmit power and the size of offloading data for each user. Since the formulated optimization problem is non-convex, we decompose it into two subproblems, and then solve the subproblems alternately to obtain the solution. Simulation results demonstrate that users prefer to offload more task bits with the deployment of STAR-RIS. Further, the STAR-RIS in MEC outperforms conventional reflecting/transmiting-only RIS in terms of sum energy consumption, and the performance gain is more significant for a larger amount of task-input bits.

FedTAR: Task and Resource-Aware Federated Learning for Wireless Computing Power Networks

In the 6G era, the proliferation of data and data-intensive applications poses unprecedented challenges on the current communication and computing networks. The collaboration among cloud computing, edge computing, and networking is imperative to process such massive data, eventually realizing ubiquitous computing and intelligence. In this article, we propose a wireless computing power network (WCPN) by orchestrating the computing and networking resources of heterogeneous nodes toward specific computing tasks. To enable intelligent service in WCPN, we design a task and resource-aware federated learning model, coined FedTAR, which minimizes the sum energy consumption of all computing nodes by the joint optimization of the computing strategies of individual computing nodes and their collaborative learning strategy. Based on the solution of the optimization problem, the neural network depth of computing nodes and the collaboration frequency among nodes are adjustable according to specific computing task requirements and resource constraints. To further adapt to heterogeneous computing nodes, we then propose an energy-efficient asynchronous aggregation algorithm for FedTAR, which accelerates the convergence speed of federated learning in WCPN. Numerical results show that the proposed scheme outperforms the existing studies in terms of learning accuracy, convergence rate, and energy saving.

Joint Allocations of Radio and Computational Resource for User Energy Consumption Minimization Under Latency Constraints in Multi-Cell MEC Systems

This paper investigates the joint allocation of radio resources (i.e., base stations (BSs), sub-channels, and uplink transmission powers) and computational resources for a multicell mobile edge computing (MEC) system, aimed at minimizing the sum energy consumption of all mobile terminals under strict delay and signal-to-interference-plus-noise ratio (SINR) constraints. The problem is challenging to solve due to the highly coupled mixed-integer variables and the complicated expressions of the constraints. To tackle the difficulty, we decompose the problem into two sub-problems: the power and computational resource allocation sub-problem and the user-BS-subchannel association sub-problem. For the first sub-problem, we derive the optimal computational resource as a function of the optimal transmission power. Then the power optimization problem is distributed to each cell, and the local-optimal power solution within each cell is derived in closed-form expressions. For the second sub-problem, we design an optimal modified-cutting-plane (MCP) algorithm with exponential time-complexity. A polynomial-time near-optimal pivoting-and-subgradient (PS) algorithm is further proposed to reduce the complexity. Simulation results demonstrate that the PS algorithm achieves comparable performance to the optimal MCP algorithm, while they both outperform the benchmark schemes in most cases.

Cooperative task offloading and resource allocation for UAV-enabled mobile edge computing systems

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems can provide flexible computation services to user equipments (UEs). On the one hand, the UAV can be flexibly deployed to provide computation services to UEs in remote areas or where intensive computing is required. On the other hand, the UAV can approach UEs conveniently to enhance the offloading performance. However, when UEs are widely distributed, the UEs will consume high energy to offload tasks to the UAV, or the UAV will need to consume significant energy to fly close to different UEs to ensure reliable computation offloading, which is unfriendly to the energy-constrained UAV. To tackle these issues, in this paper, we propose a cooperative task offloading scheme for the UAV-enabled MEC systems. Specifically, we consider a UAV-enabled MEC system consisting of a UAV serving as a MEC server for multiple near and far UEs, where each UE has a dividable computation task to be computed and can offload a part of the task to the UAV for computing. The task offloading of each far UE is proposed to be assisted by an associated near UE, where each far UE first sends its task to the associated near UE, and then the near UE offloads its own task and the task from the far UE to the UAV. An iterative algorithm based on the block coordinate descent method is proposed to optimize the UAV’s trajectory, the computation and communication resources for minimizing the weighted sum energy consumption of the UEs and the UAV. Specifically, the UAV’s trajectory is optimized based on the successive convex approximation method, and the computation and communication resources are optimized via the Lagrangian dual method. Simulation results are presented to verify the effectiveness of the proposed algorithm. It is shown that compared to the state-of-the-art algorithms in existing literature, the proposed algorithm achieves much lower energy consumption, especially when the UEs carry more task data.

Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile-Edge Computing

With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, the IoT-oriented network architecture, mobile-edge computing (MEC), has emerged as a promising technique to reinforce the computation capability of the resource-constrained IoT devices. To exploit the cloud-like functions at the network edge, service caching has been implemented to reuse the computation task input/output data, thus effectively reducing the delay incurred by data retransmissions and repeated execution of the same task. In a multiuser cache-assisted MEC system, users’ preferences for different types of services, possibly dependent on their locations, play an important role in the joint design of communication, computation, and service caching. In this article, we consider multiple representative locations, where users at the same location share the same preference profile for a given set of services. Specifically, by exploiting the location-aware users’ preference profiles, we propose joint optimization of the binary cache placement, the edge computation resource, and the bandwidth (BW) allocation to minimize the expected sum-energy consumption, subject to the BW and the computation limitations as well as the service latency constraints. To effectively solve the mixed-integer nonconvex problem, we propose a deep learning (DL)-based offline cache placement scheme using a novel stochastic quantization-based discrete-action generation method. The proposed hybrid learning framework advocates both benefits from the model-free DL approach and the model-based optimization. The simulations verify that the proposed DL-based scheme saves roughly 33% and 6.69% of energy consumption compared with the greedy caching and the popular caching, respectively, while achieving up to 99.01% of the optimal performance.

Cellular-Connected Multi-UAV MEC Networks: An Online Stochastic Optimization Approach

In this paper, we consider a mobile edge computing (MEC) network where multiple cellular-connected unmanned aerial vehicles (UAVs) can offload their computation tasks to multiple ground base stations (GBSs). In practice, the UAVs are generally unable to master stochastic information of task arrival and channel changes in advance, which may cause a severe issue in terms of energy consumption. Therefore, we formulate a stochastic optimization problem with the goal of minimizing the average weighted sum energy consumption, by jointly optimizing UAV-GBS associations, communication and computation resource allocation, and three-dimensional (3D) UAV trajectories, during which a velocity-triggered penalty term (VTPT) is designed to suppress a large amount of the energy consumption of the UAVs. To handle the stochastic problem, we propose an online resource allocation and trajectory optimization algorithm with outer and inner structures. The outer structure transforms the original problem to a deterministic one by applying the Lyapunov-based optimization framework. The inner structure solves the obtained deterministic problem via the Lagrange duality method and the successive convex approximation technique, based on the block coordinate descent framework. Numerical results demonstrate that: 1) VTPT dramatically decreases the UAVs’ energy consumption, and 2) the proposed algorithm not only reduces the energy consumption but also ensures the computation queue stability compared with other benchmark schemes.

An α-Fairness Approach to Balancing the Energy Consumption Among Sensors for UAV–IoT Systems

The rise of Internet of Things (IoT) systems has enabled us to access real-time information about our surrounding environments. However, IoT data collection in hostile and inaccessible areas without infrastructure supports is a challenging issue due to the inherent physical constraints associated with the tiny sensors. A viable solution to this problem is to use agile and controllable unmanned aerial vehicles (UAVs) to collect the ground data and relay it to the remote cloud for further processing. Under this UAV–IoT scenario, the limited battery supply carried by the sensor must be efficiently utilized so as to prolong the lifetime of the IoT system. Nevertheless, lifetime extension does not merely entail the reduction of the sum energy expenditure of sensors. In this article, we first show that minimizing the sum energy consumption cannot effectively extend the system lifetime due to the imbalance in energy expenditure among sensors, which, in fact, can render early energy depletion for some overburdened sensors. We also reveal a tradeoff between energy efficiency and energy fairness. To tackle this imbalance issue, we then propose an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -fairness approach to balance the energy consumption among IoT sensors. Specifically, in our study, the heterogeneities among the sensor nodes—different data loads, diverse residual energy levels, and distinct channel gains, have been taken into consideration. Based on this, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -utility function is designed. In the maximization of the utility function, the bandwidth allocation, transmission power, and the UAV’s trajectory are jointly optimized. In addition, we also demonstrate how to properly set the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> value according to the specific application scenarios, thus to achieve different levels of energy fairness and promote the functional longevity of the system to the best effort.

Energy-Efficient Design for IRS-Assisted NOMA-Based Mobile Edge Computing

This letter studies a non-orthogonal multiple-access (NOMA)-based mobile edge computing (MEC) system. In order to enhance communication coverage and energy efficiency, intelligent reflecting surface (IRS) is proposed, which is deployed to assist computation offloading from end users to an access point (AP) equipped with an edge server. We aim to minimize the sum energy consumption by jointly optimizing the transmit powers, transmission time, offloading partitions and IRS phase shift. To address the non-convex formulated problem, we first derive the optimal solution of transmit power in closed-form, and then decouple the reformulated problem into two subproblems and employ alternating optimization (AO) technique to solve it. Numerical results validate that the proposed IRS assisted NOMA-based MEC scheme can yield superior performance in comparison with other benchmark schemes.

Energy-Efficient Computational Offloading for Secure NOMA-Enabled Mobile Edge Computing Networks

Computational offloading and nonorthogonal multiple access (NOMA) are two promising technologies for alleviating the problems of limited battery capacity, insufficient computational capability, and massive deployment of terminal equipment in the Internet of Things (IoT) era. However, offloading data may be threatened by malicious eavesdroppers, which leads to more energy consumptions to avoid being eavesdropped. In this work, we study the energy-efficient way of computational offloading under the condition of certain security requirement in a secure NOMA-enabled mobile-edge computing (MEC) networks, where K end users are intended to offload their data to the N -antenna access point (AP) through the same resource block under the threat of an eavesdropper. We first address energy-efficient local resource allocation by minimizing sum-energy consumption of end users, subject to CPU frequencies, offloading bits, secrecy offloading rate, and transmit power. We then optimize the local resources to obtain the minimum computation latency of task for each end user, with the constraint of certain energy budget. The solutions to the above two optimization problems are given and demonstrated numerically by a 3-user scenario.

Joint Optimization of Transmission and Computation Resources for Satellite and High Altitude Platform Assisted Edge Computing

In this paper, we investigate a satellite-aerial integrated edge computing network (SAIECN) to combine a low-earth-orbit (LEO) satellite and aerial high altitude platforms (HAPs) to provide edge computing services for ground user equipment (GUE). In the SAIECN, GUE’s computing tasks can be offloaded to HAP(s) or LEO satellite. In this paper, we minimize the weighted sum energy consumption of SAIECN via joint GUE association, multi-user multiple input and multiple output (MU-MIMO) transmit precoding, computation task assignment, and resource allocation. To solve the nonconvex problem, we decompose the optimization problem into four subproblems and solve each one iteratively. For the GUE association subproblem, quadratic transform based fractional programming (QTFP) and difference of convex function are utilized. The MU-MIMO transmit precoding subproblem is solved via QTFP and the weighted minimum mean-squared method. The computation task assignment is addressed using the classic interior point method while the computation resource allocation is derived in closed form. The numerical results show that the proposed SAIECN and the corresponding algorithm can solve the satellite based edge computing quite well and the energy cost is maintained at a relative low level.

The short-term demand response potential and thermal characteristics of a ventilated floor heating system in a nearly zero energy building

Exploiting the thermal storage of building thermal mass is promising to increase the load shifting capacity. In this study, the energy consumption and thermal performance of a novel ventilated electric heating floor in a nearly zero-energy building in an extremely cold region in China were investigated. The numerical model of the heating system was first established and validated, then the energy consumption over one month were simulated through a rule-based control strategy in a traditional power system and a system with demand response, meanwhile, the impact of key structural and operational parameters was also analyzed. Results show that the energy consumption flexibility can be significantly increased through ventilation in both energy systems. Specifically, when ventilation is on throughout the period in the first energy system, the sum of energy consumption is reduced by 62.3% compared to that under non-ventilation mode. In the second energy system, the consumed energy can be reduced through ventilation in downward demand response period by 26.1%, and the energy storage is increased in upward demand response period by 4.6%. Besides, thermal stability can be increased through ventilation, and heat recovery time is reduced.

Joint Resource Allocation and Trajectory Optimization for Multi-UAV-Assisted Multi-Access Mobile Edge Computing

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has been considered as a promising approach to offering extensive coverage and massive computing capacities for Internet of Things (IoT). In this letter, we propose a novel multi-UAV-assisted multi-access MEC model by allowing each IoT user to offload task bits to multiple MEC servers deployed at UAVs simultaneously for parallel computing, which can effectively reduce the energy consumption of users and UAVs. The weighted sum energy consumption of UAVs and users is minimized by jointly optimizing the bit allocation, transmit power, CPU frequency, bandwidth allocation and UAVs' trajectories. Due to the non-convexity of the formulated problem, it is decomposed into two subproblems and a joint resource allocation and trajectory design algorithm is proposed by alternative optimization. Simulation results show that our proposed algorithm with multiple radio access outperforms the fixed trajectory, fixed bandwidth allocation and the single access schemes.

Energy Efficient Reconfigurable Intelligent Surface Enabled Mobile Edge Computing Networks With NOMA

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for achieving high spectrum and energy efficiency in future wireless communication networks. In this paper, we investigate an RIS-aided single-cell multi-user mobile edge computing (MEC) system where an RIS is deployed to support the communication between a base station (BS) equipped with MEC servers and multiple single-antenna users. To utilize the scarce frequency resource efficiently, we assume that users communicate with BS based on a non-orthogonal multiple access (NOMA) protocol. Each user has a computation task which can be computed locally or partially/fully offloaded to the BS. We aim to minimize the sum energy consumption of all users by jointly optimizing the passive phase shifters, the size of transmission data, transmission rate, power control, transmission time and the decoding order. Since the resulting problem is non-convex, we use the block coordinate descent method to alternately optimize two separated subproblems. More specifically, we use the dual method to tackle a subproblem with given phase shift and obtain the closed-form solution; and then we utilize penalty method to solve another subproblem for given power control. Moreover, in order to demonstrate the effectiveness of our proposed algorithm, we propose three benchmark schemes: the time-division multiple access (TDMA)-MEC scheme, the full local computing scheme and the full offloading scheme. We use an alternating 1-D search method and the dual method that can solve the TDMA-based transmission problem well. Numerical results demonstrate that the proposed scheme can increase the energy efficiency and achieve significant performance gains over the three benchmark schemes.

Computation Offloading in LEO Satellite Networks With Hybrid Cloud and Edge Computing

Low earth orbit (LEO) satellite networks can break through geographical restrictions and achieve global wireless coverage, which is an indispensable choice for future mobile communication systems. In this article, we present a hybrid cloud and edge computing LEO satellite (CECLS) network with a three-tier computation architecture, which can provide ground users with heterogeneous computation resources and enable ground users to obtain computation services around the world. With the CECLS architecture, we investigate the computation offloading decisions to minimize the sum energy consumption of ground users, while satisfying the constraints in terms of the coverage time and the computation capability of each LEO satellite. The considered problem leads to a discrete and nonconvex since the objective function and constraints contain binary variables, which makes it difficult to solve. To address this challenging problem, we convert the original nonconvex problem into a linear programming problem by using the binary variables relaxation method. Then, we propose a distributed algorithm by leveraging the alternating direction method of multipliers (ADMMs) to approximate the optimal solution with low computational complexity. Simulation results show that the proposed algorithm can effectively reduce the total energy consumption of ground users.

Efficient Computation Offloading in Edge Computing Enabled Smart Home

Mobile edge computing which provides computing capabilities at the edge of the radio access network can help smart home reduce response time. However, the limited computing capacity of edge servers is the bottlenecks for the development of edge computing. We integrate cloud computing and edge computing in the Internet of Things to expand the capabilities. Nevertheless, the cost of leasing computing resources has been seldom considered. In this paper, we study the joint transmission power and resource allocation to minimize the users' overhead which is measured by the sum of energy consumption and cost leasing servers. We formulate the problem as a Mixed Integer Linear Programming which is NP-hard and present the Branch-and-Bound to solve it. Due to high complexity, a learning method is proposed to accelerate the algorithm. The branching policy can be learned to reduce the time-cost of the Branch-and-Bound algorithm. Simulation results show our approach can improve the Branch-and-Bound efficiency and performs closely to the traditional branching scheme.

Joint Optimal Software Caching, Computation Offloading and Communications Resource Allocation for Mobile Edge Computing

As software may be used by multiple users, caching popular software at the wireless edge has been considered to save computation and communications resources for mobile edge computing (MEC). However, fetching uncached software from the core network and multicasting popular software to users have so far been ignored. Thus, existing design is incomplete and less practical. In this paper, we propose a joint caching, computation and communications mechanism which involves software fetching, caching and multicasting, as well as task input data uploading, task executing (with non-negligible time duration) and computation result downloading, and mathematically characterize it. Then, we optimize the joint caching, offloading and time allocation policy to minimize the weighted sum energy consumption subject to the caching and deadline constraints. The problem is a challenging two-timescale mixed integer nonlinear programming (MINLP) problem, and is NP-hard in general. We convert it into an equivalent convex MINLP problem by using some appropriate transformations and propose two low-complexity algorithms to obtain suboptimal solutions of the original non-convex MINLP problem. Specifically, the first suboptimal solution is obtained by solving a relaxed convex problem using the consensus alternating direction method of multipliers (ADMM), and then rounding its optimal solution properly. The second suboptimal solution is proposed by obtaining a stationary point of an equivalent difference of convex (DC) problem using the penalty convex-concave procedure (Penalty-CCP) and ADMM. Finally, by numerical results, we show that the proposed solutions outperform existing schemes and reveal their advantages in efficiently utilizing storage, computation and communications resources.

Optimal Multi-View Video Transmission in OFDMA Systems

In this letter, we study the transmission of a multi-view video (MVV) to multiple users in an Orthogonal Frequency Division Multiple Access (OFDMA) system. To maximally improve transmission efficiency, we exploit both natural multicast opportunities and view synthesis-enabled multicast opportunities. First, we establish a communication model for transmission of a MVV to multiple users in an OFDMA system. Then, we formulate the minimization problem of the average weighted sum energy consumption for view transmission and synthesis with respect to view selection and transmission power and subcarrier allocation. The optimization problem is a challenging mixed discrete-continuous optimization problem with huge numbers of variables and constraints. A low-complexity algorithm is proposed to obtain a suboptimal solution. Finally, numerical results further demonstrate the value of view synthesis-enabled multicast opportunities for MVV transmission in OFDMA systems.

Optimal Multi-View Video Transmission in Multiuser Wireless Networks by Exploiting Natural and View Synthesis-Enabled Multicast Opportunities

Multi-view videos (MVVs) provide immersive viewing experience, at the cost of traffic load increase for wireless networks. In this paper, we would like to optimize MVV transmission in a multiuser wireless network by exploiting both natural multicast opportunities and view synthesis-enabled multicast opportunities. Specifically, we first establish a mathematical model to specify view synthesis at the server and each user, and characterize its impact on multicast opportunities. This model is highly nontrivial and fundamentally enables the optimization of view synthesis-based multicast opportunities. For given video quality requirements of all users, we consider the optimization of view selection, transmission time and power allocation to minimize the average weighted sum energy consumption for view transmission and synthesis. In addition, under the energy consumption constraints at the server and each user respectively, we consider the optimization of view selection, transmission time and power allocation and video quality selection to maximize the total utility. These two optimization problems are challenging mixed discrete-continuous optimization problems. For the first problem, we propose an algorithm to obtain an optimal solution with reduced computational complexity by exploiting optimality properties. For each problem, to reduce computational complexity, we also propose a low-complexity algorithm to obtain a suboptimal solution, using Difference of Convex (DC) programming. Finally, numerical results show the advantage of the proposed solutions over existing ones, and demonstrate the importance of the optimization of view synthesis-enabled multicast opportunities in MVV transmission.