Energy Consumption Of Mobile Devices Research Articles

Evolutionary Multitasking for Costly Task Offloading in Mobile-Edge Computing Networks

The offloading of computation-intensive tasks to an edge server near resource-constrained mobile devices can provide improved application performance and user experience. However, with the rapid growth of mobile devices connected to the edge server, it is challenging to directly obtain an optimal task offloading scheme due to increasing computational cost and problem scale. In this study, we model the costly task offloading problem (CTOP) in mobile edge computing networks to achieve efficient joint optimization of energy consumption and processing latency for mobile devices. Inspired by the success of evolutionary multitasking in solving complex optimization problems by leveraging the experience of simple optimization problems, we develop a novel multitasking framework whose effectiveness is demonstrated in solving the CTOP. In this framework, auxiliary tasks are created to optimize the local processing overhead and the edge processing overhead of task offloading. On this basis, we propose an effective multitask evolutionary algorithm that includes segmented knowledge transfer and auxiliary task update. Specifically, source and extended decision variables are considered as different knowledge to be utilized, while the auxiliary tasks are allowed to be updated dynamically. Related knowledge that is learned from cheap and simple auxiliary tasks promotes the evolutionary search for CTOP. Experimental results verify the effectiveness of knowledge transfer. Compared to existing multitasking and single-tasking algorithms, the proposed algorithm shows competitive performance in CTOP instances and achieves better comprehensive performance in terms of energy consumption and processing latency.

Ellipsoidal Trajectory Optimization for Minimizing Latency and Data Transmission Energy in UAV-Assisted MEC Using Deep Reinforcement Learning

Due to their flexible deployment and movement capability, unmanned aerial vehicles (UAVs) are being utilized as flying mobile edge computing (MEC) platforms, offering real-time computational resources and low-latency data processing for a wide range of applications. This article aims to explore a UAV-assisted MEC system where multiple UAVs provide MEC services to mobile devices (MDs) using an ellipsoidal trajectory. Depending on the position, size, and orientation of the ellipsoidal trajectories, the coverage area of the UAV, the energy consumption, and the task transmission latency of MDs change. This has rarely been investigated in the existing works. Furthermore, unlike other studies, we consider that each MD has varying task offloading rates, which, together with varying user densities, makes the problem more challenging. Therefore, we formulate an optimization problem that finds the center position, major radius, minor radius, and rotation angle of the ellipsoidal trajectory of UAV-assisted MEC servers, to minimize the total transmission latency and energy consumption of mobile devices while taking into account the required data transmission rate, task transmission time, and energy consumption constraints. Then, we transform this optimization problem into a Markov decision process and propose a deep Q-learning-based ellipsoidal trajectory optimization (DETO) algorithm, to resolve it. The results from our simulations demonstrate that DETO efficiently computes the optimal position and trajectory for each UAV, and can achieve better performance compared to other baselines, leading to the reduced data transmission latency and energy consumption of mobile devices across a range of simulation scenarios.

Explore Deep Reinforcement Learning to Energy-Efficient Data Synchronism in 5G Self-Powered Sensor Networks

Recently, the explosive data traffic growth and large-scale Internet of Things (IoT) equipment connection have brought 5G mobile communication technology many challenges. The communication delay of network services such as augmented reality, smart grid, disaster warning and emergency communication have been relieved by 5G. However, these services are still confronted with the serious challenge in energy conservation. Most of traditional optimization methods usually need complex operations and iterations to get the optimal results, which are not suitable for communication systems with high real-time performance. Based on this argument, this paper has focused on the green communication in Mobile Edge Computing (MEC)-based self-powered sensor network and proposed a novel approach called MIDS (Mobile Intelligent Data Synchronization based on deep reinforcement learning). It exploits Deep Deterministic Policy Gradient (DDPG), continuous interacting with the environment and making tryouts to evaluate the feedback from the environment to optimize future decision-making on the path selection scheme for data transmission as well as achieve the energy efficiency and energy consumption balance of mobile devices with self-powered sensors. We provide experiments to show the capability of our approach in reducing the additional energy consumption of mobile devices and the base station as well as balancing the energy consumption of mobile devices with sensor communication. The superiority of our approach for energy conservation in data synchronization is convincingly demonstrated by comparing with state-of-the-art methods in MEC-based self-powered sensor network.

Quantum Particle Swarm Optimization for Task Offloading in Mobile Edge Computing

Mobile Edge Computing (MEC) deploys servers on the edge of the mobile network to reduce the data transmission delay between servers and mobile devices, and can meet the computing demand of mobile computing tasks. It alleviates the problem of computing power and delay requirements of mobile computing tasks and reduces the energy consumption of mobile devices. However, the MEC server has limited computing and storage resources and mobile network bandwidth, making it impossible to offload all mobile computing tasks to MEC servers for processing. Therefore, MEC needs to reasonably offload and schedule mobile computing tasks, to achieve efficient utilization of server resources. To solve the above problems, in this paper, the task offloading problem is formulated as an optimization problem, and Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO) based task offloading strategies are proposed. Extensive simulation results show that the proposed algorithm can significantly reduce the system energy consumption, task completion time, and running time compared to recent advanced strategies, namely, Ant Colony Optimization (ACO), Multi-Agent Deep Deterministic Policy Gradients (MADDPG), Deep Meta Reinforcement Learning-based Offloading (DRMO), Iterative Proximal Algorithm (IPA), and Parallel Random Forest (PRF).

Dynamic adaptive workload offloading strategy in mobile edge computing networks

With the increasing popularity of mobile devices and explosive growth of task processing requirements, edge computing attracts more attention from researchers nowadays since it can improve the QoS and utilize resources of the cloudlets, including mobile devices and base stations, as much as possible. In a Mobile Edge Computing (MEC) network, the workload offloading problem is quite important since it directly influences the latency of the task processing, and many efficient algorithms have been proposed to deal with it. However, most of the existing algorithms only consider the static or quasi-static scenario, and are not suitable for a system with several fast-moving devices. Since that more and more mobile devices with high speed are involved in a MEC system, a new workload offloading strategy is required to adapt to such a dynamic scenario. In this paper, we sufficient consider the dynamic properties of a MEC system, and proposed a dynamic adaptive workload offloading algorithm based on Lyapunov theories and an FC-LSTM based schedule determining algorithm to balance the workload of different cloudlets and minimize the weighted average energy and time consumption of mobile devices. Theoretical analysis and extensive experimental results show that all the proposed algorithms achieve high performance in terms of energy consumption, convergence and latency.

Research on the optimization of energy consumption for multi-priority tasks in mobile computing offloading

To solve the problem of insufficient energy consumption for multi-priority tasks in the computing and unloading environments of mobile devices, we designed evaluation methods to improve the traditional simulated annealing algorithm and obtain the optimal allocation scheme for these tasks under multiple computing resources. Firstly, we proposed the task model and discussed the classification and definition of task priority as well as the procedure of task processing in detail. Secondly, a calculation model for the energy consumption of mobile devices is provided. Thirdly, an evaluation mechanism is designed to evaluate the unloading distribution scheme of mobile devices effectively. Finally, the improved traditional simulated annealing algorithm was applied to find the optimal distribution scheme for this computing environment. All allocation schemes obtained in this study were compared and analyzed via simulation. The results showed that the proposed algorithm can reduce the energy consumption of mobile devices more significantly, shorten the system response time, and complete high-priority tasks based on time constraints.

Multiobjective Optimization for Adaptive Offloading in Distributed Multiuser MIMO Cell-Free 6G Networks

By offloading some computational tasks to the edge server, edge computing can help relieve the increasing computation burden of mobile users and improve the quality of experience of users. In this paper, we investigate the computation offloading in edge computing-enabled multiuser cell-free (CF) multi-input multi-output (MIMO) networks with multiple building baseband units (BBUs). The user association, transmit covariances, CPU-cycle frequencies, and allocated bandwidths are jointly optimized to minimize two objectives: the energy consumption of mobile devices (MDs) and execution delay of tasks. We formulate it as a vector optimization problem and adopt the scalarization technique to transform it into a scalar mixed integer nonlinear programming (MINLP). Then, to solve the MINLP, we introduce user sorting into the framework of branch and bound and propose a sorted MD association (MDA) method. Two key issues are tackled in the proposed MDA, i.e., the lower bound of the MINLP and the updation of incumbent solution. For the first issue, we present a Lagrangian dual relaxation algorithm based on subgradient projection, while the second one involving another nonconvex minimization problem, we propose a successive convex approximation (SCA) based algorithm to solve it. Finally, extensive simulations demonstrate that the proposed method is able to reduce the system cost significantly and achieve better system performance by comparing with the benchmark schemes.

Broadcast Attention Learning for Real Telephone Speech Keyword Spotting

With the development of mobile smart devices, keyword spotting plays an important role in the interaction between machines and users. However, low storage and low energy consumption of mobile devices limit the accuracies of keyword spotting tasks. Therefore, how to achieve a balance between the high accuracy and low consumption is a research hotspot for a keyword spotting system. Convolutional neural networks have been widely adopted in recent keyword spotting systems due to their superior accuracies, and the success of the transformer architecture in many areas demonstrates the effectiveness of self-attention. In this paper, we combine self-attention and convolutional neural networks, and propose the broadcast attention learning network (BA-net), using a small number of parameters while achieving the accuracies of 97.18% and 78.44% respectively on the Google speech command dataset and a real telephone speech dataset.

Energy-aware JPEG image compression: A multi-objective approach

Customer satisfaction is crucially affected by energy consumption in mobile devices. One of the most energy-consuming parts of an application is images. This paper, first, investigates that there is a correlation between energy consumption and image quality as well as image file size. Therefore, these two can be considered as a proxy for energy consumption. In the next step, we focused on proposing a multi-objective strategy to enhance image quality and reduce image file size based on the quantisation table (QT) in JPEG image compression. To this end, we have used two general multi-objective approaches: scalarisation and Pareto-based. In this paper, we embed our strategy into five scalarisation algorithms, including energy-aware multi-objective genetic algorithm (EnMOGA), energy-aware multi-objective particle swarm optimisation (EnMOPSO), energy-aware multi-objective differential evolution (EnMODE), energy-aware multi-objective evolutionary strategy (EnMOES), and energy-aware multi-objective pattern search (EnMOPS). Also, two Pareto-based methods, including a non-dominated sorting genetic algorithm (NSGA-II) and a reference-point-based NSGA-II (NSGA-III) are used for the embedding scheme, and two Pareto-based algorithms, EnNSGAII and EnNSGAIII, are presented. With our proposed scalarisation method, user’s preferences can be set before starting the optimisation process and the algorithm generates only one solution based on the preference, while our Pareto-based approaches generate a set of solutions so that a user can select one of the preferred solutions after the optimisation process.Experimental studies show that the performance of the baseline algorithm is improved by embedding the proposed strategy into metaheuristic algorithms. In particular, EnMOGA, EnMOPS, and EnNSGA-II can perform competitively, among others. From the results, the baseline algorithm in all cases and in comparison to all algorithms yields the worst results. Among the scalarisation methods, EnMOGA and EnMOPS can achieve the first rank in 6 and 7 out of 13 cases and the second rank in 7 and 5 cases in terms of objective function. Also, EnMOES achieved the fifth or worst rank among the scalarisation algorithms. Regarding the Pareto-based algorithms, the table shows that EnNSGAII outperforms EnNSGAIII in 10 out of 13 cases in terms of hyper-volume measure, while it fails in 3 cases. Furthermore, we statistically verify the proposed algorithm’s effectiveness based on the Wilcoxon-signed rank test. Finally, a sensitivity analysis of the parameters is provided. The source code for reproducing the results is available in: https://github.com/SeyedJalaleddinMousavirad/MultiobjectiveJPEGImageCompression.

Computation Offloading and User-Clustering Game in Multi-Channel Cellular Networks for Mobile Edge Computing.

Mobile devices may use mobile edge computing to improve energy efficiency and responsiveness by offloading computation tasks to edge servers. However, the transmissions of mobile devices may result in interference that decreases the upload rate and prolongs transmission delay. Clustering has been shown as an effective approach to improve the transmission efficiency for dense devices, but there is no distributed algorithm for the optimization of clustering and computation offloading. In this work, we study the optimization problem of computation offloading to minimize the energy consumption of mobile devices in mobile edge computing by adaptively clustering devices to improve the transmission efficiency. To address the optimization problem in a distributed manner, the decision problem of clustering and computation offloading for mobile devices is formulated as a potential game. We introduce the construction of the potential game and show the existence of Nash equilibrium in the game with a finite enhancement ability. Then, we propose a distributed algorithm of clustering and computation offloading based on game theory. We conducted a simulation to evaluate the proposed algorithm. The numerical results from our simulation show that our algorithm can improve offloading efficiency for mobile devices in mobile edge computing by improving transmission efficiency. By offloading more tasks to edge servers, both the energy efficiency of mobile devices and the responsiveness of computation-intensive applications can be improved simultaneously.

Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks

Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices. In this paper, we study a new model of multiple federated learning services at the multi-access edge computing server. Accordingly, the sharing of CPU resources among learning services at each mobile device for the local training process and allocating communication resources among mobile devices for exchanging learning information must be considered. Furthermore, the convergence performance of different learning services depends on the hyper-learning rate parameter that needs to be precisely decided. Towards this end, we propose a joint resource optimization and hyper-learning rate control problem, namely <inline-formula><tex-math notation="LaTeX">${{\sf MS-FEDL}}$</tex-math></inline-formula> , regarding the energy consumption of mobile devices and overall learning time. We design a centralized algorithm based on the block coordinate descent method and a decentralized JP-miADMM algorithm for solving the <inline-formula><tex-math notation="LaTeX">${{\sf MS-FEDL}}$</tex-math></inline-formula> problem. Different from the centralized approach, the decentralized approach requires many iterations to obtain but it allows each learning service to independently manage the local resource and learning process without revealing the learning service information. Our simulation results demonstrate the convergence performance of our proposed algorithms and the superior performance of our proposed algorithms compared to the heuristic strategy.

Privacy-Preserving Task Offloading Strategies in MEC

In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.

Research on Task Scheduling Strategy under Mobile Cloud Computing Based on ICSO

&lt;p&gt;With the gradual application of mobile terminals such as cell phones in production and life, mobile cloud computing has become an important part of the internet. Different from traditional cloud computing task scheduling methods, mobile cloud computing task scheduling needs to consider not only task time minimization but also the lowest possible mobile device energy consumption. We propose an improved chicken swarm optimization (ICSO) algorithm applied to the task scheduling strategy under mobile cloud computing. First, we establish a multiobjective optimization strategy with minimum completion time and minimum energy consumption. Second, for the shortcomings of the chicken swarm optimization algorithm that easily fall into local optimums leading to algorithm stagnation, we use reverse learning initialization for the chicken flock population to expand the space of understanding and an adaptive strategy for learning factors and following coefficients. To illustrate the effectiveness of our algorithm in scheduling, we chose the number of mobile devices as 50, 100, and 150 and compared the improved chicken swarm optimization algorithm, ant colony algorithm, particle swarm algorithm, and chicken swarm optimization algorithm. The results illustrate that our proposed algorithm can reduce the task completion time, control the energy consumption of mobile devices well, and save energy.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

A novel data size‐aware offloading technique for resource provisioning in mobile cloud computing

SummaryMobile device users are involved in social networking, gaming, learning, and even some office work, so the end users expect mobile devices with high‐response computing capacities, storage, and high battery power consumption. The data‐intensive applications, such as text search, online gaming, and face recognition usage, have tremendously increased. With such high complex applications, there are many issues in mobile devices, namely, fast battery draining, limited power, low storage capacity, and increased energy consumption. The novelty of this work is to strike a balance between time and energy consumption of mobile devices while using data‐intensive applications by finding the optimal offloading decisions. This paper proposes a novel efficient Data Size‐Aware Offloading Model (DSAOM) for data‐intensive applications and to predict the appropriate resource provider for dynamic resource allocation in mobile cloud computing. Based on the data size, the tasks are separated and gradually allocated to the appropriate resource providers for execution. The task is placed into the appropriate resource provider by considering the availability services in the fog nodes or the cloud. The tasks are split into smaller portions for execution in the neighbor fog nodes. To execute the task in the remote side, the offloading decision is made by using the min‐cut algorithm by considering the monetary cost of the mobile device. This proposed system achieves low‐latency time 13.2% and low response time 14.1% and minimizes 24% of the energy consumption over the existing model. Finally, according to experimental findings, this framework efficiently lowers energy use and improves performance for data‐intensive demanding application activities, and the task offloading strategy is effective for intensive offloading requests.

Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing

Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.

Multiple Local-Edge-Cloud Collaboration Strategies in Industrial Internet of Things: A Hybrid Genetic-Based Approach

To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading mode of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge computing architecture of an intelligent factory, this paper puts forward five different computation offloading methods. From a global perspective, the energy consumption and delay of tasks offloading in local, edge, cloud center, local-edge collaboration, and local-edge-cloud collaboration are considered. In this paper, the algorithm based on the genetic algorithm and particle swarm optimization is used to design and obtain the decision task flow offloading strategy with the lowest energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices.

Blockchain-based mobile crowdsourcing model with task security and task assignment

Crowdsourcing is a task assignment technology that has emerged in recent years, enabling companies to reduce costs and increase work efficiency. Most of the current crowdsourcing systems (BCSs) built on blockchain focus on solving privacy issues and use rigorous proof protocols for task and solution security. Such systems cannot be directly applied in mobile environments because it would lead to excessive computational overhead for the endpoint. To achieve confidentiality of crowdsourcing tasks during transmission while reducing the energy consumption of mobile devices and optimizing task assignment results, in this paper, we develop a blockchain-based crowdsourcing model. We first build a model structure consisting of basic components, such as users, smart contracts, and blockchain, and design contracts to implement various crowdsourcing operations, such as registering, publishing tasks, and acquiring solutions. Unlike before, the ciphertext of the task and the encrypted secret key are embedded into the contract. This, together with the three operations we designed for users to interact with the smart contracts, can effectively protect the crowdsourcing data from being stolen while using the address anonymity of Ethereum to ensure the privacy of users. During task assignment, we use the DBSCAN algorithm to delineate each valid service area and implement the established multiobjective planning model within each area to achieve as many benefit goals as possible in the best way. Finally, we implement our prototype on the Ethereum test network Ropsten. The experimental results show that the cost overhead of the designed model is acceptable to mobile users, and the security analysis establishes the confidentiality of the designed operations.

A Game-Theoretic Scheme for Parked Vehicle-Assisted MEC Computation Offloading

By offloading computation tasks, multi-access edge computing (MEC) supports diverse services and reduces delay and energy consumption of mobile devices (MDs). However, limited resources of edge servers may be the bottleneck for task computing in high-density scenarios. To address this challenge, by leveraging the underutilized resources of parked vehicles to execute tasks, we propose a parked vehicle-assisted multi-access edge computing (PV-assisted MEC) architecture, which enables MEC servers to expand their capability flexibly. To achieve efficient offloading, we propose a PV-assisted MEC offloading scheme in a multi-MD environment. We design a game-based distributed algorithm to minimize the overhead of MDs and further reduce the burden on the MEC server. Simulation results show that compared with the common MEC system, our scheme can reduce the burden on the MEC server by 5% and the offloading overhead by 17%.

Multiobjective Optimization for Joint Task Offloading, Power Assignment, and Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) is an emerging computational paradigm for providing storage and computing capabilities in network edge, to improve the experience of users, to shorten the delay, and to reduce the energy consumption of mobile devices. In this article, we consider a multiuser and multiserver scenario, where each user has an application composed of multiple independent tasks that need to be executed, and each MEC server is equipped on a base station (BS) for assisting mobile users to execute computation-intensive and time-sensitive tasks. Multiobjective optimization for joint task offloading, power assignment, and resource allocation is studied to maximize the offloading gains of users. A multivariable and multiobjective optimization problem with three objectives is constructed. An efficient multiobjective evolutionary algorithm is developed to solve the problems of minimizing the response time, minimizing the energy consumption, and minimizing the cost. Simulation results verify the effectiveness of our algorithm, and show the method significantly improves the user’s offloading benefits. According to the author’s knowledge, this is the first paper on the exploration of multiobjective optimization of multiuser with multiple tasks and multiserver MEC system, in which the worst user offloading revenue is regarded as the optimization objectives.

Joint task offloading and resource allocation in mobile edge computing with energy harvesting

Mobile edge computing (MEC) is considered to be a promising technique to enhance the computation capability and reduce the energy consumption of smart mobile devices (SMDs) in the sixth-generation (6G) networks. With the huge increase of SMDs, many applications of SMDs can be interrupted due to the limited energy supply. Combining MEC and energy harvesting (EH) can help solve this issue, where computation-intensive tasks can be offloaded to edge servers and the SMDs can also be charged during the offloading. In this work, we aim to minimize the total energy consumption subject to the service latency requirement by jointly optimizing the task offloading ratio and resource allocation (including time switching (TS) factor, uplink transmission power of SMDs, downlink transmission power of eNodeB, computation resources of SMDs and MEC server). Compared with the previous studies, the task uplink transmission time, MEC computation time and the computation results downloading time are all considered in this problem. Since the problem is non-convex, we first reformulate it, and then decompose it into two subproblems, i.e., joint uplink and downlink transmission time optimization subproblem (JUDTT-OP) and joint task offloading ratio and TS factor optimization subproblem (JTORTSF-OP). By solving the two subproblems, a joint task offloading and resource allocation with EH (JTORAEH) algorithm is proposed to solve the considered problem. Simulation results show that compared with other benchmark methods, the proposed JTORAEH algorithm can achieve a better performance in terms of the total energy consumption.