Mobile Computing System Research Articles

Computation Offloading and Resource Allocation for Wireless Powered Mobile Edge Computing With Latency Constraint

In this letter, we consider a multi-user wireless powered mobile edge computing (MEC) system, in which a base station (BS) integrated with an MEC server transfers energy to wireless devices (WDs) as an incentive to encourage them to offload computing tasks to the MEC server. We formulate an optimization problem to contemporaneously maximize the data utility and minimize the energy consumption of the operator under the offloaded delay constraint, by jointly controlling wireless-power allocation at the BS as well as offloaded data size and power allocation at the WDs. To solve this problem, the offloaded delay constraint is first transformed into an offloaded data rate constraint. Then an iterative algorithm is designed to obtain the optimal offloaded data size and power allocation at the WDs by using Lagrangian dual method. The results are applied to derive the optimal wireless-power allocation at the BS. Finally, simulation results show that our algorithm outperforms existing schemes in terms of operator's reward.

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

This paper considers a UAV-enabled mobile edge computing (MEC) system, where a UAV first powers the Internet of things device (IoTD) by utilizing Wireless Power Transfer (WPT) technology. Then each IoTD sends the collected data to the UAV for processing by using the energy harvested from the UAV. In order to improve the energy efficiency of the UAV, we propose a new time division multiple access (TDMA) based workflow model, which allows parallel transmissions and executions in the UAV-assisted system. We aim to minimize the total energy consumption of the UAV by jointly optimizing the IoTDs association, computing resources allocation, UAV hovering time, wireless powering duration and the services sequence of the IoTDs. The formulated problem is a mixed-integer non-convex problem, which is very difficult to solve in general. We transform and relax it into a convex problem and apply flow-shop scheduling techniques to address it. Furthermore, an alternative algorithm is developed to set the initial point closer to the optimal solution. Simulation results show that the total energy consumption of the UAV can be effectively reduced by the proposed scheme compared with the conventional systems.

Joint Deployment and Task Scheduling Optimization for Large-Scale Mobile Users in Multi-UAV-Enabled Mobile Edge Computing.

This article establishes a new multiunmanned aerial vehicle (multi-UAV)-enabled mobile edge computing (MEC) system, where a number of unmanned aerial vehicles (UAVs) are deployed as flying edge clouds for large-scale mobile users. In this system, we need to optimize the deployment of UAVs, by considering their number and locations. At the same time, to provide good services for all mobile users, it is necessary to optimize task scheduling. Specifically, for each mobile user, we need to determine whether its task is executed locally or on a UAV (i.e., offloading decision), and how many resources should be allocated (i.e., resource allocation). This article presents a two-layer optimization method for jointly optimizing the deployment of UAVs and task scheduling, with the aim of minimizing system energy consumption. By analyzing this system, we obtain the following property: the number of UAVs should be as small as possible under the condition that all tasks can be completed. Based on this property, in the upper layer, we propose a differential evolution algorithm with an elimination operator to optimize the deployment of UAVs, in which each individual represents a UAV's location and the entire population represents an entire deployment of UAVs. During the evolution, we first determine the maximum number of UAVs. Subsequently, the elimination operator gradually reduces the number of UAVs until at least one task cannot be executed under delay constraints. This process achieves an adaptive adjustment of the number of UAVs. In the lower layer, based on the given deployment of UAVs, we transform the task scheduling into a 0-1 integer programming problem. Due to the large-scale characteristic of this 0-1 integer programming problem, we propose an efficient greedy algorithm to obtain the near-optimal solution with much less time. The effectiveness of the proposed two-layer optimization method and the established multi-UAV-enabled MEC system is demonstrated on ten instances with up to 1000 mobile users.

Caching mechanism for mobile edge computing in V2I networks

AbstractThe caching‐enabled mobile edge computing (MEC) system becomes increasingly crucial to the future long‐term evolution vehicle‐to‐infrastructure (LTE‐V2I) network, especially for fast delivery of popular content of delay‐sensitive applications in the backhaul capacity–limited vehicular networks. Most related studies mainly focus on computation offloading strategy design and coding caching, while the deployment strategy design of caching capacities for high‐speed vehicles has been overlooked. In this paper, we investigate the deployment problem in LTE‐V2I networks with particular consideration on high‐speed vehicles. First, in a one‐way scenario, a joint optimization framework is formulated to minimize the cache size of the MEC system, meanwhile maximizing the average downloading percentage, where the MEC system's backhaul capacities, vehicle speeds, content popularity distributions, and sides of the highway are taken into account. The impacts of LTE‐V2I system parameters, eg, vehicle speed, on the objective function are revealed through the one‐way optimization. Then, the two‐way optimization is formulated and solved by developing an iterative optimization approach based on the aforementioned one‐way scenario. Particularly, the optimal caching allocation between two ways is derived. Simulation results validate the effectiveness of the proposed approaches even with low backhaul capacities and high vehicle speed.

Offloading and system resource allocation optimization in TDMA based wireless powered mobile edge computing

• A WP-MEC system with N WDs and one HAP is considered, where WDs and HAP are single antenna devices. • The goal is to maximize the weighted sum computation rate by joint optimization of system resources management and task computing time allocation. • An alternating direction multiplier method (ADMM) based distributed optimization method is proposed. • Experimental results show that the proposed method greatly increases the weighted sum computation rate while keeping the energy consumption at a low level. In this paper, a wireless powered mobile edge computing (WP-MEC) system is considered, in which a hybrid access point integrated with MEC servers can charge N wireless devices (WDs) by broadcasting radio-frequency signals, and the time division multiple access (TDMA) protocol is used for task offloading of WDs. The goal of this paper is to maximize the weighted sum computation rate by joint optimization of system resources management and task computing time allocation. To solve this optimization problem, an alternating direction multiplier method (ADMM) based distributed optimization method is proposed. The proposed method can decompose the optimization problem into N sub-problems, which are solved by N WDs. Experimental results show that the proposed method outperforms the benchmarks and greatly increases the weighted sum computation rate while keeping the energy consumption at a low level under the premise of time complexity O ( N ).

Optimal Resource Allocation for Scalable Mobile Edge Computing

In this letter, we would like to investigate the optimal tradeoff between user experience and consumptions of communications and computation resources in designing mobile edge computing (MEC) systems. First, we propose a novel computation task model, where each computation task is scalable with different service levels, corresponding to different user experiences (i.e., utilities) and consumptions of communications and computation resources. Then, we formulate the system utility maximization problem to optimize the service level selection and transmission resource allocation under the transmission power constraints, transmission energy constraints, and deadline constraint. The problem is a challenging mixed discrete-continuous optimization problem. We develop an algorithm to obtain an optimal solution by exploiting optimality properties. We also propose a low-complexity algorithm to obtain a suboptimal solution by the difference-of-convex (DC) programming. This letter provides an effective model for trading off user experience against the consumptions of communications and computation resources in designing efficient MEC systems.

Joint Computation and Communication Cooperation for Energy-Efficient Mobile Edge Computing

This paper proposes a novel user cooperation approach in both computation and communication for mobile edge computing (MEC) systems to improve the energy efficiency for latency-constrained computation. We consider a basic three-node MEC system consisting of a user node, a helper node, and an access point (AP) node attached with an MEC server, in which the user has latency-constrained and computation-intensive tasks to be executed. We consider two different computation offloading models, namely, the partial and binary offloading, respectively. For partial offloading, the tasks at the user are divided into three parts that are executed at the user, helper, and AP, respectively; while for binary offloading, the tasks are executed as a whole only at one of three nodes. Under this setup, we focus on a particular time block and develop an efficient four-slot transmission protocol to enable the joint computation and communication cooperation . Besides the local task computing over the whole block, the user can offload some computation tasks to the helper in the first slot, and the helper cooperatively computes these tasks in the remaining time; while in the second and third slots, the helper works as a cooperative relay to help the user offload some other tasks to the AP for remote execution in the fourth slot. For both cases with partial and binary offloading, we jointly optimize the computation and communication resources allocation at both the user and the helper (i.e., the time and transmit power allocations for offloading, and the central process unit frequencies for computing), so as to minimize their total energy consumption while satisfying the user’s computation latency constraint. Although the two problems are nonconvex in general, we develop efficient algorithms to solve them optimally. Numerical results show that the proposed joint computation and communication cooperation approach significantly improves the computation capacity and energy efficiency at the user and helper, as compared to other benchmark schemes without such a joint design.

ERMO2 algorithm: an energy efficient mobility management in mobile cloud computing system for 5G heterogeneous networks

Recently, mobile devices are becoming the primary platforms for every user who always roam around and access the cloud computing applications. Mobile cloud computing (MCC) combines the both mobile and cloud computing, which provides optimal services to the mobile users. In next-generation mobile environments, mainly due to the huge number of mobile users in conjunction with the small cell size and their portable information‟s, the influence of mobility on the network performance is strengthened. In this paper, we propose an energy efficient mobility management in mobile cloud computing (E2M2MC2) system for 5G heterogeneous networks. The proposed E2M2MC2 system use elective repeat multi-objective optimization (ERMO2) algorithm to determine the best clouds based on the selection metrics are delay, jitter, bit error rate (BER), packet loss, communication cost, response time, and network load. ERMO2 algorithm provides energy efficient management of user mobility as well as network resources. The simulation results shows that the proposed E2M2MC2 system helps in minimizing delay, packet loss rate and energy consumption in a heterogeneous network.

Stochastic computation resource allocation for mobile edge computing powered by wireless energy transfer

Limited battery capacity and poor computing capability of wireless devices have been longstanding performance limitations in the Internet of Things (IoT) era. Employing Wireless Energy Transfer (WET) technology, wireless devices of the Mobile Edge Computing (MEC) systems can be released from these limitations and achieve a better quality of experience (QoE). This paper considered a wireless powered mobile edge computing (WP-MEC) system with one mobile device, where a double antenna hybrid access point (HAP) (integrated with a MEC server) transmits wireless energy to the device and communicates with the wireless terminal to assist in its data processing. We investigated the average computation rate maximization problem in this system and proposed an online service rate maximization (OSRM) algorithm to tackle this problem. In each time slot, the proposed algorithm optimally decides the time allocation policy and the CPU-frequency for the mobile device. Simulation results show that the proposed algorithm can balance the time average computation rate and the task buffer queue length, and outperforms the benchmark schemes.

A Dynamic Pricing Strategy for Vehicle Assisted Mobile Edge Computing Systems

The idle computing resources of parked vehicles could be utilized to improve performance by assisting task executions in mobile edge computing (MEC) systems. As a result, the owner of a vehicle could be compensated, resulting in a win-win situation. A dynamic pricing strategy is proposed to minimize the average cost of the MEC system under the constraints on quality of service by adjusting the price constantly based on the current system state. To do so, a cost minimization problem is solved to obtain the optimal dynamic pricing strategy efficiently. Finally, the optimization results are validated with extensive simulations.

Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems

This paper studies a multiuser mobile edge computing (MEC) system in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancelation (SIC) to efficiently decode all users’ offloaded tasks for remote execution. In particular, we pursue energy-efficient MEC designs by considering two cases with partial and binary offloading, respectively. We aim to minimize the weighted sum-energy consumption at all users subject to their computation latency constraints, by jointly optimizing the communication and computation resource allocation as well as the BS’s decoding order for SIC. For the case with partial offloading, the weighted sum-energy minimization is a convex optimization problem, for which an efficient algorithm based on the Lagrange duality method is presented to obtain the globally optimal solution. For the case with binary offloading, the weighted sum-energy minimization corresponds to a mixed Boolean convex optimization problem that is generally more difficult to be solved. We first use the branch-and-bound (BnB) method to obtain the globally optimal solution and then develop two low-complexity algorithms based on the greedy method and the convex relaxation, respectively, to find suboptimal solutions with high quality in practice. Via numerical results, it is shown that the proposed NOMA-based computation offloading design significantly improves the energy efficiency of the multiuser MEC system as compared to other benchmark schemes. It is also shown that for the case with binary offloading, the proposed greedy method performs close to the optimal BnB-based solution, and the convex relaxation-based solution achieves a suboptimal performance but with lower implementation complexity.

Understanding Effects of Collaborations in Developing Mobile Computing Systems: Popularity, Efficiency, and Quality

The development of mobile computing systems is an inherently collaborative activity, and the nature of the collaboration likely affects systems success. Collaboration can lead to faster development of systems and increased creativity, but it may also adversely affect the quality of systems. As one of the most popular and active Web services for developing open source software, Github has been a place of origin of many novel mobile computing systems with the Internet of Things, such as OpenEdge and CloudSim. Meanwhile, Github provides a place for people to collaborate with each other on developing novel mobile service computing systems. In this paper, we explore the effects of collaboration in developing systems hosted by Github and use a model of collaboration to detect bugs of systems aiming to enhance their quality. We conduct an at-scale study of more than five million projects on GitHub to reveal collaboration’s effects on the popularity of software systems and speed of development. We find that having more collaborators results in higher popularity but slower development. Projects that use Objective-C are at an even more significant risk for lower development speeds than other projects. We define collaboration networks and co-commit networks, which capture the projects’ collaboration patterns and use structural features of these networks to identify files within commits with bugs. By examining 18 large popular projects, we find that involving these features statistically significantly improves bug detection by 6.7% on average.

Resource Allocation for a UAV-Enabled Mobile-Edge Computing System: Computation Efficiency Maximization

Energy-efficient computation is important in mobile edge computing (MEC) systems. However, the computation efficiency problem in the unmanned aerial vehicle (UAV)-enabled MEC systems has been rarely researched. In this paper, a UAV-enabled MEC system under partial computation offloading mode is investigated. The computation efficiency is maximized by jointly optimizing the offloading times, the central processing unit frequencies, the transmit powers of the user and the trajectory of the UAV. For the non-convex computation efficiency problem, a two-stage iterative algorithm is proposed. Moreover, we derive the closed-form expressions for the local computation frequency and the transmit power of the user, which facilitates our algorithm implementation. Simulation results demonstrate that the computation efficiency of our proposed joint optimization scheme is better than those of other benchmark schemes.

Energy-Efficient Joint Resource Allocation Algorithms for MEC-Enabled Emotional Computing in Urban Communities

This paper considers a mobile edge computing (MEC) system, where the MEC server first collects data from emotion sensors and then computes the emotion of each user. We give the formula of the emotional prediction accuracy. In order to improve the energy efficiency of the system, we propose resources allocation algorithms. We aim to minimize the total energy consumption of the MEC server and sensors by jointly optimizing the computing resources allocation and the data transmitting time. The formulated problem is a non-convex problem, which is very difficult to solve in general. However, we transform it into convex problems and apply convex optimization techniques to address it. The optimal solution is given in closed form. Simulation results show that the total energy consumption of our system can be effectively reduced by the proposed scheme compared with the benchmark.

Fundamental concepts of reactive control for autonomous drones

Autonomous drones represent a new breed of mobile computing system. Compared to smartphones and connected cars that only opportunistically sense or communicate, drones allow motion control to become part of the application logic. The efficiency of their movements is largely dictated by the low-level control enabling their autonomous operation based on high-level inputs. Existing implementations of such low-level control operate in a time-triggered fashion. In contrast, we conceive a notion of reactive control that allows drones to execute the low-level control logic only upon recognizing the need to , based on the influence of the environment onto the drone operation. As a result, reactive control can dynamically adapt the control rate. This brings fundamental benefits, including more accurate motion control, extended lifetime, and better quality of service in end-user applications. Based on 260+ hours of real-world experiments using three aerial drones, three different control logic, and three hardware platforms, we demonstrate, for example, up to 41% improvements in motion accuracy and up to 22% improvements in flight time.

Wireless Powered Cooperation-Assisted Mobile Edge Computing

This paper studies a mobile edge computing (MEC) system in which two mobile devices are energized by the wireless power transfer (WPT) from an access point (AP) and they can offload part or all of their computation-intensive latency-critical tasks to the AP connected with an MEC server or an edge cloud. This harvest-then-offload protocol operates in an optimized time-division manner. To overcome the doubly near-far effect for the farther mobile device, cooperative communications in the form of relaying via the nearer mobile device is considered for offloading. Our aim is to minimize the AP’s total transmit energy subject to the constraints of the computational tasks. We illustrate that the optimization is equivalent to a min–max problem, which can be optimally solved by a two-phase method. The first phase obtains the optimal offloading decisions by solving a sum-energy-saving maximization problem for given an energy transmit power. In the second phase, the optimal minimum energy transmit power is obtained by a bisection search method. Numerical results demonstrate that the optimized MEC system utilizing cooperation has significant performance improvement over systems without cooperation.

Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury.

Effective neurological rehabilitation requires long term assessment and treatment. The rapid progress of virtual reality-based assistive technologies and tele-rehabilitation has increased the potential for self-rehabilitation of various neurological injuries under clinical supervision. The objective of this study was to develop a fuzzy inference mechanism for a smart mobile computing system designed to support in-home rehabilitation of patients with neurological injury in the hand by providing an objective means of self-assessment. A commercially available tablet computer equipped with a Bluetooth motion sensor was integrated in a splint to obtain a smart assistive device for collecting hand motion data, including writing performance and the corresponding grasp force. A virtual reality game was also embedded in the smart splint to support hand rehabilitation. Quantitative data obtained during the rehabilitation process were modeled by fuzzy logic. Finally, the improvement in hand function was quantified with a fuzzy rule database of expert opinion and experience. Experiments in chronic stroke patients showed that the proposed system is applicable for supporting in-home hand rehabilitation. The proposed virtual reality system can be customized for specific therapeutic purposes. Commercial development of the system could immediately provide stroke patients with an effective in-home rehabilitation therapy for improving hand problems.

Editorial

This first issue of 2018 is a significant expansion of the number of pages for our journal to continue to reduce the backlog of papers published in the IEEE Xplore as advance posting, but not yet physically included in an issue. This is an extraordinary effort of the IEEE Systems Council to better serve our scientific, technical, and professional community. The first part contains three guest editorials for special issues that appeared in the fourth issue of 2017. By mistake, they were not included with the respective papers. The first guest editorial is the one for the special issue on "Traffic Forensics: Systems, Tools, and Experimentations,” whose Guest Editors are Y.-D. Lin, Y.-C. Lai, G. Kesidis, and A. V. Vasilakos. The second one is for the special issue on "Visual Signal Applications Over Networks," whose GuestEditors are H.-C. Chao, L. Zhou, C. Xu, and P. Lorenz. The third guest editorial is the one for the special issue on "Multimedia Services Provision Over Future Mobile Computing Systems," whose Guest Editors are G. Mastorakis, E. Pallis, C.X. Mavromoustakis, L. Shu, and J.J.P.C. Rodrigues. The second part consists of the special issue on "Communications Technologies and Infrastructures for Smart systems," whose Guest Editors are S. Ullah, W. Pedrycz, G.K. Karagiannidis, H.-C. Chao, A. Gacek, and C. Verikoukis. The third part collects papers on various aspects of complex systems, systems of systems, communications, power, and energy. Papers address various topics encompassing theory, technologies, design methodologies, and applications.

Guest Editorial Special Issue on Multimedia Services Provision Over Future Mobile Computing Systems

Guest Editorial Special Issue on Multimedia Services Provision Over Future Mobile Computing Systems

RETRACTED ARTICLE: An automated and intelligent Parkinson disease monitoring system using wearable computing and cloud technology

This paper exhibits the outline and advancement of a pervasive remote monitoring system for the Parkinson’s disease (PD) patients. The proposed system gathers various PD related information such as voice samples, gait information etc. and would empower in-home monitoring of early PD symptoms. We accomplished this objective by utilizing various wearable sensors technology, mobile computing system, Internet, cloud computing technologies. Such an incorporated framework guarantees the compelling and effective utilization of data gathered for evaluating early PD symptom’s as well as identifies critical PD severity levels. In particular, the proposed system can evaluate PD patients’ voice disorders or Dysphonia and thus enables doctors to detect patient’s PD symptoms or severity levels. Trial comes about demonstrate that our proposed system achieves very high accuracy for detecting PD symptoms as compared to existing approaches.