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Sixth generation will exploit satellite, aerial, and terrestrial platforms jointly to improve radio access capability and unlock the support of on‐demand edge cloud services in three‐dimensional (3D) space, by incorporating mobile edge computing (MEC) functionalities on aerial platforms and low‐orbit satellites. This will extend the MEC support to devices and network elements in the sky and forge a space‐borne MEC, enabling intelligent, personalized, and distributed on‐demand services. End users will experience the impression of being surrounded by a distributed computer, fulfilling their requests with apparently zero latency. In this paper, we consider an architecture that provides communication, computation, and caching (C3) services on demand, anytime, and everywhere in 3D space, integrating conventional ground (terrestrial) base stations and flying (non‐terrestrial) nodes. Given the complexity of the overall network, the C3 resources and management of aerial devices need to be jointly orchestrated via artificial intelligence‐based algorithms, exploiting virtualized network functions dynamically deployed in a distributed manner across terrestrial and non‐terrestrial nodes. [ABSTRACT FROM AUTHOR]

In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original nonconvex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Enhanced Whale Optimization Algorithm (EWA) to optimize dependent task offloading in a multi-edge cloud computing environment. Our proposed algorithm aims to identify the most suitable offloading scenario for dependent tasks, focusing on minimizing total processing latency, energy consumption, and associated costs. We operate within a system comprising many decentralized Mobile Edge Computing servers (MECs) and a centralized cloud server. Two novel improvement operations, namely Frame Shifting (FS) and Load Redistribution Strategy (LRS), are introduced to enhance the performance of the whale algorithm. Through simulation, our results demonstrate the superior performance of EWA. Specifically, compared to the Whale Optimization Algorithm (WOA), EWA achieves a remarkable reduction in latency by 22.84%, a substantial decrease in energy consumption by 78.28%, and a notable reduction in cost usage by 61.47%. These outcomes underscore the efficacy and practical significance of the proposed EWA in addressing the challenges posed by dependent task offloading in the multi-edge cloud computing landscape. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study that examines the fundamental concept of the non-orthogonal multiple access (NOMA) scheme and provides its detailed comparison with the orthogonal multiple access (OMA) technique. Furthermore, the paper explores the application of the generalized singular value decomposition (GSVD) method in conjunction with NOMA, accompanied by a detailed review of GSVD-based NOMA systems. This study also introduces the concept of mobile edge computing (MEC) and extensively discusses its key parameters. Furthermore, a comprehensive analysis of NOMA MEC is presented, shedding light on its potential advantages and challenges. The aims of this study are to provide a comprehensive understanding of the aforementioned topics and contribute to the advancement of MIMO-NOMA systems. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of efficient resource allocation in a Mobile Edge Computing (MEC) system, taking into account the trade-off between energy consumption and operation latency. The increasing deployment of connected devices and data-intensive services in the Internet of Things (IoT) poses significant challenges in terms of managing computational resources. In this study, we propose a MEC system model that considers energy constraints and the need to minimize latency to ensure optimal performance. We formulate the resource allocation problem in terms of a trade-off between energy consumption and latency, and explore solutions based on heuristic task offloading techniques. Our experiments demonstrate that our approach achieves improved latency performance while reducing energy consumption. We also evaluate the impact of various parameters, such as workload and resource availability, on the energy-latency trade-off. [ABSTRACT FROM AUTHOR]

In this day and age, the Industrial Internet of Things (IIoT) has been considered to revolutionize industrial manufacturing by capturing and accessing massive data sources with incredible speed and efficiency than before. Combined with it, Mobile Edge Computing (MEC) is a comprehensive Digital Transformation tendency to solve the problems Cloud computing faces. However, the fundamental challenges of energy and latency make deploying IIoT MEC networks difficult. Accordingly, this paper considers the efficient design of time allocation for successful computation probability (SCP) maximization for multiple energy-constrained mobile devices (MD) and multiple antennas access point (AP) in uplink radio frequency energy harvesting (RF EH) non-orthogonal multiple access (NOMA) IIoT network. Specifically, multiple MDs need to receive the energy and compute support of a MEC server placed in a multiple antenna wireless AP to complete the task immediately. Accordingly, a four-phase communication protocol is proposed to ensure system performance. The system follows the cluster head (CH) scheme based on the channel state information (CSI) to harvest RF energy from the AP. To ensure the highest system performance, we propose two algorithms for determining the optimal EH time for two CHs: SCPM-GSS and SCPM-GA. In addition, we derive the closed-form expressions for the SCP of the system and each CH. Monte Carlo simulations are used to verify the results of the analysis. The numerical results demonstrate the effects of crucial system parameters of our proposed NOMA scheme with those of conventional orthogonal multiple access (OMA) schemes. Furthermore, the proposed optimization algorithms allow the system to avoid outages like the random parameters setting approach and improve the SCP by 3 to 30% compared to the fixed parameters set when the transmit power is low and medium. [ABSTRACT FROM AUTHOR]

• A taxonomy discussing application execution strategies in fog computing. • A comprehensive discussion about components of application execution in fog computing. • Critical analysis providing in detail information about key aspects of application execution strategies. • Identifying research gaps and highlight new research directions which needs to be addressed in the future. With tremendous advancements in smart phone industry and IoT devices, edge computing has emerged to provide computational services at edge of the network. As a result, applications can be executed in a distributive manner to meet the latency requirements of applications. Despite these advancements, these devices are still considered to have limited energy, memory and computational resources compared to stationary standalone devices. Mobile edge computing extends mobile cloud computing close to end-users, decreasing latency and bandwidth utilisation by offloading tasks to edge servers. Fog computing places nodes between cloud and edge servers. A challenging approach in these paradigms is establishing offloading decision to edge/fog server, and distributed application execution at run time for heterogeneous devices. This paper reviews execution of resource-intensive tasks on mobile devices in distributed frameworks. The objective is to identify research gaps and highlight new research directions that can be pursued in future. Application execution involves application partitioning, task allocation, task execution and task resumption. In this paper, we propose a separate taxonomy for each step in application execution, reviews state-of-the-art techniques, and analyses critical aspects of application execution in a distributed manner. It further investigates similarities and differences among these techniques based on each step of application execution. [ABSTRACT FROM AUTHOR]

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption. [ABSTRACT FROM AUTHOR]

Mobile edge computing can use the wireless access network to provide the services required by telecom users and cloud computing functions nearby, thereby creating a service environment with high performance, low latency and high bandwidth. Computational task offloading is a core issue in mobile edge computing. In order to improve the security when computing tasks are partially offloaded in non-orthogonal multiple access-based mobile edge computing systems, this paper studies the physical layer security of MEC networks considering the presence of eavesdroppers. We employ the secrecy outage probability to measure the secrecy performance of computation offloading. Under the premise of comprehensively considering the joint constraints of transmit power, local task calculation and confidentiality outage probability, we introduce the energy consumption weight factor to balance the transmission energy consumption and calculation energy consumption, and finally achieve the minimum weighted sum of system energy consumption. On the premise of satisfying two user priorities, in order to reduce system overhead, we design a joint task offloading and resource allocation mechanism. The mechanism uses binary search-based iterative optimization algorithm to seek the optimal solution after problem transformation, and obtain optimal task offloading and power allocation. A large number of simulation results show that the algorithm proposed in this paper can effectively reduce system energy consumption. [ABSTRACT FROM AUTHOR]

New and innovative wearable IoT devices for health monitoring systems (HMS) have been invented one after another. However, most of these devices are resource-constrained with restricted energy and computation power. The HMS data need to be processed via mobile edge computing (MEC) to improve the response time to fulfill the latency-sensitive and computation-intensive applications and to reduce bandwidth consumption. This paper presents an efficient task scheduling and resource allocation mechanism in MEC to meet these demands in contemplating emergency conditions under HMS. We propose a priority-based task-scheduling and resource-allocation (PTS-RA) mechanism that can assign different priorities to different tasks by considering the tasks' emergency levels computed with respect to the data aggregated from a patient's smart wearable devices. The mechanism can optimally determine whether a task should be processed locally at the hospital workstations (HW) or in the cloud. This is aimed to reduce the total task processing time and the bandwidth cost as much as possible. The proposed approach is to ensure that tasks related to the emergency are given higher priorities and to run first. After the tasks' computations, results are sent to the doctor to response promptly with quick decisions. The proposed PTS-RA was benchmarked against state-of-the-art algorithms concerning average latency, task scheduling efficiency, task execution time, network usage, CPU utilization, and energy consumption. The benchmarking results are promising as PTS-RA is capable to manage the emergency conditions and is meeting the latency-sensitive tasks' requirements with reduced bandwidth cost. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-unmanned aerial vehicle (UAV)-enabled autonomous mobile edge computing (MEC) system, in which several UAVs are deployed to provide services to user devices (UDs). The aim is to reduce/minimize the overall energy consumption of the autonomous system via designing the optimal trajectories of multiple UAVs. The problem is very complicated to be solved by traditional methods, as one has to take into account the deployment updation of stop points (SPs), the association of SPs with UDs and UAVs, and the optimal trajectories designing of UAVs. To tackle this problem, we propose a variable-length trajectory planning algorithm (VLTPA) consisting of three phases. In the first phase, the deployment of SPs is updated via presenting a genetic algorithm (GA) having variable-length individuals. Accordingly, the association between UDs and SPs is addressed by using a close rule. Finally, a multi-chrome GA is proposed to jointly handle the association of SPs with UAVs and their order for UAVs. The proposed VLTPA is tested via performing extensive experiments on eight instances ranging from 60 to 200 UDs, which reveal that the proposed VLTPA outperforms other compared state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Mobile edge processing is a cutting-edge technique that addresses the limitations of mobile devices by enabling users to offload computational tasks to edge servers, rather than relying on distant cloud servers. This approach significantly reduces the latency associated with cloud processing, thereby enhancing the quality of service. In this paper, we propose a system in which a cellular network, comprising multiple users, interacts with both cloud and edge servers to process service requests. The system assumes non-orthogonal multiple access (NOMA) for user access to the radio spectrum. We model the interactions between users and servers using queuing theory, aiming to minimize the total energy consumption of users, service delivery time, and overall network operation costs. The problem is mathematically formulated as a multi-objective, bounded non-convex optimization problem. The Structural Correspondence Analysis (SCA) method is employed to obtain the global optimal solution. Simulation results demonstrate that the proposed model reduces energy consumption, delay, and network costs by approximately 50%, under the given assumptions. [ABSTRACT FROM AUTHOR]

With the development of communication and computation technologies, mobile edge computing (MEC) has become an emerging technology being deployed in 5G/B5G mobile networks to improve the mobile user experience. In this paper, we study the asymptotic performance of parallel computing in MEC networks with massive multi-input and multi-output (MIMO) in the millimeter wave band. First, a massive MIMO enabled MEC network with a parallel-limited computing model is constructed, and the task completed probability is defined as a new network performance metric. In the proposed model, we derive the task offloading and computing probabilities via the probability generation functional of the Poisson point process, from which, the task completed probability is obtained. Then we propose a computing resource allocation optimization algorithm to maximize the task completed probability within a given delay constraint. The numerical simulation results prove the accuracy of the obtained theoretical results and verify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Chiming in at the advent of 6 G, communication security is increasingly taken into account. • Use efficient iterative method to optimize the variables that affect the cost. • In order to cut system costs down, we propose an interference power price control algorithm based on simulated annealing algorithm, which prioritizes ground users based on this mechanism. • We have innovatively utilized idle roadside units by collaborating with ground users to interfere with eavesdroppers eavesdropping on data, thereby improving the security of the data transmission link. • Ensure that safety performance is maximized in the case of the highest group interest. For the past few years, the security of data transmission links has been increasingly valued. However, with the increasing requirements for the security performance of data transmission links, how to cut the cost of upgrading while improving security has become a serious challenge. In response to the issue of ensuring low-cost and secure data transmission in wireless communication environments with eavesdroppers, this article innovatively starts with idle roadside units and studies how to incentivize roadside units to transmit interference power to interfere with eavesdroppers, thereby efficiently and economically improving the security performance of mobile user data transmission. Firstly, the impact of roadside unit transmission interference power on the security performance of data transmission links was analyzed by establishing a theoretical model; Then, this paper proposes a three-layer model consisting of ground user groups, eavesdropping users, roadside units, and UAVs equipped with mobile edge computing servers. In this model, by encouraging idle roadside units to send interference power, ground users are assisted to safely unload their own data to mobile edge computing servers. Meanwhile, considering the selfishness of idle roadside units, an improved algorithm based on simulated annealing algorithm is proposed to achieve the optimal mapping between ground users and idle roadside units. Finally, through the analysis of simulation results, this scheme can achieve the goal of reducing system costs while improving the security of data transmission. [ABSTRACT FROM AUTHOR]

The appearance of Edge Computing with the possibility to bring powerful computation servers near the mobile device is a major stepping stone towards better user experience and resource consumption optimization. Due to the Internet of Things invasion that led to the constant demand for communication and computation resources, many issues were imposed in order to deliver a seamless service within an optimized cost of time and energy, since most of the applications nowadays require real response time and rely on a limited battery resource. Therefore, Mobile Edge Computing is the new reliable paradigm in terms of communication and computation consumption by the mobile devices. Mobile Edge Computing rely on computation offloading to surpass cloud-based technologies issues and break the limitations of mobile devices such as computing, storage and battery resources. However, computation offloading is not always the optimal choice to adopt, which makes the offloading decision a crucial part in which many parameters should be taken in consideration such as delegating the heavy tasks to the appropriate machine within the network by migrating the high-resource node to an edge server and lend these capabilities to the low-resources one. In this paper, we use an Edge Computing simulator to see how network delay can impact the delivery of a certain result, we also experiment computation offloading using a two-tier with Edge Orchestration architecture, which turns out to be efficient in terms of processing time. [ABSTRACT FROM AUTHOR]

This paper focuses on a mobile edge‐computing‐enabled heterogeneous network. A battery level‐aware task‐scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery‐powered mobile devices. The formulated optimization problem is a typical mixed‐integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)‐hard problem, a decomposition‐based task‐scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed‐form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power‐allocation subproblem is a unimodal problem; this subproblem is solved using a gradient‐based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level‐aware scheme on the operating hours of battery‐powered mobile devices is also investigated. [ABSTRACT FROM AUTHOR]

In Mobile Edge Computing (MEC), attackers can speculate and mine sensitive user information by eavesdropping wireless channel status and offloading usage pattern, leading to user privacy leakage. To solve this problem, this paper proposes a Privacy-preserving and Energy-efficient Offloading Algorithm (PEOA) based on Lyapunov optimization. In this method, a continuous Markov process offloading model with a buffer queue strategy is built first. Then the amount of privacy of offloading usage pattern in wireless channel is defined. Finally, by introducing the Lyapunov optimization, the problem of minimum average energy consumption in continuous state transition process with privacy constraints in the infinite time domain is transformed into the minimum value problem of each timeslot, which reduces the complexity of algorithms and helps obtain the optimal solution while maintaining low energy consumption. The experimental results show that, compared with other methods, PEOA can maintain the amount of privacy accumulation in the system near zero, while sustaining low average energy consumption costs. This makes it difficult for attackers to infer sensitive user information through offloading usage patterns, thus effectively protecting user privacy and safety. [ABSTRACT FROM AUTHOR]

The huge prospects of the internet of things (IoT) have led to an ever-growing demand for computing power by IoT users to enable various applications. Multi-access edge computing (MEC) research and development has rapidly gained attention during the last decade. The ability to deploy edge servers at different points across a content delivery network that can offer communication and computing services close to mobile user devices is one of the main factors driving the evolution of MEC. Furthermore, MEC has been considered a potentially transformational approach for fifth-generation (5 G) and beyond 5 G (B5G) networks, as well as a potential improvement to conventional cloud computing. Unmanned aerial vehicles (UAVs) can be used as effective aerial platforms to offer reliable and ubiquitous connections in wireless communication networks due to their distinctive qualities, such as high cruising altitude, on-demand deployment, and three-dimensional (3D) maneuverability. The number of research studies published in this area has dramatically increased due to the growing interest in UAV-enabled MEC. Although UAV-enabled MEC systems have been well studied, the existing models are becoming increasingly heterogeneous and scattered without harmony. This paper provides a comprehensive analysis of the literature on UAV-enabled MEC systems with a special focus on the system modeling, and optimization techniques for five identified domains, such as energy efficiency, resource allocation, trajectory control, latency, and security. For each domain, we have highlighted the recent advances, critical findings, and the advantages and disadvantages. Additionally, the identified proposed techniques were analyzed and discussed, with emphasize on the constraints and performance metrics. We also discuss a general system model for each highlighted domain. Moreover, the lessons are also derived from the study on system optimization and system modeling techniques identified in this paper. Then we discuss open issues related to UAV-enabled MEC systems in each highlighted domain, including problem formulation and optimization techniques. Finally, this paper lay out directions for future research to solve the aforementioned problems associated with UAV-enabled MEC systems. [ABSTRACT FROM AUTHOR]

As a promising technique to support tremendous numbers of Internet of Things devices and a variety of applications efficiently, mobile edge computing (MEC) has attracted extensive studies recently. In this paper, we consider a MEC-assisted peer-to-peer (P2P) mobile communication system where MEC servers are deployed at access points to accelerate the communication process between mobile terminals. To capture the tradeoff between the time delay and the energy consumption of the system, a cost function is introduced to facilitate the optimization of the computation and communication resources. The formulated optimization problem is non-convex and is tackled by an iterative block coordinate descent algorithm that decouples the original optimization problem into two subproblems and alternately optimizes the computation and communication resources. Moreover, the MEC-assisted P2P communication system is compared with the conventional P2P communication system, then a condition is provided in closed-form expression when the MEC-assisted P2P communication system performs better. Simulation results show that the advantage of this system is enhanced when the computing capability of the receiver increases whereas it is reduced when the computing capability of the transmitter increases. In addition, the performance of this system is significantly improved when the signal-to-noise ratio of hop-1exceeds that of hop-2. [ABSTRACT FROM AUTHOR]

Mobile technologies is evolving so rapidly in every aspect, utilizing every single resource in the form of applications which creates advancement in day to day life. This technological advancements overcomes the traditional computing methods which increases communication delay, energy consumption for mobile devices. In today's world, Mobile Edge Computing is evolving as a scenario for improving in these limitations so as to provide better output to end users. This paper proposed a secure and energy-efficient computational offloading scheme using LSTM. The prediction of the computational tasks done using the LSTM algorithm. A strategy for computation offloading based on the prediction of tasks, and the migration of tasks for the scheme of edge cloud scheduling based on a reinforcement learning routing algorithm help to optimize the edge computing offloading model. Experimental results show that our proposed algorithm Intelligent Energy Efficient Offloading Algorithm (IEEOA), can efficiently decrease total task delay and energy consumption, and bring much security to the devices due to the firewall nature of LSTM. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches. [ABSTRACT FROM AUTHOR]

• Chiming in at the advent of 6 G, communication security is increasingly taken into account. • The security of UAV-assisted wireless communication links is increasingly threatened. • The comprehensive model of the security, computing unloading and compression scheme of the multiuser multitask MEC system in the mobile IoT is constructed as an integer nonlinear optimization problem, it can improve the security of the data unloading process on the premise of reducing the energy consumption of the whole system. • A traditional compression algorithm is used to reduce the transmission delay of the entire offloading process by reducing the size of the data for the offloading computation task in a low bandwidth state. Finally, the overall system performance will be improved. Ensure that safety performance is maximized in the case of the highest group interest. • A security layer is added during the unloading process. At the security layer, secure multi-party computing technology is adopted to protect the sensitive data of mobile users from network attacks before being transmitted to the MEC server. At the same time, since the problem to be solved in this paper is an NP-hard problem, we transform it into a more easily solved convex problem through linearization and bivariate relaxation methods. Then, an intelligent collaborative data transfer algorithm is proposed to derive approximately optimal offload decisions for computing tasks of all mobile users in the MEC system. To solve the problem that mobile users cannot handle all data tasks by themselves due to the huge amount of data at present, this paper proposes a four-layer model of efficient and secure multiuser multitask computing offload. Since the user needs to offload the data to the server for processing through the wireless communication link, and users have mobility, it is difficult to achieve dynamic updates of user status and data information based on traditional cloud computing data processing methods, and it is difficult to ensure security during data unloading. In this paper, we studied how to realize the efficient and safe transmission of mobile users' data tasks when deploying Mobile Edge Computing (MEC) servers with artificial intelligence to assist in data processing on Unmanned Aerial vehicles (UAVs). First of all, to ensure the efficient use of computing resources, we use compression algorithms to reduce transmission overhead. Secondly, to enhance the security of the data transmission process, we have designed a security layer in the system model, which mainly uses secure multiparty technology to encrypt sensitive data, and a data transmission algorithm based on artificial intelligence is designed to assure the efficient and secure transmission of data in the network. Finally, through the analysis of the simulation results, we can know that the proposed strategy in this study has better performance than other existing strategies. [ABSTRACT FROM AUTHOR]

Due to increasing complexity of mobile applications, and limited computation resources of smart mobile devices, the quality of service requirements of mobile application can be enhanced by offloading the computation tasks of the mobile applications to edge servers, such as cloudlets, which exist at the edge of wireless networks. However, improper placement of mobile tasks on the edge servers may increase the waiting time and the transmission time. This, in turn, will increase the response time, and eventually violates the quality of service. This paper proposes an effective offloading strategy in a mobile edge environment using the queuing networks and an evolutionary algorithm, namely the genetic algorithm (GA). The queuing network is used to model the waiting time and the service time of the mobile tasks on the edge servers. The genetic algorithm finds the best allocation of mobile tasks on the edge servers to minimize tasks response time considering the transmission times and the load conditions on edge servers represented by the waiting times and the service times which are calculated using the queuing network. The proposed GA-based offloading algorithm is compared with another evolutionary algorithm, namely particle swarm optimization (PSO). Experimental evaluations show that the GA-based offloading algorithm outperforms both of round robin offloading and the PSO-based offloading algorithms, and effectively improves mobile applications response time. [ABSTRACT FROM AUTHOR]

With the rapid increased growth of Internet of Things (IoT) and 5G networks in smart cities, big data can be generated, resulting in higher latency for the conventional cloud computing model. To enhance users experience, commodity servers were built at the edges of decentralized networks of mini data centers serving regions. Our paper deals with this problem, in purposes of making a total cost optimization assignment cell to servers in Edge computing network where a so-called meta-heuristic (MH) optimization method was chosen. The first task of the present work is devoted to different methods used in server's Edge placement network optimization including MH methods. Second, the cell assignment to Edge server problem is formulated. The third task is taken up by developing a Tabu Search (TS) application (of fourteen cells network and three Edge servers as a quasi-optimal procedure) for total costs of networks, simulated under MATLAB software. As perspectives, the results compared with other MH methods should be validated, and the server capacity and traffic constraints for each cell have to be added. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a promising paradigm that helps mobile terminals save energy and increase battery life by offloading tasks to MEC servers. To obtain full benefit from MEC, the joint optimization of offloading decision-making and resource allocation, while considering user mobility, is crucial and challenging. The objective of this paper was to propose mobility-aware and energy-efficient offloading schemes for MEC in cellular networks. The proposed schemes helped to make offloading decisions, allocate transit power, and schedule transmissions among multiple moving users. An optimal scheme was proposed, in terms of minimizing total energy consumed by all users, which was based on Mixed-Integer Programming (MIP). Considering the high computational complexity of solving MIP problems, a practical heuristic scheme was also proposed. Simulation results showed the effectiveness in reducing energy consumption of both the proposed optimal and heuristic schemes and demonstrated the importance of mobility awareness as well. • The variations in transmission power caused by end-user mobility is considered. • An optimal solution for joint resource allocation is proposed. • A low-complexity heuristic algorithm is proposed for practical applications. • It is demonstrated that mobility awareness enhances energy efficiency effectively. [ABSTRACT FROM AUTHOR]

Metaverse, the core of the next-generation Internet, is a computer-generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sustainable, interoperable, and data-sensitive characteristics. It cleverly blends the virtual and real worlds, allowing users to create, communicate, and transact in virtual form. With the rapid development of emerging technologies including augmented reality, virtual reality and blockchain, the metaverse system is becoming more and more sophisticated and widely used in various fields such as social, tourism, industry and economy. However, the high level of interaction with the real world also means a huge risk of privacy leakage both for individuals and enterprises, which has hindered the wide deployment of metaverse. Then, it is inevitable to apply privacy computing techniques in the framework of metaverse, which is a current research hotspot. In this paper, we conduct comprehensive research on the necessity, taxonomy and challenges when privacy computing meets metaverse. Specifically, we first introduce the underlying technologies and various applications of metaverse, on which we analyze the challenges of data usage in metaverse, especially data privacy. Next, we review and summarize state-of-the-art solutions based on federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs for different privacy problems in metaverse. Finally, we show the current security and privacy challenges in the development of metaverse and provide open directions for building a well-established privacy-preserving metaverse system. For easy access and reference, we integrate the related publications and their codes into a GitHub repository:. • Discussing the necessity and feasibility of applying privacy computing in metaverse. • Summarizing cutting-edge research on the application of privacy computing in metaverse. • Outlining the challenges in metaverse and urging companies and researchers to pay attention. [ABSTRACT FROM AUTHOR]

The current technologies such as computer vision and artificial intelligence are continuously being applied to disaster rescue robots, enabling their adaptability to different environments. However, the challenge arising from this is that these robots will generate computationally intensive tasks, constrained by the size and energy supply of the robots. This limitation leads to a reduction in device endurance and an increase in processing latency, becoming a bottleneck for the application of artificial intelligence technologies. To address this concern, we propose a novel edge offloading system based on unmanned aerial vehicles (UAVs) and mobile edge service vehicles (MESVs) to assist with communication and computation tasks, aiming to minimize the overall computational burden of rescue robots in extreme scenarios where communication infrastructure is unavailable due to sudden natural disasters or accidents. Leveraging the high maneuverability of UAVs, we utilize an improved dung beetle optimization (DBO) algorithm to optimize the UAV's flight positions and assist robots in task processing and data relay. Moreover, we address the optimization problem of robot offloading strategies by employing a deep reinforcement learning-based Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which effectively reduces processing latency and energy consumption. Through simulation experiments and real-world simulation tests, our proposed method, compared to other UAV position optimization algorithms and computational strategy offloading methods, can effectively reduce the computational energy consumption of robots. In comparison with other methods presented in this paper, it can also reduce latency by nearly 40%. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) enables computation tasks of end users to be offloaded and processed at nearby edge servers, which is a promising technology that can effectively alleviate end users' computing pressure. However, how to balance two important metrics for MEC computation offloading, i.e., latency and energy efficiency, is still a key challenge. In this paper, we investigate joint computation offloading and resource allocation for latency-sensitive applications in order to maximize system-centric computation energy efficiency (SCEE) in MEC systems. An optimization problem is formulated to maximize the sum computation energy efficiency of all users which contains the energy consumption at both user and edge server sides. Meanwhile, task completion latency constraints for all users are considered. We decompose the original optimization problem into two tractable subproblems. The optimal computation offloading as well as communication and computing resource allocation solutions are obtained by solving these subproblems. Furthermore, a hierarchical system-centric energy efficient computation offloading and resource allocation (SEECR) algorithm is developed. Numerical results show that compared with the benchmark schemes, the proposed algorithm can effectively improve the computation energy efficiency performance while guaranteeing the latency requirements of all users. [ABSTRACT FROM AUTHOR]

As a new network architecture, the air-ground cooperative network is a support for future 6G network to achieve ubiquitous connectivities. To effectively relieve the computational pressure of massive data in 6G wireless networks, Unmanned Aerial Vehicles (UAVs) equipped with Mobile Edge Computing (MEC) servers have become an emerging technology that provides computing resources for Mobile Devices (MDs). Due to limited on-board energy and computational capabilities, this paper investigates a multi-UAV collaborative assisted MEC architecture. The optimization problem of minimizing the total computational cost is constructed by jointly optimizing the UAVs trajectories and MDs offloading strategies scheduling. The coupling between the optimization variables and the non-convexity of the problem can make it difficult to solve directly. To address the above concerns, the non-convex optimization problem is converted into a Markov decision process. The UAVs-assisted Offloading Strategy based on Reinforcement Learning (UOS-RL) algorithm is proposed to address the convergence difficulties caused by the high-dimensional continuous action space. Furthermore, the experience data generated by agents interacting with the environment is highly differentiated due to the highly dynamic variation of the environment. Hence, a Priority Experience Replay (PER) mechanism is proposed to improve the training efficiency of the UOS-RL algorithm based on the priority of the experience data. Simulation results show that the proposed PER-UOS-RL algorithm outperforms the existing works in terms of the computational cost. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the task scheduling in resource-limited mobile edge computing (MEC) network, where multiple base stations (BSs), each equipped with a MEC server, assist multiple latency-sensitive user equipments (UEs) in computing. We aim to jointly minimize the system energy consumption and maximize the number of offloaded tasks by optimizing the task scheduling between UEs and BSs. A multiple-objective and mix-integer problem is formulated, which is difficult to solve. To tackle the problem, we combine the ant colony optimization (ACO) algorithm with load balancing, and propose an efficient algorithm. The simulation demonstrates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Mobile edge computing is a new paradigm which provides cloud computing capabilities at the edge of pervasive radio access networks in close proximity to users. The problem of edge server selection in mobile edge environment in terms of user's overhead is investigated in this paper. Due to the limited resources of edge server, we firstly study the task completion probability of edge servers. Secondly, we formally model the problem of edge server selection in terms of time latency and energy consumption. More especially, the computation overhead method for completing the task in cases of both service migration and non-migration is investigated. Then, a new optimized edge server selection algorithm, called combined Genetic algorithm and simulated Annealing algorithm for edge Server Selection (GASS) is designed. Finally, a series of experiments on a real-word data-trace are conducted to evaluate the performance of GASS. The results show that GASS can effectively minimize the overhead of the user and outperform traditional heuristic algorithms. [ABSTRACT FROM AUTHOR]

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly. [ABSTRACT FROM AUTHOR]

In this paper, we study the joint radio and computational resource allocation in the ultra-dense mobile-edge computing networks. In which, the scenario which including both computation offloading and communication service is discussed. That is, some mobile users ask for computation offloading, while the others ask for communication with the minimum communication rate requirements. We formulate the problem as a joint channel assignment, power control and computational resource allocation to minimize the offloading cost of computing offloading, with the precondition that the transmission rate of communication nodes are satisfied. Since the formulated problem is a mixed-integer nonlinear programming (MINLP), which is NP-hard. By leveraging the particular mathematical structure of the problem, i.e., the computational resource allocation variable is independent with other variables in the objective function and constraints, and then the original problem is decomposed into a computational resource allocation subproblem and a joint channel assignment and power allocation subproblem. Since the former is a convex programming, the KKT (Karush–Kuhn–Tucker) conditions can be used to find the closed optimal solution. For the latter, which is still NP-hard, is further decomposed into two subproblems, i.e., the power allocation and the channel assignment, to optimize alternatively. Finally, two heuristic algorithms are proposed, i.e., the Co-channel Equal Power allocation algorithm (CEP) and the Enhanced CEP (ECEP) algorithm to obtain the suboptimal solutions. Numerical results are presented at last to verify the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The growing number of IoT devices and the different computing and communication capabilities of IoT devices require an efficient offloading scheme. This offloading scheme need to consider the mobility of IoT devices and helps to intelligently select the optimal server for offloading. An efficient offloading scheme need to take in consideration important factors such as mobility of the IoT user device, speed and direction of the IoT user device as well as the computational capabilities of the user mobile device and the load of nearby servers. Unbalanced load of data or task offloading lead to high latency and poor services. An optimal selection of offloading server will clearly improve latency and QoS. Some new architecture of cellular network suggest the deployment of small-cell base stations (SBS) [1], [2] with a certain computing capabilities which can help offloading task of IoT mobile device or of their nearby SBS. In smart city environment, the mobile IoT device user needs to choose an SBS from several available SBSs within the its communication proximity. In this paper, we propose a Smart Ranking based Task Offloading approach for selecting an SBS and to improve the Quality of Service. This approach uses Q-Learning for SBS selection which will be modelled in Software Defined Network controller to deal with the problem of choosing the SBS in an intelligent way for Task offloading. [ABSTRACT FROM AUTHOR]

The evolution of wide variety of applications that are used by vehicular users includes a lot of data hungry applications. This increases the workload on the cellular networks, thereby delivering poor service to the users. We can overcome this problem by sharing this workload with open wireless networks. As such, this improves the Quality of Service provided by cellular networks. Road-Side Units (RSU) are a wireless network which plays a major role in data offloading. Our approach discusses switching the communication network from cellular to RSU whenever there is an opportunity for a vehicle to offload vehicles data. Busy roads/urban traffic consists of several RSUs with many users. In urban environment, the vehicular user needs to choose an RSU from several available RSUs within the vehicle communication proximity. For seamless connectivity, the delay in network communication because of selecting the best RSU and frequent switching of connection between vehicles and RUSs must be minimized. In this paper, we propose a Smart Ranking based Data Offloading (SRDO) algorithm for selecting an RSU and to improve the Quality of Service. In SRDO algorithm, Q-Learning is utilized for RSU selection. This algorithm is modelled in Software Defined Network controller to deal with the problem of choosing the RSU in an intelligent way for data offloading. [ABSTRACT FROM AUTHOR]

This paper studies a single-user Mobile Edge Computing (MEC) system where mobile device (MD) includes an application consisting of multiple computation components or tasks with dependencies. MD can offload part of each computation-intensive latency-sensitive task to the AP integrated with MEC server. In order to accomplish the application faultlessly, we calculate out the optimal task offloading strategy in a time-division manner for a predetermined execution order under the constraints of limited computation and communication resources. The problem is formulated as an optimization problem that can minimize the energy consumption of mobile device while satisfying the constraints of computation tasks and mobile device resources. The optimization problem is equivalently transformed into solving a nonlinear equation with a linear inequality constraint by leveraging the Lagrange Multiplier method. And the proposed dual Bi-Section Search algorithm Bi-JOTD can efficiently solve the nonlinear equation. In the outer Bi-Section Search, the proposed algorithm searches for the optimal Lagrangian multiplier variable between the lower and upper boundaries. The inner Bi-Section Search achieves the Lagrangian multiplier vector corresponding to a given variable receiving from the outer layer. Numerical results demonstrate that the proposed algorithm has significant performance improvement than other baselines. The novel scheme not only reduces the difficulty of problem solving, but also obtains less energy consumption and better performance. [ABSTRACT FROM AUTHOR]

Driven by the Quality of Experience (QoE) demands for video analysis applications within contexts such as smart cities, Industrial Internet of Things (IoT), and Internet of Vehicles (IoV), networks are confronted with numerous challenges, including, but not limited to, the requirements for ultra-high bandwidth, ultra-low latency, reliability, and stability. Mobile Edge Computing (MEC) as a technology aims to decentralize computational and storage resources closer to data sources and end-users, meeting the demands of computation-intensive and latency-sensitive video analysis applications and thereby effectively addressing QoE requirements. This has led to a new intersection, edge video offloading, which has attracted widespread attention. However, to date, there are but a few extant papers on this topic with limited relevance. To bridge this gap, we offer a comprehensive review of the state-of-the-art approaches in computational offloading techniques applied to video data, innovatively revisiting existing task offloading technologies from the perspective of mobility of edge server nodes, including solutions based on both fixed and dynamic MEC servers. Additionally, we explore the differences in video offloading techniques between scenarios involving a single MEC server and those involving multiple MEC servers. Meanwhile, open challenges are presented to direct future research in this field. [ABSTRACT FROM AUTHOR]

Dynamically moving Unmanned Aerial Vehicles (UAVs) have emerged as an effective means to significantly enhance the flexibility and transmission performance of mobile edge computing (MEC). However, in practical scenarios, UAVs often face limitations in terms of data storage capacity and computational power. In this paper, a UAV-enabled MEC network with multiple users and multiple edge computing servers is proposed, where the UAV is equipped with limited-size buffers. An optimization problem is formulated to jointly optimize UAV flight trajectories, offload server pairings, task offload ratios, and UAV transmit power to minimize transmission delay, computation delay, and system energy consumption. To tackle the intractable non-convex optimization issue, an intelligent optimization algorithm based on Deep Dueling Double Q-Network (D3QN)-Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed, which is able to efficiently determine the optimal solution. Simulation results demonstrate that our proposed intelligent algorithm exhibits good convergence and achieves a favorable balance between delay and energy consumption. [ABSTRACT FROM AUTHOR]

With the advent of the fifth generation (5G) era, Mobile edge computing (MEC) has become a promising technology. In order to increase the spectrum utilization efficiency and mitigate interference between users, Orthogonal Frequency-Division Multiple Access (OFDMA) technology is introduced into MEC systems. However, uplink offloading signals from users to servers are vulnerable to malicious eavesdropping. We utilize physical layer security, specifically, jamming caused by servers and co-frequency interference provided by users in non-orthogonal multiple access (NOMA) groups to address this issue. This paper investigates an OFDMA based MEC system which consists of multiple users, multiple servers and an eavesdropper. For this system, an innovative algorithm is designed to minimize latency of the system through optimizing resource allocation and task offloading policy which has lower computation complexity while ensuring its security. The proposed scheme can decrease system latency and ensure secure communication according to experimental results. [ABSTRACT FROM AUTHOR]

Computation offloading, as an efficient and promising computing paradigm for mobile edge computing (MEC), provides optimal computation allocation between MEC servers and Internet of Things (IoT) devices. However, in modern wireless communications, most of the computational tasks are latency-sensitive, presenting an efficiency challenge for offloading services. Moreover, to make the best use of computation resources, it remains an issue to ensure stable optimal offloading for the allocation of computation resources. In this paper, a novel adaptive deep reinforcement learning-based offloading (ADRLO) framework is developed to achieve a stable optimal computation revenue with low execution latency for MEC offloading. Structurally, we implement a residual connected deep neural network (DNN) to generate a series of offloading strategies and use them to obtain the global system computation rate. Especially, the strategy that achieves the maximum computation rate is then selected as experience to support the DNN training, forming a closed-loop deep reinforcement learning (DRL) mechanism. To further enhance the efficiency and stability of the MEC offloading, an adaptive regulator is presented for the framework parameters including the size of action space and the DNN learning rate. Numerical results show that, compared to existing state-of-the-art approaches, our proposal achieves optimal computation revenue, exhibits better convergence performance, and significantly reduces the execution latency by almost 50%. [ABSTRACT FROM AUTHOR]

The deployment of intelligent reflecting surfaces (IRS) on dynamically moving unmanned aerial vehicles (UAVs) can enhance the communication performance of mobile edge computing (MEC), improve the system flexibility, and alleviate eavesdropping on air–ground channels. In this paper, an IRS-equipped unmanned aerial vehicle (UAV)-assisted secure MEC network is proposed. By jointly optimizing the Relay-UAV stopping point, IRS-UAV stopping point, IRS reflection coefficients and the task offloading ratio, the objective of our proposed optimization scheme is to minimize the transmission delay and computing delay while considering the secure transmission performance. To solve this non-convex optimization problem with coupled variables, we propose an intelligent optimization algorithm based on dueling double deep Q networks (D3QN)-deep deterministic policy gradient (DDPG) that can efficiently explore the trajectories and a great number of the IRS reflection elements. Simulation results demonstrate that the intelligent algorithm exhibits good convergence and our proposed scheme can achieve a good balance between system consumption and secrecy rate. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) and mobile edge computing (MEC) are two advanced technologies that could improve the computing power and range of mobile devices. However, by integrating the unmanned aerial vehicle (UAV) into wireless powered MEC systems, wireless energy transfer will be susceptible to the "double near–far" effect. Therefore, in order to further overcome the influence of the "double near–far" effect, this paper considers the optimization of time slot allocation for UAV-assisted wireless powered cooperative MEC system, which includes an access point (UAV) and two mobile devices. The purpose of the study is to minimize the total transmission energy of the UAV while satisfying the delay and size of the computational tasks, so this paper proposed a 2:1:1 time-slot optimization allocation method. The method exploits the synergy of users so that the mobile device which is closer to the UAV acts as an offloading relay, by combining power and time slot optimization to minimize the total energy consumption of the UAV. Compared with the equal time slot scheme before the improvement, this method can not only utilize the wireless transmission energy to charge the mobile device for more time in the first period, but also can save the time of data transmission of the closer device in the third period, and it can enhance the rate of data transmission of the mobile devices at the same time. The results show that the task capacity of the system computed will be increased compared to the original scheme; the total transmission rate of the whole system is also improved by the same order of magnitude. The simulation results verify the effectiveness and reliability of the algorithm of the paper, and the comprehensive performance of the system can be maximized by the flexible offloading algorithm. [ABSTRACT FROM AUTHOR]

Modern applications such as augmented reality, connected vehicles, video streaming and gaming have stringent requirements on latency, bandwidth and computation resources. The explosion in data generation by mobile devices has further exacerbated the situation. Mobile Edge Computing (MEC) is a recent addition to the edge computing paradigm that amalgamates the cloud computing capabilities with cellular communications. The concept of MEC is to relocate the cloud capabilities to the edge of the network for yielding ultra-low latency, high computation, high bandwidth, low burden on the core network, enhanced quality of experience (QoE), and efficient resource utilization. In this paper, we provide a comprehensive overview on different traits of MEC including its use cases, architecture, computation offloading, security, economic aspects, research challenges, and potential future directions. [ABSTRACT FROM AUTHOR]

Recently, new services using virtual reality (VR)/augmented reality (AR) have appeared and then exploded in entertainment fields like video games and multimedia contents. In order to efficiently provide these services to users, an infrastructure for mobile cloud computing with powerful computing capabilities is widely utilized. However, existing mobile cloud system utilizes a cloud server located at a relatively long distance, so that there are problems that a user is not effectively provided with personalized immersive multimedia service. So, this paper proposes the home VR streaming system that can provide fast content access time and high immersiveness by using mobile edge computing (MEC). [ABSTRACT FROM AUTHOR]

With the considerable growth of mobile computing and IoT devices these days, cloud computing faces some difficulties in responding to a sudden, momentous increase in high number of requests for network resources. In 2012, Cisco introduced the idea of bringing the computation process to the periphery of the network and geographically closer to the end users to offload the main cloud and bandwidth, leading to a faster response time and less cost. Leveraging on this concept, the cloud computing and IoT technologies, we propose in this paper a novel fog-computing related framework to mitigate the problem of bandwidth bottleneck and packet delay during the Hajj season in Saudi Arabia, where millions of pilgrims are gathered in a small area for a short period of time along with all service providers, health and security personnel. We also demonstrate the potential applicability of our proposed framework in terms of efficient data entry and information retrieval. [ABSTRACT FROM AUTHOR]

Highlights • We propose a task offloading and resource scheduling mechanism with three offloading destinations. • We propose a distributed algorithm containing strategy selection, clock frequency control and transmission power allocation. • We give the optimal task offloading rate and queue delay by using convex optimization method. Abstract Mobile edge computing is an emerging computing paradigm to augment computational capabilities of mobile devices by offloading computation-intensive tasks from resource-constrained smart mobile device onto edge clouds nearby with potential computation capability. However, in general, edge clouds have limited computation resource and energy. Thus it is critical to achieve high energy efficiency while ensuring satisfactory user experience. In this paper, we first formulate the computation offloading problem into the system energy-efficiency cost minimization problem by taking into account the completion time and energy. Then, by solving the optimization problem, we propose a distributed algorithm consisting of clock frequency configuration, transmission power allocation, channel rate scheduling and offloading strategy selection. In this algorithm, the clock frequency for local execution and the transmission power in edge cloud execution are optimized jointly. Furthermore, to optimize the queue delay, we formulate an M/M/n queue model with individual capacity between different windows and propose an algorithm for optimal task offloading rate. Finally, the experimental results show that our algorithm can achieve energy-efficient offloading performance compared to other existing algorithms. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency. [ABSTRACT FROM AUTHOR]

The proposed mobile edge computing can transfer the computing tasks in mobile applications to the nearby edge devices, effectively reducing the processing pressure of local servers and avoiding delays in backhaul and core networks, thus better solving the problem that cloud computing cannot effectively handle resource allocation. However, the complexity of the wireless environment during the communication process leads to the fact that the computing tasks are easily caused by the increase in the number of traffic and packet loss when they are uploaded to the MEC side through the wireless link, which cannot guarantee a lower total system energy consumption and shorter total delay. To address the problem that mobile devices cannot handle many computationally intensive tasks in a timely manner, this paper proposes a task offloading optimization scheme for SDN-enabled MEC environments. Modeling the computation offloading problem based on Lyapunov optimization, and then analyzes the offloading delay with respect to the offloading gain. In order to guarantee application requirements, minimize energy consumption and latency, and better satisfy user QoS requests, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy designs a DQN-based resource allocation algorithm to deploy a joint optimal offloading decision and resource allocation scheme in a mobile edge computing environment under the limited computational resources and the latency constraints of the computational tasks. Based on the experimental results, it is shown that the proposed task offloading strategy can reduce the overall latency; the proposed resource allocation strategy can reduce the total energy consumption and total latency of the system and improve the successful execution rate of tasks. [ABSTRACT FROM AUTHOR]

• We propose a new UAVs-empowered MEC system, where a number of UAVs are deployed to serve user equipments. • In order to reduce the weighted sum of the energy consumption and task completion time of the system, we optimize the trajectories of UAVs in a UAVs-empowered MEC system. • We propose an energy and task completion time minimization algorithm (ETCTMA) to solve the trajectory optimization problem in three phases. • To optimize the association between SPs and UAVs, we adopt an efficient low-complexity clustering algorithm. • Extensive experiments have been conducted on a set of seven instances ranging from 100 to 700 UEs. The simulation results reveal the superiority of the proposed algorithm. This paper presents an energy and task completion time minimization scheme for the unmanned aerial vehicles (UAVs)-empowered mobile edge computing (MEC) system, where several UAVs are deployed to serve large-scale users' equipment (UEs). The aim is to minimize the weighted sum of energy consumption and task completion time of the system by planning the trajectories of UAVs. The problem is NP-hard, non-convex, non-linear, and mixed-decision variables. Therefore, it is very challenging to be solved by conventional optimization techniques. To handle this problem, this paper proposes an energy and task completion time minimization algorithm (ETCTMA) that solves the above problem in three steps. In the first step, the deployment updation of stop points (SPs) is handled by adopting a differential evolution algorithm with a variable population size. Then, in the second step, the association between SPs and UAVs is determined. Specifically, a clustering algorithm is proposed to associate SPs with UAVs. Finally, in the third step, a low-complexity tabu search algorithm is adopted to construct the trajectories of all UAVs. The performance of the proposed ETCTMA is tested on seven instances with up to 700 UEs. The results reveal that our proposed algorithm ETCTMA outperforms other variants in terms of minimizing the weighted sum of energy consumption and task completion time of the system. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have been used to extend the Internet of things (IoT) framework owing to their vast applications, monitoring and surveillance capability, ubiquity, and mobility. To support IoT requirements, UAVs must be capable of aggregating, processing, and transmitting data in real-time basis. As not only the number of IoT devices but also the amount of data to be collected is increased, data aggregation is of great importance. Recently, the UAV can also function as a mobile edge computing server in association with aerial data aggregation. This paper is the first to survey the various aspects and techniques of UAV-based aerial data aggregation for IoT networks. After addressing key design issues, we review the existing data aggregation techniques along with possible future direction. They are then compared with each other in terms of major operational features, performance characteristics, advantages, and limitations. Open issues and research challenges are also discussed with possible solution approaches. [ABSTRACT FROM AUTHOR]