Task Offloading with Task Classification and Offloading Nodes Selection for MEC-Enabled IoV

In this paper, we consider a cooperative computing system which consists of a number of mobile edge computing (MEC) servers deployed with convolutional neural network (CNN) model, a remote mobile cloud computing (MCC) server deployed with CNN model and a number of mobile devices (MDs). We assume that each MD has a computation task and is allowed to offload its task to one MEC server where the CNN model with various layers is applied to conduct task execution, and one MEC server can accept multiple tasks of MDs. To enable the cooperative between the MEC servers and the MCC server, we assume that the task of MD which has been processed partially by the CNN model of the MEC server will be sent to CNN model of the MCC server for further processing. We study the joint task offloading, CNN layer scheduling and resource allocation problem. By stressing the importance of task execution latency, the joint optimization problem is formulated as an overall task latency minimization problem. As the original optimization problem is NP hard, which cannot be solved conveniently, we transform it into three subproblems, i.e., CNN layer scheduling subproblem, task offloading subproblem and resource allocation subproblem, and solve the three subproblems by means of extensive search algorithm, reformulation-linearization-technique (RLT) and Lagrangian dual method, respectively. Numerical results demonstrate the effectiveness of the proposed algorithm.

In the environment of multiple edges, an unbalanced distribution of offloaded tasks can result in a lack of edge resources, which in turn which in turn leads to lower performance. On the other hand, fast decisions regarding edge selection are crucial for efficient performance. Therefore, this paper suggests an edge-edge network based on software-defined networks to manage resources and tasks at the mobile edge of computing in the Internet of Things (IoT) environment. The proposed technique in this paper introduces an effective method for making selections concerning collaborative offloading tasks within edge computing environments based on Software-Defined Networks (SDN), which will decide where to offload and process tasks on the optimal Mobile Edge Computing (MEC) server among five MEC servers based on currently available resources when tasks need processing during a specific time using SDN controller that view the status of all network. The rank of the feasible MEC server is based on the presently available CPU frequency of the MEC server relative to the required computing resources for the task. To calculate the final height score of the MEC server, this work used Min-Max normalization and a high score for the MEC server from these servers that were considered optimal for offloading tasks. This paper aims to maintain the total latency as little as much as possible.

In the Industrial Internet of Things (IIoT), various types of tasks are processed for the small quantity batch production. But there are many challenges due to the limited battery lifespan and computational capabilities of devices. To overcome the limitations, Mobile Edge Computing (MEC) has been introduced. In MEC, a task offloading technique to execute the tasks attracts much attention. A MEC server (MECS) has limited computational capability, which increases the burden on the server and a cellular network if a larger number of tasks are offloaded to the server. It can reduce the quality of service for task execution. Thus, offloading between nearby devices through device-to-device (D2D) communication is drawing attention. We propose the optimal task offloading decision strategy in the MEC and D2D communication architecture. We aim to minimize the energy consumption of devices and task execution delay under delay constraints. To solve the problem, we adopt Q-learning algorithm as one of Reinforcement Learning (RL). Simulation results show that the proposed algorithm outperforms the other methods in terms of energy consumption of devices and task execution delay.

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a mobile edge computing (MEC) server and sensors could provide vehicle infrastructure cooperation services for vehicles, such as optimization and cooperative driving, enhanced visibility, and so on. In view of this, the MEC server needs to fuse the sensing information from sensors on the vehicles and RSU, respectively. In the case of bad channel conditions, uploading the raw sensing information from the vehicles results in high uplink transmission latency. To deal with it, the vehicles can process the information locally and just deliver the results to the RSU. However, due to the limited computing resources on the vehicles, the processing accuracy of the raw information on the vehicles is lower than that on the MEC server. Besides, processing locally leads to higher vehicle energy consumption. Thus, in this paper, we aim to jointly optimize execution latency, processing accuracy, and energy consumption of the MEC-based cooperative vehicle infrastructure system. Firstly, we design the terminal machine learning task model and the edge machine learning task model on the vehicle side and RSU side, respectively. Then, we formulate a long-term multi-objective optimization problem. Owing to the stochastic traffic and time-varying communication conditions, we reformulate it as a Markov decision process and propose a two-stage deep reinforcement learning-based offloading and resource allocation (TDORA) strategy to determine the task offloading and the transmit power of each vehicle. Simulation results demonstrate the efficacy of the proposed strategy.

A cluster-based cooperative computation offloading scheme for C-V2X networks

An increasing number of cloud providers now offer Mobile Edge Computing (MEC) services for their customers to support task offloading. This is undertaken to reduce latency associated with forwarding data from IoT devices owned by customers to cloud platforms. However, two challenges remain in existing MEC scenarios: (i) the coverage of MEC services is limited; (ii) there is limited ability to develop an audit trail about which MEC service providers have processed a user’s data. A new architecture for automatically offloading user tasks in MEC scenarios is proposed which addresses the two challenges above. The architecture makes use of drones to dynamically cache data generated from IoT devices and forward this data to MEC servers that participate in a private blockchain network. Our simulated experiments demonstrate the flexibility of the task offloading process through the proposed architecture which can provide greater visibility of MEC service providers involved in processing users’ data

Collaborative computation offloading for scheduling emergency tasks in SDN-based mobile edge computing networks

Mobile edge computing (MEC) has been envisaged as one of the most promising technologies in the fifth generation (5G) mobile networks. It allows mobile devices to offload their computation‐demanding and latency‐critical tasks to the resource‐rich MEC servers. Accordingly, MEC can significantly improve the latency performance and reduce energy consumption for mobile devices. Nonetheless, privacy leakage may occur during the task offloading process. Most existing works ignored these issues or just investigated the system‐level solution for MEC. Privacy‐aware and user‐level task offloading optimization problems receive much less attention. In order to tackle these challenges, a privacy‐preserving and device‐managed task offloading scheme is proposed in this paper for MEC. This scheme can achieve near‐optimal latency and energy performance while protecting the location privacy and usage pattern privacy of users. Firstly, we formulate the joint optimization problem of task offloading and privacy preservation as a semiparametric contextual multi‐armed bandit (MAB) problem, which has a relaxed reward model. Then, we propose a privacy‐aware online task offloading (PAOTO) algorithm based on the transformed Thompson sampling (TS) architecture, through which we can (1) receive the best possible delay and energy consumption performance, (2) achieve the goal of preserving privacy, and (3) obtain an online device‐managed task offloading policy without requiring any system‐level information. Simulation results demonstrate that the proposed scheme outperforms the existing methods in terms of minimizing the system cost and preserving the privacy of users.

Truthful mechanism for joint resource allocation and task offloading in mobile edge computing

Mobile edge computing (MEC) has been recognized as a promising technique which provides mobile devices (MDs) with enhanced computation capability. In this paper, we consider a multi-user, multi-server MEC system which consists of a number of MDs and multiple base stations (BSs) deployed with MEC servers. We assume that computation tasks can be executed locally at the MDs or be offloaded to the MEC servers. Further assume that each MEC server may execute computation tasks for multiple MDs, however, the tasks sharing one MEC server should be scheduled sequentially. We jointly study computation task offloading and scheduling scheme for the MDs and formulate the problem of joint task offloading and scheduling as a task execution latency minimization problem. Since the optimization problem is a mixed integer nonlinear problem which cannot be solved using conventional methods, we transform it into two subproblems, i.e., task partition subproblem and task scheduling subproblem. Under the assumption that task scheduling strategy is given, task partition subproblem is a set of single variable optimization problems, which can be solved easily. To tackle the task scheduling subproblem, we propose a heuristic algorithm, which first determines complete local computing mode for the MDs, then calculates local optimal strategy for the MDs. In the case that multiple MDs may share one MEC server, various priorities are then assigned to the MDs and corresponding computing mode and task scheduling strategy are determined for the MDs with different priorities. Numerical results demonstrate the effectiveness of the proposed scheme.

Benefiting from its abundant computing resources and low computing latency, mobile edge computing (MEC) is a promising approach for enhancing the computing capacity of the 5G Internet of vehicles (IoV). Because of the high mobility, handover is frequent and inevitable in IoV networks. In this paper, we investigate an edge collaborative task offloading and splitting strategy in MEC-enabled IoV networks, in which the task is splitted on the edge and paralleling executed by each part of the task on several MEC servers when handover is occured. Applications in IoV networks have flexible requirements on latency and energy consumption. To realize the tradeoff between latency and energy consumption, we formulate the task offloading and splitting as an optimization problem with the aim of minimizing the total cost of latency and energy consumption by jointly optimizing the task splitting ratio and uplink transmit power of vehicle terminal (VT). Because the proposed problem is non-smooth and non-convex, we divide the original problem into two convex subproblems, and apply an alternate convex search (ACS) algorithm to obtain the optimized solution with low computational complexity. Numerical simulation results show that the proposed method can adjust the offloading strategy properly according to task preference, and obtain a lower total cost compared with the baseline algorithms.

In mobile edge computing (MEC), it is challenging to offload tasks to appropriate edge nodes due to the heterogeneity in both tasks and edge nodes. Most existing task offloading mechanisms mainly aim at optimizing the global system performance, e.g., social welfare, while ignoring the personal preferences of the individual tasks and edge nodes. However, in an open MEC system, a task offloading decision is prone to be unstable if edge nodes or task owners have incentives to deviate from the decided allocation, and seek for alternative choices to improve their own utilities. In addition, to win the competition, task owners may gradually adjust their payments, which brings new challenge in achieving the stability of the system. To address the above issues, this paper constructs a distributed many-to-many matching model to capture the interaction between mobile tasks and edge nodes, with the consideration of their diverse resource requirements and availabilities. Based on this, we design both distributed and centralized stable matching based algorithms to jointly offload the tasks to edge nodes, and determine their payments. We prove that the proposed mechanisms achieve several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed schemes can get optimal social welfare, when the considered tasks are homogeneous in terms of their resource requirements. Finally, we conduct simulations to validate the effectiveness of the proposed work.

With the Internet of Things (IoT) and communication technologies are snowballing, various applications (e.g., e-health and face recognition) are generated by IoT devices (IoTDs). Nevertheless, these IoTDs generally have constrained computation resources. By offloading the IoT applications to be processed by the MEC servers, mobile edge computing (MEC) is envisioned as a promising and effective solution to address this problem. Meanwhile, security is a critical issue for task offloading in MEC. While plenty of studies have focused on IoT tasks offloading, many of them ignored the security issue. Moreover, many previous works ignored the resource allocation of MEC servers. In addition, as dynamic voltage scaling (DVS) technology is flexible in the design of MEC systems, we integrate this technology with task offloading. In this paper, the problem of IoT applications offloading in an MEC system is studied, whose goal is to minimize computation overheads measured by the task processing delay and energy consumption of IoTDs. The AES cryptographic technique is adopted to make sure that the security of the data of the offloaded tasks is guaranteed. An optimization problem of security-aware task offloading is formulated and solved by proposing an efficient resource-allocation scheme. Experimental results are performed to evaluate and confirm the performance of the proposed security model.

Mobile Edge Computing (MEC) is a promising and fast-developing paradigm that provides cloud services at the edge of the network. MEC enables IoT devices to offload and execute their real-time applications at the proximity of these devices with low latency. Such applications include efficient manufacture inspection, virtual/augmented reality, image recognition, Internet of Vehicles (IoV), and e-Health. However, task offloading experiences security and privacy attacks such as data tampering, private data leakage, data replication, etc. To this end, in this paper, we propose a new blockchain-based framework for secure task offloading in MEC systems with guaranteed performance in terms of execution delay and energy consumption. First, blockchain technology is introduced as a platform to achieve data confidentiality, integrity, authentication, and privacy of task offloading in MEC. Second, we formulate an integration model of resource allocation and task offloading for a multi-user with multi-task MEC systems to optimize the energy and time cost. This is an NP-hard problem because of the curse-of-dimensionality and dynamic characteristics challenges of the considered scenario. Therefore, a deep reinforcement learning-based algorithm is developed to derive the close-optimal task offloading decision efficiently. Theoretical analysis and experimental results demonstrate that the proposed framework is resilient to several task offloading security attacks and it can save about 22.2% and 19.4% of system consumption with respect to the local and edge execution scenarios. Moreover, the benchmark analysis proves that the framework consumes few resources in terms of memory and disk usage, CPU utilization, and transaction throughput.