Multi-access edge computing (MEC) has emerged as a powerful paradigm for increasing the computation performance of mobile devices (MDs). Applying non-orthogonal multiple access (NOMA) to MEC can further improve the spectrum efficiency and reduce offloading delays caused by the upload congestion. In this paper, we examine the joint computation offloading and resource allocation problem in the NOMA-MEC system, which benefits from the combination of NOMA and MEC. Our optimization objective is to minimize the computational overhead (the weighted sum of the execution delay and the energy consumption) in dynamic environments with time-varying wireless fading channels. The optimization problem is formulated as a mixed integer programming (MIP), which involves jointly optimizing the task offloading decisions, channel assignment, and transmit power allocation. To solve such an optimization problem, we formalize the task offloading and the resource allocation as a Markov decision process (MDP). Then, we propose a deep reinforcement learning (DRL) based approach, which combines multiple deep neural networks (DNNs) to directly approximate different statistical models for continuous and discrete control. The simulation results demonstrate that the proposed approach can rapidly converge and efficiently decrease the total computational overhead compared to other baseline approaches in different scenarios.

The Software Defined Network in Internet of Things (SDN-IoT) is enjoying growing popularity due to its flexibility, automaticity and programmability. However, there is still a lack of proper permission management on SDN-IoT applications (SIApps), especially when the SIApp’s required northbound interfaces are located in multiple heterogeneous controllers without mutual trust. Existing access control methods are usually based on centralized models, proprietary controllers, trusting conditions or manual operations. It can incur unnecessary performance degradation and poor scalability. To solve this problem, this paper proposes a SIApps’ ledger (SILedger), an open, trusted, and decentralized access control mechanism based on blockchain and attribute-based encryption (ABE). It can not only support effective authorization of SIApps in heterogeneous and untrusted SDN-IoT control domains, but also record all interactions between SIApps and resources, and thus facilitate SIApps further charging, analysis and audit. The main idea is that the SIApps are authorized using access tokens encrypted by ABE, and these tokens are seen as the currency of blockchain to be distributed. Specifically, we re-design blockchain transaction, token encryption, token initialization and token update schemes to achieve cross-domain, fine-grained and flexible SIApps’ permission management. In order to mitigate the delay and complexity problem of blockchain and ABE, we devise an access control framework that separates authorization from call process of SIApps. Finally, we perform security analysis and implement a FISCO-BCOS-based prototype for SILedger. The experimental results show that it can provide effective access control for SIApps with negligible overheads.

The mobile edge computing architecture successfully solves the problem of high latency in cloud computing. However, current research focuses on computation offloading and lacks research on service caching issues. To solve the service caching problem, especially for scenarios with high mobility in the Sensor Networks environment, we study the mobility-aware service caching mechanism. Our goal is to maximize the number of users who are served by the local edge-cloud, and we need to make predictions about the user’s target location to avoid invalid service requests. First, we propose an idealized geometric model to predict the target area of a user’s movement. Since it is difficult to obtain all the data needed by the model in practical applications, we use frequent patterns to mine local moving track information. Then, by using the results of the trajectory data mining and the proposed geometric model, we make predictions about the user’s target location. Based on the prediction result and existing service cache, the service request is forwarded to the appropriate base station through the service allocation algorithm. Finally, to be able to train and predict the most popular services online, we propose a service cache selection algorithm based on back-propagation (BP) neural network. The simulation experiments show that our service cache algorithm reduces the service response time by about 13.21% on average compared to other algorithms, and increases the local service proportion by about 15.19% on average compared to the algorithm without mobility prediction.

In the Internet of Things (IoT) environment, due to the limited computing power of terminal equipment, edge-cloud computing is a good solution for sharing the computation resources. Terminals can offload partial computations to the edge servers for energy saving and reduce the data transmission delays since the edge servers are usually deployed near the terminal devices. However, in the existing solutions, there is still a lot of unnecessary data transmission when the data used in the offloading jobs of several terminals are related. Adding a shareable cache to the edge server and allowing different computing jobs to share the related data can further reduce the data transmission cost and job delay. In this paper, we study the computation offloading method with cached data and propose a novel cache-aware computation offloading strategy for edge-cloud computing in IoT. First, we formulize the cache-aware computation offloading location problem, our goal is to minimize the equivalent weighted response time of all jobs with computing power and cache capacity constraints. Then, we derive the global optimum solution based on transforming the problem to the transportation problem. Next, since the terminal device is difficult to obtain global status information, we propose an online computation offloading strategy, which is convenient in practical deployment. Finally, experiments show that our online offloading strategy approximates the global optimal solution and it reduces the weighted response time by about 26.13% on average compared to other competing algorithms.

Mobile-edge computing (MEC) has been a promising solution for Internet-of-Things (IoT) applications to obtain latency reduction and energy savings. In view of the loosely coupled application, multiple devices can use the same task code and different input parameters to obtain diverse results. This motivates us to study the cooperation between devices for eliminating the repeated data transmission. Leveraging coalitional game theory, we formalize the cooperative offloading process of a reusable task into a coalitional game to maximize the cost savings. In particular, we first propose an efficient coalitional game-based cooperative offloading (CGCO) algorithm for the single-task model, and then expand it into a CGCO-M algorithm for the multiple-task model with jointly applying a two-stage flow shop scheduling approach, which helps to obtain an optimal task schedule. It is proved that our CGCO and CGCO-M can achieve the Nash-stable solution with convergence guarantee, and CGCO can obtain an optimal solution. The simulations show that CGCO is equal to the optimal exhaustive search (ES) method and CGCO-M is close to ES in terms of cost ratios. Cost ratios of CGCO and CGCO-M are significantly down by 41.08% and 83.70% compared to local executions, respectively. Meanwhile, CGCO-M obtains 41.46% and 89.74% reductions when reuse factors are 0.1 and 1, which means CGCO-M can save more cost with higher reuse density.