关键词： Internet of Things   Predictive models   Computational modeling   Topology   Training   Stress   Data models   Trust   mobile edge computing   machine learning  

摘要： We propose a data-driven distributed machine learning approach to scalably predict the trustworthiness of homogeneous IoT services in heterogeneous Mobile Edge Computing (MEC)-based IoT systems. The proposed approach formulates training distributed trust prediction models within an MEC-based IoT system as a Network Lasso problem. We then introduce a variant of the Stochastic Alternating Method of Multipliers framework enriched with the ability for feature selection at each MEC layer. To verify the effectiveness of the proposed approach, we carried out a comprehensive evaluation on three real-world datasets adjusted to exhibit the context-dependent trust information accumulated in MEC environments within a given MEC topology. The experimental results affirmed the effectiveness of our approach and its suitability to predict trustworthiness of IoT services in MEC-based IoT systems.

关键词： NOMA   Task analysis   Uplink   Resource management   Energy consumption   Computational modeling   5G mobile communication   Mobile edge computing   network slicing   uplink energy consumption minimization   energy efficiency   spectral efficiency  

摘要： The 5G network presents a new horizon with tremendous opportunities for future generation wireless networks. Mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing (NS) are some of the key enablers for 5G. MEC reduces the latency to a great extent for a wireless network, while NOMA gives access to more users with resource constraints. NS provides users with a better quality of service and network operators with more flexibility. In this work, we propose an NS technique enabled with NOMA for a MEC network. The proposed NS technique improves service latency for MEC users and reduces unnecessary allocation of radio resources in NOMA. The saved resources can be leveraged to accommodate more users, thus increasing the spectral efficiency of the network. We consider different types of services based on the task completion time of users in this work. The primary focus is to optimize the total energy consumption for wireless uplink transmission for the NOMA-enabled sliced MEC network. We also propose a heuristic algorithm as an alternative to reduce the time and computational complexities of the optimization algorithm and simulate the results extensively to show the effectiveness of our proposed algorithm.

关键词： Task analysis   Servers   Cloud computing   Resource management   Optimization   Real-time systems   Delays   Offloading strategy optimization   mobile edge computing   Index Terms   hierarchy   average response time   energy consumption  

摘要： Cloud computing and mobile edge computing techniques supply efficient ways to solve the contradiction between the increasing computing and storage demands of portable terminals and the limited capacity. In this paper, we conduct a three-tier hierarchical service system with multiple mobile users(UEs), multiple mobile edge computing servers(MECs), and a single cloud center(CC). It is worth noting that multiple UEs with personalized options generate a large number of different tasks in real time. To deal with this complex offloading problem, a response ratio offloading strategy (RROS) centered on user preference and real-time nature is designed to make MECs or CC serve as many UEs as possible. Therefore, a MEC-choosing preference list of each UE is created based on its past experiences at first. Then, each MEC iteratively sorts UEs with its ranking in the UEs' preference list. In order to avoid that the first task arriving at MEC occupies too many resources of MEC and cannot achieve global optimization, we also adopt loop iterative sequencing for multiple tasks arriving within a stipulated time. Lastly, by comparing the optimal response ratio on different MECs and CC, multiple MECs and the CC collaborative offload computing tasks of multiple UEs. We demonstrate numerical examples and data of the proposed strategy. Experimental results show that the algorithm significantly outperforms conventional techniques even with the increase of users.

关键词： Mobile edge computing   resource reservation   resource brokerage   competitive analysis   online algorithm  

摘要： With the development of MEC services, MEC brokers will emerge to facilitate the purchase and management of resources for individual MEC users. Both data communication and computing resources offered by MEC service providers can be purchased by pay-as-you-go (PAYG) or reserved plans. Besides data and computing plans for each type of resource, we also consider combo plans specifically designed for MEC services covering both resources. In this paper, we propose a smart online aggregated reservation (SOAR) framework for MEC brokers to minimize their cost of reserving resources for multiple users without the knowledge of future demands. In our framework, a task aggregation algorithm is designed to aggregate the users' demands in each PAYG billing cycle to improve the plan utilization, and plan reservation algorithms are proposed to decide when to reserve which plans. The performance gap (competitive ratio) between SOAR and optimal solution which knows all future demands in advance, is analyzed and derived in closed-form. The performance gap is proved to be the minimum among all deterministic online algorithms. Trace-driven simulations verify the cost advantage of our SOAR framework, which can save nearly 40 percent of cost for users through the brokerage service.

关键词： Blockchain   Security   key management   group communication   mobile edge computing  

摘要： Mobile edge computing (MEC) is a promising edge technology to provide high bandwidth and low latency shared services and resources to mobile users. However, the MEC infrastructure raises major security concerns when the shared resources involve sensitive and private data of users. This paper proposes a novel blockchain-based key management scheme for MEC that is essential for ensuring secure group communication among the mobile devices as they dynamically move from one subnetwork to another. In the proposed scheme, when a mobile device joins a subnetwork, it first generates lightweight key pairs for digital signature and communication, and broadcasts its public key to neighbouring peer users in the subnetwork blockchain. The blockchain miner in the subnetwork packs all the public key of mobile devices into a block that will be sent to other users in the subnetwork. This enables the mobile device to communicate with its peers in the subnetwork by encrypting the data with the public key stored in the blockchain. When the mobile device moves to another subnetwork in the tree network, all the mobile devices of the new subnetwork can quickly verify its identity by checking its record in the local or higher hierarchy subnetwork blockchain. Furthermore, when the mobile device leaves the subnetwork, it does not need to do anything and its records will remain in the blockchain which is an append-only database. Theoretical security analysis shows that the proposed scheme can defend against the 51 percent attack and malicious entities in the blockchain network utilizing Proof-of-Work consensus mechanism. Moreover, the backward and forward secrecy is also preserved. Experimental results demonstrate that the proposed scheme outperforms two baselines in terms of computation, communication and storage.

关键词： Device to device   Ant colony optimization   Weighted genetic algorithm   Hybrid energy harvesting   Mobile edge computing  

摘要： The constraints owing to the usage of batteries as well as cellular clients' restrictions in computing capability cause concern. This is related to allocating resources in Device to Device (D2D) aided edge computing systems. Hence, these prompt the respective approaches to investigate the harnessing of electrical energy in a hybrid way. The computing resource of the Mobile Edge Computing (MEC) server reaches the limit of its computing capability. The adjacent base station's user can act as a relay node by setting up the D2D relay links for the computing responsibilities of the users. Earlier these were left under the previous base station. Now they can be transferred to the new base station's MEC server. This has enough resources. The goal of the resource allocation approaches is to improvise energy efficiency under computation delay constraints and energy harvesting constraints. The formulation of the problem in the proposal is done as a mixed-integer non-linear problem (MINLP). It achieves one of the best solutions. The computational intricacy is small. Hence, the paper proposes a Weighted Genetic Algorithm (WGA) pivoted resource allocating method for 5G networks' D2D type communications. Genetic algorithms assumed due popularity for allocating networks as well as rendering them optimized. An optimal effect is realized by adopting the optimization algorithm like WGA rather than the algorithms namely Ant Colony Optimization (ACO), Standard Particle Swarm Optimization (SPSO) and Quantum Behaved Particle Swarm Optimization (QPSO). Simulated outcomes infer that WGA has an edge over the mentioned algorithms in energy efficiency, SINR and throughput. Added to that studies imply that such schemes' self-learning ability (i.e. WGA, ACO) yields improved results for problems with higher complexity.

关键词： Mobile edge computing   Non-Orthogonal Multiple Access   Delay-sensitive   Industrial internet of things   Prediction-based deep reinforcement learning  

摘要： Mobile Edge Computing (MEC) and Non-Orthogonal Multiple Access (NOMA) have been treated as promising technologies to process the delay-sensitive tasks in the Industrial Internet of Things (IIoT) network. The cooperation among multiple MEC servers is essential to improve the processing capacity of MEC systems. However, the dynamic IIoT environment with unknown changing models, including time-varying wireless channels, diversified task requests, and dynamic load on wireless resources and multiple MEC servers, may continuously affect the task offloading decision and NOMA user pairing, which brings great challenges to the resource management in the NOMA-MEC-based IIoT network. In order to solve this problem, we design a distributed deep reinforcement learning (DRL) based solution to improve the task satisfaction ratio by jointly optimizing the task offloading decision and the sub-channel assignment to support the binary computing offloading policy. For each IIoT device agent, to deal with the problem of partial state observability, the Recurrent Neural Network (RNN) is employed to predict the load states of sub-channels and MEC servers, which is further used for the decision of the RL agent. Simulation results show that the proposed prediction-based-DRL (P-DRL) method can achieve higher task satisfaction ratio than exiting schemes.

关键词： unmanned aerial vehicle communication   mobile edge computing   secrecy energy efficiency   convex optimization algorithm  

摘要： A secrecy energy efficiency optimization scheme for a multifunctional unmanned aerial vehicle (UAV) assisted mobile edge computing system is proposed to solve the computing power and security issues in the Internet-of-Things scenario. The UAV can switch roles between a computing UAV and jamming UAV based on the channel conditions. To ensure the security of the content and the system energy efficiency in the process of offloading computing tasks, the UAV trajectory, uplink transmit power, user scheduling, and offload task are jointly optimized, and an updated-rate assisted block coordinate descent (BCD) algorithm is used. Simulation results show that this scheme efficiently improves the secrecy performance and energy efficiency of the system. Compared with the benchmark scheme, the secrecy energy efficiency of the scheme is improved by 38.5%.

关键词： Mobile edge computing   Task offloading scheme   Time-correlated traffic   VM repair   Delay-energy tradeoff  

摘要： Mobile Edge Computing (MEC) has evolved into a key technology that can leverage resources of computing, storage and network deployed at the proximity of the Mobile Devices (MDs). How to offload delay-sensitive and energy-constraint tasks is of research importance. In this paper, considering time-correlated traffic and Virtual Machine (VM) repair, we come up with a method to evaluate the task offloading scheme in a MEC system. Applying a Markovian Arrival Process (MAP) to describe the task arrivals in a MEC system, the local computing offloading and the MEC offloading are modeled as a MAP/M/1 queue and a MAP/M/N/N+K queue with VM repair, respectively. By exploiting the matrix-geometric solution approach and the Gauss-Seidel approach, we give the average delay, the energy consumption level and computation resource availability. Next, we provide numerical results to investigate the influence of the offload rate on the response performance and the energy efficiency with different correlation coefficients and the influence of the repair rate and the service rate of a VM on the availability. In addition, an improved Sine and Cosine Algorithm (SCA) is developed to acquire the optimal offload rate with a delay-energy tradeoff.

关键词： Servers   Internet of Things   Resource management   Array signal processing   Optimization   Backscatter   Wireless communication   Backscatter communications   mobile edge computing   multi-agent deep reinforcement learning  

摘要： Mobile edge computing (MEC) provides an economical way for the resource-constrained edge users to offload computational workload to MEC servers co-located with the access point (AP). In this article, we consider a hybrid computation offloading scheme that allows edge users to offload workloads by using active RF communications and backscatter communications. We aim to maximize the overall energy efficiency subject to the completion of all workload by jointly optimizing the AP's beamforming and the users' offloading decisions. Considering a dynamic environment, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework to solve this problem. The high-level agent resides in the AP and optimizes the beamforming strategy, while the low-level user agents learn and adapt individuals' offloading strategies. To further improve the learning efficiency, we propose a novel optimization-driven learning algorithm that allows the AP to estimate the low-level users' actions by solving approximate optimization problem efficiently. Then, the action estimation can be shared with all users and drive them to update individuals' actions independently. Simulation results reveal that our algorithm can improve the system performance by 50%. The learning efficiency and reliability are also improved significantly comparing to the model-free learning methods.