Proactive content caching in future generation communication networks: Energy and security considerations

The proliferation of hand-held devices and Internet of Things (IoT) applications has heightened demand for popular content download. A high volume of content streaming/downloading services during peak hours can cause network congestion. Proactive content caching has emerged as a prospective solution to tackle this congestion problem. In proactive content caching, data storage units are used to store popular content in helper nodes at the network edge. This contributes to a reduction of peak traffic load and network congestion. However, data storage units require additional energy, which offers a challenge to researchers that intend to reduce energy consumption up to 90% in next generation networks. This thesis presents proactive content caching techniques to reduce grid energy consumption by utilizing renewable energy sources to power-up data storage units in helper nodes. The integration of renewable energy sources with proactive caching is a significant challenge due to the intermittent nature of renewable energy sources and investment costs. In this thesis, this challenge is tackled by introducing strategies to determine the optimal time of the day for content caching and optimal scheduling of caching nodes. The proposed strategies consider not only the availability of renewable energy but also temporal changes in network trac to reduce associated energy costs. While proactive caching can facilitate the reduction of peak trac load and the integration of renewable energy, cached content objects at helper nodes are often more vulnerable to malicious attacks due to less stringent security at edge nodes. Potential content leakage can lead to catastrophic consequences, particularly for cache-equipped Industrial Internet of Things (IIoT) applications. In this thesis, the concept of \trusted caching nodes (TCNs) is introduced. TCNs cache popular content objects and provide security services to connected links. The proposed study optimally allocates TCNs and selects the most suitable content forwarding paths. Furthermore, a caching strategy is designed for mobile edge computing systems to support IoT task offloading. The strategy optimally assigns security resources to offloaded tasks while satisfying their individual requirements. However, security measures often contribute to overheads in terms of both energy consumption and delay. Consequently, in this thesis, caching techniques have been designed to investigate the trade-off between energy consumption and probable security breaches. Overall, this thesis contributes to the current literature by simultaneously investigating energy and security aspects of caching systems whilst introducing solutions to relevant research problems

Efficient and Secure Resource Allocation in Mobile Edge Computing Enabled Wireless Networks

To support emerging applications such as autonomous vehicles and smart homes and to build an intelligent society, the next-generation internet of things (IoT) is calling for up to 50 billion devices connected world wide. Massive devices connection, explosive data circulation, and colossal data processing demand are driving both the industry and academia to explore new solutions. Uploading this vast amount of data to the cloud center for processing will significantly increase the load on backbone networks and cause relatively long latency to time-sensitive applications. A practical solution is to deploy the computing resource closer to end-users to process the distributed data. Hence, Mobile Edge Computing (MEC) emerged as a promising solution to providing high-speed data processing service with low latency. However, the implementation of MEC networks is handicapped by various challenges. For one thing, to serve massive IoT devices, dense deployment of edge servers will consume much more energy. For another, uploading sensitive user data through a wireless link intro-duces potential risks, especially for those size-limited IoT devices that cannot implement complicated encryption techniques. This dissertation investigates problems related to Energy Efficiency (EE) and Physical Layer Security (PLS) in MEC-enabled IoT networks and how Non-Orthogonal Multiple Access (NOMA), prediction-based server coordination, and Intelligent Reflecting Surface (IRS) can be used to mitigate them. Employing a new spectrum access method can help achieve greater speed with less power consumption, therefore increasing system EE. We first investigated NOMA-assisted MEC networks and verified that the EE performance could be significantly improved. Idle servers can consume unnecessary power. Proactive server coordination can help relieve the tension of increased energy consumption in MEC systems. Our next step was to employ advanced machine learning algorithms to predict data workload at the server end and adaptively adjust the system configuration over time, thus reducing the accumulated system cost. We then introduced the PLS to our system and investigated the long-term secure EE performance of the MEC-enabled IoT network with NOMA assistance. It has shown that NOMA can improve both EE and PLS for the network. Finally, we switch from the single antenna scenario to a multiple-input single-output (MISO) system to exploit space diversity and beam forming techniques in mmWave communication. IRS can be used simultaneously to help relieve the pathloss and reconfigure multi-path links. In the final part, we first investigated the secure EE performance of IRS-assisted MISO networks and introduced a friendly jammer to block the eavesdroppers and improve the PLS rate. We then combined the IRS with the NOMA in the MEC network and showed that the IRS can further enhance the system EE

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

© 2019 Binbin Huang et al. With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device's energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment

Energy Efficient Secure Computation Offloading in NOMA-based mMTC Networks for IoT

In the era of Internet of Everything, massive connectivity and various demands of latency for Internet of Thing (IoT) devices will be supported by the massive Machine Type Communication (mMTC). Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) have the advantages of improving network capacity, reducing MTC devices’ (MTCDs) latency and enhancing Quality of Service. Exploiting these benefits, we focus on the energy efficient secure computation offloading in NOMA based mMTC networks for IoT, where the relay equipped with an MEC server and a passive malicious eavesdropper are presented. We optimize the joint computation and communication resource allocation to maximize the secrecy energy efficiency of computation offloading while guaranteeing the delay requirements of MTCDs. Furthermore, we model the subchannels allocation problem as MTCD-to-Subchannel matching. Exploiting difference of convex programming and successive convex approximation, we formulate the Dinkelbach-based SEE optimization algorithm and obtain the closed-form expression of power allocation for MTCDs’ on each subchannel. Based on the communication resources allocation schemes, we propose the Knapsack algorithm to solve the problem of computation resource allocation. Furthermore, we formulate the joint computation and communication resource allocation algorithm for secure computation offloading. Simulation results demonstrate the effectiveness of proposed algorithm for supporting IoT devices energy efficient secure computation offloading