Recently, solutions based on Mobile Edge Computing (MEC) paradigm have been widely discussed in academia and industry. This paradigm offers solutions to address limitations, in terms of battery lifetime and processing power, of mobile and constrained devices. Despite the ever-increasing capabilities of these devices, resource requirements of applications can often transcend what is available within a single device. Offloading intensive computation tasks to a distant server can help applications reach their desired performances. In this work, we tackle the problem of offloading heavy computation tasks of Unmanned Aerial Vehicles (UAVs) while achieving the best possible tradeoff between energy consumption, time delay and computation cost. We focus on a scenario of a fleet of small UAVs performing an exploration mission. During their mission, these constrained devices have to carry-out highly intensive computation tasks such as pattern recognition and video preprocessing. We formulate the problem using a non-cooperative theoretical game with N players and three pure strategies. We provide a comprehensive proof for the existence of a Nash Equilibrium and implement accordingly a distributed algorithm that converges to such an equilibrium. Extensive simulations are performed in order to provide thorough results and assess the performances of the approach compared to three other models. Results show that our algorithm outperforms all the three approaches. Our approach achieved in average about 19%, 58% and 55% better results compared to local computing, offloading to the Edge Server (ES) and offloading to Base Station (BS) respectively.

Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this paper, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

In the smart grid, huge amounts of consumption data are used to train deep learning models for applications such as load monitoring and demand response. However, these applications raise concerns regarding security and have high accuracy requirements. In one hand, the data used is privacysensitive. For instance, the fine-grained data collected by a smart meter at a consumer's home may reveal information on the appliances and thus the consumer's behaviour at home. On the other hand, the deep learning models require big data volumes with enough variety and to be trained adequately. In this paper, we evaluate the use of Edge computing and federated learning, a decentralized machine learning scheme that allows to increase the volume and diversity of data used to train the deep learning models without compromising privacy. This paper reports, to the best of our knowledge, the first use of federated learning for household load forecasting and achieves promising results. The simulations were done using Tensorflow Federated on the data from 200 houses from Texas, USA.

5G technologies promise faster connections, lower latency, higher reliability, more capacity and wider coverage. We are looking to rely on these technologies to achieve Vehicle-to-Everything (V2X) communications, which increase the safety and autonomy of vehicles in addition to road safety, saving energy and costs. The integration of vehicular communication systems and 5G is the subject of many research. Nowadays, researchers address challenges such as automated and intelligent networks, cloud and edge data processing, network management, virtualization, security, privacy and finally interoperability. This paper provides a survey of the latest V2X use cases including requirements, and various 5G enabling technologies under consideration for vehicular communications. Subsequently, we first provide an interesting mapping between the three 5G pillars and V2X use case groups. Then, we present a summary of potential applications of enabling technologies for V2X use case groups. Finally, the open directions of research are discussed, and the challenges that await to be met are pointed out.

The blockchain is a distributed technology which allows establishing trust among unreliable users who interact and perform transactions with each other. While blockchain technology has been mainly used for crypto-currency, it has emerged as an enabling technology for establishing trust in the realm of the Internet of Things (IoT). Nevertheless, a naive usage of the blockchain for IoT leads to high delays and extensive computational power. In this paper, we propose a blockchain architecture dedicated to being used in a supply chain which comprises different distributed IoT entities. We propose a lightweight consensus for this architecture, called LC4IoT. The consensus is evaluated through extensive simulations. The results show that the proposed consensus uses low computational power, storage capability and latency.

The interconnection of vehicles in the future fifth generation (5G) wireless ecosystem forms the so-called Internet of vehicles (IoV). IoV offers new kinds of applications requiring delay-sensitive, compute-intensive and bandwidth-hungry services. Mobile edge computing (MEC) and network slicing (NS) are two of the key enabler technologies in 5G networks that can be used to optimize the allocation of the network resources and guarantee the diverse requirements of IoV applications. As traditional model-based optimization techniques generally end up with NP-hard and strongly non-convex and non-linear mathematical programming formulations, in this paper, we introduce a model-free approach based on deep reinforcement learning (DRL) to solve the resource allocation problem in MECenabled IoV network based on network slicing. Furthermore, the solution uses non-orthogonal multiple access (NOMA) to enable a better exploitation of the scarce channel resources.The considered problem addresses jointly the channel and power allocation, the slice selection and the vehicles selection (vehicles grouping). We model the problem as a single-agent Markov decision process. Then, we solve it using DRL using the wellknown DQL algorithm. We show that our approach is robust and effective under different network conditions compared to benchmark solutions.

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