Mobile Edge Computing Partial Offloading Techniques for Mobile Urban Scenarios

Edge Computing refers to a recently introduced approach aiming to bring the storage and computational capabilities of the cloud to the proximity of the edge devices. Edge Computing is one of the main techniques enabling Fog Computing and Networking. Among several application scenarios, the urban scenario seems one of the most attractive for exploiting edge computing approaches. However, in an urban scenario, mobility becomes a challenge to be addressed, affecting the edge computing. By gaining from the the presence of two types of devices, Fog Nodes (FNs) and Fog-Access Points (F-APs), the idea in this paper is that of exploiting Device to Device (D2D) communications between FNs for assisting computation offloading requests between FNs and F-APs by exchanging status information related to the F-APs. With this knowledge, this paper proposes a partial offloading approach where the optimal tasks amount to be offloaded is estimated for minimizing the outage probability due to the mobility of the devices. In order to reduce the outage probability we have further considered a relaying approach among F-APs. Moreover, the impact of the number of tasks that each F-AP can manage is shown in terms of task processing delay. Numerical results show that the proposed approaches allow to achieve performance closer to the lower bound, by reducing the outage probability and the task processing delay

Energy and Delay Efficient Computation Offloading Solutions for Edge Computing

This thesis collects a selective set of outcomes of a PhD course in Electronics, Telecommunications, and Information Technologies Engineering and it is focused on designing techniques to optimize computational resources in different wireless communication environments. Mobile Edge Computing (MEC) is a novel and distributed computational paradigm that has emerged to address the high users demand in 5G. In MEC, edge devices can share their resources to collaborate in terms of storage and computation. One of the computational sharing techniques is computation offloading, which brings a lot of advantages to the network edge, from lower communication, to lower energy consumption for computation. However, the communication among the devices should be managed such that the resources are exploited efficiently. To this aim, in this dissertation, computation offloading in different wireless environments with different number of users, network traffic, resource availability and devices' location are analyzed in order to optimize the resource allocation at the network edge. To better organize the dissertation, the studies are classified in four main sections. In the first section, an introduction on computational sharing technologies is given. Later, the problem of computation offloading is defined, and the challenges are introduced. In the second section, two partial offloading techniques are proposed. While in the first one, centralized and distributed architectures are proposed, in the second work, an Evolutionary Algorithm for task offloading is proposed. In the third section, the offloading problem is seen from a different perspective where the end users can harvest energy from either renewable sources of energy or through Wireless Power Transfer. In the fourth section, the MEC in vehicular environments is studied. In one work a heuristic is introduced in order to perform the computation offloading in Internet of Vehicles and in the other a learning-based approach based on bandit theory is proposed

Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists

A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks

Vehicular Edge Computing (VEC) enables the integration of edge computing facilities in vehicular networks (VNs), allowing data-intensive and latency-critical applications and services to end-users. Though VEC brings several benefits in terms of reduced task computation time, energy consumption, backhaul link congestion, and data security risks, VEC servers are often resource-constrained. Therefore, the selection of proper edge nodes and the amount of data to be offloaded becomes important for having VEC process benefits. However, with the involvement of high mobility vehicles and dynamically changing vehicular environments, proper VEC node selection and data offloading can be challenging. In this work, we consider a joint network selection and computation offloading problem over a VEC environment for minimizing the overall latency and energy consumption during vehicular task processing, considering both user and infrastructure side energy-saving mechanisms. We have modeled the problem as a sequential decision-making problem and incorporated it in a Markov Decision Process (MDP). Numerous vehicular scenarios are considered based upon the users' positions, the states of the surrounding environment, and the available resources for creating a better environment model for the MDP analysis. We use a value iteration algorithm for finding an optimal policy of the MDPs over an uncertain vehicular environment. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy

Collaborative Reinforcement Learning for Multi-Service Internet of Vehicles

Internet of Vehicles (IoV) is a recently introduced paradigm aiming at extending the Internet of Things (IoT) toward the vehicular scenario in order to cope with its specific requirements. Nowadays, there are several types of vehicles, with different characteristics, requested services, and delivered data types. In order to efficiently manage such heterogeneity, Edge Computing facilities are often deployed in the urban environment, usually co-located with the Roadside Units (RSUs), for creating what is referenced as Vehicular Edge Computing (VEC). In this paper, we consider a joint network selection and computation offloading optimization problem in multi-service VEC environments, aiming at minimizing the overall latency and the consumed energy in an IoV scenario. Two novel collaborative Q-learning based approaches are proposed, where Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication paradigms are exploited, respectively. In the first approach, we define a collaborative Q-learning method in which, through V2I communications, several vehicles participate in the training process of a centralized Q-agent. In the second approach, by exploiting the V2V communications, each vehicle is made aware of the surrounding environment and the potential offloading neighbors, leading to better decisions in terms of network selection and offloading. In addition to the tabular method, an advanced deep learning-based approach is also used for the action value estimation, allowing to handle more complex vehicular scenarios. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy with respect to some benchmark solutions

A Survey on UAV-enabled Edge Computing: Resource Management Perspective

Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU

Offloading Content with Self-organizing Mobile Fogs

Mobile users in an urban environment access content on the internet from different locations. It is challenging for the current service providers to cope with the increasing content demand from a large number of collocated mobile users. In-network caching to offload content at nodes closer to users alleviate the issue, though efficient cache management is required to find out who should cache what, when and where in an urban environment, given nodes limited computing, communication and caching resources. To address this, we first define a novel relation between content popularity and availability in the network and investigate a node's eligibility to cache content based on its urban reachability. We then allow nodes to self-organize into mobile fogs to increase the distributed cache and maximize content availability in a cost-effective manner. However, to cater rational nodes, we propose a coalition game for the nodes to offer a maximum "virtual cache" assuming a monetary reward is paid to them by the service/content provider. Nodes are allowed to merge into different spatio-temporal coalitions in order to increase the distributed cache size at the network edge. Results obtained through simulations using realistic urban mobility trace validate the performance of our caching system showing a ratio of 60-85% of cache hits compared to the 30-40% obtained by the existing schemes and 10% in case of no coalition