Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

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Mobile Edge Computing: A Survey

Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges

Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which prestores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing toward low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a workload allocation problem which suggests the optimal workload allocations between fog and cloud toward the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be, respectively, solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.

Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.

Fog Computing: A Taxonomy, Survey and Future Directions

Survey on fog computing

Fog Computing is a recent computing paradigm that is extending cloud computing towards the edge of network. Due to its proximity to end-users, dense geographical distribution, open platform and support for high mobility, Fog Computing platforms can provide services with reduced latency and improved QoS. Thus it is becoming an important enabler for consumer centric Internet of Things based applications and services that require real time operations e.g. connected vehicles, smart road intersection management and smart grid. The paper discusses one such architecture for connected vehicles with Road Side Units (RSUs) and M2M gateways including the Fog Computing Platform. M2M data processing with semantics, discovery and management of connected vehicles are briefly discussed as consumer centric IoT services enabled by the distinct characteristics of Fog Computing.

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Fog Computing architecture to enable consumer centric Internet of Things services

With the introduction of smartphones, cloud and edge computing, and mobile Internet, the automotive ecosystem is shifting toward the Internet of Vehicles (IoV). This article looks at the evolution leading to the IoV and identifies related research and engineering challenges, including 1) coexistence of cloud, edge computing, and data caching strategies at the edge; 2) integration of data processing and management as IoV services; and 3) seamless interoperability among vehicular sensors, computing platforms, and consumer devices. To address these challenges, we present an Internet of Things (IoT) architecture that considers vehicles as IoT resources and provides 1) mechanisms to integrate them in an IoV ecosystem and 2) seamless interoperation among components (e.g., vehicular sensors, computational platform, and consumers). The functional elements and operational stages of the architecture also assist in maintaining interoperability among the components.

Vehicles as Connected Resources: Opportunities and Challenges for the Future

In this article, we explore the recent Third Generation Partnership Project (3GPP) long-term evolution (LTE) device-to-device (D2D) radio resource management specification (Release 14) to identify the challenges and evaluate the potential of unsupervised LTE D2D (mode 2) for safety-critical vehicle-to-everything (V2X) communications. We also propose two distributed resource allocation strategies for unlicensed band access. Complementary to dedicated short-range communication (DSRC)/intelligent transport system (ITS)-G5, unsupervised LTE D2D presents an opportunity to provide redundancy for ultrareliable systems, such as safety-critical V2X communications.

Unsupervised Long- Term Evolution Device-to-Device: A Case Study for Safety-Critical V2X Communications

A centralized controller computes controls and transmits them to automated vehicles. In case of a communication failure, control values will be missing and vehicles might take incorrect driving decisions. Although modern controllers may resist spurious communication errors, burst errors are expected to be more challenging. This paper introduces a centralized controller robust to burst errors. We propose a model predictive controller (MPC) computing intended future controls and sharing them with vehicles to be stored in a buffer. Through such buffer, vehicles may still retrieve control values even under burst errors. The proposed concept is evaluated in a mixed multi vehicle braking scenario and its superiority over other approaches in terms of distance and acceleration errors compared is shown.

Centralized Model Predictive CACC Control Robust to Burst Communication Errors

This document states the problem of optimizing the structures created
by mobile network or MANET protocols to a mobile network topology. It
also justifies the use and the transmission of position information to
mitigate this problem.

Jerome Haerri

Papers

Fog Computing architecture to enable consumer centric Internet of Things services

Vehicles as Connected Resources: Opportunities and Challenges for the Future

Unsupervised Long- Term Evolution Device-to-Device: A Case Study for Safety-Critical V2X Communications

Centralized Model Predictive CACC Control Robust to Burst Communication Errors

MANET Position and Mobility Signaling: Problem Statement