The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey toward the fifth generation of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of multi-access edge computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC’s ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultralow latency and high quality of service due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.
Security and privacy challenges in MEC like edge computing paradigms are surveyed in @cite_173 and @cite_0 . A partially distributed approach that allows edge intelligence that can meet the privacy requirements of IoT use cases such as eHealth services is presented in @cite_68 . The possibility of exploiting edge computing to solve the problem of loss of privacy by releasing personal and social data to centralized services such as e-commerce sites, rating services, search engines, social networks, and location services are presented in @cite_46 . Possibilities of improving the data privacy of IoT data by using edge computing is presented in @cite_12 .
@cite_173 @cite_0 @cite_46 @cite_68 @cite_12
2768475350 2619310525 2730940305 2593138756
The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures. The advent of Internet of Things and 5G applications renders the need for integration of both centralized cloud computing and emerging mobile-edge computing (MEC) with existing network infrastructures to enhance storage, processing, and caching capabilities in not only centralized but also distributed fashions for supporting both delay-tolerant and mission-critical applications. This paper investigates performance gains of centralized cloud and MEC enabled integrated fiber-wireless (FiWi) access networks. A novel unified resource management scheme incorporating both centralized cloud and MEC computation offloading activities into the underlying FiWi dynamic bandwidth allocation process is proposed. Both MEC and cloud traffic are scheduled outside the transmission slot of FiWi traffic by leveraging time division multiple access. An analytical framework is developed to model packet delay, response time efficiency, gain-offload overhead ratio, and communication-to-computation ratio for both cloud and broadband access traffic. In addition, given the importance of reliability in optical backhaul and MEC, this paper develops a probabilistic survivability analysis model to assess the impact of both fiber cuts and MEC server failures. The obtained results demonstrate the feasibility of implementing conventional cloud and MEC in FiWi access networks, without affecting network performance of broadband access traffic. Many different technologies fostering and supporting distributed and decentralized computing scenarios emerged recently. Edge computing provides the necessary on-demand computing power for Internet-of-Things (IoT) devices where it is needed. Computing power is moved closer to the consumer, with the effect of reducing latency and increasing fail-safety due to absent centralized structures. This is an enabler for applications requiring high-bandwidth uplinks and low latencies to computing units. In this paper, a new use case for edge computing is identified. Mobile devices can overcome their battery limitations and performance constraints by dynamically using the edge-computing-provided computational power. We call this new technology Hybrid Mobile Edge Computing. We present a general architecture and framework, which targets the mobile device use case of hybrid mobile edge computing, not only considering the improvement of performance and energy consumption, but also providing means to protect user privacy, sensitive data and computations. The achieved results are backed by the results of our analysis, targeting the energy saving potentials and possible performance improvements. Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with MEC. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using the graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.