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
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Fog computing is a promising computing paradigm that extends cloud computing to the edge of networks. Similar to cloud computing but with distinct characteristics, fog computing faces new security and privacy challenges besides those inherited from cloud computing. In this paper, we have surveyed these challenges and corresponding solutions in a brief manner. In many aspects of human activity, there has been a continuous struggle between the forces of centralization and decentralization. Computing exhibits the same phenomenon; we have gone from mainframes to PCs and local networks in the past, and over the last decade we have seen a centralization and consolidation of services and applications in data centers and clouds. We position that a new shift is necessary. Technological advances such as powerful dedicated connection boxes deployed in most homes, high capacity mobile end-user devices and powerful wireless networks, along with growing user concerns about trust, privacy, and autonomy requires taking the control of computing applications, data, and services away from some central nodes (the "core") to the other logical extreme (the "edge") of the Internet. We also position that this development can help blurring the boundary between man and machine, and embrace social computing in which humans are part of the computation and decision making loop, resulting in a human-centered system design. We refer to this vision of human-centered edge-device based computing as Edge-centric Computing. We elaborate in this position paper on this vision and present the research challenges associated with its implementation. In the Internet of Things, services can be provisioned using centralized architectures, where central entities acquire, process, and provide information. Alternatively, distributed architectures, where entities at the edge of the network exchange information and collaborate with each other in a dynamic way, can also be used. In order to understand the applicability and viability of this distributed approach, it is necessary to know its advantages and disadvantages - not only in terms of features but also in terms of security and privacy challenges. The purpose of this paper is to show that the distributed approach has various challenges that need to be solved, but also various interesting properties and strengths. The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.