Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Industry investment and research interest in edge computing, in which computing and storage nodes are placed at the Internet's edge in close proximity to mobile devices or sensors, have grown dramatically in recent years. This emerging technology promises to deliver highly responsive cloud services for mobile computing, scalability and privacy-policy enforcement for the Internet of Things, and the ability to mask transient cloud outages. The web extra at www.youtube.com/playlist?list=PLmrZVvFtthdP3fwHPy_4d61oDvQY_RBgS includes a five-video playlist demonstrating proof-of-concept implementations for three tasks: assembling 2D Lego models, freehand sketching, and playing Ping-Pong.

The Emergence of Edge Computing

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties Finally, this paper overviews the current standardization activities and elaborates further on open research challenges

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On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

Fog computing extends the cloud computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are 1) low latency and location awareness, 2) widespread geographical distribution, 3) mobility, 4) very large number of nodes, 5) predominant role of wireless access, 6) strong presence of streaming and real time applications, and 7) heterogeneity. In this chapter, the authors argue that the above characteristics make the Fog the appropriate platform for a number of critical internet of things (IoT) services and applications, namely connected vehicle, smart grid, smart cities, and in general, wireless sensors and actuators networks (WSANs).

Fog Computing and Its Role in the Internet of Things

Multi-cloud computing is the computing platform in the cloud environment, and it differs from hybrid computing that is being implemented in most of the IT enterprises. The primary concern in the usage of the multi-cloud platform is for the handling and processing of data for the effective prediction of the online users in the recommender system. In the existing system, there is no effective clustering approach available for large data set processing, and deployment platform is not with good accuracy and execution time. This paper proposes a novel methodology using the parallel CLARANS, an algorithm for clustering that was embedded with the temporal fuzzy formal concept analysis (FCA). This method employs the homomorphic MapReduce framework (HMRF)and the time-based slope one approach to enhance the multi-cloud computation for recommender system application. The usage of these methods aids the performance to a greater extent. The input of the user is preprocessed to reduce the data inadequacy and mapper-reduce concept implemented in the HMRF before being fed into the recommender algorithm. The output of the algorithm was evaluated based on the mean average error (MAE) that was employed to predict the QoS value. The recommended approach was compared with the existing methodologies and showed enhanced results.

Parallel CLARANS Algorithm for Recommendation System in Multi-cloud Environment

Internet of things (IoT) is playing a vital role in the development of various high-performance smart systems. So much research is being done to improve the quality of human life in various ways. One such research is in the field of hospital management. After the recent COVID situation which has led to concept of social distancing and contactless transactions, we propose a centralized hospital management system using IoT. The proposed system is a mobile app that can be made use of by the doctor to access the patient history from the centralized database. The doctor can then make an E-prescription based on his diagnosis. The E-prescription is generated as a QR code on the patient-side app. The patient can show the QR code to the automatic medical dispensing machine (AMDM) which dispenses the prescribed medicines to the patient by matching it against a QR code. This helps to avoid 70% of the medical errors due to manual prescription and achieve the concept of social distancing and contactless transaction.

Medical IoT—Automatic Medical Dispensing Machine

In this IoT era, we have billions of devices connected to the internet. These devices generate tons of data that has to be stored, processed, and used for making intelligent decisions. This calls for the need for a smart heterogeneous network which could handle this data and make the real-time systems work intelligently. IoT applications leads to increasing demands in high traffic volume, M2M communications, low latency, and MIMO operations. Mobile communication has evolved from 2G voice services into a complex, interconnected environment with multiple services built on a system that supports innumerable applications and provides high-speed access. Hence the sustainability of the IoT applications do rely on next generation networks. Due to the significant increase in the network components, computational complexity, and heterogeneity of resources, there arise the need for a secure architectural framework for internet of things. For this, the authors propose a secure architectural framework for IoT that provides a solution to the lightweight devices with low computational complexity.

C. V. Nisha Angeline

Papers

Fog Computing and Its Role in the Internet of Things

Parallel CLARANS Algorithm for Recommendation System in Multi-cloud Environment

Medical IoT—Automatic Medical Dispensing Machine

Lightweight Secure Architectural Framework for Internet of Things