A Survey on Context-Aware Fog Computing Systems

AbstractIn the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

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Time-constrained strong multi-designated verifier signature suitable for Internet of things–based collaborative fog computing systems

International Journal of Distributed Sensor Networks ◽

10.1177/15501477211001760 ◽

2021 ◽

Vol 17 (3) ◽

pp. 155014772110017

Author(s):

Han-Yu Lin

Keyword(s):

Internet Of Things ◽

Fog Computing ◽

Discrete Logarithm ◽

Signature Scheme ◽

Computing Systems ◽

Extended Technique ◽

Diffie Hellman ◽

Designated Verifier ◽

Designated Verifier Signature ◽

Cloud Servers

Fog computing is viewed as an extended technique of cloud computing. In Internet of things–based collaborative fog computing systems, a fog node aggregating lots of data from Internet of things devices has to transmit the information to distributed cloud servers that will collaboratively verify it based on some predefined auditing policy. However, compromised fog nodes controlled by an adversary might inject bogus data to cheat or confuse remote servers. It also causes the waste of communication and computation resources. To further control the lifetime of signing capability for fog nodes, an appropriate mechanism is crucial. In this article, the author proposes a time-constrained strong multi-designated verifier signature scheme to meet the above requirement. In particular, a conventional non-delegatable strong multi-designated verifier signature scheme with low computation is first given. Based on its constructions, we show how to transform it into a time-constrained variant. The unforgeability of the proposed schemes is formally proved based on the famous elliptic curve discrete logarithm assumption. The security requirement of strong signer ambiguity for our substantial constructions is also analyzed by utilizing the intractable assumption of decisional Diffie–Hellman. Moreover, some comparisons in terms of the signature size and computational costs for involved entities among related mechanisms are made.

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Context Aware Information Classification in Fog Computing

2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) ◽

10.1109/icaecc.2018.8479464 ◽

2018 ◽

Cited By ~ 2

Author(s):

Prashant Sangulagi ◽

Ashok V Sutagundar

Keyword(s):

Fog Computing ◽

Context Aware ◽

Information Classification

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Divisible Load Framework and Close Form for Scheduling in Fog Computing Systems

Advances in Intelligent Systems and Computing - Recent Advances on Soft Computing and Data Mining ◽

10.1007/978-3-030-36056-6_31 ◽

2019 ◽

pp. 323-333 ◽

Cited By ~ 1

Author(s):

Mojtaba Kazemi ◽

Shamsollah Ghanbari ◽

Manochehr Kazemi

Keyword(s):

Fog Computing ◽

Computing Systems ◽

Divisible Load ◽

Close Form

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Enablement of IoT Based Context-Aware Smart Home With Fog Computing

Fog Computing ◽

10.4018/978-1-5225-5649-7.ch013 ◽

2018 ◽

pp. 251-263 ◽

Cited By ~ 1

Author(s):

Maggi Bansal ◽

Inderveer Chana ◽

Siobhan Clarke

Keyword(s):

Smart Home ◽

Fog Computing ◽

Context Aware ◽

Monitoring And Control ◽

Context Processing ◽

Context Aware Computing ◽

Smart Lighting ◽

Human In The Loop ◽

Smart Appliances ◽

Situational Information

The recent advent of Internet of Things (IoT), has given rise to a plethora of smart verticals- smart homes being one of them. Smart Home is a classic example of IoT, wherein smart appliances connected via home gateways constitute a local home network to assist people in activities of daily life. Smart Home involves IoT-based automation (such as smart lighting, heating, surveillance etc.), remote monitoring and control of smart appliances. Besides automation, human-in-the-loop is a unique characteristic of Smart home to offer personalized services. Understanding the human behavior requires context processing. Thus, enablement of Smart home involves two prominent technologies IoT and context-aware computing. Further, local devices lying in the smart home have the implicit location and situational information, hence fog computing can offer real-time smart home services. In this paper, the authors propose ICON (IoT-based CONtext-aware) framework for context-aware IoT applications such as smart home, further ICON leverages fog-based IoT middleware to perform context-aware processing.

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Design of Wearable Computing Systems for Future Industrial Environments

Handbook of Research on Mobility and Computing ◽

10.4018/978-1-60960-042-6.ch075 ◽

2011 ◽

pp. 1226-1245

Author(s):

Pierre Kirisci ◽

Ernesto Morales Kluge ◽

Emanuel Angelescu ◽

Klaus-Dieter Thoben

Keyword(s):

Mobile Computing ◽

Design Process ◽

Reference Model ◽

Wearable Computing ◽

Computing System ◽

Context Aware ◽

Context Model ◽

Computing Systems ◽

Model Driven ◽

Industrial Environments

During the last two decades a lot of methodology research has been conducted for the design of software user interfaces (Kirisci, Thoben 2009). Despite the numerous contributions in this area, comparatively few efforts have been dedicated to the advancement of methods for the design of context-aware mobile platforms, such as wearable computing systems. This chapter investigates the role of context, particularly in future industrial environments, and elaborates how context can be incorporated in a design method in order to support the design process of wearable computing systems. The chapter is initiated by an overview of basic research in the area of context-aware mobile computing. The aim is to identify the main context elements which have an impact upon the technical properties of a wearable computing system. Therefore, we describe a systematic and quantitative study of the advantages of context recognition, specifically task tracking, for a wearable maintenance assistance system. Based upon the experiences from this study, a context reference model is proposed, which can be considered supportive for the design of wearable computing systems in industrial settings, thus goes beyond existing context models, e.g. for context-aware mobile computing. The final part of this chapter discusses the benefits of applying model-based approaches during the early design stages of wearable computing systems. Existing design methods in the area of wearable computing are critically examined and their shortcomings highlighted. Based upon the context reference model, a design approach is proposed through the realization of a model-driven software tool which supports the design process of a wearable computing system while taking advantage of concise experience manifested in a well-defined context model.

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