Context Aware Information Classification in Fog Computing

2018 ◽  
Author(s):  
Prashant Sangulagi ◽  
Ashok V Sutagundar
Keyword(s):  
Fog Computing ◽  
Context Aware ◽  
Information Classification

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

Fog Computing ◽  
2018 ◽  
pp. 251-263 ◽  
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.

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

2017 ◽  
Vol 19 (4) ◽  
pp. 1-12 ◽  
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 ◽  
And Control

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.

scholarly journals Driver Behavior Modeling: Developments and Future Directions

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Najah AbuAli ◽  
Hatem Abou-zeid
Keyword(s):  
Fog Computing ◽  
Driver Behavior ◽  
Behavior Modeling ◽  
Mobile Cloud ◽  
Context Aware ◽  
Design Development ◽  
Future Directions ◽  
Research Challenges ◽  
Recent Developments ◽  
Behavior Models

The advances in wireless communication schemes, mobile cloud and fog computing, and context-aware services boost a growing interest in the design, development, and deployment of driver behavior models for emerging applications. Despite the progressive advancements in various aspects of driver behavior modeling (DBM), only limited work can be found that reviews the growing body of literature, which only targets a subset of DBM processes. Thus a more general review of the diverse aspects of DBM, with an emphasis on the most recent developments, is needed. In this paper, we provide an overview of advances of in-vehicle and smartphone sensing capabilities and communication and recent applications and services of DBM and emphasize research challenges and key future directions.

Context Aware Resource and Service Provisioning Management in Fog Computing Systems

2017 ◽  
pp. 213-223 ◽  
Author(s):  
Saša Pešić ◽  
Milenko Tošić ◽  
Ognjen Iković ◽  
Mirjana Ivanović ◽  
Miloš Radovanović ◽  
...  
Keyword(s):  
Fog Computing ◽  
Context Aware ◽  
Service Provisioning ◽  
Computing Systems

Context-Aware Placement of Industry 4.0 Applications in Fog Computing Environments

2020 ◽  
Vol 16 (11) ◽  
pp. 7004-7013
Author(s):  
Redowan Mahmud ◽  
Adel N. Toosi ◽  
Kotagiri Ramamohanarao ◽  
Rajkumar Buyya
Keyword(s):  
Industry 4.0 ◽  
Fog Computing ◽  
Context Aware ◽  
Computing Environments

scholarly journals Dynamic Priority-Based Service Resource Allocation for Context-Aware Conflict Resolution in Wisdom Network with Fog Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Lin Duo ◽  
Qianqian Li ◽  
Haitao Xu ◽  
Yunhui Zhou
Keyword(s):  
Resource Allocation ◽  
Conflict Resolution ◽  
Differential Game ◽  
Optimal Allocation ◽  
Fog Computing ◽  
Context Aware ◽  
Dynamic Priority ◽  
Service Resource ◽  
Research Goal

With the development of wisdom network, this paper assumes that intelligent devices become more and more intelligent, which can easily collect and provide a variety of context awareness data. The research goal is to design a dynamic conflict resolution strategy for context-aware resource allocation. The limited availability of resources inevitably leads to conflicts. Considering the characteristics of wisdom network, the quality of service when solving conflicts, a mechanism is proposed to improve the quality of services and to solve the resources allocation conflicts. This paper constructs the optimal model of context-aware based on a differential game and optimizes the resource allocation of context-aware based on the priority of scenarios. Fog computing is used to provide enough computing resources for the control of resource allocation of context-aware. The Bellman dynamic programming is introduced to solve the feedback Nash equilibrium solution of the proposed differential game model, to obtain the optimal allocation of service resources and solve the effectiveness of resource allocation.

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