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

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.

Fog Computing and Virtualization

The essence of Cloud computing is moving out the processing from the local systems to remote systems. Cloud is an umbrella of physical/virtual services/resources easily accessible over the internet. With more companies adopting cloud either fully through public cloud or Hybrid model, the challenges in maintaining a cloud capable infrastructure is also increasing. About 42% of CTOs say that security is their main concern for moving into cloud. Another problem which is mainly problem with infrastructure is the connectivity issue. The datacenter could be considered as the backbone of cloud computing architecture. As the processing power and storage capabilities of the end devices like mobile phones, routers, sensor hubs improve we can increasing leverage these resources to improve your quality and reliability of services.

APT

The global routing system has seen a rapid increase in table size and routing changes in recent years, mostly driven by the growth of edge networks. This growth reflects two major limitations in the current architecture: (a) the conflict between provider-based addressing and edge networks' need for multihoming, and (b) flat routing's inability to provide isolation from edge dynamics. In order to address these limitations, we propose A Practical Tunneling Architecture (APT), a routing architecture that enables the Internet routing system to scale independently from edge growth. APT partitions the Internet address space in two, one for the transit core and one for edge networks, allowing edge addresses to be removed from the routing table in the transit core. Packets between edge networks are tunneled through the transit core. In order to automatically tunnel the packets, APT provides a mapping service between edge addresses and the addresses of their transit-core attachment points. We conducted an extensive performance evaluation of APT using trace data collected from routers at two major service providers. Our results show that APT can tunnel packets through the transit core by incurring extra delay on up to 0.8% of all packets at the cost of introducing only one or a few new or repurposed devices per AS.

Privacy Enhanced Cloud-Based Recommendation Service for Implicit Discovery of Relevant Support Groups in Healthcare Social Networks

Recommending support-groups in healthcare social networks is the problem of detecting for each patient his/her membership to one support-group of relevant patients. The patients in each support-group share some relevant preferences which guarantee that the support-group as a whole satisfies some desired properties of similarity. As a result, forming these support-groups requires the availability of personal data of different patients. This is a crucial requirement for different recommender services. With the increasing trend of service providers to collect a large volume of personal data regarding their end-users, presumably to better serve them. However, a significant part of the data that is typically collected is not essential to the service being offered, or to the completion of the services it was presumably released for. Gathering such unnecessary data can be seen as a privacy threat, and storing it exposes the end-users to further unavoidable risks. In this paper, a privacy enhanced cloud-based recommendation service is proposed for the implicit discovery of appropriate support groups in healthcare social network. A fog based middleware (FMCP) was introduced that runs at patients' sides and allows exchanging of their information to facilities recommending and creating support-groups without disclosing their real preferences to other parties. The membership of patients in various support groups allows receiving highly appropriate and reliable healthcare-related advices. The system utilizes two protocols to attain this goal. Experiments were performed on real dataset.

Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System

This chapter proposes an efficient centralized secure architecture for end to end integration of IoT based healthcare system deployed in Cloud environment. The proposed platform uses Fog Computing environment to run the framework. In this chapter, health data is collected from sensors and collected sensor data are securely sent to the near edge devices. Finally, devices transfer the data to the cloud for seamless access by healthcare professionals. Security and privacy for patients' medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems. The proposed system uses asynchronous communication between the applications and data servers deployed in the cloud environment.

Smart XSS Attack Surveillance System for OSN in Virtualized Intelligence Network of Nodes of Fog Computing

This article introduces a distributed intelligence network of Fog computing nodes and Cloud data centres for smart devices against XSS vulnerabilities in Online Social Network (OSN). The cloud data centres compute the features of JavaScript, injects them in the form of comments and saved them in the script nodes of Document Object Model (DOM) tree. The network of Fog devices re-executes the feature computation and comment injection process in the HTTP response message and compares such comments with those calculated in the cloud data centres. Any divergence observed will simply alarm the signal of injection of XSS worms on the nodes of fog located at the edge of the network. The mitigation of such worms is done by executing the nested context-sensitive sanitization on the malicious variables of JavaScript code embedded in such worms. The prototype of the authors' work was developed in Java development framework and installed on the virtual machines of Cloud data centres (typically located at the core of network) and the nodes of Fog devices (exclusively positioned at the edge of network). Vulnerable OSN-based web applications were utilized for evaluating the XSS worm detection capability of the authors' framework and evaluation results revealed that their work detects the injection of XSS worms with high precision rate and less rate of false positives and false negatives.

Proactive Mobile Fog Computing Using Work Stealing

A common design of the Internet of Things (IoT) system relies on distant Cloud for management and processing, which faces the challenge of latency, especially when the application requires rapid response in the edge network. Therefore, researchers have proposed the Fog computing architecture, which distributes the computational data processing tasks to the edge network nodes located in the vicinity of data sources and end-users to reduce the latency. Although the Fog computing architecture is promising, it still faces a challenge in mobility when the tasks come from ubiquitous mobile applications in which the data sources are moving objects. In order to address the challenge, this article proposes a proactive Fog service provisioning framework, which hastens the task distribution process in Mobile Fog use cases. Further, the proposed framework provides an optimization scheme in task allocation based on runtime context information. A proof-of-concept prototype has been implemented and tested on real devices.

An Extension of the MiSCi Middleware for Smart Cities Based on Fog Computing

In a Smart City is required computational platforms, which allow environments with multiple interconnected and embedded systems, where the technology is integrated with the people, and can respond to unpredictable situations. One of the biggest challenges in developing Smart City is how to describe and dispose of enormous and multiple sources of information, and how to share and merge it into a single infrastructure. In previous works, we have proposed an Autonomic Reflective Middleware with emerging and ubiquitous capabilities, which is based on intelligent agents that can be adapted to the existing dynamism in a city for, ubiquitously, respond to the requirements of citizens, using emerging ontologies that allow the adaptation to the context. In this work, we extend this middleware using the fog computing paradigm, to solve this problem. The fog extends the cloud to be closer to the things that produce and act on the smart city. In this paper, we present the extension to the middleware, and examples of utilization in different situations in a smart city.

Fog Computing

The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.

Image Mosaicing Using Binary Edge Detection Algorithm in a Cloud-Computing Environment

Image Mosaicing is an image processing technique that arises from the need of having a more realistic view of the real world wider than the view captured by the lenses of the available cameras. In this paper, a sequence of images will be mosaiced using binary edge detection algorithm in a cloud-computing environment to improve processing speed and accuracy. The authors have used Platform as a Service (PaaS) to provide a number of nodes in the cloud to run the computational intensive image processing and stitching algorithms. This increased the processing speed as most of image processing algorithms deal with every single pixel in the image. Message Passing Interface (MPI) is used for message passing among the compute-nodes in the cloud and a MapReduce technique is used for image distribution and collection, where the root node is used as reducer and the others as mappers. After applying the algorithm on different sequence of images and different machines on JUST cloud, the authors have achieved high mosaicing accuracy, and the execution time has been improved when comparing it with sequential execution on the images.