Extending a Conventional Chatbot Knowledge Base to External Knowledge Source and Introducing User-Based Sessions for Diabetes Education

Chatbots or conversational agents are computer programs that interact with users using natural language through artificial intelligence in a way that the user thinks he is having dialogue with a human. One of the main limits of chatbot technology is associated with the construction of its local knowledge base. A conventional chatbot knowledge base is typically hand constructed, which is a very time-consuming process and may take years to train a chatbot in a particular field of expertise. This chapter extends the knowledge base of a conventional chatbot beyond its local knowledge base to external knowledge source Wikipedia. This has been achieved by using Media Wiki API to retrieve information from Wikipedia when the chatbot's local knowledge base does not contain the answer to a user query. To make the conversation with the chatbot more meaningful with regards to the user's previous chat sessions, a user-specific session ability has been added to the chatbot architecture. An open source AIML web-based chatbot has been modified and programmed for use in the health informatics domain. The chatbot has been named VDMS – Virtual Diabetes Management System. It is intended to be used by the general community and diabetic patients for diabetes education and management.

Classification of Handoff Schemes in a Wi-Fi-Based Network

Handoff management of the users is one of the major issues wi-fi-based wireless LAN. The total handoff process can be divided into three phases, namely scanning, authentication, and re-association. If mobile client frequently changes its position while accessing internet, number of handoffs also increases proportionally. Frequent handoffs affect the quality of service of different wireless applications because of large handoff latency. Many schemes have been developed for reducing handoff delay. In this chapter, handoff management schemes have been classified based on the phase in which the scheme works. Thus, the techniques have been classified as scanning-based schemes, authentication-based schemes, and re-association-based schemes. This chapter also classifies the handoff schemes into two categories based on the number of radios used: single-radio-based handoff schemes and multi-radio-based handoff schemes. The schemes under each of the class have been discussed in detail. A comprehensive comparison of all the schemes has also been presented in this chapter.

Quality Evaluation of Cloud and Fog Computing Services in 5G Networks

Because of the increased computing and intelligent networking demands in 5G network, cloud computing alone encounters too many limitations, such as requirements for reduced latency, high mobility, high scalability, and real-time execution. A new paradigm called fog computing has emerged to resolve these issues. Fog computing distributes computing, data processing, and networking services to the edge of the network, closer to end users. Fog applied in 5G significantly improves network performance in terms of spectral and energy efficiency, enable direct device-to-device wireless communications, and support the growing trend of network function virtualization and separation of network control intelligence from radio network hardware. This chapter evaluates the quality of cloud and fog computing services in 5G network, and proposes five algorithms for an optimal selection of 5G RAN according to the service requirements. The results demonstrate that fog computing is a suitable technology solution for 5G networks.

Characterization of Online Social Network

The rapid growth of internet with large number of social network sites makes it easy to interconnect people from all over the world on a shared platform. Social network can be represented by a graph, where individual users are represented as nodes/vertices and connections between them are represented as edges of the graph. As social network inherits the properties of graph, its characterization includes centrality and community detection. In this chapter we discuss three centrality measures and its effects for information propagation. We discuss three popular hierarchical community detection measures and make a comparative analysis of them. Moreover we propose a new ego-based community detection algorithm which can be very efficient in terms of time complexity for very large network like online social network. In this chapter, a network is formed based on the data collected from Twitter account using hashtag(#).

Software-Defined Network Security

The future networks are expected to lead a hyper-connected society with the promise of high social and economic value. The goal is to solve today's network problems and provide satisfactory security. Thus, the future networks require a flexible infrastructure that is secure against cyberattacks. Software defined networking (SDN) can be considered as one of the building blocks of upcoming networking technologies. In this chapter, first, the limitations of today's networks are presented. Then, solutions to secure the networks with SDN components are given. This concept is referred to as “SDN for Security.” While SDN facilitates securing networks in general, it introduces additional challenges, mainly, the vulnerabilities of the SDN components such as the controller have to be addressed. Security for SDN aims at securing SDN assets and is discussed in the sequel. After reading this chapter, readers will obtain a comprehensive overview of the limitations of traditional networks, such as how SDN overcomes those limitations and the security issues thereof.

Network Support for IoT Ecosystems

IoT is a new paradigm that enables cloud environments to capture, interpret, and analyze data at a real time from some sensors distributed in the environment and able to act on the environment based on decision-making algorithms. Communication between the sensors and the cloud is performed via the network. This chapter presents a bibliographic study that aims to map out which network technologies are more adapted to the IoT environments. In this sense, the many characteristics are presented, such as security, distance range, energy consumption, and versions of each illustrated network technology in order to support the choice of better network technology for new IoT projects.

5G Networks

Mobile communication witnesses a new generation after every 10 years. Following the same trend, the roll out of next generation called 5G network is anticipated by 2020. In this chapter, a critical review of enabling technologies and research issues of 5G network is provided. The novelty of the chapter lies in providing a holistic view of 5G networks spanning discussions on technologies and issues across all the layers of protocol stack. Specifically, the chapter talks about the higher-level research issues of 5G network. The chapter is primarily structured as follows: It starts with a brief overview of various generations of mobile communication. Then, the problems with existing generation of mobile communication are presented, thus providing the motivation for a new generation of mobile network. A survey of different enabling technologies of 5G network is provided afterwards. After having brief discussions on key enablers, the chapter presents various research issues of 5G network. The chapter concludes with highlighting current challenges and future research issues.

Spectrum Sharing for D2D Communications in Fifth-Generation Wireless Networks

In this chapter, the authors discuss various spectrum sharing techniques to enable device-to-device (D2D) communications over the licensed spectrum. First, they highlight the need of spectrum sharing in fifth-generation (5G) wireless and mobile networks. Then, they formulate the expressions of useful performance metrics e.g., outage probability, achievable sum-rate, and spectral efficiency of these schemes to refine physical layer design aspects. To give a better picture, they deduce some major practical scenarios where these techniques can play a crucial role in deploying future generation wireless networks. They also cover relevant literature on the spectrum sharing and D2D communications. Numerical and simulation results are provided to elucidate the effect of various system/channel parameters on the considered spectrum sharing schemes over Nakagami-m fading channels.

Routing Optimization for Integrated Optical and Mobile Ad hoc Networks

Resource consumption in access network will continue to draw attention due to the increasing trend of mobile user device and application. This chapter will address the issue of resource utilization efficiency via alternate specific method known as Taguchi offline optimization-based on mobile ad hoc network (MANET) to be applied into cooperating multiple layers framework of deploy over passive optical network (PON) called the last mile mobile hybrid optical wireless access network (LMMHOWAN). Adhering to this method, the chapter is also to propose the design of experiment simulation model using OMNeT++ software and its impact is investigated on the identified performance metrics like the end-to-end delay, packet delivery ratio (PDR), network capacity, packet loss probability (PLP), and energy consumption. Simulation result shows that the heterogeneous optical wireless network under the influence of random mobile connection can perform better with the optimized front-end wireless ad hoc.

Blending Association Rules for Knowledge Discovery in Big Data

Data mining techniques have been widely applied in several domains to support a variety of business-related applications such as market basket analysis. For instance, basket market transaction accumulate large amounts of customer purchase data from their day-to-day operations. This paper delivers a strategy for the implementation of a systematic analysis framework built on the established principles used in data mining and machine learning areas.We employ Apriori and FP-growth algorithms coupled with support vector machine to implement our recommendation systems. Experiments are done using a real market dataset and the 0.632+ bootstrap method is used here in order to evaluate our framework. The obtained results suggest that the proposed framework will be able to generate benefits for grocery chain using a real-world grocery store data. FP-growth algorithm shows better performance over Apriori in terms of time complexity.