Virtual Vidyalaya

Pervasive computing is an evolving environment for the next generation providing with Information & Communication Technology everywhere, for everyone, at all times. e-learning is a phenomenon which is catching up the fire fiercely not only in corporate training world but also in the different sections of society. Pervasive computing is still in the evolving stage and opens a great naïve market yet unexplored. This chapter studies the features of Pervasive Computing which can take e-learning to the greater heights opening a new horizon for its growth and development. It also tries to analyze the factors that can contribute to the success of e-learning not only in making it reach the corporate training world but also in the rural areas of the world making a dream of education to all a reality in the environment of Pervasive Computing. It also studies and tries to find out the scope of e-learning in the light of Pervasive Computing.

An Intelligent Framework for Automatic Question Set Generation

Automated question selection is an emerging problem in the industry of Online Test Management. The Test Management Suites, offer administration of question sets, either precompiled by experts, or randomized over the database of questions. Presently available literature in this domain is sparse and primarily focuses on automated question classification problem. This paper proposes a novel technique for administering question sets in an intelligent and automated approach. Artificial Intelligence, in the form of Self Organizing Feature Maps is utilized for question selection process. Finally, results from experiments are compiled for an illustration of the whole technique. Optimal design parameters for further research are also proposed alongside plausible future direction in pervasive computing.

A Software Component Generation Model for Pervasive Computing Environment

In order to develop software components that are reusable across the pervasive computing applications it would be required to consider the variations and properties (mobility, adaptability, composability, context awareness etc.) that may be required for different pervasive computing applications (application types). It should go without saying that various requirements and variations may not always be known a priori and hence developing all the multiple variants may not always be possible or feasible. It is quite unlikely that all the pervasive computing applications would be able to reuse a component ‘as-is’ always. One idea is to use lightweight components such that the overheads (those that are not required in a particular pervasive computing application) do not get transported with the body of the component. Based on this idea, a model of “Generic Component” with ‘Component Generator’ has been proposed that will generate components according to the requirements of a specific pervasive computing application. This work starts a discussion and calls for more extensive research oriented studies by professionals and academicians for perfection of the model.

Recent Trends in Pervasive and Ubiquitous Computing

Pervasive computing is the trend towards increasingly ubiquitous connected computing devices in the environment, a trend being brought about by a convergence of advanced electronic – and particularly, wireless - technologies and the Internet. Pervasive computing devices are not personal computers but very tiny - even invisible - devices, either mobile or embedded in almost any type of object imaginable, including cars, tools, appliances, clothing and various consumer goods – all communicating through increasingly interconnected networks. In the future these smart devices will maintain current information about their locations, the contexts in which they are being used, and relevant data about the users. The goal of researchers is to create a system that is pervasively and unobtrusively embedded in the environment, completely connected, intuitive, effortlessly portable, and constantly available. Among the emerging technologies expected to prevail in the pervasive computing environment of the future are wearable computers, smart homes and smart buildings. Among the myriad of tools expected to support these are: application-specific integrated circuitry (ASIC); speech recognition; gesture recognition; system on a chip (SoC); perceptive interfaces; smart matter; flexible transistors; reconfigurable processors; field programmable logic gates (FPLG); and micro electromechanical systems (MEMS).

Pervasive Computing and Ambient Intelligence Development

Ambient Intelligence (AmI) as an environment, where pervasive computing methods, tools, or products are taking place, is a concept envisioning information society in the future. AmI can be investigated from several points of view, whereas a technological perspective is the one most frequently presented. However, as the fragments of AmI vision are becoming reality, the educational perspective is of growing importance. Managerial workplace may be considered as a typical environment where pervasive, or ubiquitous, computing and AmI are being currently introduced. Hence, the proper education of future managers or IT specialists should aim at students’ ability to define requirements for the structure and behaviour of AmI from different viewpoints and from final users’ perspective namely. The problem is that AmI is a system with high level of complexity due to the need to unite many related aspects from different problem domains. Therefore, relevant tools and methods should be taught so that students can comprehend such a complexity. A brief description of the situation in the selected Czech university and results based on particular tools and methodological approaches are depicted in the following chapter.

Neuro Linguistic Programming

The power of computers is now beginning to be exploited in subjective areas of human study like those related to human psychology in order to make the interaction between humans and computers more natural. An effective interaction must involve automatic analysis of human behavior by the computer, and responding to it. Neuro-Linguistic Programming was developed, drawing its inspiration from the computer programs, so as to change the perception of the human brain to a more successful behavior. In addition to that, Neuro Linguistic Programming gave an algorithmic approach to ‘observe’ and analyze human behavior, both verbal and non-verbal, and serve to effectively perceive the human behavior and interaction. The interaction is a more deep rooted cognitive interaction. With the use of Neuro Linguistic Programming and the fundamentals of cybernetics, the humans and computer can be brought closer, with automatic transfer of valuable information between the two. This chapter describes the fundamentals of Neuro Linguistic Programming and aims at developing a hypothetical model on how Neuro Linguistic Programming can be used to better understand the interaction between the humans and the computers.

Software Parallel Processing in Pervasive Computing

This chapter proposes the application of periodic wave concepts in management of software Parallel processing projects or processes. This chapter lays special emphasis on Runaway project, which create a lot of problems in Project Management for the stakeholders. This chapter proposes a new and dynamic way to control the software project estimation activity of Runaway project/processes, thereby reducing their future occurrences. This chapter also explains how the equations of unidirectional periodic Waves can be applied in software Parallel processing to measure quality and project execution in a dynamic way at any point in time. The concepts proposed here are dynamic unlike PERT/CPM and other metrics, which fail in worst-case scenarios. The Propagation Speed ‘C’ at any point in time of a stage or part of a software system executing in Parallel can be given by: C = H/ k (1). Where, H = length of the Wave (i.e. highest point), k = time taken in completing the stage.

Kernel Parameter Selection for SVM Classification

The choice of kernel function and its parameter is very important for better performance of support vector machine. In this chapter, the authors proposed few new kernel functions which satisfy the Mercer’s conditions and a robust algorithm to automatically determine the suitable kernel function and its parameters based on AdaBoost to improve the performance of support vector machine. The performance of proposed algorithm is evaluated on several benchmark datasets from UCI repository. The experimental results for different datasets show that the Gaussian kernel is not always the best choice to achieve high generalization of support vector machine classifier. However, with the proper choice of kernel function and its parameters using proposed algorithm, it is possible to achieve maximum classification accuracy for all datasets.

Optimal Resource Allocation Model for Pervasive Healthcare Using Genetic Algorithm

Number of mobile devices, equipped with sophisticated services apart from communication, is increasing day by day. Today a vast set of high speed network infrastructure, both wired and wireless, exists. This work studies the mobility management model for healthcare services developed for the efficient utilization of the network infrastructure. The model assumes that the physicians (doctors) are highly mobile and are periodically changing their location to perform their daily work, which includes serving patients at different nodes (serving as health centre). The mobility information about these doctors dependents on their current location. A location-aware medical information system is developed to provide information about resources such as the location of a medical specialist and patient’s records. In the author’s previous work, a framework is developed to describe the relations between types of hospitals and specialists with the use of Hospital Information Systems (HIS). The model for pervasive healthcare is to manage the specialists’ movements between the hospital nodes with the objective to serve the maximum number of patients in minimum amount of time. In this work, they carried out simulation experiments to evaluate the performance of the proposed model towards servicing the patients. It has been observed that the model performs better in servicing the patients in the service area.

Security in Pervasive Computing

Pervasive computing has wide application in military, medical and smart home domain. In pervasive computing, a large number of smart objects interact with one another without the user intervention. Although the technology is promising but security needs to be addressed before the technology is widely deployed. Pervasive networks are formed spontaneously and the devices communicate via radio. Thus, mobile ad hoc networking is an essential technology for pervasive computing. An ad hoc network is a collection of wireless mobile nodes, which acts as a host as well as a router. The communication between the nodes is multihop without any centralized administration. AODV (Ad Hoc On demand Distance Vector) is a prominent on-demand reactive routing protocol for mobile ad hoc networks. But in existing AODV, there is no security provision against well-known attack known as “Black hole attack”. Black hole nodes are those malicious nodes that agree to forward the packets to destination but do not forward the packets intentionally. Thischapter extends the watchdog mechanism for the AODV routing protocol to detect such misbehavior based on promiscuous listening. The proposed method first detects a black hole node and then gives a new route bypassing this node. The experimental results show that in a lightly loaded, hostile environment, the proposed scheme improves the throughput compared to an unprotected AODV protocol.