Communication Trends in Internet of Things

The central hypothesis of Internet of Things is the term “connectivity”. The IoT devices are connected to the Internet through a wide variety of communication technologies. This chapter explains the various technologies involved in IoT connectivity. The diversity in communication raises the query of which one to choose for the proposed application. The key objective of the application needs to be defined very clearly. The application features such as the power requirement, data size, storage, security and battery life highly influence the decision of selecting one or more communication technology. Near Field Communication is a good choice for short-range communication, whereas Wi-Fi can be opted for a larger range of coverage. Though Bluetooth is required for higher data rate, it is power hungry, but ZigBee is suitable for low power devices. There involves always the tradeoff between the technologies and the requirements. This chapter emphasizes that the goal of the application required to be more precise to decide the winner of the IoT connectivity technology that suits for it.

A Quantitative Study of Factors Affecting Value of Adopting Self-Service Banking Technology (SSBT) Among Customers in Developing and Developed Countries

Self-service banking technology (SSBT) allow customers to perform services on their own without direct assistance from staff. This study focuses on factors affecting the value of adopting self-Service banking technology (SSBT) among customers. It is believed that the successful usage of self-service banking technology will be increasingly advantageous for all (banks & customers). This chapter's purpose is an extension to the technology acceptance model (TAM) and views customer responses to technology as an integrated part of SSBT. The sample used for this study was selected from users of banks in both Libya and Australia, with a total size of 141 respondents. Reliability and validity of the data collection instrument was tested using Cronbach Alpha. Descriptive and regression tests for data analysis were used. The domains in which subjects were tested were “ease of use of SSBT”, “Usefulness of SSBT”, “Quality of SSBT”, “privacy of information” and “Trust of SSBT”.

Beyond Service-Oriented Architectures

Predictions for Service Oriented Architectures (SOA) to deliver transformational results to the role and capabilities of IT for businesses have fallen short. Unforeseen challenges have often emerged in SOA adoption. They fall into two categories: technical issues stemming from service components reuse difficulties and organizational issues stemming from inadequate support or understanding of what is required from the executive management in an organization to facilitate the technical rollout. This paper first explores and analyses the hindrances to the full exploitation of SOA. It then proposes an alternative service delivery approach that is based on even a higher degree of loose coupling than SOA. The approach promotes knowledge services and agent-based support for integration and identification of services. To support the arguments, this chapter sketches as a proof of concept the operationalization of such a service delivery system in disaster management.

Smart City Portals for Public Service Delivery

The future cities of our societies need to integrate their citizens into a value-co-creation process in order to transform to smart cities with an increased quality of life for their citizens. Therefore, administrations need to radically improve the delivery of public services, providing them citizen- and user-centric. In this context, online portals represent a cost effective front-end to deliver services and engage customers and new organizational approaches as back-ends which decouple the service interface from the departmental structures emerged. The research presented in this book chapter makes two main contributions: Firstly, the findings of a usability study comparing the online presences of the Queensland Government, the UK Government and the South Australian Government are reported and discussed. Secondly, the findings are reflected in regard to a broader “Transformational Government” approach and current smart city research and developments. Service bundling and modularization are suggested as innovative solutions to further improve online service delivery.

A Genetic-Algorithms-Based Technique for Detecting Distributed Predicates

One of the techniques that have been used in the literature to enhance the dependability of distributed applications is the detection of distributed predicates techniques (also referred to as runtime verification). These techniques are used to verify that a given run of a distributed application satisfies certain properties (specified as predicates). Due to the existence of multiple processes running concurrently, the detection of a distributed predicate can incur significant overhead. Several researchers have worked on the development of techniques to reduce the cost of detecting distributed predicates. However, most of the techniques presented in the literature work efficiently for specific classes of predicates, like conjunctive predicates. This chapter presents a technique based on genetic algorithms to efficiently detect distributed predicates under the possibly modality. Several experiments have been conducted to demonstrate the effectiveness of the proposed technique.

Intelligent Radial Basis Function Neural Network for Intrusion Detection in Battle Field

Defense at boundary is nowadays well equipped with perimeter protection, cameras, fence sensors, radars etc. However, in battlefield there is more feasibility of entering of a non-native human and unknowing stamping of the explosives placed in the various paths by the native soldiers. There exists no alert system in the battlefield for the soldiers to identify the intruder or the explosives in the field. Therefore, there is a need for an automated intelligent intrusion detection system for battlefield monitoring. This chapter proposes an intelligent radial basis function neural network (RBFNN) technique for intrusion detection and explosive identification. The proposed intelligent RBFNN implements some intellectual components in the algorithm to make the neural network think before learning the training samples. Involvement of intellectual components makes the learning process simple, effective and efficient. The proposed technique helps to reduce false alarm and encourages timely detection thereby providing extensive support for the native soldiers and save the life of the mankind.

A Pattern-Mining Approach for Wearable Sensor-Based Remote Health Care

Rapid advancement in Wireless Sensor Network (WSN) technology facilitates remote health care solutions without hindering the mobility of a person using Wearable Wireless Body Area Network (WWBAN). Activity recognition, fall detection and finding abnormalities in vital parameters play a major role in pervasive health care for making accurate decision on health status of a person. This chapter presents the proposed two pattern mining algorithms based on associative classification and fuzzy associative classification which models the association between the attributes that characterize the activity or health condition and handles the uncertainty in data respectively for an accurate decision making. The algorithms mine the data from WWBAN to detect abnormal health status of the person and thus facilitate remote health care. The experimental results on the proposed algorithms show that they work par with the popular traditional algorithms and predicts the activity class, fall or health status in less time compared to existing traditional classifiers.

Applying Semantic Relations for Automatic Topic Ontology Construction

The rapid growth of web technologies had created a huge amount of information that is available as web resources on Internet. Authors develop an automatic topic ontology construction process for better topic classification and present a corpus based novel approach to enrich the set of categories in the ODP by automatically identifying concepts and their associated semantic relationships based on external knowledge from Wikipedia and WordNet. The topic ontology construction process relies on concept acquisition and semantic relation extraction. Initially, a topic mapping algorithm is developed to acquire the concepts from Wikipedia based on semantic relations. A semantic similarity clustering algorithm is used to compute similarity to group the set of similar concepts. The semantic relation extraction algorithm derives associated semantic relations between the set of extracted topics from the lexical patterns in WordNet. The performance of the proposed topic ontology is evaluated for the classification of web documents and obtained results depict the improved performance over ODP.

Clustering Mixed Datasets Using K-Prototype Algorithm Based on Crow-Search Optimization

Data mining techniques are useful to discover the interesting knowledge from the large amount of data objects. Clustering is one of the data mining techniques for knowledge discovery and it is the unsupervised learning method and it analyses the data objects without knowing class labels. The k-prototype is the most widely-used partitional clustering algorithm for clustering the data objects with mixed numeric and categorical type of data. This algorithm provides the local optimum solution due to its selection of initial prototypes randomly. Recently, there are number of optimization algorithms are introduced to obtain the global optimum solution. The Crow Search algorithm is one the recently developed population based meta-heuristic optimization algorithm. This algorithm is based on the intelligent behavior of the crows. In this paper, k-prototype clustering algorithm is integrated with the Crow Search optimization algorithm to produce the global optimum solution.

Crow-Search-Based Intuitionistic Fuzzy C-Means Clustering Algorithm

Data clustering is an unsupervised technique that segregates data into multiple groups based on the features of the dataset. Soft clustering techniques allow an object to belong to various clusters with different membership values. However, there are some impediments in deciding whether or not an object belongs to a cluster. To solve these issues, an intuitionistic fuzzy set introduces a new parameter called hesitancy factor that contributes to the lack of domain knowledge. Unfortunately, selecting the initial centroids in a random manner by any clustering algorithm delays the convergence and restrains from getting a global solution to the problem. To come across these barriers, this work presents a novel clustering algorithm that utilizes crow search optimization to select the optimal initial seeds for the Intuitionistic fuzzy clustering algorithm. Experimental analysis is carried out on several benchmark datasets and artificial datasets. The results demonstrate that the proposed method provides optimal results in terms of objective function and error rate.