Open Source Software in the Arab World

A literature survey study was conducted to explore the state-of-the-art of Open Source Software and the opportunities and challenges faced by this segment of the software industry in seven Arab countries — Tunisia, Egypt, Jordan, KSA, Qatar, Oman and UAE. A framework and road map for OSS is presented derived from interviews conducted in the UAE with at least four experts from each of the following categories: governments and ministries, IT companies, universities and IT enthusiasts. This is the first study of its kind in this part of the world and is expected to make a significant contribution to the direction for Open Source Software in the region and beyond.

IoT in Healthcare

The fields of computer science and electronics have merged to result into one of the most notable technological advances in the form of realization of the Internet of Things. The market for healthcare services has increased exponentially at the same time security flaws could pose serious threats to the health and safety of patients using wearable technologies and RFID. The volume and sensitivity of data traversing the IoT environment makes dangerous to messages and data could be intercepted and manipulated while in transit. This scenario must absolutely respect the confidentiality and privacy of patient's medical information. Therefore, this chapter presents various security issues or vulnerabilities with respect to attacks and various situations how information will be attacked by the attacker in healthcare IoT. The working principle of healthcare IoT also discussed. The chapter concludes the performance of various attacks based on the past work. In the future this work can be extended to introduce a novel mechanism to resolve various security issues in healthcare IoT.

IoT Based Agriculture as a Cloud and Big Data Service

Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service (AaaS) through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service (QoS) is also better in terms of QoS parameters.

Semantic Approach to Opening Museum Collections of Everyday Life History for Services in Internet of Things Environments

Technologies of the Internet of Things (IoT) and of smart spaces support creating smart museums based on digitized infrastructures and information systems already deployed in modern museums. Cultural heritage knowledge in such a museum is used by interested visitors as well as by personnel. This work continues the authors' research on the smart museum concept and its case study of everyday life history in the History Museum of Petrozavodsk State University (PetrSU). The authors develop an ontological model for the needs of studying the everyday life history. The ontology supports integrating descriptions of collected exhibits into a semantic network, where the links reflect meaningful relations between exhibits and other historical objects. They apply the wiki technology within the smart spaces-based architecture of a smart museum. The wiki implements an ontology-enabled system that experts use to extract and represent knowledge hidden in the museum collection. The authors discuss possible semantic algorithms for data mining in the museum semantic network.

Ontology-Driven Situation Assessment System Design and Development in IoT Domains

An ontology-driven approach to software design and development of situation assessment systems (SAS) for IoT applications is considered. As SAS is used to build the situational model for the external environment, it highly depends on the operational domain. To simplify the transition from the domain description to SAS dataflow process the ontology-driven approach is proposed. The main idea of the approach is to explicitly formalize SAS dataflow process in an ontological form. For this purpose, a domain-independent SAS ontology is proposed that allows automation of the dataflow process design. The dataflow process ontology is used to automate development and runtime stages of SAS lifecycle. The proposed ontology is included into the proposed instrument set. The set can be used to build SAS systems for different domains described with OWL ontology. The set is evaluated on a traffic control scenario.

An Approach of Role Updating in Context-Aware Role Mining

With the rapid development of Internet of Things (IoT) and mobile technologies, the service offerings available in the IoT and mobile environments are increasing dramatically. How to provide intelligent and personalized services for users becomes a challenging issue. Several context aware service recommendation approaches have been reported to leverage roles to represent common knowledge within user communities, based on which services can be recommended for users. Prior studies on context aware role mining mainly focus on mining roles from a fixed data set of user behavior patterns, while most of them neglect the dynamic change of the input data. The frequent change of the user data will result in the change of extracted roles, and how to efficiently update extracted roles according to change of the input user data remains a challenging issue. In this paper, towards this issue, the authors introduce a novel role updating approach in context aware role mining. In the apporach, several algorithms are presented towards various scenarios such as new users and new contexts are removed from and added into the input data. Experiments show that compared with existing solutions, the proposed algorithms can guarantee the completeness of updating results while keeping good updating efficiency.

Applying Security to a Big Stream Cloud Architecture for the Internet of Things

The Internet of Things (IoT) is expected to interconnect billions (around 50 by 2020) of heterogeneous sensor/actuator-equipped devices denoted as “Smart Objects” (SOs), characterized by constrained resources in terms of memory, processing, and communication reliability. Several IoT applications have real-time and low-latency requirements and must rely on architectures specifically designed to manage gigantic streams of information (in terms of number of data sources and transmission data rate). We refer to “Big Stream” as the paradigm which best fits the selected IoT scenario, in contrast to the traditional “Big Data” concept, which does not consider real-time constraints. Moreover, there are many security concerns related to IoT devices and to the Cloud. In this paper, we analyze security aspects in a novel Cloud architecture for Big Stream applications, which efficiently handles Big Stream data through a Graph-based platform and delivers processed data to consumers, with low latency. The authors detail each module defined in the system architecture, describing all refinements required to make the platform able to secure large data streams. An experimentation is also conducted in order to evaluate the performance of the proposed architecture when integrating security mechanisms.

Cognitive Internet of Everything (CIoE)

In today's world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions (Wanglei, 2015). The ambient intelligence is a sensational new information technology paradigm in which people are empowered for assisted living through multiple IoTs sensors environment that are aware of inhabitant presence and context and highly sensitive, adaptive and responsive to their needs. A noble ambient intelligent environment are characterized by their ubiquity, transparency and intelligence which seamlessly integrated into the background and invisible to surrounded users/inhabitant. Cognitive IoE (Internet of Everything) is a new type of pervasive computing. As the ambient smart home is into research only from a couple of years, many research outcomes are lacking potentials in ambient intelligence and need to be more dug around for better outcomes. As a result, an effective architecture of CIoE for ambient intelligent space is missing in other researcher's work. An unsupervised and supervised methods of machine learning can be applied in order to classify the varied and complex user activities. In the first step, by using fuzzy set theory, the input dataset value can be fuzzified to obtain degree of membership for context from the physical layer. In the second step, using K-pattern clustering algorithms to discover pattern clusters and make dynamic rules based on identified patterns. This chapter provides an overview, critical evaluation of approaches and research directions to CIoE.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

An Architecture to Implement the Internet-of-Things using the Prometheus Methodology

The rapid expansion of the Internet of Things (IoT) will generate a diverse range of data types that needs to be handled, processed and stored. This paper aims to create a multi-agent system that suits the needs introduced by the IoT expansion, thus being able to oversee the Big Data collection and processing and also to maintain the semantic links between the data sources and data consumers. In order to build a complex agent oriented architecture, we have assessed the existing agent oriented methodologies searching for the best solution that is not bound to a specific programming language of framework, and it is flexible enough to be applied in such a divers domain like IoT. As complex scenario, the proposed approach has been applied to medical diagnosis and motoring of mental disorders.