A Robust IOT-Cloud IaaS for Data Availability within Minimum Latency

Sensors in Internet of Things generate a huge amount of data. The massive volume of the captured data is stored on cloud servers. Over time, the unbalanced load servers prevent better resource utilization. It also increases the input and the output response time. Hence, applying load balancing techniques is very important to achieve efficient system performance. Ensuring the critical data availability in such dynamic systems is very essential. In this article, the authors propose ROBUST for ensuring data availability mechanism and a fault-tolerance architecture. ROBUST also realises load balancing among servers within minimum latency, by avoiding the problem of the overloaded sites and unbalanced use of the resources on the servers. Comparing the response time using the ROBUST and Load Balancing in the Cloud Using Specialization (LBCS), ROBUST architecture has given satisfactory results in terms of critical data latency. Compared with LBCS, ROBUST gains 42% critical data recovery from a primary server and 45% when searching for duplicated critical data. The authors implemented the system using the JADE platform.

Transmission Range Changing Effects on Location Privacy-Preserving Schemes in the Internet of Vehicles

The internet of vehicles (IoV) is getting a considerable amount of attention from different research parties. IoV aims at enhancing the driving experience with its wide range of applications varying from safety, road management to entertainment; however, some of such applications bring severe security and privacy issues; identity exposing, and location tracking are good examples. By enabling vehicles to send their statuses to themselves via beacon messages, this creates an environmental awareness for safety purposes but also exposes them to the aforementioned attacks. A lot of work has been done to mitigate the effect of such attacks but still does not provide a holistic solution. In this article, which is an extension to a prior work, the authors investigate the effects of changing the transmission range while sending beacons on the achieved level of location privacy based on two location privacy schemes: SLOW and CAPS. The authors use additional privacy metrics in addition to comparing the strategies in some well-known security attacks. The outcomes confirm the feasibility of using such a mechanism.

A Fast and Effective Watermarking Method for Medical Data security

Personal health information is considered a target for illegal users that attempt to read, delete, and/or steal data. Therefore, watermarking techniques offer new ways to protect sensitive data transferred over the Internet. This article presents a novel watermarking approach for securing sensitive data in e-health applications based on DWT and Schur decomposition coefficients. The proposed approach aims to offer a valuable balance between robustness and imperceptibility. Several experiments were conducted using a standard dataset of medical images. The experiments achieved promising results for high-level data protection and good imperceptibility values compared to existing works.

Blockchain Innovation and Information Technology at GCC

Blockchain has become an epidemic and significant decision that organizations may make in the next few years, enabling institutions to integrate business functions, operations, and processes in a decentralized distributed ledger technology. This technology will transform the business world and economy in solving the limitations created by centralization and system inefficiency. Accordingly, with the high demand and complexity of growing economies such as the Gulf Cooperation Council GCC countries, the need for a typical solution technology is a game changer. This will lead GCC to a solid economic base. Blockchain technology can be applicable in many different fields such as Banking, education, Health, finance, government and trade. This article will address the literature review and methodology of Blockchain technology and innovation at the GCC, particularly in Saudi Arabia. Also, more research can be conducted in the future as the system may be integrated in these countries.

Big Data Analytics Adoption Factors in Improving Information Systems Security

This research measured determinants that influence the willingness of IT/IA professionals to recommend Big Data analytics to improve information systems security in an organization. A review of the literature as well as the works of prior researchers provided the basis for formulation of research questions. Results of this study found that security effectiveness, organizational need, and reliability play a role in the decision to recommend big data analytics to improve information security. This research has implications for both consumers and providers of big data analytics services through the identification of factors that influence IT/IA professionals. These factors aim to improve information systems security, and therefore, which service offerings are likely to meet the needs of these professionals and their organizations.

Digital Innovation to Transform the Customer Experience

Digital innovation is becoming a strategic priority for business and IT leaders in many companies. This article explores digital innovation at Six Flags Entertainment Corporation, the world's largest regional theme park company, with emphasis on the strategic leadership role of its Chief Information Officer (CIO). The article shows that digital innovation requires appropriate technology infrastructure, organization, leadership skills and a systematic process to evaluate existing and emerging technologies. Innovation ideas may come from many internal or external sources. Five digital initiatives illustrate how digital innovation can create new revenue streams and transform customer experience. Insights are provided on how to justify such initiatives and how to strike the right balance between a focus on innovation, while meeting the traditional operational needs. Overall, a focus on digital innovation is an opportunity for a strategic role of IT leaders, but that requires new skills and new education.

Towards a New Data Replication Management in Cloud Systems

Applications produce huge volumes of data that are distributed on remote and heterogeneous sites. This generates problems related to access and sharing data. As a result, managing data in large-scale environments is a real challenge. In this context, large-scale data management systems often use data replication, a well-known technique that treats generated problems by storing multiple copies of data, called replicas, across multiple nodes. Most of the replication strategies in these environments are difficult to adapt to cloud environments. They aim to achieve the best performance of the system without meeting the important objectives of the cloud provider. This article proposes a new dynamic replication strategy. The proposed algorithm significantly improves provider gain without neglecting customer satisfaction.

Using Resources Competition and Memory Cell Development to Select the Best GMM for Background Subtraction

Background subtraction is an essential step in the process of monitoring videos. Several works have proposed models to differentiate the background pixels from the foreground pixels. Mixtures of Gaussian (GMM) are among the most popular models for a such problem. However, the use of a fixed number of Gaussians influence on their results quality. This article proposes an improvement of the GMM based on the use of the artificial immune recognition system (AIRS) to generate and introduce new Gaussians instead of using a fixed number of Gaussians. The proposed approach exploits the robustness of the mutation function in the generation phase of the new ARBs to create new Gaussians. These Gaussians are then filtered into the resource competition phase in order to keep only ones that best represent the background. The system tested on Wallflower and UCSD datasets has proven its effectiveness against other state-of-art methods.

Location-Privacy Evaluation Within the Extreme Points Privacy (EPP) Scheme for VANET Users

The main purpose of designing vehicular ad-hoc networks (VANETs) is to achieve safety by periodically broadcasting the vehicle's coordinates with a high precision. This advantage brings a threat represented in the possible tracking and identification of the vehicles. A possible solution is to use and change pseudonyms. However, even by changing pseudonyms, the vehicle could still be tracked if the adversary has a prior knowledge about the potential start and end points of a particular driver who has social interactions (e.g., with neighbors) which introduces the concept of vehicular social networks (VSNs). This article extends the authors previous work, namely: “EPP Extreme Points Privacy for Trips and Home Identification in Vehicular Social Networks,” which exploits the nature of the end points that are common between VSN users in order to create shared zones to anonymize them. The extension is represented by (a) the evaluation of the enjoyed location privacy of VSN users after quitting the district in addition to (b) detailing the used environment during the evaluation.

Large-Scale Ontology Alignment- An Extraction Based Method to Support Information System Interoperability

Ontology alignment is an important way of establishing interoperability between Semantic Web applications that use different but related ontologies. Ontology alignment is the process of identifying semantically equivalent entities from multiple ontologies. This is not always obvious because technical constraints such as data volume and execution time are determining factors in the choice of an alignment algorithm. Nowadays, partitioning and modularization are two main strategies for breaking down large ontologies into blocks or ontology modules respectively to align ontologies. This article proposes ONTEM as an effective alignment method for large-scale ontology based on the ontology entities extraction. This article conducts a comprehensive evaluation using the datasets of the OAEI 2018 campaign. The obtained results are promising, and they revealed that ONTEM is one of the most effective systems.