Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy.

Purpose: The purpose of this paper is to investigate the prospects of current storage technologies for long-term preservation of big data in digital libraries. Design/methodology/approach: The study employs a systematic and critical review of the relevant literature to explore the prospects of current storage technologies for long-term preservation of big data in digital libraries. Online computer databases were searched to identify the relevant literature published between 2000 and 2016. A specific inclusion and exclusion criterion was formulated and applied in two distinct rounds to determine the most relevant papers. Findings: The study concludes that the current storage technologies are not viable for long-term preservation of big data in digital libraries. They can neither fulfil all the storage demands nor alleviate the financial expenditures of digital libraries. The study also points out that migrating to emerging storage technologies in digital libraries is a long-term viable solution. Research limitations/implications: The study suggests that continuous innovation and research efforts in current storage technologies are required to lessen the impact of storage shortage on digital libraries, and to allow emerging storage technologies to advance further and take over. At the same time, more aggressive research and development efforts are required by academics and industry to further advance the emerging storage technologies for their timely and swift adoption by digital libraries. Practical implications: The study reveals that digital libraries, besides incurring significant financial expenditures, will suffer from potential loss of information due to storage shortage for long-term preservation of big data, if current storage technologies are employed by them. Therefore, policy makers and practitioners should meticulously choose storage technologies for long-term preservation of big data in digital libraries. Originality/value: This type of holistic study that investigates the prospects of magnetic drive technology, solid-state drive technology, and data-reduction techniques for long-term preservation of big data in digital libraries has not been conducted in the field previously, and so provides a novel contribution. The study arms academics, practitioners, policy makers, and industry with the deep understanding of the problem, technical details to choose storage technologies meticulously, greater insight to frame sustainable policies, and opportunities to address various research problems.

The voluminous nature of big data and technological limitations of storage technology employed in data centers has led to a gap between storage demand and supply. This article investigates multiple factors that lead to data-capacity gap in big data storage such as technological limitations of magnetic storage technology and shortcomings of data-reduction techniques. Furthermore, the article discusses the recent advances in emerging storage technologies to assess their potential in avoiding the gap.

Forensics paved the way for the growth of anti-forensics, and the time has come for anti-forensics to return the favour. For that purpose, it is imperative that forensic investigators and practitioners are armed with the knowledge of contemporary anti-forensics types, techniques and tools. This article aims to provide technical information and a comprehensive understanding of file system anti-forensics types, techniques and tools so as to facilitate investigators' ability to collect technically credible and legally admissible digital evidence from crime scenes. Forensics paved the way for the growth of anti-forensics, which tries to prevent, hinder or corrupt the forensic process of evidence acquisition and analysis. The time has come for anti-forensics to return the favour. Mohamad Ahtisham Wani, Ali AlZahrani and Wasim Ahmad Bhat provide technical information and a comprehensive understanding of file system anti-forensics types, techniques and tools so as to facilitate investigators' ability to collect technically credible and legally admissible digital evidence from crime scenes.