M. Abdullah-Al-Wadud

Mahnaz Rafia Islam, Israt Jahan Jenny, Moniruzzaman Nayon et al.

Selecting an appropriate clustering method as well as an optimal number of clusters in road accident data is at times confusing and difficult. This paper analyzes shortcomings of different existing techniques applied to cluster accident-prone areas and recommends using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) to overcome them. Comparative performance analysis based on real-life data on the recorded cases of road accidents in North Carolina also show more effectiveness and efficiency achieved by these algorithms.

Lewis Nkenyereye, S. M. Riazul Islam, Mahmud Hossain et al.

The rapid development of personal health records (PHR) systems enables an individual to collect, create, store and share his PHR to authorized entities. Health care systems within the smart city environment require a patient to share his PRH data with a multitude of institutions' repositories located in the cloud. The cloud computing paradigm cannot meet such a massive transformative healthcare systems due to drawbacks including network latency, scalability and bandwidth. Fog computing relieves the burden of conventional cloud computing by availing intermediate fog nodes between the end users and the remote servers. Aiming at a massive demand of PHR data within a ubiquitous smart city, we propose a secure and fog assisted framework for PHR systems to address security, access control and privacy concerns. Built under a fog-based architecture, the proposed framework makes use of efficient key exchange protocol coupled with ciphertext attribute based encryption (CP-ABE) to guarantee confidentiality and fine-grained access control within the system respectively. We also make use of digital signature combined with CP-ABE to ensure the system authentication and users privacy. We provide the analysis of the proposed framework in terms of security and performance.

Lewis Nkenyereye, S. M. Riazul Islam, Mahmud Hossain et al.

The Internet of Medical Things (IoMT) offers an infrastructure made of smart medical equipment and software applications for health services. Through the internet, the IoMT is capable of providing remote medical diagnosis and timely health services. The patients can use their smart devices to create, store and share their electronic health records (EHR) with a variety of medical personnel including medical doctors and nurses. However, unless the underlying combination within IoMT is secured, malicious users can intercept, modify and even delete the sensitive EHR data of patients. Patients also lose full control of their EHR since most health services within IoMT are constructed under a centralized platform outsourced in the cloud. Therefore, it is appealing to design a decentralized, auditable and secure EHR system that guarantees absolute access control for the patients while ensuring privacy and security. Using the features of blockchain including decentralization, auditability and immutability, we propose a secure EHR framework which is mainly maintained by the medical centers. In this framework, the patients' EHR data are encrypted and stored in the servers of medical institutions while the corresponding hash values are kept on the blockchain. We make use of security primitives to offer authentication, integrity and confidentiality of EHR data while access control and immutability is guaranteed by the blockchain technology. The security analysis and performance evaluation of the proposed framework confirms its efficiency.

Md. Selim, Saeed Siddik, Alim Ul Gias et al.

The potential benefit of migrating software design from Structured to Object Oriented Paradigm is manifolded including modularity, manageability and extendability. This design migration should be automated as it will reduce the time required in manual process. Our previous work has addressed this issue in terms of optimal graph clustering problem formulated by a quadratic Integer Program (IP). However, it has been realized that solution to the IP is computationally hard and thus heuristic based methods are required to get a near optimal solution. This paper presents a Genetic Algorithm (GA) for optimal clustering with an objective of maximizing intra-cluster edges whereas minimizing the inter-cluster ones. The proposed algorithm relies on fitness based parent selection and cross-overing cluster elements to reach an optimal solution step by step. The scheme was implemented and tested against a set of real and synthetic data. The experimental results show that GA outperforms our previous works based on Greedy and Monte Carlo approaches by 40% and 49.5%.