Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future.
The Internet of Things (IoT) invention has taken the growth of sensors technology to a completely high step. New challenges in terms of data delivery have emerged due to strict QoS conditions. Among the solutions proposed in the literature is the subdivision of the large-scale network into several clusters. Except that most of these solutions are conventional. However, prior research generally confirms that bio-inspired paradigms are more flexible and effective compared to traditional methods. When it comes to a heterogeneous network, additional constraints appear. Nodes have different buffer sizes. Then, data captured must be sent before their buffers are full, otherwise, some data will be lost. This is not suitable for a real-time application where time and information are crucial elements. In this study, a comprehensive overview of the use of sensors in IoT contexts is performed. Two algorithms as Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA), combined with the Imperialist Competitive Algorithm (ICA) based Cluster Head (CH) selection with a novel approach for heterogeneous networks are proposed. These algorithms can support data exchange over a heterogeneous Wireless Sensor Network (WSN) infrastructure with taking into consideration the buffer overflow problem. Simulation results are presented and discussed in different network designs. The research demonstrated that knowing well how to manage buffers using bio-inspired techniques, leads to a significant reduction in data loss.INDEX TERMS Bio-inspired optimization, heterogeneous WSN, CH selection, IoT, model checking.
In recent years, the interest in using wireless communication technologies and mobile devices in the healthcare environment has increased. However, despite increased attention to the security of electronic health records, patient privacy is still at risk for data breaches. Thus, it is quite a challenge to involve an access control system especially if the patients' medical data are accessible by users who have diverse privileges in different situations. Blockchain is a new technology that can be adopted for decentralized access control management issues. Nevertheless, different scalability, security, and privacy challenges affect this technology. To address these issues, we suggest a novel Decentralized Self-Management of data Access Control (DSMAC) system using a blockchain-based Self-Sovereign Identity (SSI) model for privacy-preserving medical data, empowering patients with mechanisms to preserve control over their personal information and allowing them to self-grant access rights to their medical data. DSMAC leverages smart contracts to conduct Role-based Access Control policies and adopts the implementation of decentralized identifiers and verifiable credentials to describe advanced access control techniques for emergency cases. Finally, by evaluating performance and comparing analyses with other schemes, DSMAC can satisfy the privacy requirements of medical systems in terms of privacy, scalability, and sustainability, and offers a new approach for emergency cases.INDEX TERMS Blockchain, data privacy, decentralized access control, decentralized IDentifier (DID), IoMT sensors, Self Sovereign Identity (SSI), smart contract, verifiable credential (VC).
scite is a Brooklyn-based startup that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.