Modern healthcare is a data-intensive domain representing an amalgamation of long-term electronic medical records, real-time patient monitoring data, and more recently sensor data from wearable computing. Blockchain in healthcare can address a multitude of challenges in healthcare, including care coordination, data security, and interoperability concerns, as technology advances. Technical challenges such as processing speed and massive data duplication will be resolved as improved technology. This data needs to be accessed seamlessly by a multitude of players from the general physicians to hospitals, medical service providers to insurance companies. Thus, healthcare-related data needs to be verified, securely stored, and shared while maintaining patient privacy and control over what portion of the data is shared, with whom it is shared, and how it is consumed. Blockchain has emerged as a technology stack of choice for distributed authentication, secure storage, and automated analysis of stored data in diverse domains including healthcare. Its distributed nature is a natural fit to the healthcare ecosystem with multiple participating entities and patients in different geographic locations. In this paper, we review the technology of blockchain to the healthcare domain analyzing and classifying work done in the field. Open challenges are identified and future directions for research are also presented.

We propose a method for building a simple electronic nose based on commercially available sensors used to sniff in the market and identify spoiled/contaminated meat stocked for sale in butcher shops. Using a metal oxide semiconductor-based electronic nose, we measured the smell signature from two of the most common meat foods (beef and fish) stored at room temperature. Food samples were divided into two groups: fresh beef with decayed fish and fresh fish with decayed beef. The prime objective was to identify the decayed item using the developed electronic nose. Additionally, we tested the electronic nose using three pattern classification algorithms (artificial neural network, support vector machine and k-nearest neighbor), and compared them based on accuracy, sensitivity, and specificity. The results demonstrate that the k-nearest neighbor algorithm has the highest accuracy.

Governments rely on collected data to analyze their citizens’ health requirements and needs. Such data is usually of scattered manner gathered by several agencies and entities that fall under different business models and goals. Hence, the scattered nature of this data creates a burden on governments in precisely planning adequate health bills that guarantee the wellbeing of their citizens. Furthermore, the issue of synchronizing and having a real-time access to such data exacerbates the problem even more. Therefore, a system that guarantees the integrity, accessibility, correctness and security of medical data in a highly synchronized environment is crucial for governments to accurately project their future needs. In this paper, we present Smart-Health (SHealth), a blockchain-based health management system. In its crux, SHealth is a private multi-layered blockchain integrated with a multi-tiered addressing scheme that defines the privileges and permissions of entities in the system. Blockchain-based systems assure security, reliability, availability, resistance against tampering and malicious attacks, seamless integration, and easy data management. SHealth proposes a complete autonomy to its users. Through a user-friendly graphical interface it capitalizes on smart contracts to initiate various requests and inquiries pertaining to patients such as appointments, medical tests, medications, medical procedures, or history. SHealth is simple, robust, efficient, secured, and completely automated. All the stakeholders in the system can access the health related data stored in a distributed database without compromising its authenticity. SHealth covers all the possible scenarios in health systems from which some of them are explained in this work.

Reliable spectrum sensing is one of the most crucial aspects for the successful deployment of cognitive radio (CR) technology. For CR, it is not possible to transmit on a licensed band and sense it simultaneously, therefore sensing must be interleaved with transmission. Spectrum sensing in CR is challenged by a number of uncertainties, which degrade the sensing performance and in turn require much more time to achieve the targeted sensing efficiency. Hence, the authors propose a spectrum sensing scheme which obtains reliable results with less mean detection time. First, the scheme determines a better matched filter, or a combination of energy and cyclostationary detectors based on the power and band of interest. In the combined energy and cyclostationary detector, an energy detector with a bi-threshold is used, and the cyclostationary detector is applied only if the energy of the signal lies between two thresholds. Second, sensing is performed by the selection choice resulting from the first step. To evaluate the scheme’s performance, the results are compared with those where only an energy detector, matched filter, or cyclostationary detector are performed. The performance metrics are the probability of detection, probability of false alarm, and mean detection time.