The Internet of Medical Things (IoMT) is a kind of connected infrastructure of smart medical devices along with software applications, health systems and services. These medical devices and applications are connected to healthcare systems through the Internet. The Wi-Fi enabled devices facilitate machine-to-machine communication and link to the cloud platforms for data storage. IoMT has the ability to make accurate diagnoses, with fewer mistakes and lower costs of care. IoMT with smartphone applications permits the patients to exchange their health related confidential and private information to the healthcare experts (i.e., doctors) for the better control of diseases, and also for tracking and preventing chronic illnesses. Due to insecure communication among the entities involved in IoMT, an attacker can tamper with the confidential and private health related information for example an attacker can not only intercept the messages, but can also modify, delete or insert malicious messages during communication. To deal this sensitive issue, we design a novel blockchain enabled authentication key agreement protocol for IoMT environment, called BAKMP-IoMT. BAKMP-IoMT provides secure key management between implantable medical devices and personal servers and between personal servers and cloud servers. The legitimate users can also access the healthcare data from the cloud servers in a secure way. The entire healthcare data is stored in a blockchain maintained by the cloud servers. A detailed formal security including the security verification of BAKMP-IoMT using the widely-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is performed to demonstrate its resilience against the different types of possible attack. The comparison of BAKMP-IoMT with relevant existing schemes is conducted which identifies that the proposed system furnishes better security and functionality, and also needs low communication and computational costs as compared to other schemes. Finally, the simulation of BAKMP-IoMT is conducted to demonstrate its impact on the performance parameters.
To guarantee the uninterrupted operation of an IoT node, IoT nodes are installed with energy harvesting techniques to prolong their lifetime and recharge their batteries. Mostly energy harvesting systems collect energy from sunlight and wind. However, the energy harvested from the sunlight is non-continuous and energy harvested from the wind is insufficient for continuously powering an IoT node. Thus, to resolve this problem, authors proposed an energy harvesting system namely SWEH which harvests energy from solar light and wind. In this article, authors proposed a scheduling algorithm to balance the energy produced by SWEH and the energy consumption of an IoT node that results in the energy neutral system. Results from simulation analysis clearly manifest that the proposed SWEH system extracts more energy as compared to energy produced by a single solar panel or wind turbine. With the help of simulation results, authors also show that the proposed algorithm leaves the system in energy neutral state at the end of particular time frame.
Clustering is one of the most effective methods for summarizing and analyzing datasets that are collection of data objects similar or dissimilar in nature. Clustering aims at finding groups, or clusters, of objects with similar attributes. Most clustering methods work efficiently for low dimensional data since distance measures are used to find dissimilarities between objects. High dimensional data, however, may contain attributes which are not required for defining clusters and irrelevant dimension may produce noise and will hide the clusters that are required to be created. The discovery of groups of objects that are highly similar within some subsets of relevant attributes becomes an important but challenging task. In this paper we provide a short introduction to various approaches and challenges for high-dimensional data clustering.
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