Role of a 24-hour Ambulatory Internet of Things System in Preeclampsia Monitoring: Technologies, Challenges, and Future Path Survey

The Internet of Things (IoT) is a technology that integrates different sensor actuators, working together for data management towards efficient communication within the digital world. IoT has been applied in many sectors to achieve sustainable development goals. Massive devices and a huge amount of data have been the major components of the technology, which has presented new challenges. IoT has been applied in healthcare to improve several ways of managing health, including antenatal care. Worldwide, the cost of having preeclampsia monitoring has been a major concern. A 24-hour ambulatory IoT system, an integration of a smartwatch, a mobile device, and a cloud-based application, is one of the technologies used to help in preeclampsia monitoring. IoT and its functionalities have been evaluated in previous studies and assessments. However, they concentrated on its application in other areas, such as animal husbandry, and little on ambulatory care. The impact of a real-time ambulatory IoT system on preeclampsia monitoring are comprehensively and methodically examined in this paper, focusing on three categories: the challenges and its benefits in ambulatory care. The application’s effects, performance, and safety have been thoroughly described. Generally, this paper explores potential initiatives of the IoT system to address existing ambulatory care issues

Computing paradigms in emerging vehicular environments: a review

Determining how to structure vehicular network environments can be done in various ways. Here, we highlight vehicle networks' evolution from vehicular ad-hoc networks (VANET) to the internet of vehicles (IoVs), listing their benefits and limitations. We also highlight the reasons in adopting wireless technologies, in particular, IEEE 802.11p and 5G vehicle-to-everything, as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments. We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems' requirements. The presentation of each paradigm is given from a historical and logical standpoint. In particular, vehicular fog computing improves on the deficiences of vehicular cloud computing, so both are not exclusive from the application point of view. We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks, showing that they complement each other and share problems and limitations. As these networks still have many opportunities to grow in both concept and application, we finally discuss concepts and technologies that we believe are beneficial. Throughout this work, we emphasize the crucial role of these concepts for the well-being of humanity

A novel Byzantine fault tolerance consensus for Green IoT with intelligence based on reinforcement

To enhance the consensus performance of Blockchain in the Green Internet of Things (G-IoT) and improve the static network structure and communication overheads in the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, in this paper, we propose a Credit Reinforce Byzantine Fault Tolerance (CRBFT) consensus algorithm by using reinforcement learning. The CRBFT algorithm divides the nodes into three types, each with different responsibilities: master node, sub-nodes, and candidate nodes, and sets the credit attribute to the node. The node's credit can be adjusted adaptively through the reinforcement learning algorithm, which can dynamically change the state of nodes. CRBFT algorithm can automatically identify malicious nodes and invalid nodes, making them exit from the consensus network. Experimental results show that the CRBFT algorithm can effectively improve the consensus network's security. Besides, compared with the PBFT algorithm, in CRBFT, the consensus delay is reduced by about 40%, and the traffic overhead is reduced by more than 45%. This reduction is conducive to save energy and reduce emissions

M-SMDM: A model of security measures using Green Internet of Things with Cloud Integrated Data Management for Smart Cities

In recent years, the Green Internet of Things (G-IoT) has gained a lot of attention to developing energy-efficient communication systems. It consists of electronic devices and is integrated with numerous tight constraint sensors for observing the real world and provide communication services to end-users. However, optimal data collection and its management among the heterogeneous G-IoT objects are one of the main challenges. Many researchers are still proposing different solutions to cope with such problems and offering IoT-cloud paradigms for processing, storage, and scalability services. However, the data of smart cities is forwarded using the open-source IoT platform, and sensitive information may be compromised. Therefore, this research aims to propose a model of security measures using the Green Internet of Things with Cloud Integrated Data Management (M-SMDM) for Smart Cities. Firstly, it forms a long-run and energy-efficient connectivity using self-balancing trees and distributing load factors uniformly in green communication systems. Secondly, it addresses the problem of secret key distribution between peer nodes and attained trust for both partial and direct communication. In the end, it securing the transmission system from mobile gateways to application users against threats with improved overheads and data latency. The security analysis of the proposed M-SMDM model is done along with simulation-based experiments. The attained results disclose the importance of the proposed model in terms of network parameters compared to existing work