Background: In developing countries, the healthcare system is facing numerous challenges. One of the major challenges faced by the healthcare system is that the healthcare service providers are meager and geographically far from the densely populated area.
Objective: To overcome the above challenge, the present research work proposes SHRMS (Smart Heart Rate Monitoring System) which provides the ad-hoc services to the patients who are in the transit mode in the emergency vehicle.
Methods: A pulse sensor is attached to the patient’s fingertip to fetch the heart rate of the patient. The patient’s data is further transmitted to the microcontroller which in turn transmits the data to the Thing- Speakcloud service.
Result: SHRMS provides the real-time monitoring of the patient and helps to provide emergency aid as per the patient’s current situation.
Conclusion: This device is beneficial for developing countries where the healthcare service providers are very less and geographically scattered. Read now: https://bit.ly/3AhStne
Aims: This study aimed to propose a routing protocol for IoT-based WBANs that is reliable, power-efficient, and has a high throughput.
Background: A variety of services and applications that use wireless connections such as LTE, 3G, Wi-Fi, Bluetooth, and ZigBee communication technologies have become popular in daily life as a result of the rapid development of network hardware technology. Remote medical monitoring and care is one such service. Governments have developed new policies in response to the healthcare needs of aging populations. Their goal is to create a comprehensive medical network based on new wireless technologies like sensor networks and cloud computing. Their purpose is to take the medical industry and the Internet of Things (IoT) to the next level of development.
Objective: The goal of our proposed study is to improve the network nodes’ ability to stay alive for a longer period of time and to maintain stability. A longer stability period contributes to high packet delivery of the node to the sink that enhances the efficiency of the WBAN network.
Methods: The Wireless Body Area Network (WBAN) Internet of Things (IoT) for healthcare applications has attracted attention from various fields of study in the last few years. In this paper, we propose a routing protocol for IoT-based WBANs that is reliable, power-efficient and has a high throughput. To achieve low energy consumption and a longer network lifetime, we used a multi-hop topology. To choose a parent node or forwarder, we propose a cost function that selects a parent node with the highest residual energy and the shortest distance to sink. The residual energy parameter balances energy consumption among sensor nodes, while the distance parameter ensures packet delivery to the sink. Our key goal is to increase WBAN’s total network by raising cumulative energy usage. The residual energy parameter governs the usage of energy by the sensor nodes, while the distance parameter ensures that the packet is effectively transmitted to the sink.
Result: Simulation results demonstrate that our proposed protocol is energy efficient and maximizes network stability for longer periods.
Conclusion: Real-time health and activity recognition with wearable sensors is a prerequisite for assistive paradigms. In this paper, we suggest a method for routing data to WBANs. The proposed scheme employs a cost function to determine the best route to the sink. The residual energy of nodes and their distance from the sink is used to calculate the cost function. Nodes with a lower cost function value are selected as the parent node. Other nodes are children nodes and send their data to the parent node. The proposed routing scheme improves network stability time and packet delivery to sink, according to our simulation results. Path loss is also investigated in this protocol and will be considered in future work. Read now: https://bit.ly/3dXI5tz
Background: Internet of Things (IoT) plays a vital role by connecting several heterogeneous devices seamlessly via the Internet through new services. Every second, the scale of IoT keeps on increasing in various sectors like smart home, smart city, health, smart transportation and so on. Therefore, IoT becomes the reason for the massive rise in the volume of data which is computationally difficult to work out on such a huge amount of heterogeneous data. This high dimensionality in data has become a challenge for data mining and machine learning. Hence, with respect to efficiency and effectiveness, dimensionality reduction techniques show the roadmap to resolve this issue by removing redundant, irrelevant and noisy data, making the learning process faster with respect to computation time and accuracy.
Methods: In this study, we provide a broad overview on advanced dimensionality reduction techniques to facilitate selection of required features necessary for IoT based data analytics and for machine learning on the basis of criterion measure, training dataset and inspired by soft computation technology followed by significant challenges of dimensionality reduction techniques for IoT generated data that exists as scalability, streaming datasets and features, stability and sustainability.
Results & Conclusion: In this survey, the various dimensionality reduction algorithms reviewed delivers the essential information in order to recommend the future prospect to resolve the current challenges in the use of dimensionality reduction techniques for IoT data. In addition, we highlight the comparative study of various methods and algorithms with respect to certain factors along with their pros and cons. Read now:https://bit.ly/3BMX7wf
Background: With the development of technologies such as the Internet of Things (IoT) and cloud/edge computing, a large number of edge devices in the smart grid accomplish information interconnection and generate massive amounts of data. On the one hand, smart grids need to obtain a large amount of grid data from edge terminal devices. On the other hand, edge computing also brings some security threats. Therefore, edge data security faces major challenges.
Objective: In order to ensure the security of data in the edge computing environment of the smart grid, we propose an edge data storage scheme (BlockSE) for the smart grid edge computing environment.
Methods: BlockSE combines permissioned blockchain and smart contract technology to achieve secure storage of data on edge. We adapted ring signature technology to achieve privacy preserving of participating nodes. To allocate the storage resources of edge servers fairly and transparently, we designed a storage resource allocation algorithm based on the smart contract, whereby edge devices can obtain storage resources fairly according to demand.
Results: Simulation experiments were performed; the results show the effectiveness of the proposed BlockSE scheme.
Conclusion: This paper focused on data security in the smart grid’s edge computing environment and proposed a secure storage scheme for edge data based on permissioned blockchain. The evaluation shows the safety, effectiveness, and practicability of the proposed BlockSE scheme. Read more: https://bit.ly/3S5ttrM
Bentham Science 
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