The framework presented in this article provides a guide for designing secure and sustainable internet of medical things (IoMT) solutions. The main objective is to address the challenges related to safety and sustainability in the medical field. The critical conditions driving these challenges are identified, and future trends in the field of IoMT are discussed. To assess the effectiveness of the proposed framework, a case study was carried out in a private medical clinic. In this study, an IoMT system was implemented to monitor patients’ vital signs, even when they were not in the clinic. The positive results demonstrated that the implemented IoMT system met the established security and sustainability requirements. The main statistical findings of the case study include the real-time monitoring of the vital signs of the patients, which improved the quality of care and allowed for the early detection of possible complications. In addition, medical devices such as the blood pressure monitor, pulse oximeter, and electrocardiograph were selected, proving safe, durable, and energy and maintenance efficient. These results were consistent with previous research that had shown the benefits of IoMT in remote monitoring, the early detection of health problems, and improved medical decision-making.

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Framework for a Secure and Sustainable Internet of Medical Things, Requirements, Design Challenges, and Future Trends

This paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures’ foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms.

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Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

The emergence of Electric Vehicles (EVs) is a turning point in decarbonizing the road transport sector. In spite of the various apprehensions of the customers, such as range anxiety, long charging times, higher costs, and the lack of charging infrastructures, EVs have managed to considerably penetrate into the market. Appreciable subsidies in EV purchase and possibilities of renewable energy-based local charging equipment have encouraged more and more people to own EVs. Electrifying road transport also calls for scaling up of all stages of the supply chain as it involves a lot of raw materials and critical metals used for battery technology. One of the most important factors determining the range of an EV is the energy density of the battery, which has reached over 300 Wh/kg, from 100-150 Wh/kg a decade ago. This clearly means that the same vehicle can travel double the distance with the same mass. Understanding and modeling the energy consumption in an EV is quintessential in alleviating the fear of range anxiety. This paper presents a detailed mathematical equation-based energy consumption analysis of a particular EV model for Indian roads. Very few researchers have worked on drive cycles suitable for India. The novelty of the current work is that the energy consumption calculation can be worked out for any EV model or vehicle type through simple mathematical equations.

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Energy Consumption Analysis for the Prediction of Battery Residual Energy in Electric Vehicles

Battery management solutions for li-ion batteries based on artificial intelligence

The health monitoring system is one of the most innovative technologies that has gained traction in the Internet of Medical Things (IoMT). It allows the connection of multiple sensors and actuators that can capture and monitor the data through the web page or mobile application. IoMT technology not only provides communications but also will provide monitoring, recording, storage, and display. IoMT in healthcare is used for measuring the vital signs of the human body, which allows medical professionals to assess the well-being of a patient. The doctor may recommend lifestyle modifications, prescribe more tests, or diagnose a disorder according to the results. This paper illustrates the remote-control health monitoring system (HMS) with the integration of a UAV, which allows the doctor to access the data and analyze the patient data remotely. Thus, the proposed HMS-UAV system aims to measure the temperature, humidity, blood pressure, heart rate, and SpO2 and stores the data on the UAV. Several sensors were thus used namely DHT11, MAX30102, Myoware and K24C16, and the Raspberry Pi camera. Reduced hospital stays and avoidance of readmissions are benefits of remote patient monitoring with IoMT-based UAVs. Contrary to its advantages, IoMT has flaws in information processing since a huge volume of data are needed to be handled in a single environment. One major novel inclusion in this work is to measure multiple parameters and provide a comparative analysis for all of them. Furthermore, the functionality of video recorded and stored is included where the doctor can surveil the patient.

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Smart IoMT Framework for Supporting UAV Systems with AI

This paper gives the development of a firmware for a Smart Switch that might manage the on-off of any electric tool at home via the usage of net. The Smart Switch is installed to internet through Wi-Fi TM; through a laptop, Smartphone, pill or any tool with net get right of get entry to. In order to perform this connection it's miles vital to put in writing down the IP pre-programmed into the Smart Switch in an internet browser with the cause to load the Smart Switch server, so you can open a configuration web page to put in writing the facts of the purchaser’s network. Then, the purchaser will select in the computerized mode the community, the protection kind, and the man or woman want to have written a passphrase. Once those information is uploaded and stored, it's far vital to restart the Smart Switch so you can get entry to net, from which the person can control the Smart Switch surely sending a number one or some of 0 to alternate the electric tool, this system is accomplished in precept via the internet, but it could be achieved without the use of net, i.e. With the useful resource of the usage of a community network.

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N Shiva Shankar

Papers

Battery management solutions for li-ion batteries based on artificial intelligence

Smart IoMT Framework for Supporting UAV Systems with AI

To activate the electrical applications by using elegant control technology with iot