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The purpose of this paper is energy consumption and security. To extend the sensor’s life span, saving the energy in a sensor is important. In this paper, biosensors are implanted or suited on the human body, and then, transposition has been applied for biosensors for reducing the sensor distance from the sink node. After transposition path loss has been calculated, security is maintained and also compared the results with the existing strategies.

Nowadays, one of the most emergent technologies is wireless body area network (WBAN), which represents to improve the quality of life and also allow for monitoring the remote patient and other health-care applications. Traffic routing plays a main role together with the relay nodes, which is used to collect the biosensor’s information and send it towards the sink.

To calculate the distance and observe the position, Euclidean distance technique is used. Path loss is the main parameter, which is needed to reduce for making better data transmission and to make the network stability. Routing protocols can be designed, with the help of proposed values of sensors locations in the human body, which gives good stability of network and lifetime. It helps to achieve as the less deplete energy.

This scheme is compared with the two existing schemes and shows the result in terms of parameter path loss. Moreover, this paper evaluated a new method for improving the security in WBAN. The main goal of this research is to find the optimal sensor location on the body and select the biosensor positions where they can get less energy while transmitting the data to the sink node, increasing the life span in biosensors, decreasing memory space, giving security, controlling the packet complexity and buffer overflow and also fixing the damages in the existing system.

An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

Internet addiction (IA) has become a global health problem. As the research on IA has progressed, this study aims to explore the antecedents and consequences of IA, particularly in the Indian context. A conceptual model was developed, and hypotheses were formulated based on the conceptual model and the hypotheses were tested.

This study investigated 752 schools and collected students from the southern part of India. First, psychometric properties of the survey instrument were tested, and hierarchical regression was used to test the hypotheses.

The results revealed that time spent on the internet every day is positively related to IA, internet experience in terms of years is positively related to IA, income and gender moderates the relationship between time spent every day on the internet and internet experience and IA and IA is positively related to time spent on networking, video streaming, short video apps, educational apps, chat apps, online shopping apps, money-involved apps, etc.

The outcomes of this study are essential for the school and college students and their parents. As IA has become chronic in the present-day digital world, it is necessary to take rectification measures to avoid facing the perils of IA. The conceptual model provides a simple framework of explaining how young students spend their time on the internet to become addicted gradually. Furthermore, this study highlights the importance of controlling the younger generation's behavior, particularly regarding internet use.

This study is unique and innovative to the extent that it explores the antecedents of IA and the moderating role of gender and income in the relationship between the time spent on the internet and the IA. To the best of the authors’ knowledge, developing a conceptual model is the first of its kind to study school and college students in India.