A Centralized Cluster-Based Hierarchical Approach for Green Communication in a Smart Healthcare System

The emergence of the Internet of Things (IoT) has revolutionized our digital and virtual worlds of connected devices. IoT is a key enabler for a wide range of applications in today's world. For example, in smart healthcare systems, the sensor-embedded devices monitor various vital signs of the patients. These devices operate on small batteries, and their energy need to be utilized efficiently. The need for green IoT to preserve the energy of these devices has never been more critical than today. The existing smart healthcare approaches adopt a heuristic approach for energy conservation by minimizing the duty-cycling of the underlying devices. However, they face numerous challenges in terms of excessive overhead, idle listening, overhearing, and collision. To circumvent these challenges, we have proposed a cluster-based hierarchical approach for monitoring the patients in an energy-efficient manner, i.e., green communication. The proposed approach organizes the monitoring devices into clusters of equal sizes. Within each cluster, a cluster head is designated to gather data from its member devices and broadcast to a centralized base station. Our proposed approach models the energy consumption of each device in various states, i.e., idle, sleep, awake, and active, and also performs the transitions between these states. We adopted an analytical approach for modeling the role of each device and its energy consumption in various states. Extensive simulations were conducted to validate our analytical approach by comparing it against the existing schemes. The experimental results of our approach enhance the network lifetime with a reduced energy consumption during various states. Moreover, it delivers a better quality of data for decision making on the patient's vital signs

An intelligent heart disease prediction system based on swarm-artificial neural network

The accurate prediction of cardiovascular disease is an essential and challenging task to treat a patient efficiently before occurring a heart attack. In recent times, various intelligent healthcare frameworks have been designed with different machine learning and swarm optimization techniques for cardiovascular disease prediction. However, most of the existing strategies failed to achieve higher accuracy for cardiovascular disease prediction due to the lack of data-recognized techniques and proper prediction methodology. Motivated by the existing challenges, in this paper, we propose an intelligent healthcare framework for predicting cardiovascular heart disease based on Swarm-Artificial Neural Network (Swarm-ANN) strategy. Initially, the proposed Swarm-ANN strategy randomly generates predefined numbers of Neural Networks (NNs) for training and evaluating the framework based on their solution consistency. Additionally, the NN populations are trained by two stages of weight changes and their weight is adjusted by a newly designed heuristic formulation. Finally, the weight of the neurons is modified by sharing the global best weight with other neurons and predicts the accuracy of cardiovascular disease. The proposed Swarm-ANN strategy achieves 95.78% accuracy while predicting the cardiovascular disease of the patients from a benchmark dataset. The simulation results exhibit that the proposed Swarm-ANN strategy outperforms the standard learning techniques in terms of various performance matrices. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature

Rare earth element and radionuclide distribution in surface sediments along an estuarine system affected by fertilizer industry contamination

Site-specific contamination related to fertilizer industry activity was demonstrated by light rare earth element (REE) anomalies (sum of La, Ce, Pr, Nd, Sm, and Eu concentrations up to 4.141 mg kg−1) and radionuclides (210Pb and 226Ra activities up to 994 and 498 Bq kg−1, respectively) from industrial contamination, within a subtropical estuary (SE Brazil). Anthropogenic influence was also supported by the site-specific 210Pb and 226Ra distribution down the estuarine system. The distribution of REE and radionuclide contamination varied along the estuary, which reflected differing sedimentation patterns of phosphogypsum and/or phosphate ore pollutants as identified downstream from the source, likely influenced by sediment–hydrodynamic processes within the estuarine system. Redox- and ion exchange-sensitive pollutants are mobile at the fresh–sea water interface causing an uneven distribution of the pollutants, indicating that the phosphgypsum and/or phosphate ore pollutant deposition can be also influenced by physical and/or geochemical processes associated to estuarine systems