The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.

There has been an explosion in research focused on Internet of Things (IoT) devices in recent years, with a broad range of use cases in different domains ranging from industrial automation to business analytics. Being battery-powered, these small devices are expected to last for extended periods (i.e., in some instances up to tens of years) to ensure network longevity and data streams with the required temporal and spatial granularity. It becomes even more critical when IoT devices are installed within a harsh environment where battery replacement/charging is both costly and labour intensive. Recent developments in the energy harvesting paradigm have significantly contributed towards mitigating this critical energy issue by incorporating the renewable energy potentially available within any environment in which a sensor network is deployed. Radio Frequency (RF) energy harvesting is one of the promising approaches being investigated in the research community to address this challenge, conducted by harvesting energy from the incident radio waves from both ambient and dedicated radio sources. A limited number of studies are available covering the state of the art related to specific research topics in this space, but there is a gap in the consolidation of domain knowledge associated with the factors influencing the performance of RF power harvesting systems. Moreover, a number of topics and research challenges affecting the performance of RF harvesting systems are still unreported, which deserve special attention. To this end, this article starts by providing an overview of the different application domains of RF power harvesting outlining their performance requirements and summarizing the RF power harvesting techniques with their associated power densities. It then comprehensively surveys the available literature on the horizons that affect the performance of RF energy harvesting, taking into account the evaluation metrics, power propagation models, rectenna architectures, and MAC protocols for RF energy harvesting. Finally, it summarizes the available literature associated with RF powered networks and highlights the limitations, challenges, and future research directions by synthesizing the research efforts in the field of RF energy harvesting to progress research in this area.

Thanks to its inherent capabilities (such as fairly long radio coverage with extremely low power consumption), long-range wide area network (LoRaWAN) can support a wide spectrum of low-rate use-cases in Industry 4.0. In this article, both plain and energy harvesting (EH) industrial environments are considered to study the performance of LoRa radios for industrial automation. In the first instance, a model is presented to investigate LoRaWAN in Industry 4.0 in terms of battery life, battery replacement cost, and damage penalty. Then, the EH potential, available within an Industry 4.0, is highlighted to demonstrate the impact of harvested energy on the battery life and sensing interval of LoRa motes deployed across a production facility. The key outcome of these investigations is the cost trade-off analysis between battery replacement and damage penalty along different sensing intervals which demonstrates a linear increase in aggregate cost up to £1500 in case of 5 min sensing interval in the plain (nonenergy harvesting) industrial environment while it tends to decrease after a certain interval up to five times lower in EH scenarios. In addition, the carbon emissions due to the presence of LoRa motes and the annual CO ₂ emission savings per node have been recorded up to 3 kg/kWh when fed through renewable energy sources. The analysis presented herein could be of great significance toward a green industry with cost and energy efficiency optimization.

The primary objective of this study is to accumulate, summarize, and evaluate the state-ofthe- art for spatio-temporal crime hotspot detection and prediction techniques by conducting a systematic literature review (SLR). The authors were unable to find a comprehensive study on crime hotspot detection and prediction while conducting this SLR. Therefore, to the best of author’s knowledge, this study is the premier attempt to critically analyze the existing literature along with presenting potential challenges faced by current crime hotspot detection and prediction systems. The SLR is conducted by thoroughly consulting top five scientific databases (such as IEEE, Science Direct, Springer, Scopus, and ACM), and synthesized 49 different studies on crime hotspot detection and prediction after critical review. This study unfolds the following major aspects: 1) the impact of data mining and machine learning approaches, especially clustering techniques in crime hotspot detection; 2) the utility of time series analysis techniques and deep learning techniques in crime trend prediction; 3) the inclusion of spatial and temporal information in crime datasets making the crime prediction systems more accurate and reliable; 4) the potential challenges faced by the state-of-the-art techniques and the future research directions. Moreover, the SLR aims to provide a core foundation for the research on spatio-temporal crime prediction applications while highlighting several challenges related to the accuracy of crime hotspot detection and prediction applications.

Smart manufacturing is a vision and major driver for change in today’s industry. The goal of smart manufacturing is to optimize manufacturing processes through constantly monitoring, controlling, and adapting processes towards more efficient and personalised manufacturing. This requires and relies on technologies for connected machines incorporating a variety of computation, sensing, actuation, and machine to machine communications modalities. As such, understanding the change towards smart manufacturing requires knowledge of the enabling technologies, their applications in real world scenarios and the communication protocols and their performance to meet application requirements. Particularly, wireless communication is becoming an integral part of modern smart manufacturing and is expected to play an important role in achieving the goals of smart manufacturing. This paper presents an extensive review of wireless communication protocols currently applied in manufacturing environments and provides a comprehensive review of the associated use cases whilst defining their expected impact on the future of smart manufacturing. Based on the review, we point out a number of open challenges and directions for future research in wireless communication technologies for smart manufacturing.