IET Smart Cities最新文献

This article presents an approach to model and simulate Plug&Play social things. Confined into silos, existing (not social) things are restricted to basic operations like sensing and actuating, which deprive them from participating in the satisfaction of complex business applications. Contrarily, social things are expected to engage in collaborative scenarios and to tap into specific relations that connect them to peers when achieving these scenarios. These relations are referred to as complimentary, antagonism, and competition, and allow to develop networks of things. To capitalize on such networks, the approach to model and simulate Plug&Play social things puts forward four stages that are referred to as connecting to demystify social relations between things, influencing to examine the impact of social relations on things, playing to make things perform while considering influence, and incentivizing to reward things based on their performance. A smart system for elderly the care centers has been developed to showcase the technical doability of Plug&Play social things. The system is an integrated development environment allowing IoT engineers to define the collaboration of social things, thanks to a set of drag&drop operations.

Changes in vital signs are an important indicator of physiological decline and provide opportunities for early recognition and intervention. The collected vital signs data can be evaluated using several approaches such as the Early warning score (EWS) approach to predict the risk level of patients. By exploring the Internet of things (IoT), vital signs monitoring solutions are automated based on various medical devices and sensors. However, there is a lack of efficient tools that enable an adaptative monitoring depending on the patient situations. This article explores the IoT technologies to provide an EWS system in smart hospital situation. The proposed solution presents an adaptative configuration of the vital signs monitoring process depending on the patient’s health status variation and the medical staff decisions. Also, an intelligent notification mechanism that reduces the delay of the medical staff intervention in the case of risk detection is proposed.

Currently, the world witnesses a boom in the sensing-based applications where the number of connected devices is becoming higher than the people. Such small sensing devices are now deployed in billions around the world, collecting data about the surroundings and reporting them to the data analysis centres. This fact allows a better understanding of the world and helps to reduce the effects of potential risks. However, while the benefits of such devices are real and significant, sensing-based applications face two major challenges: big data collection and restricted power of sensor battery. In order to overcome these challenges, data reduction and sampling sensor adaptation techniques have been proposed to reduce data collection and to save the sensor energy. The authors propose an orchestration-based mechanism (OM) for adapting the sampling rate of the sensors in the network. OM is two-fold: first, it proposes a data transmission model at the sensor level, based on the clustering and Spearman coefficient, in order to reduce the amount of data transmitted to the sink; second, it proposes a sampling rate mechanism at the cluster-head level that allows searching the similarity between data collected by the neighbouring sensors, and then to adapt their sensing frequencies accordingly. A set of simulations on real sensor data have been conducted to evaluate the efficiency of OM, in terms of data reduction and energy conservation, compared to other exiting techniques.

Fast and reliable evaluation of regional traffic congestion is beneficial to more effective traffic control. Based on data accumulation in modern society, more and more data-driven methods are proposed. However, it is still not easy to process the raw data to an interpretable level in practical applications. In this article, the GPS data are obtained from floating cars covering a large scale region in Xi'an, China. To link the original data to the spatiotemporal relationship of driving behaviour, a pre-processing method with specified time–frequency rules is proposed. Through map matching and landmark mapping, it can be seen that the data dispersion degree has decreased and the quality of the original data has been improved. At the same time, deep learning methods and non-parametric survival analysis methods are used to compare and evaluate traffic congestion. In addition, four different distributions (Exponential, Weibull, Log-normal, and Log-logistic) are tested to fit the accelerated failure time model (AFT), which is then compared with the Cox proportional hazards model (Cox). It is concluded that the most suitable parameter model for the test section of Xi'an South Second Ring Road is AFT (Lognormal). All those methods are tested on a randomly selected segment on the ring road in Xi'an. The results suggest dramatic improvement of data quality and successful evaluation of traffic conditions with high reliability. Potential application could be effective methods for traffic control and management in the smart city.