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Recently people pay more and more attention on how to effectively and efficiently analyze the result of regular physical examinations to provide the most helpful information for individual health management. In this paper, we design and develop an interactive system of virtual healthcare assistant to help people, especially for those who suffer from chronic diseases (e.g., metabolic syndrome) to easily understand their health conditions and then well manage it. This system analyzes the result of regular physical examination to evaluate the health risk and provide personalized healthcare services for users in terms of diet and exercise guideline recommendations. We developed some interactive ways for users to easily feedback their vital signs to the system and quickly get the suggestions for health management from the system. Besides the browser-based system, we also developed a mobile App that can regularly remind users to carry out the recommendations, which are provided by the system. To prove the system is feasible in the real-world clinical environment, we also applied the Institutional Review Board (IRB) for a human subject research to validate this system. Other than the functional features, there are also several important non-functional features of the extensibility and the convenience for use. First, we use the physical examination result as the raw data to be analyzed. It's very convenient for users with very low cost. Second, the system design is extendable, so it can be easily adjusted to work for any chronic ills, even other kinds of diseases. Moreover, it can be extended to provide other kinds of healthcare guideline recommendations as well. These features constitute the main contributions of this work.

Abstract:Commercial vehicle operation (CVO) has been a popular application of intelligent transportation systems. Location determination and route tracing of an on-board unit (OBU) in a vehicle is an important capability for CVO. However, large location errors from global positioning system (GPS) receivers may occur in cities that shield GPS signals. Therefore, a highly efficient mobile positioning method is proposed based on the collection and analysis of the cellular network signals of CVO data. Parallel-and cloud-computing techniques are designed into the proposed method to quickly determine the location of an OBU for CVO. Furthermore, this study proposes analytical models to analyze the availability of the proposed mobile positioning method with various outlier filtering criteria. Experimentally, a CVO system was designed and implemented to collect CVO data from Chunghwa Telecom vehicles and to analyze the cellular network signals of CVO data for location determination. A case study found that the average errors of location determination using the proposed method vs. using the traditional cell-ID-based location method were 163.7 m and 521.2 m, respectively. Furthermore, the practical results show that the average location error and availability of using the proposed method are better than using GPS or the cell-ID-based location method for each road type, particularly urban roads. Therefore, this approach is feasible to determine OBU locations for improving CVO.

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² With the growing popularity of cellular phones, using cellular signaling data to collect traffic information has become an attractive technique to replace VDs (Vehicle Detectors). However, most previous studies have focused on GSM network instead of the ongoing UMTS due to some special properties in UMTS. In this paper, a passive framework with multiple handover pattern exploring and traffic information estimating strategy is proposed to fit characteristics of UMTS signaling data. Experiments from real-time UMTS signals result in a mean relative error around 14% to the information collected from VDs.

This editorial introduces the special issue, entitled “Applications of Internet of Things”, of Symmetry. The topics covered in this issue fall under four main parts: (I) communication techniques and applications, (II) data science techniques and applications, (III) smart transportation, and (IV) smart homes. Four papers on sensing techniques and applications are included as follows: (1) “Reliability of improved cooperative communication over wireless sensor networks”, by Chen et al.; (2) “User classification in crowdsourcing-based cooperative spectrum sensing”, by Zhai and Wang; (3) “IoT’s tiny steps towards 5G: Telco’s perspective”, by Cero et al.; and (4) “An Internet of things area coverage analyzer (ITHACA) for complex topographical scenarios”, by Parada et al. One paper on data science techniques and applications is as follows: “Internet of things: a scientometric review”, by Ruiz-Rosero et al. Two papers on smart transportation are as follows: (1) “An Internet of things approach for extracting featured data using an AIS database: an application based on the viewpoint of connected ships”, by He et al.; and (2) “The development of key technologies in applications of vessels connected to the Internet”, by Tian et al. Two papers on smart home are as follows: (1) “A novel approach based on time cluster for activity recognition of daily living in smart homes”, by Liu et al.; and (2) “IoT-based image recognition system for smart home-delivered meal services”, by Tseng et al.

In recent years, the improvement of cloud computing and mobile computing techniques has led to the availability of a variety of mobile applications ('apps') in the app store. For instance, a garbage truck app that can provide the immediate location of a garbage truck, the location of collection points, and forecasted arrival times of garbage trucks would be useful for mobile users. Since the power consumption of apps on mobile devices if of concern to mobile users, an optimised power-saving mechanism for updating messages, which is based on location information, for a proposed garbage truck fleet management system (GTFMS) is proposed and implemented in this paper. The GTFMS is a threecomponent system that includes the on-board units on garbage trucks, a fleet management system, and a garbage truck app. In this study, an arrival time forecasting method is designed and implemented in the fleet management system, so that the garbage truck app can retrieve the forecasted arrival time via web services. A message updating event is then triggered that reports the location of garbage truck and the forecasted arrival time. In experiments conducted on case studies, the results showed that the mean accuracy of predicted arrival time by the proposed method is about 81.45 per cent. As for power consumption, the cost of traditional mobile apps is 2,880 times that of the mechanism proposed in this study. Consequently, the GTFMS can provide the precise forecasted arrival time of garbage trucks to mobile users, while consuming less power. OPSOMMINGDie verbetering van wolkverwerking en mobiele verwerkingstegnieke het gelei tot die beskikbaarheid van 'n groot verskeidenheid mobiele toepassings. 'n Voorbeeld hiervan is 'n toepassing wat die onmiddellike ligging van 'n vullistrok, die ligging van vullis versamelpunte en die voorspelde aankomstye van die vullistrokke aan die gebruiker verskaf. Die energieverbruik van die toepassings is ook van belang en 'n geoptimeerde energiebesparingsmeganisme vir die opdateer van boodskappe (wat inligting rakende die vullistrok se ligging bevat) word in hierdie artikel ontwerp en geïmplementeer. 'n Opdateringsboodskap rapporteer die vullistrok se ligging en voorspelde aankomstyd. Gevallestudies toon dat die gemiddelde akkuraatheid van die voorspelde aankomstyd 81.45% is. Die energieverbruik van die toepassing is 2880 keer minder as dié van 'n tradisionele mobiele toepassing. Gevolglik kan die voorgestelde vullistrokvlootbestuurstelsel 'n baie akkurate aankomstyd voorspelling aan gebruikers gee terwyl dit min energie verbuik. 33 INTRODUCTIONIn recent years, the improvement of cloud computing and mobile computing techniques has led to the availability of a variety of mobile applications in the app store [1][2][3]. Furthermore, several mobile applications of intelligent transportation systems (ITS), which include bus apps [4], public bicycle systems [5], mass rapid transit systems [6], and railway apps [7], have been developed and implemented to provide convenient transport services for resid...

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