Nowadays, rapid developments of Internet of Things (IoT) technologies have increased possibilities of realizing smart cities where collaborations and integrations of various IoT application systems are essential. However, IoT application systems have often been designed and deployed independently without considering the standards of devices, logics, and data communications. In this paper, we present the design and implementation of the IoT server platform called Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) for integrating IoT application systems using standards. SEMAR offers Big Data environments with built-in functions for data aggregations, synchronizations, and classifications with machine learning. Moreover, plug-in functions can be easily implemented. Data from devices for different sensors can be accepted directly and through network connections, which will be used in real-time for user interfaces, text files, and access to other systems through Representational State Transfer Application Programming Interface (REST API) services. For evaluations of SEMAR, we implemented the platform and integrated five IoT application systems, namely, the air-conditioning guidance system, the fingerprint-based indoor localization system, the water quality monitoring system, the environment monitoring system, and the air quality monitoring system. When compared with existing research on IoT platforms, the proposed SEMAR IoT application server platform offers higher flexibility and interoperability with the functions for IoT device managements, data communications, decision making, synchronizations, and filters that can be easily integrated with external programs or IoT applications without changing the codes. The results confirm the effectiveness and efficiency of the proposal.
Nowadays, indoor localization systems using IEEE 802.11 have been actively explored for location-based services, since GPS cannot identify floors or rooms in buildings. However, the user-side device is usually large and consumes high energy. In this paper, the authors propose a fingerprint-based indoor localization system using IEEE 802.15.4 that allows the use of a small device with a long-life battery, named FILS15.4. A user carries a small transmitter whose signal is received by multiple receivers simultaneously. The received signal strengths are compared with the fingerprints to find the current location. To address signal fluctuations caused by the low-power narrow-band signal, FILS15.4 limits one room as the localization unit, prepares plural fingerprints for each room, and allocates a sufficient number of receivers in the field. For evaluations, extensive experiments were conducted at #2 Engineering Building in Okayama University and confirmed high detection accuracy with sufficient numbers of receivers and fingerprints.
Nowadays, human indoor localization services inside buildings or on underground streets are in strong demand for various location-based services. Since conventional GPS cannot be used, indoor localization systems using wireless technologies have been extensively studied. Previously, we studied a fingerprint-based indoor localization system using IEEE802.15.4 devices, called FILS15.4, to allow use of inexpensive, tiny, and long-life transmitters. However, due to the narrow channel band and the low transmission power, the link quality indicator (LQI) used for fingerprints easily fluctuates by human movements and other uncontrollable factors. To improve the localization accuracy, FILS15.4 restricts the detection granularity to one room in the field, and adopts multiple fingerprints for one room, considering fluctuated signals, where their values must be properly adjusted. In this paper, we present a fingerprint optimization method for finding the proper fingerprint parameters in FILS15.4 by extending the existing one. As the training phase using the measurement LQI, it iteratively changes fingerprint values to maximize the newly defined score function for the room detecting accuracy. Moreover, it automatically increases the number of fingerprints for a room if the accuracy is not sufficient. For evaluations, we applied the proposed method to the measured LQI data using the FILS15.4 testbed system in the no. 2 Engineering Building at Okayama University. The validation results show that it improves the average detection accuracy (at higher than 97%) by automatically increasing the number of fingerprints and optimizing the values.
Abstrak – Dunia digital kini telah sampai pada era di mana begitu banyak unsur fisik dapat terhubung dandimonitor secara jarak jauh dengan penggunaan sensor yang terhubung dalam suatu jaringan komunikasinirkabel yang berbasis internet (internet of things). Pelayanan kesehatan juga tak luput dari sorotanpenggunaan IoT terutama dengan meningkatnya berbagai isu penyakit kronis yang dapat menurunkanharapan hidup manusia. Jaringan yang secara khusus menggunakan berbagai sensor yang ditempatkan padatubuh manusia ini disebut wireless body area network (WBAN). Artikel ini mengulas tentang bagaimanaperkembangan WBAN dalam menjawab berbagai kebutuhan peningkatan layanan kesehatan secarakomprehensif dan kontinyu tanpa terhalang keterbatasan jarak dan waktu antara pasien dengan paramedis.Teknologi pemantauan kesehatan yang bersifat mobile (m-Health) terus dikembangkan demi meningkatkanefektivitas dan efisiensi layanan kesehatan. Berbagai isu dan tantangan juga dikemukakan sehingga dapatmenjadi telaah referensi untuk berbagai penelitian lanjutan.Kata Kunci: WBAN, IoT, WSN, pelayanan kesehatan, jaringan sensor Abstract – The digital world has now arrived in an era where every physical thing can be remotelymonitored by using sensors connected to an internet-based wireless communication network (internetof things). Health care services is also become a concern for the development of this service especiallybecause the increasing chronic health problem which can decrease the life expectancy. The networkwhich specifically worked by a set of sensors which attached around human’s body is called wirelessbody area network (WBAN). This article is meant to discuss about the development of WBAN insolving various health care services comprehensively and continuously without any restrictions relatedto distance and time between the patient and the paramedics. Mobile health monitoring (m-Health)continues to be developed to improve the effectivity and efficiency of health care services. Issues andopen challenges are also discussed in the article as a reference for the further researches.Keywords: WBAN, IoT, WSN, health care service, sensor networks
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