Wireless body area network (WBAN) is a term used to describe a network of sensor devices connected wirelessly for communication on, in and near the body to obtain physiological data from sensor devices. This paper explain the implementation of WBAN for monitoring body temperature, heart beat rate and oxygen saturation in blood. We develop the sensor reads physiological data to the desktop application, then will read the printout of the series and create a visual in the tables and graphs, as well as storing the data into a database and displayed via a website in the form of reports that can be accessed remotely. We analyze the data received from sensor nodes to server receiver with a variety of different distances 10, 20, 30, 40, and 50 meters. The experiment results show that the physiological data can be accessed through ZigBee wirelessly in short distance which suitable for WBAN.

Hypothermia is a state where the body cannot balance the body heat due to excessive cold temperature pressure so that there will be loss of heat in the body. The result will be a quickly decreasing body temperature. A condition where a body temperature is less than 35°C and that it is accompanied by a decrease in consciousness may cause a death threat. Hypothermia is more common when climbers perform activities in cold temperatures and strong winds, and when they wear wet clothes. It is not easy for a mountain climber to find out his own body temperature as well as that of his friends and to determine whether the body temperature is safe or affected by hypothermia symptoms. Therefore, this research formed a device to detect hypothermia. It consists of temperature sensor, pulse sensor, and accompanied by heating elements based on wireless body area network (WBAN). This device will connect to the user's smartphone by using Bluetooth, so the user can see the condition of his body, adjust the temperature of the heating element, and make a group of climbers. The experiment result shows that the device can detect hypothermia symptoms and inform these symptoms to the users and groups of mountaineers in WBAN area, and the climbers can be more vigilant in responding to hypothermia so that mortality cases caused by hypothermia can be minimized.

Nowadays, the use of Electrocardiogram (ECG) as a detection tool of the condition of human heartbeat is still considered difficult. So, the idea of implementing this sensor is to enhance the quality of service and to minimize the cost of using and maintaining by determining the best way in implementing and monitoring ECG sensor. This research proposes a novel approach to observe the condition of human heartbeat using ECG sensor with e-Health sensor and Raspberry Pi, mini single-board computer with robust capability, which acts as central processing unit. Those hardware sets were assembled to collect ECG voltage signals in human body. The signals are transmitted into the device and the data are stored in MySQL database for long-term monitoring access. The experimental results show effectiveness of this approach as it is successfully fulfill the idea of Body Area Network (BAN).

The semantic sensor web (SSW) is a combination of sensor networks, web services, database and semantic web technologies. The SSW describes sensor data with spatial, temporal, and thematic semantic metadata. One of the challenges for sensor web application is the integration and fusion of data coming from autonomously deployed sensor network. Gas Sensor technology is one of the wireless sensor network (WSN), which has a wide range of sensors that can be used to monitor the concentration of gas in a region. This paper implements SSW with existing gas sensor to perform readings from the environment, store it into a database and provide services application program interface (API) data. Sensor data used in this study are temperature, CO, CO₂ and Humidity Sensor. The method we use is to build a Semantic Web to connect the sensor data that is spread by harnessing service API on the sensor semantic web servers interconnected. Furthermore, the data retrieved from the sensors will be represented in the form of ontology with RDF/OWL type and stored into RDF Repository (OPENRDF-SESAME) in order to be processed and published to the semantic web service. SPARQL library and curl serves to bridge the gap between service and Apache Tomcat. Existing data in the RDF Triple Store file will be displayed to the user to utilize the service using standard API RESTFUL. Then the existing data in the RDF database is displayed in a JSON data format.

Target tracking is an application in WSN (Wireless Sensor Network) that estimating directly the mobile target position. However, in indoor location with large coverage area it is difficult to get the precision estimation, especially using RSSI (received strength signal indicator) with poor accuracy. The condition of an indoor location with many walls, obstacle, pillar, doors, multipath fading, and attenuation is influenced to the RSSI measurement in determining the distance estimation from PLE (path loss exponent) value. In this paper, we propose mobile cooperative tracking based on extended RSSI ranging to obtain localization information from three nearest reference devices continuously. RSSI measurement has data fluctuating and nonlinear characteristic. Using EKF (extended Kalman filter) algorithm the error estimation position from the mobile cooperative tracking result could be reduced as well. The combination of mobile cooperative tracking and EKF algorithm for indoor wireless sensor network yield error reduction percentage 47.47%, with MSE (mean square error) 1.09 m. Although this combination has the capability to reduce error estimated position, the time computation is in 120.74 seconds longer than mobile cooperative tracking which only needs 41.73 seconds.