Prima Kristalina

An implementation of secure data sensor transmission in Wireless Sensor Network for monitoring environmental health

Recently, conveying latest information related to the status of environmental health surrounding human dwelling through Wireless Sensor Networks (WSNs) is one of very important things to be known by everyone at anytime and anywhere as well as very critical information in disaster warning and any other high-risk information. However, since the adversaries are able to illegally intercept and manipulate data, a secure data sensor transmission would be strongly expected. In this paper, we propose an implementation of secure data sensor transmission in WSN, such as temperature, humidity, O2, CO, CO2, O3, and NH3 data transferring from WSN sensor nodes to data center server through a gateway node for monitoring environmental health with consideration on data confidentiality, data integrity and light-weight cryptography computation. Our experimental results showed that total computation time for secure data sensor processing is about 320 ms on Waspmote ATmega1281 microcontroller 14MHz.

SCLoc: Secure localization platform for indoor wireless sensor network

Security scheme in indoor object positioning has become a special challenge in the recent time. The characteristic of indoor environment due to obstacles or building material leads a lack of range-based position estimation. A security scheme determined to protect data towards unauthorized party. The proposed SCLoc is an object position platform with AES 128 cryptography system equipped on each node in wireless sensor networks. This algorithm is used to secure anchor node coordinates information and estimated position which has been calculated in unknown nodes. The scheme is examined using Waspmote nodes and compared with a DES cryptography algorithm in similar nodes. The experimental result shows that average position error which calculated by the proposed platform in corridor A was 0.043% of the total observation area. The security algorithm which employed to secure the position of anchor node and unknown node during data exchange in transmission process is satisfy confidentiality and data integrity security requirements simultaneously.

Cluster-based PLE areas for mobile cooperative localization in indoor wireless sensor network

Localization is an important requirement in wireless sensor networks to determine the position of the object. Localization based on RSSI (Received Signal Strength Indicator) has low accuracy is caused by attenuation of the transmitted signal as a function of the distance between nodes. In indoor environment, the PLE (path loss exponent) values are varied depend on the environment characteristic. This value is required to estimate the distance among nodes and it obtained from the measurement process. However, the fluctuation of PLE value based on RSSI is strongly influencing the output of estimation distance. In this paper we propose a cluster-based PLE areas measurement procedure for indoor mobile cooperation localization. The variation values of PLE can be used to calculate distance estimation from RSSI data of mobile object based on nearest distance from anchor node in cooperative localization. The simulation result shows that cluster-based PLE measurement values have capability in decreasing MSE localization up to 16.16 %, meanwhile without clustering the observation area the MSE localization has 32.39 % decreased. The improvement of PLE value on cluster-based area using linear regression yields 18.06 % of MSE localization.

Mobile cooperative tracking with RSSI ranging in EKF algorithm for indoor wireless sensor network

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.

Cluster-based pathloss exponential modeling for indoor positioning in wireless sensor network

GPS (Global Positioning System) cannot be used for localization object in indoor environments. Even though, estimation of localization is an important requirement in wireless sensor networks to determine the position of the object. Localization based on RSSI (Received Signal Strength Indicator) has low accuracy because of attenuation of the transmitted signal as a function of the distance between nodes. In indoor environment, the path loss exponent values are varied depending on the environment characteristic. This value is required to estimate the distance between nodes and it obtained from the measurement process. However, the fluctuating value based on measurement, it is strongly influencing the output of estimating distance based on RSSI. There for, we proposed a new model for cluster-based PLE in three conditions. After this measurement phase, the variation values of PLE using purposed method and linear regression can be used to calculate distance estimated from RSSI data. On the simulation result shows that cluster-based PLE model condition 1 (anchor node area) have the capability in decreasing MSE estimation distance up to 14.64 %, meanwhile MSE estimation distance PLE using regression liner has 23.55%, cluster-based PLE model condition 3 (cluster area) has 26.27 % and cluster-based PLE model condition 2 (sub cluster area) has 35.54 %.

Skema Lokalisasi Posisi Node Terdistribusi pada Lingkungan Free Space Path Loss

A wireless sensor network consists of interconnected nodes that exchange information and use shared resource in a wireless transmission medium. Sensor nodes are randomly deployed in observation area in static or moving term. During this situation, the position of each sensor nodes is required to be known to monitor the circumstances around the node according to the information collected by sensor. Localization is the process to determine the position of nodes. This process could be done in centralized or distributed manner. In this paper, a distributed localization mechanism is proposed, where the calculation of node position is carried out on the node itself. Trilateration method is employed to calculate the position of node based on estimated distance measured by Received Signal Strength Indicator (RSSI) technique using Zigbee module in Free-Space Path Loss (FSPL) outdoor area. The experiment result shows that, based on log-normal shadowing model, the path loss coefficient for observation area is 2.5443, whereas average estimated position error from three different measured nodes are 23.504 m, 17.369 m, and 17.95 m respectively. Each node needs 2.73 second to undertake localization process completely.