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Abstract-Awireless 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. I. PENDAHULUAN Perkembangan teknologi komunikasi nirkabel yang semakin pesat beberapa tahun belakangan ini mendorong berkembangnya perangkat-perangkat telekomunikasi yang semakin canggih. Salah satunya adalah jaringan ad-hoc wireless, yaitu kumpulan node terinterkoneksi yang saling bertukar informasi dan menggunakan daya bersama dalam sebuah media transmisi nirkabel. Salah satu aplikasi jaringan nirkabel yang dapat diimplementasikan di bidang e-health khususnya pencarian posisi adalah sistem lokalisasi antar perangkat node [1]. Lokalisasi adalah proses penentuan posisi dari perangkat node yang digunakan secara acak pada jaringan nirkabel [2]. Sistem lokalisasi diperlukan untuk memberikan informasi posisi dari node ke server [3]. Intisari-Global Positioning System (GPS) merupakan salah satu teknologi yang dapat digunakan untuk identifikasi posisi node. Namun, GPS mengonsumsi energi dalam jumlah besar dan apabila diaplikasikan pada jaringan sensor nirkabel (JSN) akan menyebabkan biaya yang mahal karena harus diimplementasikan pada node dalam jumlah besar. Dibandingkan dengan teknologi yang lain, GPS memberikan akurasi lokalisasi yang sangat tinggi, tetapi GPS menyerap sejumlah besar energi pada node sensor, sehingga dengan alasan-alasan tersebut penggunaan GPS tidak disarankan untuk sebagian besar aplikasi berbasis JSN. Kalaupun terpaksa digunakan, maka hanya diaplikasikan pada nodenode referensi saja, tidak pada seluruh node dalam sistem [3].Pada makalah ini, diusulkan skema lokalisasi posisi secara terdistribusi pada jaringan nirkabel berdasarkan kuat sinyal yang diterima dari node lain. Lokalisasi terdistribusi menyatakan bahwa proses kalkulasi estimasi posisi sebuah node dilakukan pada node itu sendiri. Node-node yang menghitung posisinya berkomunikasi secara kooperatif dengan node-node referensi dan se...

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In wireless sensor network applications, the position of nodes is randomly distributed following the contour of the observation area. A simple solution without any measurement tools is provided by range-free method. However, this method yields the coarse estimating position of the nodes. In this paper, we propose Adaptive Connectivity-based (ACC) algorithm. This algorithm is a combination of Centroid as range-free based algorithm, and hop-based connectivity algorithm. Nodes have a possibility to estimate their own position based on the connectivity level between them and their reference nodes. Each node divides its communication range into several regions where each of them has a certain weight depends on the received signal strength. The weighted value is used to obtain the estimated position of nodes. Simulation result shows that the proposed algorithm has up to 3 meter error of estimated position on 100x100 square meter observation area, and up to 3 hop counts for 80 meters' communication range. The proposed algorithm performs an average error positioning up to 10 meters better than Weighted Centroid algorithm.Keywords: adaptive, connectivity, centroid, range-free.

Alzheimer's disease is a disease of the nerves that are irreversible, resulting in memory impairment. This condition resulted in Alzheimer's patients easily lost because they forget the existence. In this research, we designed a tracking system for Alzheimer's patients in a hospital environment, incorporating Kalman method to estimate the position of the patient. As known Received Signal Strength Indicator value is strongly influenced by environmental conditions that lead to the acquisition of position estimation is inaccurate. From the test results showed that the optimal Kalman estimated value obtained when the value of R = 0:01 and Q = 0.1 with the average percentage of error only 7.01 % of the actual patient position. The test results with various data variations also indicate the reliability of the Kalman method, because of the average estimated position approach the actual patient position.