Amang Sudarsono

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 %.

Reliability analysis of GSM network using Software Defined Radio-based system

Recently, the development of telecommunication fields is growing rapidly. One of the important issues in the development of telecommunications is the expansion of public mobile network infrastructures, i.e., base stations. However, the development of base stations requires substantial funds. Currently, there is a solution in building base station infrastructure with more efficient and cheaper by using Software Defined Radio (SDR) and Universal Software Radio Peripheral (USRP). USRP is working as a base station and SDR as a software complement in the development of the system. In this paper, we propose a GSM 2.5G network system. We utilize several software such as soft-switch, GNU radio and OpenBTS to support Mobile Switching Center (MSC), Base Station Controller (BSC), and Base Transceiver Station (BTS) functionalities. We demonstrate handover mechanism between two and more base stations, and authentication mechanism in the system. We also measure the throughput of the system provided by General Packet Radio Service (GPRS). The system capacity provides in average of 4 Kbps for download and 3 Kbps for upload. The main factor that affects the throughput is the factor of multi slot channel allocations towards download and upload. We also demonstrate handover mechanism when MS moves away from USRP 1 towards USRP 2, signal level of USRP 1 received by MS is weaker than signal level of USRP 2. Thus, MS selects USRP which has a strong signal as its base station. In addition, to prevent hacking and provide user authentication on the system, user and MSC must have a same key of Ki. By using a secret key of Ki which owned GSM network, only users that have the same key of Ki can be registered on the system.

Secure data exchange using authenticated Ciphertext-Policy Attributed-Based Encryption

Easy sharing files in public network that is intended only for certain people often resulting in the leaking of sharing folders or files and able to be read also by others who are not authorized. Secure data is one of the most challenging issues in data sharing systems. Here, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a reliable asymmetric encryption mechanism which deals with secure data and used for data encryption. It is not necessary encrypted to one particular user, but recipient is only able to decrypt if and only if the attribute set of his private key match with the specified policy in the ciphertext. In this paper, we propose a secure data exchange using CP-ABE with authentication feature. The data is attribute-based encrypted to satisfy confidentiality feature and authenticated to satisfy data authentication simultaneously.

Performance analysis of OLSR Routing for secure medical data transmission for rural areas with Delay Tolerant Network

Wireless data communications commonly use public networks to exchange data packets. Public networks of rural area are lack of network infrastructure so it will inflict high delay for wireless communications. The rural areas are same with intermittent area that need a mechanism of data transfer to tolerate with delay. The Delay Tolerant Network (DTN) is a solution for data transmission in rural areas that have coverless TCP/IP network. DTN uses store-and-forward mechanism to send data packets from the source to the destination through intermediary nodes. DTN uses dynamic routing protocols to determine routes of data packets delivery. DTN implements a reliable routing protocol of Optimized Link State Routing (OLSR) algorithm to looking for a delivery route of data packets to the end node. The OLSR routing protocol uses MPRs node as selected nodes, which forwards the control messages of networks topology information. This could streamline the network traffic of DTN to determine the best route of delivery data packets from the sender to the receiver. This paper presents transmission of medical data from the source of the node in the rural area to the destination node through the nodes of intermediate. This telemedicine system sends medical data of patients private data that is encrypted using Advanced Encryption System (AES) to guarantee the medical data safety. The AES encryption system takes no more than 0.4 ms for data encrypting and decrypting process.

An implementation of secure medical data delivery for rural areas through delay tolerant network

The telemedicine system commonly uses a public network to exchange medical data. Data security implemented before transmission using telemedicine system because patients data are private. This paper presents the architecture is recommended to support the transmission of medical data using data security of a telemedicine system for rural areas in the developing country with limited internet. The patients medical data is encrypted before it is sent to the main hospital in this area for further diagnosis. The total computing in the encryption and decryption of medical data file was no more than 0.4 ms so that the implemented for the application security system is very light and is not burdensome.

Smart presentation system using hand gestures and Indonesian speech command

The development of technology for supporting learning system at this time takes place very rapidly. Human Computer Interaction provides users that allows to control presentations in a natural way by their body gestures and voice commands. The authors propose a simple system that can be use to control presentation by using hand gestures and also by using Indonesian speech recognition. Our proposed system consist of gesture recognition with Kinect 3D skeletal data, and utilize Google Speech API to recognize Indonesian language as voice command. In this system we built applications to perform hand gestures and speech recognitions and utilize libraries to perform mouse and keyboard actions in presentation application. From the experiments, hand gestures application can process in realtime with the accuracy more than 90%, and for Indonesian language speech recognition gave accuracy almost 99% with average speed of recognition around 1 second.