Selo Sulistyo

FoSBaS: A bi-directional secrecy and collusion resilience key management scheme for BANs

Body Area Network (BAN) consists of various types of small physiological sensors, transmission modules and low computational components and can thus form an E-health solution for continuous all-day and any-place health monitoring. To protect confidentiality of collected data, a shared group key is usually deployed in a BAN, and consequently a secure communication group is generated. In this paper, we propose a bi-directional security and collusion resilience key management scheme for BAN, referred to as FoSBaS. Detailed analysis shows that the scheme can provide both forward security and backward security and resist against collusion attacks. Furthermore, the FoSBaS is implemented on a Sun SPOT based sensor network testbed to evaluate its performance. Experimental results show that a group key can be updated within 102.13 ms with 60.22 mJ energy consumption on a 12 node BAN with 28 bits pairwise key.

SCARKER: A sensor capture resistance and key refreshing scheme for mobile WSNs

How to discover a captured node and to resist node capture attack is a challenging task in Wireless Sensor Networks (WSNs). In this paper, we propose a node capture resistance and key refreshing scheme for mobile WSNs which is based on the Chinese remainder theorem. The scheme is able of providing forward secrecy, backward secrecy and collusion resistance for diminishing the effects of capture attacks. By implementing our scheme on a Sun SPOT based sensor network testbed, we demonstrate that the time for updating a new group key varies from 56 ms to 546 ms and the energy consumption is limited to 16.5–225 mJ, depending on the length of secret keys and the number of sensors in a group.

Recursive modeling for completed code generation

Model-Driven Development is promising to software development because it can reduce the complexity and cost of developing large software systems. The basic idea is the use of different kinds of models during the software development process, transformations between them, and automatic code generation at the end of the development. But unlike the structural parts, fully-automated code generation from the behavior parts is still hard, if it works at all, restricted to specific application areas using a domain specific language, DSL. This paper proposes an approach to model the behavior parts of a system and to embed them into the structural models. The underlying idea is recursive refinements of activity elements in an activity diagram. With this, the detail generated code depends on the depth at which the refinements are done, i.e. if the lowest level of activities is mapped into activities executors, the completed code can be obtained.

Feasible solution of centralized power control for multi channel cognitive femtocell network

The importance of power control is related to the interference problem between users on the cognitive radio network. It also keeps the battery device remains durable. A completion method uses the algorithm feasible solution to the centralized power control by looking at the feasibility of power vector value. Feasible solution can be achieved if the value of the power user is non-negative, meaning that SINR target can be achieved and the system can be implemented. This study is focused on the multi-channel that applied to multi-user. It can be concluded that the addition of the channels will increase the SINR of user. The smaller the size of the user group, the higher the SIR can be achieved.

Road density prediction: Updated methods of turning probabilities and highway capacities manual for achieving the best route

Currently, some information technology applications related to the traffic guidance of metropolitan city could only present the shortest route information, but the road density prediction was not informed. In this research, we proposed the model both to predict the road density and to inform the shortest cycle time of road. The methods of Turning Propobabilities and Highway Capacities Manual were implemented in this proposed model by updating some related formulas in order to predict the road density. The proposed model resulted the new procedure to present the prediction of the road density and suggesting the information of the best route. The model was contributed to solve the problem of distributing vehicles in metropolitan city to overcome the congestion problem.

PMG-Pro: a model-driven development method of service-based applications

In the Internet of Things, billions of networked and software-driven devices will be connected to the Internet. They can communicate and cooperate with each other to form a composite system. In this paper, we propose PMG-pro (present, model, generate and provide), a language independent, bottom-up and model-driven method for the development of such composite system. We envision that all devices in the Internet of Things provide their functionalities as services. From a service description, a service presenter generates source code (i.e., for the service invocations) and uses an abstract graphical representation to represent a service. The code is connected to the abstract graphical service representation. A service abstractor constructs the abstract graphical representations even more abstract in hierarchical service taxonomy. Software developers use the abstract graphical service presentations to specify new service-based applications, while the source code is used for the automation of code generation.

Software development methods in the internet of things

In the Internet of Things, billions of networked and software- driven devices will be connected to the Internet. They can communicate and cooperate with each other to function as a composite system. This paper proposes the AMG (abstract, model and generate) method for the development of such composite systems. With AMG, the development of software application can be done in an automatic manner, and therefore reducing the cost and develpment time. The method has been prototyped and tested with use cases.

A comparative study on centralized and distributed power control in cognitive femtocell network

The need for an efficient transmit power is affected by the condition of user and power control methods used. User conditions that categorized in cognitive femtocell networks included in the category as distributed user, so it required a distributed power control (DPC). However, centralized power control (CPC) is also important, especially in the framework as a reference for the independent power control in a distributed user. Therefore, it is important to do a comparison of both methods related to power control the transmit power and the achievement of the target SINR. Comparison of the two schemes was conducted during the feasibility and convergence requirements are met. The results show that CPC can achieve SINR whose value exceeds the SINR targets, while DPC maximum achieving SINR is only up to SINR target. But the power consumed by the CPC is very large when compared to the power consumption at the DPC. So it can be concluded that the method of DPC can save the power better than the CPC.

Implementation of scalable K-Means++ clustering for passengers temporal pattern analysis in public transportation system (BRT Trans Jogja case study)

The popularity of Bus Rapid Transit (BRT) makes Trans Jogja an alternative of a mass public transportation system for urban mobility. However, without supervision on temporal patterns of passengers behavior in Trans Jogja on supply and demand, it will result in the decreases of the number of BRT users and the increasing number of private vehicle users, so that traffic jams remain difficult to avoid. Smart Card Automated Fare Collection System (SCAFCS) which is currently used as e-ticketing in Trans Jogja public transport can be used to analyze passengers pattern with data mining approaches. This paper applied SCAFCS data preprocessing with data warehouse mechanism and implemented Hadoop Platform as distributed computing to improve K-Means++ clustering performance on large datasets scalability; in this case, SCAFCS Trans Jogja has a large dataset (volume) and rapid growth data (velocity). Scalable K-Means++ algorithm generates five clusters with characteristics in number of clusters, namely: Very Low, Low, Average, High, Very High. The clusters were used to analyze passengers pattern based on the dimensions of time (temporal), segmentation of passengers (structure) to determine the variability of passengers based on the card they used and transaction peak on boarding location (spatio). Experimental and testing setup was performed by comparing Sum of Square Error (SSE) which is the total squared error of k cluster at the centroid on three algorithms, simple K-Means, K-Means++ and K-Means++ implementation using Hadoop Platform as parallel and distributed computing. K-Means++ with Hadoop Platform implementation generates smaller SSE value than simple K-Means and K-Means++ algorithms; that shows it has good SSE value.

Implementation of Wireless Sensor Network to reduce cashier queue

The use of Wireless Sensor Network (WSN) has been widely used in many fields, especially for environmental monitoring. This present study attempts to apply the WSN technology into the retail industry or the modern market. Utilizing barcode scanning technology as a medium of sensors that are commonly used, combined with a microcontroller and XBee modules as data transmission module. This study adds load sensor module on the checkout system based Wireless Sensor Network (WSN) to increase the accuracy of the system during the process of checking out at the cashier. Error processes towards the identification of goods can be reduced, thus the customer queuing time can also be reduced.