Muhammad Ariff Baharudin

Evaluation of the SCTP Optimal Path Selection with Ant Colony Optimization Probabilistic Equation Implementation

This paper evaluates the proposed SCTP optimal path selection mechanism which implements the Ant Colony Optimization probabilistic equation. The proposed mechanism uses three parameters, which are the path delay, RSSI level at the mobile node and the RSSI level at the corresponding node, as the decision criteria. A cross-layer architecture is proposed to obtain the value from the Physical layer. In addition, a new parameter for the SCTP Heartbeat chunk is introduced in order to share the RSSI information between the nodes in the SCTP association. A brief comparison between using two parameters (path delay and RSSI at mobile node only) and three parameters (plus RSSI at the corresponding node) is also included. The experimental evaluations show that the proposed mechanism is able to select the optimal path.

Smart grid communication concept for frequency control in distribution system

The advancement of electrical device technologies can be used to balance the frequency of the system by adjusting their power consumption. However, this can only be achieved by having good data communication management. The introduction of Smart Home System (which consists of Smart Appliance (SA) and Smart Meter (SM)), Smart Regional Server (SRS) and Smart Server (SS) will be used to manage data communication in smart distribution system. The communications between these devices will be via Power Line Communication (PLC), and WiMAX will be used as a medium to transfer the data to the SRS. Transport Control Protocol (TCP) will increase the reliability of transferring data. By having good communication and architecture, as well as good management and decision making by the servers and smart metering, the use of load-balancing control to maintain the frequency of the system within the acceptable limit in smart distribution grid system will be achieved.

Evaluations of Ant Colony Optimization Inspired SCTP Optimal Path Selection Using E-model

This paper proposes an SCTP optimal path selection mechanism which is inspired by the Ant Colony Optimization probabilistic equation. The proposed mechanism uses three parameters, which are the path delay, RSSI level at the mobile node and the RSSI level at the corresponding node, as the decision criteria. A cross-layer architecture is proposed to obtain the value from the Physical layer. In addition, a new parameter for the SCTP Heartbeat chunk is introduced in order to share the RSSI information between the nodes in the SCTP association. The proposed work was evaluated using two types of scenarios. Then the result was translated into the R-factor using the E-Model recommended by ITU-T. The results show that the mechanism was able to find the optimal path for the user.

Synergistic approaches to mobile intelligent transportation systems considering low penetration rate

This paper investigates the effect of low penetration rate on mobile phone-based traffic state estimation (M-TES) models. Synergistic approaches, including an appropriate genetic algorithm (GA) based velocity-density estimation model and a notable artificial neural network (ANN) based prediction method for unacceptably low penetration rate, are proposed. The GA-based traffic state estimation model not only improves the effectiveness but also reduces the critical penetration rate required in the M-TES model. When the critical penetration rate is reduced the error-tolerance and the scalability of the estimation model can be significantly improved. The ANN-based prediction approach is introduced to overcome the weakness remaining in the GA-based traffic state estimation model when the penetration rate becomes unacceptably low or unknown. In addition, the effect of related road segments on the prediction effectiveness is thoroughly discussed. This work, therefore, provides practical instructions in narrowing the search space for finding prediction rules of the ANN model, thus improving the computational performance without compromising the prediction accuracy. The experimental evaluations confirm the effectiveness as well as the robustness of the proposed approaches. As a result, this research contributes to accelerating the realization of mobile phone-based intelligent transportation systems (M-ITS) or, of the M-TES systems in specific, since the essential issue of low penetration rate has been solved.

Commonsense knowledge extraction for Tidy-up robotic service in domestic environments

Commonsense is one of the keys to enable human-robot communication in daily life scenarios. It is very difficult for a robot to do tasks ordered by a human without having some basic knowledge to understand the humans commands. This paper proposes a method to automatically build commonsense knowledge for the “Tidy-up” service, in which a robot is asked to take objects such as books, cups, dishes on a table to appropriate places automatically. We defined three object classes that are necessary for the service, namely “Washable”-objects that need to be washed, “Reusable”-objects that need to be stored for reuse, and “Trashable”-objects that need to be disposed of. For each object, multiple attributes were extracted from both the ConceptNet knowledge base and the Google search engine, and fed to classifiers to classify the object into the appropriate class. To evaluate the proposed method, output from classifiers were compared with the result from actual human. The result showed that the proposed approach is efficient in classifying objects and in providing object type as commonsense knowledge, hence, helping the robots to understand human intention and to provide intuitive service.

Context-Aware Mobile Intelligent Transportation Systems

This paper proposes a practical quantification model for mobile phone based traffic state estimation systems (M-TES). The low penetration rate issue, an inherent issue impeding the realization of a mobile phone based application such as the M-TES, is thoroughly discussed. A notable solution framework, namely the intelligent context-aware velocity-density inference circuit (ICIC), is proposed to effectively resolve the low penetration rate issue. In the ICIC model, velocities and densities calculated directly from the sensed data and inferred by using different inference models such as the Greeshields or the moving average model are appropriately integrated. In addition, appropriate contexts extracted from data reported by mobile devices are utilized to identify the optimal estimation parameters leading to the optimal estimation effectiveness. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.

Uncertain Low Penetration Rate -- A Practical Issue in Mobile Intelligent Transportation Systems

Low penetration rate is one of the essential issues in the mobile phone based traffic state estimation model. This paper proposes an appropriate genetic algorithm (GA) mechanism to optimize the traffic state estimation model even in cases of low penetration rate. This mechanism also reduces the critical penetration rate, thus improves the error-tolerance as well as the scalability of the traffic state estimation system. The paper also investigates the ANN-based prediction model to overcome the weakness of the GA-based traffic state estimation approach when the penetration rate becomes unacceptably low. In addition, the effect of different level related road segments on the prediction effectiveness is thoroughly discussed. Consequently, this study provides practically useful instructions in verifying the data missing rate at different level related road segments to ensure the prediction accuracy. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.

Distributed generator sizing via evolutionary particle swarm optimization

With the existence of Distribution Generator (DG) in distribution network, the power losses in the network can be minimized by resizing the power output of each DG units. In line with this objective, most researchers have concentrated on the optimization technique to regulate the DG.s output to compute its optimal size. In this paper, the concept of Evolutionary Particle Swarm Optimization (EPSO) method is implemented in sizing the DG units. By applying the concept of Evolutionary Programming (EP) in Particle Swarm Optimization (PSO), it will allow the only successful particles to move toward the optimal value and make the process of convergence become faster. The algorithm has been tested in 33 bus distribution system with 3 units of DG that operate in PV mode. Its performance was compared with the performance when using the traditional PSO. In terms of power loss reduction and voltage profile, the EPSO gives similar performance as PSO. Morever, it requires less number of iteration to converge. Thus, EPSO can reach the optimal value in shorter time while maintaining the traditional PSO.s performance.