International journal of simulation: systems, science and technology 

About: International journal of simulation: systems, science and technology is an academic journal published by UK Simulation Society. The journal publishes majorly in the area(s): Computer science & Routing protocol. It has an ISSN identifier of 1473-8031. Over the lifetime, 823 publications have been published receiving 2363 citations. The journal is also known as: International journal of simulation & International journal of simulation: systems, science and technology (Online).

Multihop clustering algorithm for load balancing in wireless sensor networks

Abstract: The paper presents a new cluster based routing algorithm that exploits the redundancy properties of the sensor networks in order to address the traditional problem of load balancing and energy efficiency in the WSNs.The algorithm makes use of the nodes in a sensor network of which area coverage is covered by the neighbours of the nodes and mark them as temporary cluster heads. The algorithm then forms two layers of multi hop communication. The bottom layer which involves intra cluster communication and the top layer which involves inter cluster communication involving the temporary cluster heads. Performance studies indicate that the proposed algorithm solves effectively the problem of load balancing and is also more efficient in terms of energy consumption from Leach and the enhanced version of Leach.

Novel methods for stress features identification using EEG signals

Abstract: This paper introduces new methods to extract stress features from electroencephalogram (EEG) signals during two cognitive states; Closed-Eyes (CE) and Open-Eyes (OE) using Relative Energy Ratio (RER), Shannon Entropy (SE) and Spectral Centroids (SC). The group with the stress features was identified and classified using k-Nearest Neighbor (k-NN). The RER in term of Energy Spectral Density (ESD) for each frequency band (delta, theta, alpha and beta) in four different groups consisted of 180 EEG data were calculated and analyzed. Then, the SE was used to confirm the pattern of stress features. Meanwhile, SC was applied to the RER of each group and then the results were selected as input features to k-Nearest Neighbor (k-NN) for the classification purposes. The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The proposed method showed promising results where the combination of RER, SE and SC techniques with the training and testing of k-NN set at 70:30 able to detect and classify the group with the unique stress features at 88.89% accuracy

An agent-based approach to modelling pedestrian behaviour

Abstract: The modelling of pedestrian behaviour in a real-world environment is a complex problem, mainly due to the unpredictable nature of human decision making. Agent-oriented simulation moves away from traditional all-knowing and "controlling" simulations and towards reality, where pedestrians exhibit different behaviours depending on their knowledge of the environment and other personal characteristics. We investigate the behaviours that pedestrians may exhibit, the different techniques used for pedestrian modelling, and the appropriateness of each technique for particular domains. The classification framework developed will play a role in the decision making process for planning and design of pedestrian areas. We then explore the agent-based approach in more detail, in particular the belief-desireintention (BDI) architecture, by presenting the development of a sample model using Prometheus, an agent-oriented design methodology, and JACK, an agent-oriented programming language. Although the BDI architecture is useful for high-level decision making, further work is required in representing and updating the environment.

Q-Learning Based Traffic Optimization in Management of Signal Timing Plan

Abstract: Occurrences of traffic congestions within the urban traffic network are increasing in a rapid rate due to the rising traffic demands of the outnumbered vehicles on road. The effectiveness of management from traffic signal timing planner is the key solution to solve the traffic congestions, but unfortunately the current traffic light signal system is not fully optimized based on the dynamic traffic conditions on the road. Adaptable traffic signal timing plan system with ability to learn from their past experiences is needed to overcome the dynamic changes of the urban traffic network. The ability of Q-learning to prospect gains from future actions and obtain rewards from its past experiences allows Q-learning to improve its decisions for the best possible actions. A good valuable performance has been shown by the proposed learning algorithm that able to improve the traffic signal timing plan for the dynamic traffic flows within a traffic network.

Automatic street lighting system for energy efficiency based on low cost microcontroller

Abstract: This paper proposes energy efficient of automatic street lighting system based on low cost microcontroller. The main objective is to design energy efficient based controller for controlling the Light Emitting Diode (LED) based street lamp via appropriate lighting levels control. This system is consists of a microcontroller, light sensor, rain sensor, laser sensor and a set of the light emitting diode (LED) module. While, the controlling and managing of the system is based on the number of traffic and five different level of street light brightness has been used for lighting up the street and proportional to the number of traffic. The system was programmed to automatically turn off during the hours of daylight and only operate during the night and heavy raining or bad weather. Several numbers of tests have been conducted to test and validate the proposed prototype in the different environment. As conclusion, around 77%-81% reduction in power consumption can be achieved through this proposed automatic street lighting system for energy efficiency system design.