Informatics Publishing Limited

Chun Hoe Lee ¹, Kit Guan Lim ¹, Min Keng Tan ¹, Renee Ka Yin Chin ¹, Kenneth Tze Kin Teo ¹

Affiliations
1 Simulation and Computing Laboratory, Universiti Malaysia Sabah, MY

Abstract

Intelligent Transportation Systems (ITS) plays a vital role in providing different means of traffic management and enables users to be better informed of traffic condition, promoting safer, coordinated and efficient use of transport network. Vehicular Ad Hoc Network (VANET) shows promising reliability and validity in ITS. But, it poses challenges to researchers in designing protocol specifically for VANET as the deployment of VANET in real world will incur high cost. Therefore, simulation and non-physical testbed implementation have been widely adopted by the VANET research community in the development and assessment of the new or improved system and protocol of VANET. This paper presents a viable simulation platform for network development. Besides, a code cast or better known as network coding, a data packet transmission method has been developed and introduced into VANET protocol using the presented platform to assess and determine the potential of the introduced simulation platform.

Keywords

Vehicular Ad hoc Network, Traffic Simulation, Network Simulation, Intelligent Transportation System, Network Coding.

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Nurul Izyan Kamaruddina ¹, Ahmad Razani Haron ¹, Bih Lii Chua ¹, Min Keng Tan ¹, Kit Guan Lim ¹, Kenneth Tze Kin Teo ¹

Affiliations
1 Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, MY

Abstract

Photovoltaic system (PV) is an important technological asset for renewable energy production. It works by converting solar cell energy from the sun into electrical direct current. In reality, the photovoltaic module usually receives non-uniform solar irradiance at different light intensity due to non-atmospheric hindrance. Under such conditions, the PV system exhibits multiple peaks on the energy characteristic curve, generally known as the partial shading condition (PSC). Therefore, in order to maximize the energy harvested by the photovoltaic system (PV), maximum power point tracking (MPPT) method is suggested to extract all possible maxima that have been produced by the PV system under various circumstances through the non-uniform irradiance of the sunlight. Based on previous researches, it is found that conventional method such as perturb and observed (P&O) method failed to track the maximum power and was trapped at the local maximum power (LMPP). This paper focuses on exploring a metaheuristic method which is the differential evolution (DE) algorithm in optimizing the energy harvested by the PV system. The platform chosen for modelling in this paper is a 3 × 3 PV array. The PV array is tested with different conditions of partial shading where random irradiance values are set. Comparing the performance of PV between P&O and DE based MPPT controller, the DE based MPPT controller is inferred to have a higher success rate to escape from being trapped in LMPP and thus produce more total energy.

Keywords

Photovoltaic system, Maximum Power Point Tracking, Partial Shading Condition, Perturb and Observed, Differential Evolution.

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Vincent Chung ¹, Hamzarul Alif Hamzah ¹, Norah Tuah ², Kit Guan Lim ¹, Min Keng Tan ¹, Kenneth Tze Kin Teo ¹

Affiliations
1 Simulation and Computing Laboratory, Faculty of Engineering, Universiti Malaysia Sabah, MY
2 Faculty of Computer Science and Mathematics, Universiti Teknologi MARA, MY

Abstract

Wireless sensor network (WSN) is an embedded system comprises of spatially distributed sensor nodes where an energy-efficient mechanism is needed to prolong the network lifetime. Existing approaches for this optimization problem have several drawbacks, including non-adaptive network configuration that may cause premature death of sensor nodes. Genetic-based evolutionary algorithms such as Genetic Algorithm (GA) and Differential Evolution (DE) have been popularly used to optimize cluster head selection in WSN to improve energy efficiency for the extension of network lifetime. Therefore, the performances of GA and DE are evaluated through comparative analysis to determine their efficiency in cluster head selection optimization. Simulation results show that GA outperforms DE with higher round number for first node dies (FND) but lower round number for last node dies (LND) in terms of network lifetime. Besides, GA also leads to a network with lower number of transmission failures than DE. On the other hand, fitness convergence of GA is slower but it has higher fitness value of population.

Keywords

Brent’s Method, Optimal Power Allocation, Ant Colony Optimization, Secrecy Rate.

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