Nor Azlina Ab Aziz

A wireless sensor network coverage optimization algorithm based on particle swarm optimization and Voronoi diagram

The coverage problem is a crucial issue in wireless sensor networks (WSN), where a high coverage rate ensures a high quality of service of the WSN. This paper proposes a new algorithm to optimize sensor coverage using particle swarm optimization (PSO) and Voronoi diagram. PSO is used to find the optimal deployment of the sensors that gives the best coverage while Voronoi diagram is used to evaluate the fitness of the solution. The algorithm is evaluated through simulation in different WSN. The simulation results show that the proposed algorithm achieves a good coverage with a better time efficiency.

Particle Swarm Optimization and Voronoi diagram for Wireless Sensor Networks coverage optimization

The focus of this study is the sensor coverage problem. It is a crucial issue in wireless sensor networks (WSN), where a high coverage rate will ensure a high quality of service of the WSN. This paper proposes a new algorithm to optimize sensor coverage using particle swarm optimization (PSO). PSO is chosen to find the optimal position of the sensors that gives the best coverage and Voronoi diagram is used to evaluate the fitness of the solution.

Particle swarm optimization for coverage maximization and energy conservation in wireless sensor networks

Two key issues in mobile Wireless Sensors Network (WSN) are coverage and energy conservation. A high coverage rate ensures a high quality of service of the WSN. Energy conservation prolongs the network lifetime. These two issues are correlated, as coverage improvement in mobile WSN requires the sensors to move, which is one of the main factors of energy consumption. Therefore coverage optimization should take into consideration the available energy. Considering the sensors limited energy, this paper proposes a PSO based algorithm for maximizing the coverage subject to a constraint on the maximum distance any sensor can move. The simulation results show that the proposed algorithm achieves good coverage and significantly reduces the energy consumption for sensors repositioning.

A Kalman Filter approach to PCB drill path optimization problem

Drill path optimization problem is an important problem in holes drilling with computer numerically controlled (CNC) machine. Due to the exponential increase in the number of possible solutions when the number of holes to be drilled increase, the metaheuristic optimization algorithm seems to be a good choice in solving this type of optimization problem. This paper presents a Kalman Filter approach in solving printed circuit board (PCB) routing problem by using the Simulated Kalman Filter (SKF) algorithm. The experimental results are compared with those obtained by swarm intelligence approach, which are the Particle Swarm Optimization (PSO) variants, Ant Colony System (ACS) and Cuckoo Search (CS). The implementation proves to be effortless with good global convergence capability.

Gravitational search algorithm with a more accurate newton’s gravitational principle

Gravitational search algorithm (GSA) is a metaheuristic population-based optimization algorithm inspired by the Newtonian law of gravity and law of motion. However, GSA has a fundamental problem. It has been reported that the force calculation in GSA is not genuinely based on the Newtonian law of gravity. Based on the Newtonian law of gravity, force between two masses in the universe is inversely proportional to the square of the distance between them. However, in the original GSA, R has been used. In this paper, a modification is done to GSA by considering the square of the distance between masses, which is R 2. The CEC2014 benchmark functions for real-parameter single objective optimization problems are employed in the evaluation. An important finding is that by considering the square of the distance between masses, significant improvement over the original GSA is observed provided a large gravitational constant should be used at the beginning of the optimization process.

Synchronous gravitational search algorithm vs asynchronous gravitational search algorithm: a statistical analysis

Gravitational search algorithm (GSA) is a new member of swarm intelligence algorithms. It stems from Newtonian law of gravity and motion. The performance of synchronous GSA (S-GSA) and asynchronous GSA (A-GSA) is studied here using statistical analysis. The agents in S-GSA are updated synchronously, where the whole population is updated after each member’s performance is evaluated. On the other hand, an agent in A-GSA is updated immediately after its performance evaluation. Hence an agent in A-GSA is updated without the need to synchronize with the entire population. Asynchronous update is more attractive from the perspective of parallelization. The results show that both implementations have similar performance.

Synchronous vs Asynchronous Gravitational Search Algorithm

Gravitational search algorithm (GSA) is a new member of swarm intelligence algorithms. It stems from Newtonian law of gravity and mass interaction. Typically the agents in GSA are updated synchronously, where the whole population is updated together after every members performance is evaluated. However, asynchronous update of agent has been used by other optimization algorithms. Therefore the performance of asynchronous GSA (A-GSA) is studied in this work. An agent in A-GSA is updated immediately after its performance evaluation. Hence an agent in A-GSA is updated without using complete and updated information of its entire population. Asynchronous update is more attractive from the perspective of parallelization. The results show that improvement to the straight forward implementation of A-GSA is needed.

Commercialisation of University Research: An Investigation of Researchers ' Behaviour

Higher Education Institutions (HEIs) have been identified as one of the key factors for the growth and development of a nation. This is reflected in the vast amount of investment of public funds into research activities among the HEIs by the Malaysia government. Such a move is embraced by both developed and developing nations in the hopes of reaping the benefits in terms of the enrichment of knowledge, development of expertise and human capital and more tangibly in terms of the intellectual properties (IPs) produced by the research activities. Often the IPs would have commercial potential and there are numerous avenues for commercialising such IPs. However, often only small percentages of the RD most are treated as just another academic exercise. Thus, the challenge is driving the researchers in the HEIs to ensure R&D doesn’t end with publication of findings only, but it flows continuously into C – commercialisation, which includes the creation of university spin-out (USO). This study investigates the researchers’ behaviour in terms of conducting research, exploiting the results and ultimately commercialising their innovations.

Intellectual Property Valuation Decision Support System for University Research Output: A Conceptual Model

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Single-Solution Simulated Kalman Filter Algorithm for Routing in Printed Circuit Board Drilling Process

Optimal drilling path for printed circuit board is crucial in increasing productivity and reduce production costs. Single-solution Simulated Kalman Filter (ssSKF) is a new optimizer inspired by the Kalman filtering process. It uses only a single agent to solve optimization process by finding the estimate of the optimal solution. Principally, ssSKF algorithm uses the standard Kalman filter framework, aided by a local neighborhood technique during its prediction step. This paper reveals the potential of ssSKF as a good routing method in printed circuit board (PCB) drilling process. Experimental results indicate that the ssSKF algorithm outperforms the existing methods in searching a good route to speed up a PCB drilling process.