Kenneth Tze Kin Teo

Energy efficient clustering algorithm in wireless sensor networks using fuzzy logic control

In general, environment monitoring cluster based hierarchical routing protocol is among the most common protocol being opted due to the load balancing among each other sensor. Sensors are randomly deployed in a specific area to collect useful information periodically for a few months or even a few years. Therefore, battery power limitation becomes a challenging issue. It is also impractical to maintain the network lifetime by changing the battery frequently. Low energy adaptive cluster hierarchical (LEACH) is one of the common clustering protocols that will elect the cluster head based on the probability model which will possibly lead to a reduce in network lifetime due to election of cluster head with a least desired location in the network. For wireless sensor networks, the distribution of cluster head selection directly influences the networks lifetime. This paper presents factors which will affect the network lifetime and apply fuzzy logic based cluster head selection conducted in base station. The base station considers two selection criteria from sensor nodes which are energy level and distance to the base station to select the suitable cluster head that will prolong the first node die (FND) time, data stream guaranteed for every round and also increase the throughput received by the base station before FND.

Maximum Power Point Tracking for PV Array Under Partially Shaded Conditions

Solar photovoltaic (PV) array which is exposed to the uniform solar irradiance shows the non-linear P-V characteristic. Nevertheless, the P-V characteristic becomes more complex with multiple maximum power point (MPP) when the array is operated under partially shaded condition. Conventional maximum power point tracking (MPPT) approach which is designed to track the MPP will be trapped at the local MPP. Therefore, one of the challenges is to continuously track the absolute MPP while the environmental factors such as solar irradiation, PV temperature and partial shaded condition are rapidly changing. This paper presents a novel method of parallel tracking function to assist the on-line fuzzy logic perturb and observe (P&O) MPPT to continuously search beyond the trapped MPP operating voltage point. The information of PV array such as the operating voltage and the corresponding generation current are stored in the database. The characteristics of the shaded array can be estimated via simulation and the absolute MPP is identified via the tracking function. The simulation results show that the enhanced fuzzy logic P&O MPPT is capable of tracking the real absolute MPP during the partially shaded condition.

Fuzzy Logic Based MPPT for Photovoltaic Modules Influenced by Solar Irradiation and Cell Temperature

This paper presents fuzzy based perturb and observe maximum power point tracking in solar panel. The solar system is modelled and analysed in MATLAB/SIMULINK. The photovoltaic panel has an optimal operating voltage where the PV panel can produce maximum power at this particular point. Due to the nonlinearity of the voltage-current characteristic in solar panel, it is difficult to determine analytically the maximum power operating voltage that varies with the change of solar irradiance and cell temperature. Maximum power point tracking (MPPT) is implemented to identify the maximum power operating point, subsequently regulate the solar panel to operate at that particular operating voltage for maximum power gaining. Perturb and observe (P&O) MPPT and fuzzy based optimized P&O MPPT are developed and the performances of both controllers are examined at variable solar irradiances at different temperatures. Simulation results show that fuzzy based P&O MPPT has better performance where it can facilitate the solar panel to produce a more stable power.

Image segmentation via normalised cuts and clustering algorithm

Image segmentation has been widely applied in image analysis for various areas such as biomedical imaging, intelligent transportation systems and satellite imaging. The main goal of image segmentation is to simplify an image into segments that have a strong correlation with objects in the real world. Homogeneous regions of an image are regions containing common characteristics and are grouped as single segment. One of the graph partitioning methods in image segmentation, normalised cuts, has been recognised producing reliable segmentation result. To date, normalised cuts in image segmentation of various sized images is still lacking of analysis of its performance. In this paper, segmentation on synthetic images and natural images are covered to study the performance and effect of different image complexity towards segmentation process. This study gives some research findings for effective image segmentation using graph partitioning method with computation cost reduced. Because of its cost expensive and it becomes unfavourable in performing image segmentation on high resolution image especially in online image retrieval systems. Thus, a graph-based image segmentation method done in multistage approach is introduced here.

Multiple intersections traffic signal timing optimization with genetic algorithm

Traffic congestion in the urban area occurs more frequent than the past due to rapidly increasing on road vehicle usage rates. It could seriously hinder the development of urban area if a well management system has not being established. These scenarios necessitate the development of advance traffic management systems to increase the performance of signalized intersection. Traffic signal timing management (TSTM) system which comprise of genetic algorithm based optimization is proposed. Using a proper TSTM system, network traffic flow can be improved with considerably less cost than other infrastructural improvements. The proposed genetic algorithm based optimization approach allows signal timing parameters such as offset, cycle time, green split and phase sequence to be optimized with objective of minimum delay and better traffic fluency. The proposed GATSTM system has the ability to handle and manage the dynamic changes of the traffic networks condition by calibrating the system parameters accordingly.

Cluster Heads Distribution of Wireless Sensor Networks via Adaptive Particle Swarm Optimization

Wireless sensor networks consists of hundreds or thousands of sensor nodes supported by small capacity battery. For environmental monitoring purposes, sensor nodes must have high endurance capabilities. Therefore, selecting suitable cluster heads (CH) location becomes a challenging issue. In this work, cluster heads distribution based on adaptive particle swarm (PSO) is proposed. PSO is one of the swarm intelligence methods designed to find optimum solution by mimicking the behavior of bird flocking and fish schooling. Adaptive cognitive and social learning factor can achieve better convergence speed and particles reselection mechanism can reduce the chances of getting trapped in local maximum. The performance of the proposed method is compared with low energy adaptive cluster hierarchical (LEACH). Simulation result shows that proposed method outperforms LEACH in terms of first node die (FND) round, total data received by base station and energy consume per round.

Optimization of partially shaded PV array using fuzzy MPPT

Solar photovoltaic (PV) array shows non-linear P-V characteristic when the array is illuminated under uniform solar irradiance. In this circumstance, the P-V characteristic consists only one maximum power point (MPP) along the functional operating voltage. However, the P-V characteristic has changed and become more complex with multiple MPP when the array is exposed under partially shaded conditions. This circumstance has caused maximum power point tracking (MPPT) algorithm faces difficulty to allocate the exact optimal operating voltage for PV array to generate maximum power. In this paper, initial voltage tracking function (IVTF) has been introduced to assign new initial voltage for tracking the absolute MPP when the PV array is at shaded conditions. Nevertheless, MPPT approach deals with the challenge of tracking the MPP in the faster mode while maintaining the power stability of the PV array toward the MPP. Therefore, fuzzy logic is embedded in the conventional MPPT to adapt the size of the perturbed voltage. The obtained results show that fuzzy logic based MPPT with IVTF is able to track the absolute MPP faster while minimizing power lost due to the power fluctuations.

Genetic algorithm based PID optimization in batch process control

The primary aim in batch process is to enhance the process operation in order to achieve high quality and purity product while minimising the production of undesired by-product. However, due to the difficulties to perform online measurement, batch process supervision is based on the direct measurable quantities, such as temperature. During the process, a large amount of exothermic heat is released when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently the quality and purity of the final product will be affected. Therefore, it is important to have a control scheme which is able to balance the needs of process safety with the product quality and purity. Since the chemical industries are still applying PI and PID to control the batch process, researchers are keen to optimize PID parameters using artificial intelligence (AI) techniques. However, most of these PID optimization techniques need online process model to predetermine the optimizer parameters. However in practice, the dynamic model of the batch process is poorly known. As a result, majority of the studies focused on acceptable performance instead of optimum performance of the batch process control. This paper proposes a new genetic algorithm (GA) optimizer which consists of additional information of the online estimated model parameters in addition to the PID parameters as the string of the GA. The simulation results show that the proposed GA auto-tuning method is a better candidate than the regular GA where the estimated model parameters in fitness function is capable to control the process temperature while avoiding model mismatch and disturbance condition.

Optimization of Distributed and Collaborative Beamforming in Wireless Sensor Networks

Collaborate beamforming in wireless sensor networks (WSNs) is a concept of using beamforming technology to establish link in the networks. It can effectively increase the transmission distance and improve the energy efficiency of the networks. Due to random deployment of the sensor nodes in the networks, proper assignment for the sensor nodes in wireless sensor networks is vital to achieve better array pattern synthesis. In this paper, a node selection method based on uniform space linear array synthesis is presented. A virtual line was constructed in the network topology as a reference guide to optimize the selection of nodes to mimic the uniform space linear array. The node selection for collaborate beamforming is further optimized using genetic algorithm. Using the method, simulation results show an improvement of radiation beam pattern.

Queue Management for Network Coding in Ad Hoc Networks

Network Coding has been proven to be a method that will increase the throughput of network. Network coding will perform an XOR operation in the intermediate nodes to improve the throughput of the network. The simulation of network coding in AODV to search for the route to destination will be conducted in MATLAB. This paper introduces the development of simulation to illustrate the performance of network coding in wireless ad hoc network. The simulation will calculate the transmit packet time according to the size of the packet. Lastly, average network throughput performance between AODV network without network coding and network with network coding is shown and compared.