Aroland Kiring

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.

Adaptive hybrid channel assignment in wireless mobile network via genetic algorithm

In wireless mobile network, the challenge is to assign appropriate frequency spectrum channels to requested calls while maintaining a desirable level of electromagnetic compatibility (EMC) constraint. An effective channel assignment technique is important to improve the system capacity and to reduce the effect of the interference. Most of the existing channel assignment approaches are based on deterministic methods. In this paper, an adaptive hybrid channel assignment (HCA) technique based on genetic algorithm (GA) is proposed. The proposed GA based optimization in HCA scheme is capable to adapt the population size to the number of eligible channels for a particular cell upon new call arrivals in order to achieve faster convergence speed. Besides, the proposed approach can handle both the reassignment of existing calls as well as the allocation of channel to a new call in an adaptive process to maximize the utility of the limited frequency spectrum. The simulation for both uniform and nonuniform traffic distributions on a 49-cells network model show that the average new incoming call blocking or call dropping probability for the proposed hybrid channel optimization method is lower than the deterministic HCA methods.

Fuzzy Logic Based MPPT for PV Array under Partially Shaded Conditions

This paper presents the fuzzy logic based maximum power point tracking for the optimization of the solar photovoltaic (PV) array under partially shaded conditions. The PV system is modelled in MATLAB/SIMULINK where the PV array is formed by five PV modules connected in series. The PV characteristic of PV module and PV array under uniform solar irradiance are nonlinear but there are one maximum power point (MPP) can be identified. Nevertheless, the PV characteristic becomes more complex with multiple MPP when the PV array under partially shaded conditions (PSC). In this paper, maximum power point tracking (MPPT) approach based on perturb and observe algorithm has been investigated. Fuzzy logic is adopted into the conventional MPPT to enhance the overall performance of the PV system. The performances of MPPT and FMPPT are investigated particularly on the transient response and the steady state response when the PV array is exposed under different partially shaded conditions. The simulation results show that FMPPT has better performance where it can facilitate the PV array to reach the MPP faster and provide more stable output power.

Kernel-based object tracking via particle filter and mean shift algorithm

One of the critical tasks in object tracking is the tracking of fast-moving object in random motion, especially in the field of machine vision applications. An approach towards the hybrid of particle filter (PF) and mean shift (MS) algorithm in visual tracking is proposed. In this proposed system, complete occlusion and random movement of object can be handled due to its ability in predicting the object location with adaptive motion model. In addition, the PF is capable to maintain multiple hypotheses to handle clutters in background and temporary failure. However PF requires a large number of particles to approximate the true posterior of the target dynamics. Therefore, MS algorithm is applied to the sampling process of the PF to move these particles in gradient ascent direction. Consequently a small sample size will be sufficient to represent the system dynamics accurately. The proposed approach is aimed to track the moving object in random directions under varying conditions with acceptable computational time.

Foetus Ultrasound Medical Image Segmentation via Variational Level Set Algorithm

There is a challenge to segment the medical image which is often blurred and consists of noise. The objects to be segmented are always changing shape. Thus, there is a need to apply a method to automated segment well the objects for future analysis without any assumptions about the objects topology are made. In general, when performing pregnancy ultrasound scanning, obstetrician needs to find out the best position or angle of the foetus and freeze the scene. The obstetrician will click on the crown and the rump of the foetus to get the foetus length. The segmentation technique applied is level set method. A variational level set algorithm has been successfully implemented in medical image segmentation (Xray image, MRI image and ultrasound image). The results showed the level set contour evolved well on the low contrast and noise consisting medical image, especially the ultrasound image.

Performance Measurement of Evolutionary Routing Protocol in Network Coding

Wireless networks are suffer from throughput limitation, one of the effective method to solve limitation of wireless throughput are network coding which is combine several packets from difference input to a single packet size length and forwards the packet in a single transmission time slot. Furthermore, network coding also reduce energy usage in wireless devices. However, while network coding improve throughput of network it also introduce side effect such as increasing on buffer capacity and delay. In this paper, an evolutionary approach is proposed to optimize the advantages and disadvantages of network coding in a network. The simulation result shows that evolutionary algorithm is able to search multipath with minimal usage of coding nodes to balance the overhead and throughputs of network.

Node collaboration for distributed beamforming in wireless sensor networks

Collaborative beamforming (CB) is introduced in wireless sensor networks (WSNs) to improve the transmission power and the energy efficiency of the networks. Using a subset number of nodes from a network of sensors, they collectively transmit a common message with different proper weights to an intended location. Different geometrical location of the subset of nodes will produce different transmission gains. Due to erratic deployment of the sensor nodes in the networks, the 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 circular array is presented. Simulation and performance analysis are carried out to demonstrate the beam pattern performance of uniform circular in performing collaborative beamforming.

Distributed beamforming with uniform circular array formation in wireless sensor networks

Collaborative beamforming (CB) has been introduced in the context of wireless sensor networks (WSNs) to enhance the communication range and energy efficiency of the networks. CB relies on a subset of nodes in the network which acts as a virtual antenna array to collectively transmit a common signal to an intended location. Due to random deployment of the sensor nodes in the networks, proper selection of participating nodes to perform distributed CB is vital to achieve better array pattern synthesis. Different topologies of the participating nodes will produce different impact on transmission gains. In this paper, a node selection method based on uniform circular array formation is presented. The analysis is carried out with the assumption that the sensor nodes are randomly distributed with uniform distribution. Statistical evaluation on the average beampattern is carried out to demonstrate the behavior and properties of the mainlobe, and sidelobe level (SLL) for the selected scheme.