Optimizing acquisition geometry in shallow gas cloud using particle swarm optimization approach

Many hydrocarbon explorations in mature fields have been severely affected by complex and overburdening issues, such as shallow gas accumulation, gas pockets, and gas seepage. In this work, a new forward modelling technique is proposed in evaluating the potential survey design for fields affected by shallow gas cloud. In recent years, the implementation of innovative acquisition layouts has been producing significantly better seismic images, especially in the low illumination subsurface area. However, the uncertainty of the effectiveness in new acquisition design subsurface coverage always become a major stumbling block. To overcome this constraint, an optimization approach is suggested through the smart source and receiver location arrangement on the surface, with significant alignment to the conventional source and receiver arrangement approach. The particle swarm optimization (PSO) method is used to find the source-receiver configuration with maximum subsurface illumination coverage for the gas affected field situated in Malaysia Basin. Implementation of the PSO algorithm requires both a velocity model building process and wave field extrapolation from a target reflector to the surface level. The wave field data then was used to simulate receiver optimization outputs which eventually determined the subsurface illumination coverage. The results from the new optimization method for both synthetic model and Malaysia Basin data, offer a greater understanding of the consequences of obstacles caused by shallow anomalies with respect to seismic acquisition, data processing, and interpretation

Implementation of energy-efficient protocol for wireless sensor networks on telosB mote

Wireless Sensor Networks (WSNs) are formed by hundreds of sensor nodes that are distributed autonomously within the sensing area. It also incorporates with a gateway that provides wireless connection to communicate among nodes and pass data to one and another. There are various applications using WSNs such as wildlife onitoring, environmental monitoring and smart space. The limitation of WSNs is that they are only dependable on power battery to ensure their lifetime as a sensing device. Thus, in order to prolong the network lifetime, various research has been done including the development of energy efficient routing protocols. One of the earliest techniques is Low Energy Adaptive Clustering Hierarchy (LEACH). LEACH protocol uses randomization to select cluster head in order to have an evenly distributed energy among nodes. This work provides an in depth knowledge of LEACH protocol and how it is implemented on TinyOS using a TelosB mote. By implementing a conventional protocol which is Direct Transmission (DT) along with LEACH protocol in nodes, a significant impact on energy dissipation of protocols can be examined. In the findings, LEACH protocol energy usage in transmitting data can be evenly minimized thus lifetime of nodes can be longer. The result shows LEACH saves up to 30% of energy saving than using DT protocol

Wireless water quality cloud monitoring system with self-healing algorithm

The need of a water quality monitoring system is crucial for aquaculture and environmental control evaluation. This paper focuses on the development of the Water Quality (WQ) monitoring module that consists of hardware and software components. It highlights the details of the hardware components and the algorithm as well as the software that is connected to the cloud. There are many works on storing environmental data in cloud storage in Malaysia. The new platform to date for the Internet of Things (IoT) and cloud database is Favoriot. Favoriot is a platform for IoT and machine-to-machine (M2M) development. For this project, Favoriot platform is used for real time data. The self-healing algorithm is design to reduce human intervention and continuous data collected in the remote areas. The result shows that the self-healing algorithm is able to recover itself without physical reseting, in case during distruption of wireless service connection failure

Multi-objectives adaptive array synthesis using speedy-particle swarm method

A method of computing the optimum element distance position of multi-objectives adaptive linear antenna arrays (MLAA) is developed by taking several objectives (eg. adaptive capability, beamwidth and minimum sidelobe level (SLL)) into consideration. In this paper, the recently invented algorithm, known as Speedy-Particle Swarm Optimization (SpPSO) algorithm is adopted to optimize the distance between the MLAA elements. Different numerical examples of 8- and 12-element MLAA are presented to validate and illustrate the capability of SpPSO for pattern synthesis with a prescribed adaptive angle, controllable beamwidth and minimum SLL. It was found that by employing SpPSO method, the results provide considerable improvement over the conventional array. It is observed that the maximum normalized SLL of -12.27 dB has been achieved by using SpPSO for 8-element MLAA. The proposed SpPSO-based LAA also able to achieve a beampattern with sufficiently low sidelobes for 12-element MLAA by having maximum SLL of -16.46 dB, a desired wider FNBW of 50° and main beam that is pointing to 20°

Schelkunoff array synthesis methods using adaptive-iterative algorithm

The adaptive-iterative algorithm is an error-reduction algorithm that has been extensively studied in recent years. Basically, this algorithm is a combination of iterative algorithm with adaptive algorithm. By the co-emergence of these algorithms, a better performance and wider application are hoped to be achieved since each of these algorithms has its own advantages respectively. Although considerable research has been devoted to apply this algorithm in variety of DSP applications such as filter design and signal reconstruction, rather less attention has been paid for application in antenna array synthesis. For that reason, it is the purpose of this thesis to outline the implementation and use of adaptive-iterative algorithm in designing antenna array. In doing sa, Schelkunoff Polynomial Method will be used in order to have z-domain information. Intended to achieve the aim of this project, MATLAB program will be employed to present the outcomes of modulus reconstruction on antenna array using adaptive-iterative algorithm. The results and performance of other error-reduction algorithm such as Papoulis algorithm and optimal algorithm are also presented and discussed in this thesis, along with the discussion of adaptive-iterative algorithm for comparison purposes

Particle swarm optimisation for clustering in wireless sensor networks

One of the mam characteristics which set wireless sensor networks apart from traditional networks is the inclusion of energy consumption as the highest priority optimisation goal. This is because these types of networks work under the general concept that the system lifetime needs to be extended as much as possible, whilst at the same time achieving efficient data forwarding and preventing route disconnections due to sensor node failure. Hence, the use of energy efficient infrastructure such as clustering n:ay lengthen the lifetime of the network and prevent network connectivity degradation through the utilisation of cluster heads. Since the optimal selection of cluster heads in a network belongs to nondeterministic polynomial (NP) hard problem, the use of approximation algorithms such as Particle Swarm Optimisation (PSO) are generally more suitable due to its simplicity and outstanding search strength. This PhD thesis investigates the application of the PSO algorithm in clustering ofwireless sensor networks. In view of the need to prolong sensor network lifetime, a centralised, energy efficient, cluster-based protocol is developed using the PSO algorithm. A new cost function has been defined, which takes into consideration three important factors, namely the expected network energy consumption, the intracluster distance and the remaining energy of the cluster heads. The clustering problem is then transformed into an optimisation problem, and the PSO algorithm is employed to search for the optimal set of cluster heads. Simulation results demonstrate that the proposed protocol using PSO obtains better data delivery and network lifetime, as well as improves network connectivity over its comparatives. In addition, the results confirm the efficiency of PSG in clustering problems, compared to other evolutionary algorithms. This thesis also considers the use of the PSO algorithm in clustering the wireless sensor networks with mobile nodes. For this purpose, the 'mobility factor is taken into account when defining the cluster membership and selecting the cluster heads in order to maintain network connectivity. Simulation results prove that this approach outperforms other well known protocols in terms of data delivery and network lifetime. Finally, a dynamic multi-objective clustering algorithm which automatically determines the optimum number of clusters is introduced. This algorithm, based on binary PSG, eliminates the need to set the number of clusters a priori. Furthermore, the use of multi-objective PSG can tackle the difficulty of tuning the cost function weights that properly scales the sub-objectives. Performance evaluation through simulation exhibits the superior strength of this algorithm in enhancing the network survivability.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Mobile base station for wireless sensor networks using particle swarm optimization

Wireless sensor networks are a family of networks in wireless communication system and have the potential to become significant subsystem of engineering applications. In view of the fact that the sensor nodes in wireless sensor networks are typically irreplaceable, this type of network should operate with minimum possible energy in order to improve overall energy efficiency. Therefore, the protocols and algorithms developed for sensor networks must incorporate energy consumption as the highest priority optimization goal. Since the base station in sensor networks is usually a node with high processing power, high storage capacity and the battery used can be rechargeable, the base station can be utilized to collect data from each sensor node in the sensing area by moving closer to the transmitting node. The main objective of this research is to propose an energy-efficient protocol for the movement of mobile base station using particle swarm optimization (PSO) method in wireless sensor networks. Simulation results demonstrate that the proposed protocol can improve the network lifetime, data delivery and energy consumption compared to existing energy-efficient protocols developed for this network

A green clustering protocol for mobile sensor network using particle swarm optimization

Energy consumption of sensor nodes is one of the crucial issues in prolonging the lifetime of wireless sensor networks. One of the methods that can improve the utilization of sens or nodes batteries is the clustering method. In this paper, we propose a green clustering protocol for mobile sensor networks using particle swarm optimization (PSO) algorithm. We define a new fitness function that can optimize the energy consumption of the whole network and minimize the relative distance between cluster heads and their respective member nodes. We also take into account the mobility factor when defining the cluster membership, so that the sensor nodes can join the cluster that has the similar mobility pattern. The performance of the proposed protocol is compared with well-known clustering protocols developed for wireless sensor networks such as LEACH (low-energy adaptive clustering hierarchy) and protocols designed for sensor networks with mobile nodes called CM-IR (clustering mobility-invalid round). In addition, we also modify the improved version of LEACH called MLEACH-C, so that it is applicable to the mobile sensor nodes environment. Simulation results demonstrate that the proposed protocol using PSO algorithm can improve the energy consumption of the network, achieve better network lifetime, and increase the data delivered at the base station