Browsing by Author "Kalid Abdlkader Marsal"
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Publication Formulation Of 3D Euclidean Distance For Network Clustering In Wireless Sensor Network(COMPUSOFT, 2019) ;Kalid Abdlkader Marsal ;A.H AzniFarida RidzuanIn wireless sensor networks, nodes operating under dynamic topology are often correlated with their behavior. Correlated behavior may pose devastating impact towards network connectivity. A node may change its behaviour from cooperative node to misbehave node which directly affects the network’s connectivity. Misbehaviour nodes tend to have correlated effect which creates partitioning within the network. To improve network connectivity in providing an efficient communication in the events of the correlated behaviors, a new formulation of correlated degree to perform network clustering is required. This paper proposes a formulation on correlated degree using 3D Euclidean distance to achieve higher network connectivity under correlated node behavior. The key idea behind the 3D Euclidean distance in network clustering is to identify a set of sensors whose sensed values present some data correlation referring to correlated degree. The correlated degree is formulated based on three-point distance within a correlation region to identify the level of node correlation within neighboring nodes. In addition, the correlated degree also be able to detect the same group of node behavior which is grouped in correlated regions. 3D Euclidean distance is shown in mathematical analysis and how the new formulation calculates correlated degree is also discussed. It is also expected that the new 3D Euclidean distance formulation may help correlation region to change it cluster formation dynamically to achieve the required network connectivity. - Some of the metrics are blocked by yourconsent settings
Publication On Clustering Algorithm Of Coverage Area Problems In Wireless Sensor Networks(Scientific Federation, 2018) ;Ismail AbdullahKalid Abdlkader MarsalWireless sensor networks (WSN) has applications in many areas such as in surveillance, healthcare, national security, military and environmental monitoring. For WSNs, the coverage problem has proven to be one of the fundamental issues, because of which the sensor network's efficiency and capability are directly influenced. This also proved to be the most complicated area for research, which was to detect the maximal total of active nodes or sleep, specifically the node scheduling part, its business with the WSN being to determine the optimal coverage area at which exist the most number of active nodes that manage connectivity and coverage. The size of a WSN can be up to hundreds or maybe even thousands of sensor nodes. The corresponding deployment technique can be figured out if the placement of the sensors is exactly where they are needed. The lifespan of a WSN can be increased by efficient routing techniques, but the following research, for the most part, focuses on the length of the survival of networks, without bearing in mind the 'quality' of the network in its last or final iterations. In the following paper, the effect that changing the fitness cap used in the genetic algorithm has, on a WSN, in terms of lifespan and the quality of lifespan, is documented. For the proliferation of wireless sensor network, in different environments, an escalation in the lifetime of wireless sensors is mandatory, because among the basic issues concerning WSN is a successful effort to document the coverage of the number of target fields, while maximizing the lifetime of this network. In the case study, many algorithms have been proposed in the literature, in order to find the maximum number of disjoint or non-disjoint sets of sensor cover sets, we can see where one set can be active at any one time. The link between energy consumption and the radio bandwidth is requested to be hampered with, to escalate the lifespan of the sensors. An important problem in the wireless sensor networks is connectivity of the consumption of energy, because a network is linked for the communication of two active nodes. Because the information collected by the sensor nodes has to be repeated back to the data controllers or sinks, when the sensors are arranged, they deploy into a network that must be connected. In fact, the energy utilization or consumption of point coverage can be managed. In the following study, the radio bandwidth's energy consumption is the target under study, which in turn is predicted to optimize WSN. - Some of the metrics are blocked by yourconsent settings
Publication Single fitness function analysis of energy-consumption and radio bandwidth management in coverage area problems(Universiti Sains Islam Malaysia, 2015)Kalid Abdlkader MarsalThe Wireless Sensor Network (WSN) has emerged as a promising tool for monitoring the physical world, utilizing self-organizing networks of battery-powered wireless sensors that can sense, process and communicate. It can be deployed rapidly and cheaply, thereby enabling large-scale, on-demand monitoring and tracking over a wide range of applications. Sensor nodes in such a network usually have limited on-board processing and wireless communication capabilities, and are equipped with batteries prolong the network lifetime. However, if all the sensor nodes simultaneously operated, redundant sensing data, corresponding wireless communication collision and interference will cause much energy to be wasted. How does one cover all the sensing area with the least active nodes so that no blind-point exists and connectivity is kept significant? Coverage becomes a serious problem in large scale sensor networks where hundreds and thousands of nodes are randomly deployed. The coverage problem is one of the most fundamental issues in wireless sensor. Current solutions are based for the most part on node scheduling, the main idea of which is to find the optimal number of active nodes while maintaining coverage and connectivity. The problem in finding the maximal coverage in a sensor network addressed in where coverage is defined as a set of nodes that can completely cover the monitored are, and a centralized solution to this problem is proposed. Several algorithms aim to find a close-to-optimal solution based on local information. In this work, a new method for controlling WSN main parameters (such as energy consumption, bandwidth, signal strength and coverage) using single fitness function proposed, developed and tested. In order to complete this research a network simulates is developed main Microsoft visual C# and a few experiments are done on the simulator. In future research, more and more work will be focused on distributed and localized solutions for practical deployment by simulation wireless sensor networks. In this simulation can be run either be reset with a new seed or with the previous seed for replay. - Some of the metrics are blocked by yourconsent settings
Publication Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network(Scientific Federation Adobe for Researchers, 2017) ;Ismail AbdullahKalid Abdlkader MarsalA Single fitness function is a particular type of objective function that is used to summarize, as a single figure of merit, how close a given design solution is to achieve the set aims. The Wireless Sensor Network (WSN) has emerged as a promising tool for monitoring the physical world, utilizing self-organizing networks of battery-powered wireless sensors that can sense, process and communicate. A fitness function is used in Genetic Algorithm in each iteration of the algorithm to evaluate the quality of all the proposed solutions to your problem in the current population. The fitness function evaluates how good a single solution in a population is, e.g. if you are trying to find for what x-value a function has it's y-minimum with a Genetic algorithm, the fitness function for a unit might simply be the negative y-value (the smaller is better for fitness function).A reasonable solution to a problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. GA is a optimization tool, so generally fitness function is a max/min value function consisting of all the variables. If we want to find the best optimal threshold value (i.e. min value of the fitness function), we have to generate a function with these parameters such as Single-to Noise Ratio (SNR), probability of false alarm and number of samples of received data for detection in such a way that the value of the function must be approaching zero. This function is called fitness function and the final value of this function after performing GA will be the optimal outcome. The creation of the function is totally depends on our approach towards the solution of the problem. In this paper, an overview is presented describing single fitness function to optimize energy using genetic algorithms in WSNs. GA are customized to accommodate multi-objective problems by using specialized fitness functions, introducing methods to promote solution diversity, and other approaches.