Ahmad Ali Alhasanat

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— This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using(More)
Wireless Sensor Networks (WSNs) have been emerged in many important aspects in the real world, such as industry, agriculture, and military applications. As the main challenge that WSNs facing is the energy consumption, it is necessary to investigate the suitability of using mobile sinks for data collection in these networks. In this paper, therefore, a new(More)
Energy consumption is an essential concern to Wireless Sensor Networks (WSNs).The major cause of the energy consumption in WSNs is due to the data aggregation. A data aggregation is a process of collecting data from sensor nodes and transmitting these data to the sink node or base station. An effective way to perform such a task is accomplished by using(More)
Using multiple mobile sinks for data gathering, such as Area Splitting Algorithm (ASA), has improved the performance of Wireless Sensor Networks (WSN) in terms of network latency and power consumption. However, for time and energy constrained mobile sinks in large scale WSNs, a large number of mobile sinks is required. Furthermore, a load balance method on(More)
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