Densely deployment of sensors is generally employed in wireless sensor networks (WSNs) to ensure energy-efficient covering of a target area. Many sensors scheduling techniques have been recently proposed for designing such energy-efficient WSNs. Sensors scheduling has been modeled, in the literature, as a generalization of minimum set covering problem (MSCP) problem. MSCP is a well-known NP-hard optimization problem used to model a large range of problems arising from scheduling, manufacturing, service planning, information retrieval, etc. In this paper, the MSCP is modeled to design an energy-efficient wireless sensor networks (WSNs) that can reliably cover a target area. Unlike other attempts in the literature, which consider only a simple disk sensing model, this paper addresses the problem of scheduling the minimum number of sensors (i.e., finding the minimum set cover) while considering a more realistic sensing model to handle uncertainty into the sensors' target-coverage reliability. The paper investigates the development of a genetic algorithm (GA) whose main ingredient is to maintain scheduling of a minimum number of sensors and thus to support energy-efficient WSNs. With the aid of the remaining unassigned sensors, the reliability of the generated set cover provided by the GA, can further be enhanced by a post-heuristic step. Performance evaluations on solution quality in terms of both sensor cost and coverage reliability are measured through extensive simulations, showing the impact of number of targets, sensor density and sensing radius.
Krawtchouk polynomials (KPs) and their moments are promising techniques for applications of information theory, coding theory, and signal processing. This is due to the special capabilities of KPs in feature extraction and classification processes. The main challenge in existing KPs recurrence algorithms is that of numerical errors, which occur during the computation of the coefficients in large polynomial sizes, particularly when the KP parameter (p) values deviate away from 0.5 to 0 and 1. To this end, this paper proposes a new recurrence relation in order to compute the coefficients of KPs in high orders. In particular, this paper discusses the development of a new algorithm and presents a new mathematical model for computing the
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The problem motivation of this work deals with how to control the network overhead and reduce the network latency that may cause many unwanted loops resulting from using standard routing. This work proposes three different wireless routing protocols which they are originally using some advantages for famous wireless ad-hoc routing protocols such as dynamic source routing (DSR), optimized link state routing (OLSR), destination sequenced distance vector (DSDV) and zone routing protocol (ZRP). The first proposed routing protocol is presented an enhanced destination sequenced distance vector (E-DSDV) routing protocol, while the second proposed routing protocol is designed based on using the advantages of DSDV and ZRP and we named it as
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This study aims to analyze the flow migration of individuals between Iraqi governorates using real anonymized data from Korek Telecom company in Iraq. The purpose of this analysis is to understand the connection structure and the attractiveness of these governorates through examining the flow migration and population densities. Hence, they are classified based on the human migration at a particular period. The mobile phone data of type Call Detailed Records (CDRs) have been observed, which fall in a 6-month period during COVID-19 in the year 2020-2021. So, according to the CDRs nature, the well-known spatiotemporal algorithms: the radiation model and the gravity model were applied to analyze these data, and they are turned out to be comp
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Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener
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In this paper, a Modified Weighted Low Energy Adaptive Clustering Hierarchy (MW-LEACH) protocol is implemented to improve the Quality of Service (QoS) in Wireless Sensor Network (WSN) with mobile sink node. The Quality of Service is measured in terms of Throughput Ratio (TR), Packet Loss Ratio (PLR) and Energy Consumption (EC). The protocol is implemented based on Python simulation. Simulation Results showed that the proposed protocol provides better Quality of Service in comparison with Weighted Low Energy Cluster Hierarchy (W-LEACH) protocol by 63%.
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... Show MoreVoice over Internet Protocol (VoIP) is important technology that’s rapidly growing in the wireless networks. The Quality of Service (QoS) and Capacity are two of the most important issues that still need to be researched on wireless VoIP. The main aim of this paper is to analysis the performance of the VoIP application in wireless networks, with respect to different transport layer protocols and audio codec. Two scenarios used in the simulation stage. In the first scenario VoIP with codec G.711 transmitted over User Datagram Protocol (UDP), Stream Control Transmission Protocol (SCTP), and Real-Time Transport Protocol (RTP). While, in the second scenario VoIP with codec G.726 transmitted over UDP, SCTP, and RTP protocols. Network simulator
... Show MoreIn this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from
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