K-Means clustering of optimized wireless network sensor using genetic algorithm

Kadim, Azhar M.; Al-Mukhtar, Farah Saad; Hasan, Najwan Abed; Alnajjar, Aseel B.; Altaei, Mohammed Sahib Mahdi

doi:10.21533/pen.v10i3.3059

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…In contemporary object detection, deep convolutional neural networks (CNNs) are prominent, addressing objects of varying sizes and aspect ratios [15]. Scene-adaptive people recognition systems, trained with target data [16], showcase remarkable effectiveness in pedestrian detection. While CNN methodologies have triumphed, the utility of manually constructed feature-based algorithms, like histogram of oriented gradients (HOG), remains evident.…”

Section: Related Workmentioning

confidence: 99%

Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades

Khalaf,

M. Abdulrahman,

Al_Barazanchi

et al. 2024

TELKOMNIKA

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Numerous technologies and systems, including autonomous vehicles, surveillance systems, and robotic applications, rely on the capability to accurately detect pedestrians to ensure their safety. As the demand for realtime object detection continues to rise, many researchers have dedicated their efforts to developing effective and trustworthy algorithms for pedestrian recognition. By integrating learning complexity-aware cascades with an enhanced you only look once (YOLO) algorithm, the paper presents a real-time system for identifying both items and pedestrians. The performance of the proposed approach is evaluated using the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) pedestrian dataset across both the v4 and v8 versions of the YOLO framework. Prioritizing both speed and accuracy, the enhanced YOLO algorithm outperforms its baseline counterpart. The demonstrated superiority of the suggested technique on the KITTI pedestrian dataset underscores its effectiveness in real-world contexts. Furthermore, the complexity-aware learning cascades contribute to a streamlined detection model without compromising performance. When applied to scenarios requiring real-time identification of objects and individuals, the proposed method consistently delivers promising outcomes.

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Section: Related Workmentioning

confidence: 99%

Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades

Khalaf,

M. Abdulrahman,

Al_Barazanchi

et al. 2024

TELKOMNIKA

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“…Such models can be used among others in the rural area that lacks communication infrastructures [11]. For multi-hop WMN, carrier sense multiple access with collision avoidance (CSMA-CA) protocol is inappropriate way due to hidden node problems and exposed terminal problems [12]. In contrast, the time-division multiple access (TDMA) scheduling can offer spatial reuse and thus is suitable for this type of network [13,14].…”

Section: Wireless Mesh Networkmentioning

confidence: 99%

Sustainable optimizing WMN performance through meta-heuristic TDMA link scheduling and routing

Sekhar,

Abdulshaheed,

Penubadi

et al. 2024

Heritage and Sustainable Development

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Wireless mesh networks (WMNs) have become a popular solution for expanding internet service and communication in both urban and rural areas. However, the performance of WMNs depends on generating optimized time-division multiple access (TDMA) schedules, which distribute time into a list of slots called superframes. This study proposes novel meta-heuristic algorithms to generate TDMA link schedules in WMNs using two different interference/constraint models: multi-transmit-receive (MTR) and full-duplex (FD). The objectives of this study are to optimize the TDMA frame for packet transmission, satisfy the constraints, and minimize the end-to-end delay. The significant contributions of this study are: (1) proposing effective and efficient heuristic solutions to solve the NP-complete problem of generating optimal TDMA link schedules in WMNs; (2) investigating the new FD interference model to improve the network capacity above the physical layer. To achieve these objectives and contributions, the study uses two popular meta-heuristics, the artificial bee colony (ABC) and/or genetic algorithm (GA), to solve the known NP-complete problems of joint scheduling, power control, and rate control. The results of this study show that the proposed algorithms can generate optimized TDMA link schedules for both MTR and FD models. The joint routing and scheduling approach further minimizes end-to-end delay while maintaining the schedule's minimum length and/or maximum capacity. The proposed solution outperforms the existing solutions in terms of the number of active links, end-to-end delay, and network capacity. The research aims to improve the efficiency and effectiveness of WMNs in most applications that require high throughput and fast response time.

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“…The genetic algorithm was adopted in the process of optimizing the WSN model, where it was considered that each WSN model is an individual with one chromosome containing four genes: 1, 2, and 3 are representing the symbols of the types of CH, HSR, and LSR sensors respectively, while the gene 0 indicates there is no sensor in that location [25][26][27][28][29][30][31][32][33][34][35]. The parameters of GA were set to be as given in Table 1, while the implementation of the GA (Fig.…”

Section: A Wsn Model Optimizationmentioning

confidence: 99%

Wireless Sensor Network Optimization Using Genetic Algorithm

Alnajjar

Kadim

Jaber

et al. 2023

Journal of Robotics and Control

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Wireless Sensor Network (WSN) is a high potential technology used in many fields (agriculture, earth, environmental monitoring, resources union, health, security, military, and transport, IoT technology). The band width of each cluster head is specific, thus, the number of sensors connected to each cluster head is restricted to a maximum limit and exceeding it will weaken the connection service between each sensor and its corresponding cluster head. This will achieve the research objective which refers to reaching the state where the proposed system energy is stable and not consuming further more cost. The main challenge is how to distribute the cluster heads regularly on a specified area, that’s why a solution was supposed in this research implies finding the best distribution of the cluster heads using a genetic algorithm. Where using an optimization algorithm, keeping in mind the cluster heads positions restrictions, is an important scientific contribution in the research field of interest. The novel idea in this paper is the crossover of two-dimensional integer encoded individuals that replacing an opposite region in the parents to produce the children of new generation. The mutation occurs with probability of 0.001, it changes the type of 0.05 sensors found in handled individual. After producing more than 1000 generations, the achieved results showed lower value of fitness function with stable behavior. This indicates the correct path of computations and the accuracy of the obtained results. The genetic algorithm operated well and directed the process towards improving the genes to be the best possible at the last generation. The behavior of the objective function started to be regular gradually throughout the produced generations until reaching the best product in the last generation where it is shown that all the sensors are connected to the nearest cluster head. As a conclusion, the genetic algorithm developed the sensors’ distribution in the WSN model, which confirms the validity of applying of genetic algorithms and the accuracy of the results.

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K-Means clustering of optimized wireless network sensor using genetic algorithm

Cited by 5 publications

References 15 publications

Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades

Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades

Sustainable optimizing WMN performance through meta-heuristic TDMA link scheduling and routing

Wireless Sensor Network Optimization Using Genetic Algorithm

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