IPDCA: Intelligent Proficient Data Collection Approach for IoT-Enabled Wireless Sensor Networks in Smart Environments

Osamy, Walid; Khedr, Ahmed M.; El‐Sawy, A. A.; Salim, Ahmed; Vijayan, Dilna

doi:10.3390/electronics10090997

Cited by 16 publications

(10 citation statements)

References 67 publications

(102 reference statements)

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“…In this section, we present a comprehensive comparison between the performance of the Intelligent Mobile Data Collection with Frequency-Based Reinforcement Learning (IMDC-FRL) framework and the Intelligent Pro cient Data Collection Approach (IPDCA) [2]. The comparison is conducted across multiple key metrics, including packet delivery ratio, packet drop rate, computation cost, and average residual energy.…”

Section: Comparison Resultsmentioning

confidence: 99%

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Utilizing Stacked Ensemble AI Algorithm for Comprehensive Water Quality Analysis and Health Assessment in Complex Aquatic Systems

Al-Otaibi

et al. 2024

Preprint

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A novel framework, referred to as the Intelligent Mobile Data Collection (MDC) framework, is proposed to enhance data collection efficiency in Internet of Things (IoT) based sensor networks. This framework organizes IoT devices and sensors into clusters based on their geographical proximity or region. Within each cluster, a gateway node is designated to collect and consolidate data from its constituent members before transmitting it to the central MDC. To optimize data collection, the framework employs a learning mechanism known as Frequency-Based Reinforcement Learning (FRL). This technique analyzes data generation patterns, such as time intervals between transmissions, the quantity and type of packets generated, to classify clusters into categories: Frequent, Less Frequent, Rare, and Very Rare. Within FRL, each IoT sensor or device independently trains its local model using Reinforcement Learning (RL) techniques, encompassing states, actions, and rewards. These local models capture the specific behaviors and characteristics of the sensors. Subsequently, IoT sensors transmit their local model parameters to the gateway, where they are aggregated into a global model. This aggregated global model is then disseminated back to the IoT sensors, enabling them to adjust their behavior based on collective insights. Based on the categorized clusters, the framework dynamically adjusts parameters such as Time Division Multiple Access (TDMA) slot allocations, sleep durations for sensors, and the visiting schedule of the MDC. This adaptive approach ensures efficient utilization of network resources while accommodating varying data generation rates and priorities across different clusters. In summary, the proposed Intelligent Mobile Data Collection framework integrates FRL and RL techniques to optimize data collection in IoT sensor networks. By dynamically adapting to changing data generation patterns and cluster characteristics, it enhances overall network performance and resource utilization.

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Section: Comparison Resultsmentioning

confidence: 99%

“…The IPDCA, suggested by Walid Osamy et al [2], proposes a method for delivering public data in a largescale smart city setup. Data collectors (D-collection vehicles) that gather data from a variety of Access Points (APs) and relay it back to a central Base Station are used by IPDCA (BS).…”

Section: Related Workmentioning

confidence: 99%

Utilizing Stacked Ensemble AI Algorithm for Comprehensive Water Quality Analysis and Health Assessment in Complex Aquatic Systems

Al-Otaibi

et al. 2024

Preprint

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“…The main target of the modified Bee is to form disjoint dominating sets that work as collectors in each round. Intelligent Proficient DC Approach (IPDCA) [90] utilizes public vehicles as the mobile data collectors (D-collectors) that read (or collect) data from multiple Access Points (APs) and send them back to the central Base Station (BS). IPDCA adopts a modified Bat algorithm for path finding of D-collectors, where the Bat algorithm is extended to solve a discrete optimization problem.…”

Section: Ai Based Solutions To Data Collection Aggregation and Dissem...mentioning

confidence: 99%

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

et al. 2022

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The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

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“…The WSN technology can be enhanced through the integration of artificial intelligence (AI), cognitive technology, and spectrum sensing for big data achievement and distant communication (Li et al 2015;Luna et al 2018). This WSN found its application in the various areas of smart technology such as smart cities, smart homes (Rawi and Al-Anbuky, 2011;Ajao et al 2021), smart agriculture, smart healthcare, and smart factories (Zhang et al 2012;Osamy et al 2021;Ngabo et al 2021). Another significant area of WSN application is the military for tracking opposition, battlefield surveillance, defensive measures, and field monitoring as illustrated in "Figure 2".…”

Section: Introductionmentioning

confidence: 99%

Intelligent Wireless Sensor Network for Surveillance and Security Information Gathering using Computer Vision and Deep Convolutional Neural Network

Apeh,

Ajao,

Nyitamen

et al. 2024

Preprint

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The use of a wireless sensor network (WSN) for gathering security information about terrorism patterns in war zones renders a wide range of advantages. Which reduces huge personnel fatalities, minimizing attendant personnel, and maintenance costs. As well as improving the efficiency of the sophisticated machinery that is more resilient than humans at the front line through autonomous surveillance. However, this research aims to develop an intelligent wireless sensing system (IWSS) for autonomous surveillance, firearm detection, and defense systems at the front line through the deployment of intelligent wireless sensor nodes. This prototypical model of the autonomous defense and surveillance system involves several sensors and intelligent cameras. These all are integrated into the ARM Cortex A53 processor for data collection, and image processing using the Support Vector Machine (SVM), Histogram of Gradient (HOG), and Eye Aspect Ratio (EAR) algorithms of the computer vision algorithm. The surveillance video clips/imagery extracted have experimented with the YOLOv3 model for object training, detection, and classification using a deep convolutional neural network (DCNN). The result obtained for the detection accuracy of humans in possession of the weapon is 100%, with a processing time of 0.875 seconds. Also, the deployment of the multi-agent sensing prototype for the autonomous surveillance system is implemented and simulated in a spanning tree network testbed model. The average detection accuracy results obtained are 94.85%, 95.10%, 96.58%, 93.57%, 95.26%, and 97.17% respectively.

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IPDCA: Intelligent Proficient Data Collection Approach for IoT-Enabled Wireless Sensor Networks in Smart Environments

Cited by 16 publications

References 67 publications

Utilizing Stacked Ensemble AI Algorithm for Comprehensive Water Quality Analysis and Health Assessment in Complex Aquatic Systems

Utilizing Stacked Ensemble AI Algorithm for Comprehensive Water Quality Analysis and Health Assessment in Complex Aquatic Systems

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

Intelligent Wireless Sensor Network for Surveillance and Security Information Gathering using Computer Vision and Deep Convolutional Neural Network

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