3D Localization for Mobile Node in Wireless Sensor Network

Javed, Iram; Tang, Xianlun; Saleem, Muhammad Asim; Sarwar, Muhammad Umer; Tariq, Muhammad; Shivachi, Casper Shikali

doi:10.1155/2022/3271265

Cited by 13 publications

(5 citation statements)

References 35 publications

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“…Addressing availability and mobility challenges in vehicular networks requires robust strategies that account for the dynamic nature of vehicle movements and ensure reliable connectivity and service availability [27]. In [28], the authors propose a novel system that combines positioning and vehicle direction to provide a predictive evaluation.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Intelligent Sensor Virtualization and Provisioning in Internet of Vehicles (IoV)

Abbes,

Rekhis

2024

IEEE Access

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The Internet of Vehicles (IoV) is a powerful application of the Internet of Things (IoT) in the Intelligent Transportation System (ITS). It enables device connectivity, and interaction with the environment, and improves efficiency in utilizing sensor data. Leveraging the capabilities of vehicle sensors to provide virtual sensor service presents an opportunity to make the most of underutilized sensor resources and offer on-demand sensor services. However, challenges remain in ensuring spatiotemporal sensor availability due to the high mobility of vehicles and frequent changes in the IoV network topology. Furthermore, providing sensor services in the dynamic IoV-Cloud market, while balancing costeffectiveness for consumers and profitability for service providers, poses a significant challenge. To address these challenges, we develop an IoV-Cloud architecture tailored for vehicle virtual sensor provisioning, integrating interactive functional layers and components for a comprehensive solution. This architecture supports vehicle sensor virtualization and management, facilitating the intelligent provisioning of ondemand, elastic, and scalable vehicle virtual sensors from existing physical sensors within mobile vehicles. To address vehicle mobility and enhance sensor availability, our design permits virtual sensors to maintain data provision even when switched to different physical sensors. Further optimizing sensor utilization, our system allows multiple virtual vehicle sensors to share a single physical sensor by employing a configuration mapping mechanism. At the heart of our strategy is a reinforcement learning model that dynamically selects the most suitable vehicle physical sensor for each time slot throughout the service period, considering both physical availability and cost-effectiveness for the Sensor Cloud Service Provider (SCSP). We simulate the proposed intelligent sensor selection model using both Q-learning and SARSA algorithms, demonstrating its effectiveness in intelligent and dynamic sensor provisioning.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Intelligent Sensor Virtualization and Provisioning in Internet of Vehicles (IoV)

Abbes,

Rekhis

2024

IEEE Access

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“…Sekhar et al (2021) [13] proposed a metaheuristics-based node localization technique for WSNs that aims to find the coordinates of unknown nodes by the anchor nodes with minimum error and maximum accuracy. Javed et al (2022) [14] proposed a mobile node localization algorithm for WSNs that improves positioning accuracy. Aroba et al (2023) [15] proposed an algorithm to address the problem of determining sensor node localization with the minimum error when the data is transmitted over the wireless channel.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Supported Nano-Router Localization in WNSNs

Güleç

2023

Sakarya University Journal of Science

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Sensing data from the environment is a basic process for the nano-sensors on the network. This sensitive data need to be transmitted to the base station for data processing. In Wireless Nano-Sensor Networks (WNSNs), nano-routers undertake the task of gathering data from the nano-sensors and transmitting it to the nano-gateways. When the number of nano-routers is not enough on the network, the data need to be transmitted by multi-hop routing. Therefore, there should be more nano-routers placed on the network for efficient direct data transmission to avoid multi-hop routing problems such as high energy consumption and network traffic. In this paper, a machine learning-supported nano-router localization algorithm for WNSNs is proposed. The algorithm aims to predict the number of required nano-routers depending on the network size for the maximum node coverage in order to ensure direct data transmission by estimating the best virtual coordinates of these nano-routers. According to the results, the proposed algorithm successfully places required nano-routers to the best virtual coordinates on the network which increases the node coverage by up to 98.03% on average and provides high accuracy for efficient direct data transmission.

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“…Transmitting sources are connected to R1 and receiving sources are connected to R2. All link bandwidths are set to 100 Mbps and the propagation delay is set to 20 ms. A TCP connection is established between the transmitting source Sn and the corresponding receiving source Dn [28]- [30]. All six algorithms are implemented sequentially on the router R1 queue.…”

Section: Figure 3 Topology Of the Single-bottleneck Linkmentioning

confidence: 99%

A Novel Active Queue Management Algorithm Based on Average Queue Length Change Rate

et al. 2022

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The rapid development of information technology has promoted the transformation of traditional networks into intelligent networks. Huge data traffic is generated by various types of traffic services in the intelligent networks, which can easily lead to network congestion, system instability, and other problems. These problems may incur great requirements and pose challenges for queue management algorithms. Most traditional active queue management (AQM) algorithms judge the congestion level of the network based only on the size of the average queue length while ignoring the network traffic variations. This makes these algorithms difficult to achieve effective improvement of congestion control efficiency. To address this problem, a novel network congestion control algorithm, namely the average queue length and change rate -RED (AC-RED) is proposed in this paper. AC-RED can better relieve network congestion by reconfiguring the packet loss strategy model based on the average queue length change rate. The simulation results of NS2 show that in complex and dynamic network environments, the performance of AC-RED algorithm in the average queue length, packet loss rate, delay, and delay jitter is improved in most load conditions compared with the other five algorithms.INDEX TERMS Active queue management; network congestion; average queue length change rate; AC-RED; NS2

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3D Localization for Mobile Node in Wireless Sensor Network

Cited by 13 publications

References 35 publications

Reinforcement Learning for Intelligent Sensor Virtualization and Provisioning in Internet of Vehicles (IoV)

Reinforcement Learning for Intelligent Sensor Virtualization and Provisioning in Internet of Vehicles (IoV)

Machine Learning Supported Nano-Router Localization in WNSNs

A Novel Active Queue Management Algorithm Based on Average Queue Length Change Rate

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