IoT Sensor Selection for Target Localization: A Reinforcement Learning based Approach

Shurrab, Mohammed; Singh, Shakti; Mizouni, Rabeb; Otrok, Hadi

doi:10.1016/j.adhoc.2022.102927

Cited by 36 publications

(7 citation statements)

References 18 publications

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“…Algorithms like Bayesian networks and weighted averages rely on statistical methods and information theory for data integration. Besides, there are also machine learning-based adaptive sensor algorithms [5]. Sensor fusion encompasses a broad spectrum of algorithms, each with inherent constraints, necessitating careful selection based on the specific environmental conditions [6].…”

Section: Advances In Sensor Data Integration and Algorithmic Processingmentioning

confidence: 99%

Sensor-Based Target Positioning Technologies in Industrial Automation

Gao

2024

HSET

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This thesis examines the critical role of sensor-based target positioning technologies in industrial automation within the context of Industry 4.0, highlighting their importance for operational efficiency and adaptability. Emphasizing the significance of sensors in automation, the study explores the principles, capabilities, and constraints of RFID, LiDAR, and WSN technologies, each pivotal for accurate target localization. It presents a comparative analysis of these technologies, focusing on their applications across various industrial scenarios, such as production lines, warehousing, and logistics, and evaluates their effectiveness through a series of experiments. The experimental analysis reveals RFID's precision improvement within 10 centimeters and LiDAR's enhanced accuracy with light information integration. It also shows that WSN's precision is contingent on noise levels, with the distance-corrected iterative localization algorithm significantly reducing localization error. The study concludes that while advancements have been made, future research should address the limitations observed in harsh industrial environments, with an emphasis on machine learning and AI integration for data processing to navigate environmental challenges.

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Section: Advances In Sensor Data Integration and Algorithmic Processingmentioning

confidence: 99%

Sensor-Based Target Positioning Technologies in Industrial Automation

Gao

2024

HSET

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“…RL algorithms address the challenge of achieving both efficient and fair coexistence between long-term evolution and Wi-Fi technologies [36,37]. Numerous algorithms driven by RL have been suggested to enhance the efficiency of IoT devices, such as a computation offloading scheme for healthcare applications [38], spectrum access [39] for IoT networks, and target localization for IoT sensor selection [40]. The resource management problem is also handled by using RL for efficient networking protocols.…”

Section: Motivationmentioning

confidence: 99%

Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges

Musaddiq,

Olsson,

Ahlgren

2023

Sensors

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Internet of Things (IoT) devices are increasingly popular due to their wide array of application domains. In IoT networks, sensor nodes are often connected in the form of a mesh topology and deployed in large numbers. Managing these resource-constrained small devices is complex and can lead to high system costs. A number of standardized protocols have been developed to handle the operation of these devices. For example, in the network layer, these small devices cannot run traditional routing mechanisms that require large computing powers and overheads. Instead, routing protocols specifically designed for IoT devices, such as the routing protocol for low-power and lossy networks, provide a more suitable and simple routing mechanism. However, they incur high overheads as the network expands. Meanwhile, reinforcement learning (RL) has proven to be one of the most effective solutions for decision making. RL holds significant potential for its application in IoT device’s communication-related decision making, with the goal of improving performance. In this paper, we explore RL’s potential in IoT devices and discuss a theoretical framework in the context of network layers to stimulate further research. The open issues and challenges are analyzed and discussed in the context of RL and IoT networks for further study.

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“…They proposed a dynamic approach to active node selection in which the trained RL agent was deployed in the first phase to select an appropriate grid. In the second phase, a selection mechanism was used to select the best nodes for that grid based on their attributes, such as location, cost, residual energy, and node confidence, with the goal of locating an unknown source [ 50 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“… Classification of the reviewed literature, with emphasis on the learning algorithms used [ 28 , 29 , 31 , 32 , 33 , 34 , 37 , 38 , 39 , 40 , 41 , 43 , 44 , 46 , 47 , 49 , 50 , 51 , 52 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]. …”

Section: Figurementioning

confidence: 99%

Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

Shahbazian

Macrina

Scalzo

et al. 2023

Sensors

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The widespread use of the internet and the exponential growth in small hardware diversity enable the development of Internet of things (IoT)-based localization systems. We review machine-learning-based approaches for IoT localization systems in this paper. Because of their high prediction accuracy, machine learning methods are now being used to solve localization problems. The paper’s main goal is to provide a review of how learning algorithms are used to solve IoT localization problems, as well as to address current challenges. We examine the existing literature for published papers released between 2020 and 2022. These studies are classified according to several criteria, including their learning algorithm, chosen environment, specific covered IoT protocol, and measurement technique. We also discuss the potential applications of learning algorithms in IoT localization, as well as future trends.

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IoT Sensor Selection for Target Localization: A Reinforcement Learning based Approach

Cited by 36 publications

References 18 publications

Sensor-Based Target Positioning Technologies in Industrial Automation

Sensor-Based Target Positioning Technologies in Industrial Automation

Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges

Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

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