A deep reinforcement learning-based multi-agent area coverage control for smart agriculture

Din, Ahmad; Ismail, Muhammed Yousoof; Shah, Babar; Babar, Mohammad; Ali, Farman; Baig, Siddique Ullah

doi:10.1016/j.compeleceng.2022.108089

Cited by 28 publications

(14 citation statements)

References 16 publications

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“…DRL-based advanced agricultural techniques effectively filter out suboptimal choices, significantly enhancing crop production within the crop recommendation system [598]. Furthermore, RL is essential in addressing the area coverage problem related to monitoring crop health in semi-structured farm settings [640]. RL agents adeptly learn from environmental feedback provided by sensors, ultimately enhancing precision and efficiency in crop management [599].…”

Section: ) Reinforcement Learning (Rl)mentioning

confidence: 99%

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Mowla,

Shah

et al. 2023

IEEE Access

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The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing food production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization and control, early-stage pest and crop disease management, and energy conservation. Wireless application protocols such as ZigBee, WiFi, SigFox, and LoRaWAN are commonly employed to collect real-time data for monitoring purposes. Embracing advanced technology is imperative to ensure efficient annual production. Therefore, this study emphasizes a comprehensive, futureoriented approach, delving into IoT-WSNs, wireless network protocols, and their applications in agriculture since 2019. It thoroughly discusses the overview of IoT and WSNs, encompassing their architectures and summarization of network protocols. Furthermore, the study addresses recent issues and challenges related to IoT-WSNs and proposes mitigation strategies. It provides clear recommendations for the future, emphasizing the integration of advanced technology aiming to contribute to the future development of smart agriculture systems. INDEX TERMSInternet of things, wireless sensor networks, wireless network protocols, smart agriculture applications.

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Section: ) Reinforcement Learning (Rl)mentioning

confidence: 99%

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Mowla,

Shah

et al. 2023

IEEE Access

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“…The clause ([ ] > 0) can be replaced with [ ] in (6) in light of (2). Since = , the clause ([ ] > 0) can be replaced with [ ] , yielding…”

Section: Definition 7 the Matrices Of Binary Entriesmentioning

confidence: 99%

“…There is a growing interest in the use of MAS formations in diverse domains, ranging from aquatic to aerospace applications 2,3,4 , demonstrating the intent for the ubiquitous use of collaborating agents. There are many use cases where MAS have been demonstrated to be necessary or advantageous, such as the inspection of underground facilities 5 , smart agriculture 6 , and autonomous exploration 7 .…”

Section: Introductionmentioning

confidence: 99%

Linear constraints for ensuring k -hop connectivity using Mixed-Integer Programming for Multi-Agent Systems

Afonso

Galvão

et al. 2023

Preprint

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This note concerns the problem of k-hop connectivity in a network of mobile agents, which is achieved if any pair of agents can communicate with each other through a link of k-1 or fewer intermediate nodes. We propose linear constraints involving binary optimization variables to ensure k-hop connectivity. Such constraints are then integrated into a Mixed-Integer Linear Programming (MILP) trajectory planning model. Simulation results illustrate the application of the proposed method and the effect of varying k in the context of a mission involving the visitation of multiple targets.

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“…1 There is a growing interest in the use of MAS formations in diverse domains, ranging from aquatic to aerospace applications, [2][3][4] demonstrating the intent for the ubiquitous use of collaborating agents. There are many use cases where MAS have been demonstrated to be necessary or advantageous, such as the inspection of underground facilities, 5 smart agriculture, 6 and autonomous exploration. 7 Trajectory planning algorithms are fundamental to the operation of MAS, 1 as they provide the required coordination that enables agents to efficiently complete tasks while retaining important features such as inter-agent communication.…”

Section: Introductionmentioning

confidence: 99%

“…There is a growing interest in the use of MAS formations in diverse domains, ranging from aquatic to aerospace applications, 2‐4 demonstrating the intent for the ubiquitous use of collaborating agents. There are many use cases where MAS have been demonstrated to be necessary or advantageous, such as the inspection of underground facilities, 5 smart agriculture, 6 and autonomous exploration 7 …”

Section: Introductionmentioning

confidence: 99%

Linear constraints for ensuring k‐hop connectivity using mixed‐integer programming for multi‐agent systems

Afonso,

Galvão,

Souza

et al. 2023

Intl J Robust & Nonlinear

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This note concerns the problem of ‐hop connectivity in a network of mobile agents, which is achieved if any pair of agents can communicate with each other through a link of or fewer intermediate nodes. We propose linear constraints involving binary optimization variables to ensure ‐hop connectivity. Such constraints are then integrated into a mixed‐integer linear programming (MILP) trajectory planning model. Simulation results illustrate the application of the proposed method and the effect of varying in the context of a mission involving the visitation of multiple targets.

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A deep reinforcement learning-based multi-agent area coverage control for smart agriculture

Cited by 28 publications

References 16 publications

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Linear constraints for ensuring k -hop connectivity using Mixed-Integer Programming for Multi-Agent Systems

Linear constraints for ensuring k‐hop connectivity using mixed‐integer programming for multi‐agent systems

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