التفاصيل البيبلوغرافية
| العنوان: |
Reinforcement Learning-based Topology-Aware Routing Protocol with Priority Scheduling for Internet of Drones in Agriculture Application. |
| المؤلفون: |
Najim, Ali Hamzah, Abbas, Ali Hashim, Al-sharhanee, Kareem, Hariz, Hussein Muhi |
| المصدر: |
International Journal of Intelligent Engineering & Systems; 2023, Vol. 16 Issue 5, p395-405, 11p |
| مصطلحات موضوعية: |
DECISION theory, END-to-end delay, REINFORCEMENT learning, HEBBIAN memory, PRECISION farming, NETWORK routing protocols, INTERNET, AGRICULTURE, DECISION making |
| مستخلص: |
Internet of drones (IoD) are commonly constructed with unmanned vehicles, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. These IoDs are interact with zone service providers (ZSPs) to achieve the goal of assisting drones in accessing controlled agriculture services. The utilization of drones in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area by analysing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. objectively and equitably assigns a weight to multiple service scheduling parameters based on maldistributed decision making theory, calculates the serving priority of each service request group, and then serves the service request groups based on the calculated serving priori-ty accordingly Hence, this paper proposes reinforcement learning-based topology-aware routing protocol with priority scheduling (RLTARP) to provide reliable combinations between the source and destination. It also improves the routing decision by considering two-hop neighbour nodes, extending the local view of the network topology. The priority scheduling method adopts maldistributed decision making theory, to find the group of priorities based on service request. The proposed RLTARP is compared with three existing methods such as DroneCOCoNet, Markov decision process (MDP)and deep deterministic policy gradient (DDPG) and hence it produces 46.34% of packet delivery ratio, 67.49% of end to end delay, 16.45% of routing overhead, 13% of energy consumption and 97.6% of network lifetime. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
