Energy-efficient cooperative routing scheme with recurrent neural network based decision making system for wireless multimedia sensor networks

Nagalingayya, M; Mathpati, Basavaraj S

doi:10.1007/s11042-022-12938-5

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Nagalingayya and Mathpati study the maximum energy cooperative route in WMSNs. They introduce the recurrent neural network oriented decision-making system to select the appropriate cooperative nodes based on energy, reliability, and delay [ 34 ]. In addition to improving QoS and reducing energy consumption, the transmission security of WMSNs is also worth considering.…”

Section: Related Workmentioning

confidence: 99%

“…The related works could be summarized as a research line from a multimedia routing perspective. In wireless multimedia sensor networks (WMSNs), stationary nodes are relatively easy to construct the shortest path and ensure QoS [ 2 , 17 , 28 , 34 ]. However, energy-saving is an enormous obstacle to further improving their performances.…”

Section: Introductionmentioning

confidence: 99%

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Hedge transfer learning routing for dynamic searching and reconnoitering applications in 3D multimedia FANETs

Zhang

Liu

et al. 2023

Multimed Tools Appl

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With the fast development of unmanned aerial vehicles (UAVs) and the user increasing demand of UAV video transmission, UAV video service is widely used in dynamic searching and reconnoitering applications. Video transmissions not only consider the complexity and instability of 3D UAV network topology but also ensure reliable quality of service (QoS) in flying ad hoc networks (FANETs). We propose hedge transfer learning routing (HTLR) for dynamic searching and reconnoitering applications to address this problem. Compared with the previous transfer learning framework, HTRL has the following innovations. First, hedge principle is introduced into transfer learning. Online model is continuously trained on the basis of offline model, and their weight factors are adjusted in real-time by transfer learning, so as to adapt to the complex 3D FANETs. Secondly, distributed multi-hop link state scheme is used to estimate multi-hop link states in the whole network, thus enhancing the stability of transmission links. Among them, we propose the multiplication rule of multi-hop link states, which is a new idea to evaluate link states. Finally, we use packet delivery rate (PDR) and energy efficiency rate (EER) as two main evaluation metrics. In the same NS3 experimental scenario, the PDR of HTLR is at least 5.11% higher and the EER is at least 1.17 lower than compared protocols. Besides, we use Wilcoxon test to compare HTLR with the simplified version of HTLR without hedge transfer learning (N-HTLR). The results show that HTRL is superior to N-HTRL, effectively ensuring QoS.

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Section: Related Workmentioning

confidence: 99%