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. 2023 Nov 22:9:e1682.
doi: 10.7717/peerj-cs.1682. eCollection 2023.

A P2P multi-path routing algorithm based on Skyline operator for data aggregation in IoMT environments

Ismail Kertiou  1 , Abdelkader Laouid  1 , Benharzallah Saber  2 , Mohammad Hammoudeh  3 , Muath Alshaikh  4
Affiliations

A P2P multi-path routing algorithm based on Skyline operator for data aggregation in IoMT environments

Ismail Kertiou et al. PeerJ Comput Sci. .

Abstract

The integration of Internet of Things (IoT) technologies, particularly the Internet of Medical Things (IoMT), with wireless sensor networks (WSNs) has revolutionized the healthcare industry. However, despite the undeniable benefits of WSNs, their limited communication capabilities and network congestion have emerged as critical challenges in the context of healthcare applications. This research addresses these challenges through a dynamic and on-demand route-finding protocol called P2P-IoMT, based on LOADng for point-to-point routing in IoMT. To reduce congestion, dynamic composite routing metrics allow nodes to select the optimal parent based on the application requirements during the routing discovery phase. Nodes running the proposed routing protocol use the multi-criteria decision-making Skyline technique for parent selection. Experimental evaluation results show that P2P-IoMT protocol outperforms its best rivals in the literature in terms of residual network energy and packet delivery ratio. The network lifetime is extended by 4% while achieving a comparable packet delivery ratio and communication delay compared to LRRE. These performances are offered on top of the dynamic path selection and configurable route metrics capabilities of P2P-IoMT.

Keywords: Data routing; Distributed systems; Healthcare; Internet of Medical Things; Routing metrics; Skyline; Wireless sensor networks.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. (A–D) The selection process of the best route.
Figure 2
Figure 2. Skyline routes arrangement.
Figure 3
Figure 3. A flowchart of the proposed routing protocol.
Figure 4
Figure 4. A use case example network with 10 nodes.
Figure 5
Figure 5. (A–B) Selection of the best route using a single routing metric.
Figure 6
Figure 6. (A–B) Selection of the best route using node cost and transmission delay as routing metrics.
Figure 7
Figure 7. (A–B) Selection of the best route using node cost, node energy, and transmission delay as routing metrics.
Figure 8
Figure 8. (A–B) Selection of the best route with six routing metrics.
Figure 9
Figure 9. PDR comparison.
Figure 10
Figure 10. Average residual energy comparison.
Figure 11
Figure 11. Comparison of the network lifetime.
See this image and copyright information in PMC

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