Outage Probability Analysis of Multi-hop Relay Aided IoT Networks

Multi-hop Relay Aided IoT Networks

Authors

  • Fusheng Wei Guangdong Power Grid Co., Ltd
  • Jiajia Huang Guangdong Power Grid Co.
  • Jingming Zhao Guangdong Power Grid Co.
  • Huakun Que Guangdong Power Grid Co.

DOI:

https://doi.org/10.4108/eetsis.3780

Keywords:

IoT networks, multi-hop relaying, outage probability, achievable rate

Abstract

This study delves into Internet of Things (IoT) networks wherein a transmitting source communicates information to a designated recipient. The presence of signal attenuation challenges the direct transmission of information from the source to the recipient. To surmount this obstacle, we investigate IoT network communication facilitated by multi-hop relays, whereby multiple relays collaboratively enable the conveyance of data from the source to the recipient across intermediate stages. For the considered IoT networks augmented by multi-hop relays, we assess the performance of the system by analyzing the probability of transmission outage. This analysis entails the derivation of an analytical expression for evaluating the occurrence of IoT network outage. Additionally, we gauge the system's effectiveness by examining the attainable transmission rate, wherein an analytical expression is furnished to assess the IoT data rate. The empirical results, along with the analytical findings, are subsequently presented to validate the formulated expressions in the context of IoT networks empowered by multi-hop relays. Notably, the utilization of multi-hop relaying emerges as a efficacious strategy for substantially expanding the coverage scope of IoT networks.

References

Z. Li, J. Xie, W. Liu, H. Zhang, and H. Xiang, “Joint strategy of power and bandwidth allocation for multiple maneuvering target tracking in cognitive MIMO radar with collocated antennas,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 190–204, 2023.

T. Gafni, B. Wolff, G. Revach, N. Shlezinger, and K. Cohen, “Anomaly search over discrete composite hypotheses in hierarchical statistical models,” IEEE Trans. Signal Process., vol. 71, pp. 202–217, 2023.

A. Gupta, M. Sellathurai, and T. Ratnarajah, “End-to-end learning-based full-duplex amplify-and-forward relay networks,” IEEE Trans. Commun., vol. 71, no. 1, pp. 199– 213, 2023.

C. Chaieb, F. Abdelkefi, and W. Ajib, “Deep reinforcement learning for resource allocation in multi-band and hybrid OMA-NOMA wireless networks,” IEEE Trans. Commun., vol. 71, no. 1, pp. 187–198, 2023.

Z. Song, J. An, G. Pan, S. Wang, H. Zhang, Y. Chen, and M. Alouini, “Cooperative satellite-aerial-terrestrial systems: A stochastic geometry model,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 220–236, 2023.

A. Favano, M. Ferrari, M. Magarini, and L. Barletta, “A sphere packing bound for vector gaussian fading channels under peak amplitude constraints,” IEEE Trans. Inf. Theory, vol. 69, no. 1, pp. 238–250, 2023.

J. Han, J. Zhang, C. He, C. Lv, X. Hou, and Y. Ji, “Distributed finite-time safety consensus control of vehicle platoon with senor and actuator failures,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 162–175, 2023.

S. Shanmugam and S. Kalyani, “Unrolling SVT to obtain computationally efficient SVT for n-qubit quantum state tomography,” IEEE Trans. Signal Process., vol. 71, pp. 178–188, 2023.

N. Zhang, M. Tao, J.Wang, and F. Xu, “Fundamental limits of communication efficiency for model aggregation in distributed learning: A rate-distortion approach,” IEEE Trans. Commun., vol. 71, no. 1, pp. 173–186, 2023.

X. Niu and E. Wei, “Fedhybrid: A hybrid federated optimization method for heterogeneous clients,” IEEE Trans. Signal Process., vol. 71, pp. 150–163, 2023.

R. Yang, Z. Zhang, X. Zhang, C. Li, Y. Huang, and L. Yang, “Meta-learning for beam prediction in a dualband communication system,” IEEE Trans. Commun., vol. 71, no. 1, pp. 145–157, 2023.

L. F. Abanto-Leon, A. Asadi, A. Garcia-Saavedra, G. H. Sim, and M. Hollick, “Radiorchestra: Proactive management of millimeter-wave self-backhauled small cells via joint optimization of beamforming, user association, rate selection, and admission control,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 153–173, 2023.

K. A. S. Immink and J. H. Weber, “Minimally modified balanced codes,” IEEE Trans. Inf. Theory, vol. 69, no. 1, pp. 187–193, 2023.

J. Sun, J. Yang, G. Gui, and H. Sari, “In-motion alignment method of SINS under the geographic latitude uncertainty,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 125–135, 2023.

Z. Zhang, Z. Shi, and Y. Gu, “Ziv-zakai bound for doas estimation,” IEEE Trans. Signal Process., vol. 71, pp. 136– 149, 2023.

S. Guo and X. Zhao, “Multi-agent deep reinforcement learning based transmission latency minimization for delay-sensitive cognitive satellite-uav networks,” IEEE Trans. Commun., vol. 71, no. 1, pp. 131–144, 2023.

L. Hu, H. Li, P. Yi, J. Huang, M. Lin, and H. Wang, “Investigation on AEB key parameters for improving car to two-wheeler collision safety using in-depth traffic accident data,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 113–124, 2023.

Y. Liu, Z. Tan, A. W. H. Khong, and H. Liu, “An iterative implementation-based approach for joint source localization and association under multipath propagation environments,” IEEE Trans. Signal Process., vol. 71, pp. 121–135, 2023.

K. Ma, S. Du, H. Zou, W. Tian, Z. Wang, and S. Chen, “Deep learning assisted mmwave beam prediction for heterogeneous networks: A dual-band fusion approach,” IEEE Trans. Commun., vol. 71, no. 1, pp. 115–130, 2023.

B. Banerjee, R. C. Elliott, W. A. Krzymien, and H. Farmanbar, “Downlink channel estimation for FDD massive MIMO using conditional generative adversarial networks,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 122–137, 2023.

F. Hu, Y. Deng, and A. H. Aghvami, “Scalable multiagent reinforcement learning for dynamic coordinated multipoint clustering,” IEEE Trans. Commun., vol. 71, no. 1, pp. 101–114, 2023.

H. Hui and W. Chen, “Joint scheduling of proactive pushing and on-demand transmission over shared spectrum for profit maximization,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 107–121, 2023.

W. Yu, Y. Xi, X. Wei, and G. Ge, “Balanced set codes with small intersections,” IEEE Trans. Inf. Theory, vol. 69, no. 1, pp. 147–156, 2023.

P. Tichavský, O. Straka, and J. Duník, “Grid-based bayesian filters with functional decomposition of transient density,” IEEE Trans. Signal Process., vol. 71, pp. 92–104, 2023.

C. Zeng, J. Wang, C. Ding, M. Lin, and J. Wang, “MIMO unmanned surface vessels enabled maritime wireless network coexisting with satellite network: Beamforming and trajectory design,” IEEE Trans. Commun., vol. 71, no. 1, pp. 83–100, 2023.

S. Arya and Y. H. Chung, “Fault-tolerant cooperative signal detection for petahertz short-range communication with continuous waveform wideband detectors,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 88–106, 2023.

Y. Xu, C. Ji, R. Tao, and S. Hu, “Extended cyclic codes sandwiched between reed-muller codes,” IEEE Trans. Inf. Theory, vol. 69, no. 1, pp. 138–146, 2023.

X. Zhou, D. He, M. K. Khan, W. Wu, and K. R. Choo, “An efficient blockchain-based conditional privacypreserving authentication protocol for vanets,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 81–92, 2023.

D. Malak and M. Médard, “A distributed computationally aware quantizer design via hyper binning,” IEEE Trans. Signal Process., vol. 71, pp. 76–91, 2023.

Y. Xiong, S. Sun, L. Liu, Z. Zhang, and N. Wei, “Performance analysis and bit allocation of cell-free massive MIMO network with variable-resolution adcs,” IEEE Trans. Commun., vol. 71, no. 1, pp. 67–82, 2023.

D. Malak and M. Médard, “A distributed computationally aware quantizer design via hyper binning,” IEEE Trans. Signal Process., vol. 71, pp. 76–91, 2023.

Y. Xiong, S. Sun, L. Liu, Z. Zhang, and N. Wei, “Performance analysis and bit allocation of cell-free massive MIMO network with variable-resolution adcs,” IEEE Trans. Commun., vol. 71, no. 1, pp. 67–82, 2023.

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Published

14-11-2023

How to Cite

1.
Wei F, Huang J, Zhao J, Que H. Outage Probability Analysis of Multi-hop Relay Aided IoT Networks: Multi-hop Relay Aided IoT Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Nov. 14 [cited 2024 Dec. 4];11(4). Available from: https://publications.eai.eu/index.php/sis/article/view/3780