An Experimental End-to-End Delay Study of a Sub-1GHz Wireless Sensor Network with LTE Backhaul

Arjona, Ricardo; Fumagalli, Andrea; Lee, Craig; Vijayasankar, Kumaran

doi:10.1109/glocom.2018.8647618

Cited by 2 publications

(1 citation statement)

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“…As mentioned by Ref. [7], most real-environment experimental results show that delay in IoT devices varies depending on various circumstances. So, it is essential to evaluate the AMTL for cognitive WSN communication.…”

Section: Introductionmentioning

confidence: 96%

Cognition-Based Delay Analysis to Determine the Average Minimum Time Limit for Wireless Sensor Communications

Besher¹,

Hipólito²,

Sanchez-Lopez³

et al. 2020

IEICE Trans. Inf. & Syst.

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End-to-end delay, aiming to realize how much time it will take for a traffic load generated by a Mobile Node (MN) to reach Sink Node (SN), is a principal objective of most new trends in a Wireless Sensor Network (WSN). It has a direct link towards understanding the minimum time delay expected where the packet sent by MN can take to be received by SN. Most importantly, knowing the average minimum transmission time limit is a crucial piece of information in determining the future output of the network and the kind of technologies implemented. In this paper, we take network load and transmission delay issues into account in estimating the Average Minimum Time Limit (AMTL) needed for a health operating cognitive WSN. To further estimate the AMTL based on network load, an end-to-end delay analysis mechanism is presented and considers the total delay (service, queue, ACK, and MAC). This work is proposed to answer the AMTL needed before implementing any cognitive based WSN algorithms. Various time intervals and cogitative channel usage with different application payload are used for the result analysis. Through extensive simulations, our mechanism is able to identify the average time intervals needed depending on the load and MN broadcast interval in any cognitive WSN.

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Section: Introductionmentioning

confidence: 96%