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

Abstract

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.

Spectrum Based Power Management for Congested IoT Networks

“…So, to extend the battery life of IoT devices it is important to decrease the packets delivery time delay. Lower latency improves power wastage caused by retransmission and wait time [4,11]. Furthermore, solving problems like retransmission and delay without having good spectrum management in congested IoT environment is almost impossible.…”

“…In order to have an estimation of the device lifetime for a given battery capacity, network configuration, and transmission frequency, we need to understand the power consumption of an IoT device during (1) initial turn-on, (2) connected state, (3) re-transmission and (4) sleep/listen state. To the best of our knowledge, literature research is more focused on optimizing energy efficiency through different scheduling sachems, which is not on solving the cause [4,5,[10][11][12]23,24]. Instead, this work is based on empirical evaluation of real deployments, which shed light on the relation between power consumption and spectrum management in congested IoT environment.…”

“…This adjustment allowed data to be processed rapidly and faster transmission. It is mentioned in reference [11] that, among main factors of power consumption in IoT communication, retransmission and queue delay are the highest, which is caused because of busy channel. So, solving the busy channel will improve the use of power and overall network performance.…”