Channel-Adaptive Packetization Policy for Minimal Latency and Maximal Energy Efficiency

TL;DR

This paper introduces a channel-adaptive packetization policy that minimizes delay and maximizes energy efficiency in wireless sensor networks by optimizing packet length based on channel conditions and error rates.

Contribution

It presents a novel packetization algorithm that adapts to channel conditions to reduce delay and improve energy efficiency, supported by detailed delay analysis and convex optimization.

Findings

Optimal packetization interval minimizes end-to-end delay.

Algorithm adapts to slowly varying channel conditions.

Method enhances energy efficiency under delay constraints.

Abstract

This article considers the problem of delay optimal bundling of the input symbols into transmit packets in the entry point of a wireless sensor network such that the link delay is minimized under an arbitrary arrival rate and a given channel error rate. The proposed policy exploits the variable packet length feature of contemporary communications protocols in order to minimize the link delay via packet length regularization. This is performed through concrete characterization of the end-to-end link delay for zero error tolerance system with First Come First Serve (FCFS)queuing discipline and Automatic Repeat Request (ARQ) retransmission mechanism. The derivations are provided for an uncoded system as well as a coded system with a given bit error rate. The proposed packetization policy provides an optimal packetization interval that minimizes the end-to-end delay for a given channel with certain bit error probability. This algorithm can also be used for near-optimal bundling of input symbols for dynamic channel conditions provided that the channel condition varies slowly over time with respect to symbol arrival rate. This algorithm complements the current network-based delay-optimal routing and scheduling algorithms in order to further reduce the end-to-end delivery time. Moreover, the proposed method is employed to solve the problem of energy efficiency maximization under an average delay constraint by recasting it as a convex optimization problem.