Joint Latency-Energy Minimization for Fog-Assisted Wireless IoT Networks

TL;DR

This paper proposes a joint resource allocation method for fog-assisted IoT networks that minimizes latency and energy consumption simultaneously, considering practical device power models and using game theory for device cooperation.

Contribution

It introduces a novel joint optimization framework for latency and energy in fog-assisted IoT, incorporating realistic power models and a cooperative game-theoretic solution approach.

Findings

The proposed algorithm effectively balances latency and energy consumption.

Numerical results validate the efficiency of the joint optimization approach.

The method outperforms fixed-power models in practical scenarios.

Abstract

This work aims to present a joint resource allocation method for a fog-assisted network wherein IoT wireless devices simultaneously offload their tasks to a serving fog node. The main contribution is to formulate joint minimization of service latency and energy consumption objectives subject to both radio and computing constraints. Moreover, unlike previous works that set a fixed value to the circuit power dissipated to operate a wireless device, practical models are considered. To derive the Pareto boundary between two conflicting objectives we consider, Tchebyshev theorem is used for each wireless device. The competition among devices is modeled using the cooperative Nash bargaining solution and its unique cooperative Nash equilibrium (NE) is computed based on block coordinate descent algorithm. Numerical results obtained using realistic models are presented to corroborate the effectiveness of the proposed algorithm.