Joint Dynamic Pricing and Radio Resource Allocation Framework for IoT Services

TL;DR

This paper introduces a joint pricing and resource allocation framework for IoT networks, optimizing revenue and fairness among stakeholders through multi-objective optimization and convex approximation methods.

Contribution

It presents a novel joint optimization model for pricing and resource allocation in IoT, using scalarization and SCA to find Pareto optimal solutions.

Findings

Increased total revenue with the proposed framework.

Achieved near-complete fairness among IoT network stakeholders.

Outperformed conventional independent revenue maximization approaches.

Abstract

In this paper, we study the problem of resource allocation as well as pricing in the context of Internet of things (IoT) networks. We provide a novel pricing model for IoT services where all the parties involved in the communication scenario as well as their revenue and cost are determined. We formulate the resource allocation in the considered model as a multi-objective optimization problem where in addition to the resource allocation variables, the price values are also optimization variables. To solve the proposed multi-objective optimization problem, we use the scalarization method which gives different Pareto optimal solutions. We solve the resulting problems using the alternating approach based on the successive convex approximation (SCA) method which converges to a local solution with few iterations. We also consider a conventional approach where each entity tries to maximize its own revenue independently. Simulation results indicate that by applying the proposed joint framework, we can increase the total revenue compared to the conventional case while providing an almost complete fairness among the players. This is while the conventional approach fails to provide such a fairness.