Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey

TL;DR

This survey reviews how economic analysis and pricing models are applied to optimize data collection, communication, and incentives in IoT, focusing on wireless sensor networks, crowdsensing, and M2M communication.

Contribution

It provides a comprehensive overview of economic and pricing strategies used in IoT, highlighting recent applications and identifying open research challenges.

Findings

Economic models improve WSN adaptability and robustness.

Pricing strategies incentivize user participation in crowdsensing.

Economic approaches are applied to M2M communication for efficiency.

Abstract

This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the main component of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in Machine-to-Machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.