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

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 components 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.

Description

Keywords

Wireless sensor networks, crowdsensing network, M2M communication, IoT, pricing models, economic models

Citation

Copyright 2016 IEEE Communications Surveys & Tutorials. This is a pre-print version of a published paper that is available at: https://ieeexplore.ieee.org/abstract/document/7496795. Recommended citation: Luong, Nguyen Cong, Dinh Thai Hoang, Ping Wang, Dusit Niyato, Dong In Kim, and Zhu Han. "Data collection and wireless communication in Internet of Things (IoT) using economic analysis and pricing models: A survey." IEEE Communications Surveys & Tutorials 18, no. 4 (2016): 2546-2590. DOI: 10.1109/COMST.2016.2582841. This item has been deposited in accordance with publisher copyright and licensing terms and with the author's permission.