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

TLDR

Wireless sensor networks, the core of IoT, gather environmental data and forward it to sink nodes, requiring adaptive, robust designs to address challenges such as topology, routing, resource optimization, coverage, task allocation, and security, with sensors making optimal decisions based on current capabilities. The paper surveys economic analysis and pricing models applied to data collection and wireless communication in IoT, examining their use in adaptive algorithms, crowdsensing incentives, and machine‑to‑machine communication, and identifies open research directions. The authors conduct a comprehensive literature review, compiling and evaluating various pricing strategies and intelligent decision‑making methods to develop adaptive protocols for wireless sensor networks.

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.