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

By Nguyen Cong Luong, Dinh Thai Hoang, Ping Wang, Dusist Niyato, Dong In Kim, Zhu Han. IEEE Communications Surveys and Tutorials, 2016

297 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Computer Science Applications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/3d70970af19f70064115962220d6cc7ecf3408a3

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Citations in the corpus

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, background): “Networks: Pricing and economic models have been widely applied to wireless networks [195], [196].”
A Survey on Big Data Market: Pricing, Trading and Protection (2018, background): “There are several different pricing strategies for evaluating cost according to the different scenarios [56].”
A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues (2017, background): “[27] surveyed a wide range of economic and pric-”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “More recently, ANNs have started to attract significant attention in the context of wireless communications and networking [2], [4], [5], [31] and [32], since the development of smart devices and mobile applications has significantly increased the level at which human users interact with the wireles”
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies (2017, background): “[9] survey the economic and pricing theory and their applications in data collection and communication for”
Securing Fog Computing for Internet of Things Applications: Challenges and Solutions (2018, unknown): “applications deployment with minimal cost [30].”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “The smartphone-centric networks of yesteryears are gradually morphing into an Internet of Things (IoT) system [1]–[3] that integrates a heterogeneous mix of wireless-enabled devices ranging from smartphones, to drones, connected vehicles, wearables, sensors, and virtual reality devices.”