Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey (1608.03475v1)

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.

• The paper provides a comprehensive survey on applying economic theories to optimize data collection and wireless communication in IoT networks.
• It categorizes methods into economic models, game theory/auctions, and optimization techniques to address resource constraints and efficient network operations.
• The study highlights practical implications and future research directions for enhancing energy efficiency, security, and dynamic resource allocation in IoT systems.

Economic Analysis and Pricing Models in IoT: A Comprehensive Survey

The paper provides an extensive survey on the application of economic analysis and pricing models within the scope of the Internet of Things (IoT), focusing specifically on aspects of data collection and wireless communication. This research integrates economic and pricing theories in IoT to address complex problems related to Wireless Sensor Networks (WSNs), crowdsensing networks, and Machine-to-Machine (M2M) communication.

Overview

The paper begins by delineating the fundamental components of IoT systems, emphasizing the role of WSNs as critical to IoT infrastructure. These networks are tasked with environmental data collection and data transmission to sink nodes. Given the resource constraints of WSNs, which necessitate efficient energy and bandwidth use, the application of adaptive and intelligent economic models has become imperative. This reflects in tasks like data aggregation, topology formation, task allocation, and security maintenance within IoT architectures.

Economic and Pricing Models

The authors categorize economic and pricing approaches under economic concepts, game theory and auctions, and optimization-based pricing. Each category addresses specific challenges in IoT:

1. Economic Concepts: Models such as cost-based pricing, consumer perceived value pricing, and the use of supply-demand dynamics are discussed. These approaches typically involve setting prices based on production costs, perceived consumer value, or through balancing supply and demand principles.
2. Game Theory and Auctions: Non-cooperative and cooperative game theories, including Nash equilibrium and the Stackelberg game, provide frameworks for distributed decision-making among IoT entities. Auctions, both sealed-bid and combinatorial, allow for dynamic resource allocation, accounting for the competitive nature of IoT markets.
3. Optimization and Utility Maximization: This involves optimizing resource allocation through utility functions, often within a Network Utility Maximization (NUM) framework, to ensure equitable and efficient distribution of resources among IoT components.

Applications

The applications of these models are surveyed across various IoT scenarios:

• Data Aggregation and Routing: Reverse auctions and value-based pricing optimize the selection of sensors for data collection, balancing cost and data quality, while minimizing energy consumption.
• Resource and Task Allocation: The implementations include combinatorial and double auctions for resource allocation to sensors, ensuring optimal energy use and prolonging network lifetime.
• Security and Privacy: Game-theoretical models address security threats such as DoS attacks by isolating malicious nodes and protecting user privacy through incentivized participation.
• M2M Communication: Pricing models facilitate efficient communication between machines, often involving dynamic pricing strategies to manage network congestion and resource distribution effectively.

Practical Implications and Future Directions

The use of economic models in IoT supports scalable, efficient, and incentive-compatible solutions, crucial in environments characterized by high device heterogeneity and resource constraints. Future research can explore integrating auctions with real-time pricing, enhancing mobility models, and addressing bid confidentiality and security in pricing strategies. Moreover, the potential for expanded integration with contract theory and sophisticated economic models needs exploration to further refine resource allocation and maximize system utility.

In conclusion, this survey highlights the pivotal role of economic and pricing models in optimizing the operations and interactions within IoT networks, guiding future advancements in adapting these economic principles to evolving IoT technologies.