A market-based optimization approach to sensor and resource management

Schrage, Dan; Farnham, Christopher; Gonsalves, Paul

doi:10.1117/12.665153

Cited by 16 publications

(3 citation statements)

References 6 publications

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“…1) First-price sealed-bid auction: The authors in [183] designed a task allocation scheme by employing the first-price sealed-bid auction, called the Rust-In-Time (JIT) market. The task allocation problem is modeled as a multi-agent negotiation including: sensor agents, i.e., sellers, which perform tasks, external systems known as buyers who may require data from the sensor network, a market entity, i.e., an auctioneer, to assign tasks to sensors, and resource agents to provide resources for sensors.…”

Section: B Task Allocationmentioning

confidence: 99%

“…The seller will select a winning buyer if the buyer's bidding price is higher than a predefined threshold. Similar to [183], to get the highest bidding price, the seller waits for a deadline which is set according to the application requirement to decide the winner. Since the price is proportional to the remaining energy, the proposed strategy maintains the energy balance among the sensors in the network.…”

Section: B Task Allocationmentioning

confidence: 99%

“…This is because the mobile sensors do not know the largest hole beforehand. This problem can be Competitive equilibrium [184] First-price sealed-bid auction Sensor Neighboring nodes Task Same as [183] but the seller accepts a task from the first arrival bid Energy balance Competitive equilibrium [185] First-price sealed-bid auction Aggregator Phone users Sensing task…”

Section: A Area Coveragementioning

confidence: 99%

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

Luong

Hoang

Wang

et al. 2016

IEEE Commun. Surv. Tutorials

292

124

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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.

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Section: B Task Allocationmentioning

confidence: 99%

Section: B Task Allocationmentioning

confidence: 99%

Section: A Area Coveragementioning

confidence: 99%

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