3,670 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Incentivizing Private Data Sharing in Vehicular Networks: A Game-Theoretic Approach
In the context of evolving smart cities and autonomous transportation
systems, Vehicular Ad-hoc Networks (VANETs) and the Internet of Vehicles (IoV)
are growing in significance. Vehicles are becoming more than just a means of
transportation; they are collecting, processing, and transmitting massive
amounts of data to make driving safer and more convenient. However, this
advancement ushers in complex issues concerning the centralized structure of
traditional vehicular networks and the privacy and security concerns around
vehicular data. This paper offers a novel, game-theoretic network architecture
to address these challenges. Our approach decentralizes data collection through
distributed servers across the network, aggregating vehicular data into
spatio-temporal maps via secure multi-party computation (SMPC). This strategy
effectively reduces the chances of adversaries reconstructing a vehicle's
complete path, increasing privacy. We also introduce an economic model grounded
in game theory that incentivizes vehicle owners to participate in the network,
balancing the owners' privacy concerns with the monetary benefits of data
sharing. This model aims to maximize the data consumer's utility from the
gathered sensor data by determining the most suitable payment to participating
vehicles, the frequency in which these vehicles share their data, and the total
number of servers in the network. We explore the interdependencies among these
parameters and present our findings accordingly. To define meaningful utility
and loss functions for our study, we utilize a real dataset of vehicular
movement traces.Comment: To Appear in the Proceedings of The 2023 IEEE 98th Vehicular
Technology Conference (VTC2023-Fall), 6 pages, 5 figure
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