46 research outputs found
A Blockchain-Based Reward Mechanism for Mobile Crowdsensing
Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator's utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS
Modeling and Analysis of Data Trading on Blockchain-based Market in IoT Networks
Mobile devices with embedded sensors for data collection and environmental
sensing create a basis for a cost-effective approach for data trading. For
example, these data can be related to pollution and gas emissions, which can be
used to check the compliance with national and international regulations. The
current approach for IoT data trading relies on a centralized third-party
entity to negotiate between data consumers and data providers, which is
inefficient and insecure on a large scale. In comparison, a decentralized
approach based on distributed ledger technologies (DLT) enables data trading
while ensuring trust, security, and privacy. However, due to the lack of
understanding of the communication efficiency between sellers and buyers, there
is still a significant gap in benchmarking the data trading protocols in IoT
environments. Motivated by this knowledge gap, we introduce a model for
DLT-based IoT data trading over the Narrowband Internet of Things (NB-IoT)
system, intended to support massive environmental sensing. We characterize the
communication efficiency of three basic DLT-based IoT data trading protocols
via NB-IoT connectivity in terms of latency and energy consumption. The model
and analyses of these protocols provide a benchmark for IoT data trading
applications.Comment: 10 pages, 8 figures, Accepted at IEEE Internet of Things Journa
Blockchain-empowered decentralized storage in air-to-ground industrial networks
Blockchain has created a revolution in digital networking by using distributed storage, cryptographic algorithms, and smart contracts. Many areas are benefiting from this technology, including data integrity and security, as well as authentication and authorization. Internet of Things (IoTs) networks often suffers from such security issues, which is slowing down wide-scale adoption. In this paper, we describe the employing of blockchain technology to construct a decentralized platform for storing and trading information in the air-to-ground IoT heterogeneous network. To allow both air and ground sensors to participate in the decentralized network, we design a mutual-benefit consensus process to create uneven equilibrium distributions of resources among the participants. We use a Cournot model to optimize the active density factor set in the heterogeneous air network and then employ a Nash equilibrium to balance the number of ground sensors, which is influenced by the achievable average downlink rate between the air sensors and the ground supporters. Finally, we provide numerical results to demonstrate the beneficial properties of the proposed consensus process for air-to-ground networks and show the maximum active sensor's density utilization of air networks to achieve a high quality of service
Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges
In recent years, blockchain has gained widespread attention as an emerging
technology for decentralization, transparency, and immutability in advancing
online activities over public networks. As an essential market process,
auctions have been well studied and applied in many business fields due to
their efficiency and contributions to fair trade. Complementary features
between blockchain and auction models trigger a great potential for research
and innovation. On the one hand, the decentralized nature of blockchain can
provide a trustworthy, secure, and cost-effective mechanism to manage the
auction process; on the other hand, auction models can be utilized to design
incentive and consensus protocols in blockchain architectures. These
opportunities have attracted enormous research and innovation activities in
both academia and industry; however, there is a lack of an in-depth review of
existing solutions and achievements. In this paper, we conduct a comprehensive
state-of-the-art survey of these two research topics. We review the existing
solutions for integrating blockchain and auction models, with some
application-oriented taxonomies generated. Additionally, we highlight some open
research challenges and future directions towards integrated blockchain-auction
models
Blockchain-Empowered Decentralized Storage in Air-to-Ground Industrial Networks
Blockchain has raised an evolution in the cyber
network by using distributed storage, cryptography algorithms
and smart contract. Many areas are benefiting from this technology, such as data integrity, security as well as authentication
and authorization. Internet of Things network is often suffering
from such security issues, obstructing its development in scales.
In this paper, we employ blockchain technology to construct a
decentralized platform for storing and trading information in the
air-to-ground IoT heterogeneous network. To make both air and
ground sensors trading in the decentralized network, we design
a mutual benefit consensus process to the uneven equilibrium
distribution of resources among the participators. We use the
Cournot model to optimize the active density factor set in the
heterogeneous air network and then employ Nash equilibrium
to balance the number of ground supporters, which is subject
to the achievable average downlink rate between the air sensors
and the ground sensors. Finally, numerical results are provided to
demonstrate the beneficial properties of the proposed consensus
process for air-to-ground network, and show the maximum active
sensors density utilization of air network to achieve a higher
quality of service
Game Theory Based Privacy Protection for Context-Aware Services
In the era of context-aware services, users are enjoying remarkable services based on data collected from a multitude of users. To receive services, they are at risk of leaking private information from adversaries possibly eavesdropping on the data and/or the un--trusted service platform selling off its data. Malicious adversaries may use leaked information to violate users\u27 privacy in unpredictable ways. To protect users\u27 privacy, many algorithms are proposed to protect users\u27 sensitive information by adding noise, thus causing context-aware service quality loss. Game theory has been utilized as a powerful tool to balance the tradeoff between privacy protection level and service quality. However, most of the existing schemes fail to depict the mutual relationship between any two parties involved: user, platform, and adversary. There is also an oversight to formulate the interaction occurring between multiple users, as well as the interaction between any two attributes. To solve these issues, this dissertation firstly proposes a three-party game framework to formulate the mutual interaction between three parties and study the optimal privacy protection level for context-aware services, thus optimize the service quality. Next, this dissertation extends the framework to a multi-user scenario and proposes a two-layer three-party game framework. This makes the proposed framework more realistic by further exploring the interaction, not only between different parties, but also between users. Finally, we focus on analyzing the impact of long-term time-serial data and the active actions of the platform and adversary. To achieve this objective, we design a three-party Stackelberg game model to help the user to decide whether to update information and the granularity of updated information