70 research outputs found
Virtual Track: Applications and Challenges of the RFID System on Roads
The RFID System on Roads (RSR), which includes RFID tags deployed on roads and RFID readers installed on vehicles, is an essential platform for future transportation systems. It can provide unique features that are missing from the current systems, including lane level position, road traffic control information, vehicle distance estimation, real time driving behavior analysis, and so on. Based on these features, several novel vehicular applications can be implemented, which can significantly improve the transportation safety and efficiency. Specifically, the proposed applications on RSR include Assisted Navigation Systems, Electrical Traffic Control, Unmanned Patrol Systems, Vehicle Distance Estimation, Parking Assistant System, Route Tracing and Access Control, Unmanned Ground Vehicles. We also investigate the corresponding engineering/system and research challenges for implementing RSR and its applications in this article
A game theoretic analysis on block withholding attacks using the zero-determinant strategy
In Bitcoin's incentive system that supports open mining pools, block withholding attacks incur huge security threats. In this paper, we investigate the mutual attacks among pools as this determines the macroscopic utility of the whole distributed system. Existing studies on pools' interactive attacks usually employ the conventional game theory, where the strategies of the players are considered pure and equal, neglecting the existence of powerful strategies and the corresponding favorable game results. In this study, we take advantage of the Zero-Determinant (ZD) strategy to analyze the block withholding attack between any two pools, where the ZD adopter has the unilateral control on the expected payoffs of its opponent and itself. In this case, we are faced with the following questions: who can adopt the ZD strategy? individually or simultaneously? what can the ZD player achieve? In order to answer these questions, we derive the conditions under which two pools can individually or simultaneously employ the ZD strategy and demonstrate the effectiveness. To the best of our knowledge, we are the first to use the ZD strategy to analyze the block withholding attack among pools
Active Defense Analysis of Blockchain Forking through the Spatial-Temporal Lens
Forking breaches the security and performance of blockchain as it is
symptomatic of distributed consensus, spurring wide interest in analyzing and
resolving it. The state-of-the-art works can be categorized into two kinds:
experiment-based and model-based. However, the former falls short in
exclusiveness since the derived observations are scenario-specific. Hence, it
is problematic to abstractly reveal the crystal-clear forking laws. Besides,
the models established in the latter are spatiality-free, which totally
overlook the fact that forking is essentially an undesirable result under a
given topology. Moreover, few of the ongoing studies have yielded to the active
defense mechanisms but only recognized forking passively, which impedes forking
prevention and cannot deter it at the source. In this paper, we fill the gap by
carrying out the active defense analysis of blockchain forking from the
spatial-temporal dimension. Our work is featured by the following two traits:
1) dual dimensions. We consider the spatiality of blockchain overlay network
besides temporal characteristics, based on which, a spatial-temporal model for
information propagation in blockchain is proposed; 2) active defense. We hint
that shrinking the long-range link factor, which indicates the remote
connection ability of a link, can cut down forking completely fundamentally. To
the best of our knowledge, we are the first to inspect forking from the
spatial-temporal perspective, so as to present countermeasures proactively.
Solid theoretical derivations and extensive simulations are conducted to
justify the validity and effectiveness of our analysis.Comment: 10 pages,10 figure
Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity
Spatial crowdsourcing (SC) engages large worker pools for location-based
tasks, attracting growing research interest. However, prior SC task allocation
approaches exhibit limitations in computational efficiency, balanced matching,
and participation incentives. To address these challenges, we propose a
graph-based allocation framework optimized for massive heterogeneous spatial
data. The framework first clusters similar tasks and workers separately to
reduce allocation scale. Next, it constructs novel non-crossing graph
structures to model balanced adjacencies between unevenly distributed tasks and
workers. Based on the graphs, a bidirectional worker-task matching scheme is
designed to produce allocations optimized for mutual interests. Extensive
experiments on real-world datasets analyze the performance under various
parameter settings
Privacy-aware Data Trading
The growing threat of personal data breach in data trading pinpoints an
urgent need to develop countermeasures for preserving individual privacy. The
state-of-the-art work either endows the data collector with the responsibility
of data privacy or reports only a privacy-preserving version of the data. The
basic assumption of the former approach that the data collector is trustworthy
does not always hold true in reality, whereas the latter approach reduces the
value of data. In this paper, we investigate the privacy leakage issue from the
root source. Specifically, we take a fresh look to reverse the inferior
position of the data provider by making her dominate the game with the
collector to solve the dilemma in data trading. To that aim, we propose the
noisy-sequentially zero-determinant (NSZD) strategies by tailoring the
classical zero-determinant strategies, originally designed for the
simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies
can empower the data provider to unilaterally set the expected payoff of the
data collector or enforce a positive relationship between her and the data
collector's expected payoffs. Both strategies can stimulate a rational data
collector to behave honestly, boosting a healthy data trading market. Numerical
simulations are used to examine the impacts of key parameters and the feasible
region where the data provider can be an NSZD player. Finally, we prove that
the data collector cannot employ NSZD to further dominate the data market for
deteriorating privacy leakage.Comment: 10 pages, 11 figure
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