36 research outputs found
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
Single module identifiability in linear dynamic networks with partial excitation and measurement
Identifiability of a single module in a network of transfer functions is determined by whether a particular transfer function in the network can be uniquely distinguished within a network model set, on the basis of data. Whereas previous research has focused on the situations that all network signals are either excited or measured, we develop generalized analysis results for the situation of partial measurement and partial excitation. As identifiability conditions typically require a sufficient number of external excitation signals, this article introduces a novel network model structure such that excitation from unmeasured noise signals is included, which leads to less conservative identifiability conditions than relying on measured excitation signals only. More importantly, graphical conditions are developed to verify global and generic identifiability of a single module based on the topology of the dynamic network. Depending on whether the input or the output of the module can be measured, we present four identifiability conditions which cover all possible situations in single module identification. These conditions further lead to synthesis approaches for allocating excitation signals and selecting measured signals, to warrant single module identifiability. In addition, if the identifiability conditions are satisfied for a sufficient number of external excitation signals only, indirect identification methods are developed to provide a consistent estimate of the module. All the obtained results are also extended to identifiability of multiple modules in the network.</p
Exploiting unmeasured disturbance signals in identifiability of linear dynamic networks with partial measurement and partial excitation
Identifiability conditions for networks of transfer functions require a sucientnumber of external excitation signals, which are typically measured reference signals. In this abstract, we introduce an equivalent network model structure to address the contribution of unmeasured noises to identifiability analysis in the setting with partial excitation and partial measurement. With this model structure, unmeasured disturbance signals can be exploited as excitation sources, which leads to less conservative identifiability conditions
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
Proof of User Similarity: the Spatial Measurer of Blockchain
Although proof of work (PoW) consensus dominates the current blockchain-based
systems mostly, it has always been criticized for the uneconomic brute-force
calculation. As alternatives, energy-conservation and energy-recycling
mechanisms heaved in sight. In this paper, we propose proof of user similarity
(PoUS), a distinct energy-recycling consensus mechanism, harnessing the
valuable computing power to calculate the similarities of users, and enact the
calculation results into the packing rule. However, the expensive calculation
required in PoUS challenges miners in participating, and may induce plagiarism
and lying risks. To resolve these issues, PoUS embraces the best-effort schema
by allowing miners to compute partially. Besides, a voting mechanism based on
the two-parties computation and Bayesian truth serum is proposed to guarantee
privacy-preserved voting and truthful reports. Noticeably, PoUS distinguishes
itself in recycling the computing power back to blockchain since it turns the
resource wastage to facilitate refined cohort analysis of users, serving as the
spatial measurer and enabling a searchable blockchain. We build a prototype of
PoUS and compare its performance with PoW. The results show that PoUS
outperforms PoW in achieving an average TPS improvement of 24.01% and an
average confirmation latency reduction of 43.64%. Besides, PoUS functions well
in mirroring the spatial information of users, with negligible computation time
and communication cost.Comment: 12 pages,10 figure
Allocation of Excitation Signals for Generic Identifiability of Linear Dynamic Networks
A recent research direction in data-driven modeling is the identification of
dynamic networks, in which measured vertex signals are interconnected by
dynamic edges represented by causal linear transfer functions. The major
question addressed in this paper is where to allocate external excitation
signals such that a network model set becomes generically identifiable when
measuring all vertex signals. To tackle this synthesis problem, a novel graph
structure, referred to as \textit{directed pseudotree}, is introduced, and the
generic identifiability of a network model set can be featured by a set of
disjoint directed pseudotrees that cover all the parameterized edges of an
\textit{extended graph}, which includes the correlation structure of the
process noises. Thereby, an algorithmic procedure is devised, aiming to
decompose the extended graph into a minimal number of disjoint pseudotrees,
whose roots then provide the appropriate locations for excitation signals.
Furthermore, the proposed approach can be adapted using the notion of
\textit{anti-pseudotrees} to solve a dual problem, that is to select a minimal
number of measurement signals for generic identifiability of the overall
network, under the assumption that all the vertices are excited
Approximate Dynamic Programming for Constrained Piecewise Affine Systems with Stability and Safety Guarantees
Infinite-horizon optimal control of constrained piecewise affine (PWA)
systems has been approximately addressed by hybrid model predictive control
(MPC), which, however, has computational limitations, both in offline design
and online implementation. In this paper, we consider an alternative approach
based on approximate dynamic programming (ADP), an important class of methods
in reinforcement learning. We accommodate non-convex union-of-polyhedra state
constraints and linear input constraints into ADP by designing PWA penalty
functions. PWA function approximation is used, which allows for a mixed-integer
encoding to implement ADP. The main advantage of the proposed ADP method is its
online computational efficiency. Particularly, we propose two control policies,
which lead to solving a smaller-scale mixed-integer linear program than
conventional hybrid MPC, or a single convex quadratic program, depending on
whether the policy is implicitly determined online or explicitly computed
offline. We characterize the stability and safety properties of the closed-loop
systems, as well as the sub-optimality of the proposed policies, by quantifying
the approximation errors of value functions and policies. We also develop an
offline mixed-integer linear programming-based method to certify the
reliability of the proposed method. Simulation results on an inverted pendulum
with elastic walls and on an adaptive cruise control problem validate the
control performance in terms of constraint satisfaction and CPU time
A Necessary Condition for Network Identifiability With Partial Excitation and Measurement
This article considers dynamic networks where vertices and edges represent manifest signals and causal dependencies among the signals, respectively. We address the problem of how to determine if the dynamics of a network can be identified when only partial vertices are measured and excited. A necessary condition for network identifiability is presented, where the analysis is performed based on identifying the dependency of a set of rational functions from excited vertices to measured ones. This condition is further characterized by using an edge-removal procedure on the associated bipartite graph. Moreover, on the basis of necessity analysis, we provide a necessary and sufficient condition for identifiability in circular networks.</p