We extend and verify diagnosability for a class of set intersection refinement strategies, which can be used for distributed state estimation and fault diagnosis in nondeterministic finite automata that are observed a...
详细信息
We extend and verify diagnosability for a class of set intersection refinement strategies, which can be used for distributed state estimation and fault diagnosis in nondeterministic finite automata that are observed at multiple observation sites. These strategies allow observation sites to (periodically) communicate their diagnostic information (i.e., possible states along with corresponding status, such as normal operation and/or fault type) to other observation sites, which subsequently fuse all available diagnostic information (via set intersection operations), and continue operation based on the refined diagnostic information. We verify diagnosability using the proposed distributed protocol, with polynomial complexity, via compositions of (extended versions of) local verifiers, which are capable of capturing the refinement of information under the set intersection operations, as well as the influence of the refinement process on immediate or future diagnosis decisions.
This paper focuses on distributed estimation using networked sensor agents with local measurements and local communication. A distributed Kalman-Bucy filter implementation is proposed and analyzed for the general case...
详细信息
This paper focuses on distributed estimation using networked sensor agents with local measurements and local communication. A distributed Kalman-Bucy filter implementation is proposed and analyzed for the general case of continuous-time linear time-varying systems as compared to linear time-invariant systems. The sensor agents employ a distributed average tracking algorithm and its extension that accounts for bounded noise to estimate the averages of certain time-varying signals by communicating with their local neighbors in the network. Such estimates are then used to recover certain information in the implementation of the distributed Kalman-Bucy filter. It is shown that using the proposed distributed filter, in the absence of measurement noise, the distributed local estimates approach the centralized filter's estimate asymptotically. In the presence of bounded measurement noise, the distributed local estimates asymptotically approach the centralized filter's estimate within some bound. Simulation results illustrate the good performance of the distributed filter.
The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. Traditionally, OPF is solved in a centralized manner. With increasing...
详细信息
The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. Traditionally, OPF is solved in a centralized manner. With increasing penetration of renewable energy in distribution system, we need faster and distributed solutions for real-time feedback control. This is difficult due to the nonlinearity of the power flow equations. In this paper, we propose a solution for balanced radial networks. It exploits recent results that suggest solving for a globally optimal solution of OPF over a radial network through the secondorder cone program relaxation. Our distributed algorithm is based on alternating direction method of multiplier (ADMM), but unlike standard ADMM-based distributed OPF algorithms that require solving optimization subproblems using iterative method, our decomposition allows us to derive closed form solutions for these subproblems, greatly speeding up each ADMM iteration. We illustrate the scalability of the proposed algorithm by simulating it on a real-world 2065-bus distribution network.
We propose distributed solutions to the problem of Robust Subspace Recovery (RSR). Our setting assumes a huge dataset in an ad hoc network without a central processor, where each node has access only to one chunk of t...
详细信息
We propose distributed solutions to the problem of Robust Subspace Recovery (RSR). Our setting assumes a huge dataset in an ad hoc network without a central processor, where each node has access only to one chunk of the dataset. Furthermore, part of the whole dataset lies around a low-dimensional subspace and the other part is composed of outliers that lie away from that subspace. The goal is to recover the underlying subspace for the whole dataset, without transferring the data itself between the nodes. We first apply the Consensus Based Gradient method to the Geometric Median Subspace algorithm for RSR. For this purpose, we propose an iterative solution for the local dual minimization problem and establish its r-linear convergence. We then explain how to distributedly implement the Reaper and Fast Median Subspace algorithms for RSR. The proposed algorithms display competitive performance on both synthetic and real data.
We describe the Kilobot Soft Robot, a novel soft-bodied robot that is modular and reconfigurable. The Kilobot Soft Robot is realized by inter-connecting a group of miniature mobile modules, based on the commercially a...
详细信息
ISBN:
(纸本)9781728128764
We describe the Kilobot Soft Robot, a novel soft-bodied robot that is modular and reconfigurable. The Kilobot Soft Robot is realized by inter-connecting a group of miniature mobile modules, based on the commercially available Kilobot, through an elastic material. It moves and deforms fully autonomously. Each module executes a distributed algorithm that exploits only information that is locally obtained using omnidirectional, infrared based signaling. A series of experiments were conducted to validate the algorithm, investigating the ability of the robot to follow a predefined trajectory, to squeeze and extend its shape and to control its motion independently of the number of modules.
Emerging networked systems become increasingly flexible and "reconfigurable". This introduces an opportunity to adjust networked systems in a demand-aware manner, leveraging spatial and temporal locality in ...
详细信息
ISBN:
(纸本)9783030249212;9783030249229
Emerging networked systems become increasingly flexible and "reconfigurable". This introduces an opportunity to adjust networked systems in a demand-aware manner, leveraging spatial and temporal locality in the workload for online optimizations. However, it also introduces a tradeoff: while more frequent adjustments can improve performance, they also entail higher reconfiguration costs. This paper initiates the formal study of linear networks which self-adjust to the demand in an online manner, striking a balance between the benefits and costs of reconfigurations. We show that the underlying algorithmic problem can be seen as a distributed generalization of the classic dynamic list update problem known from self-adjusting datastructures: in a network, requests can occur between node pairs. This distributed version turns out to be significantly harder than the classical problem in generalizes. Our main results are a O(log n) lower bound on the competitive ratio, and a (distributed) online algorithm that is O(log n)-competitive if the communication requests are issued according to a linear order.
A shared communication channel (also known as a multiple access channel) is among the most popular and widely studied models of communication and distributed computing. In this model, stations are able to communicate ...
详细信息
ISBN:
(纸本)9781728125190
A shared communication channel (also known as a multiple access channel) is among the most popular and widely studied models of communication and distributed computing. In this model, stations are able to communicate by transmitting and listening to a shared channel. A fundamental problem, called contention resolution, is to allow any station to successfully deliver its message by resolving the conflicts that arise when several stations transmit simultaneously on the channel. Despite a long history, many fundamental questions remain open in the realistic scenario when up to k stations out of N join the channel at different times. In this work we explore the impact of asynchrony, knowledge (or linear estimate) of contenders, and acknowledgments, on latency and channel utilization of non-adaptive deterministic algorithms. We show that if the number of contenders k (or a linear upper bound on it) is known and the stations switchoff after acknowledgment of their successful transmissions, the channel admits efficient solutions. In the same settings, we show that the ignorance of contention k makes the channel nearly quadratically less efficient, even if the stations could switch-off after acknowledgments. We present an algorithm which nearly matches this complexity (for unknown k) which is achieved even if acknowledgments are not provided. We show how the above algorithm could be further improved if stations could switch off upon acknowledgment. Surprisingly, our results imply an exponential impact of knowledge of contention on deterministic utilization of asynchronous channel by deterministic algorithms - it is known that for synchronized channel this feature does not influence asymptotically the channel utilization. The second implication concerns the impact of acknowledgments - they exponentially improve deterministic channel utilization if (some estimate of) k is known, unlike in the case of randomized algorithms where the improvement is only polynomial, while they a
The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, band...
详细信息
The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large-scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the alternating direction method of multipliers (ADMM) that tackles the multi-path fair resource allocation problem in a distributed SDN control architecture. Our ADMM-based algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal-dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances at scale.
Efficient data aggregation is crucial for mobile wireless sensor networks, as their resources are significantly constrained. Over recent years, the average consensus algorithm has found a wide application in this tech...
详细信息
Efficient data aggregation is crucial for mobile wireless sensor networks, as their resources are significantly constrained. Over recent years, the average consensus algorithm has found a wide application in this technology. In this paper, we present a weight matrix simplifying the average consensus algorithm over mobile wireless sensor networks, thereby prolonging the network lifetime as well as ensuring the proper operation of the algorithm. Our contribution results from the theorem stating how the Laplacian spectrum of an undirected simple finite graph changes in the case of adding an arbitrary edge into this graph. We identify that the mixing parameter of Best Constant weights of a complete finite graph with an arbitrary order ensures the convergence in time-varying topologies without any reconfiguration of the edge weights. The presented theorems and lemmas are verified over evolving graphs with various parameters, whereby it is demonstrated that our approach ensures the convergence of the average consensus algorithm over mobile wireless sensor networks in spite of no edge reconfiguration.
In the field of interactive coding, two or more parties wish to carry out a distributed computation over a communication network that may be noisy. The ultimate goal is to develop efficient coding schemes that can tol...
详细信息
ISBN:
(纸本)9781450362177
In the field of interactive coding, two or more parties wish to carry out a distributed computation over a communication network that may be noisy. The ultimate goal is to develop efficient coding schemes that can tolerate a high level of noise while increasing the communication by only a constant factor (i.e., constant rate). In this work we consider synchronous communication networks over an arbitrary topology, in the powerful adversarial insertion-deletion noise model. Namely, the noisy channel may adversarially alter the content of any transmitted symbol, as well as completely remove a transmitted symbol or inject a new symbol into the channel. We provide efficient, constant rate schemes that successfully conduct any computation with high probability as long as the adversary corrupts at most epsilon/m fraction of the total communication, where m is the number of links in the network and epsilon is a small constant. This scheme assumes an oblivious adversary which is independent of the parties' inputs and randomness. We can remove this assumption and resist a worst-case adversary at the price of being resilient to epsilon/m log m errors. While previous work considered the insertion-deletion noise model in the two-party setting, to the best of our knowledge, our scheme is the first multiparty scheme that is resilient to insertions and deletions. Furthermore, our scheme is the first computationally efficient scheme in the multiparty setting that is resilient to adversarial noise.
暂无评论