Carbon emission rights (CER) are a market mechanism adopted to reduce carbon dioxide emissions. In this paper, a decentralized market model integrating electricity and CER trading is established for a microgrid. The p...
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Carbon emission rights (CER) are a market mechanism adopted to reduce carbon dioxide emissions. In this paper, a decentralized market model integrating electricity and CER trading is established for a microgrid. The proposed trading model not only satisfies the demand for transactions but also ensures the constraint of total carbon emissions for the microgrid. Energy storage (ES) is introduced to balance loads more economically, and the constraint specifying that the ES cannot be simultaneously charged and discharged is proven to be satisfied automatically, ensuring the convexity of the model. Furthermore, by inserting local trackers to global constraints for each node, a scalable fully distributed algorithm is designed to solve the model locally for both global equality and inequality constraints. The proposed algorithm decomposes the arithmetic demand to each user without intermediate agents, which can effectively reduce the cost and ensure the transparency of trading. The algorithm is also proven to be convergent. Numerical results verify the effectiveness of the proposed market model.
We consider networks of small, autonomous devices that communicate with each other wirelessly. Minimizing energy usage is an important consideration in designing algorithms for such networks, as battery life is a cruc...
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We consider networks of small, autonomous devices that communicate with each other wirelessly. Minimizing energy usage is an important consideration in designing algorithms for such networks, as battery life is a crucial and limited resource. Working in a model where both sending and listening for messages deplete energy, we consider the problem of finding a maximal matching of the nodes in a radio network of arbitrary and unknown topology. We present a distributed randomized algorithm that produces, with high probability, a maximal matching. The maximum energy cost per node is O ((log n)(log Delta)), and the time complexity is O(Delta log n). Here n is any upper bound on the number of nodes, and Delta is any upper bound on the maximum degree;n and Delta are parameters of our algorithm that we assume are known a priori to all the processors. We note that there exist families of graphs for which our bounds on energy cost and time complexity are simultaneously optimal up to polylog factors, so any significant improvement would need additional assumptions about the network topology. We also consider the related problem of assigning, for each node in the network, a neighbor to back up its data in case of eventual node failure. Here, a key goal is to minimize the maximum load, defined as the number of nodes assigned to a single node. We present an efficient decentralized low-energy algorithm that finds a neighbor assignment whose maximum load is at most a polylog (n) factor bigger that the optimum.
In this article, we present a parallelized primal-dual algorithm for solving constrained convex optimization problems. The algorithm is "block-based," in which vectors of primal and dual variables are partit...
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In this article, we present a parallelized primal-dual algorithm for solving constrained convex optimization problems. The algorithm is "block-based," in which vectors of primal and dual variables are partitioned into blocks, each of which is updated only by a single processor. We consider four behaviors that could be asynchronous: 1) updates to primal variables;2) updates to dual variables;3) communications of primal variables;and 4) communications of dual variables. We show that any amount of asynchrony in the communications of dual variables can preclude convergence, though the other forms of asynchrony are permitted. A first-order primal-dual update law is then developed and shown to be robust to these other forms of asynchrony. We next derive convergence rates to an approximate Lagrangian saddle point in terms of the operations agents execute, without specifying any timing or pattern with which they must be executed. The distance between the approximate solution we obtain and the exact solution is explicitly bounded. Convergence rates include an "asynchrony penalty" that we quantify and present ways to mitigate. Numerical results illustrate these developments.
Listing dense subgraphs is a fundamental task with a variety of network analytics applications. A lot of research has been done focusing on $k$-cliques, i.e. complete subgraphs on $k$ nodes. However, requiring complet...
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Listing dense subgraphs is a fundamental task with a variety of network analytics applications. A lot of research has been done focusing on $k$-cliques, i.e. complete subgraphs on $k$ nodes. However, requiring complete connectivity between the nodes of a subgraph may be too restrictive in many real applications. Hence, in this paper, we consider a natural relaxation of cliques, called $k$-diamonds and defined as cliques of size $k$ with one missing edge. We first provide a sequential algorithm that, in $O(nm<^>{(k-1)/2})$ time, counts and lists all the $k$-diamonds in large graphs, for any constant $k \geq 4$. A parallel extension of the sequential algorithm is then proposed and analyzed in a MapReduce-style model, achieving the same local and total space usage of the state-of-the-art algorithms for $k$-cliques. The running time is optimal on dense graphs and $O(\sqrt{m})$ larger than $k$-clique counting if the graph is sparse. Our algorithms compute induced diamonds by analyzing the structure of directed stars formed by the graph nodes and their neighbors.
It is always desired for optimizing energy trading to disable manipulation and preserve individual privacy. These two features become increasingly appealing for an energy market where interest parties do not mutually ...
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It is always desired for optimizing energy trading to disable manipulation and preserve individual privacy. These two features become increasingly appealing for an energy market where interest parties do not mutually trust each other, such as peer-to-peer energy trading. Traditional centralized or hierarchical optimization schemes are vulnerable to an untrusted coordinator who may dishonestly broadcast coordination results or be curious about individual privacy. Recent blockchain-based optimization schemes resist dishonesty but increase the risk of privacy leakage. This paper proposes a privacy-preserving blockchain-based method to optimize energy trading. In the proposed method, participants submit encrypted bids/offers based on a bid/offer encryption algorithm to preserve their privacy. A privacy-preserving-Byzantine-fault-tolerance-based coordination algorithm is proposed to ensure the correctness of trading results with considering dishonesty. Numerical results in a peer-to-peer energy trading case demonstrate the performance of our method on convergence, resisting dishonesty, preserving privacy, and scalability.
This article revisits the problem of secure state estimation of multiagent systems under homologous attacks in [1]. We first characterize the condition on agents dynamics such that agents states can be uniquely solved...
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This article revisits the problem of secure state estimation of multiagent systems under homologous attacks in [1]. We first characterize the condition on agents dynamics such that agents states can be uniquely solved from the attacked measurements of agents outputs. This condition implies that the conclusion in [1] that the attack signal and agents states can be uniquely reconstructed by adding longer time-windowed measurements is incomplete. Based on this condition, when the communication graph of agents is undirected, we propose two different distributed secure state estimators by reformulating the state reconstruction as optimization problems. The first estimator does not need agents to exchange with others their dynamics information, which is used by the second, but requires updating and exchanging more variables. Both estimators are much simpler and easier to understand than that proposed in [1]. Moreover, when the communication graph is directed and strongly connected, we also proposed two distributed state estimators, adjusted from the estimators for undirected graphs. Both of them require no global information of communication graphs but the network size. At last, we verify all the theoretical results with simulation examples.
Consider a team of kappa <= n autonomous mobile robots initially placed at a node of an arbitrary graph G with n nodes. The dispersion problem asks for a distributed algorithm that allows the robots to reach a conf...
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Consider a team of kappa <= n autonomous mobile robots initially placed at a node of an arbitrary graph G with n nodes. The dispersion problem asks for a distributed algorithm that allows the robots to reach a configuration in which each robot is at a distinct node of the graph. If the robots are anonymous, i.e., they do not have any unique identifiers, then the problem is not solvable by any deterministic algorithm. However, the problem can be solved even by anonymous robots if each robot is given access to a fair coin which they can use to generate random bits. In this setting, it is known that the robots require O(log.) bits of memory to achieve dispersion, where. is the maximum degree of G. On the other hand, the best known memory upper bound is min{Delta,max{log Delta, log D}} (D = diameter of G), which can be omega(log Delta), depending on the values of Delta and D. In this paper, we close this gap by presenting an optimal algorithm requiring O(log Delta) bits of memory. (c) 2023 Elsevier B.V. All rights reserved.
Byzantine broadcast is a fundamental primitive in distributed computing. A highly efficient Byzantine broadcast protocol, motivated by the real-world performance of practical state machine replication protocols, is in...
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Byzantine broadcast is a fundamental primitive in distributed computing. A highly efficient Byzantine broadcast protocol, motivated by the real-world performance of practical state machine replication protocols, is increasingly needed. This article focuses on the state-of-the-art partially synchronous Byzantine broadcast protocol proposed by Abraham et al. (PODC'21), which achieves optimal good-case latency of two rounds and optimal resilience of n & GE;5f - 1 in this setting. Each step of the protocol is analysed, and then improved by cutting down the number of messages required to be collected and transmitted in the heaviest step of the protocol by about half, without adding any extra cost. This benefits from a new property, named "spread", that we identify and extract from the original protocol. It helps us to eliminate non-essential work in its view-change procedure. The authors also show that no further reduction is possible without violating security. A prototype is implemented and the performances of improved and original protocols are evaluated in the same environment. The results show that our improvement can achieve about 50% lower communication cost and 40% shorter latency at a scale of 100 replicas. The latency gap becomes wider as the scale further increases.
Research in theoretical swarm robotics focuses on models that assign to robots a minimal set of capabilities. One of the models well investigated is certainly OBLOT, addressing the case of distributed robots that are,...
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Research in theoretical swarm robotics focuses on models that assign to robots a minimal set of capabilities. One of the models well investigated is certainly OBLOT, addressing the case of distributed robots that are, anonymous, without means of communication, and oblivious. Here we propose MOBLOT, an extension of OBLOT that allows to resolve a larger spectrum of cases. MOBLOT stands for molecular oblivious robots: like atoms combine themselves to form molecules, in MOBLOT simple robots can bond with each other in order to create possibly bigger computational units with more intrinsic capabilities with respect to robots (called molecules also in the model);like in nature, molecules can further bond to create more complex structures (e.g., the matter), the MOBLOT version of molecules can exploit their own capabilities to accomplish new tasks or simply to arrange themselves to form any shape defined according to some compositional properties. In order to better understand the potentials of MOBLOT, we introduce a new problem called matter formation (MF). We do provide a necessary condition for the solvability of MF, in general. This relies on the "amount of symmetries' arising by the disposal of the robots. In practice, we show how molecules can break certain symmetries that cannot be broken in OBLOT. Finally, as a case study of MOBLOT, we consider a representative problem derived from the general MF problem along with a distributed resolution algorithm and show its correctness.
In this article, we propose a load balancing problem formulation where agents cooperate with the aim of simultaneously minimizing both the workload disparity among the agents and the overall workload transfer, under n...
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In this article, we propose a load balancing problem formulation where agents cooperate with the aim of simultaneously minimizing both the workload disparity among the agents and the overall workload transfer, under network capacity constraints. Notably, in our computational setting, the network is not just a device for the distributed solution of an optimization problem;on the contrary, the problem shares the same sparsity pattern as the network, and this aspect allows us to solve it without the need for the agents to store large amount of data. In particular, while the load balancing process occurs over directed links, agents' communication is assumed to be bidirectional. For this optimization setting, first, an optimality condition is derived;then, a provably convergent distributed algorithm to compute the optimal solution is developed, and an upper bound on the convergence rate is characterized. Simulation results are provided to corroborate the validity and performance of our theoretical findings.
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