Optimization of sensors position is a challenging problem in wireless sensor networks since the processing process significantly affects energy consumption, surveillance ability and network lifetime. Vectorbased algorithm (VEC) and Voronoi-based algorithm (VOR) are two existing approaches. However, VEC is sensitive to initial deployment, while VOR always moves to the coverage holes. Moreover, the nodes in a network may oscillate for a long time before they reach the equilibrium state. This paper presents an initially central deployment model that is cost effective and easy to implement. In this model, we present a new distributed deployment algorithm based on boundary expansion and virtual force (BEVF). The proposed scheme enables nodes to move to the boundary rapidly and ultimately reach equilibrium quickly. For a node, only the location of its nearby nodes and boundary information are needed in the algorithm, thereby avoiding communication cost for transmitting global information. The distance threshold is adopted to limit node movement and to avoid node oscillations. Finally, we compare BEVF with existing algorithms Results show that the proposed algorithm achieves a much larger coverage and consumes lower energy.
This paper is concerned with solving the distributed resource allocation optimization problem by multi-agent systems over undirected graphs. The optimization objective function is a sum of local cost functions associated to individual agents, and the optimization variable satisfies a global network resource constraint. The local cost function and the network resource are the private data for each agent, which are not shared with others. A novel gradient-based continuous-time algorithm is proposed to solve the distributed optimization problem. We take an event-triggered communication strategy and an event-triggered gradient measurement strategy into account in the algorithm. With strongly convex cost functions and locally Lipschitz gradients, we show that the agents can find the optimal solution by the proposed algorithm with exponential convergence rate, based on the construction of a suitable Lyapunov function. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed scheme.
As embedded microprocessors are applied widerly to multi-agent systems, control scheduling and time-delay problems arose in the case of limited energy and computational ability. It has been shown that the event-triggered actuation strategy is an effective methodology for designing distributed control of multi-agent systems with limited computational resources. In this paper, a tracking control problem of leader-follower multi-agent systems with/without communication delays is formulated and a distributed dynamic tracking control is designed by employing event-triggered technique. Then, the input-to-state stability of the closed-loop multi-agent system with directed interconnections is analyzed. Finally, a numerical example is given to validate the proposed control.
Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research.
In this paper, the distributed H∞ estimation problem is investigated for a moving target with local communication and switching topology. Based on the solution of the algebraic Riccati equation, a recursive algorithm is proposed using constant gain. The stability of the proposed algorithm is analysed by using the Lyapounov method, and a lower bound for estimation errors is obtained for the proposed common H∞ filter. Moreover, a bound for the H∞ parameter is obtained by means of the solution of the algebraic Riccati equation. Finally, a simulation example is employed to illustrate the effectiveness of the proposed estimation algorithm.
Distributed optimization over unbalanced graphs is an important problem in multi-agent systems. Most of literatures, by introducing some auxiliary variables, utilize the Push-Sum scheme to handle the widespread unbalance graph with row or column stochastic matrix only. But the introduced auxiliary dynamics bring more calculation and communication tasks. In this paper, based on the in-degree and out-degree information of each agent, we propose an innovative distributed optimization algorithm to reduce the calculation and communication complexity of the conventional Push-Sum scheme. Furthermore, with the aid of small gain theory, we prove the linear convergence rate of the proposed algorithm.
In this paper, we study the distributed optimization problem using approximate first-order information. We suppose the agent can repeatedly call an inexact first-order oracle of each individual objective function and exchange information with its time-varying neighbors. We revisit the distributed subgradient method in this circumstance and show its suboptimality under square summable but not summable step sizes. We also present several conditions on the inexactness of the local oracles to ensure an exact convergence of the iterative sequences towards the global optimal solution. A numerical example is given to verify the efficiency of our algorithm.
In this paper, the distributed output regulation problem of linear multi-agent systems with parametric-uncertain leaders is considered. The existing distributed output regulation results with exactly known leader systems is not applicable. To solve the leader-following with unknown parameters in the leader dynamics, a distributed control law based on an adaptive internal model is proposed and the convergence can be proved.
This paper addresses the distributed resilient filtering for discrete-time large-scale systems (LSSs) with energy constraints, where their information are collected by sensor networks with a same topology structure. As a typical model of information physics systems, LSSs have an inherent merit of modeling wide area power systems, automation processes and so forth. In this paper, two kinds of channels are employed to implement the information transmission in order to extend the service time of sensor nodes powered by energy-limited batteries. Specifically, the one has the merit of high reliability by sacrificing energy cost and the other reduces the energy cost but could result in packet loss. Furthermore, a communication scheduling matrix is introduced to govern the information transmission in these two kind of channels. In this scenario, a novel distributed filter is designed by fusing the compensated neighboring estimation. Then, two matrix-valued functions are derived to obtain the bounds of the covariance matrices of one-step prediction errors and the filtering errors. In what follows, the desired gain matrices are analytically designed to minimize the provided bounds with the help of the gradient-based approach and the mathematical induction. Furthermore, the effect on filtering performance from packet loss is profoundly discussed and it is claimed that the filtering performance becomes better when the probability of packet loss decreases. Finally, a simulation example on wide area power systems is exploited to check the usefulness of the designed distributed filter.
Distribution of a word across contexts has proved to be a very useful ap- proximation of the word’s meaning. This paper reflects on the recent attempts to enhance distributional (or vector space) semantics of words with meaning composition, in particular with Fregean compositionality. I discuss the nature and performance of distributional semantic representations and argue against the thesis that semantics is in some sense identical with distribution (which seems to be a strong assumption of the compositional efforts). I propose instead that distribution is merely a reflection of semantics, and a substantially imperfect one. That raises some doubts regarding the very idea of obtaining semantic representations for larger wholes (phrases, sentences) by combining the distributional representations of particular items. In any case, I reject the generally unquestioned assumption that formal semantics provides a good theory of semantic composition, which it would be desirable to combine with distributional semantics (as a theory that is highly successful on the lexical field). I suggest that a positive alternative to the strong reading of the distributional hypothesis can be seen in the philosophy of inferentialism with respect to language meaning. I argue that the spirit of inferentialism is reasonably compatible with the current practice of distributional semantics, and I discuss the motivations for as well as the obstacles in the way of implementing the philosophical position in a computational framework., Ukázalo se, že rozložení slova napříč kontextem je velmi užitečným přiblížením významu slova. Příspěvek se zabývá nedávnými snahami o rozšíření sémantiky slov s významovou skladbou, zejména pak o fregovskou kompozici. Diskutuji o povaze a výkonu distribučních sémantických reprezentací a argumentuji proti té tezi, že sémantika je v určitém smyslu identická s distribucí (což se zdá být silným předpokladem kompozičního úsilí). Místo toho navrhuji, aby distribuce byla pouze odrazem sémantiky a podstatně nedokonalým. To vyvolává určité pochybnosti o samotné myšlence získat sémantické reprezentace pro větší celky (fráze, věty) kombinací distribučních reprezentací jednotlivých položek. V každém případě, Odmítám obecně nezpochybnitelný předpoklad, že formální sémantika poskytuje dobrou teorii sémantické kompozice, kterou by bylo žádoucí kombinovat s distribuční sémantikou (jako teorie, která je na lexikálním poli velmi úspěšná). Navrhuji, že pozitivní alternativu k silnému čtení distribuční hypotézy lze vidět ve filozofii inferenciality s ohledem na jazykový význam. Tvrdím, že duch inferenciality je přiměřeně slučitelný se současnou praxí distribuční sémantiky a diskutuji o motivacích i překážkách ve způsobu implementace filosofické pozice do výpočetního rámce. které by bylo žádoucí kombinovat s distribuční sémantikou (jako teorie, která je na lexikálním poli velmi úspěšná). Navrhuji, že pozitivní alternativu k silnému čtení distribuční hypotézy lze vidět ve filozofii inferenciality s ohledem na jazykový význam. Tvrdím, že duch inferenciality je přiměřeně slučitelný se současnou praxí distribuční sémantiky a diskutuji o motivacích i překážkách ve způsobu implementace filosofické pozice do výpočetního rámce. které by bylo žádoucí kombinovat s distribuční sémantikou (jako teorie, která je na lexikálním poli velmi úspěšná). Navrhuji, že pozitivní alternativu k silnému čtení distribuční hypotézy lze vidět ve filozofii inferenciality s ohledem na jazykový význam. Tvrdím, že duch inferenciality je přiměřeně slučitelný se současnou praxí distribuční sémantiky a diskutuji o motivacích i překážkách ve způsobu implementace filosofické pozice do výpočetního rámce., and Radek Ocelák