Using Network Dynamical Influence to Drive Consensus

Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the "steering" refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks

New results on the local linear convergence of ADMM: A joint approach

Thanks to its versatility, its simplicity, and its fast convergence, alternating direction method of multipliers (ADMM) is among the most widely used approaches for solving a convex problem in distributed form. However, making it running efficiently is an art that requires a fine tuning of system parameters according to the specific application scenario, and which ultimately calls for a thorough understanding of the hidden mechanisms that control the convergence behavior. In this framework, we aim at providing new theoretical insights on the convergence process and specifically on some constituent matrices of ADMM whose eigenstructure provides a close link with the algorithm's convergence speed. One of the key techniques that we develop allows to effectively locate the eigenvalues of a (symmetric) matrix product, thus being able to estimate the contraction properties of ADMM. In the comparison with the results available from the literature, we are able to strengthen the precision of our speed estimate thanks to the fact that we are solving a joint problem (i.e., we are identifying the spectral radius of the product of two matrices) in place of two separate problems (the product of two matrix norms)

Elastic and Predictive Allocation of Computing Tasks in Energy Harvesting IoT Edge Networks

We consider a distributed IoT edge network whose end nodes generate computation jobs that can be processed locally or be offloaded, in full or in part, to other IoT nodes and/or edge servers having the necessary computation and energy resources. That is, jobs can either be partitioned and executed at multiple nodes (including the originating node) or be atomically executed at the designate server. IoT nodes and servers harvest ambient energy and jobs have a completion deadline. For this setup, we are concerned with the temporal allocation of jobs that maximizes the minimum level among all energy buffers in the network while meeting all the deadlines, i.e., that makes the network as much as possible energy neutral. Jobs continuously and asynchronously arrive at the IoT nodes, and computing resources are allocated dynamically at runtime, automatically adapting the processing load across nodes and servers. To achieve this, we present a Model Predictive Control based algorithm, where the job scheduler solves a sequence of low complexity convex problems and exploits future job and energy arrival estimates. The proposed technique is numerically evaluated, showing excellent adaptation capabilities, and performance close to that of an offline optimal scheduler with perfect information of all processes

On trading the spreading gain with the coding rate and its application to GNSS data component design

The ubiquity of global navigation satellite system (GNSS)-based positioning and timing services is often frustrated by the necessity to operate in harsh environments, where the carrier-to-noise ratio is low, and hence, decoding of navigation data and even tracking of an acquired symbol are difficult. We consider the possibility of improving the decoding performance of the GNSS data component by trading the spreading gain against the coding rate. The rationale is that spreading codes can be seen as a form of repetition coding that can be (at least partially) replaced by more robust coding forms to improve robustness to (any form of) noise. This is true both for the classical additive white Gaussian noise channel case and for more realistic GNSS channel formats severely degraded by multiple access interference and near-far effects. By bringing results on finite-block-length channel capacity and coding rates from information/communication theory to the GNSS domain, we are able to establish and discuss the expected performance gap, as well as the limits of such tradeoff

Towards Sustainable Edge Computing through Renewable Energy Resources and Online, Distributed and Predictive Scheduling

In this work, we tackle the energy consumption problem of edge computing technology looking at two key aspects: (i) reducing the energy burden of modern edge computing facilities to the power grid and (ii) distributing the user-generated computing load within the edge while meeting computing deadlines and achieving network level benefits (server load balancing vs consolidation and reduction of transmission costs). In the considered setup, edge servers are co-located with the base stations of a mobile network. Renewable energy sources are available to power base stations and servers, and users generate workload that is to be processed within certain deadlines. We propose a predictive, online and distributed algorithm for the scheduling of computing jobs that attains objectives (i) and (ii). The algorithm achieves fast convergence, leading to an energy efficient use of edge computing facilities, and obtains in the best case a reduction of 50% in the amount of renewable energy that is sold to the power grid by heuristic policies and that is, in turn, used at the network edge for processing

Feature-based Vehicle Tracking at Roundabouts in Visual Sensor Networks

During the last decade, visual monitoring has gained an increasing interest especially in the field of smart cities and intelligent transportation systems. This paper describes a feature-based vehicle tracking system in visual sensor networks at roundabouts. In the proposed system, the tasks for vehicle tracking are divided among smart camera nodes and the receiver. Smart camera nodes are embedded devices equipped with camera sensor, multiprocessors for video processing and computer vision tasks, and an RF transceiver for wireless communication. They are responsible for the vehicle detection and classification as well as features extraction. In order to satisfy the constraints of the visual sensor network in terms of power and communication resources, we assume that only the features are transmitted to the sink node. The received features will then be used to retrieve the vehicle trajectories. In order to train the vehicle classifier used at the camera nodes and to test the proposed tracking approach, a training database was built by placing cameras around a realworld roundabout. The system is shown to be capable of tracking vehicles even in partial occlusion cases

Immigrations, styles and topics: A study about Italian politics tweet

Immigration has been one of the most relevant topics during the Brexit Referendum, United States presidential election, and Italian and European Parliament elections (e.g., Zappettini, 2019; Faris, Roberts, Etling, Bourassa, Zuckerman, & Benkler, 2017). In particular, during these events, the anti-immigration-politicalgroups gain consensus (Muis and Immerzeel, 2017). In this study, we investigate, with a text-Analysis of 1000 Italian political leaders' tweets, which topics are associated with negative or positive sentiment toward immigrants. The results show that negative messages are associated, in a logistic regression model, with a populistic communication style, and with conspiracy suspicions. Moreover, a network analysis reveals that anti-immigration messages are associated with security and justice topics, whereas pro-immigration messages are associated with the topics of civil rights, culture, and Europe. Stylistics elements of populist and conspiratorial communication and their relation with the diffusion of anti-immigration contents and related consequences on composition and transmission of values, in social media context, are discussed

Distributed Learning Algorithms for Optimal Data Routing in IoT Networks

We consider the problem of joint lossy data compression and data routing in distributed Internet of Things (IoT). Heterogeneous sources compress their data using a source-specific lossy compression scheme, where heterogeneity is meant in terms of signal type and/or transmission rates. The compressed data is thus disseminated in a multi-hop fashion until it reaches a data collector (the IoT gateway). The problem we address is to compute a suitable rate-distortion working point for the compression scheme at the source nodes, while jointly assessing the most energy efficient routing paths for the data they transmit, under channel access, distortion and capacity constraints. This is formulated as a multi-objective optimization problem that is solved through distributed learning algorithms, where source coding and routing configurations emerge as the result of local interactions among the network devices. Our final algorithm is based on the alternating direction method of multipliers (ADMM), which is accelerated using the most recent findings from the literature. As a result, it has faster convergence (up to three times) to the global optimum than standard ADMM. Numerical results are discussed for selected network scenarios, emphasizing the interrelations that exist between signal reconstruction quality at the IoT gateway and total transport energy in the network