Decentralized Communication Networks Research Articles

Unveiling the Impact of Communication Network on Engineering Project Team Performance: The Interplay of Centralization and Tie Strength.

Recent research has focused on the impact of communication networks on the performance of construction project teams, attempting empirical exploration from various social network analysis perspectives. However, there is still a significant gap in understanding the variations in performance and the mechanisms for teams using different communication networks. Drawing from organizational learning theory and social network theory, this study, based on the Input-Mediator-Output (IMO) model, explores the effects of the interaction between centralization and tie strength in communication networks on team performance, as well as the mediating mechanisms of knowledge sharing behavior and team resilience performance in engineering project teams. Drawing on classic group communication experiment, we design an online communication and collaboration platform to simulate the execution of a construction engineering project. Finally, data was collected through the communication experiment with 720 participants, and hypotheses were tested using ANOVA and PROCESS. The results indicate that under conditions of weak tie strength, centralized communication networks yield higher performance. Conversely, under conditions of strong tie strength, decentralized communication networks demonstrate superior performance. Furthermore, this study also verifies the mediating role of knowledge sharing behavior and team resilience performance when tie strength is strong. This study focuses on engineering project team, exploring the evolutionary development of knowledge sharing behavior and team resilience performance from the perspective of the interaction of communication network structural characteristics, as well as the paths to enhancing team performance. Our research results highlight the interactive effects of structural indicators and relational indicators of communication networks, revealing the mechanism by which the structure of communication networks impacts team performance. Additionally, from the perspectives of forming and timely adjusting team communication models, and motivating and supporting employee communication behavior, our study provides practical insights for project managers and relevant administrators.

Centralized or Decentralized? Communication Network and Collective Effectiveness of PBOs—A Task Urgency Perspective

In the construction industry, there are a large number of project-based organizations (PBOs), where the efficiency of communication and collaboration among organizational members greatly impacts the success of projects. For PBOs employing both centralized and decentralized communication networks, it is worth delving into the question of under what circumstances which type of network will yield better results. Based on the IMO model and organizational learning theory, this paper conducts a grouped communication experiment involving 598 engineering management personnel to explore the differences in collective effectiveness of varying communication networks from the perspective of task urgency. Beyond task performance assessments, we have included organizational member perception to form evaluation criteria for collective effectiveness. Our research results show that under conditions of weak task urgency, decentralized networks yield higher collective effectiveness. Conversely, under conditions of strong task urgency, centralized networks demonstrate superior collective effectiveness. Furthermore, this study also verifies the mediating role of knowledge sharing behavior when task urgency is strong. This research provides significant managerial insights for the establishment of appropriate communication networks for PBOs in the construction industry.

Multi-agent actor centralized-critic with communication

Multiple real-world problems are naturally modeled as cooperative multi-agent systems, ranging from satellite formation to traffic monitoring. These systems require algorithms that can learn successful policies with independent agents that rely solely on local partial-observations of the environment. However, multi-agent environments are more complex, due to their partial-observability and non-stationarity from an agent’s perspective, as well as the structural credit assignment problem and the curse of dimensionality, and achieving coordination in such systems remains a complex challenge. To this end, we propose a multi-agent actor-critic algorithm called Asynchronous Advantage Actor Centralized-Critic with Communication (A3C3). A3C3 uses a centralized critic to estimate a value function, decentralized actors to approximate each agent’s policy function, and decentralized communication networks for each agent to share relevant information with its team. The critic can incorporate additional information, like the environment’s global state, when available, and optimizes the actor networks. The actor networks of an agent’s teammates optimize that agent’s communication network, such that each agent learns to output information that is relevant to the policies of others. A3C3 supports a dynamic amount of agents, noisy communication mediums, and can be horizontally scaled to shorten its learning phase. We evaluate A3C3 in two partially-observable multi-agent suites where agents benefit from communicating local information to each other. A3C3 outperforms state-of-the-art multi-agent algorithms, independent approaches, and centralized controllers with access to all agents’ observations.

Fault isolation technique for decentralized survivable communication network systems via regions and paths

<p>The rapid continuous growth of communication networks in size, complexity and dependencies, makes them extremely challenging to maintain the survivability of the complete large network. The complexity may be due to advance voice and video services like IP TVs, IP telephony, video streaming which demands high reliability and survivability. Network management has become a great challenge as Faults are expected only in these complex networks. Once a failure is detected, the next step in the diagnosis is fault isolation which locates the source of that failure. The necessitate of decentralized diagnosis is justified by various applications, like spacecrafts. This article presents model based fault isolation technique using a graph theoretical concepts, regions and paths for decentralized communication networks.</p>

Network dynamics of social influence in the wisdom of crowds

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton's discovery of the "wisdom of crowds" [Galton F (1907) Nature 75:450-451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals' estimates became more similar when subjects observed each other's beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020-9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.

Performance evaluation of a distributed storage service in community network clouds

SummaryCommunity networks are self‐organized and decentralized communication networks built and operated by citizens, for citizens. The consolidation of today's cloud technologies offers now, for community networks, the possibility to collectively develop community clouds, building upon user‐provided networks and extending toward cloud services. Cloud storage, and in particular secure and reliable cloud storage, could become a key community cloud service to enable end‐user applications. In this paper, we evaluate in a real deployment the performance of Tahoe least‐authority file system (Tahoe‐LAFS), a decentralized storage system with provider‐independent security that guarantees privacy to the users. We evaluate how the Tahoe‐LAFS storage system performs when it is deployed over distributed community cloud nodes in a real community network such as Guifi.net. Furthermore, we evaluate Tahoe‐LAFS in the Microsoft Azure commercial cloud platform, to compare and understand the impact of homogeneous network and hardware resources on the performance of the Tahoe‐LAFS. We observed that the write operation of Tahoe‐LAFS resulted in similar performance when using either the community network cloud or the commercial cloud. However, the read operation achieved better performance in the Azure cloud, where the reading from multiple nodes of Tahoe‐LAFS benefited from the homogeneity of the network and nodes. Our results suggest that Tahoe‐LAFS can run on community network clouds with suitable performance for the needed end‐user experience. Copyright © 2015 John Wiley & Sons, Ltd.