1,721,478 research outputs found
Coordination Problem in Cognitive Wireless Mesh Networks
No full textCognitive wireless mesh networks are an emerging technology and they are at-
tracting an always growing community of researchers thanks to the ability of
creating and extending pervasive communication applications to cognitive envi-
ronments considering decentralized models.
The major challenge in cognitive networks is the adaptation to time and
space variability of the available resources, i.e. chunks of the frequency spec-
trum called channels. In particular, this problem is exacerbated in cognitive
mesh networks because there is no direct communication among devices and
hence they cannot establish a global common control channel to coordinate the
entire network. Instead, local control channels, that vary depending on time
instant and location, can be established to coordinate cognitive devices among
themselves.
In this paper, challenges and approaches proposed in the literature to ad-
dress the absence of a static and global control channel are analyzed and a
Control channel formation protocol (Connor) is proposed. Connor is a fully
distributed coordination scheme where cognitive mesh devices self-organize into
clusters based on similarity of available channels and on topological constraints.
Compared with the existing clustering algorithms in the literature, which re-
quires synchronization, Connor performs better in most cases without imposing
synchronization
Clustering the Research at the Intersection of Industry 4.0 Technologies, Environmental Sustainability and Circular Economy: Evidence from Literature and Future Research Directions
Full text linkDigital technologies may enable the adoption of Circular Economy models in production and consumption processes, thereby promoting environmental sustainability. Literature on these topics has grown exponentially over the last decades, focusing on the adoption of Industry 4.0 technologies and its implications for environmental sustainability or circularity. However, extant literature reviews failed to cover the vast amount of literature produced, since they either have a narrow scope or focus on a limited sample of articles. To fill this gap, a bibliometric literature review was carried out on a sample of 1002 scientific articles on Circular Economy, Industry 4.0 technologies, and environmental sustainability. Descriptive statistics are coupled with a cluster-based analysis to provide a comprehensive coverage of the broader subject matter. Eight research clusters have been identified, with two general clusters (linkages between Industry 4.0, Circular Economy, environmental sustainability) and six topic-specific clusters (Big Data analytics for supply chain circularity, circular and sustainable additive manufacturing, urban sustainability, sustainable circular and digital (re)manufacturing, blockchain and data integration for a sustainable Circular Economy, miscellaneous and sectorial applications). Clusters are discussed in terms of research themes, methodologies, technologies, and circular strategies. Finally, a research agenda is drafted, pointing out six cluster-specific and four more transversal research directions. Hence, this research offers a detailed and quantitative overview of the research landscape, helping researchers and managers in understanding past contributions, assessing current standings, and identifying future directions of the research at the intersection of Industry 4.0 technologies, environmental sustainability, and Circular Economy
A Fully Distributed Game Theoretic Approach to Guarantee Self-Coexistence among WRANs
No full textAlthough the proliferation of wireless applications operating in unlicensed spectrum bands has resulted in overcrowding, recent analysis has shown that license bands are still underutilized. Cognitive Radio is seen as the key enabling technology to address the spectrum shortage problem, opportunistically using the spectrum allocated for TV bands. In this paper, we present a novel game theoretic framework that uses the potentialities of the new IEEE 802.22 Standard to guarantee self-coexistence among Wireless Regional Area Networks. We address this problem as a channel assignment problem where each WRAN acquires a chunk of spectrum free of interference in a dynamic and distributed way. Using a novel technique to compute backoff windows, we show that the channel assignment problem can be formulated as a multi-player non-cooperative repeated potential game that converges to a Nash Equilibrium point. We consider each WRAN as a player of our game and we use two different types of utility functions to maximize the spatial reuse and minimize the interference. An extensive simulation study shows that having the interference minimization as objective is not necessarily the best solution with selfish players
Drone-Truck Cooperated Delivery Under Time Varying Dynamics
No full textRapid technological developments in autonomous unmanned aerial vehicles (or drones) could soon lead to their large-scale implementation in the last-mile delivery of products. However, drones have a number of problems such as limited energy budget, limited carrying capacity, etc. On the other hand, trucks have a larger carrying capacity, but they cannot reach all the places easily. Intriguingly, last-mile delivery cooperation between drones and trucks can synergistically improve delivery efficiency. In this paper, we present a drone-truck co-operated delivery framework under time-varying dynamics. Our framework minimizes the total delivery time while considering low energy consumption as the secondary objective. The empirical results support our claim and show that our algorithm can help to complete the deliveries time efficiently and saves energy
Digital technologies for the sustainability of circular manufacturing processes: a review
No full textDigital technologies have been recognized as a potential enabling factor for the implementation of circular economy in companies and organizations. They can support manufacturing companies in the redesign of products, processes, business models and supply chains according to the circular economy paradigm. For instance, the Internet of Things can be employed to monitor production data in a way to reduce materials scraps and increase the energy efficiency of production equipment. Big Data analytics can be used to transform product-in-use data into valuable knowledge to inform decision making, e.g. during the design phase of products to increase their modularity and ease disassembly. 3D printing may be adopted for enabling local and on-demand spare parts production for repair purposes. In general, these technologies can enable circularity and help in achieving sustainability benefits in different manufacturing and supply chain processes, ranging from product design to raw material acquisition, production, distribution, maintenance, reverse logistics and end-of-life. Despite an increasing interest on the application of digital technologies for the circular economy, the link between digital technologies, their implementation into different manufacturing and supply chain processes and the generation of sustainability benefits is still at a nascent stage of investigation. Therefore, the aim of this paper is to shed light on how digital technologies can bring sustainability benefits through the application of different R-strategies at different manufacturing and supply chain processes. A systematic literature review is carried out for that purpose, and a final sample of 24 scientific articles has been analyzed. Selected papers have been classified according to (i.) digital technologies investigated; (ii.) circular economy strategies; and (iii.) manufacturing processes. Results have been used to structure a preliminary framework which highlights potential circular economy adoption paths to generate sustainability benefits at different manufacturing processes
Analysis and Optimization of a Protocol for Mobile Element Discovery in Sensor Networks
No full textRecent studies have demonstrated that mobile elements (MEs) are an efficient solution to help decrease dramatically energy consumption in wireless sensor networks (WSNs). However, in most of cases, sensors use duty cycle schemes to save energy, and unless the ME mobility pattern is deterministic, each sensor node has to discover the presence of the ME in the nearby area before starting to exchange data with it. Therefore, in such wireless sensor networks with mobile elements (in short, WSN-MEs), the definition and analysis of a protocol for efficient ME discovery becomes of fundamental importance. In this paper, we propose an extensive performance analysis of an easy-to-implement, hierarchical discovery protocol for WSN-MEs, called Dual Beacon Discovery (2BD) protocol, taking into account stochastic, multi-path, variable speed ME mobility patterns. We also derive the optimal parameter values that minimize the energy consumption of sensor nodes, while guaranteeing the minimum node throughput required by the applications under consideration. Finally, we compare the 2BD protocol with a classical solution based on Periodic Listening (PL). Our results show that 2BD can exploit its hierarchical mechanism and thus significantly increase lifetime, especially when the ME discovery phase is relatively long
Robust Federated Learning against Backdoor Attackers
No full textFederated learning is a privacy-preserving alter-native for distributed learning with no involvement of data transfer. As the server does not have any control on clients' actions, some adversaries may participate in learning to introduce corruption into the underlying model. Backdoor attacker is one such adversary who injects a trigger pattern into the data to manipulate the model outcomes on a specific sub-task. This work aims to identify backdoor attackers and to mitigate their effects by isolating their weight updates. Leveraging the correlation between clients' gradients, we propose two graph theoretic algorithms to separate out attackers from the benign clients. Under a classification task, the experimental results show that our algorithms are effective and robust to the attackers who add backdoor trigger patterns at different location in targeted images. The results also evident that our algorithms are superior than existing methods especially when numbers of attackers are more than the normal clients
Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning
No full textFederated learning distributes model training among multiple clients who, driven by privacy concerns, perform training using their local data and only share model weights for iterative aggregation on the server. In this work, we explore the threat of collusion attacks from multiple malicious clients who pose targeted attacks (e.g., label flipping) in a federated learning configuration. By leveraging client weights and the correlation among them, we develop a graph-based algorithm to detect malicious clients. Finally, we validate the effectiveness of our algorithm in presence of varying number of attackers on a classification task using a well-known Fashion-MNIST dataset
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