An Effective Algorithm to Find a Cost Minimizing Gateway Deployment for Node-Replaceable Wireless Sensor Networks

In this paper, we present an efficient way to find a gateway deployment for a given sensor network topology. We assume that the expired sensors and gateways can be replaced and the locations of the gateways are chosen among the given sensor nodes. The objective is to find a gateway deployment that minimizes the cost per unit time, which consists of the maintenance and installation costs. The proposed algorithm creates a cost reference and uses it to find the optimal deployment via a divide and conquer algorithm. Comparing all cases is the most reliable way to find the optimal gateway deployment, but this is practically impossible to calculate, since its computation time increases exponentially as the number of nodes increases. The method we propose increases linearly, and so is suitable for large scale networks. Additionally, compared to stochastic algorithms such as the genetic algorithm, this methodology has advantages in computational speed and accuracy for a large number of nodes. We also verify our methodology through several numerical experiments.

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A Scalable Redefined Stochastic Blockmodel

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3442589 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1-28

Author(s):

Xueyan Liu ◽

Bo Yang ◽

Hechang Chen ◽

Katarzyna Musial ◽

Hongxu Chen ◽

...

Keyword(s):

Large Scale ◽

Network Science ◽

Learning Algorithm ◽

State Of The Art ◽

Real World Data ◽

Computational Overhead ◽

Stochastic Blockmodel ◽

Np Hard Problem ◽

Large Scale Networks ◽

The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

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A divide-and-conquer algorithm for large-scale de novo transcriptome assembly through combining small assemblies from existing algorithms

BMC Genomics ◽

10.1186/s12864-017-4270-9 ◽

2017 ◽

Vol 18 (S10) ◽

Author(s):

Sing-Hoi Sze ◽

Jonathan J. Parrott ◽

Aaron M. Tarone

Keyword(s):

Large Scale ◽

De Novo ◽

Transcriptome Assembly ◽

Divide And Conquer ◽

De Novo Transcriptome Assembly ◽

De Novo Transcriptome ◽

Divide And Conquer Algorithm

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Functional observability and target state estimation in large-scale networks

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2113750119 ◽

2021 ◽

Vol 119 (1) ◽

pp. e2113750119

Author(s):

Arthur N. Montanari ◽

Chao Duan ◽

Luis A. Aguirre ◽

Adilson E. Motter

Keyword(s):

Large Scale ◽

Phase Measurement ◽

Scale Up ◽

Measurement Data ◽

State Observer ◽

Sensor Nodes ◽

Scalable Algorithms ◽

Functional Observer ◽

Full State ◽

Large Scale Networks

The quantitative understanding and precise control of complex dynamical systems can only be achieved by observing their internal states via measurement and/or estimation. In large-scale dynamical networks, it is often difficult or physically impossible to have enough sensor nodes to make the system fully observable. Even if the system is in principle observable, high dimensionality poses fundamental limits on the computational tractability and performance of a full-state observer. To overcome the curse of dimensionality, we instead require the system to be functionally observable, meaning that a targeted subset of state variables can be reconstructed from the available measurements. Here, we develop a graph-based theory of functional observability, which leads to highly scalable algorithms to 1) determine the minimal set of required sensors and 2) design the corresponding state observer of minimum order. Compared with the full-state observer, the proposed functional observer achieves the same estimation quality with substantially less sensing and fewer computational resources, making it suitable for large-scale networks. We apply the proposed methods to the detection of cyberattacks in power grids from limited phase measurement data and the inference of the prevalence rate of infection during an epidemic under limited testing conditions. The applications demonstrate that the functional observer can significantly scale up our ability to explore otherwise inaccessible dynamical processes on complex networks.

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MOBILE NETWORKING FOR “SMART DUST” WITH RFID SENSOR NETWORKS

International Journal of Smart Sensor and Adhoc Network. ◽

10.47893/ijssan.2013.1179 ◽

2013 ◽

pp. 272-276

Author(s):

SHYAM D. BAWANKAR ◽

SONAL B. BHOPLE ◽

VISHAL D. JAISWAL

Keyword(s):

Sensor Networks ◽

Large Scale ◽

Mobile Network ◽

Base Station ◽

Sensor Nodes ◽

Node Localization ◽

Fine Grained ◽

Mobile Networking ◽

Smart Dust ◽

Large Scale Networks

Large-scale networks of wireless sensors are becoming an active topic of research.. We review the key elements of the emergent technology of “Smart Dust” and outline the research challenges they present to the mobile networking and systems community, which must provide coherent connectivity to large numbers of mobile network nodes co-located within a small volume. Smart Dust sensor networks – consisting of cubic millimeter scale sensor nodes capable of limited computation, sensing, and passive optical communication with a base station – are envisioned to fulfil complex large scale monitoring tasks in a wide variety of application areas. RFID technology can realize “smart-dust” applications for the sensor network community. RFID sensor networks (RSNs), which consist of RFID readers and RFID sensor nodes (WISPs), extend RFID to include sensing and bring the advantages of small, inexpensive and long-lived RFID tags to wireless sensor networks. In many potential Smart Dust applications such as object detection and tracking, fine-grained node localization plays a key role.

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Machine-Learning-Based White-Hat Worm Launcher in Botnet Defense System

International Journal of Software Science and Computational Intelligence ◽

10.4018/ijssci.291713 ◽

2022 ◽

Vol 14 (1) ◽

pp. 0-0

Keyword(s):

Machine Learning ◽

Petri Net ◽

Cyber Security ◽

Large Scale ◽

Security System ◽

Divide And Conquer ◽

Defense System ◽

Divide And Conquer Algorithm

This article proposes a white-hat worm launcher based on machine learning (ML) adaptable to large-scale IoT network for Botnet Defense System (BDS). BDS is a cyber-security system that uses white-hat worms to exterminate malicious botnets. White-hat worms defend an IoT system against malicious bots, the BDS decides the number of white-hat worms, but there is no discussion on the white-hat worms' deployment in IoT network. Therefore, the authors propose a machine-learning-based launcher to launch the white-hat worms effectively along with a divide and conquer algorithm to deploy the launcher to large-scale IoT networks. Then the authors modeled BDS and the launcher with agent-oriented Petri net and confirmed the effect through the simulation of the PN2 model. The result showed that the proposed launcher can reduce the number of infected devices by about 30-40%.

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