Статті в журналах: "Sensor network and distributed systems"

1

Wang, Jing, In Soo Ahn, Yufeng Lu, Tianyu Yang, and Gennady Staskevich. "A Distributed Least-Squares Algorithm in Wireless Sensor Networks With Unknown and Limited Communications." International Journal of Handheld Computing Research 8, no. 3 (July 2017): 15–36. http://dx.doi.org/10.4018/ijhcr.2017070102.

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In this article, the authors propose a new distributed least-squares algorithm to address the sensor fusion problem in using wireless sensor networks (WSN) to monitor the behaviors of large-scale multiagent systems. Under a mild assumption on network observability, that is, each sensor can take the measurements of a limited number of agents but the complete multiagent systems are covered under the union of all sensors in the network, the proposed algorithm achieves the estimation consensus if local information exchange can be performed among sensors. The proposed distributed least-squares algorithm can handle the directed communication network by explicitly estimating the left eigenvector corresponding to the largest eigenvalue of the sensing/communication matrix. The convergence of the proposed algorithm is analyzed, and simulation results are provided to further illustrate its effectiveness.

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2

Zhang, Shiyong, and Gongliang Chen. "Micro-Trivium: A lightweight algorithm designed for radio frequency identification systems." International Journal of Distributed Sensor Networks 13, no. 2 (February 2017): 155014771769417. http://dx.doi.org/10.1177/1550147717694171.

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Distributed sensor networks have been widely applied to healthcare, environmental monitoring and management, intelligent transportation, and other fields in which network sensors collect and transmit information about their surrounding environment. Radio frequency identification technology transmits an object’s identification as a unique serial number—using radio waves as the transmission carrier—and is becoming an important building block for distributed sensor networks. However, the security of radio frequency identification systems is a major industrial concern that can significantly hinder the market growth of distributed sensor networks. Trivium is a well-known lightweight synchronous stream cipher that was submitted to the European eSTREAM project in April 2005. In this article, we generalize Trivium to the Trivium-Model algorithm and highlight that security is mainly determined by the internal state bits and the number of nonlinear terms. We propose principles for choosing parameters and generating better parameters that are feasible for low-cost radio frequency identification tags in distributed sensor networks. The new algorithm, named Micro-Trivium, requires less power and a smaller chip area than the original Trivium, which is proven using experimental data and results. Mathematical analysis shows that using Micro-Trivium is as secure as using Trivium.

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3

Singh, Mitali, and Viktor K. Prasanna. "A HIERARCHICAL MODEL FOR DISTRIBUTED COLLABORATIVE COMPUTATION IN WIRELESS SENSOR NETWORKS." International Journal of Foundations of Computer Science 15, no. 03 (June 2004): 485–506. http://dx.doi.org/10.1142/s012905410400256x.

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In-network collaborative computation is essential for implementation of a large number of sensor applications. We approach the problem of computation in sensor networks from a parallel and distributed system's perspective. We define COSMOS, the Cluster-based, heterOgeneouSMOdel for Sensor networks. The model abstracts the key features of the class of cluster-based sensor applications. It assumes a hierarchical network architecture comprising of a large number of low cost sensors with limited computation capability, and fewer number of powerful clusterheads, uniformly distributed in a two dimensional terrain. The sensors are organized into single hop clusters, each managed by a distinct clusterhead. The clusterheads are organized in a mesh-like topology. All sensors in a cluster are time synchronized, whereas the clusterheads communicate asynchronously. The sensors are assumed to have multiple power states and a wakeup mechanism to facilitate power management. To illustrate algorithm design using our model, we discuss implementation of algorithms for sorting and summing in sensor networks.

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4

Sujihelen, L., Rajasekhar Boddu, S. Murugaveni, Ms Arnika, Anandakumar Haldorai, Pundru Chandra Shaker Reddy, Suili Feng, and Jiayin Qin. "Node Replication Attack Detection in Distributed Wireless Sensor Networks." Wireless Communications and Mobile Computing 2022 (May 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/7252791.

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Wireless sensor network (WSN) is an emerging technology used in emergency scenarios. There are a number of possible threats to WSNs because they use unsupervised IP addresses. Securing networks with unattended sensors is a real challenge nowadays. Sensor nodes lack power and storage, making them incompatible with normal security checks. It will be vital to make advancements in sensor network architecture and protocol design. There will be more vulnerability to attack if there is a lack of security. Especially, one key attack is node replication which induces the sensor node to acts as an original node, collecting data from the network and sending it to the attacker. In dynamic WSN, detecting an assault is difficult to find replica nodes. Therefore, this paper proposes a Strategic Security System (SSS) to discover replica nodes in static and dynamic distributed WSNs. It is mainly focused on enhancing detection accuracy, time delay, and communication overhead. The present system includes Single Stage Memory Random Walk with Network Division (SSRWND) and a Random-walk-based approach to detect clone attacks (RAWL). The proposed system has less memory and better detection accuracy.

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5

Heo, Taewook, Hyunhak Kim, Yoonmee Doh, Kwangsoo Kim, Jongjun Park, Naesoo Kim, JongArm Jun, and JeongGil Ko. "Multitiered and Distributed WSAN for Cooperative Indoors Environment Management." Mobile Information Systems 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6979178.

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For the past decade, wireless sensor networks have focused primarily on data collection. As a result the network topology for these systems was usually heavily centralized. However, for these networks to form a full system, the introduction of proper actuation units and decision-making intelligence is inevitable. Such a new wireless sensor and actuator network system enables new architectural research issues that have not been previously studied. In this work, we introduce the DWSAN system architecture, which effectively combines both sensor and actuation hardware devices to a single network and manages this network so that the actuation decisions are made in a distributed manner and the topology of the network maintains a multitier architecture. Our intensive set of evaluations reveal that, compared to the centralized approach that has been used in most wireless sensor network systems until now, when actuation units are introduced to the system, the DWSAN architecture reduces the transmission load of the network and the actuation decision-making latency by close to twofold and threefold, respectively. Furthermore, we show that this benefit naturally leads to better scalability of the system, making it suitable for various sensing applications in different environments.

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6

Likhttsinder, Boris Ya. "Ensor networks – distributed information measuring and information control systems." Vestnik of Samara State Technical University. Technical Sciences Series 29, no. 2 (August 30, 2021): 76–87. http://dx.doi.org/10.14498/tech.2021.2.5.

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The problems of control and management of geographically distributed objects are considered. The sensor networks operating on the ZigBee technology are considered. The characteristics of the 802.15.4 ZigBee standard are given. The advantages of this technology are shown when building networks that are not very critical to traffic delays. The elements of such a network are considered. The primary converters used in such networks and their energy characteristics are considered. The issues of reducing and compensating delays in control circuits are considered. It is shown that modern wireless sensor networks can be considered as distributed information measuring and information control systems.

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7

García-Ligero, María Jesús, Aurora Hermoso-Carazo, and Josefa Linares-Pérez. "Distributed Fusion Estimation in Network Systems Subject to Random Delays and Deception Attacks." Mathematics 10, no. 4 (February 20, 2022): 662. http://dx.doi.org/10.3390/math10040662.

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This paper focuses on the distributed fusion estimation problem in which a signal transmitted over wireless sensor networks is subject to deception attacks and random delays. We assume that each sensor can suffer attacks that may corrupt and/or modify the output measurements. In addition, communication failures between sensors and their local processors can delay the receipt of processed measurements. The randomness of attacks and transmission delays is modelled by different Bernoulli random variables with known probabilities of success. According to these characteristics of the sensor networks and assuming that the measurement noises are cross-correlated at the same time step between sensors and are also correlated with the signal at the same and subsequent time steps, we derive a fusion estimation algorithm, including prediction and filtering, using the distributed fusion method. First, for each sensor, the local least-squares linear prediction and filtering algorithm are derived, using a covariance-based approach. Then, the distributed fusion predictor and the corresponding filter are obtained as the matrix-weighted linear combination of corresponding local estimators, checking that the mean squared error is minimised. A simulation example is then given to illustrate the effectiveness of the proposed algorithms.

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8

Ji, Hao, Meng Wang, Ting Yang, and Junjie Zhao. "State feedback control aware stochastic transmitting latency in cyber-physical power system." International Journal of Distributed Sensor Networks 14, no. 9 (September 2018): 168781401878740. http://dx.doi.org/10.1177/1550147718802260.

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Distributed sensor networks with a great number of sensors realize information gathering, transmitting, and controlling, which will greatly improve the reliability and efficiency of industrial infrastructure systems, such as cyber-physical power system. Cyber-physical power system is one of the heuristic systems tightly coupled by a continuous-time electrical power system and a discrete-time information system. The transmitting latency, packets disorder, or loss will fail system convergence, even unstable under disturbance. This article studies the effect of long latency on cyber-physical power system frequency stability based on network control theory and establishes a heuristic model to express the continuous-time distributed generation system and the discrete-time distributed sensor network. A new state feedback controller is designed based on the stochastic optimal control theory to solve the micro-grid’s frequency stability problem with stochastic latency disturbance. Finally, we have conducted an extensive evaluation study using a real distributed generation micro-grid system. The simulation results show that the new controller reduces the influence of distributed sensor networks transmitting latency on the frequency stability, and the frequency dynamic process in the distributed generation micro-grid has smaller overshoot and obtains faster dynamic response speed.

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9

Khaytbaev, A. E., and A. M. Eshmuradov. "APPLICATIONS OF NEURAL NETWORK TECHNOLOGIES IN WIRELESS SENSOR NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 46–51. http://dx.doi.org/10.14489/vkit.2020.09.pp.046-051.

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The purpose of the article is to study the possibilities of improving the efficiency of the sensory network management technique, using the neural network method. The presented model of the wireless sensor network takes into account the charging of the environment. The article also tests the hypothesis of the possibility of organizing distributed computing in wireless sensor networks. To achieve this goal, a number of tasks are allocated: review and analysis of existing methods for managing BSS nodes; definition of simulation model components and their properties of neural networks and their features; testing the results of using the developed method. The article explores the major historical insights of the application of the neural network technologies in wireless sensor networks in the following practical fields: engineering, farming, utility communication networks, manufacturing, emergency notification services, oil and gas wells, forest fires prevention equipment systems, etc. The relevant applications for the continuous monitoring of security and safety measures are critically analyzed in the context of the relevancy of specific decisions to be implemented within the system architecture. The study is focused on the modernization of methods of control and management for the wireless sensor networks considering the environmental factors to be allocated using senor systems for data maintenance, including the information on temperature, humidity, motion, radiation, etc. The article contains the relevant and adequate comparative analysis of the updated versions of node control protocols, the components of the simulation model, and the control method based on neural networks to be identified and tested within the practical organizational settings.

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10

Khaytbaev, A. E., and A. M. Eshmuradov. "APPLICATIONS OF NEURAL NETWORK TECHNOLOGIES IN WIRELESS SENSOR NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 195 (September 2020): 46–51. http://dx.doi.org/10.14489/vkit.2020.09.pp.046-051.

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The purpose of the article is to study the possibilities of improving the efficiency of the sensory network management technique, using the neural network method. The presented model of the wireless sensor network takes into account the charging of the environment. The article also tests the hypothesis of the possibility of organizing distributed computing in wireless sensor networks. To achieve this goal, a number of tasks are allocated: review and analysis of existing methods for managing BSS nodes; definition of simulation model components and their properties of neural networks and their features; testing the results of using the developed method. The article explores the major historical insights of the application of the neural network technologies in wireless sensor networks in the following practical fields: engineering, farming, utility communication networks, manufacturing, emergency notification services, oil and gas wells, forest fires prevention equipment systems, etc. The relevant applications for the continuous monitoring of security and safety measures are critically analyzed in the context of the relevancy of specific decisions to be implemented within the system architecture. The study is focused on the modernization of methods of control and management for the wireless sensor networks considering the environmental factors to be allocated using senor systems for data maintenance, including the information on temperature, humidity, motion, radiation, etc. The article contains the relevant and adequate comparative analysis of the updated versions of node control protocols, the components of the simulation model, and the control method based on neural networks to be identified and tested within the practical organizational settings.

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11

Wang, Shenquan, Yuenan Wang, Yulian Jiang, and Yuanchun Li. "Event-triggered based distributed H∞ consensus filtering for discrete-time delayed systems over lossy sensor network." Transactions of the Institute of Measurement and Control 40, no. 9 (December 11, 2017): 2740–47. http://dx.doi.org/10.1177/0142331217741959.

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This paper investigates the issue of event-triggered distributed H∞ consensus filtering for discrete time-varying delay systems over lossy sensor networks with stochastic switching topologies. For each sensor node, the event-triggering mechanism is given by an event detector, which determines whether to transmit the output measurement or not. The communication links between the event detector and the distributed filter are assumed to be over a lossy network, and the missing probability is governed by a set of random variables. Through available output measurements from not only the individual sensor but also its neighbouring sensors, according to the interconnection topology to estimate the system states, a sufficient condition is established for the desired distributed filter to ensure that the overall filtering dynamics are stochastically stable and achieve a prescribed distributed H∞ average performance. Meanwhile, the corresponding solvability conditions for the desired distributed filter gains are characterized in terms of feasibility linear matrix inequalities. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed approaches.

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12

Qoutb, Abdelrahman G., and Eby G. Friedman. "Distributed Spintronic/CMOS Sensor Network for Thermal-Aware Systems." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 28, no. 6 (June 2020): 1505–12. http://dx.doi.org/10.1109/tvlsi.2020.2981443.

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13

Hong, Bo, and Viktor K. Prasanna. "Maximum Data Gathering in Networked Sensor Systems." International Journal of Distributed Sensor Networks 1, no. 1 (February 2005): 57–80. http://dx.doi.org/10.1080/15501320590901847.

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We focus on data gathering problems in energy-constrained networked sensor systems. We study store-and-gather problems where data are locally stored at the sensors before the data gathering starts, and continuous sensing and gathering problems that model time critical applications. We show that these problems reduce to maximization of network flow under vertex capacity constraint. This flow problem in turn reduces to a standard network flow problem. We develop a distributed and adaptive algorithm to optimize data gathering. This algorithm leads to a simple protocol that coordinates the sensor nodes in the system. Our approach provides a unified framework to study a variety of data gathering problems in networked sensor systems. The performance of the proposed method is illustrated through simulations.

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14

Campos, Nídia G. S., Danielo G. Gomes, Flávia C. Delicato, Augusto J. V. Neto, Luci Pirmez, and José Neuman de Souza. "Autonomic Context-Aware Wireless Sensor Networks." Journal of Sensors 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/621326.

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Autonomic Computing allows systems like wireless sensor networks (WSN) to self-manage computing resources in order to extend their autonomy as much as possible. In addition, contextualization tasks can fuse two or more different sensor data into a more meaningful information. Since these tasks usually run in a single centralized context server (e.g., sink node), the massive volume of data generated by the wireless sensors can lead to a huge information overload in such server. Here we propose DAIM, a distributed autonomic inference machine distributed which allows the sensor nodes to do self-management and contextualization tasks based on fuzzy logic. We have evaluated DAIM in a real sensor network taking into account other inference machines. Experimental results illustrate that DAIM is an energy-efficient contextualization method for WSN, reducing 48.8% of the number of messages sent to the context servers while saving 19.5% of the total amount of energy spent in the network.

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15

Sun, Hui, Xianyu Wang, Kaixin Yang, and Tongrui Peng. "Analysis of Distributed Wireless Sensor Systems with a Switched Quantizer." Complexity 2021 (July 29, 2021): 1–14. http://dx.doi.org/10.1155/2021/6690761.

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In this article, a switched quantizer is proposed to solve the bandwidth limitation application problem for distributed wireless sensor networks (WSNs). The proposed estimator based on switched quantitative event-triggered Kalman consensus filtering (KCF) algorithm is used to monitor the aircraft cabin environmental parameters when suffering packet loss and path loss issues during the communication process for WSN. The quantization error of the novel switched quantizer structure is bounded, and the corresponding stability theory for the quantitative estimation approach is proved. Compared with other methods, the simulation results for the introduced method verify that the environmental parameters can be estimated accurately and timely and reduce the burden of network communication bandwidth.

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16

Zárraga-Rodríguez, Marta, Xabier Insausti, and Jesús Gutiérrez-Gutiérrez. "On the topology design of large wireless sensor networks for distributed consensus with low power consumption." International Journal of Distributed Sensor Networks 15, no. 12 (December 2019): 155014771989674. http://dx.doi.org/10.1177/1550147719896742.

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Sensor-based structural health monitoring systems are commonly used to provide real-time information and detect damage in complex structures. In particular, wireless structural health monitoring systems are of low cost but, since wireless sensors are powered with batteries, a low power consumption is critical. A common approach for wireless structural health monitoring is to use a distributed computation strategy, which is usually based on consensus algorithms. Power consumption in such wireless consensus networks depends on the number of connections of the network. If sensors are randomly connected, there is no control on the power consumption. In this article, we present a novel strategy to connect a large number of wireless sensors for distributed consensus with low power consumption by combining small networks with basic topologies using the Kronecker product.

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17

Chen, Xiyuan, Tanay Topac, Wyatt Smith, Purim Ladpli, Cheng Liu, and Fu-Kuo Chang. "Characterization of Distributed Microfabricated Strain Gauges on Stretchable Sensor Networks for Structural Applications." Sensors 18, no. 10 (September 28, 2018): 3260. http://dx.doi.org/10.3390/s18103260.

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Smart structures mimic biological systems by using thousands of sensors serving as a nervous system analog. One approach to give structures this sensing ability is to develop a multifunctional sensor network. Previous work has demonstrated stretchable sensor networks consisting of temperature sensors and impact detectors for monitoring external environments and interacting with other objects. The objective of this work is to develop distributed, robust and reliable strain gauges for obtaining the strain distribution of a designated region on the target structure. Here, we report a stretchable network that has 27 rosette strain gauges, 6 resistive temperature devices and 8 piezoelectric transducers symmetrically distributed over an area of 150 × 150 mm to map and quantify multiple physical stimuli with a spatial resolution of 2.5 × 2.5 mm. We performed computational modeling of the network stretching process to improve measurement accuracy and conducted experimental characterizations of the microfabricated strain gauges to verify their gauge factor and temperature coefficient. Collectively, the results represent a robust and reliable sensing system that is able to generate a distributed strain profile of a common structure. The reported strain gauge network may find a wide range of applications in morphing wings, smart buildings, autonomous cars and intelligent robots.

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18

Park, Bongsang, Junghyo Nah, Jang-Young Choi, Ick-Jae Yoon, and Pangun Park. "Robust Wireless Sensor and Actuator Networks for Networked Control Systems." Sensors 19, no. 7 (March 29, 2019): 1535. http://dx.doi.org/10.3390/s19071535.

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The stability guarantee of wireless networked control systems is still challenging due to the complex interaction among the layers and the vulnerability to network faults, such as link and node failures. In this paper, we propose a robust wireless sensor and actuator network (R-WSAN) to maintain the control stability of multiple plants over the spatial-temporal changes of wireless networks. The proposed joint design protocol combines the distributed controller of control systems and the clustering, resource scheduling, and control task sharing scheme of wireless networks over a hierarchical cluster-based network. In particular, R-WSAN decouples the tasks from the inherently unreliable nodes and allows control tasks to share between nodes of wireless networks. Our simulations demonstrate that R-WSAN provides the enhanced resilience to the network faults for sensing and actuation without significantly disrupting the control performance.

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19

Bordim, Jacir Luiz, Koji Nakano, and Hong Shen. "Sorting on Single-Channel Wireless Sensor Networks." International Journal of Foundations of Computer Science 14, no. 03 (June 2003): 391–403. http://dx.doi.org/10.1142/s0129054103001807.

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A Wireless Sensor Network (WSN) is a distributed system consisting of a large number of wireless sensing devices and a base station. Due to their compactness and low-cost, sensor networks can be distributed at a fraction of the cost of conventional wired sensors and actuator systems. The physical world generates an unlimited amount of data that can be observed and monitored. Hence, designing protocols to coordinate WSNs with hundreds, or even thousands, of sensors will face many challenges. In this work we focus on the design of protocols that enable the sensor nodes to coordinate among themselves to achieve a larger task. From this standpoint, we present a sorting protocol for wireless sensor networks. We show that in a WSN consisting of n sensor nodes, where each sensor stores an element and has a fixed transmission range r. sorting can be performed in [Formula: see text] time slots when [Formula: see text]. We also reason that future applications of wireless sensor networks are very likely to employ short-range radio communications (i.e., r less than 100 meters). If this is the case, the time complexity of our sorting protocol is optimal.

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20

Li, Shancang, Xinheng Wang, and Deyun Zhang. "An adaptive distributed localisation in wireless sensor network." Enterprise Information Systems 3, no. 2 (May 2009): 117–32. http://dx.doi.org/10.1080/17517570902749144.

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Patan, Maciej, and Dariusz Uciński. "Configuring A Sensor Network for Fault Detection in Distributed Parameter Systems." International Journal of Applied Mathematics and Computer Science 18, no. 4 (December 1, 2008): 513–24. http://dx.doi.org/10.2478/v10006-008-0045-4.

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Configuring A Sensor Network for Fault Detection in Distributed Parameter SystemsThe problem of fault detection in distributed parameter systems (DPSs) is formulated as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A computational scheme is provided for the design of a network of observation locations in a spatial domain that are supposed to be used while detecting changes in the underlying parameters of a distributed parameter system. The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset can be selected because of the cost constraints. As a suitable performance measure, theDs-optimality criterion defined on the Fisher information matrix for the estimated parameters is applied. Then, the solution of a resulting combinatorial problem is determined based on the branch-and-bound method. As its essential part, a relaxed problem is discussed in which the sensor locations are givena prioriand the aim is to determine the associated weights, which quantify the contributions of individual gauged sites. The concavity and differentiability properties of the criterion are established and a gradient projection algorithm is proposed to perform the search for the optimal solution. The delineated approach is illustrated by a numerical example on a sensor network design for a two-dimensional convective diffusion process.

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Yang, Wen, Guanrong Chen, Xiaofan Wang, and Ling Shi. "Stochastic sensor activation for distributed state estimation over a sensor network." Automatica 50, no. 8 (August 2014): 2070–76. http://dx.doi.org/10.1016/j.automatica.2014.05.025.

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23

Joo-Ho Lee and H. Hashimoto. "Controlling mobile robots in distributed intelligent sensor network." IEEE Transactions on Industrial Electronics 50, no. 5 (October 2003): 890–902. http://dx.doi.org/10.1109/tie.2003.817488.

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Niu, Ruixin, Pramod K. Varshney, and Qi Cheng. "Distributed detection in a large wireless sensor network." Information Fusion 7, no. 4 (December 2006): 380–94. http://dx.doi.org/10.1016/j.inffus.2005.06.003.

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Liang, Jing, and Chengchen Mao. "Distributed compressive sensing in heterogeneous sensor network." Signal Processing 126 (September 2016): 96–102. http://dx.doi.org/10.1016/j.sigpro.2015.10.026.

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26

Sreedevi, Indu, Shubham Mankhand, Santanu Chaudhury, and Asok Bhattacharyya. "Bio-Inspired Distributed Sensing Using a Self-Organizing Sensor Network." Journal of Engineering 2013 (2013): 1–16. http://dx.doi.org/10.1155/2013/959430.

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Nature offers several examples of self-organizing systems that automatically adjust to changing conditions without adversely affecting the system goals. We propose a self-organizing sensor network that is inspired from real-life systems for sampling a region in an energy-efficient manner. Mobile nodes in our network execute certain rules by processing local information. These rules enable the nodes to divide the sampling task in a manner such that the nodes self-organize themselves to reduce the total power consumed and improve the accuracy with which the phenomena are sampled. The digital hormone-based model that encapsulates these rules, provides a theoretical framework for examining this class of systems. This model has been simulated and implemented on cricket motes. Our results indicate that the model is more effective than a conventional model with a fixed rate sampling.

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Caballero-Águila, Raquel, Irene García-Garrido, and Josefa Linares-Pérez. "Distributed Fusion Filtering in Networked Systems with Random Measurement Matrices and Correlated Noises." Discrete Dynamics in Nature and Society 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/398605.

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The distributed fusion state estimation problem is addressed for sensor network systems with random state transition matrix and random measurement matrices, which provide a unified framework to consider some network-induced random phenomena. The process noise and all the sensor measurement noises are assumed to be one-step autocorrelated and different sensor noises are one-step cross-correlated; also, the process noise and each sensor measurement noise are two-step cross-correlated. These correlation assumptions cover many practical situations, where the classical independence hypothesis is not realistic. Using an innovation methodology, local least-squares linear filtering estimators are recursively obtained at each sensor. The distributed fusion method is then used to form the optimal matrix-weighted sum of these local filters according to the mean squared error criterion. A numerical simulation example shows the accuracy of the proposed distributed fusion filtering algorithm and illustrates some of the network-induced stochastic uncertainties that can be dealt with in the current system model, such as sensor gain degradation, missing measurements, and multiplicative noise.

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Wang, Shiqiang, Jianchun Xing, Ziyan Jiang, and Juelong Li. "A decentralized sensor fault detection and self-repair method for HVAC systems." Building Services Engineering Research and Technology 39, no. 6 (May 7, 2018): 667–78. http://dx.doi.org/10.1177/0143624418775881.

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This study proposes a novel decentralized sensor fault detection and self-repair method for heating, ventilation and air-conditioning systems. From the perspective of network structure, sensor fault diagnosis in heating, ventilation and air-conditioning systems is distributed to the updated smart sensors without the monitoring host, which is necessary in the traditional centralized method. A fully distributed flat sensor network is established based on fundamental physical equations. Similar to the structure, mechanism and characteristics of biological communities, a smart sensor needs only to communicate with adjacent nodes and operate collaboratively to complete sensor fault detection and self-repair tasks. These tasks are formulated as a constrained optimization and are solved by a decentralized algorithm with a penalty function executed in all the sensor nodes in parallel. The diagnosis model introduces an exponential function method to determine the precise location and undertake self-repair of a fault node. Simulation results on a chilled water system illustrate the effectiveness of the proposed method. Practical application: The traditional sensor fault detection and diagnosis methods for heating, ventilation and air-conditioning systems are based on a centralized structure with several deficiencies, such as high maintenance and labor costs, link congestion and operational lag. This study presents a decentralized sensor network structure and exponential-function-based method that possess the advantages of plug-and-play, rapid deployment, high flexibility and convenience for engineering implementation without having to build a central monitor. The efficiency and effectiveness of the proposed method are demonstrated via a case study.

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29

Brucie, James, Tessa Daniel, Elena Gaura, and John Halloran. "DISTRIBUTED EXTRACTION OF INFORMATION FROM LARGESCALE WIRELESS SENSOR NETWORKS." SOFT MEASUREMENTS AND COMPUTING 1, no. 8 (2021): 5–48. http://dx.doi.org/10.36871/2618-9976.2021.08.001.

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The article discusses distributed information extraction from large-scale wireless sensor networks. The focus is on agentbased approaches to information retrieval, query-based systems and macro programming approaches are considered, and a research question is raised regarding the usefulness of network processing from an information point of view. Examples of developments are given, in which those that were used in practical placements are highlighted. Where possible, the architecture and the way the information is processed (centralized or distributed) are defined.

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30

Reppa, Vasso, Marios M. Polycarpou, and Christos G. Panayiotou. "Distributed Sensor Fault Diagnosis for a Network of Interconnected Cyberphysical Systems." IEEE Transactions on Control of Network Systems 2, no. 1 (March 2015): 11–23. http://dx.doi.org/10.1109/tcns.2014.2367362.

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31

Stecklina, Oliver, Peter Langendörfer, and Christian Goltz. "A Lifetime Forecast Scheme for a Distributed Low Duty Cycle Multi-Hop Routing in Wireless Sensor Networks." International Journal of Business Data Communications and Networking 9, no. 4 (October 2013): 1–22. http://dx.doi.org/10.4018/ijbdcn.2013100101.

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Wireless sensor nodes become more and more attractive for a broad variety of application scenarios. Wireless Sensor Networks (WSNs) can be easily deployed and they require by design low maintenance effort. But running installations are still rare, because real world requirements and environmental conditions are even today a big challenge. Especially in multi-hop networks a minimum lifetime of several years cannot be achieved globally. In this paper, the authors present a Distributed Low Duty Cycle (DLDC) based Multi-Hop Routing (MHR) protocol for Wireless Sensor Networks guaranteeing a minimum network lifetime. The authors introduce a forecast scheme to calculate the expected life of a node with a minimal effort. The authors are convinced that by using a forecast scheme the network topology and the used protocols can be easily optimized before deploying the network. The authors evaluated their forecast scheme by measuring real sensor node parameters and simulating an example network in the Castalia simulation framework. The authors demonstrated that by using the proposed scheme an energy consumption forecast with a deviation of less than three per cent can be achieved.

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32

Kim, Kyungdoh, Chunghun Ha, and Changsoo Ok. "Network Structure-Aware Ant-Based Routing in Large-Scale Wireless Sensor Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/521784.

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Routing algorithms for large-scale sensor networks should be capable of finding energy efficient paths to prolong the lifetime of the networks in a decentralized manner. With this respect, Ant System has several proper characteristics for routing algorithm in large-scale wireless sensor networks. First, its distributed mechanism enables routing algorithm to find a solution with only local information and be robust for uncertainties in wireless sensor networks. Second, the framework of the Ant System is proper to solve dynamic problems such as routing problem. Transition probability in Ant System can be used to estimate how good a given routing path is. Capturing these features, this work proposes two Ant Systems based routing algorithms, which are AS-RWSNs (Ant System for Routing in Wireless Sensor Networks) and SAAS-RWSN (Structure-Aware AS-RWSN). The AS-RWSN applies the original Ant System to routing algorithm for wireless sensor network and SAAS-RSN upgrades AS-RWSN with considering properties of network structure such as degree of node. In SAAS-RSN, sensors with high node degree have high data traffic since they have more routing paths. Consequently, SAAS-RSN achieves an energy balance over sensor network through this routing scheme. We demonstrate the effectiveness of the proposed algorithms by comparing three existing routing algorithms.

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33

Stella, K., and E. N. Ganesh. "Distributed Energy Efficient Zonal Relay Node Based Multi Path Secure Routing Protocol (DEZMSR) for Wireless Sensor Networks." Journal of Computational and Theoretical Nanoscience 15, no. 2 (February 1, 2018): 403–8. http://dx.doi.org/10.1166/jctn.2018.7102.

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Wireless Sensor Networks are used in various applications such as target detection, military, environmental monitoring, disaster relief operations, health care systems, etc. Due to restriction of energy resources in wireless sensor networks, increasing the security and network life time while maintaining the network performance and fairness is challenging. We propose Distributed energy efficient Zonal relay node based Secure Routing Protocol (DEZMSR) to increase security and network life time while maintaining network performance and fairness. Sensor nodes are randomly deployed in hostile environment and hence they are captured by attackers. In the proposed protocol, Zonal relay node is selected based on zone radius and it is protected by using Symmetric Key encryption algorithm. The transmission of data from source node to sink node follows multiple path via zonal relay node. So it will increase the network life time, security and reduce the routing overhead.

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34

Raha, Arnab, Shovan Maity, Souvik Kr Mitra, Ramesh Jana, and Mrinal K. Naskar. "A Low Complexity Multivariate Regression Based Flood Forecasting Model Using an Optimized WSN Deployment Scheme." Advanced Materials Research 403-408 (November 2011): 3484–94. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3484.

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Very recently work has been done to develop efficient disaster forecasting systems utilizing WSN technology. Such networks pose a tremendous design challenge such as the ability to cope with node failure, limited power, distributed prediction, wide variety of sensors and the need for communication over a large area. Our paper introduces a Predictive Environmental Sensor Network (PESN) Architecture which employs a minimal deployment scheme to ensure connectivity among the nodes involved within the network. On this connected network we run our distributed statistical model for forecasting. The statistical process used for this real time prediction uses multiple variable regression method providing the advantages of simplicity and robustness much needed in low power and limited ability sensor nodes. The versatility of the forecasting model is proved on its independence on the number of parameters, as it can incorporate as many variables into the algorithm as required, as long as there is sufficient positive correlation with the instantaneous river water level.

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35

Soro, Stanislava, and Wendi Heinzelman. "A Survey of Visual Sensor Networks." Advances in Multimedia 2009 (2009): 1–21. http://dx.doi.org/10.1155/2009/640386.

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Visual sensor networks have emerged as an important class of sensor-based distributed intelligent systems, with unique performance, complexity, and quality of service challenges. Consisting of a large number of low-power camera nodes, visual sensor networks support a great number of novel vision-based applications. The camera nodes provide information from a monitored site, performing distributed and collaborative processing of their collected data. Using multiple cameras in the network provides different views of the scene, which enhances the reliability of the captured events. However, the large amount of image data produced by the cameras combined with the network's resource constraints require exploring new means for data processing, communication, and sensor management. Meeting these challenges of visual sensor networks requires interdisciplinary approaches, utilizing vision processing, communications and networking, and embedded processing. In this paper, we provide an overview of the current state-of-the-art in the field of visual sensor networks, by exploring several relevant research directions. Our goal is to provide a better understanding of current research problems in the different research fields of visual sensor networks, and to show how these different research fields should interact to solve the many challenges of visual sensor networks.

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36

Ometov, Aleksandr, Sergey Bezzateev, Natalia Voloshina, Pavel Masek, and Mikhail Komarov. "Environmental Monitoring with Distributed Mesh Networks: An Overview and Practical Implementation Perspective for Urban Scenario." Sensors 19, no. 24 (December 16, 2019): 5548. http://dx.doi.org/10.3390/s19245548.

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Almost inevitable climate change and increasing pollution levels around the world are the most significant drivers for the environmental monitoring evolution. Recent activities in the field of wireless sensor networks have made tremendous progress concerning conventional centralized sensor networks known for decades. However, most systems developed today still face challenges while estimating the trade-off between their flexibility and security. In this work, we provide an overview of the environmental monitoring strategies and applications. We conclude that wireless sensor networks of tomorrow would mostly have a distributed nature. Furthermore, we present the results of the developed secure distributed monitoring framework from both hardware and software perspectives. The developed mechanisms provide an ability for sensors to communicate in both infrastructure and mesh modes. The system allows each sensor node to act as a relay, which increases the system failure resistance and improves the scalability. Moreover, we employ an authentication mechanism to ensure the transparent migration of the nodes between different network segments while maintaining a high level of system security. Finally, we report on the real-life deployment results.

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37

Vançin, Sercan, and Ebubekir Erdem. "Threshold Balanced Sampled DEEC Model for Heterogeneous Wireless Sensor Networks." Wireless Communications and Mobile Computing 2018 (November 6, 2018): 1–12. http://dx.doi.org/10.1155/2018/4618056.

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Due to the restricted hardware resources of the sensor nodes, modelling and designing energy efficient routing methods to increase the overall network lifetime have become one of the most significant strategies in wireless sensor networks (WSNs). Cluster-based heterogeneous routing protocols, a popular part of routing technology, have proven effective in management of topology, energy consumption, data collection or fusion, reliability, or stability in a distributed sensor network. In this article, an energy efficient three-level heterogeneous clustering method (DEEC) based distributed energy efficient clustering protocol named TBSDEEC (Threshold balanced sampled DEEC) is proposed. Contrary to most other studies, this study considers the effect of the threshold balanced sampled in the energy consumption model. Our model is compared with the DEEC, EDEEC (Enhanced Distributed Energy Efficient Clustering Protocol), and EDDEEC (Enhanced Developed Distributed Energy Efficient Clustering Protocol) using MATLAB as two different scenarios based on quality metrics, including living nodes on the network, network efficiency, energy consumption, number of packets received by base station (BS), and average latency. After, our new method is compared with artificial bee colony optimization (ABCO) algorithm and energy harvesting WSN (EH-WSN) clustering method. Simulation results demonstrate that the proposed model is more efficient than the other protocols and significantly increases the sensor network lifetime.

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38

Caballero-Águila, Raquel, Aurora Hermoso-Carazo, and Josefa Linares-Pérez. "A Two-Phase Distributed Filtering Algorithm for Networked Uncertain Systems with Fading Measurements under Deception Attacks." Sensors 20, no. 22 (November 11, 2020): 6445. http://dx.doi.org/10.3390/s20226445.

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In this paper, the distributed filtering problem is addressed for a class of discrete-time stochastic systems over a sensor network with a given topology, susceptible to suffering deception attacks, launched by potential adversaries, which can randomly succeed or not with a known success probability, which is not necessarily the same for the different sensors. The system model integrates some random imperfections and features that are frequently found in real networked environments, namely: (1) fading measurements; (2) multiplicative noises in both the state and measurement equations; and (3) sensor additive noises cross-correlated with each other and with the process noise. According to the network communication scheme, besides its own local measurements, each sensor receives the measured outputs from its adjacent nodes. Based on such measurements, a recursive algorithm is designed to obtain the least-squares linear filter of the state. Thereafter, each sensor receives the filtering estimators previously obtained by its adjacent nodes, and these estimators are all fused to obtain the desired distributed filter as the minimum mean squared error matrix-weighted linear combination of them. The theoretical results are illustrated by a simulation example, where the efficiency of the developed distributed estimation strategy is discussed in terms of the error variances.

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39

Millán, P., L. Orihuela, C. Vivas, F. R. Rubio, D. V. Dimarogonas, and K. H. Johansson. "Sensor-network-based robust distributed control and estimation." Control Engineering Practice 21, no. 9 (September 2013): 1238–49. http://dx.doi.org/10.1016/j.conengprac.2013.05.002.

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40

Yang, Xiong, Yuling Chen, Xiaobin Qian, Tao Li, and Xiao Lv. "BCEAD: A Blockchain-Empowered Ensemble Anomaly Detection for Wireless Sensor Network via Isolation Forest." Security and Communication Networks 2021 (November 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/9430132.

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The distributed deployment of wireless sensor networks (WSNs) makes the network more convenient, but it also causes more hidden security hazards that are difficult to be solved. For example, the unprotected deployment of sensors makes distributed anomaly detection systems for WSNs more vulnerable to internal attacks, and the limited computing resources of WSNs hinder the construction of a trusted environment. In recent years, the widely observed blockchain technology has shown the potential to strengthen the security of the Internet of Things. Therefore, we propose a blockchain-based ensemble anomaly detection (BCEAD), which stores the model of a typical anomaly detection algorithm (isolated forest) in the blockchain for distributed anomaly detection in WSNs. By constructing a suitable block structure and consensus mechanism, the global model for detection can iteratively update to enhance detection performance. Moreover, the blockchain guarantees the trust environment of the network, making the detection algorithm resistant to internal attacks. Finally, compared with similar schemes, in terms of performance, cost, etc., the results prove that BCEAD performs better.

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41

Aslam, Muhammad, Fan Wang, Xiaopeng Hu, Muhammad Asad, and Ehsan Ullah Munir. "Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks." Journal of Sensors 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/5378403.

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Effective utilization of energy resources in Wireless Sensor Networks (WSNs) has become challenging under uncertain distributed cluster-formation and single-hop intercluster communication capabilities. So, sensor nodes are forced to operate at expensive full rate transmission power level continuously during whole network operation. These challenging network environments experience unwanted phenomena of drastic energy consumption and packet drop. In this paper, we propose an adaptive immune Multihopping Multilevel Clustering (MHMLC) protocol that executes a Hybrid Clustering Algorithm (HCA) to perform optimal centralized selection of Cluster-Heads (CHs) within radius of centrally located Base Station (BS) and distributed CHs selection in the rest of network area. HCA of MHMLC also produces optimal intermediate CHs for intercluster multihop communications that develop heterogeneity-aware economical links. This hybrid cluster-formation facilitates the sensors to function at short range transmission power level that enhances link quality and avoids packet drop. The simulation environments produce fair comparison among proposed MHMLC and existing state-of-the-art routing protocols. Experimental results give significant evidence of better performance of the proposed model in terms of network lifetime, stability period, and data delivery ratio.

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42

Palpanas, Themistoklis, Dimitris Papadopoulos, Vana Kalogeraki, and Dimitrios Gunopulos. "Distributed deviation detection in sensor networks." ACM SIGMOD Record 32, no. 4 (December 2003): 77–82. http://dx.doi.org/10.1145/959060.959074.

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43

Flanagan, R., LJ Brandt, MR Deinert, and AG Osborne. "DETECTING NUCLEAR MATERIALS IN URBAN ENVIRONMENTS USING MOBILE SENSOR NETWORKS." EPJ Web of Conferences 247 (2021): 16003. http://dx.doi.org/10.1051/epjconf/202124716003.

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Radiation detectors installed at major ports of entry are a key component of the overall strategy to protect countries from nuclear terrorism. While the goal of deploying these systems is to intercept special nuclear material as it enters the country, no detector system is fool proof. Mobile, distributed sensors have been proposed to detect nuclear materials in transit should portal monitors fail to prevent their entry in the first place. In large metropolitan areas a mobile distributed sensor network could be deployed using vehicle platforms such as taxis, Ubers and Lyfts which are already connected to communications infrastructure. However, the potential geographic coverage that could be achieved using a network of sensors mounted on commercial passenger vehicles has not been established. Here we evaluate how a mobile sensor network could perform in New York City using a combination of radiation transport and Geographic Information Systems. The Geographic Information System QGIS is used in conjunction with OpenStreetMap data to isolate roads and construct a grid over the streets. Vehicle paths are built using pickup and drop off data from Uber, and data from the New York State Department of Transportation.

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44

Arroyo, Patricia, José Herrero, José Suárez, and Jesús Lozano. "Wireless Sensor Network Combined with Cloud Computing for Air Quality Monitoring." Sensors 19, no. 3 (February 8, 2019): 691. http://dx.doi.org/10.3390/s19030691.

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Low-cost air pollution wireless sensors are emerging in densely distributed networks that provide more spatial resolution than typical traditional systems for monitoring ambient air quality. This paper presents an air quality measurement system that is composed of a distributed sensor network connected to a cloud system forming a wireless sensor network (WSN). Sensor nodes are based on low-power ZigBee motes, and transmit field measurement data to the cloud through a gateway. An optimized cloud computing system has been implemented to store, monitor, process, and visualize the data received from the sensor network. Data processing and analysis is performed in the cloud by applying artificial intelligence techniques to optimize the detection of compounds and contaminants. This proposed system is a low-cost, low-size, and low-power consumption method that can greatly enhance the efficiency of air quality measurements, since a great number of nodes could be deployed and provide relevant information for air quality distribution in different areas. Finally, a laboratory case study demonstrates the applicability of the proposed system for the detection of some common volatile organic compounds, including: benzene, toluene, ethylbenzene, and xylene. Principal component analysis, a multilayer perceptron with backpropagation learning algorithm, and support vector machine have been applied for data processing. The results obtained suggest good performance in discriminating and quantifying the concentration of the volatile organic compounds.

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45

Brucie, James, Tessa Daniel, Elena Gaura, and John Halloran. "DISTRIBUTED EXTRACTION OF INFORMATION FROM LARGESCALE WIRELESS SENSOR NETWORKS." SOFT MEASUREMENTS AND COMPUTING 1, no. 9 (2021): 5–35. http://dx.doi.org/10.36871/2618-9976.2021.09.001.

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The article discusses distributed information extraction from large-scale wireless sensor networks. The first subsection identifies a family of applications that could benefit from a higher-level information retrieval and delivery mechanism integrated into the WSN, and discusses the state of the art of deployment in a particular application. The following describes the requirements for an integrated high-level information retrieval engine and provides an overview of the prior art in complex queries and processing on a network. The next subsection defines WSN topologies, architectures and protocols suitable for modern information systems; this is followed by a description of a new hybrid approach called a distributed complex query processor.

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46

Saputra, Ferry Astika, Muhammad Salman, Jauari Akhmad Nur Hasim, Isbat Uzzin Nadhori, and Kalamullah Ramli. "The Next-Generation NIDS Platform: Cloud-Based Snort NIDS Using Containers and Big Data." Big Data and Cognitive Computing 6, no. 1 (February 7, 2022): 19. http://dx.doi.org/10.3390/bdcc6010019.

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Snort is a well-known, signature-based network intrusion detection system (NIDS). The Snort sensor must be placed within the same physical network, and the defense centers in the typical NIDS architecture offer limited network coverage, especially for remote networks with a restricted bandwidth and network policy. Additionally, the growing number of sensor instances, followed by a quick increase in log data volume, has caused the present system to face big data challenges. This research paper proposes a novel design for a cloud-based Snort NIDS using containers and implementing big data in the defense center to overcome these problems. Our design consists of Docker as the sensor’s platform, Apache Kafka, as the distributed messaging system, and big data technology orchestrated on lambda architecture. We conducted experiments to measure sensor deployment, optimum message delivery from the sensors to the defense center, aggregation speed, and efficiency in the data-processing performance of the defense center. We successfully developed a cloud-based Snort NIDS and found the optimum method for message-delivery from the sensor to the defense center. We also succeeded in developing the dashboard and attack maps to display the attack statistics and visualize the attacks. Our first design is reported to implement the big data architecture, namely, lambda architecture, as the defense center and utilize rapid deployment of Snort NIDS using Docker technology as the network security monitoring platform.

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47

Rajesh, M., and J. M. Gnanasekar. "Constructing Well-Organized Wireless Sensor Networks with Low-level Identification." Oriental journal of computer science and technology 9, no. 1 (March 17, 2016): 17–23. http://dx.doi.org/10.13005/ojcst/901.04.

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In most distributed systems, naming of nodes for low-level communication leverages topological location (such as node addresses) and is independent of any application. In this paper, we investigate an emerging class of distributed systems where low-level communication does not rely on network topological location. Rather, low-level communication is based on attributes that are external to the network topology and relevant to the application. When combined with dense deployment of nodes, this kind of named data enables in-network processing for data aggregation, collaborative signal processing, and similar problems. These approaches are essential for emerging applications such as sensor networks where resources such as bandwidth and energy are limited. This paper is the first description of the software architecture that supports named data and in-network processing in an operational, multi-application sensor-network. We show that approaches such as in-network aggregation and nested queries can significantly affect network traffic. In one experiment aggregation reduces traffic by up to 42% and nested queries reduce loss rates by 30%. Although aggregation has been previously studied in simulation, this paper demonstrates nested queries as another form of in-network processing, and it presents the first evaluation of these approaches over an operational test bed.

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48

NIU, Jian-Jun, and Zhi-Dong DENG. "Markov Chain-based Distributed Scheduling Approach for Wireless Sensor Network." Acta Automatica Sinica 36, no. 5 (May 28, 2010): 685–95. http://dx.doi.org/10.3724/sp.j.1004.2010.00685.

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49

Shu, Haining, Qilian Liang, and Jean Gao. "Distributed Sensor Networks Deployment Using Fuzzy Logic Systems." International Journal of Wireless Information Networks 14, no. 3 (March 29, 2007): 163–73. http://dx.doi.org/10.1007/s10776-007-0060-7.

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50

Chen, Juntong, Rongyao Ling, Hongjie Ni, and Dan Zhang. "Distributed Fault Estimation of Nonlinear Networked Systems: Application to Robotic Manipulator." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/121636.

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This paper is concerned with the distributed fault estimation for a class of nonlinear networked systems, where the T-S fuzzy model is utilized to approximate the nonlinear plant and the whole fault estimation task is operated by a wireless sensor network. Due to the limited power in sensors, signal is quantized before transmission. Based on the Lyapunov stability theory and the robust control approach, a sufficient condition is obtained such that the estimation error system is asymptotic stable with a prescribedH∞performance level. Finally, a case study on the actuator fault estimation of robotic manipulator is given to show the effectiveness of the proposed design.

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