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Готові списки джерел за темами / Sensor network and distributed systems

Добірка наукової літератури з теми "Sensor network and distributed systems"

Автор: Grafiati

Опубліковано: 21 червня 2022

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Статті в журналах з теми "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.

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.

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.

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.

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.

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.

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.

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.

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.

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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Дисертації з теми "Sensor network and distributed systems":

1

Jeong, Dong Hwa. "DISTRIBUTED WIRELESS SENSOR NETWORK SYSTEMS: THEORETICAL FRAMEWORK, ALGORITHMS, AND APPLICATIONS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1436541959.

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2

Tewatia, Rohit. "Security in Distributed Embedded Systems." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1379.

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Communication in a sensor network needs guaranteed reception of data without fail and providing security to it. The authenticity and confidentiality of the data has to be ensured as sensors have limited hardware resources as well as the bandwidth. This thesis addresses the security aspects in wireless sensor networks. The main task of the project is to identify the critical security parameters for these distributed embedded systems. The sensors have extremely limited resources: small amount of memory, low computation capability and poor bandwidth. For example, a sensor platform can have 8KB of flash memory, a 4MHz 8-bit Atmel processor, and a 900MHz radio interface. Various security threats posed to these small wireless sensor networks has been made and solutions proposed. Secure communication between these communicating partners is to be achieved using cryptography.

3

Dehmelt, Chris. "Integration of Smart Sensor Buses into Distributed Data Acquisition Systems." International Foundation for Telemetering, 2005. http://hdl.handle.net/10150/604924.

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ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada
As requirements for the amount of test data continues to increase, instrumentation engineers are under pressure to deploy data acquisition systems that reduce the amount of associated wiring and overall system complexity. Smart sensor buses have been long considered as one approach to address this issue by placing the appropriate signal conditioners close to their respective sensors and providing data back over a common bus. However, the inability to adequately synchronize the operation of the sensor bus to the system master, which is required to correlate analog data measurements, has precluded their use. The ongoing development and deployment of smart sensor buses has reached the phase in which integration into a larger data acquisition system environment must be considered. Smart sensor buses, such as IntelliBus™, have their own unique mode of operation based on a pre-determined sampling schedule, which however, is typically asynchronous to the operation of the (master or controller) data acquisition system and must be accounted for when attempting to synchronize the two systems. IRIG Chapter 4 type methods for inserting data into a format, as exemplified by the handling of MIL-STD-1553 data, could be employed, with the disadvantage of eliminating any knowledge as to when a particular measurement was sampled, unless it is time stamped (similar to the time stamping function that is provided to mark receipt of 1553 command words). This can result in excessive time data as each sensor bus can manage a large number of analog sensor inputs and multiple sensor buses must be accommodated by the data acquisition system. The paper provides an example, using the Boeing developed IntelliBus system and the L3 Communications - Telemetry East NetDAS system, of how correlated data can be acquired from a smart sensor bus as a major subsystem component of a larger integrated data acquisition system. The focus will be specifically on how the IntelliBus schedule can be synchronized to that of the NetDAS formatter. Sample formats will be provided along with a description of how a standalone NetDAS stack and an integrated NetDAS-IntelliBus system would be programmed to create the required output, taking into account the unique sampling characteristics of the sensor bus.

4

Luo, Bin, and 羅斌. "Distributed clock synchronization for wireless sensor networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/198812.

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Clock synchronization for Wireless Sensor Networks (WSNs) has attracted lots of attention due to its importance for operations in WSNs. In traditional centralized clock synchronization algorithms, all the local information should be transmitted to a fusion center for processing, and the results need to be forwarded back to each individual sensor, thus resulting in a heavy burden on communication and computation in the network. In addition, it also lacks of adaptability to link failures and dynamic changes in the network topology, which greatly prevents their use in WSNs. Hence, in this thesis, we focus on developing energy-efficient distributed clock synchronization algorithms for WSNs. Firstly, global clock synchronization problem is investigated with time-varying clock parameters (skew and offset) owing to imperfect oscillator circuits. A distributed Kalman filter is developed for clock parameters tracking. The proposed algorithm only requires each node to exchange limited information with its direct neighbors, thus is energy efficient, scalable with network size, and is robust against changes in network connectivity. A low-complexity distributed algorithm based on Coordinate-Descent with Bootstrap (CD-BS) is also proposed to provide rapid initialization of the tracking algorithm. Simulation results show that the proposed distributed tracking algorithm achieves the long-term accuracy for the clock parameters close to the Bayesian Cramer-Rao Lower Bound. Secondly, the problem of global clock synchronization for WSNs in the presence of unknown exponential delays is studied. The joint maximum likelihood estimator of clock offsets, clock skews and fixed delays of the network is first formulated as a global linear programming (LP) problem. Based on the Alternating Direction Method of Multipliers (ADMM), we propose a fully-distributed synchronization algorithm that has low communication overhead and computation cost. Simulation results show that the proposed algorithm achieves better accuracy than consensus algorithm and the distributed least squares algorithm, and can always converge to the centralized optimal solution. Finally, global clock synchronization for WSNs under the exponentially distributed delays is re-visited with the fast convergence min-sum algorithm. The synchronization problem is cast into an optimization problem represented by factor graph, and a closed-form expression of the messages passed between nodes are derived. Simulation results show that this distributed algorithm can approach the centralized LP solution with faster convergence speed compared to ADMM-based algorithm.
published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy

5

Cheung, Victor. "Distributed position estimation for wireless sensor networks /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20CHEUNG.

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6

Ocean, Michael James. "The Sensor Network Workbench: Towards Functional Specification, Verification and Deployment of Constrained Distributed Systems." Boston University Computer Science Department, 2009. https://hdl.handle.net/2144/1713.

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As the commoditization of sensing, actuation and communication hardware increases, so does the potential for dynamically tasked sense and respond networked systems (i.e., Sensor Networks or SNs) to replace existing disjoint and inflexible special-purpose deployments (closed-circuit security video, anti-theft sensors, etc.). While various solutions have emerged to many individual SN-centric challenges (e.g., power management, communication protocols, role assignment), perhaps the largest remaining obstacle to widespread SN deployment is that those who wish to deploy, utilize, and maintain a programmable Sensor Network lack the programming and systems expertise to do so. The contributions of this thesis centers on the design, development and deployment of the SN Workbench (snBench). snBench embodies an accessible, modular programming platform coupled with a flexible and extensible run-time system that, together, support the entire life-cycle of distributed sensory services. As it is impossible to find a one-size-fits-all programming interface, this work advocates the use of tiered layers of abstraction that enable a variety of high-level, domain specific languages to be compiled to a common (thin-waist) tasking language; this common tasking language is statically verified and can be subsequently re-translated, if needed, for execution on a wide variety of hardware platforms. snBench provides: (1) a common sensory tasking language (Instruction Set Architecture) powerful enough to express complex SN services, yet simple enough to be executed by highly constrained resources with soft, real-time constraints, (2) a prototype high-level language (and corresponding compiler) to illustrate the utility of the common tasking language and the tiered programming approach in this domain, (3) an execution environment and a run-time support infrastructure that abstract a collection of heterogeneous resources into a single virtual Sensor Network, tasked via this common tasking language, and (4) novel formal methods (i.e., static analysis techniques) that verify safety properties and infer implicit resource constraints to facilitate resource allocation for new services. This thesis presents these components in detail, as well as two specific case-studies: the use of snBench to integrate physical and wireless network security, and the use of snBench as the foundation for semester-long student projects in a graduate-level Software Engineering course.

7

Pandit, Saurav. "Approximation algorithms for distributed systems." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/870.

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Distributed Approximation is a new and rapidly developing discipline that lies at the crossroads of various well-established areas of Computer Science - Distributed Computing, Approximation Algorithms, Graph Theory and often, Computational Geometry. This thesis focuses on the design and analysis of distributed algorithms to solve optimization problems that usually arise in large-scale, heavily dynamic, resource constrained networks, e.g. wireless ad-hoc and sensor networks, P2P systems, mobile networks etc. These problems can often be abstracted by variations of well-known combinatorial optimization problems, such as topology control, clustering etc. Many of these problems are known to be hard (NP-complete). But we need fast and light-weight distributed algorithms for these problems, that yield near-optimal solutions. The results presented in this thesis can be broadly divided in two parts. The first part contains a set of results that obtain improved solutions to the classic problem of computing a sparse "backbone" for Wireless Sensor Networks (WSNs). In graph-theoretic terms, the goal is to compute a spanning subgraph of the input graph, that is sparse, lightweight and has low stretch. The term "low stretch" indicates that in spite of dropping many edges, the distance between any two nodes in the graph is not increased by much. We model WSNs as geometric graphs - unit ball graphs, quasi-unit ball graphs etc. in Euclidean spaces, as well as in more general metric spaces of low doubling dimension. We identify and exploit a variety of geometric features of those models to obtain our results. In the second part of the thesis we focus on distributed algorithms for clustering problems. We present several distributed approximation algorithms for clustering problems (e.g., minimum dominating set, facility location problems) that improve on best known results so far. The main contribution here is the design of distributed algorithms where the running time is a "tunable" parameter. The advent of distributed systems of unprecedented scale and complexity motivates the question of whether it is possible to design algorithms that can provide non-trivial approximation guarantees even after very few rounds of computation and message exchanges. We call these algorithms "k-round algorithms". We design k-round algorithms for various clustering problems that yield non-trivial approximation factors even if k is a constant. Additionally, if k assumes poly-logarithmic values, our algorithms match or improve on the best-known approximation factors for these problems.

8

Hiremath, Naveen. "SenMinCom pervasive distributed dynamic sensor data mining for effective commerce /." unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-07172008-230611/.

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Thesis (M.S.)--Georgia State University, 2008.
Title from file title page. Yanqing Zhang, committee chair; Rajshekhar Sunderraman, Ying Zhu, committee members. Electronic text (64 p. : ill. (some col.)) : digital, PDF file. Description based on contents viewed Nov. 19, 2008. Includes bibliographical references (p. 59-64).

9

Techateerawat, Piya, and piyat33@yahoo com. "Key distribution and distributed intrusion detection system in wireless sensor network." RMIT University. Electrical and Computer Systems Engineering, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080729.162610.

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This thesis proposes a security solution in key management and Intrusion Detection System (IDS) for wireless sensor networks. It addresses challenges of designing in energy and security requirement. Since wireless communication consumes the most energy in sensor network, transmissions must be used efficiently. We propose Hint Key Distribution (HKD) for key management and Adaptive IDS for distributing activated IDS nodes and cooperative operation of these two protocols. HKD protocol focuses on the challenges of energy, computation and security. It uses a hint message and key chain to consume less energy while self-generating key can secure the secret key. It is a proposed solution to key distribution in sensor networks. Adaptive IDS uses threshold and voting algorithm to distribute IDS through the network. An elected node is activated IDS to monitor its network and neighbors. A threshold is used as a solution to reduce number of repeated activations of the same node. We attempt to distribute the energy use equally across the network. In a cooperative protocol, HKD and Adaptive IDS exchange information in order to adjust to the current situation. The level of alert controls the nature of the interaction between the two protocols.

10

Breza, Michael. "Bio-inspired tools for a distributed wireless sensor network operating system." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/14404.

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The problem which I address in this thesis is to find a way to organise and manage a network of wireless sensor nodes using a minimal amount of communication. To find a solution I explore the use of Bio-inspired protocols to enable WSN management while maintaining a low communication overhead. Wireless Sensor Networks (WSNs) are loosely coupled distributed systems comprised of low-resource, battery powered sensor nodes. The largest problem with WSN management is that communication is the largest consumer of a sensor node’s energy. WSN management systems need to use as little communication as possible to prolong their operational lifetimes. This is the Wireless Sensor Network Management Problem. This problem is compounded because current WSN management systems glue together unrelated protocols to provide system services causing inter-protocol interference. Bio-inspired protocols provide a good solution because they enable the nodes to self-organise, use local area communication, and can combine their communication in an intelligent way with minimal increase in communication. I present a combined protocol and MAC scheduler to enable multiple service protocols to function in a WSN at the same time without causing inter-protocol interference. The scheduler is throughput optimal as long as the communication requirements of all of the protocols remain within the communication capacity of the network. I show that the scheduler improves a dissemination protocol’s performance by 35%. A bio-inspired synchronisation service is presented which enables wireless sensor nodes to self organise and provide a time service. Evaluation of the protocol shows an 80% saving in communication over similar bio-inspired synchronisation approaches. I then add an information dissemination protocol, without significantly increasing communication. This is achieved through the ability of our bio-inspired algorithms to combine their communication in an intelligent way so that they are able to offer multiple services without requiring a great deal of inter-node communication.

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Книги з теми "Sensor network and distributed systems":

1

Bhanu, Bir. Distributed video sensor networks. Edited by Distributed video sensor networks-research challenges and future directions workshop (2009 : Riverside, Calif.). London: Springer, 2011.

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2

Rashvand, Habib F. Distributed sensor systems: Practice and applications. Hoboken, N.J: Wiley, 2012.

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3

Patan, Maciej. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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4

Patan, Maciej. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28230-0.

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5

Buratti, Chiara. Sensor Networks with IEEE 802.15.4 Systems: Distributed Processing, MAC, and Connectivity. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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6

Hinze, Annika. Principles and applications of distributed event-based systems. Hershey, PA: Information Science Reference, 2010.

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7

DCOSS 2008 (2008 Thera Island, Greece). Distributed computing in sensor systems: 4th IEEE international conference, DCOSS 2008, Santorini Island, Greece, June 11-14, 2008 : proceedings. Berlin: Springer, 2008.

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8

DCOSS 2010 (2010 Santa Barbara, Calif.). Distributed computing in sensor systems: 6th IEEE International Conference, DCOSS 2010, Santa Barbara, CA, USA, June 21-23, 2010 ; proceedings. Berlin: Springer, 2010.

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9

Dragotti, Pier Luigi. Distributed source coding: Theory, algorithms, and applications. Amsterdam: Academic Press, 2009.

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10

Rashvand, Habib F., and Jose M. Alcaraz Calero. Distributed Sensor Systems. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119941354.

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Більше джерел

Частини книг з теми "Sensor network and distributed systems":

1

Kumar, Nitin, Dimitrios Gunopulos, and Vana Kalogeraki. "Sensor Network Coverage Restoration." In Distributed Computing in Sensor Systems, 409. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11502593_41.

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2

Dong, Hongli, Zidong Wang, and Nan Hou. "Distributed Estimation over Sensor Network." In Networked Nonlinear Stochastic Time-Varying Systems, 119–58. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003189497-6.

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3

van der Lee, Tim, Georgios Exarchakos, and Sonia Heemstra de Groot. "In-network Hebbian Plasticity for Wireless Sensor Networks." In Internet and Distributed Computing Systems, 79–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34914-1_8.

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4

Angluin, Dana, James Aspnes, Melody Chan, Michael J. Fischer, Hong Jiang, and René Peralta. "Stably Computable Properties of Network Graphs." In Distributed Computing in Sensor Systems, 63–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11502593_8.

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5

Hohlt, Barbara, and Eric Brewer. "Network Power Scheduling for TinyOS Applications." In Distributed Computing in Sensor Systems, 443–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776178_27.

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6

Abe, Rey, and Shinichi Honiden. "Suppressing Redundancy in Wireless Sensor Network Traffic." In Distributed Computing in Sensor Systems, 187–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13651-1_14.

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7

Chenji, Harsha, and Radu Stoleru. "Mobile Sensor Network Localization in Harsh Environments." In Distributed Computing in Sensor Systems, 244–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13651-1_18.

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8

Chan, Haowen, Mark Luk, and Adrian Perrig. "Using Clustering Information for Sensor Network Localization." In Distributed Computing in Sensor Systems, 109–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11502593_11.

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9

Eswaran, Sharanya, Matthew Johnson, Archan Misra, and Thomas La Porta. "Adaptive In-Network Processing for Bandwidth and Energy Constrained Mission-Oriented Multi-hop Wireless Networks." In Distributed Computing in Sensor Systems, 87–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02085-8_7.

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10

Schmitt, Jens B., and Utz Roedig. "Sensor Network Calculus – A Framework for Worst Case Analysis." In Distributed Computing in Sensor Systems, 141–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11502593_13.

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Тези доповідей конференцій з теми "Sensor network and distributed systems":

1

Licht, Torsten, and Abhijit Deshmukh. "Hierarchically Organized Bayesian Networks for Distributed Sensor Networks." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-33217.

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Анотація:

As sensor hardware becomes more sophisticated, smaller in size and increasingly affordable, use of large scale sensor networks is bound to become a reality in several application domains, such as vehicle condition monitoring, environmental sensing and security assessment. The ability to incorporate communication and decision capabilities in individual or groups of sensors, opens new opportunities for distributed sensor networks to monitor complex engineering systems. In such large scale sensor networks, the ability to integrate observations or inferences made by distributed sensors into a single hypothesis about the state of the system is critical. This paper addresses the sensor integration issue in hierarchically organized sensor networks. We propose a multi-agent architecture for distributed sensor networks. We present a new formalism to represent causal relations and prior beliefs of hierarchies of sensors, called Hierarchically Organized Bayesian Networks (HOBN), which is a semantic extension of Multiply Sectioned Bayesian Networks (MSBN). This formalism allows a sensor to reason about the integrity of a sensed signal or the integrity of neighboring sensors. Furthermore, we can also evaluate the consistency of local observations with respect to the knowledge of the system gathered up to that point.

2

Mari, P., F. P. Font, M. A. Dom, and S. Otero. "Application-Oriented Distributed Sensor Network." In 2008 Third International Conference on Systems ICONS. IEEE, 2008. http://dx.doi.org/10.1109/icons.2008.64.

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3

Madhag, Aqeel, and Jongeun Choi. "Distributed Navigation Strategy of Mobile Sensor Networks With Probabilistic Wireless Communication Links." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9964.

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Mobile sensor networks have been widely used to predict spatio-temporal physical phenomena for various scientific and engineering applications. To accommodate the realistic models of mobile sensor networks, we incorporated probabilistic wireless communication links based on packet reception ratio (PRR) with distributed navigation. We then derived models of mobile sensor networks that predict Gaussian random fields from noise-corrupted observations under probabilistic wireless communication links. For the given model with probabilistic wireless communication links, we derived the prediction error variances for further sampling locations. Moreover, we designed a distributed navigation that minimizes the network cost function formulated in terms of the derived prediction error variances. Further, we have shown that the solution of distributed navigation with the probabilistic wireless communication links for mobile sensor networks are uniformly ultimately bounded with respect to that of the distributed one with the R-disk communication model. According to Monte Carlo simulation results, agent trajectories under distributed navigation with the probabilistic wireless communication links are similar to those with the R-disk communication model, which confirming the theoretical analysis.

4

Zheng, Jun, and Asghar Dehghani. "LNNE: A Neural Network Ensembles-based Localization Algorithm in Wireless Sensor Networks." In Parallel and Distributed Computing and Systems. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.757-072.

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5

Zheng, Jun, and Asghar Dehghani. "LNNE: A Neural Network Ensembles-based Localization Algorithm in Wireless Sensor Networks." In Parallel and Distributed Computing and Systems. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.757-072.

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6

Winkler, Daniel, and Alberto E. Cerpa. "Distributed independent actuation for irrigation control." In SenSys '14: The 12th ACM Conference on Embedded Network Sensor Systems. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2674061.2674077.

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7

Jasko, Szilrd, and Gyula Simon. "Reconfigurable sensor network architecture for distributed measurement systems." In 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings. IEEE, 2010. http://dx.doi.org/10.1109/imtc.2010.5488158.

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8

Horsanali, Emre, Yagmur Yigit, Gokhan Secinti, Aytac Karameseoglu, and Berk Canberk. "Network-Aware AutoML Framework for Software-Defined Sensor Networks." In 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2021. http://dx.doi.org/10.1109/dcoss52077.2021.00076.

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9

Dreicer, Jared S. "Distributed Sensor Network With Collective Computation For Situational Awareness." In UNATTENDED RADIATION SENSOR SYSTEMS FOR REMOTE APPLICATIONS. AIP, 2002. http://dx.doi.org/10.1063/1.1513975.

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10

Sanderson, Arthur, Vadiraj Hombal, David Fries, Heather Broadbent, James Wilson, Pragnesh Bhanushali, Stanislav Ivanov, Mark Luther, and Steve Meyers. "Distributed Enviromental Sensor Network: Design and Experiments." In 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/mfi.2006.265595.

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Звіти організацій з теми "Sensor network and distributed systems":

1

Chen, Kevin P. High Spatial Resolution Distributed Fiber-Optic Sensor Networks for Reactors and Fuel Cycle Systems. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1475174.

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2

Schmidt, Harry. Integrated Sensing Processor (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada453717.

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3

Smith, Clint, Andmorgan Fischer, Alex Ly, and Michael Anderson. Autonomous QUerying and PATHogen threat agent sensor system (AQUA PATH) : monitoring source waters with geospatially wirelessly networked distributed sensing systems. Engineer Research and Development Center (U.S.), July 2020. http://dx.doi.org/10.21079/11681/37533.

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4

Schmitt, Harry. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada444037.

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5

Schmitt, Harry A. Integrated Sensing and Processing (ISP) Phase II: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada454039.

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6

Schmitt, Harry A. Integrated Sensing and Processing (ISP) Phase 2: Demonstration and Evaluation for Distributed Sensor Networks and Missile Seeker Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada468089.

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7

Brennan, Sean M. Distributed Sensor Network Software Development Testing through Simulation. Office of Scientific and Technical Information (OSTI), December 2003. http://dx.doi.org/10.2172/833222.

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8

Kadambe, Shubha, and Cindy Daniell. Theoretic Based Performance Analysis of Distributed Sensor Network. Fort Belvoir, VA: Defense Technical Information Center, October 2003. http://dx.doi.org/10.21236/ada419203.

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9

Tumer, Kagan. Distributed sensor coordination for advanced energy systems. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1191171.

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10

Tumer, Kagan. Distributed Sensor Coordination for Advanced Energy Systems. Office of Scientific and Technical Information (OSTI), July 2013. http://dx.doi.org/10.2172/1113755.

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