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in-network data processing Based on Compressed Sensing in WSN: A Survey

WIRELESS PERSONAL COMMUNICATIONS 2017年第2期96卷 2087-2124页

作者： Singh, Vishal Krishna Singh, Vivek Kumar Kumar, Manish Indian Inst Informat Technol Dept Informat Technol Network Secur & Cryptog Lab 4311 Comp Ctr 2 Allahabad 211012 Uttar Pradesh India Indian Inst Informat Technol Dept Informat Technol Comp Ctr 2 Allahabad 211012 Uttar Pradesh India

Energy conservation in resource constrained wireless sensor networks (WSNs) is an important concern. Techniques such as, energy conserving MAC protocols have been proved to be efficient to minimize the energy consumption in such networks. However, techniques based on in-network data compression have dominated recent research works for reducing the network traffic thereby improving the network lifetime. A large number of compression techniques have been proposed in recent years with diverse applications in WSNs. This work presents a comprehensive analysis of various existing in-network data processing techniques based on compressed sensing in WSNs. The essential features, requirements and characteristics of compression algorithms in WSNs, are discussed in detail with a classification of existing algorithms into different categories. A set of parameters are identified for a comparative evaluation of these algorithms to determine the efficacy of such in-network data processing techniques in an environment such as a WSN.

关键词： Compressed sensing Energy conservation in-network data processing network lifetime Wireless sensor networks

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in-network data processing Architecture for Energy Efficient Wireless Sensor networks 2

In-Network Data Processing Architecture for Energy Efficient...

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2015 IEEE 2nd World Forum on Internet of Things (WF-IoT)

作者： He, Weiwei Yang, Shuang-Hua Yang, Lili Li, Ping Univ Loughborough Dept Comp Sci Loughborough LE11 3TU Leics England Univ Loughborough Sch Business & Econ Loughborough LE11 3TU Leics England Liaoning Shihua Univ Sch Informat & Control Engn Fushun Peoples R China

ISBN: (纸本)9781509003662

In traditional WSNs, the sensor nodes are used only for capturing data, which is analyzed in a centralized base station. This requires a continuous communication but wastes the energy of sensor nodes and results in a reduction of the overall network lifetime. In this paper, we proposed an in-network data processing architecture to improve the energy efficiency and the scalability and the accuracy of sensor networks. We also adapted a data mining algorithm to process data for matching a specific event. The simulations show a reduction in energy consumption and an improvement in data accuracy.

关键词： in-network data processing Wireless Sensor network data compression Event detection

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Subjective Logic-Based in-network data processing for Trust Management in Collocated and Distributed Wireless Sensor networks

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IEEE SENSORS JOURNAL 2018年第15期18卷 6446-6460页

作者： Firoozi, Farhad Zadorozhny, Vladimir, I Li, Frank Y. Univ Agder Dept Informat & Commun Technol N-4898 Grimstad Norway Univ Pittsburgh Sch Informat Sci Pittsburgh PA 15260 USA

While analyzing an explosive amount of data collected in today's wireless sensor networks (WSNs), the redundant information in the sensed data needs to be handled. in-network data processing is a technique which can eliminate or reduce such redundancy, leading to minimized resource consumption. On the other hand, trust management techniques establish trust relationships among nodes and detect unreliable nodes. In this paper, we propose two novel in-network data processing schemes for trust management in static WSNs. The first scheme targets at networks, where sensor nodes are closely collocated to report the same event. Considering both spatial and temporal correlations, this scheme generates trustworthiness for each sensor node-based solely on the data reported by the sensor. The second scheme is designed for networks, where sensor nodes are randomly distributed to sense ongoing events. This scheme produces node trustworthiness considering the variations in node observations. In addition, we propose an energy saving mode to reduce energy consumption and/or increase the reliability of a network. For trust generation, we employ a subjective logic-based scheme to mitigate trust fluctuations caused by various factors. Extensive simulations based on four scenarios are performed to evaluate the effectiveness of our proposed schemes.

关键词： Wireless sensor networks (WSNs) in-network data processing trust management subjective logic

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A Programmable data Plane to Support in-network data processing in Software-Defined IoT 8

A Programmable Data Plane to Support In-network Data Process...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Kim, Ki-Wook Min, Sung-Gi Han, Youn-Hee Korea Univ Dept Comp & Radio Commun Engn Seoul South Korea Korea Univ Technol & Educ Sch Comp Sci & Engn Cheonan South Korea

ISBN: (纸本)9781509040322

Making the data plane of a SDN flexible enough to satisfy the various requirements of heterogeneous IoT applications is very desirable for Software Defined IoT (SD-IoT) networking. The network device having programmable data plane provides an ability to add new packet and data processing procedures dynamically to the IoT applications. Previously proposed solutions for adding programmability to the SDN data plane provide extensibility for packet forwarding operations for new protocols, but IoT applications need more flexible programmability for in-network data processing operations e.g. data aggregation of sensing data from thousands of sensor nodes. Moreover, some IoT models such as OMG DDS, oneM2M, and Eclipse SCADA use publish-subscribe model, which is difficult to represent with the operations of existing message-centric data plane models. We introduce a new in-network data processing scheme for SD-IoT data plane. It defines an event-driven data processing model that can express various in-network data processing cases in the SD-IoT environment. It also proposes a language to program the data processing procedures using the model, and presents a flexible data plane structure that can install and execute the programs at runtime. We demonstrated flexibility of the proposing scheme with sample programs for some example SD-IoT cases.

关键词： Software-Defined IoT Programmable data Plane in-network data processing

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Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR networkS 2018年第11期14卷

作者： Kaugerand, Jaanus Ehala, Johannes Motus, Leo Preden, Jurgo-Soren Tallinn Univ Technol Res Lab Proact Technol Ehitajate Tee 5 EE-19086 Tallinn Estonia Thinnect Inc Sunnyvale CA USA

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

关键词： Time-selective data fusion in-network data processing data validity data alignment data simultaneity wireless ad hoc sensor networks situation awareness middleware

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Energy Efficient In-network RFID data Filtering Scheme in Wireless Sensor networks

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SENSORS 2011年第7期11卷 7004-7021页

作者： Bashir, Ali Kashif Lim, Se-Jung Hussain, Chauhdary Sajjad Park, Myong-Soon Korea Univ Dept Comp & Radio Commun Engn Seoul 136713 South Korea

RFID (Radio frequency identification) and wireless sensor networks are backbone technologies for pervasive environments. In integration of RFID and WSN, RFID data uses WSN protocols for multi-hop communications. Energy is a critical issue in WSNs;however, RFID data contains a lot of duplication. These duplications can be eliminated at the base station, but unnecessary transmissions of duplicate data within the network still occurs, which consumes nodes' energy and affects network lifetime. In this paper, we propose an in-network RFID data filtering scheme that efficiently eliminates the duplicate data. For this we use a clustering mechanism where cluster heads eliminate duplicate data and forward filtered data towards the base station. Simulation results prove that our approach saves considerable amounts of energy in terms of communication and computational cost, compared to existing filtering schemes.

关键词： RFID data filtering energy-efficiency in WSN in-network data processing integration of RFID with WSN redundant readers

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Distributed Reference-Free Fault Detection Method for Autonomous Wireless Sensor networks

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IEEE SENSORS JOURNAL 2013年第5期13卷 2009-2019页

作者： Lo, Chun Lynch, Jerome P. Liu, Mingyan Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

Compact and low-cost sensors used in wireless sensor networks are vulnerable to deterioration and failure. As the number and scale of sensor deployments grow, the failure of sensors becomes an increasingly paramount issue. This paper presents a distributed, reference-free fault detection algorithm that is based on local pair-wise verification between sensors monitoring the same physical system. Specifically, a linear relationship is shown to exist between the outputs of a pair of sensors measuring the same system. Using this relationship, faulty sensors may be detected within subsystems of the global system. Moreover, faulty sensors suffering from sparse spikes in their measurements can be identified with spike magnitudes and times accurately estimated. An appealing feature of the proposed method is that the need for reference sensors and complete knowledge of the system input are not required. Due to the pair-wise nature of the proposed algorithm, it can also be performed in a completely decentralized fashion. This ensures the method can be scaled to large sensor networks and lead to significant energy savings derived from reduced wireless communication compared to centralized approaches.

关键词： Fault detection in-network data processing wireless sensor networks (WSNs)

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Lightweight, latency-aware routing for data compression in wireless sensor networks with heterogeneous traffics

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2016年第9期16卷 1035-1049页

作者： Wang, You-Chiun Wei, Chia-Ting Natl Sun Yat Sen Univ Dept Comp Sci & Engn 70 Lienhai Rd Kaohsiung 80424 Taiwan

In many applications and scenarios, sensors have to regularly report what they monitor from the environment and quickly notify the sink node of event occurrence in the sensing field. An in-network data reduction technique, such as data aggregation and data compression, can help diminish the amount of data sent from sensors, which not only saves the network bandwidth but also preserves sensors' energy. However, such technique does not consider packet latency because of the aggregation or compression operation. When some sensors generate regular reports in lower data rates, their packets have to spend longer time to be aggregated or compressed, resulting in higher packet delays. Besides, when events occur, the network could suffer from instant congestion due to the generation of numerous event notifications. Motivated with the aforementioned observations, the paper develops a lightweight, latency-aware routing for data compression (L2DC) scheme to reduce packet latency when applying the compression technique, to reduce the amount of data generated from sensors. L2DC gives event notifications a higher priority over regular reports and eliminates unnecessary notifications to avoid bursty network congestion. In addition, L2DC facilitates the data compression process by allowing each sensor to determine whether to keep packets for compression locally or to send them to a neighbor to be compressed in a distributed manner. Our L2DC scheme can be easily built on most ad hoc and sensor routing protocols because it provides auxiliary redundant packet elimination and relay node selection mechanisms to reduce packet latency. By using the ad hoc on-demand distance vector protocol as the example, simulation results demonstrate the effectiveness of the L2DC scheme. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： data aggregation and compression in-network data processing packet delay routing protocol wireless sensor network

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Deployment and evaluation of a fully applicable distributed event detection system in Wireless Sensor networks

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AD HOC networkS 2016年 37卷 160-182页

作者： Dziengel, Norman Seiffert, Martin Ziegert, Marco Adler, Stephan Pfeiffer, Stefan Schiller, Jochen Free Univ Berlin Dept Math & Comp Sci D-14195 Berlin Germany

Versatility and real world applicability are key features of Wireless Sensor networks (WSNs). In order to achieve these benefits we have to face the challenges of high practical relevance during application. We deploy and evaluate the concrete example of a fence monitoring task to reveal how our distributed event detection system is able to perform under real application conditions. The challenge is to bring both opposing aspects-high event detection accuracy and long service life-into one applicable ubiquitous system. If sensor nodes compose parts of events cooperatively, a comprehensive event assessment with low energy demands is possible. We propose a classifier based distributed event detection system consisting of two frameworks. The evaluation framework delivers a classification model and enables the theoretical evaluation of a given training set. The distributed event detection framework subsequently applies the classification model to assess, filter and classify events within the network. We evaluate our system by training and detecting events with our WSN composed of 49 nodes which are integrated in construction site fence elements. We compare four different data application scenarios with varying data processing concepts and varying network sizes to analyze the resulting communication load as well as the system lifetime. We compare the results of our evaluation framework with the results of our application to show that the evaluation framework reflects the real world deployment results in a credible way. We show the full applicability of our approach by comparing the resulting increased event detection accuracy against our previous work. Compared to other information fusion scenarios, our distributed event detection system reduces energy consumption beyond a communication distance of two hops which yields a prolonged lifetime of the network while additionally achieving an improved event detection accuracy. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Wireless sensor network Distributed event detection in-network data processing Pattern recognition Deployment

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A Parallel Simulated Annealing Architecture for Model Updating in Wireless Sensor networks

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IEEE SENSORS JOURNAL 2009年第11期9卷 1503-1510页

作者： Zimmerman, Andrew T. Lynch, Jerome P. Univ Michigan Ann Arbor MI 48109 USA

In recent years, wireless sensing technologies have provided a much sought-after alternative to expensive cabled monitoring systems. Wireless sensing networks forego the high data transfer rates associated with cabled sensors in exchange for low-cost and low-power communication between a large number of sensing devices, each of which features embedded data processing capabilities. As such, a new paradigm in large-scale data processing has emerged;one where communication bandwidth is somewhat limited but distributed data processing centers are abundant. By taking advantage of this grid of computational resources, data processing tasks once performed independently by a central processing unit can now be parallelized, automated, and carried out within a wireless sensor network. By utilizing the intelligent organization and self-healing properties of many wireless networks, an extremely scalable multiprocessor computational framework can be developed to perform advanced engineering analyses. In this study, a novel parallelization of the simulated annealing stochastic search algorithm is presented and used to update structural models by comparing model predictions to experimental results. The resulting distributed model updating algorithm is validated within a network of wireless sensors by identifying the mass, stiffness, and damping properties of a three-story steel structure subjected to seismic base motion.

关键词： in-network data processing parallel algorithms structural monitoring wireless sensors

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