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Enhancing data delivery with density controlled clustering in wireless sensor networks

MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-information STORAGE AND processing SYSTEMS 2017年第3期23卷 613-631页

作者： Raval, Gaurang Bhavsar, Madhuri Patel, Nitin Nirma Univ Inst Technol CSE Dept Ahmadabad Gujarat India

Wireless sensor networks (WSN) are primarily used for sensing and collecting the information from environment. This information is sent to base station (BS), where, it is processed and analyzed by the underlying application. Functioning of WSN highly depend on the deployment strategy and the coordination among the sensor nodes. Preserving the energy is an important goal that must be considered when developing a routing protocol for WSNs. Radio communication in sensor nodes is highly expensive operation in terms of energy usage. Energy can be conserved in an efficient way with specifically customized routing techniques. Data aggregation methods when applied to sensor nodes, clusters are created where data generated from cluster members is aggregated. This kind of data collection strategy results into energy efficient communication. Integration of data aggregation and clustering approach is enabled through customized hierarchical routing strategies. Clustering technique is key to apply and exploit, the advantages in-network data processing offers. This paper compares various clustering protocols like LEACHC, K-means and its variants. The protocols are compared with respect to network lifetime, data delivery and energy consumption. These protocols only consider intra cluster distance of members while doing clustering, due to this non-uniform clusters are created. These kind of clusters do not deliver data periodically and uniformly from every corner of the field. A density control based approach is proposed which does balancing of cluster members assignment in a loose way. A loose density control (LDC) based approach is integrated with K-means clustering method. LDC based approach exhibits improved average data delivery per node, ensuring regular data availability from every corner of the deployment area as long as the nodes are alive. Not only it improves the amount of data delivered, it also improves the network lifetime considerably. This performance improvement is

关键词： WIRELESS sensor networks ROUTING protocols (Computer network protocols) WIRELESS sensor nodes ENERGY consumption DATA analysis

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Sustainable life-span of WSN nodes using participatory devices in pervasive environment

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MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-information STORAGE AND processing SYSTEMS 2017年第3期23卷 651-657页

作者： Prabakaran, N. Kannan, R. Jagadeesh VIT Univ Vellore 632014 Tamil Nadu India

Wireless sensor network being a dominant prerequisite in the modern pervasive environment has nodes connected with multi-hop to transmit and reinforce continuous monitoring with real-time updates from the field environment. To achieve pervasiveness integrating wireless and physical devices is unavoidable. Numerous self-organized tiny sensor nodes cooperate with each other to form the clusters and the most prominent node act as cluster head (CH). The cluster head pioneered based on its battery forte whose failure affects rest of the communications. In this paper, we discuss on the essential of idle resource sharing using participatory devices as relay nodes along with node failure rate and node density to achieve reliable communication. The earlier performances are observed and results are revealed. Hence we concentrate on minimizing the task of CH using relay nodes such as participatory devices and the faulty nodes are identified over Poisson distribution which observes the failure probability without affecting communication and reduced resource consumption.

关键词： WIRELESS sensor networks WIRELESS sensor nodes POISSON distribution FAILURE analysis (Engineering) PROBABILITY theory

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Energy Efficient Protocol to Supervise Overground Pipelines using WSN

Energy Efficient Protocol to Supervise Overground Pipelines ...

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IEEE international Conference on Recent Trends in Electronics, information & Communication Technology

作者： Neetu Verma Neha Pandey Prince Rajpoot Information Technology REC Ambedkar Nagar Akbarpur India

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

Wireless sensor Network (WSN) is a prominent field which is actively researched by many researchers. WSN is a network which contains the huge number of tiny wireless sensor nodes having ability of communication, signal processing, sensing the information from its domain and one of the important, transmitting the information to the sink node after performing some mathematical computations. It is widely used in different applications as directory control, ecology, military, health issues and miscellaneous parts of pipelines. Each node of the network has the different amount of energy and nodes lose their energy in receiving and transmitting information. Due to usability in remote areas, the energy of these sensor nodes can not be recharged again. So it is important to find out the way of saving energy of nodes in transmitting the sensed information. In pipelines, a WSN with non-rechargable sensor nodes is needed that is having the property of sensing and routing the information to sink. So that every abnormal activity can be reported to the sink. Since, it is a remote area so the data communication should be in efficient manner to increase the lifetime of WSN. In this paper, we consider an overground gas pipeline that is monitored underground, on the pipeline and around the pipeline. For this, we have designed an architecture of WSN to collect information about any abnormal activity using an energy efficient approach.

关键词： Pipelines Wireless sensor networks Logic gates Data models Computer architecture Data communication Energy consumption

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Evaluating deep learning approaches to characterize and classify the DGAs at scale

Evaluating deep learning approaches to characterize and clas...

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3rd international symposium on Signal processing and Intelligent Recognition Systems (SIRS)

作者： Vinayakumar, R. Soman, K. P. Poornachandran, Prabaharan Kumar, S. Sachin Amrita Univ Amrita Vishwa Vidyapeetham Amrita Sch Engn Ctr Computat Engn & Networking CEN Coimbatore Tamil Nadu India Amrita Univ Amrita Vishwa Vidyapeetham Amrita Sch Engn Ctr Cyber Secur Syst & Networks Amritapuri Tamil Nadu India

In recent years, domain generation algorithms (DGAs) are the foundational mechanisms for many malware families. Mainly, due to the fact that DGA can generate immense number of pseudo random domain names to associate to a command and control (C2) infrastructures. This paper focuses on to detect and classify the pseudo random domain names without relying on the feature engineering or any other linguistic, contextual or semantics and statistical information by adopting deep learning approaches. A deep learning approach is a complex model of traditional machine learning mechanism that has received renewed interest by solving the long-standing tasks in artificial intelligence (AI) related to the field of natural language processing, image recognition, speech processing and many others. They have immense capability to extract optimal feature representations by taking input as in the form of raw input texts. To leverage this and to transfer the performance enhancement in aforementioned areas towards characterize, detect and classify the DGA generated domain names to a specific malware family, this paper adopts deep learning mechanisms with a known one million benign domain names from Alexa, OpenDNS and a corpus of malicious domain names generated from 17 DGA malware families in real time for training in character and bigram level and a trained model has been evaluated on the OSNIT data set in real-time. Specifically, to understand the effectiveness of various deep learning mechanisms, we used recurrent neural network (RNN), identity-recurrent neural network (I-RNN), long short-term memory (LSTM), convolution neural network (CNN), and convolutional neural network-long short-term memory (CNN-LSTM) architectures. Additionally, to find out an optimal architecture, experiments are done with various configurations of network parameters and network structures. All experiments run up to 1000 epochs with a learning rate set in the range [0.01-0.5]. Overall, deep learning approaches

关键词： Domain generation algorithms (DGAs) deep learning mechanisms recurrent neural network (RNN) identity-recurrent neural network (IRNN) long short-term memory (LSTM) convolution neural network (CNN) and convolutional neural network-long short-term memory (CNN-LSTM)

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3rd international symposium of Space Optical Instruments and Applications 1st ed. 2017

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丛书名： Springer Proceedings in Physics

2017年

作者： H. Paul Urbach Guangjun Zhang

ISBN: (数字)9783319491844

ISBN: (纸本)9783319491837

This volume contains selected and expanded contributions presented at the 3rd symposium on Space Optical Instruments and Applications in Beijing, China June 28 29, 2016. This conference series is organised by the Sino-Holland Space Optical Instruments Laboratory, a cooperation platform between China and the Netherlands. The symposium focused on key technological problems of optical instruments and their applications in a space context. It covered the latest developments, experiments and results regarding theory, instrumentation and applications in space optics. The book issplit acrossfive topical sections. The first section covers space optical remote sensing system design, the second advanced optical system design, the third remote sensor calibration and measurement. Remote sensing data processing and information extraction is then presented, followed by a final section on remote sensing data applications.

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Gaussian Noise Filtering Using Pulse-Coupled Neural networks

Gaussian Noise Filtering Using Pulse-Coupled Neural Networks

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international Conference on Image, Vision and Computing (ICIVC)

作者： Ke Liu Keming Long Baozhen Ma Jing Yang School of Information Science and Engineering Lan Zhou University Lan Zhou China

ISBN: (纸本)9781538649923

In the process of collecting or transmitting images, various noise interferences are often introduced, especially in a multi-image sensor network, and noise has an important influence on subsequent image processing. Gaussian noise is a common noise in such systems. In order to filter Gaussian noise better, the neighborhood gray level difference weight matrix is proposed and applied to the Pulse-coupled neural network (PCNN). The matrix corresponds to the coupling-connection matrix of the PCNN and is determined by the related constraint relationship. From the perspective of image pixels, the neighborhood gray level difference weight matrix can adaptively change the gray level of the noisy pixels in the center of the neighborhood and improve the correlation of pixel gray levels in the neighborhood. From the macro perspective, the introduction of the neighborhood gray level difference weight matrix converts the image denoising process into a two-dimensional convolution operation. When the initial conditions are determined, parallel processing can be realized, which greatly improves the efficiency of the algorithm. These advantages make the proposed algorithm can be better combined with CNN and other networks as the front-end denoising module of these networks. The specific experiments show that the denoising effect of this algorithm is better, especially under the higher variance Gaussian noise.

关键词： Gaussian noise Neurons Filtering algorithms Wiener filters Filtering Mathematical model

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Low Cost Energy Efficient Smart Security System with information Stamping for IoT networks

Low Cost Energy Efficient Smart Security System with Informa...

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international Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU)

作者： Jayendra Kumar Paidi Raja Ramesh Dept. Electronics & Communication Engg National Institute of Technology Jamshedpur India

ISBN: (纸本)9781538638330

A security system with CCTV and other network nodes in IoT deals with large amount of data, so the need for devices with larger storage space comes into the picture which is a bit costlier. Traditional security systems are more power consuming as it has to record for 24x7 with more throughput but less efficient. Thus need human in middle to upload into cloud is needed. In this work, we propose a low-cost energy efficient smart security system for CCTV with other network nodes. Whenever motion is detected by passive infrared (PIR) sensor, at that time only the camera and all other security sensor nodes in network are activated (Switched on), the captured video and information (sensor data) are stamped on the image using image processing techniques on Python platform. It will be stored in a local storage device, after certain threshold (based on sensitivity of the location) data will be uploaded into the cloud along with data log created during processing stage by Python programming model. In this way, we can reduce the amount of data to be stored (as no recording when idle), consumed power (as device is switched off when idle), maintenance cost(as fully programmed). No need for human in middle for uploading data into the cloud (as program will upload data in to cloud). Because of time, date and information (data from sensor nodes) stamping on each frame of video using python programming model video access becomes easier, as data log will be created.

关键词： Security Cameras Cloud computing Internet of Things Switches sensors Python

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Data processing in semantic sensor web: A survey 3

Data processing in semantic sensor web: A survey

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3rd international Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2017

作者： Bhajantri, Lokesh B. Pundalik, R. Department Ofinformation Science and Engineering Basaveshwar Engineering College Bagalkot Karnataka India Department of Computer Science and Engineering KLE CET Belgaum Karnataka India

ISBN: (纸本)9781538611449

Wireless sensor Network (WSN) has set of wireless sensor nodes that are used to generate or sense the high volume of raw data from the environment, transmits the data to its respective sink node. The processing of raw data requires more amount of energy from sensor nodes to sink node or base station through single-hop or multi-hop communication over the nodes. In this context, sensor data need to be quality of data with semantic information in terms of semantic data integration and fusion and data processing, and so on. In order to problem of semantic data and managing raw data in WSN effectively, the Semantic sensor Web (SSW) is taken into the wireless sensor networks. The SSW is promising technology for realizing the use of sensor data and semantic web that provide the semantic information to existing sensor networks by utilizing domain, spatial and temporal ontologies and other related semantic technology. This paper presents survey of data processing in SSW from various aspects. Besides, it presents the architecture of SSW and its advantages. © 2017 IEEE.

关键词： Semantic Web

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Optimized H-LEACH algorithm for clustering to improve lifetime of WSN

Optimized H-LEACH algorithm for clustering to improve lifeti...

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IEEE international Conference on Recent Trends in Electronics, information & Communication Technology

作者： Aaditya Robin Kumar Prince Rajpoot Information Technology REC Ambedkar Nagar Akbarpur India

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

Wireless sensor network (WSN) is one of the most standard selected services for the use of technical field, industrial applications, or in the market. WSN consists basically independent sensors that sense topographical area of implementation. In WSN, the independent nodes sense observe the area which is enclosing us such as observing, sensing of sound, vibration, temperature etc. WSN can transpose the information to the Base Station (BS) and perform different types of processing like data collection, data sensing, data processing and also used for find the best route between nodes and BS to transmit the data. These days, WSNs is being used in many areas like border security, battle field surveillance, motion tracking etc. The main issue associated with a WSN is to enhance its lifetime. Clustering is an approach to increase the network lifetime. H-LEACH is basically a hybrid clustering hierarchy for wireless sensor network. The nodes in H-LEACH get associated with CH in their respective zone. For the long distance CH guild with CH node. In this paper we use extended H-LEACH approach for clustering. In this approach the CH or zones are increased for reducing the distance between one CH to next CH or BS. The CH are selected one in every zone in rotatory using probability same as H-LEACH. When the re-clustering is done the result show minimum energy by normal node during before the transmission of information.

关键词： Wireless sensor networks Protocols Clustering algorithms Partitioning algorithms Energy consumption Data communication Data models

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Caching for Distributed Parameter Estimation in Wireless sensor networks

Caching for Distributed Parameter Estimation in Wireless Sen...

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IEEE international Conference on Communications (ICC) / Wireless Communications symposium / 3rd Workshop on Optical Wireless Communications (OWC)

作者： Chennakesavula, Pradeep Hong, Y. -W. Peter Scaglione, Anna Natl Tsing Hua Univ Inst Commun Engn Hsinchu Taiwan Arizona State Univ Dept Elect & Comp Engn Tempe AZ 85287 USA

ISBN: (纸本)9781467389990

This work examines a cross-layered caching problem for distributed estimation in wireless sensor networks (WSNs). In WSNs, large amounts of data are produced continuously over time, and storing all the data collected from the sensors can be costly. In distributed estimation applications, sensors first gather information about a common phenomenon, and then forward the information to a fusion center where the final estimate is computed. By assuming that the parameters are correlated over time, the estimation quality at the fusion center can be improved by combining both present and past information, where the latter can be obtained from cached data. Different from conventional caching problems, where the goal is to reconstruct the sensors' observations, our caching strategy is designed to minimize the long term average mean-square error (MSE) of the final estimate. This problem can be modelled as a Markov decision process but, due to the curse of dimensionality, is solved here using a greedy one-step-ahead caching strategy, which only minimizes the expected MSE in the next time slot. This results in a nonlinear fractional programming problem that is solved approximately using semi-definite relaxation and a modified Dinkelbach's algorithm. The effectiveness of the proposed scheme is demonstrated through numerical simulations.

关键词： Wireless sensor networks Estimation sensor fusion Linear programming Signal processing sensor phenomena and characterization

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