JAMIOLAS 3.0

In this article, the authors propose an improved context-aware system to support the learning of Japanese mimicry and onomatopoeia (MIO) using sensor data. In the authors’ two previous studies, they proposed a context-aware language learning assistant system named JAMIOLAS (JApanese MImicry and Onomatopoeia Learning Assistant System). The authors used wearable sensors and sensor networks, respectively, to support learning Japanese MIO. To address the disadvantages of the previous systems, the authors propose a new learning model that can support learning MIO, using sensor data and the sensor network to enable context-aware learning by either initiating the creation of context or detecting context automatically.

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Performance metrics in wireless sensor networks :a survey and outlook

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.26.12527 ◽

2018 ◽

Vol 7 (2.26) ◽

pp. 25

Author(s):

E Ramya ◽

R Gobinath

Keyword(s):

Wireless Sensor Networks ◽

Quality Of Service ◽

Wireless Sensor Network ◽

Sensor Networks ◽

Sensor Network ◽

Performance Metrics ◽

Resource Constraints ◽

Sensor Data ◽

Wireless Sensor

Data mining plays an important role in analysis of data in modern sensor networks. A sensor network is greatly constrained by the various challenges facing a modern Wireless Sensor Network. This survey paper focuses on basic idea about the algorithms and measurements taken by the Researchers in the area of Wireless Sensor Network with Health Care. This survey also catego-ries various constraints in Wireless Body Area Sensor Networks data and finds the best suitable techniques for analysing the Sensor Data. Due to resource constraints and dynamic topology, the quality of service is facing a challenging issue in Wireless Sensor Networks. In this paper, we review the quality of service parameters with respect to protocols, algorithms and Simulations.

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An Outline of Threats and Sensor Cloud Infrastructure in Wireless Sensor Network

Handbook of Research on Securing Cloud-Based Databases with Biometric Applications - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-4666-6559-0.ch019 ◽

2015 ◽

pp. 412-432

Author(s):

Bhavana Butani ◽

Piyush Kumar Shukla ◽

Sanjay Silakari

Keyword(s):

Sensor Networks ◽

Sensor Network ◽

Sensor Data ◽

Wireless Sensor ◽

Cloud Infrastructure ◽

Node Capture Attack ◽

Node Capture ◽

Sensor Cloud ◽

A New Technique ◽

Commercial Applications

Wireless sensor networks are utilized in vital situations like military and commercial applications, traffic surveillance, habitat monitoring, and many other applications. WSNs have to face various issues and challenges in terms of memory, communication, energy, computation, and storage, which require efficient management of huge amount of sensor data. Therefore, storage is an important issue in the WSN. Emergence of Sensor-Cloud infrastructure overcomes several shortcomings of WSN such as storage capacity and offers high processing capabilities for huge sensor data. Security is also the major challenge that is faced by the sensor network. This chapter includes a brief overview of the importance of cloud computing in sensor networks and the goal of DDoS and Node Capture Attack in WSN. This chapter includes descriptions of different modeling techniques of Node Capture attack and various detection and key pre-distribution schemes to invent a new technique to improve network resilience against node capture attacks.

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Knowledge Discovery for Sensor Network Comprehension

Intelligent Techniques for Warehousing and Mining Sensor Network Data ◽

10.4018/978-1-60566-328-9.ch006 ◽

2010 ◽

pp. 118-135 ◽

Cited By ~ 9

Author(s):

Pedro Pereira Rodrigues ◽

João Gama ◽

Luís Lopes

Keyword(s):

Sensor Networks ◽

Knowledge Discovery ◽

Sensor Network ◽

Data Streams ◽

High Rate ◽

Sensor Data ◽

Processing Abilities ◽

Comprehension Ability ◽

Visualization Of Data ◽

Different Characteristics

In this chapter we explore different characteristics of sensor networks which define new requirements for knowledge discovery, with the common goal of extracting some kind of comprehension about sensor data and sensor networks, focusing on clustering techniques which provide useful information about sensor networks as it represents the interactions between sensors. This network comprehension ability is related with sensor data clustering and clustering of the data streams produced by the sensors. A wide range of techniques already exists to assess these interactions in centralized scenarios, but the seizable processing abilities of sensors in distributed algorithms present several benefits that shall be considered in future designs. Also, sensors produce data at high rate. Often, human experts need to inspect these data streams visually in order to decide on some corrective or proactive operations (Rodrigues & Gama, 2008). Visualization of data streams, and of data mining results, is therefore extremely relevant to sensor data management, and can enhance sensor network comprehension, and should be addressed in future works.

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Mining Wireless Sensor Network Data: an adaptive approach based on artificial neuralnetworks algorithm

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2011.1083 ◽

2011 ◽

pp. 129-135

Author(s):

Lambodar Jena ◽

Ramakrushna Swain ◽

N.K. kamila

Keyword(s):

Data Mining ◽

Wireless Sensor Network ◽

Sensor Networks ◽

Dimensionality Reduction ◽

Sensor Network ◽

Power Supply ◽

Sensor Data ◽

Wireless Sensor ◽

Limited Power ◽

Limited Power Supply

This paper proposes a layered modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and in an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The rationale of the approach is that a modular aggregation of sensor data can serve jointly two purposes: first, the organization of sensors in clusters, then reducing the communication effort, second, the dimensionality reduction of the data mining task, then improving the accuracy of the sensing task . Here we show that some of the algorithms developed within the artificial neuralnetworks tradition can be easily adopted to wireless sensor-network platforms and will meet several aspects of the constraints for data mining in sensor networks like: limited communication bandwidth, limited computing resources, limited power supply, and the need for fault-tolerance. The analysis of the dimensionality reduction obtained from the outputs of the neural-networks clustering algorithms shows that the communication costs of the proposed approach are significantly smaller, which is an important consideration in sensor-networks due to limited power supply. In this paper we will present two possible implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several nodes, equipped with several sensors each.

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Incorporation of Non-Fictional Applications in Wireless Sensor Networks

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k7673.0991120 ◽

2020 ◽

Vol 9 (11) ◽

pp. 42-49

Keyword(s):

Wireless Sensor Networks ◽

Wireless Sensor Network ◽

Sensor Networks ◽

Sensor Network ◽

Patient Information ◽

Wireless Sensor ◽

Context Aware ◽

Efficient Manner ◽

Service Support ◽

The Way

At present, various studies regarding the Wireless Sensor Network have already published in various fields and applications but there is always a further scope and also challenges comes under the way of researchers and they have to overcome them. Wireless Sensor Network always has some potential in the field of research and we have to go through them and try to study or analyse them. In this paper, we try to study some non-fictional applications so that we can analyse in future how these applications work with wireless sensor networks for this we concisely define Wireless Sensor Network to sum things up and mainly focus on the study of non-fictional applications like: Patient information in Hospital, Tracking: searching and determining location, Context Aware and Retailing: sales and service support. It is important to know about these applications of Wireless Sensor Network so that they can be used in efficient manner by both user and developer.

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Improving IoT Predictions through the Identification of Graphical Features

Sensors ◽

10.3390/s19153250 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3250

Author(s):

Syeda Akter ◽

Lawrence Holder

Keyword(s):

Feature Selection ◽

Sensor Networks ◽

Sensor Network ◽

Activity Recognition ◽

Smart Home ◽

Smart Phone ◽

Sensor Data ◽

Feature Engineering ◽

The Impact ◽

Application Specific

IoT sensor networks have an inherent graph structure that can be used to extract graphical features for improving performance in a variety of prediction tasks. We propose a framework that represents IoT sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the most useful features that are to be used by a classifier for prediction tasks. We show that a set of generic graph-based features can improve performance of sensor network predictions without the need for application-specific and task-specific feature engineering. We apply this approach to three different prediction tasks: activity recognition from motion sensors in a smart home, demographic prediction from GPS sensor data in a smart phone, and activity recognition from GPS sensor data in a smart phone. Our approach produced comparable results with most of the state-of-the-art methods, while maintaining the additional advantage of general applicability to IoT sensor networks without using sophisticated and application-specific feature generation techniques or background knowledge. We further investigate the impact of using edge-transition times, categorical features, different sensor window sizes, and normalization in the smart home domain. We also consider deep learning approaches, including the Graph Convolutional Network (GCN), for the elimination of feature engineering in the smart home domain, but our approach provided better performance in most cases. We conclude that the graphical feature-based framework that is based on IoT sensor categorization, nodes and edges as features, and feature selection techniques provides superior results when compared to the non-graph-based features.

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Design and Implementation of Sensor Network Device Control System with AR Technology

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.2-3.131 ◽

2011 ◽

Vol 2-3 ◽

pp. 131-134

Author(s):

Naoya Sakamoto ◽

Hideki Shimada ◽

Kenya Sato

Keyword(s):

Control System ◽

Sensor Networks ◽

Sensor Network ◽

Sensor Nodes ◽

Sensor Data ◽

Visualization Tool ◽

Network Information ◽

Packet Data ◽

The Status ◽

Network Device

Sensor networks, which can immediately detect events and situations and automatically control actuators, are expected to proliferate in the future, even though their visualization of sensor networks has not been emphasized. Identifying broken nodes in real environments remains difficult using a traditional visualization tool that plots the virtual diagram on which sensor nodes are put. In this paper, we propose and implement a control system of sensor network devices with AR technology. Our proposed system displays sensor data and network information such as the link status, the packet data, and the traffic in the sensor network on an AR interface. In addition, we control the sensor devices through the AR interface. Our proposed system allows users to intuitively acquire the status of sensor networks. We can also control the devices through the AR interface.

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Modeling a Secure Sensor Network Using an Extended Elementary Object System

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/jcini.2010070101 ◽

2010 ◽

Vol 4 (3) ◽

pp. 1-17

Author(s):

Vineela Devarashetty ◽

Jeffrey J.P. Tsai ◽

Lu Ma ◽

Du Zhang

Keyword(s):

Sensor Networks ◽

Sensor Network ◽

Formal Model ◽

Geographical Area ◽

Sensor Nodes ◽

Sensor Data ◽

Security Mechanism ◽

Object System ◽

Security Issues ◽

Synchronous Firing

A sensor network consists of a large number of sensor nodes, which are spread over a geographical area. Sensor networks have found their way into many applications, from military domains to traffic or environmental monitoring, and as sensor networks reach toward wide spread deployment, security becomes a major concern. In this regard, one needs to be sure about the confidentiality, authenticity and tamper-proof of data. The research thus far has focused on how to deploy sensor networks so that they can work efficiently; however, the focus of this paper is on sensor networks’ security issues. In this paper, the authors propose a formal model to design and analyze the secure sensor network system. The model is based on an augmented Petri net formalism called Extended Elementary Object System. This proposed secure sensor network model has a multi-layered structure consisting of sink node layer, sensor node layer and security mechanism layer. At the security mechanism layer, a synchronous firing mechanism is utilized as a security measure to detect malicious node attacks to sensor data and information flow. In addition, the model applies SNEP protocol for authentication and confidentiality of sensor data.

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A Three-Tier Architecture for User-Centric Ubiquitous Networked Sensing

ISRN Communications and Networking ◽

10.5402/2012/637134 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Jin Nakazawa ◽

Hideyuki Tokuda

Keyword(s):

Sensor Networks ◽

Sensor Network ◽

New Product ◽

Sensor Nodes ◽

Sensor Data ◽

Tight Coupling ◽

The Core ◽

User Centric ◽

Ambient Sensing ◽

Device Industry

In a sensor network, sensor data are usually forwarded from sensor nodes to a database. This tight coupling between the nodes and the database has been complicating user-centric applications that traverse multiple different sensor networks. To break this coupling, thus enabling user-centric applications, we propose a three-tier architecture for ubiquitous networked sensing. Its major feature is that it contains the “core” device, which is assumed to be a terminal held by users between sensor nodes and sensor databases. This architecture supports the sensor data directly transmitted to and consumed by the core device, in addition to the classic ones that are transmitted to the sensor database first, and downloaded to the core. The major contribution of this paper are the following three-fold. First, we clarify the architecture itself. Researchers can leverage the architecture as the baseline of their development. Second, we show two types of prototype implementations of the core device. Industry is allowed to develop a new product for practical use of ambient sensing. Finally, we show a range of applications that are enabled by the architecture and indicate issues that need to be addressed for further investigation.

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