A Modeling Study of Sensor Data

2013 ◽  
Vol 765-767 ◽  
pp. 1259-1262
Author(s):  
Feng Liu ◽  
Jian Yong Wang ◽  
Ming Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Statistical Methods ◽  
Research Topic ◽  
Environmental Data ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Iot Applications ◽  
Data Estimation

Nowadays, Internet of Things (IoT) has been becoming a hot research topic. Being an important part of Internet of Things, the wireless sensor networks collect various types of environmental data and construct the fundamental structure of the IoT applications. In order to find out the characteristics of the environmental data, in this paper, we focus on four types of these sensor data: temperature, humidity, light and voltage, and employ statistical methods to analyze and model these sensor data. The results of our research can be used to solve the missing sensor data estimation problem which is inevitable in the wireless sensor networks.

scholarly journals An Efficient Superframe Structure with Optimal Bandwidth Utilization and Reduced Delay for Internet of Things Based Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1971 ◽  
Author(s):  
Sangrez Khan ◽  
Ahmad Naseem Alvi ◽  
Muhammad Awais Javed ◽  
Byeong-hee Roh ◽  
Jehad Ali
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Large Scale ◽  
Ieee 802.15.4 ◽  
Fine Tuning ◽  
Wireless Sensor ◽  
Bandwidth Utilization ◽  
Iot Applications ◽  
Ieee 802.15.4 Standard

Internet of Things (IoT) is a promising technology that uses wireless sensor networks to enable data collection, monitoring, and transmission from the physical devices to the Internet. Due to its potential large scale usage, efficient routing and Medium Access Control (MAC) techniques are vital to meet various application requirements. Most of the IoT applications need low data rate and low powered wireless transmissions and IEEE 802.15.4 standard is mostly used in this regard which offers superframe structure at the MAC layer. However, for IoT applications where nodes have adaptive data traffic, the standard has some limitations such as bandwidth wastage and latency. In this paper, a new superframe structure is proposed that is backward compatible with the existing parameters of the standard. The proposed superframe overcomes limitations of the standard by fine-tuning its superframe structure and squeezing the size of its contention-free slots. Thus, the proposed superframe adjusts its duty cycle according to the traffic requirements and accommodates more nodes in a superframe structure. The analytical results show that our proposed superframe structure has almost 50% less delay, accommodate more nodes and has better link utilization in a superframe as compared to the IEEE 802.15.4 standard.

scholarly journals An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092047
Author(s):  
Xiang Yu ◽  
Hui Lu ◽  
Xianfei Yang ◽  
Ying Chen ◽  
Haifeng Song ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Adaptive Method ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Detection Model ◽  
Implicit Information ◽  
Changes Over Time

With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantities of explicit or implicit information. When analyzing such sensor data, it is of particular importance to detect accurately and efficiently not only individual anomalous behaviors but also anomalous events (i.e. patterns of behaviors). However, most previous work has focused only on detecting anomalies while generally ignoring the correlations between them. Even in approaches that take into account correlations between anomalies, most disregard the fact that the anomaly status of sensor data changes over time. In this article, we propose an unsupervised contextual anomaly detection method in Internet of Things through wireless sensor networks. This method accounts for both a dynamic anomaly status and correlations between anomalies based contextually on their spatial and temporal neighbors. We then demonstrate the effectiveness of the proposed method in an anomaly detection model. The experimental results show that this method can accurately and efficiently detect not only individual anomalies but also anomalous events.

scholarly journals Design Of Wireless Sensor Networks For IOT Application : A Challenges and survey

2018 ◽  
Vol 7 (03) ◽  
pp. 23790-23795 ◽  
Author(s):  
Mr.K. Muruganandam ◽  
Dr.B. Balamurugan ◽  
Dr.Sibaram Khara
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Environmental Data ◽  
Wireless Sensor ◽  
The Internet ◽  
Total System ◽  
Persons With Disabilities ◽  
The Internet Of Things

Internet of things (IOT) Can be defined in many ways  it encompasses many aspects of life such as connected homes, connected cities, connected cars and roads, roads to devices that track an individual’s behaviour. It is expected that one trillion Internet-connected devices will be available with mobile phones as the eyes and ears of the applications connecting all of those connected things. IoT made it possible for billions objects to communicate over worldwide over a public, private internet protocol network In 2010. In 2010 -11 the number of everyday physical objects and devices connected to the Internet was around 12.5 billion. The essential idea of the Internet of Things (IoT) has been around for nearly two decades, and has attracted many researchers and industries because of its great estimated impact in improving our daily lives and society. When things like household appliances are connected to a network, they can work together in cooperation to provide the ideal service  as a whole, not as a collection of independently working devices. This is useful for many of the real-world applications and services, and one would for example apply it to build a smart residence; windows can be closed automatically when the air conditioner is turned on, or can be opened for oxygen when the gas oven is turned on. The idea of IoT is especially valuable or persons with disabilities, as IoT technologies can support human activities at larger scale like building or society, as the devices can mutually cooperate to act as a total system. WSNs are integrated into the “Internet of Things”, where sensor nodes join the Internet dynamically, and use it to collaborate and accomplish their tasks. Wireless sensor networks (WSN) are well suited for long-term environmental data acquisition for IoT representation. In this paper, it is proposed to implement a WSN platform that can be used for a range of long-term  environmental monitoring for IoT applications. This paper presents functional design of WSN for IoT application. To provide a comprehensive overview of the IoT scenario and reviews its enabling technologies and the sensor networks. Also, it describes a six-layered architecture of IoT and points out the related key challenges

scholarly journals A Report on Designing of Wireless Sensor Networks for IoT Applications

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2004-2009 ◽  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Environmental Data ◽  
Internet Technology ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
The Internet ◽  
Air Conditioner ◽  
Night Time

Internet of things is defined in many ways is an internet technology connected to devices, machines and tools by the means of wireless technology, such as connected homes, cities, cars and roads, etc. The IoT is the platform to connect physical objects that contain built-in technology to interact and sends or communicate with their internal blocks or external environment. Nearly one trillion devices are internet connected devices which are available with mobile applications, linking all these connected things. Billions of devices are being communicated all the way though out the world over network protocols, in 2010 which was made by an IoT. Daily around 12.5 billion things or devices are interacting with internet. Since past two decades many researchers and industries were attracted towards IoT because of its reliability. If domestic appliances like Oven, Refrigerator, Air conditioner, Geezer, Smart TV are connected in a network, they all work together in association to provide an ideal service as an entire, not as a collection of independently working devices. IoT is a powerful tool which is used in many ways in the development of real-world applications and services, for example building a smart residence where light should ON when the person enters into the room at night time, windows can be closed automatically when it rains, and automatically windows will be open if any leakage of gas. Wireless Sensor Networks are included into the “Internet of Things”, where sensor nodes are connected to the Internet vigorously, and use it to act as a team and complete their action. Wireless Sensor Networks are well apt for long-term environmental data acquirement for IoT representation. In this paper, we provide the information on designing challenges on wireless sensor networks for an IoT application.. This paper presents an overview on an IoT, functional design of WSN for IoT application, details an architecture of IoT and related key issues.

QoS Aware Multi-hop Multi-path Routing Approach in Wireless Sensor Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 43-52
Author(s):  
Omkar Singh ◽  
Vinay Rishiwal
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Life Time ◽  
Base Station ◽  
Environmental Data ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
End To End

Background & Objective: Wireless Sensor Network (WSN) consist of huge number of tiny senor nodes. WSN collects environmental data and sends to the base station through multi-hop wireless communication. QoS is the salient aspect in wireless sensor networks that satisfies end-to-end QoS requirement on different parameters such as energy, network lifetime, packets delivery ratio and delay. Among them Energy consumption is the most important and challenging factor in WSN, since the senor nodes are made by battery reserved that tends towards life time of sensor networks. Methods: In this work an Improve-Energy Aware Multi-hop Multi-path Hierarchy (I-EAMMH) QoS based routing approach has been proposed and evaluated that reduces energy consumption and delivers data packets within time by selecting optimum cost path among discovered routes which extends network life time. Results and Conclusion: Simulation has been done in MATLAB on varying number of rounds 400- 2000 to checked the performance of proposed approach. I-EAMMH is compared with existing routing protocols namely EAMMH and LEACH and performs better in terms of end-to-end-delay, packet delivery ratio, as well as reduces the energy consumption 13%-19% and prolongs network lifetime 9%- 14%.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

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