scholarly journals Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

Sensors ◽  
2015 ◽  
Vol 15 (4) ◽  
pp. 9277-9304 ◽  
Author(s):  
Juan Pardo ◽  
Francisco Zamora-Martínez ◽  
Paloma Botella-Rocamora
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Online Learning ◽  
Sensor Networks ◽  
Learning Algorithm ◽  
Low Cost ◽  
Time Series Forecasting ◽  
Wireless Sensor ◽  
Online Learning Algorithm

scholarly journals Time Series Forecasting Energy-efficient Organization of Wireless Sensor Networks

Sensors ◽  
2007 ◽  
Vol 7 (9) ◽  
pp. 1766-1792 ◽  
Author(s):  
Xue Wang ◽  
Jun-Jie Ma ◽  
Sheng Wang ◽  
Dao-Wei Bi
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Time Series Forecasting ◽  
Wireless Sensor ◽  
Efficient Organization

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.

scholarly journals A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1179
Author(s):  
Carolina Del-Valle-Soto ◽  
Carlos Mex-Perera ◽  
Juan Arturo Nolazco-Flores ◽  
Alma Rodríguez ◽  
Julio C. Rosas-Caro ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Performance Metrics ◽  
Low Cost ◽  
Detection Algorithm ◽  
Anomalous Behavior ◽  
Wireless Sensor ◽  
Jamming Detection ◽  
Minimal Energy Expenditure

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

scholarly journals Comparative Study on Various Authentication Protocols in Wireless Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
S. Raja Rajeswari ◽  
V. Seenivasagam
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Key Management ◽  
Low Cost ◽  
Wireless Sensor ◽  
Authentication Protocols ◽  
Security Services ◽  
Management Schemes ◽  
Active Research ◽  
Lightweight Authentication

Wireless sensor networks (WSNs) consist of lightweight devices with low cost, low power, and short-ranged wireless communication. The sensors can communicate with each other to form a network. In WSNs, broadcast transmission is widely used along with the maximum usage of wireless networks and their applications. Hence, it has become crucial to authenticate broadcast messages. Key management is also an active research topic in WSNs. Several key management schemes have been introduced, and their benefits are not recognized in a specific WSN application. Security services are vital for ensuring the integrity, authenticity, and confidentiality of the critical information. Therefore, the authentication mechanisms are required to support these security services and to be resilient to distinct attacks. Various authentication protocols such as key management protocols, lightweight authentication protocols, and broadcast authentication protocols are compared and analyzed for all secure transmission applications. The major goal of this survey is to compare and find out the appropriate protocol for further research. Moreover, the comparisons between various authentication techniques are also illustrated.

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