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

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.

scholarly journals Performance metrics in wireless sensor networks :a survey and outlook

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. 

scholarly journals MREA: a minimum resource expenditure node capture attack in wireless sensor networks

2016 ◽  
Vol 9 (18) ◽  
pp. 5502-5517 ◽  
Author(s):  
Chi Lin ◽  
Tie Qiu ◽  
Mohammad S. Obaidat ◽  
Chang Wu Yu ◽  
Lin Yao ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Node Capture Attack ◽  
Node Capture

Detection of Node Capture Attack in Wireless Sensor Networks

2019 ◽  
Vol 13 (1) ◽  
pp. 238-247 ◽  
Author(s):  
Sarita Agrawal ◽  
Manik Lal Das ◽  
Javier Lopez
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Node Capture Attack ◽  
Node Capture

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

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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