Least-Latency Routing over Time-Dependent Wireless Sensor Networks

2013 ◽  
Vol 62 (5) ◽  
pp. 969-983 ◽  
Author(s):  
Shouwen Lai ◽  
Binoy Ravindran
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Time Dependent ◽  
Wireless Sensor ◽  
Over Time

scholarly journals WS3N: Wireless Secure SDN-Based Communication for Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Renan C. A. Alves ◽  
Doriedson A. G. Oliveira ◽  
Geovandro C. C. F. Pereira ◽  
Bruno C. Albertini ◽  
Cíntia B. Margi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Secure Communication ◽  
Wireless Sensors ◽  
Software Defined Networking ◽  
Wireless Sensor ◽  
Resource Constrained ◽  
Security Services ◽  
Design And Implementation ◽  
Over Time

The Software Defined Networking (SDN) paradigm can provide flexible routing and potentially support the different communication patterns that exist in Wireless Sensor Networks (WSN). However applying this paradigm to resource-constrained networks is not straightforward, especially if security services are a requirement. Existing SDN-based approaches for WSN evolved over time, addressing resource-constrained requirements. However, they do not integrate security services into their design and implementation. This work’s main contribution is a secure-by-design SDN-based framework for Wireless Sensors Networks. Secure node admission and end-to-end key distribution to support secure communication are considered key services, which the framework must provide. We describe its specification, design, implementation, and experiments considering device and protocol constraints. The results indicate that our approach has achieved such goals with acceptable overheads up to medium sized networks.

scholarly journals K-Means Clustering in WSN with Koheneon SOM and Conscience Function

2019 ◽  
Vol 13 (8) ◽  
pp. 63
Author(s):  
Asia K. Bataineh ◽  
Mohammad Habib Samkari ◽  
Abdualla Abdualla ◽  
Saad Al-Azzam
Keyword(s):  
Neural Networks ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Dynamic Environments ◽  
Wireless Sensor ◽  
Average Lifetime ◽  
Self Organizing Map ◽  
Over Time

Wireless Sensor Networks (WSNs) are broadly utilized in the recent years to monitor dynamic environments which vary in a rapid way over time. The most used technique is the clustering one, such as Kohenon Self Organizing Map (KSOM) and K means. This paper introduces a hybrid clustering technique that represents the use of K means clustering algorithm with the KSOM with conscience function of Neural Networks and applies it on a certain WSN in order to measure and evaluate its performance in terms of both energy and lifetime criteria. The application of this algorithm in a WSN is performed using the MATLAB software program. Results demonstrate that the application of K-means clustering algorithm with KSOM algorithm enhanced the performance of the WSN which depends on using KSOM algorithm only in which it offers an enhancement of 11.11% and 3.33% in terms of network average lifetime and consumed energy, respectively. The comparison among the current work and a previous one demonstrated the effectiveness of the proposed approach in this work in terms of reducing the energy consumption.

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