Computational Complexity of Relay Placement in Sensor Networks

2006 ◽  
pp. 521-529 ◽  
Author(s):  
Jukka Suomela
Keyword(s):  
Sensor Networks ◽  
Computational Complexity ◽  
Relay Placement

scholarly journals Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8017
Author(s):  
Nurfazrina M. Zamry ◽  
Anazida Zainal ◽  
Murad A. Rassam ◽  
Eman H. Alkhammash ◽  
Fuad A. Ghaleb ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Computational Complexity ◽  
Anomaly Detection ◽  
Principal Component ◽  
Support Vector ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Memory Utilization

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with (𝑛𝑑) memory utilization and no communication overhead.

scholarly journals Convolutional technique for enhancing security in wireless sensor networks against malicious nodes

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Turki Ali Alghamdi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Complexity ◽  
Convolutional Codes ◽  
Time Instant ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Malicious Nodes ◽  
Security Issues ◽  
Network Operation

Abstract Wireless sensor networks (WSNs) comprise tiny devices known as sensors. These devices are frequently employed in short-range communications and can perform various operations such as monitoring, collecting, analyzing, and processing data. WSNs do not require any infrastructure, are reliable, and can withstand adverse conditions. Sensor networks are autonomous structures in which the sensor nodes can enter or leave the network at any time instant. If the entering node is attacker node it will monitor the network operation and can cause security issues in the network that can affect communication. Existing literature presents security improvements in such networks in the form of cryptography, asymmetric techniques, key distribution, and various protocols. However, these techniques may not be effective in the case of autonomous structures and can increase computational complexity. In this paper, a convolutional technique (CT) is proposed that generates security bits using convolutional codes to prevent malicious node attacks on WSNs. Different security codes are generated at different hops and the simulation results demonstrate that the proposed technique enhances network security and reduces computational complexity compared to existing approaches.

scholarly journals Optimized relay placement for wireless sensor networks federation in environmental applications

2011 ◽  
Vol 11 (12) ◽  
pp. 1677-1688 ◽  
Author(s):  
Fadi M. Al-Turjman ◽  
Hossam S. Hassanein ◽  
Waleed M. Alsalih ◽  
Mohamad Ibnkahla
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Environmental Applications ◽  
Relay Placement

scholarly journals A Low Computational Complexity SML Estimation Algorithm of DOA for Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Faming Gong ◽  
Haihua Chen ◽  
Shibao Li ◽  
Jianhang Liu ◽  
Zhaozhi Gu ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Complexity ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Search Method ◽  
Wireless Sensor ◽  
Initial Value ◽  
Effectiveness And Efficiency ◽  
Very High

We address the problem of DOA estimation in positioning of nodes in wireless sensor networks. The Stochastic Maximum Likelihood (SML) algorithm is adopted in this paper. The SML algorithm is well-known for its high resolution of DOA estimation. However, its computational complexity is very high because multidimensional nonlinear optimization problem is usually involved. To reduce the computational complexity of SML estimation, we do the following work. (1) We point out the problems of conventional SML criterion and explain why and how these problems happen. (2) A local AM search method is proposed which could be used to find the local solution near/around the initial value. (3) We propose an algorithm which uses the local AM search method together with the estimation of DML or MUSIC as initial value to find the solution of SML. Simulation results are shown to demonstrate the effectiveness and efficiency of the proposed algorithms. In particular, the algorithm which uses the local AM method and estimation of MUSIC as initial value has much higher resolution and comparable computational complexity to MUSIC.

scholarly journals A new approach of anomaly detection in wireless sensor networks using support vector data description

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668616 ◽  
Author(s):  
Zhen Feng ◽  
Jingqi Fu ◽  
Dajun Du ◽  
Fuqiang Li ◽  
Sizhou Sun
Keyword(s):  
Decision Making ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Complexity ◽  
Anomaly Detection ◽  
Wireless Sensor ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description

Anomaly detection is an important challenge in wireless sensor networks for some applications, which require efficient, accurate, and timely data analysis to facilitate critical decision making and situation awareness. Support vector data description is well applied to anomaly detection using a very attractive kernel method. However, it has a high computational complexity since the standard version of support vector data description needs to solve quadratic programming problem. In this article, an improved method on the basis of support vector data description is proposed, which reduces the computational complexity and is used for anomaly detection in energy-constraint wireless sensor networks. The main idea is to improve the computational complexity from the training stage and the decision-making stage. First, the strategy of training sample reduction is used to cut back the number of samples and then the sequential minimal optimization algorithm based on the second-order approximation is implemented on the sample set to achieve the goal of reducing the training time. Second, through the analysis of the decision function, the pre-image in the original space corresponding to the center of hyper-sphere in kernel feature space can be obtained. The decision complexity is reduced from O( l) to O(1) using the pre-image. Eventually, the experimental results on several benchmark datasets and real wireless sensor networks datasets demonstrate that the proposed method can not only guarantee detection accuracy but also reduce time complexity.

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