Prevention of Intrusion Attacks via Deep Learning Algorithm in Wireless Sensor Network in Smart Cities

2021 ◽  
pp. 501-521
Author(s):  
Deepak Choudhary ◽  
Roop Pahuja
Keyword(s):  
Deep Learning ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Wireless Sensor ◽  
Deep Learning Algorithm

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wireless Sensor Network ◽  
Convolutional Neural Network ◽  
Sensor Network ◽  
Time Series Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

Modelling a Deep Learning-Based Wireless Sensor Network Task Assignment Algorithm

2020 ◽  
pp. 84-109
Author(s):  
Titus Issac ◽  
Salaja Silas ◽  
Elijah Blessing Rajsingh
Keyword(s):  
Deep Learning ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Task Assignment ◽  
Human Interaction ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Feed Forward Neural Network ◽  
Functional Capabilities ◽  
Assignment Algorithm

The 21st century is witnessing the emergence of a wide variety of wireless sensor network (WSN) applications ranging from simple environmental monitoring to complex satellite monitoring applications. The advent of complex WSN applications has led to a massive transition in the development, functioning, and capabilities of wireless sensor nodes. The contemporary nodes have multi-functional capabilities enabling the heterogeneous WSN applications. The future of WSN task assignment envisions WSN to be heterogeneous network with minimal human interaction. This led to the investigative model of a deep learning-based task assignment algorithm. The algorithm employs a multilayer feed forward neural network (MLFFNN) trained by particle swarm optimization (PSO) for solving task assignment problem in a dynamic centralized heterogeneous WSN. The analyses include the study of hidden layers and effectiveness of the task assignment algorithms. The chapter would be highly beneficial to a wide range of audiences employing the machine and deep learning in WSN.

scholarly journals 3D GEO-CLUSTERING FOR WIRELESS SENSOR NETWORK IN SMART CITY

2019 ◽  
Vol XLII-4/W12 ◽  
pp. 11-16 ◽  
Author(s):  
S. Azri ◽  
U. Ujang ◽  
A. Abdul Rahman
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Smart City ◽  
Clustering Algorithm ◽  
Smart Cities ◽  
Wireless Sensor ◽  
Sensor Technology ◽  
Social Infrastructure ◽  
Head Node

<p><strong>Abstract.</strong> Smart city is a connection of physical and social infrastructure together with the information technology to leverage the collective intelligence of the city. Smart cities depend on a great extent on wireless sensor network to manage and maintain their services. Advanced sensor technologies are used to acquire information and help dealing with issues like air pollution, waste management, traffic optimization, and energy efficiency. However, no matter how much smart city may focus on sensor technology, data that are produced from sensors do not organize themselves in a database. Such tasks require a sophisticated database structure to produce informative data output. Besides that, wireless sensor network requires a proper design to improve the energy efficiency. The design will aid to prolong the lifespan of wireless network efficiently. In this study, we proposed a new technique that will be used to organize the information of wireless sensor network in the spatial database. Specific algorithm which is 3D geo-clustering algorithm is used to tackle several issues of location of the sensor in three-dimensional urban area in smart city. The algorithm is designed to minimizing the overlap among group clusters. Overlap plays an important role for energy efficiency. Thus, detection of sensors in two or more group clusters will avoid it from transmitting the same signal to cluster head node. It is prove that this algorithm would only create 5% to 10% overlap among group clusters. Several experiments are performed in this study to evaluate the algorithm. Based on the simulation results indicate that this algorithm can balance nodes energy consumption and prolong the network’s life span. It also has good stability and extensibility. Several tests are performed to validate the efficiency of the technique to measure the database performance.</p>

scholarly journals Performance assessment of static and dynamic routings using 6LoWPAN on small scenarios applied to monitoring of Barranquilla flash flood

2021 ◽  
Author(s):  
Piñeres -Espitia Gabriel ◽  
shariq aziz butt ◽  
Estévez -Ortiz Francisco ◽  
Cama -Pinto Alejandro
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Network ◽  
Performance Metrics ◽  
Task Force ◽  
Flash Flood ◽  
Smart Cities ◽  
Wireless Sensor ◽  
The Internet ◽  
Personal Area Networks

Abstract The Internet of Things (IoT) is growing rapidly due to the wireless network that provides connectivity to devices at anytime and anywhere. Currently, the wireless sensor network is involved in many research fields like smart health monitoring, smart cities, and smart industries. From all of these, flood monitoring is the most important field in the IoT wireless network to alert about the occurrence of any abnormalities. To monitor the environment wireless sensor network needs a decision-making protocol that sense and route the information timely. The present work includes numerous use of Low Power Wireless Personal Area Networks (6LoWPAN) with IPv6 protocol defined by the Internet Engineering Task Force (IETF) due to its different test conditions, analysing static and dynamic routing and its impact on different performance metrics such as latency and packet losses for their application in monitoring system of the flash floods in any region. In this study, we are using IPv6 6LoWPAN to develop a wireless warning system before a flood event in “The Brigade” in Barranquilla city. The basic purpose for making system is to secure the city from costly damage and life loss. Different types of traffic and different 1-/2-hops scenarios have been considered. In order to implement our system, we use the well-known TelosB platform jointly with TinyOS, BLIP 2.0 and LPL. The experiment result shows that time on 512 ms and 1024 ms with a packet of 120 B obtain good performance on the metrics used for the tests.

scholarly journals The Algorithms of Distributed Learning and Distributed Estimation about Intelligent Wireless Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1302
Author(s):  
Fuxiao Tan
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Distributed Learning ◽  
Estimation Algorithm ◽  
Distributed Estimation ◽  
Wireless Sensor ◽  
Research Progress ◽  
Theoretical Frameworks

The intelligent wireless sensor network is a distributed network system with high “network awareness”. Each intelligent node (agent) is connected by the topology within the neighborhood which not only can perceive the surrounding environment, but can adjusts its own behavior according to its local perception information to constructs a distributed learning algorithms. Therefore, three basic intelligent network topologies of centralized, non-cooperative, and cooperative are intensively investigated in this paper. The main contributions of the paper include two aspects. First, based on algebraic graph, three basic theoretical frameworks for distributed learning and distributed parameter estimation of cooperative strategy are surveyed: increment strategy, consensus strategy, and diffusion strategy. Second, based on classical adaptive learning algorithm and online updating law, the implementation process of distributed estimation algorithm and the latest research progress of above three distributed strategies are investigated.

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