scholarly journals An Anonymous Channel Categorization Scheme of Edge Nodes to Detect Jamming Attacks in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2311 ◽  
Author(s):  
Muhammad Adil ◽  
Mohammed Amin Almaiah ◽  
Alhuseen Omar Alsayed ◽  
Omar Almomani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Jamming Attacks ◽  
Edge Node ◽  
Jamming Attack ◽  
Proposed Model ◽  
The Media ◽  
Categorization Scheme ◽  
Adjacent Channels

Wireless Sensor Networks (WSNs) are vulnerable to various security threats. One of the most common types of vulnerability threat is the jamming attack, where the attacker uses the same frequency signals to jam the network transmission. In this paper, an edge node scheme is proposed to address the issue of jamming attack in WSNs. Three edge nodes are used in the deployed area of WSN, which have different transmission frequencies in the same bandwidth. The different transmission frequencies and Round Trip Time (RTT) of transmitting signal makes it possible to identify the jamming attack channel in WSNs transmission media. If an attacker jams one of the transmission channels, then the other two edge nodes verify the media serviceability by means of transmitting information from the same deployed WSNs. Furthermore, the RTT of the adjacent channel is also disturbed from its defined interval of time, due to high frequency interference in the adjacent channels, which is the indication of a jamming attack in the network. The simulation result was found to be quite consistent during analysis by jamming the frequency channel of each edge node in a step-wise process. The detection rate of jamming attacks was about 94% for our proposed model, which was far better than existing schemes. Moreover, statistical analyses were undertaken for field-proven schemes, and were found to be quite convincing compared with the existing schemes, with an average of 6% improvement.

scholarly journals Enhancing and Securing the Routing Techniques in Wireless Sensor Networks

2020 ◽  
pp. 141-145
Author(s):  
Rupinder Singh
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Network ◽  
Denial Of Service ◽  
Wireless Sensor ◽  
Physical Parameters ◽  
Network Node ◽  
Jamming Attacks ◽  
Proposed Model ◽  
Node Network

A wireless network node network (WSN) is defined as being composed of a large number of small light weighted nodes called network node nodes with routing, processing and communication facilities, which are densely deployed in physical or environmental condition. Each of these nodes collects data and its purpose is to route this information back to a sink. WSN is highly constrained type of network, having network node nodes with more capabilities. All network node nodes in the wireless network node network are interact with each other by intermediated network node nodes. Physical parameters computations are power, energy, memory, communication range and bandwidth. Wireless ad-hoc networks mainly use broadcast communication. Upon deployment, network node nodes automatically collaborate and form a network, start collecting data without any input from the user. The proposed model has been improved for the route metric calculation along with node and link load availability information module to avoid the connectivity loopholes and link congestions. The proposed model results have been obtained in the form of various network performance parameters such as network load, transmission delay, throughput, energy consumption, etc. In wireless sensor networks, there are many types of attacks that can hinder or obstruct the data to be deliver to the authenticated node so in order to check which node is authenticated various algorithms have been proposed. There are various attacks like Denial of Service, Distributed Denial of Service and various types of Jamming attacks that can disrupt or deny the communication between sender and receiver. It is important to develop some powerful tools for network analysis, design and managing the performance optimization of the network. In this paper some of the most common attacks and threats are explained and the prevention that can be taken by using various tools is implemented. Also the different routes are configured if the particular route is not available. All the nodes and the attacks are been shown by using a simulator NS2.

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 Virus Propagation in Wireless Sensor Networks with Media Access Control Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lurong Jiang ◽  
Qiaoyu Xu ◽  
Hangyi Pan ◽  
Yanyun Dai ◽  
Jijun Tong
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Access Control ◽  
Control Mechanism ◽  
Wireless Sensor ◽  
Media Access Control ◽  
Media Access ◽  
Virus Propagation ◽  
Suppression Effect ◽  
The Media

In wireless sensor networks, network security against virus propagation is one of the challenges with the applications. In severe cases, the network system may become paralyzed. In order to study the process of virus propagation in wireless sensor networks with the media access control mechanism, this paper uses the susceptible-infectious-removed (SIR) model to analyze the spreading process. It provides a theoretical basis for the development of virus immune mechanisms to solve network virus attack hidden dangers. The research shows that the media access control (MAC) mechanism in the wireless sensor network can inhibit the process of virus propagation, reduce the network virus propagating speed, and decrease the scale of infected nodes. The listen/sleep duty cycle of this mechanism will affect the suppression effect of virus propagation. The smaller the listen/sleep duty cycle, the stronger the suppression effect. Energy consumption has a peak value under specific infection probability. Meanwhile, it is also found that the spreading scale of the virus in wireless sensor networks can be effectively inhibited by the MAC mechanism.

Sign in / Sign up

Export Citation Format

Share Document