scholarly journals RSSI and LQI Data Clustering Techniques to Determine the Number of Nodes in Wireless Sensor Networks

2014 ◽  
Vol 10 (5) ◽  
pp. 380526 ◽  
Author(s):  
Yanwen Wang ◽  
Ivan G. Guardiola ◽  
Xiaoling Wu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Clustering ◽  
Wireless Sensor ◽  
Clustering Techniques

scholarly journals A Data Clustering Algorithm for Detecting Selective Forwarding Attack in Cluster-Based Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 23 ◽  
Author(s):  
Hao Fu ◽  
Yinghong Liu ◽  
Zhe Dong ◽  
Yuanming Wu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Clustering ◽  
Detection Rate ◽  
Clustering Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Packets ◽  
Selective Forwarding ◽  
Balance Energy

In cluster-based wireless sensor networks, cluster heads (CHs) gather and fuse data packets from sensor nodes; then, they forward fused packets to the sink node (SN). This helps wireless sensor networks balance energy effectively and efficiently to prolong their lifetime. However, cluster-based WSNs are vulnerable to selective forwarding attacks. Compromised CHs would become malicious and launch selective forwarding attacks in which they drop part of or all the packets from other nodes. In this paper, a data clustering algorithm (DCA) for detecting a selective forwarding attack (DCA-SF) is proposed. It can capture and isolate malicious CHs that have launched selective forwarding attacks by clustering their cumulative forwarding rates (CFRs). The DCA-SF algorithm has been strengthened by changing the DCA parameters (Eps, Minpts) adaptively. The simulation results show that the DCA-SF has a low missed detection rate of 1.04% and a false detection rate of 0.42% respectively with low energy consumption.

Introducing Machine Learning to Wireless Sensor Networks

2020 ◽  
pp. 1-22
Author(s):  
Vidit Gulyani ◽  
Tushar Dhiman ◽  
Bharat Bhushan
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
20Th Century ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clustering Techniques ◽  
Reduce Power Consumption ◽  
Overall Performance ◽  
Hostile Environments

From its advent in mid-20th century, machine learning constantly improves the user experience of existing systems. It can be used in almost every field such as weather, sports, business, IoT, medical care, etc. Wireless sensor networks are often placed in hostile environments to observe change in surroundings. Since these communicate wirelessly, many problems such as localisation of nodes, security of data being routed create barriers for proper functioning of system. Extending the horizon of machine learning to WSN creates wonders and adds credibility to the system. This chapter aids to present various machine learning aspects applied on wireless sensor networks and the benefits and drawbacks of applying machine learning to WSN. It also describes various data aggregation and clustering techniques that aim to reduce power consumption and ensure confidentiality, authentication, integrity, and availability amongst sensor nodes. This could contribute to design and alter pre-existing ML algorithms to improve overall performance of wireless sensor networks.

Network performance improvement by using TI-LEACH heterogeneous clustered protocol for wireless sensor networks

2019 ◽  
Vol 09 ◽  
Author(s):  
Rita Mahajan ◽  
Shivali Sharma ◽  
Deepak Bagai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Performance Improvement ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Network Performance ◽  
Clustering Algorithm ◽  
Research Work ◽  
Wireless Sensor ◽  
Clustering Techniques

Background and Objective: Current advancements in technology have encouraged heterogeneous networks with multi-sensors in Wireless Sensor Networks (WSN). These multi-sensors deployed in a particular region formed WSN and are continuously grabbing attention of researchers in the field of IoT application. In this research work, the clustering protocols, which have significant effect on saving energy of the network, increasing its lifetime and throughput are discussed. Based on such clustering techniques, a new clustering algorithm named as TI-LEACH is proposed which poses to an improvement over I-LEACH and LEACH in terms of network lifetime, instability period and throughput. Results and Conclusion: The simulation results divulge that TI-LEACH routing protocol is an energy efficient protocol as compare to I-LEACH and LEACH routing protocol. The results are compared in terms of dead nodes, alive nodes, network throughput and number of cluster heads (CHs) elected in each round.

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