A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Abstract

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

Scientific Developments and New Technological Trajectories in Sensor Research

“…This study reveals that networking of sensor systems is growing over time [ 48 , 52 , 81 , 82 , 83 ]. Results here also suggest that wireless sensor networks have a higher rate of evolution likely because of the interaction with specific technologies, such as Internet of Things, data security and monitoring systems.…”

“…This study reveals that technological development of sensors is due to evolutionary pathways based on interactions of sensors with other technological systems, such as information and communication technologies, artificial intelligence, Internet of Things, etc. [ 33 , 34 , 36 , 43 , 45 , 46 , 49 , 50 ] (cf., also [ 28 , 47 , 48 , 54 , 116 , 117 , 118 , 119 , 120 , 121 , 122 ]). Results suggest that sensors have, as parasite technologies (i.e., depending on other technologies; [ 17 ]), a wide spectrum of applications in medicine, environmental pollution, aircraft and automotive industries [ 123 , 124 , 125 , 126 , 127 ].…”

“…[ 20 , 22 , 31 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. New studies show that smart sensors are crucial elements for the Internet of Things [ 43 , 45 , 46 , 47 , 48 , 49 , 50 ]. The continuous interactions of sensor technologies with other technologies generate new applications in different fields, such as medicine, environmental science, telematics, the Internet of Things, etc.…”

A Centralized Mechanism for Preventing DDOS Attack in Wireless Sensor Networks

“…In [21] authors have presented a plan to distinguish a DDoS attack at the beginning to enable misfortune prohibition of information and saving in vitality after the avoidance of attacks. In [22] the authors have proposes a lightweight and secure framework for improvement of attacks in WSN through concentration on exchange amongst protection and resources. They utilized a Low Energy Adaptive Clustering Hierarchy (LEACH) and symmetric encryption method with a specific end goal for ensuring security.…”

Low-Power Wireless Sensor Network Using Fine-Grain Control of Sensor Module Power Mode

“…They employed a special schedule for the network’s root nodes and their direct neighbors, as well as the option to have multiple root nodes in a single network. Most WSNs rely on small batteries, which is a serious bottleneck in the system [ 5 , 6 ]. Battery capacity is developing at a very slow rate compared to other technologies, such as integrated circuits or software design.…”