Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks

Mittal, Mohit; Prado, Rocío Pérez de; Kawai, Yukiko; Nakajima, Shinsuke; Expósito, J. E. Muñoz

doi:10.3390/en14113125

Cited by 37 publications

(17 citation statements)

References 35 publications

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“…Figure 4 illustrates this point. As a final remark, the false positive and false negative values of recently presented energy-efficient IDS reached 5% (see, for example, [28]). Assume that the admissible system performance degradation is limited to 10%.…”

Section: Criterionmentioning

confidence: 93%

On the Suitability of Intrusion Detection System for Wireless Edge Networks

2021

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Multi-access edge computing has become a strategic concept of the Internet of Things. The edge computing market has reached USD several billion and is growing intensively. In the edge-computing paradigm, most of the data is processed close to, or at the edge of, the network. This greatly reduces the computation and communication load of the network core. Moreover, edge computing provides better support for user privacy. On the other hand, an increase in data processing locations will proportionately increase the attack surface. An edge node can be put out of service easily by being flooded with spoofed packets owing to limited capacities and resources. Furthermore, wireless edge nodes are quite vulnerable to energy exhaustion attacks. In this situation, traditional network security mechanisms cannot be used effectively. Therefore, a tradeoff between security and efficiency is needed. This study considered the requirements under which the use of an intrusion detection system (IDS) is justified. To the best of our knowledge, this is a first attempt to combine IDS quality, system performance degradation due to IDS operations, and workload specificity into a unified quantitative criterion. This paper is an extended version of a report published in the proceedings of the ICCSA 2020 and differs from it in many ways. In particular, this paper considers novel mathematical problems regarding the deployment strategies for an IDS and the corresponding inverse problems and provides closed-form solutions for a few previously unsolved problems.

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Section: Criterionmentioning

confidence: 93%

On the Suitability of Intrusion Detection System for Wireless Edge Networks

2021

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“…The authors designed a strategy to achieve no-gap and minimum overlap for the sensing area. Energy efficiency is a crucial objective for WSNs, as nodes are battery powered [7][8][9][10][11][12][13]. In [14], the connectivity and efficiency algorithm was proposed to reduce energy consumption and provide network connectivity.…”

Section: Related Workmentioning

confidence: 99%

PINC: Pickup Non-Critical Node Based k-Connectivity Restoration in Wireless Sensor Networks

Akram

Dagdeviren

Dağdeviren

et al. 2021

Sensors

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A Wireless Sensor Network (WSN) is connected if a communication path exists among each pair of sensor nodes (motes). Maintaining reliable connectivity in WSNs is a complicated task, since any failure in the nodes can cause the data transmission paths to break. In a k-connected WSN, the connectivity survives after failure in any k-1 nodes; hence, preserving the k-connectivity ensures that the WSN can permit k-1 node failures without wasting the connectivity. Higher k values will increase the reliability of a WSN against node failures. We propose a simple and efficient algorithm (PINC) to accomplish movement-based k-connectivity restoration that divides the nodes into the critical, which are the nodes whose failure reduces k, and non-critical groups. The PINC algorithm pickups and moves the non-critical nodes when a critical node stops working. This algorithm moves a non-critical node with minimum movement cost to the position of the failed mote. The measurements obtained from the testbed of real IRIS motes and Kobuki robots, along with extensive simulations, revealed that the PINC restores the k-connectivity by generating optimum movements faster than its competitors.

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“…As future IoT applications will generate a large amount of data, intelligent machine learning techniques are needed to analyze the data and get useful insights to improve the reliability of IoT [141]. Many machine learning techniques can be useful for IoT applications.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Reliable Internet of Things: Challenges and Future Trends

et al. 2021

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The Internet of Things (IoT) is a vital component of many future industries. By intelligent integration of sensors, wireless communications, computing techniques, and data analytics, IoT can increase productivity and efficiency of industries. Reliability of data transmission is key to realize several applications offered by IoT. In this paper, we present an overview of future IoT applications, and their major communication requirements. We provide a brief survey of recent work in four major areas of reliable IoT including resource allocation, latency management, security, and reliability metrics. Finally, we highlight some of the important challenges for reliable IoT related to machine learning techniques, 6G communications and blockchain based security that need further investigation and discuss related future directions.

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Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks

Cited by 37 publications

References 35 publications

On the Suitability of Intrusion Detection System for Wireless Edge Networks

On the Suitability of Intrusion Detection System for Wireless Edge Networks

PINC: Pickup Non-Critical Node Based k-Connectivity Restoration in Wireless Sensor Networks

Reliable Internet of Things: Challenges and Future Trends

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