The application of the Symbolic Aggregate Approximation algorithm (SAX) to radio frequency fingerprinting of IoT devices

2017 ◽  
Author(s):  
Gianmarco Baldini ◽  
Raimondo Giuliani ◽  
Gary Steri ◽  
Ignacio Sanchez ◽  
Claudio Gentile
Keyword(s):  
Approximation Algorithm ◽  
Radio Frequency ◽  
Symbolic Aggregate Approximation ◽  
Iot Devices

scholarly journals Practical Cross-Layer Radio Frequency-Based Authentication Scheme for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4034
Author(s):  
Arie Haenel ◽  
Yoram Haddad ◽  
Maryline Laurent ◽  
Zonghua Zhang
Keyword(s):  
Internet Of Things ◽  
Radio Frequency ◽  
Real Life ◽  
Computation Time ◽  
Cost Effective ◽  
Battery Life ◽  
Cross Layer ◽  
Security Level ◽  
Cost Of Energy ◽  
Iot Devices

The Internet of Things world is in need of practical solutions for its security. Existing security mechanisms for IoT are mostly not implemented due to complexity, budget, and energy-saving issues. This is especially true for IoT devices that are battery powered, and they should be cost effective to be deployed extensively in the field. In this work, we propose a new cross-layer approach combining existing authentication protocols and existing Physical Layer Radio Frequency Fingerprinting technologies to provide hybrid authentication mechanisms that are practically proved efficient in the field. Even though several Radio Frequency Fingerprinting methods have been proposed so far, as a support for multi-factor authentication or even on their own, practical solutions are still a challenge. The accuracy results achieved with even the best systems using expensive equipment are still not sufficient on real-life systems. Our approach proposes a hybrid protocol that can save energy and computation time on the IoT devices side, proportionally to the accuracy of the Radio Frequency Fingerprinting used, which has a measurable benefit while keeping an acceptable security level. We implemented a full system operating in real time and achieved an accuracy of 99.8% for the additional cost of energy, leading to a decrease of only ~20% in battery life.

scholarly journals Competition of Duopoly MVNOs for IoT Applications through Wireless Network Virtualization

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Wanli Zhang ◽  
Xianwei Li ◽  
Liang Zhao ◽  
Xiaoying Yang
Keyword(s):  
Radio Frequency ◽  
Wireless Network ◽  
Network Performance ◽  
Stackelberg Game ◽  
Network Virtualization ◽  
Network Services ◽  
Network Service ◽  
Iot Applications ◽  
Wireless Network Virtualization ◽  
Iot Devices

Network performance is of great importance for processing Internet of Things (IoT) applications in the fifth-generation (5G) communication system. With the increasing number of the devices, how network services should be provided with better performances is becoming a pressing issue. The static resource allocation of wireless networks is becoming a bottleneck for the emerging IoT applications. As a potential solution, network virtualization is considered a promising approach to enhancing the network performance and solving the bottleneck issue. In this paper, the problem of wireless network virtualization is investigated where one wireless infrastructure provider (WIP), mobile virtual network operators (MVNOs), and IoT devices coexist. In the system model under consideration, with the help of a software-defined network (SDN) controller, the WIP can divide and reconfigure its radio frequency bands to radio frequency slices. Then, two MVNOs, MVNO1 and MVNO2, can lease these frequency slices from the WIP and then provide IoT network services to IoT users under competition. We apply a two-stage Stackelberg game to investigate and analyze the relationship between the two MVNOs and IoT users, where MVNO1 and MVNO2 firstly try to maximize their profits by setting the optimal network service prices. Then, IoT users make decisions on which network service they should select according to the performances and prices of network services. Two competition cases between MVNO1 and MVNO2 are considered, namely, Stackelberg game (SG) where MVNO1 is the leader whose price of network service is set firstly and MVNO2 is the follower whose network service price is set later and noncooperative strategic game (NSG) under which the service prices of MVNO1 and MVNO2 are simultaneously set. Each IoT user decides whether and which MVNO to select on the basis of the network service prices and qualities. The numerical results are provided to show the effectiveness of our game model and the proposed solution method.

scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

scholarly journals Efficient Energy Utilization with Device Placement and Scheduling in the Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yanli Zhu ◽  
Xiaoping Yang ◽  
Yi Hong ◽  
Youfang Leng ◽  
Chuanwen Luo
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Approximation Algorithm ◽  
Minimum Energy ◽  
Base Station ◽  
Area Coverage ◽  
The Internet ◽  
Minimum Energy Consumption ◽  
Iot Devices ◽  
The Internet Of Things

The low-power wide-area network (LPWAN) technologies, such as LoRa, Sigfox, and NB-IoT, bring new renovation to the wireless communication between end devices in the Internet of things (IoT), which can provide larger coverage and support a large number of IoT devices to connect to the Internet with few gateways. Based on these technologies, we can directly deploy IoT devices on the candidate locations to cover targets or the detection area without considering multihop data transmission to the base station like the traditional wireless sensor networks. In this paper, we investigate the problems of the minimum energy consumption of IoT devices for target coverage through placement and scheduling (MTPS) and minimum energy consumption of IoT devices for area coverage through placement and scheduling (MAPS). In the problems, we consider both the placement and scheduling of IoT devices to monitor all targets (or the whole detection area) such that all targets (or the whole area) are (or is) continuously observed for a certain period of time. The objectives of the problems are to minimize the total energy consumption of the IoT devices. We first, respectively, propose the mathematical models for the MTPS and MAPS problems and prove that they are NP-hard. Then, we study two subproblems of the MTPS problem, minimum location coverage (MLC), and minimum energy consumption scheduling deployment (MESD) and propose an approximation algorithm for each of them. Based on these two subproblems, we propose an approximation algorithm for the MTPS problem. After that, we investigate the minimum location area coverage (MLAC) problem and propose an algorithm for it. Based on the MLAC and MESD problems, we propose an approximation algorithm to solve the MAPS problem. Finally, extensive simulation results are given to further verify the performance of the proposed algorithms.

scholarly journals An Ensemble Learning Method for Wireless Multimedia Device Identification

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhen Zhang ◽  
Yibing Li ◽  
Chao Wang ◽  
Meiyu Wang ◽  
Ya Tu ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Ensemble Learning ◽  
Data Transmission ◽  
Physical Layer ◽  
Classification Model ◽  
Identification System ◽  
Wireless Multimedia ◽  
Device Identification ◽  
Data Transmission Security ◽  
Iot Devices

In the last decade, wireless multimedia device is widely used in many fields, which leads to efficiency improvement, reliability, security, and economic benefits in our daily life. However, with the rapid development of new technologies, the wireless multimedia data transmission security is confronted with a series of new threats and challenges. In physical layer, Radio Frequency Fingerprinting (RFF) is a unique characteristic of IoT devices themselves, which can difficultly be tampered. The wireless multimedia device identification via Radio Frequency Fingerprinting (RFF) extracted from radio signals is a physical-layer method for data transmission security. Just as people’s unique fingerprinting, different Internet of Things (IoT) devices exhibit different RFF which can be used for identification and authentication. In this paper, a wireless multimedia device identification system based on Ensemble Learning is proposed. The key technologies such as signal detection, RFF extraction, and classification model are discussed. According to the theoretical modeling and experiment validation, the reliability and the differentiability of the RFFs are evaluated and the classification results are shown under the real wireless multimedia device environments.

scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Jithin Jagannath ◽  
Anu Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Jithin Jagannath ◽  
Anu Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

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