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Trustworthy Sensing with Human-and-Environment-in-the-Loop

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 21290

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Special Issue Editor

Prof. Dr. Jinsong Han

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Guest Editor

School of Cyber Science and Technology, Zhejiang University, Hangzhou 310007, China
Interests: IoT security; smart sensing; mobile computing; AI security

Special Issue Information

Dear Colleagues,

Cyberspace is the new frontier where the cyber space, physical world, and humans are intra-or interconnected. Specifically, the newly emerged techniques and paradigms in sensing bring both opportunities and challenges to cyberspace. These well-regarded sensing approaches utilize a wide spectrum of wireless signals, including acoustic, Radio Frequency(RF), WiFi, mmWave, Ultra-wide band (UWB), optical and magnetic signals. In addition, massive sensors have been deployed, such as the camera, IMU, temperature and humidity sensors. The blooming of sensing techniques and devices not only fosters promising intelligent computing solutions, but also causes severe social and economic losses via spoofing, impersonating, hacking, malware, distributed deny of service (DDoS), and other attacks. The aim of this Special Issue is to encompass research advances in all areas of trustworthy sensing in the cyberspace. The topics of interest include, but are not limited to, the following:

AI and machine learning for trustworthy sensing

Biometric-oriented authentication

Cross-modal injection detection

Hardware design for trustworthy sensing

Intrusion detection and prevention

Privacy and anonymity in wireless sensing

Resistance to replay attack

Security in integrated sensing and communication

Secure sensing protocol

Secure sensing framework design

Security metrics for sensing

Security measurement framework for sensing

Systemization of knowledge for security in wireless sensing

Security analysis for 5G sensing

Side-channel mitigation in sensing

Theoretical foundation and models for trustworthy sensing

Prof. Dr. Jinsong Han
Guest Editor

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Published Papers (7 papers)

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Research

18 pages, 1842 KiB

Open AccessArticle

An Order-Preserving Encryption Scheme Based on Weighted Random Interval Division for Ciphertext Comparison in Wearable Systems

by Ruowei Gui, Liu Yang and Xiaolin Gui

Sensors 2022, 22(20), 7950; https://doi.org/10.3390/s22207950 - 18 Oct 2022

Cited by 4 | Viewed by 2274

Abstract

With the rapid development of wearable devices with various sensors, massive sensing data for health management have been generated. This causes a potential revolution in medical treatments, diagnosis, and prediction. However, due to the privacy risks of health data aggregation, data comparative analysis under privacy protection faces challenges. Order-preserving encryption is an effective scheme to achieve private data retrieval and comparison, but the existing order-preserving encryption algorithms are mainly aimed at either integer data or single characters. It is urgent to build a lightweight order-preserving encryption scheme that supports multiple types of data such as integer, floating number, and string. In view of the above problems, this paper proposes an order-preserving encryption scheme (WRID-OPES) based on weighted random interval division (WRID). WRID-OPES converts all kinds of data into hexadecimal number strings and calculates the frequency and weight of each hexadecimal number. The plaintext digital string is blocked and recombined, and each block is encrypted using WRID algorithm according to the weight of each hexadecimal digit. Our schemes can realize the order-preserving encryption of multiple types of data and achieve indistinguishability under ordered selection plaintext attack (IND-OCPA) security in static data sets. Security analysis and experiments show that our scheme can resist attacks using exhaustive methods and statistical methods and has linear encryption time and small ciphertext expansion rate. Full article

(This article belongs to the Special Issue Trustworthy Sensing with Human-and-Environment-in-the-Loop)

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21 pages, 3523 KiB

Open AccessArticle

Enabling Fog–Blockchain Computing for Autonomous-Vehicle-Parking System: A Solution to Reinforce IoT–Cloud Platform for Future Smart Parking

by Aamir Shahzad, Abdelouahed Gherbi and Kaiwen Zhang

Sensors 2022, 22(13), 4849; https://doi.org/10.3390/s22134849 - 27 Jun 2022

Cited by 19 | Viewed by 3677

Abstract

With the advent of modern technologies, including the IoT and blockchain, smart-parking (SP) systems are becoming smarter and smarter. Similar to other automated systems, and particularly those that require automation or minimal interaction with humans, the SP system is heuristic in delivering performances, such as throughput in terms of latency, efficiency, privacy, and security, and it is considered a long-term cost-effective solution. This study looks ahead to future trends and developments in SP systems and presents an inclusive, long-term, effective, and well-performing smart autonomous vehicle parking (SAVP) system that explores and employs the emerging fog-computing and blockchain technologies as robust solutions to strengthen the existing collaborative IoT–cloud platform to build and manage SP systems for autonomous vehicles (AVs). In other words, the proposed SAVP system offers a smart-parking solution, both indoors and outdoors, and mainly for AVs looking for vacant parking, wherein the fog nodes act as a middleware layer that provides various parking operations closer to IoT-enabled edge devices. To address the challenges of privacy and security, a lightweight integrated blockchain and cryptography (LIBC) module is deployed, which is functional at each fog node, to authorize and grant access to the AVs in every phase of parking (e.g., from the parking entrance to the parking slot to the parking exit). A proof-of-concept implementation was conducted, wherein the overall computed results, such as the average response time, efficiency, privacy, and security, were examined as highly efficient to enable a proven SAVP system. This study also examined an innovative pace, with careful considerations to combatting the existing SP-system challenges and, therefore, to building and managing future scalable SP systems. Full article

(This article belongs to the Special Issue Trustworthy Sensing with Human-and-Environment-in-the-Loop)

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24 pages, 4953 KiB

Open AccessArticle

BioTouch: Reliable Re-Authentication via Finger Bio-Capacitance and Touching Behavior

by Chong Zhang, Songfan Li, Yihang Song, Qianhe Meng, Li Lu and Mengshu Hou

Sensors 2022, 22(9), 3583; https://doi.org/10.3390/s22093583 - 8 May 2022

Cited by 1 | Viewed by 3343

Abstract

Re-authentication continuously checks to see if a user is authorized during a whole usage session, enhancing secrecy capabilities for computational devices, especially against insider attacks. However, it is challenging to design a reliable re-authentication scheme with accuracy, transparency and robustness. Specifically, the approaches of using biometric features (e.g., fingerprint, iris) are often accurate in identifying users but not transparent to them due to the need for user cooperation. On the other hand, while the approaches exploiting behavior features (e.g., touch-screen gesture, movement) are often transparent in use, their applications suffer from low accuracy and robustness as behavior information collected is subjective and may change frequently over different use situations and even user’s motion. In this paper, we propose BioTouch, a reliable re-authentication scheme that satisfies all the above requirements. First, BioTouch utilizes multiple features (finger capacitance and touching behavior) to identify the user for better accuracy. Second, BioTouch automatically works during user operation on capacitive-touch devices, achieving transparency without the need for manual assistance. Finally, by applying finger bio-capacitance, BioTouch is also robust to various conditions, as this feature is determined by the user’s physical characteristics and will not change by different user positions and motions. We implement BioTouch for proof-of-concept and conduct comprehensive evaluations. The results show that BioTouch can flag 98% of anomalous behaviors within ten touching operations and achieve up to 99.84% accuracy during usage. Full article

(This article belongs to the Special Issue Trustworthy Sensing with Human-and-Environment-in-the-Loop)

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23 pages, 7366 KiB

Open AccessArticle

Butterfly: μW Level ULP Sensor Nodes with High Task Throughput

by Chong Zhang, Li Lu, Yihang Song, Qianhe Meng, Junqin Zhang, Xiandong Shao, Guangyuan Zhang and Mengshu Hou

Sensors 2022, 22(8), 3082; https://doi.org/10.3390/s22083082 - 17 Apr 2022

Viewed by 3106

Abstract

The rapid development of Internet of Things (IoT) applications calls for light-weight IoT sensor nodes with both low-power consumption and excellent task execution efficiency. However, in the existing system framework, designers must make trade-offs between these two. In this paper, we propose an “edge-to-end integration” design paradigm, Butterfly, which assists sensor nodes to perform sensing tasks more efficiently with lower power consumption through their (high-performance) network infrastructures (i.e., a gateway). On the one hand, to optimize the power consumption, Butterfly offloads the energy-intensive computational tasks from the nodes to the gateway with only microwatt-level power budget, thereby eliminating the power-consuming Microcontroller (MCU) from the node. On the other hand, we address three issues facing the optimization of task execution efficiency. To start with, we buffer the frequently used instructions and data to minimize the volume of data transmitted on the downlink. Furthermore, based on our investigation on typical sensing data structures, we present a novel last-bit transmission and packaging mechanism to reduce the data amount on the uplink. Finally, we design a task prediction mechanism on the gateway to support efficient scheduling of concurrent tasks on multiple MCU-free Butterfly nodes. The experiment results show that Butterfly can speed up the task rate by 4.91 times and reduce the power consumption of each node by 94.3%, compared to the benchmarks. In addition, Butterfly nodes have natural security advantages (e.g., anti-capture) as they offload the control function with all application information up to the gateway. Full article

(This article belongs to the Special Issue Trustworthy Sensing with Human-and-Environment-in-the-Loop)

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19 pages, 886 KiB

Open AccessArticle

Rethinking Power Efficiency for Next-Generation Processor-Free Sensing Devices

by Yihang Song, Songfan Li, Chong Zhang, Shengyu Li and Li Lu

Sensors 2022, 22(8), 3074; https://doi.org/10.3390/s22083074 - 16 Apr 2022

Cited by 2 | Viewed by 3164

Abstract

The last decade has seen significant advances in power optimization for IoT sensors. The conventional wisdom considers that if we reduce the power consumption of each component (e.g., processor, radio) into

μ

W-level of power, the IoT sensors could achieve overall ultra-low power [...] Read more.

μ

W-level of power, the IoT sensors could achieve overall ultra-low power consumption. However, we show that this conventional wisdom is overturned, as bus communication can take significant power for exchanging data between each component. In this paper, we analyze the power efficiency of bus communication and ask whether it is possible to reduce the power consumption for bus communication. We observe that existing bus architectures in mainstream IoT devices can be classified into either push-pull or open-drain architecture. push-pull only adapts to unidirectional communication, whereas open-drain inherently fits for bidirectional communication which benefits simplifying bus topology and reducing hardware costs. However, open-drain consumes more power than push-pull due to the high leakage current consumption while communicating on the bus. We present Turbo, a novel approach introducing low power to the open-drain based buses by reducing the leakage current created on the bus. We instantiate Turbo on I

^{2}

C bus and evaluate it with commercial off-the-shelf (COTS) sensors. The results show a

76.9 %

improvement in power efficiency in I

^{2}

C communication. Full article

(This article belongs to the Special Issue Trustworthy Sensing with Human-and-Environment-in-the-Loop)

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18 pages, 1792 KiB

Open AccessArticle

An Effective Gaze-Based Authentication Method with the Spatiotemporal Feature of Eye Movement

by Jinghui Yin, Jiande Sun, Jing Li and Ke Liu

Sensors 2022, 22(8), 3002; https://doi.org/10.3390/s22083002 - 14 Apr 2022

Cited by 13 | Viewed by 2389

Abstract

Eye movement has become a new behavioral feature for biometric authentication. In the eye movement-based authentication methods that use temporal features and artificial design features, the required duration of eye movement recordings are too long to be applied. Therefore, this study aims at using eye movement recordings with shorter duration to realize authentication. And we give out a reasonable eye movement recording duration that should be less than 12 s, referring to the changing pattern of the deviation degree between the gaze point and the stimulus point on the screen. In this study, the temporal motion features of the gaze points and the spatial distribution features of the saccade are using to represent the personal identity. Two datasets are constructed for the experiments, including 5 s and 12 s of eye movement recordings. On the datasets constructed in this paper, the open-set authentication results show that the Equal Error Rate of our proposed methods can reach 10.62% when recording duration is 12 s and 12.48% when recording duration is 5 s. The closed-set authentication results show that the Equal Error Rate of our proposed methods can reach 5.25% when recording duration is 12 s and 7.82% when recording duration is 5 s. It demonstrates that the proposed method provides a reference for the eye movements data-based identity authentication. Full article

(This article belongs to the Special Issue Trustworthy Sensing with Human-and-Environment-in-the-Loop)

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13 pages, 525 KiB

Open AccessCommunication

Deep-Learning-Based Resource Allocation for Time-Sensitive Device-to-Device Networks

by Zhe Zheng, Yingying Chi, Guangyao Ding and Guanding Yu

Sensors 2022, 22(4), 1551; https://doi.org/10.3390/s22041551 - 17 Feb 2022

Cited by 4 | Viewed by 2214

Abstract

Ultra-reliable and low-latency communication (URLLC) is considered as one of the major use cases in 5G networks to support the emerging mission-critical applications. One of the possible tools to achieve URLLC is the device-to-device (D2D) network. Due to the physical proximity of communicating devices, D2D networks can significantly improve the latency and reliability performance of wireless communication. However, the resource management of D2D networks is usually a non-convex combinatorial problem that is difficult to solve. Traditional methods usually optimize the resource allocation in an iterative way, which leads to high computational complexity. In this paper, we investigate the resource allocation problem in the time-sensitive D2D network where the latency and reliability performance is modeled by the achievable rate in the short blocklength regime. We first design a game theory-based algorithm as the baseline. Then, we propose a deep learning (DL)-based resource management framework using deep neural network (DNN). The simulation results show that the proposed DL-based method achieves almost the same performance as the baseline algorithm, while it is more time-efficient due to the end-to-end structure. Full article

(This article belongs to the Special Issue Trustworthy Sensing with Human-and-Environment-in-the-Loop)

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