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Data and Privacy Management in Sensor Networks

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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 (31 December 2022) | Viewed by 23046

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Prof. Dr. Richard Chbeir

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IUT de Bayonne, 2 Allée du Parc de Montaury, Campus Montaury/Anglet, Université de Pau et des Pays de l'Adour (UPPA), Office 200, 64600 Anglet, France
Interests: multimedia information retrieval; XML and RSS similarity; access control models; digital ecosystems
Special Issues, Collections and Topics in MDPI journals

Dr. Taoufik Yeferny

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Guest Editor

LIUPPA, Université de Pau et des Pays de l'Adour, 2 Allée du Parc Montaury, 64600 Anglet, France
Interests: mobile computing; connected envirenement; vehicular ad-hoc networks; intelligent transportation systems

Special Issue Information

Dear Colleagues,

Recent years have witnessed a widespread interest in innovative sensor networks capable of providing valuable data for various applications (e.g., home automation, energy management), the connected objects and environments impact numerous application domains. From smart homes and buildings to cities, vehicle networks, and electrical grids, they have become a novel trend that is revolutionizing how people interact with their surroundings, how they accomplish their daily tasks in the workplace, and how they handle their health security, and safety. Sensor networks markets are currently booming and are projected to continue their growth for the years to come. The rising interest in intelligent connected environments (e.g., smart buildings, cities, factories) and the evolution of sensors, data management/communication technologies have paved the way for exciting and valuable applications that help users in their everyday tasks (e.g., increasing comfort, reducing energy consumption).

Indeed, the sensor network ecosystem have made it easy to collect and exchange a large amount of data, connect heterogeneous systems, create complex systems for new forms of collaboration and interoperability. Typically, the sensed data are transmitted to the edge nodes, or directly to the cloud/server where it will be stored, indexed, processed and analysed to offer a new class of advanced services, such as envirenement monitoring, objects tracking, event detection, advanced data analytics, etc. Despite the progress made, however, data and privacy management in sensor networks remains a core and challenging issues. In fact, due to the nature of sensor networks, there exist complexity in gathering, aggregation, indexing, storage, processing and analysing big data generated by resource-constrained sensor nodes per unit time. Furthermore, sensor networks also impose new challenges related to knowledge discovery and decision-making automation, security, privacy, and trust.

This special issue will promote the state-of-the-art research covering all aspects of the data and privacy management in sensor networks. High quality contributions addressing related theoretical and practical aspects are expected. The topics of interest for this special issue include, but are not limited to :

Modelling, simulation of sensor networks
Architecture and Protocols for sensor networks
Data gathering, storage and aggregation in sensor networks
Data processing, indexing and discovery in sensor networks
Data analytics solutions for sensor networks
Knowledge discovery and decision-making automation in sensor networks
Modelling, analysis, simulation, and verification of security, privacy, and trustworthiness for sensor networks
Detection, evaluation, and prevention of threats and attacks in sensor networks
Data security, privacy, and trustworthiness in sensor networks

Dr. Richard Chbeir
Dr. Taoufik Yeferny
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

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33 pages, 552 KiB

Open AccessArticle

A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks

by Kyle DeMedeiros, Abdeltawab Hendawi and Marco Alvarez

Sensors 2023, 23(3), 1352; https://doi.org/10.3390/s23031352 - 25 Jan 2023

Cited by 20 | Viewed by 7145

Abstract

Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly detection (AD). With the rapid increase in the number of Internet-connected devices, the growing desire for Internet of Things (IoT) devices in the home, on our person, and in our vehicles, and the transition to smart infrastructure and the Industrial IoT (IIoT), anomaly detection in these devices is critical. This paper is a survey of anomaly detection in sensor networks/the IoT. This paper defines what an anomaly is and surveys multiple sources based on those definitions. The goal of this survey was to highlight how anomaly detection is being performed on the Internet of Things and sensor networks, identify anomaly detection approaches, and outlines gaps in the research in this domain. Full article

(This article belongs to the Special Issue Data and Privacy Management in Sensor Networks)

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17 pages, 458 KiB

Open AccessArticle

An Improved Lightweight User Authentication Scheme for the Internet of Medical Things

by Keunok Kim, Jihyeon Ryu, Youngsook Lee and Dongho Won

Sensors 2023, 23(3), 1122; https://doi.org/10.3390/s23031122 - 18 Jan 2023

Cited by 8 | Viewed by 1689

Abstract

The Internet of Medical Things (IoMT) is used in the medical ecosystem through medical IoT sensors, such as blood glucose, heart rate, temperature, and pulse sensors. To maintain a secure sensor network and a stable IoMT environment, it is important to protect the medical IoT sensors themselves and the patient medical data they collect from various security threats. Medical IoT sensors attached to the patient’s body must be protected from security threats, such as being controlled by unauthorized persons or transmitting erroneous medical data. In IoMT authentication, it is necessary to be sensitive to the following attack techniques. (1) The offline password guessing attack easily predicts a healthcare administrator’s password offline and allows for easy access to the healthcare worker’s account. (2) Privileged-insider attacks executed through impersonation are an easy way for an attacker to gain access to a healthcare administrator’s environment. Recently, previous research proposed a lightweight and anonymity preserving user authentication scheme for IoT-based healthcare. However, this scheme was vulnerable to offline password guessing, impersonation, and privileged insider attacks. These attacks expose not only the patients’ medical data such as blood pressure, pulse, and body temperature but also the patients’ registration number, phone number, and guardian. To overcome these weaknesses, in the present study we propose an improved lightweight user authentication scheme for the Internet of Medical Things (IoMT). In our scheme, the hash function and XOR operation are used for operation in low-spec healthcare IoT sensor. The automatic cryptographic protocol tool ProVerif confirmed the security of the proposed scheme. Finally, we show that the proposed scheme is more secure than other protocols and that it has 266.48% better performance than schemes that have been previously described in other studies. Full article

(This article belongs to the Special Issue Data and Privacy Management in Sensor Networks)

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26 pages, 791 KiB

Open AccessArticle

Detecting Inference Attacks Involving Raw Sensor Data: A Case Study

by Paul Lachat, Nadia Bennani, Veronika Rehn-Sonigo, Lionel Brunie and Harald Kosch

Sensors 2022, 22(21), 8140; https://doi.org/10.3390/s22218140 - 24 Oct 2022

Cited by 1 | Viewed by 1360

Abstract

With the advent of sensors, more and more services are developed in order to provide customers with insights about their health and their appliances’ energy consumption at home. To do so, these services use new mining algorithms that create new inference channels. However, the collected sensor data can be diverted to infer personal data that customers do not consent to share. This indirect access to data that are not collected corresponds to inference attacks involving raw sensor data (IASD). Towards these new kinds of attacks, existing inference detection systems do not suit the representation requirements of these inference channels and of user knowledge. In this paper, we propose RICE-M (Raw sensor data based Inference ChannEl Model) that meets these inference channel representations. Based on RICE-M, we proposed RICE-Sy an extensible system able to detect IASDs, and evaluated its performance taking as a case study the MHEALTH dataset. As expected, detecting IASD is proven to be quadratic due to huge sensor data managed and a quickly growing amount of user knowledge. To overcome this drawback, we propose first a set of conceptual optimizations that reduces the detection complexity. Although becoming linear, as online detection time remains greater than a fixed acceptable query response limit, we propose two approaches to estimate the potential of RICE-Sy. The first one is based on partitioning strategies which aim at partitioning the knowledge of users. We observe that by considering the quantity of knowledge gained by a user as a partitioning criterion, the median detection time of RICE-Sy is reduced by 63%. The second approach is H-RICE-SY, a hybrid detection architecture built on RICE-Sy which limits the detection at query-time to users that have a high probability to be malicious. We show the limits of processing all malicious users at query-time, without impacting the query answer time. We observe that for a ratio of 30% users considered as malicious, the median online detection time stays under the acceptable time of 80 ms, for up to a total volume of 1.2 million user knowledge entities. Based on the observed growth rates, we have estimated that for 5% of user knowledge issued by malicious users, a maximum volume of approximately 8.6 million user’s information can be processed online in an acceptable time. Full article

(This article belongs to the Special Issue Data and Privacy Management in Sensor Networks)

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18 pages, 1822 KiB

Open AccessArticle

Examining the Suitability of NetFlow Features in Detecting IoT Network Intrusions

by Mohammed Awad, Salam Fraihat, Khouloud Salameh and Aneesa Al Redhaei

Sensors 2022, 22(16), 6164; https://doi.org/10.3390/s22166164 - 17 Aug 2022

Cited by 12 | Viewed by 2117

Abstract

The past few years have witnessed a substantial increase in cyberattacks on Internet of Things (IoT) devices and their networks. Such attacks pose a significant threat to organizational security and user privacy. Utilizing Machine Learning (ML) in Intrusion Detection Systems (NIDS) has proven advantageous in countering novel zero-day attacks. However, the performance of such systems relies on several factors, one of which is prediction time. Processing speed in anomaly-based NIDS depends on a few elements, including the number of features fed to the ML model. NetFlow, a networking industry-standard protocol, offers many features that can be used to predict malicious attacks accurately. This paper examines NetFlow features and assesses their suitability in classifying network traffic. Our paper presents a model that detects attacks with (98–100%) accuracy using as few as 13 features. This study was conducted using a large dataset of over 16 million records released in 2021. Full article

(This article belongs to the Special Issue Data and Privacy Management in Sensor Networks)

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17 pages, 2292 KiB

Open AccessArticle

Trust-Based Intelligent Routing Protocol with Q-Learning for Mission-Critical Wireless Sensor Networks

by DooHo Keum and Young-Bae Ko

Sensors 2022, 22(11), 3975; https://doi.org/10.3390/s22113975 - 24 May 2022

Cited by 11 | Viewed by 1708

Abstract

Mission-critical wireless sensor networks require a trustworthy and punctual routing protocol to ensure the worst-case end-to-end delay and reliability when transmitting mission-critical data collected by various sensors to gateways. In particular, the trustworthiness of mission-critical data must be guaranteed for decision-making and secure communications. However, it is a challenging issue to meet the requirement of both reliability and QoS in sensor networking environments where cyber-attacks may frequently occur and a lot of mission-critical data is generated. This study proposes a trust-based routing protocol that learns the trust elements using Q-learning to detect various attacks and ensure network performance. The proposed mechanism ensures the prompt detection of cyber threats that may occur in a mission-critical wireless sensor network and guarantees the trustworthy transfer of mission-critical sensor data. This paper introduces a distributed transmission technology that prioritizes the trustworthiness of mission-critical data through Q-learning results considering trustworthiness, QoS, and energy factors. It is a technology suitable for mission-critical wireless sensor network operational environments and can reliably operate resource-constrained devices. We implemented and performed a comprehensive evaluation of our scheme using the OPNET simulator. In addition, we measured packet delivery rates, throughput, survivability, and delay considering the characteristics of mission-critical sensor networks. The simulation results show an enhanced performance when compared with other mechanisms. Full article

(This article belongs to the Special Issue Data and Privacy Management in Sensor Networks)

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27 pages, 1784 KiB

Open AccessArticle

Location-Aware Resource Discovery and QoR-Driven Resource Selection for Hybrid Web Environments

by Lara Kallab, Richard Chbeir and Michael Mrissa

Sensors 2021, 21(20), 6835; https://doi.org/10.3390/s21206835 - 14 Oct 2021

Cited by 1 | Viewed by 1488

Abstract

In the Web of Things (WoT) context, an increasing number of stationary and mobile objects provide functions as RESTful services, also called resources, that can be combined with other existing Web resources, to create value-added processes. However, nowadays resource discovery and selection are challenging, due to (1) the growing number of resources providing similar functions, making Quality of Resource (QoR) essential to select appropriate resources, (2) the transient nature of resource availability due to sporadic connectivity, and (3) the location changes of mobile objects in time. In this paper, we first present a location-aware resource discovery that relies on a 3-dimensional indexing schema, which considers object location for resource identification. Then, we present a QoR-driven resource selection approach that uses a Selection Strategy Adaptor (SSA) to form i-compositions (with i

\in N^{*}

) offering different implementation alternatives. The defined SSA allows forming resource compositions while considering QoR constraints and Inputs/Outputs matching of related resources, as well as resource availability and users different needs (e.g., optimal and optimistic compositions obtained using a scoring system). Analyses are made to evaluate our service quality model against existing ones, and experiments are conducted in different environments setups to study the performance of our solution. Full article

(This article belongs to the Special Issue Data and Privacy Management in Sensor Networks)

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Review

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24 pages, 543 KiB

Open AccessReview

Access Control for IoT: A Survey of Existing Research, Dynamic Policies and Future Directions

by Kaushik Ragothaman, Yong Wang, Bhaskar Rimal and Mark Lawrence

Sensors 2023, 23(4), 1805; https://doi.org/10.3390/s23041805 - 06 Feb 2023

Cited by 14 | Viewed by 5620

Abstract

Internet of Things (IoT) provides a wide range of services in domestic and industrial environments. Access control plays a crucial role in granting access rights to users and devices when an IoT device is connected to a network. However, many challenges exist in designing and implementing an ideal access control solution for the IoT due to the characteristics of the IoT including but not limited to the variety of the IoT devices, the resource constraints on the IoT devices, and the heterogeneous nature of the IoT. This paper conducts a comprehensive survey on access control in the IoT, including access control requirements, authorization architecture, access control models, access control policies, access control research challenges, and future directions. It identifies and summarizes key access control requirements in the IoT. The paper further evaluates the existing access control models to fulfill the access control requirements. Access control decisions are governed by access control policies. The existing approaches on dynamic policies’ specification are reviewed. The challenges faced by the existing solutions for policies’ specification are highlighted. Finally, the paper presents the research challenges and future directions of access control in the IoT. Due to the variety of IoT applications, there is no one-size-fits-all solution for access control in the IoT. Despite the challenges encountered in designing and implementing the access control in the IoT, it is desired to have an access control solution to meet all the identified requirements to secure the IoT. Full article

(This article belongs to the Special Issue Data and Privacy Management in Sensor Networks)

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