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Embedded Operating Systems (OSs) are often developed in the C programming language. Developers justify this choice by the performance that can be achieved, the low memory footprint, and the ease of mapping hardware to software, as well as the strong adoption by industry of this programming language. The downside is that C is prone to security vulnerabilities unknowingly introduced by the software developer. Examples of such vulnerabilities are use-after-free, and buffer overflows. Like C, Rust is a compiled programming language that guarantees memory safety at compile time by adhering to a set of rules. There already exist a few OSs and frameworks that are entirely written in Rust, targeting sensor nodes. In this work, we give an overview of these OSs and frameworks and compare them on the basis of the features they provide, such as application isolation, scheduling, inter-process communication, and networking. Furthermore, we compare the OSs on the basis of the performance they provide, such as cycles and memory usage.

Time slotted channel hopping (TSCH) has become the standard multichannel MAC protocol for low-power lossy networks. The procedure for associating nodes in a TSCH-based network is not included in the standard and has been defined in the minimal 6TiSCH configuration. Faster network formation ensures that data packet transmission can start sooner. This paper proposes a dynamic beacon transmission schedule over the TSCH mechanism that achieves a shorter network formation time than the default minimum 6TiSCH static schedule. A theoretical model is derived for the proposed mechanism to estimate the expected time for a node to get associated with the network. Simulation results obtained with different network topologies and channel conditions show that the proposed mechanism reduces the average association time and average power consumption during network formation compared to the default minimal 6TiSCH configuration.

Elliptic curve cryptography is a widely deployed technology for securing digital communication. It is the basis of many cryptographic primitives such as key agreement protocols, digital signatures, and zero-knowledge proofs. Fast elliptic curve cryptography relies on heavily optimised modular arithmetic operations, which are often tailored to specific micro-architectures. In this article, we study and evaluate optimisations of the popular elliptic curve Curve25519 for ARM processors. We specifically target the ARM NEON single instruction, multiple data (SIMD) architecture, which is a popular architecture for modern smartphones. We introduce a novel representation for 128-bit NEON SIMD vectors, optimised for SIMD parallelisation, to accelerate elliptic curve operations significantly. Leveraging this representation, we implement an extended twisted Edwards curve Curve25519 back-end within the popular Rust library “curve25519-dalek”. We extensively evaluate our implementation across multiple ARM devices using both cryptographic benchmarks and the benchmark suite available for the Signal protocol. Our findings demonstrate a substantial back-end speed-up of at least 20% for ARM NEON, along with a noteworthy speed improvement of at least 15% for benchmarked Signal functions.

The Internet of Things (IoT) facilitates the integration of diverse devices, leading to the formation of networks such as Low-power Wireless Personal Area Networks (LoWPANs). These networks have inherent constraints that make header and payload compression an attractive solution to optimise communication. In this work, we evaluate the performance of Generic Header Compression (6LoWPAN-GHC), defined in RFC 7400, for IEEE 802.15.4-based networks running the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Through simulation and real-device experiments, we study the impact of 6LoWPAN-GHC on energy consumption and delays and investigate for which scenarios 6LoWPAN-GHC is beneficial. We show that all RPL control packets are compressible by 6LoWPAN-GHC, which reduces their transmission delay and as such their vulnerability to interference. However, for the devices under study transmitting at 250 kbit/s, the energy gain obtained from sending a compressed packet is outweighed by the energy needed to compress it. The use of 6LoWPAN-GHC causes an energy increase of between 2% and 26%, depending on the RPL packet type. When the range is more important than the bandwidth and a sub-GHz band is used at 10 kbit/s, an energy gain of 11% to 29% can be obtained, depending on the type of RPL control packet.

Driven by the increased consciousness in data ownership and privacy, zero-knowledge proofs (ZKPs) have become a popular tool to convince a third party of the truthfulness of a statement without disclosing any further information. As ZKPs are rather complex to design, frameworks that transform high-level languages into ZKPs have been proposed. We propose Circuitree, a Datalog reasoner in zero-knowledge. Datalog is a high-level declarative logic language that is generally used for querying. Furthermore, as a logic language, it can also be used to solve logic problems. An application using Circuitree can efficiently generate ZKPs, based on Datalog rules and encrypted data, to prove that a certain conclusion follows from a Datalog ruleset and encrypted input data. Compared to existing frameworks, which generally use their own limited imperative languages, Circuitree uses an existing high-level declarative language. We point out several applications for Circuitree, including EU Digital COVID Certificates and privacy-preserving access control for peer-to-peer (p2p) networks. Circuitree’s performance is evaluated for access control in a p2p network. First results show that our approach allows for fast proofs and proof verification for this application.

Typical wearable devices use a dedicated mobile phone as relay node to transfer the collected sensor data to a server. However, such relay nodes can be faulty or inactive due to various reasons, leading to interruptions of the communication link. To mitigate this challenge, we propose a novel security-enhanced emergency situation detection system, where 3rd party unknown mobile relays are used instead of dedicated gateways as opposed to many existing solutions for IoT healthcare applications. The proposed underlying key agreement and authentication scheme ensures anonymity and untraceability for both sensors (wearable devices) and relay nodes, and relies on symmetric key-based operations to function under resource-constrained environments. We have also developed a prototype of the system using commercial off-the-shelf devices to verify the proposed method's validity and evaluate the performance advantage over existing approaches. Bluetooth Low Energy (BLE) communication technology is used to connect sensor nodes (wearable devices) and mobile relays. After sending medical data to the cloud server, the relay node is responsible for emergency detection and alert generation.

We are witnessing the evolution from Internet of Things (IoT) to Internet of Vehicles (IoV). Internet connected vehicles can sense, communicate, analyze and make decisions. Rich vehicle-related data collection allows to apply artificial intelligence (AI) such as machine learning and deep learning (DL) to develop advanced services in Intelligent Transportation Systems (ITS). However, AI/DL-based ITS applications require intensive computation, both for model training and deployment. The exploitation of the huge computational power obtained through aggregation of resources present in individual vehicles and ITS infrastructure brings an efficient solution. In this work, oneVFC, a tangible vehicular fog computing (VFC) platform based on oneM2M is proposed. It benefits from the oneM2M standard to facilitate interoperability as well as hierarchical resource organization. oneVFC manages the distributed resources, orchestrates information flows and computing tasks on vehicle fog nodes and feeds back results to the application users. On a lab scale model consisting of Raspberry Pi modules and laptops, we demonstrate how oneVFC manages the AI-driven applications running on various machines and how it succeeds in significantly reducing application processing time, especially in cases with high workload or with requests arriving at high pace. We also show how oneVFC facilitates the deployment of AI model training in Federated Learning (FL), an advanced privacy preserving and communication saving training approach. Our experiments deployed in an outdoor environment with mobile fog nodes participating in the computation jobs confirm the feasibility of oneVFC for IoV environments whenever the communication links among fog nodes are guaranteed by V2X technology.

Message Queuing Telemetry Transport (MQTT) is a lightweight publish/subscribe protocol, which is currently one of the most popular application protocols in Internet of Things (IoT) thanks to its simplicity in use and its scalability. The secured version, MQTTS, which combines MQTT with the Transport Layer Security (TLS) protocol, has several shortcomings. It only offers one-to-one security, supports a limited number of security features and has high computation and communication costs. In this paper, we propose a flexible and lightweight security solution to be integrated in MQTT, addressing many-to-many communication, which reduces the communication overhead by 80% and the computational overhead by 40% for the setup of a secure connection on the client side.

Fog architectures are currently present in many applications. Constrained devices equipped with sensors produce measurements that will be sent to a nearby gateway, called the fog. The fog verifies, aggregates and forwards them to the server. Group authentication among these devices allows them to securely accept messages of the group members, resulting in faster updates in their process. When defining a security scheme, it should be considered that edge and fog devices are susceptible to attacks. Privacy of the devices should be guaranteed, with respect to outsiders and the fog. It should be impossible to track the connection pattern of devices with different fogs, even if several fogs are captured by an attacker. Inclusion of protection against potentially malicious fogs has not yet been considered in literature, especially not for group-based communications. We present a server-controlled group authentication and key agreement scheme, executed by the fog in collaboration with the devices that it can reach. The server, assumed to be fully trusted, is responsible for the registration and authorisation of the devices and initiates the key update process, whereas the fog takes care of the secure distribution process among its members. At the end, all entities in the group are ensured to possess the correct group key. Moreover, a pairwise secret key between device and server is obtained during the process. The proposed scheme is very efficient as it relies on elliptic curve cryptography and Lagrange interpolation. No initially shared secret key material among the entities needs to be pre-stored.

Wireless sensor networks (WSNs) are becoming increasingly prevalent in numerous fields. Industrial applications and natural-disaster-detection systems need fast and reliable data transmission, and in several cases, they need to be able to cope with changing traffic conditions. Thus, time-slotted channel hopping (TSCH) offers high reliability and efficient power management at the medium access control (MAC) level; TSCH considers two dimensions, time and frequency when allocating communication resources. However, the scheduler, which decides where in time and frequency these communication resources are allotted, is not part of the standard. Orchestra has been proposed as a scheduler which allocates the communication resources based on information collected through the IPv6 routing protocol for low-power and lossy networks (RPL). Orchestra is a very elegant solution, but does not adapt to high traffic. This research aims to build an Orchestra-based scheduler for applications with unpredictable traffic bursts. The implemented scheduler allocates resources based on traffic congestion measured for the children of the root and RPL subtree size of the same nodes. The performance analysis of the proposed scheduler shows lower latency and higher packet delivery ratio (PDR) compared to Orchestra during bursts, with negligible impact outside them.

It has been demonstrated that LoRa-based wide area networks (WANs) can cover extended areas under harsh propagation conditions. Traditional LoRaWAN solutions based on single-hop access face important drawbacks related to the presence of blind spots. This paper aims to tackle blind spots and performance issues by using a relaying approach. Many researchers investigating multi-hop solutions consider a fixed spreading factor (SF). This simplifies synchronization and association processes, but does not take advantage of the orthogonality between the virtual channels (i.e., frequency, SF) that help to mitigate blind spots. This paper proposes a time-slotted spreading factor hopping (TSSFH) mechanism that combines virtual channels and time slots into a frame structure. Pseudo-random scheduling is used inside blind spots, which simplifies the end-devices’ communication process and network organization. The results show how collisions decrease inside blind spots when more communication opportunities become available as more relaying nodes can be listening in different cells (i.e., frequency, SF-offset, time-offset). This has a direct impact on the collision-free packet delivery ratio (PDR) metric, which improves when more listening windows are opened, at the expense of faster battery depletion.

To enable today’s industrial automation, a significant number of sensors and actuators are required. In order to obtain trust and isolate faults in the data collected by this network, protection against authenticity fraud and nonrepudiation is essential. In this paper, we propose a very efficient symmetric-key-based security mechanism to establish authentication and nonrepudiation among all the nodes including the gateway in a distributed cooperative network, without communicating additional security parameters to establish different types of session keys. The solution also offers confidentiality and anonymity in case there are no malicious nodes. If at most one of the nodes is compromised, authentication and nonrepudiation still remain valid. Even if more nodes get compromised, the impact is limited. Therefore, the proposed method drastically differs from the classical group key management schemes, where one compromised node completely breaks the system. The proposed method is mainly based on a hash chain with multiple outputs defined at the gateway and shared with the other nodes in the network.

The so-called Internet of Things (IoT) aims at connecting every single object to the Internet with the purpose to automate every aspect of daily life. The IoT relies heavily on wireless low-power and lossy networks (LLNs) that collect information from the physical world and send the measurements to data aggregation and processing nodes. Most LLNs operate in the non-licensed industrial, scientific, and medical radio band, which is shared by a considerable number of systems. Coexisting wireless systems cause interference to each other, limiting their achievable performance. Multichannel communications enable frequency diversity, which in turn provides robustness against interference as well as increased network capacity. There is a considerable interest in multichannel medium access control (MAC) protocols for LLNs, including an evolving standard for the MAC layer of LLNs. In this paper, we review the latest advances in the topic and introduce a new classification framework for multichannel MAC protocols for LLNs. While our framework builds on previous review and classification studies, it adds aspects of a MAC protocol that reflect its interactions with the surrounding network stack. Seeing the resource constraints of the LLN devices, the study of such interactions-which is missing in prior classification efforts-can be the key for improving future designs. Relevant protocols published since 2006 are discussed and classified using the presented framework, including the recent multichannel MAC protocols for LLNs, such as the latest version of the IEEE 802.15.4 standard for time slotted channel hopping.

The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive results. The advancement of non-visible light sensors, such as acoustic imaging sensors, has attracted much attention, as they allow people to visualize the intensity of sound waves beyond the visible spectrum. However, because of the limitations imposed on acquiring acoustic data, new methods for improving the resolution of the acoustic images are necessary. At this time, there is no acoustic imaging dataset designed for the SR problem. This work proposed a novel backprojection model architecture for the acoustic image super-resolution problem, together with Acoustic Map Imaging VUB-ULB Dataset (AMIVU). The dataset provides large simulated and real captured images at different resolutions. The proposed XCycles BackProjection model (XCBP), in contrast to the feedforward model approach, fully uses the iterative correction procedure in each cycle to reconstruct the residual error correction for the encoded features in both low- and high-resolution space. The proposed approach was evaluated on the dataset and showed high outperformance compared to the classical interpolation operators and to the recent feedforward state-of-the-art models. It also contributed to a drastically reduced sub-sampling error produced during the data acquisition.

Smart buildings benefit from IEEE 802.15.4e time slotted channel hopping (TSCH) medium access for creating reliable and power aware wireless sensor and actuator networks (WSANs). As in these networks, sensors are supposed to communicate to each other and with actuators, IPv6 multicast forwarding is seen as a valuable means to reduce traffic. A promising approach to multicast, based on the Routing Protocol for Low Power and Lossy Networks (RPL) is Bidirectional Multicast RPL Forwarding (BMRF). This paper aimed to analyze the performance of BMRF over TSCH. The authors investigated how an adequate TSCH scheduler can help to achieve a requested quality of service (QoS). A theoretical model for the delay and energy consumption of BMRF over TSCH is presented. Next, BMRF’s link layer (LL) unicast and LL broadcast forwarding modes were analyzed on restricted and realistic topologies. On topologies with increased interference, BMRF’s LL broadcast on top of TSCH causes high energy consumption, mainly because of the amount of energy needed to run the schedule, but it significantly improves packet delivery ratio and delay compared to ContikiMAC under the same conditions. In most cases, the LL unicast was found to outperform the LL broadcast, but the latter can be beneficial to certain applications, especially those sensitive to delays.

Serverless computing, especially implemented through Function-as-a-Service (FaaS) platforms, has recently been gaining popularity as an application deployment model in which functions are automatically instantiated when called and scaled when needed. When a warm start deployment mode is used, the FaaS platform gives users the perception of constantly available resources. Conversely, when a cold start mode is used, containers running the application’s modules are automatically destroyed when the application has been executed. The latter can lead to considerable resource and cost savings. In this paper, we explore the suitability of both modes for deploying Internet of Things (IoT) applications considering a low resources testbed comparable to an edge node. We discuss the implementation and the experimental analysis of an IoT serverless platform that includes typical IoT service elements. A performance study in terms of resource consumption and latency is presented for the warm and cold start deployment mode, and implemented using OpenFaaS, a well-known open-source FaaS framework which allows to test a cold start deployment with precise inactivity time setup thanks to its flexibility. This experimental analysis allows to evaluate the aptness of the two deployment modes under different operating conditions: Exploiting OpenFaaS minimum inactivity time setup, we find that the cold start mode can be convenient in order to save edge nodes limited resources, but only if the data transmission period is significantly higher than the time needed to trigger containers shutdown.

This paper describes a new approach and implementation methodology for indoor ranging based on the time difference of arrival using code division multiple access with ultrasound signals. A novel implementation based on a field programmable gate array using finite impulse response filters and an optimized correlation demodulator implementation for ultrasound orthogonal signals is developed. Orthogonal codes are modulated onto ultrasound signals using frequency shift keying with carrier frequencies of 24.5 kHz and 26 kHz. This implementation enhances the possibilities for real-time, embedded and low-power tracking of several simultaneous transmitters. Due to the high degree of parallelism offered by field programmable gate arrays, up to four transmitters can be tracked simultaneously. The implementation requires at most 30% of the available logic gates of a Spartan-6 XC6SLX45 device and is evaluated on accuracy and precision through several ranging topologies. In the first topology, the distance between one transmitter and one receiver is evaluated. Afterwards, ranging analyses are applied between two simultaneous transmitters and one receiver. Ultimately, the position of the receiver against four transmitters using trilateration is also demonstrated. Results show enhanced distance measurements with distances ranging from a few centimeters up to 17 m, while keeping a centimeter-level accuracy.

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass’s hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field.

This paper presents a new key management protocol for group-based communications in non-hierarchical wireless sensor networks (WSNs), applied on a recently proposed IP-based multicast protocol. Confidentiality, integrity, and authentication are established, using solely symmetric-key-based operations. The protocol features a cloud-based network multicast manager (NMM), which can create, control, and authenticate groups in the WSN, but is not able to derive the actual constructed group key. Three main phases are distinguished in the protocol. First, in the registration phase, the motes register to the group by sending a request to the NMM. Second, the members of the group calculate the shared group key in the key construction phase. For this phase, two different methods are tested. In the unicast approach, the key material is sent to each member individually using unicast messages, and in the multicast approach, a combination of Lagrange interpolation and a multicast packet are used. Finally, in the multicast communication phase, these keys are used to send confidential and authenticated messages. To investigate the impact of the proposed mechanisms on the WSN, the protocol was implemented in ContikiOS and simulated using COOJA, considering different group sizes and multi-hop communication. These simulations show that the multicast approach compared to the unicast approach results in significant smaller delays, is a bit more energy efficient, and requires more or less the same amount of memory for the code.

Large-scale noise pollution sensor networks consist of hundreds of spatially distributed microphones that measure environmental noise. These networks provide historical and real-time environmental data to citizens and decision makers and are therefore a key technology to steer environmental policy. However, the high cost of certified environmental microphone sensors render large-scale environmental networks prohibitively expensive. Several environmental network projects have started using off-the-shelf low-cost microphone sensors to reduce their costs, but these sensors have higher failure rates and produce lower quality data. To offset this disadvantage, we developed a low-cost noise sensor that actively checks its condition and indirectly the integrity of the data it produces. The main design concept is to embed a 13 mm speaker in the noise sensor casing and, by regularly scheduling a frequency sweep, estimate the evolution of the microphone’s frequency response over time. This paper presents our noise sensor’s hardware and software design together with the results of a test deployment in a large-scale environmental network in Belgium. Our middle-range-value sensor (around €50) effectively detected all experienced malfunctions, in laboratory tests and outdoor deployments, with a few false positives. Future improvements could further lower the cost of our sensor below €10.

This paper highlights the benefits and possible drawbacks of a DC-based lighting infrastructure for powering Light Emitting Diode (LED)-lamps. It also evaluates the efforts needed for integrating the so called smart lighting and other sensor/actuator based control systems, and compares existing and emerging solutions. It reviews and discusses published work in this field with special focus on the intelligent DC-based infrastructure named EDISON that is primarily dedicated to lighting, but is applicable to building automation in general. The EDISON “PowerLAN” consists of a DC-based infrastructure that offers telecommunication abilities and can be applied to lighting retrofitting scenarios for buildings. Its infrastructure allows simple and efficient powering of DC-oriented devices like LED lamps, sensors and microcontrollers, while offering a wired communication channel. This paper motivates the design choices for organizing DC lighting grids and their associated communication possibilities. It also shows how the EDISON based smart lighting solution is evolving today to include new communication technologies and to further integrate other parts of building management solutions through the OneM2M (Machine to Machine) service bus.

Geosensor networks and sensor webs are two technologies widely used for determining our exposure to pollution levels and ensuring that this information is publicly available. However, most of these networks are independent from each other and often designed for specific domains, hindering the integration of sensor data from different sources. We contributed to the integration of several environmental sensor networks in the context of the IDEA project. The objective of this project was to measure noise and air quality pollution levels in urban areas in Belgium using low-cost sensors. This paper presents the IDEA Environmental Measurement Cloud as a proof-of-concept Data-as-a-Service (DaaS) cloud platform that integrates environmental sensor networks with a sensor web. Our DaaS platform implements a federated two-layer architecture to loosely couple together sensor networks deployed over a wide geographical area with web services. It offers several data access, discovery, and visualization services to the public while serving as a scientific tool for noise pollution research. After one year of operation, it hosts approximately 6.5 TB of environmental data and offers to the public near real-time noise pollution measurements from over 40 locations in Belgium.

Multichannel communication protocols have been developed to alleviate the effects of interference and consequently improve the network performance in wireless sensor networks requiring high bandwidth. In this paper, we propose a contention-free multichannel protocol to maximize network throughput while ensuring energy-efficient operation. Arguing that routing decisions influence to a large extent the network throughput, we formulate route selection and transmission scheduling as a joint problem and propose a Reinforcement Learning based scheduling algorithm to solve it in a distributed manner. The results of extensive simulation experiments show that the proposed solution not only provides a collision-free transmission schedule but also minimizes energy waste, which makes it appropriate for energy-constrained wireless sensor networks.

We report two experiments designed to assess the key sensory drivers underlying people’s association of a specific auditory pitch with Belgian beer. In particular, we assessed if people would rely mostly on the differences between beers in terms of their relative alcohol strength, or on the contrast between the most salient taste attributes of the different beers. In Experiment 1, the participants rated three bitter beers (differing in alcohol content), using a narrow range of pitch choices (50–500 Hz). The results revealed that the beers were all rated around the same pitch (Mean = 232 Hz, SD = 136 Hz). In Experiment 2, a wider range of pitch choices (50–1500 Hz), along with the addition of a much sweeter beer, revealed that people mostly tend to match beers with bitter-range profiles at significantly lower pitch ranges when compared to the average pitch of a much sweeter beer. These results therefore demonstrate that clear differences in taste attributes lead to distinctly different matches in terms of pitch. Having demonstrated the robustness of the basic crossmodal matching, future research should aim to uncover the basis for such matches and better understand the perceptual effects of matching/non-matching tones on the multisensory drinking experience.

Wireless Sensor Networks (WSNs) are composed of small wireless nodes equipped with sensors, a processor, and a radio communication unit, all normally powered by batteries. For most WSN applications, the network is expected to function for several months or years. In the common monitoring application scenario, adjacent nodes in a WSN often sense spatially correlated data. Suppressing this correlation can significantly improve the lifetime of the network. The maximum possible network data compression can be achieved using distributed source coding (DSC) techniques when nodes encode at Slepian-Wolf rates. This paper presents contributions to the lifetime optimization problem of WSNs in the form of two algorithms: the Updated-CMAX (UCMAX) power-aware routing algorithm to optimize the routing tree and the Rate Optimization (RO) algorithm to optimize the encoding rates of the nodes. The two algorithms combined offer a solution that maximizes the lifetime of a WSN measuring spatially correlated data. Simulations show that our proposed approach may significantly extend the lifetime of multihop WSNs with nodes that are observing correlated data.