Voice-based Speaker Identification (SI) can be framed as the problem of Closed-Set Speaker Identification (CSSI), recognizing a speaker from a known set, or OSSI, additionally recognizing unknown speakers. Precise and accurate Open-Set Speaker Identification (SI) can enable a variety of applications, ranging from human presence detection to authentication. Existing SI solutions are typically driven by deep learning approaches, which involve computationally demanding models often running in cloud back-ends. Enabling local SI models running directly on resource-constrained embedded devices can enable new use cases while preserving speaker privacy. In this work, we fill this gap and present SPIDER, a lightweight, on-device CSSI and OSSI solution capable of running on the off-the-shelf Nordic nRF52840 and nRF5340 system-on-chip microcontrollers, which feature as little as 256 and 512 kB of RAM, respectively, and 1 MB of flash memory. SPIDER is 16x-67x smaller than currently-available SI models, and yet, the 16x smaller version achieves a comparable accuracy of 94.33 % for CSSI and 91.8% for OSSI. Our evaluation across multiple datasets confirms the viability of performing accurate SI directly on resource-constrained embedded devices using only low-cost microphones. To foster further research and development, we open source our implementation of SPIDER, empowering the community to explore new SI use cases where cloud connectivity or backhaul infrastructure is impractical or undesirable.

The growing proliferation of Wi-Fi 6E devices, which operate at a high transmission power in the same frequency band used by ultra-wideband (UWB) technology, poses significant challenges to the reliability of UWB-based systems. Prior work has explored the impact of Wi-Fi 6E traffic on UWB performance experimentally using real-world testbeds, but lacks a theoretical framework capturing how Wi-Fi 6E’s physical-layer (PHY) settings, signal strength, and traffic profile affect UWB performance. We address this gap by introducing a probabilistic error model that accurately estimates the packet error rate (PER) of UWB systems operating alongside Wi-Fi 6E traffic. Using a conducted test setup, we analyze coexisting transmissions to characterize real-world Wi-Fi 6E traffic (e.g., airtime, inter-arrival times, and channel occupancy) and quantify the sensitivity of UWB transmissions to Wi-Fi 6E interference for different PHY settings, traffic profiles, and signal strengths. We leverage this characterization to parametrize the model using different approaches, ranging from a trace-based estimation of the collision probability to an analytical derivation of Wi-Fi 6E’s channel occupancy. This allows us to explore different trade-offs w.r.t. the model’s accuracy and generality. Our experimental evaluation demonstrates that our model accurately captures the UWB PER, with average deviations as low as 3.6% on conducted measurements and below 9.7% on real-world experiments in a university building.

Careful parametrization of networking protocols is crucial to maximize the performance of low-power wireless systems and ensure that stringent application requirements can be met. This is a non-trivial task involving thorough characterization on testbeds and requiring expert knowledge. Unfortunately, the community still lacks a tool to facilitate parameter exploration while minimizing the necessary experimentation time on testbeds. Such a tool would be invaluable, as exhaustive parameter searches can be time-prohibitive or unfeasible given limited testbed availability, whereas non-exhaustive unguided searches rarely deliver satisfactory results. In this article, we present APEX, a framework enabling an automated and informed parameter exploration for low-power wireless protocols and allowing convergence to the best parameter set within a limited number of testbed trials. We design APEX using Gaussian processes to effectively handle noisy experimental data and estimate the optimality of a parameter combination. After developing a prototype of APEX, we demonstrate its effectiveness by parametrizing two IEEE 802.15.4 protocols across a wide range of application requirements. Our results show that APEX can return the best parameter set with up to 10.6×, 4.5×, 4.3×, and 3.25× less testbed trials than traditional solutions based on exhaustive search, greedy approaches, support vector regression and reinforcement learning, respectively.

Battery-free sensing devices have gained growing popularity as they can operate relying solely on harvested energy and environmentally friendly capacitors. However, despite the increasing number of battery-free solutions, their design remains a difficult task. In fact, the limited energy storage capacity and the resulting coupling between energy supply and demand introduce new design trade-offs that cannot be explored using conventional tools that consider a constant power supply. To enable fast design space exploration and facilitate the development of battery-free systems, we introduce Simba, an open-source simulation framework that allows to investigate in detail the complex interplay between various device components. We demonstrate the benefits of Simba in two case studies, evaluated experimentally, targeting real-world, state-of-the-art battery-free devices. First, we illustrate how Simba can explore the dependencies between different component configurations and assess their impact on the overall system performance. Among others, we show that changing the storage capacity or slightly modifying the load behavior can improve data throughput by a factor of up to 5.1x and 9.7x, respectively. Second, we present how Simba allows to automatically select key parameters that optimize the operations of a battery-free system (e.g., its checkpointing mechanism), and showcase how Simba enables performance evaluations based on real-world energy harvesting traces.CCS CONCEPTS• Computer systems organization → Embedded systems.

The scalability of the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is increasingly attracting the interest of both academia and industry. In light of this, we present experiences from a pilot experimental study conducted over several weeks on the protocol’s state-of-the-art implementation, RPL-Lite, in a large multi-hop IEEE 802.15.4 network of up to 700 nodes dispersed over 3390 m2. Our results show that RPL is capable of reliable and self-managed data collection even in such large-scale deployments, but its performance may drop significantly because of transmission power settings, border router locations, and insufficient load balancing, notably due to a phenomenon that, to the best of our knowledge, has not been reported in literature. We believe that our observations can be of value to designers and administrators of low-power wireless networks as well as to the community developing RPL.

Ultra-wideband (UWB) devices operate only on a few frequency channels and commonly lack clear channel assessment capabilities: this makes it difficult to support several devices operating concurrently within a single network or to avoid coexistence issues with other UWB-based systems operating in close proximity. To address this issue, the IEEE 802.15.4 standard proposes the use of complex channels (i.e., diverse combinations of frequency channels and preamble codes) to enable multiple orthogonal transmissions. However, existing studies have shown that concurrent UWB transmissions on different complex channels are unreliable and incur high packet loss. In this paper, we investigate and shed light on the reason for this packet loss. We then present concrete methods to boost the reliability of concurrent UWB communications over different complex channels and demonstrate their effectiveness experimentally. In detail, we show that the synchronization and clock frequency offset among concurrent transmitters as well as the employed physical layer settings can be used to increase communication performance over different complex channels. Our study shows the feasibility of more than eight concurrent UWB transmissions on the same frequencies, sustaining a packet reception rate above 99% while retaining the ability to carry out centimetre-accurate ranging.

Adapting the transmission (TX) power of Bluetooth Low Energy (BLE) devices at runtime is pivotal to maximize the reliability and efficiency of their communications. Unfortunately, research in this area is scant. Firstly, there is a notable gap in exploring the efficacy of utilizing received signal strength information to adjust the TX power, as mandated by BLE's newly-introduced LEPC feature. Secondly, the performance of existing approaches is constrained by their reliance on connection-wide information. In this work, we fill this gap and investigate in detail the performance of different schemes used to adapt the TX power of BLE devices at runtime. After shedding light on the suitability of different link quality indicators to inform the adaptation, we demonstrate experimentally that adjusting the TX power on a per-channel basis allows to drastically improve the performance of existing solutions. Finally, we propose and implement a novel TX power control mechanism for BLE devices embedding our empirical observations, showcasing its superior reliability and energy efficiency over traditional approaches.

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This article fills this gap and provides an extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by relative carrier frequency offsets on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware and with varying environmental conditions through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based data collection and dissemination protocols in the presence of RF interference on a large-scale testbed, deriving insights on how the employed physical layer affects their dependability.

The selection of the spreading factor (SF) has important implications on the radio on-time, energy consumption, achievable data rate, and communication range of LoRa devices. In practical applications, LoRa packets can only be exchanged when the SF between transmitter and receiver matches. To ensure that this is the case, current approaches either statically hard-code the SF used to communicate between two devices, or negotiate which SF to use through handshaking mechanisms. Unfortunately, statically assigning the SF may lead to sub-optimal performance, and changing the assignment at runtime through a negotiation process incurs a significant overhead in terms of both latency and energy consumption. In this paper, we propose RSSF, a scheme that allows an off-the-shelf LoRa device to receive and decode a packet without prior knowledge of the SF used by the transmitter. RSSF leverages the observation that the SF with which a packet was sent can be inferred by analyzing the received signal strength (RSS) samples captured while receiving the first preamble symbols, and by characterizing their periodicity. In real-world systems, however, the waveform obtained by sampling the RSS during the reception of the first preamble symbols contains several spikes due to the receiver's DC offset cancellation, which makes it difficult to accurately identify periods. We show that this problem can be solved by letting an off-the-shelf LoRa receiver sample the RSS on a frequency that is shifted by half of the bandwidth from the original frequency at which the packet was transmitted. We then implement a lightweight algorithm that determines the SF by partitioning the RSS samples into sliding windows of different size (corresponding to each possible SF) and by measuring the zero-crossing intervals for each window size. We evaluate RSSF's performance experimentally using both software-defined radios and off-the-shelf LoRa nodes, showing that RSSF can accurately determine the SF within the first 5 preamble symbols.

Indoor localization systems based on UltraWideBand (UWB) technology can typically achieve cm-level accuracy, but their performance degrades in Non-Line-of-Sight (NLoS) conditions. To cope with this problem, Machine Learning (ML) techniques have been applied to detect such NLoS conditions and adapt the localization algorithm accordingly. However, such ML techniques are typically optimized for accuracy, resulting in computationally-complex models that cannot be run on resource-constrained UWB devices. In this paper, we study and propose methods to reduce the computational complexity of NLoS classification models by applying ML-based feature selection and by reducing the window length of the channel impulse response for feature extraction. Specifically, we consider 29 features and study the effect of feature selection across five different datasets to obtain generalizable results. We show that we can extract two sets of only 3 and 8 features, which result in tiny ML models (smaller than 1kB), and low computation times ($3.6 \mathrm{~ms}$ and $27.7 \mathrm{~ms}$ on a 80MHz ESP8266 microcontroller, respectively). This allows a reduction of the runtime by more than $90 \%$ compared to the state of the art, while still maintaining an average classification accuracy above $85 \%$ across all five datasets.

Voice is a convenient and popular way to interact with our digital world. Besides translating speech to text, it is also possible to identify speakers based on their voice profile. To date, speaker identification has predominantly been limited to high-performance computational platforms owing to the intricate nature of the underlying algorithms. In this work, we demonstrate that it is possible to reduce model complexity by the required factor of ~10, such that speaker identification can be made feasible for embedded devices with limited resources. We further describe and discuss novel use cases, such as voice-based presence detection and authentication, that become feasible on these class of devices.

LoRaWAN is one of the most popular protocols to build low-power wide area networks. Unfortunately, it adopts a star topology, which limits network coverage and may also cause an unnecessary decrease in energy efficiency as well as scalability. In fact, end-devices that are deployed far away from a gateway need to increase their transmission power or spreading factor (SF) to sustain reliable communications, which increases their energy expenditure as well as the size of the collision domain. The only alternative is the deployment of additional gateways or dedicated relay nodes, which results in higher costs and deployment efforts. In this work, we introduce LoRaHop, an extension of LoRaWAN that enriches end-devices with the ability to form a mesh network and to seamlessly relay packets to/from a gateway, thereby providing LoRaWAN networks with multihop support for both uplink and downlink messaging. LoRaHop leverages concurrent transmissions to enable a reliable and efficient data collection or dissemination over the mesh network, as well as to simplify network formation. Furthermore, LoRaHop embeds a mechanism that simplifies rendezvous across devices and that minimizes the impact of mesh operations on existing LoRaWAN transmissions. We implement LoRaHop on off-the-shelf LoRa end-devices (ensuring their interoperability with commercial LoRaWAN gateways and network servers), and evaluate its performance on an outdoor testbed. Our results show that LoRaHop can effectively extend the coverage of an LoRaWAN network while improving reliability by up to 98.33% and reducing energy consumption by up to 48.02%. Our findings further demonstrate that using LoRaHop to create a multihop LoRaWAN network that communicates using low SFs brings significant benefits in terms of energy efficiency and scalability compared to the use of a single-hop LoRaWAN network using high SFs.

The introduction of the Wi-Fi 6E standard operating in the 6 GHz frequency band is a serious threat for IoT systems based on ultra-wideband technology, as they share portions of the same spectrum. Wi-Fi 6E devices can in fact support channel bandwidths up to 160 MHz and operate at a much higher transmission power compared to ultra-wideband devices, which may lead to severe coexitence issues and degraded performance. However, whether and to which extent the performance of ultra-wide band systems worsens due to Wi-Fi 6E interference has not been investigated in detail yet. In this paper, we fill this gap and study how Wi-Fi 6E traffic affects ultra-wideband performance. Our experiments on a large-scale testbed demonstrate that Wi-Fi 6E transmissions may largely disrupt ultra-wideband communications and decrease the accuracy as well as the precision of ranging measurements, with significant consequences on the efficiency of localization systems. We investigate in detail the root causes for the degraded performance and derive empirical observations that can be used to design countermeasures mitigating the impact of Wi-Fi 6E interference. These include, among others, an optimal selection of physical layer settings, as well as the use of a tight synchronization to prevent a false detection of Wi-Fi 6E traffic as ultra-wide band frames and an overshooting of the radio's automatic gain control. We further devise a technique to detect the presence of Wi-Fi 6E traffic and postpone ultra-wideband transmissions accordingly. Our experiments demonstrate that these countermeasures effectively mitigate the impact of Wi-Fi 6E interference on the performance of ultra-wide band systems.

This paper presents EMU, a framework that enables the emulation, snipping, and multiplexing of LoRa chirps on commercial IoT devices equipped with low-power sub-GHz transceivers, including those supporting LoRa itself. Chirp snipping consists in artificially removing a sequence of chips and in putting the radio in low-power mode, which allows to reduce energy consumption while still commu-nicating reliably. Chirp multiplexing exploits the gaps introduced by chirp snipping to transmit portions of another chirp on a sep-arate channel, which allows to concurrently transmit two LoRa packets and to increase the throughput. We build EMU as a modu-lar framework and implement support for off-the-shelf LoRa and non-LoRa transceivers. We then evaluate its performance by com-paring the reliability, efficiency, and receiver sensitivity achieved by EMU with that of traditional LoRa for different physical layer settings. We finally showcase EMU's ability to send packets over two channels simultaneously, thereby improving the uplink throughput of LoRaWan, and demonstrate that even non-LoRa transceivers employing EMU can communicate to a LoRaWan gateway, enabling new use cases and expanding the applicability of LoRa technology.

Time-Slotted Channel Hopping (TSCH) is a popular link-layer pro-tocol defined in the IEEE 802.15.4e standard that improves the reli-ability and throughput of wireless sensor networks by exploiting diversity in both time and frequency. Despite the body of literature proposing several scheduling schemes for TSCH, a gap yet to be filled is the design of an effective way to deal with internal and external interference, which are both known to strongly affect communication performance. In fact, existing works either make use of a fixed schedule (and hence cannot cope with interference), or re-quire extra control traffic (and hence increase energy consumption). In this paper, we present SmarTiSCH, an interference-aware en-gine for IEEE 802.15.4e-based networks that retains the simplicity and energy-efficiency of autonomous scheduling, while increasing the awareness as well as robustness to both internal and external interference. With SmarTiSCH, the nodes in the network infer the presence of interference and react to it without the need of extra control traffic. Specifically, SmarTiSCH enables each node to infer the interference by passively observing existing data exchanges. It then lets a pair of nodes exchange information and mutually agree on a proper strategy to mitigate interference without the need of extra transmissions. We implement SmarTiSCH in Contiki-NG and evaluate its performance on a testbed of 20 off-the-shelf IEEE 802.15.4 devices based on the nRF52840. Our results show that SmarTiSCH increases the reliability of transmissions by up to 2.9 times compared to state-of-the-art approaches in the presence of interference, while even lowering the duty cycle by 54.3%.

Battery-free sensor nodes rely solely on energy harvested from the environment and thus employ supercapacitors as energy storage to allow perpetual operation in absence of ambient energy. To guarantee that the sensor nodes can survive in periods where no harvested energy is available, it is crucial to accurately estimate the lifetime of these devices. However, as we show experimentally in this paper, an accurate lifetime estimation is non-trivial due to the supercapacitors' complex discharge characteristics (e.g., leakage currents) and large capacitance tolerances. After showing that empirical data capturing the supercapacitors' characteristics is essential towards an accurate estimation of the system's lifetime, we introduce an enhanced leakage model that is computationally lightweight and evaluate its accuracy experimentally.

Despite the growing interest in cross-technology communication, its application to real-world systems is still limited, as existing schemes are mostly unidirectional and technology-specific. The lack of generic solutions as well as the complexity of their integration reduces the applicability in a broader scope. In this paper, we propose a solution to augment Wi-Fi, BLE, and ZigBee devices with the ability to transmit and receive cross-technology broadcast frames alongside their existing functionality. After experimentally evaluating the performance of our solution on a variety of hardware platforms, we leverage it to build a gateway-free smart home, where a smartphone can simultaneously control heterogeneous smart objects. The smart objects, which include an off-the-shelf ZigBee light bulb and a BLE-enabled door lock from different vendors, perform cross-technology communication while retaining their original functionality and can maintain duty-cycled operations.

Cross-technology interference is a major threat to the dependability of low-power wireless communications. Due to power and bandwidth asymmetries, technologies such as Wi-Fi tend to dominate the RF channel and unintentionally destroy low-power wireless communications from resource-constrained technologies such as ZigBee, leading to severe coexistence issues. To address these issues, existing schemes make ZigBee nodes individually assess the RF channel's availability or let Wi-Fi appliances blindly reserve the medium for the transmissions of low-power devices. Without a two-way interaction between devices making use of different wireless technologies, these approaches have limited scenarios or achieve inefficient network performance. This paper presents BiCord, a bidirectional coordination scheme in which resource-constrained wireless devices such as ZigBee nodes and powerful Wi-Fi appliances coordinate their activities to increase coexistence and enhance network performance. Specifically, in BiCord, ZigBee nodes directly request channel resources from Wi-Fi devices, who then reserve the channel for ZigBee transmissions on-demand. This interaction continues until the transmission requirement of ZigBee nodes is both fulfilled and understood by Wi-Fi devices. This way, BiCord avoids unnecessary channel allocations, maximizes the availability of the spectrum, and minimizes transmission delays. We evaluate BiCord on off-the-shelf Wi-Fi and ZigBee devices, demonstrating its effectiveness experimentally. Among others, our results show that BiCord increases channel utilization by up to 50.6% and reduces the average transmission delay of ZigBee nodes by 84.2% compared to state-of-the-art approaches.

Clock synchronization in distributed IoT systems is a necessary feature to allow a coherent data collection and event detection. This task is challenging, as today's IoT systems often consist of heterogeneous wireless devices using incompatible technologies. Because of this, existing solutions often make use of multi-radio gateways, which allow an indirect synchronization across heterogeneous devices, but increase end-to-end delays and suffer from an increased overhead. In this work, we present X-Sync, a novel approach allowing a direct and bidirectional clock synchronization among off-the-shelf wireless IoT devices with incompatible physical layer. X-Sync leverages cross-technology communication to convey timing information among heterogeneous devices and presents novel techniques to compensate for the inaccuracies in reliably detecting the start of a cross-technology frame. We seamlessly integrate X-Sync into the Contiki-NG operating system and evaluate its performance experimentally on off-the-shelf Bluetooth Low Energy and IEEE 802.15.4 devices, showing that X-Sync achieves a μs-level synchronization accuracy.

Recently, several datasets shedding light on connectivity aspects in real-world LoRa networks have been provided to the community. However, they typically only involve a limited number of nodes, deal with unidirectional communication only, or focus on very specific physical layer settings. More importantly, existing datasets typically lack fine-grained environmental information such as the temperature in the surroundings of each node, which is known to have a strong impact on communication performance. In this work, we provide the community with a comprehensive dataset that fills all these gaps. We have collected detailed connectivity information in an outdoor LoRa network composed of 21 nodes for more than four months. Our dataset does not only focus on network-level performance (e.g., the average number of correctly-exchanged packets), but sheds light on link-level information such as the received signal strength, signal-to-noise ratio, and the number of available neighbours over time. We further collect environmental information from an online weather site, as well as the on-board temperature of each node in the network, which varies considerably across the deployed locations. We collect all this information while perpetually changing physical layer settings such as the spreading factor and the RF channel. A preliminary analysis of our dataset, which is available in Zenodo1, reveals that temperature has a significant correlation with the link quality and connectivity in the outdoor LoRa network, confirming the findings of earlier studies.

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This paper fills this gap and provides the first extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by de-synchronization and beating on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based flooding protocols in the presence of radio interference on a large-scale, and derive important insights on how the used physical layer affects their dependability.

The increasing congestion of the RF spectrum is a key challenge for low-power wireless networks using concurrent transmissions. The presence of radio interference can indeed undermine their dependability, as they rely on a tight synchronization and incur a significant overhead to overcome packet loss. In this paper, we present Harmony, a new data collection protocol that exploits the benefits of concurrent transmissions and embeds techniques to ensure a reliable and timely packet delivery despite highly congested channels. Such techniques include, among others, a data freezing mechanism that allows to successfully deliver data in a partitioned network as well as the use of network coding to shorten the length of packets and increase the robustness to unreliable links. Harmony also introduces a distributed interference detection scheme that allows each node to activate various interference mitigation techniques only when strictly necessary, avoiding unnecessary energy expenditures while finding a good balance between reliability and timeliness. An experimental evaluation on real-world testbeds shows that Harmony outperforms state-of-the-art protocols in the presence of harsh Wi-Fi interference, with up to 50% higher delivery rates and significantly shorter end-to-end latencies, even when transmitting large packets.

Positioning systems based on ultra-wideband (UWB) technology are becoming ubiquitous and enable a plethora of attractive Internet of Things applications, ranging from smart access and asset tracking to the navigation of autonomous vehicles. As these positioning systems are often deployed over large areas, the focus of UWB-based research has recently shifted to the development of scalable solutions that can offer a high positioning accuracy for countless tags while maximizing energyefficiency. At the same time, as positioning systems are increasingly used in safety-critical settings, several academic efforts and the standardization activities of the IEEE 802. 15.4 z working group have laid the foundations for a secure distance estimation using UWB technology. Unfortunately, these two endeavours have followed independent tracks that do not blend together. In this paper, we highlight this issue and describe the challenge of securing modern UWB-based positioning systems that are designed with scalability in mind. We first illustrate how the use of unidirectional communications, the need for synchronized anchors, and the use of quasi-simultaneous responses, which are common features of recent scalable UWB systems based on timedifference-of-arrival, make these solutions vulnerable to several attacks, despite the use of IEEE 802. 15.4 z. After carrying out a security analysis and describing how scalable UWB systems are exposed to several attacks, we devise a number of design concepts to counteract the identified attacks and secure these systems.

The ability to communicate within given delay bounds in noisy RF environments is crucial for Bluetooth Low Energy (BLE) applications used in safety-critical application domains, such as health care and smart cities. In this work, we experimentally study the latency of BLE communications in the presence of radio interference and show that applications may incur long and unpredictable transmission delays. To mitigate this problem, we devise a model capturing the timeliness of connection-based BLE communications in noisy RF channels by expressing the impact of radio interference in terms of the number of connection events necessary to complete a successful data transmission ( n CE ). We show that this quantity can be estimated using the timing information of commands sent over the host controller interface of common BLE devices, hence without additional communication overhead or energy expenditure. We further show that a BLE device can make use of our BLE timeliness model and recent n CE measurements to adapt its BLE communication parameters at runtime, thereby improving its performance in the presence of dynamic radio interference. We implement such an adaptive scheme on the popular nRF52840 platform and perform an extensive experimental study in multiple indoor environments using three different BLE platforms. Our results show that a BLE application can, indeed, make use of the proposed model and recent n CE measurements to adapt its connection interval at runtime to increase the timeliness of its communications, reducing the number of delayed packets in noisy RF environments by up to a factor of 40.

Industrial Internet of Things (IIoT) networks are considered the large-scale deployment of IoT devices for industrial applications such as smart manufacturing, harvesting and supply chain management. The Internet of Things (IoT) devices are typically connected over a wireless medium, given the large geographical distribution area and the increasing demand for flexible installations. In some cases, a combination of wired and wireless connectivity can be assumed as common practice. In both scenarios, wireless communications for IIoT networks is a fundamental component of the system architecture that needs to satisfy stringent requirements such as reliable connectivity and minimal delays. Therefore, the dependability of wireless communications for IIoT networks should be carefully studied to provide new solutions, which can guarantee that applications can meet their real-time and reliability requirements while optimizing the control capability of the overall network. This paper focuses on the dependable wireless communications in the IIoT networks, where wireless control and monitoring tasks need to meet stringent real-time and reliability constraints. After reviewing recent solutions and discussing their suitability for IIoT networks, we highlight the yet open challenges that needs to be tackled by both academia and industry.

Updating the software running on constrained IoT devices such as low-power sensors and actuators in a secure and efficient way is an open problem. The limited computational, memory, and storage capabilities of these devices, together with their small energy budget, indeed, restrict the number of features that can be embedded into an update system and make it also difficult to build a generic and compact solution. As a result, existing update systems for constrained IoT devices are often not portable, do not perform a proper verification of the downloaded firmware, or focus only on a single phase of the update process, which exposes them to security threats and calls for new solutions. In this paper we present UpKit, a portable and lightweight software update framework for constrained IoT devices encompassing all phases of the update process: from the generation and signature of a new firmware, to the transmission of the latter to an IoT device, its verification and installation. UpKit employs a novel update architecture that is agnostic to how new firmware images are distributed and that introduces a double-signature process to guarantee the freshness of a new firmware. This, together with an additional verification step, allows also to reject invalid software at an early stage and to prevent an unnecessary reboot of the device. We keep UpKit's design modular and provide an open-source implementation for several operating systems, hardware platforms, as well as cryptographic libraries. We further include support for differential updates and flexible memory slots, which allows to significantly increase the efficiency of the update process. An experimental evaluation shows that UpKit can be used to efficiently update highly-constrained IoT devices, and that it has a comparable memory footprint to state-of-the-art solutions, despite the introduction of several features.

A large body of work has shown that ultra-wideband (UWB) technology enables accurate indoor localization and tracking thanks to its high time-domain resolution. Existing systems, however, are typically designed to localize only a limited number of tags, and involve the exchange of several messages following a given schedule. As a result, the scalability of current solutions in terms of tag density is limited, as well as their efficiency and responsiveness. In this paper, we present SnapLoc, a UWB-based indoor localization system that allows an unlimited number of tags to self-localize at a theoretical upper bound of 2.3 kHz. In SnapLoc, a tag obtains the responses from multiple anchors simultaneously. Based on these signals, the tag derives the time difference of arrival between anchors and estimates its position. Therefore, SnapLoc does not require tags to actively transmit packets, but to receive only a single message. This allows tags to passively localize themselves and ensures that the performance of SnapLoc does not degrade with high node densities. Moreover, due to the (quasi-)simultaneous responses, a tight clock synchronization between anchors is not needed. We have implemented SnapLoc on a low-cost platform based on the Decawave DW1000 radio and solved limitations in the transceiver's timestamp resolution to sustain a high localization accuracy. An experimental evaluation shows that SnapLoc exhibits a 90% error and median error of 33 cm and 18 cm, respectively, hence enabling decimeter-level accuracy at fast update rates for countless tags.

In this demo, we present SnapLoc, a UWB-based indoor localization system that allows decimeter-accurate self-localization of mobile tags by listening to only a single message. To this end, SnapLoc leverages (quasi-)simultaneous responses of multiple anchors. Based on these responses, the tag derives the time difference of arrival between anchors and unambiguously estimates its position. Thanks to this principle, SnapLoc carries out passive localization and supports an unlimited number of tags at high update rates. Our SnapLoc implementation runs on the off-the-shelf Decawave DW1000 UWB transceiver. The latter suffers from a limited transmit timestamp resolution that affects the achievable localization accuracy when used in conjunction with concurrent anchor responses. Therefore, in this demo we also showcase techniques to overcome these limitations and achieve nevertheless a high localization accuracy.

Cross-technology communication (CTC) allows devices employing incompatible wireless technologies to directly exchange information without the need of expensive gateways. Existing work on CTC has showcased the ability of exchanging data between diverse wireless standards, but has not analysed the challenges nor tackled the problem of enabling CTC between multiple constrained IoT platforms with different characteristics. Indeed, CTC schemes are often hacked on very specific hardware platforms, which results in a lack of a general, portable solution. Furthermore, CTC has always been tested as a standalone piece of functionality, and its seamless integration with the classical operations of a constrained IoT device remains an open challenge. In this paper, we present X-Burst, a portable framework that allows multiple constrained IoT platforms with diverse characteristics to seamlessly interact using CTC. X-Burst allows to customize the CTC working principle (e.g., how information is encoded, or the alphabet used to encode a symbol) and enables the combination of different encoding and decoding strategies independently of the employed hardware platform. Thanks to its high modularity, X-Burst also simplifies the development of alternative CTC implementations and makes it easy to compare different approaches. As a proof of concept, we integrate X-Burst into the Contiki operating system without changing Contiki's core functions and allow an IoT device to seamlessly support CTC in parallel to its normal operations. We then showcase the functionality of X-Burst by enabling a bidirectional CTC between off-the-shelf heterogeneous IoT platforms based on IEEE 802.15.4 and Bluetooth Low Energy (BLE). An experimental evaluation further shows X-Burst's small memory footprint and analyses the robustness and throughput of different encoding schemes.

To enable future location-aware Internet of Things (IoT) applications, Ultra-wideband (UWB) technology provides centimeter-accurate distance estimations. In the common case of a non-synchronized network, at least N·(N-1) message exchanges are required to derive the distance between N nodes. Enabling concurrent ranging between an initiator and an arbitrary number of responders can drastically reduce the amount of necessary transmissions and hence increases the efficiency of UWB systems. Although the feasibility of concurrent ranging has been proven experimentally, several key challenges still need to be addressed to practically implement concurrent ranging in real-world UWB systems, such as the automatic detection of multiple responses, the identification of a responder, as well as the detection of overlapping responses (especially in the presence of multipath components). In this paper, we provide a concurrent ranging solution tackling the aforementioned challenges. Among others, our solution enables (i) to detect responses in the CIR reliably, (ii) to encode the responder ID in the CIR to allow personalized ranging, as well as (iii) to mitigate the impact of overlapping responses and multipath components. We further show how the proposed solution increases the scalability of concurrent ranging in real-world UWB-based distributed systems.

IoT localization systems based on ultra-wideband (UWB) technology require dependable communication links to reliably acquire and efficiently share the timestamps in the network. The communication performance of UWB radios, however, is still largely unexplored and strongly affected by the employed physical layer settings. In this work, we analyze the role of different UWB physical layer settings and propose a scheme that adapts them at runtime in order to maintain a highly reliable link while minimizing energy consumption. The proposed adaptation scheme exploits the channel impulse response provided by the UWB transceiver to estimate the link quality and to extract information about the surrounding environment, such as the presence of destructive interference.

Setting up indoor localization systems is often excessively time-consuming and labor-intensive, because of the high amount of anchors to be carefully deployed or the burdensome collection of fingerprints. In this paper, we present SALMA, a novel low-cost UWB-based indoor localization system that makes use of only one anchor and that does neither require prior calibration nor training. By using only a crude floor plan and by exploiting multipath reflections, SALMA can accurately determine the position of a mobile tag using a single anchor, hence minimizing the infrastructure costs, as well as the setup time. We implement SALMA on off-the-shelf UWB devices based on the Decawave DW1000 transceiver and show that, by making use of multiple directional antennas, SALMA can also resolve ambiguities due to overlapping multipath components. An experimental evaluation in an office environment with clear line-of-sight has shown that 90% of the position estimates obtained using SALMA exhibit less than 20 cm error, with a median below 8 cm. We further study the performance of SALMA in the presence of obstructed line-of-sight conditions, moving objects and furniture, as well as in highly dynamic environments with several people moving around, showing that the system can sustain decimeter-level accuracy with a worst-case average error below 34 cm.

Ultra-wideband (UWB) technology is increasingly used to build location-aware IoT applications because of its outstanding positioning accuracy. Its communication performance, however, is unexplored and strongly affected by the chosen physical layer settings as well as by the surrounding environment. Finding an effective way to increase the dependability of UWB communications is yet an open problem. In this paper, we study the performance of different UWB physical layer settings and use them as tuning knobs to increase the energy efficiency and robustness of communications. Towards this goal, we first experimentally quantify the reliability and energy cost of each setting, in order to understand which physical layer configuration to privilege depending on the application requirements. We then use the estimated channel impulse response-a unique feature of UWB transceivers-to accurately measure the link quality and to extract relevant information about the characteristics of the surrounding environment, such as the presence of destructive interference. Capitalizing on this information, we design a scheme that adapts the UWB physical layer settings at runtime. An experimental evaluation using the Decawave DW1000 radio shows the effectiveness of the proposed adaptive scheme, highlighting the increased communication robustness and energy efficiency.

Future vehicles will be wirelessly connected to nearby vehicles, to the road infrastructure, and to the Internet, thereby becoming an integral part of the Internet of Things. New comfort features, safety functions, and a number of new vehicle-specific services will be integrated in future smart vehicles. These include a fast, secure, and reliable way to diagnose and reconfigure a vehicle, as well as the installation of new software (SW) on its integrated electronic control units (ECUs). Such wireless SW updates are beneficial for both automotive carmakers and customers, as they allow us to securely enable new features on the vehicle and to fix SW bugs by installing a new SW version over the air. A secure and dependable wireless SW update process is valuable in the entire lifetime of a modern vehicle as it can be used already during vehicle development and manufacturing process on the assembly line, as well as during vehicle maintenance in a service center. Additionally, future vehicles will allow us to remotely download up-to-date SW on the ECUs. To support this process over the entire vehicle's lifetime, a generic framework is needed. In this paper, SecUp, a generic framework enabling secure and efficient wireless automotive SW updates is proposed. SecUp utilizes IEEE 802.11s as wireless medium to interconnect vehicles and diagnostic devices in a dependable and fast way. Additionally, SecUp is enabling beneficial wireless SW update features such as parallel and partial SW updates to increase the efficiency, and comprises advanced security mechanisms to prevent abuse and attacks.

Connected vehicles allow to update the software (SW) running on their integrated electronic control units (ECUs) over-the-air. Such updates are complex procedures that involve several steps, such as the authentication with a remote device, the secure and reliable wireless transfer of the new binary, as well as its installation and verification on the target ECU. Each of these aspects affects the efficiency of the entire SW update process, and it is important to evaluate the impact of different solutions on the functionality of a vehicle and to compare their performance on real hardware. In this paper we present CESAR, a configurable testbed infrastructure that allows to evaluate the efficiency of an automotive SW update system in a highly automated way. CESAR allows to specify different update mechanisms, security configurations, wireless protocols used for the data transfer, and to carefully define the scenario of interest (i.e., pin down the number of wireless vehicle interfaces, the network topology, and the target ECU). Furthermore, CESAR can be used to measure the efficiency of a SW update on real hardware, and to derive insights about the weaknesses of a system under test or about the interaction of a specific SW with a given ECU.

In this demo, we present a low-cost indoor localization system based on the off-the-shelf ultra-wideband transceiver Decawave DW1000. To obtain an accurate position information, the system makes use of a single anchor and of multipath reflections from walls, hence removing the need of installing a network of anchors or any other additional infrastructure. The procedure of determining the position of a tag can be divided in four consecutive stages. First, the location of virtual anchors is computed by mirroring the anchor position at reflective surfaces. Using two-way ranging, the distance and channel impulse response (CIR) between anchor and tag is obtained. This actual CIR is compared with expected CIRs from possible tag locations using a maximum likelihood approach to estimate the tag's position. Finally, a switchable directional antenna can be exploited to improve the robustness of the system by suppressing undesired, interfering multipath components. By following this procedure, the proposed system can achieve a decimeter accuracy and react to position updates in real-time.

The ability to fine-tune communication performance is key to meeting the requirements of Internet of Things applications. While years of low-power wireless research now allows developers to fully optimize the performance of applications built on top of IEEE 802.15.4, this has not yet happened with Bluetooth Low Energy (BLE), whose networking performance is still largely unexplored and whose potential is not yet fully exploited. Indeed, BLE radios are often treated as a black box, because they are meant to only execute data transfer commands and manufacturers build BLE soft devices with closed-source network stacks. As a result, developers working with BLE cannot modify the radio driver or the link-layer, and hence have no direct control over radio duty cycling and packet re-transmissions. To tackle these challenges, we analyze and model how specific BLE features can be used to fine-tune communication performance at run-time. We further present the design and implementation of BLEach, an IPv6-over-BLE stack that exposes tuning knobs for controlling the energy usage and timeliness of BLE transmissions and that allows to enforce a variety of quality-of-service (QoS) metrics. We design three exemplary modules for BLEach providing novel BLE functionality: adaptive radio duty cycling, IPv6-over-BLE traffic prioritization and multiplexing, as well as indirect link-quality monitoring. We integrate BLEach into Contiki and release its code, thus addressing the lack of a full-fledged open-source IPv6-over-BLE stack. Experiments demonstrate that BLEach is lightweight, interoperable with other standard-compliant devices, and reduces energy costs by up to 50 % while giving QoS guarantees by quickly adapting to changes in interference, traffic priority, and traffic load.

Wireless software updates for vehicles are very beneficial for both customers and manufacturers, as they enable performance improvements and error correction without the need of vehicle recalls, as well as a reduction of warranty costs and continuous system upgrades over a vehicle's whole lifetime. However, adding a wireless interface to enable software updates over the air may also expose the vehicle to security threats and make it vulnerable to a variety of attacks. Hence, to protect the safety of the driver and passengers of a vehicle, a strong and comprehensive security concept is needed. In this paper, we propose SecUp: a novel security concept enabling efficient and trustworthy wireless software updates for vehicles. SecUp is based on a system-centric structured analysis enabling a secure system configuration. The concept uses, among others, symmetric and asymmetric keys securely stored on the devices in the network to prove the identity of the nodes and to ensure the integrity as well as the confidentiality of data. We evaluate the robustness of SecUp by employing an attacker-centric threat model and show that it is indeed applicable for efficient and trustworthy wireless software updates for vehicles.

Future vehicles will be wirelessly connected to nearby vehicles, to the road infrastructure and to the Internet in order to enable new comfort features, safety functions and a number of new vehicle-specific services. The latter will include a fast, secure, and reliable way to remotely diagnose and reconfigure a vehicle as well as to install new software on the electronic control units integrated in a vehicle. Such wireless software updates are beneficial for both automotive OEMs and customers, as they allow to enable new features of the vehicle remotely and to fix software bugs by installing a new software version over the air. Wireless diagnostics and software updates are required in several stages of a vehicle's lifetime: from the manufacturing stage on the assembly line and the maintenance in a workshop to the remote download of up-to-date software directly by the car owner. To support this process over a whole vehicle's lifetime, a generic framework is needed. In this paper we propose a generic framework enabling secure and efficient wireless automotive SW updates and hence supporting a vehicle's whole lifetime. We describe the IEEE 802.11s network used as wireless medium to interconnect vehicles and diagnostic devices in a reliable, trustworthy and fast way and propose a dedicated cross-layer security concept applying strong authentication as well as encryption mechanisms.

Experimental research in low-power wireless networking lacks a reference benchmark. While other communities such as databases or machine learning have standardized benchmarks, our community still uses ad-hoc setups for its experiments and struggles to provide a fair comparison between communication protocols. Reasons for this include the diversity of network scenarios and the stochastic nature of wireless experiments. Leveraging on the excellent testbeds and tools that have been built to support experimental validation, we make the case for a reference benchmark to promote a fair comparison and reproducibility of results. This abstract describes early design elements and a benchmarking methodology with the goal to gather feedback from the community rather than propose a definite solution.

Research and industrial installations have shown that the on-board temperature of wireless sensor nodes deployed outdoors can experience high fluctuations over time with a large variability across the network. These variations can have a strong impact on the efficiency of low-power radios and can significantly affect the operation of communication protocols, often compromising network connectivity. In this paper, we show the adverse effects of temperature on communication protocols and propose techniques to increase their resilience. First, we experimentally show that fluctuations of the on-board temperature of sensor nodes reduce the efficiency of data link layer protocols, leading to a substantial decrease in packet reception rate and to a considerable increase in energy consumption. Second, we investigate the reasons for such performance degradation, and show that high on-board temperatures reduce the effectiveness of clear channel assessment, compromising the ability of a node to avoid collisions and to successfully wake-up from low-power mode. After modelling the behaviour of radio transceivers as a function of temperature, we propose two mechanisms to dynamically adapt the clear channel assessment threshold to temperature changes, thus making data link layer protocols temperature-aware. An extensive experimental evaluation shows that our approaches considerably increase the performance of a network in the presence of temperature variations commonly found in real-world outdoor deployments, with up to 42% lower radio duty-cycle and 87% higher packet reception rate.

Experiments under controlled radio interference are crucial to assess the robustness of low-power wireless protocols. While tools such as JamLab augment existing sensornet testbeds with realistic interference, it remains an error-prone and time-consuming task to manually select the set of nodes acting as jammers and their individual transmit powers. We present an automated configuration approach based on simulated annealing to overcome this problem. A preliminary evaluation based on two testbeds shows that our approach can find near-optimal solutions within at most a few hours. We believe our approach can facilitate the widespread adoption of controlled interference experiments by the sensornet community.

Real-world experiments have shown that the transmission power and the received signal strength of low-power radio transceivers used in sensornets decrease when temperature increases. We analyze how this phenomenon affects the network layer, and find that temperature fluctuations may cause undesirable behavior by sensornet routing protocols such as CTP and RPL. Furthermore, we present an approach to make these protocols robust to temperature fluctuations by augmenting the ETX link metric with temperature hints.

Wireless low-power transceivers used in sensor networks typically operate in unlicensed frequency bands that are subject to external radio interference caused by devices transmitting at much higher power. Communication protocols should therefore be designed to be robust against such interference. A critical building block of many protocols at all layers is agreement on a piece of information among a set of nodes. At the MAC layer, nodes may need to agree on a new time slot or frequency channel, at the application layer nodes may need to agree on handing over a leader role from one node to another. Message loss caused by interference may break agreement in two different ways: none of the nodes uses the new information (time slot, channel, leader) and sticks with the previous assignment, or - even worse - some nodes use the new information and some do not. This may lead to reduced performance or failures. In this paper, we investigate the problem of agreement under external radio interference and point out the limitations of traditional message-based approaches. We propose JAG, a novel protocol that uses jamming instead of message transmissions to make sure that two neighbouring nodes agree, and show that it outperforms message-based approaches in terms of agreement probability, energy consumption, and time-to-completion. We further show that JAG can be used to obtain performance guarantees and meet the requirements of applications with real-time constraints.

Radio link quality estimation in Wireless Sensor Networks (WSNs) has a fundamental impact on the network performance and also affects the design of higher-layer protocols. Therefore, for about a decade, it has been attracting a vast array of research works. Reported works on link quality estimation are typically based on different assumptions, consider different scenarios, and provide radically different (and sometimes contradictory) results. This article provides a comprehensive survey on related literature, covering the characteristics of low-power links, the fundamental concepts of link quality estimation in WSNs, a taxonomy of existing link quality estimators, and their performance analysis. To the best of our knowledge, this is the first survey tackling in detail link quality estimation in WSNs. We believe our efforts will serve as a reference to orient researchers and system designers in this area.

Link quality estimation is a thorny problem in wireless sensor networks, because its accuracy affects the design and the efficiency of networking protocols and applications. Especially in the context of low-power wireless, estimating the link quality poses a sort of catch-22 dilemma, whereby a large number of packet samples are required to accurately estimate a channel, but only a few samples should be used due to limited energy resources. This paradox becomes even more severe in mobile wireless sensor networks, since the high variability of the medium imposes even stricter constraints on the timing in which the estimation has to be carried out. In this paper we propose the Triangle Metric, a metric that combines geometrically the information of PRR, LQI, and SNR into a robust estimator that guarantees a fast and reliable assessment of the link quality. Our evaluation shows that the triangle metric can identify the quality of links using as few as 10 packet samples, making it an eligible solution for highly mobile sensor networks.

Wireless sensor networks are being considered for use in industrial process and control environments. Unlike traditional deployment scenarios for sensor networks, in which energy preservation is the main design principle, industrial environments stress worker safety and uninterrupted production. To fulfill these requirements, sensor networks must be able to provide performance guarantees for radio communication. In this paper, we consider as a case study the deployment of a sensornet in an oil refinery in Portugal, where sensor nodes are deployed outdoors and might experience high temperature fluctuations. We investigate how the variations of ambient temperature influence data delivery performance and link quality in low-power radio communications. We also study the impact that specific implementation requirements, such as the ATEX fire-safety regulations, can have on the design of the overall network. Our experiments show that temperature directly affects the communication between sensor nodes, and that significantly less transmission power is required at low temperatures. We further illustrate that it is possible to save up to 16% energy during nights and cold periods of the year, while still ensuring reliable communication among sensor nodes. In view of these experimental results, we elaborate on how the temperature influences both the design and the deployment of wireless sensor networks in industrial environments.

We address the problem of generating customized, controlled interference for experimental and testing purposes in wireless sensor networks. The known coexistence problems between electronic devices sharing the same ISM radio band drive the design of new solutions to mitigate interference. The validation of these techniques and the assessment of protocols under external interference require the creation of reproducible and well-controlled interference patterns on real nodes, a nontrivial and time-consuming task. In this paper, we study methods to generate a precisely adjustable level of interference on a specific channel, with lowcost equipment and rapid calibration. We focus our work on the platforms carrying the CC2420 radio chip. We show that, by setting the CC2420 in special mode, we can easily generate repeatable and precise patterns of interference. We show how this method is extremely useful for researchers to quickly investigate the behaviour of sensor network protocols and applications under different patterns of interference. We further evaluate the performance of our proposed method.

Radio interference plays a central role for the performance of Wireless Sensor Networks (WSN). Interference not only leads to packet loss, but it also affects the function of MAC and routing protocols. Hitherto, testing the impact of interference on WSN experimentally has been difficult because of the unavailability of low-cost tools to create reproducible and well-controlled interference patterns.In this demo we present a simple and inexpensive method to generate controllable and repeatable interference patterns for 802.15.4 devices. The demo is presented as a game, where a user is required to achieve a given interference level.