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.

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.

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.

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.

Ultra-wideband technology is applied for indoor positioning systems and achieves a position accuracy in the order of decimeters. A trending approach in this context relies on combined angle-of-arrival and time-of-flight measurements, and enables localization using a single anchor, thereby reducing infrastructure overhead. Although analytical models already exist for Ultra-wideband-based distance estimation using time-of-flight, no model has been proposed for its angle-of-arrival counterpart. In this paper we cover this gap by investigating the use of 4 different machine learning regressors to generate such models. The models were trained with data from real-world experiments performed with commercial off-the-shelf Ultra-wideband modules. The models can be easily integrated in simulators, facilitating and even enabling the evaluation of scalable positioning systems using this technology. Among the tested regressors, the random forest regressor presented the best fit to the experimental data, with MAE of the “mean” parameter of 8°.

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.

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.

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.

Distance bounding has gained attention in the last decades due to the increasing need for security in applications, such as controlling drones and keyless access control. In such applications, it is important to verify if the two entities participating in a transaction are geographically close to each other. A distance fraud is a known class of attacks in this context, and it has been shown that particular attacks within this class can be successfully applied to any distance estimation approach relying on round-trip time-of-flight (ToF) measurements. In this paper, we show that by combining ToF-based with Received Signal Strength (RSS)-based distance estimations, a system comprising at least two anchors can detect distance reduction and enlargement frauds. The latter so far had no general solution. Providing such a solution constitutes the main contribution of this paper. The proposed solution is based on the fact that the apparent position of the prover is constrained to two different feasibility regions: one for RSS and another for ToF-based measurements. These feasibility regions overlap only at the true position of the prover and get inconsistent with an increasing magnitude of the attack. The proposed hybrid method adds virtually no overhead to a ToF-based system, and not only achieves a tighter bound on distance reduction frauds than the pure ToF method, but (tightly) bounds the distance enlargement fraud, which is currently unbounded when using the pure ToF method. Additionally, it requires only two verifiers, outperforming a well-known solution, which requires at least three verifiers. Simulation results show that when using verifiers separated by just 10 cm, suitable for implementation in a single device, the system is able to detect enlargement frauds as small as 120 cm with a high probability.

Recovering causality from copious time series data beyond mere correlations has been an important contributing factor in numerous scientific fields. Most existing works assume linearity in the data that may not comply with many real-world scenarios. Moreover, it is usually not sufficient to solely infer the causal relationships. Identifying the correct time delay of cause-effect is extremely vital for further insight and effective policies in inter-disciplinary domains. To bridge this gap, we propose KOMPOS, a novel algorithmic framework that combines a powerful concept from causal discovery of additive noise models with graphical ones. We primarily build our structural causal model from multivariate adaptive regression splines with inherent additive local nonlinearities, which render the underlying causal structure more easily identifiable. In contrast to other methods, our approach is not restricted to Gaussian or non-Gaussian noise due to the non-parametric attribute of the regression method. We conduct extensive experiments on both synthetic and real-world datasets, demonstrating the superiority of the proposed algorithm over existing causal discovery methods, especially for the challenging cases of autocorrelated and non-stationary time series.

With silicon technology decreasing in size, memories get more susceptible to external influences, which can lead to soft errors. Although temporary, these errors constitute a challenge for safety-critical systems. Redundancy-based error detection is commonly used in industry to increase safety and mitigate these errors. When an error is detected, safety-critical systems are usually switched to a safe state. While this prevents failures, it negatively affects the system's availability. In this work, we propose Just-in-Time fault detection, a novel method which enables a system to be switched to the safe state only in case a detected error would affect the system's behavior. A software tool enabling the deployment of this method on an off-the-shelf processor is implemented, and the method is validated and compared with a state-of-the-art alternative approach using mixed-critical memories. Our results show an availability gain between 25.2% and 100% compared with the state-of-the-art approach while executing two different standard algorithms.

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.

Using Angle-of-Arrival based on Phase-Difference-of-Arrival is a recent trend in indoor positioning. Although the basic techniques for direction estimation have been known and used for a long time in different application scenarios (e.g., RADAR), the complexity of Phase-Difference-of-Arrival transceivers discouraged its use for indoor applications. This scenario changed with the recent development of modern chips at competitive prices. Specifically, two ubiquitous technologies in the context of indoor positioning, namely Bluetooth Low Energy (BLE) and Ultra-Wideband (UWB), currently feature Angle-of-Arrival estimation. In this paper we compare these two technologies regarding their potential for indoor positioning based on a fair evaluation of their performance for estimating Angle-of-Arrival in five different realistic setups. The results obtained are compared with existing studies when available. Our results show that Ultra-Wideband is, in general, more accurate and precise, especially when subject to multipath interference. Additionally, we discuss possible approaches to improve the accuracy and precision of Angle-of-Arrival estimation using BLE, such as the impact of increasing the number of antenna elements. By using UWB, we achieved an angular accuracy of up to 5° even under obstructed LOS and multipath, while an overall mean error of nearly 25° was obtained with BLE in an outdoor scenario without obstacles.

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.

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.

Network control in microgrids is an active research area driven by a steady increase in energy demand, the necessity to minimize the environmental footprint, yet achieve socioeconomic benefits and ensure sustainability. Reducing deviation of the predicted energy consumption from the actual one, softening peaks in demand and filling in the troughs, especially at times when power is more affordable and clean, present challenges for the demand-side response. In this paper, we present a hierarchical energy system architecture with embedded control. This architecture pushes prediction models to edge devices and executes local control loops to address the challenge of managing demand-side response locally. We employ a two-step approach: At an upper level of hierarchy, we adopt a conventional machine learning pipeline to build load prediction models using automated domain-specific feature extraction and selection. Given historical data, these models are then used to label prediction failure events that force the operator to use backup energy sources to stabilize the network. On a lower level of hierarchy, computed labels are used to train impact models realized by LSTM networks running on edge devices to infer the probability that the power consumption of the player contributes to the upper level prediction failure event. The system is evaluated on clustered and aggregated energy traces from a public data set of academic buildings. The results show the benefits of the proposed hierarchical energy system architecture in terms of impact prediction with 55% accuracy. This allows minimizing the number of prediction failure events by 11.69 % by executing targeted local control.

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.

A wide gap exists between the state of the art in developing Wireless Sensor Network (WSN) software and current practices concerning the design, execution, and maintenance of business processes. WSN software is most often developed based on low-level OS abstractions, whereas business process development leverages high-level languages and tools. This state of affairs places WSNs at the fringe of industry. The makeSense system addresses this problem by simplifying the integration of WSNs into business processes. Developers use BPMN models extended with WSN-specific constructs to specify the application behavior across both traditional business process execution environments and the WSN itself, which is to be equipped with application-specific software. We compile these models into a high-level intermediate language-also directly usable by WSN developers-and then into OS-specific deployment-ready binaries. Key to this process is the notion of meta-abstraction, which we define to capture fundamental patterns of interaction with and within the WSN. The concrete realization of meta-abstractions is application-specific; developers tailor the system configuration by selecting concrete abstractions out of the existing codebase or by providing their own. Our evaluation of makeSense shows that i) users perceive our approach as a significant advance over the state of the art, providing evidence of the increased developer productivity when using makeSense; ii) in large-scale simulations, our prototype exhibits an acceptable system overhead and good scaling properties, demonstrating the general applicability of makeSense; and, iii) our prototype-including the complete tool-chain and underlying system support-sustains a real-world deployment where estimates by domain specialists indicate the potential for drastic reductions in the total cost of ownership compared to wired and conventional WSN-based solutions.

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.

Wireless networked embedded systems are increasingly used for safety-critical applications such as smart production or networked cars, where failures may have severe impact. Therefore, strict dependability requirements have to be met. This is difficult to achieve, however, as these applications often operate in harsh environments or are exposed to attacks. In this talk we present recent research results obtained in the Dependable Things research center at TU Graz which aims at increasing the dependability of the IoT for safety-critical applications. Specifically, we present a single-anchor approach to robust and accurate localization using UWB, a method to analyze software for potential side-channel leakage, and an approach to automatically learn models of protocols used in the IoT in order to formally verify their correct implementation and interoperability. There is consensus among the relevant industry and standardization communities that a key element in 5G mobile networks will be network slicing. The idea is to allow the mobile infrastructure to be “sliced” into logical networks, where each slice is a collection of resources and functions that includes software modules running at different locations as well as the nodes' computational and communication resources. The intention is to tailor each slice to support a specific service, providing only what is necessary for the service while avoiding unnecessary overheads and complexity. This provides a basis for efficient infrastructure sharing among diverse entities, ranging from classical or virtual mobile network operators to new players that simply view connectivity as a service, where each of these entities may be running one or more slices. This talk will focus on the key enablers for network slicing and the research challenges involved in realizing this technology. Current standardization activities will be reviewed along with the contributions of major research projects, such as the H2020 5G-NORMA and 5G-MoNArch projects. A key problem underlying network slicing is enabling efficient sharing of mobile network resources. Various approaches considered in 3GPP will be analysed, ranging from per-reservation based schemes (where network slices reserve the required resources in advance) to others based on network shares (where resources are allocated based on predetermined shares). The performance and behavior of the various approaches will be studied based on analytical tools including optimization, game theory and machine learning. Buildig on these analyses, we will provide some insights on the stability, peformance, optimality and level of customization enabled by the various approaches.

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.

The development of resource-constrained embedded systems that are naturally state-driven is still a challenging issue, especially in industrial applications -- developed on a bare-bone style runtime system with basic programming features. This is because of the complexity of state-driven design in embedded applications, such as parallel and complicated event-based activity flows, and complicated constraints for transitioning between program states. State machines are considered a systematic approach for such needs. However, existing approaches, in this area, either do not satisfactorily address the above complexity aspects, or force the developer to write code intermingling state handling logic with the functional code. To tackle these issues, we propose TOKEN IT, a state machine-based development framework for resource-constrained embedded systems. Using TOKEN IT, the programmer models the application as a set of parallel processes, where each process consists of sequenced activities with state constraints such as delayed transitions or interdependency between the states of parallel processes. TOKEN IT, then, processes the obtained model and associates a token to each sequential flow of activities, synthesizing them and executing state transitions according to the constraints expressed in the TOKEN IT model. The evaluation results show that TOKEN IT reduces significantly the complexity of state-driven programming in embedded systems at an acceptable memory cost and with no extra processing overhead.

We present design and implementation of a chain of particles that can be programmed to fold the chain into a given curve. The particles guide an external force to fold, therefore the particles are simple and amenable for miniaturization. A chain can consist of a large number of such particles. Using multiple of these chains, a shape-shifting display can be constructed that folds its initially flat surface to approximate a given 3D shape that can be touched and modified by users, for example, enabling architects to interactively view, touch, and modify a 3D model of a building.

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.

A warm welcome to the Twelfth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom 2014). We are pleased to introduce the technical program of the conference which this year includes 25 papers representing high-quality research conducted over a broad spectrum of topics related to pervasive computing.

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.

A sustainable, uniform, and utility-maximizing operation of energy-harvesting sensor networks requires methods for aligning consumption with harvest. This article presents a lightweight algorithm for online load adaptation of energy-harvesting sensor nodes using supercapacitors as energy buffers. The algorithm capitalizes on the elementary relationship between state of charge and voltage that is characteristic for supercapacitors. It is particularly designed to handle the nonlinear system model, and it is lightweight enough to run on low-power sensor node hardware. We define two energy policies, evaluate their performance using real-world solar-harvesting traces, and analyze the influence of the supercapacitor’s capacity and imprecisions in harvest forecasts. To show the practical merit of our algorithm, we devise a load adaptation scheme for multihop data collection sensor networks and run a 4-week field test. The results show that (i) choosing a duty cycle a priori is infeasible, (ii) our algorithm increases the achievable work load of a node when using forecasts, (iii) uniform and steady operation is achieved, and (iv) depletion can be prevented in most cases.

Long-term accurate measurements of core body temperature are essential to study human thermoregulation in ambulatory settings and during exercise, but they are traditionally carried out using highly-invasive techniques. To enable a continuous unobtrusive monitoring of core body temperature on ambulatory patients and exercising athletes, we have designed a wireless wearable system that measures the tympanic temperature inside the ear, as well as skin and environmental temperature, and that allows remote monitoring of the collected measurements. In this paper, we describe the design and implementation of the system and show that it can be used to identify the circadian rhythms of core body temperature, as well as to detect the variation in core body temperature due to prolonged physical exertion. We further describe the lessons learnt during a pilot deployment of our telemetric system on several athletes during the 5thLübeck Marathon, and discuss the impact of environmental parameters such as temperature and wind on the accuracy and meaningfulness of the measured values.

In the emerging Web of Things (WoT), the real-time state of real-world objects is published by Internet-connected sensors using Web technologies. A key service in the WoT will be a search engine that allows searching for real-world objects with a certain state. We propose content-based sensor search, where given a search query that is composed of a range of sensor values, sensors that have been reading values in that range at the time of the query are found. In this paper, we present an approach based on fuzzy sets to realize scalable content-based sensor search in the WoT. Using sensor data sets obtained from real deployments, we find that our approach results in a low communication overhead. To demonstrate the practical feasibility of our approach, we implemented a prototypical search engine that enables the user to search for sensors that are available on the Web.

While size and complexity of sensor networks software has increased significantly in recent years, the hardware capabilities of sensor nodes have been remaining very constrained. The predominant event-based programming paradigm addresses these hardware constraints, but does not scale well with the growing software complexity, often leading to software that is hard-to-manage and error-prone. Thread abstractions could remedy this situation, but existing solutions in sensor networks either provide incomplete thread semantics or introduce a significant resource overhead. This reflects the common understanding that one has to trade expressiveness for efficiency and vice versa. Our work, however, shows that this trade-off is not inherent to resource-constrained systems. We propose a comprehensive compiler-assisted cooperative threading abstraction, where full-fledged thread-based C code is translated to efficient event-based C code that runs atop an event-based operating system such as Contiki or TinyOS. Our evaluation shows that our approach outperforms thread libraries and generates code that is almost as efficient as hand-written event-based code with overheads of 1% RAM, 2% CPU, and 3% ROM.

The Internet of Things (IoT) integrates wireless sensors to provide online and real-time access to the state of things and places. However, many interesting real-world states are difficult to detect with traditional scalar sensors. Tiny wireless camera sensor nodes are an interesting alternative as a single camera can observe a large area in great detail. However, low image resolution, poor image quality, and low frame rates as well as varying lighting conditions in outdoor scenarios make the detection of real-world states using these lousy cameras a challenging problem. In this paper we introduce a framework that addresses this problem by providing an end-to-end solution that includes energy-efficient image capture, image enhancement to mitigate low picture quality, object detection with low frame rates, inference of high-level states, and publishing of these states on the IoT. The framework can be flexibly configured by end-users without programming skills and supports a variety of different applications.

Sensor networks are exposed to hostile environments that may cause failures of single nodes and communication links which affect the whole network. Localizing the cause of the problem in space and time requires to collect diagnostic data from the network. Due to resource and energy constraints, however, it is not possible to continuously collect detailed diagnostic data from all nodes. We therefore propose an incremental approach where first data is logged to flash memory and later the user can pose a sequence of diagnostic queries with decreasing scope and increasing level of detail to pinpoint the cause of the problem.

The industrial adoption of wireless sensor networks (WSNs) is hampered by two main factors. First, there is a lack of integration of WSNs with business process modeling languages and back-ends. Second, programming WSNs is still challenging as it is mainly performed at the operating system level. To this end, we provide makeSense: a unified programming framework and a compilation chain that, from high-level business process specifications, generates code ready for deployment on WSN nodes.

Recently, large sensor networks with several thousands of nodes are being deployed over large geographic areas in the context of smart city projects. In these settings, there is often a need to send a message to all sensors contained in one of multiple geographic regions, for example, to query for a free parking spot in several streets. We present Recursive Multi-region Geocasting (RMG), a novel multi-region geocast routing protocol which addresses the problem of delivering data from a source to multiple remote geocast regions in large-scale wireless sensor networks. The key idea is to treat a remote group of geocast regions as a point destination and forward data packets along a straight line towards the group, until a division point at which the group is divided, and the packet is forwarded towards the sub-groups in the same fashion. RMG is lightweight as no state has to be maintained at the nodes and the computations are simple. Simulation shows that our protocol achieves lower path length overhead and network relay load while incurring less computation overhead when compared to state-of-the-art protocols.

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.

An increasing number of sensors is being connected to the Internet and their output is published on the Web, resulting in the formation of a Web of Things (WoT) that will soon connect tens of Billions of devices. We propose sensor similarity search, where given a sensor, other sensors on the WoT are found that produced similar output in the past. At the heart of our approach is an algorithm that exploits fuzzy sets for efficiently computing a similarity score for a pair of sensors that is used to obtain a ranked list of matching sensors. Using sensor data sets from real deployments, we find that this approach results in a high accuracy.

We describe the motivation, focus, and organization of SESENA11, the 2nd International Workshop on Software Engineering for Sensor Network Applications. The workshop took place under the umbrella of ICSE 2011, the 33rd ACM/IEEE International Conference on Software Engineering, in Honolulu, Hawaii, on May 22, 2011. The aim was to attract researchers belonging to the Software Engineering (SE) and Wireless Sensor Network (WSN) communities, not only to exchange their recent research results on the topic, but also to stimulate discussion on the core open problems and define a shared research agenda. More information can be found at the workshop website: http://www.sesena.info.

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.

This editorial preface describes the aims and motivation as well as some details of the reviewing process of SESENA 2010, the First International Workshop on Software Engineering for Sensor Network Applications, which took place under the umbrella of ICSE 2010, the 32rd ACM/IEEE International Conference on Software Engineering, in Cape Town, South Africa, May 2010. See also our workshop's website at http://www.sesena.info/

Limited visibility of node states makes debugging deployed sensor networks very difficult. Higher visibility usually implies more resource consumption. As sensor networks are resource-constrained and need to operate unattended for long periods, a balance between a sufficient level of visibility and a tolerable consumption of resources needs to be found so that enough evidence can be collected to analyze the causes of observed problems. We propose a mechanism called visibility levels (vLevels) to manage the trade-off between visibility and resource consumption. Preliminary experimental results show the feasibility of vLevels.

The 25 papers in this special issue focus on simple wireless sensor networking solutions.

We are observing an increasing trend of connecting embedded sensors and sensor networks to the Internet and publishing their output on the Web. We believe that this development is a precursor of a Web of Things, which gives real-world objects and places a Web presence that not only contains a static description of these entities, but also their real-time state. Just as document searches have become one of the most popular services on the Web, we argue that the search for real-world entities (i.e., people, places, and things) will become equally important. However, in contrast to the mostly static documents on the current Web, the state of real-world entities as captured by sensors is highly dynamic. Thus, searching for real-world entities with a certain state is a challenging problem. In this paper, we define the underlying problem, outline the design space of possible solutions, and survey relevant existing approaches by classifying them according to their design space. We also present a case study of a real-world search engine called Dyser designed by the authors.

The Fourth International IEEE Workshop on Practical Issues in Building Sensor Networks Applications (SenseApp 2009) was held in Zurich, Switzerland, in conjunction with the 34thIEEE Conference on Local Computer Networks (LCN 2009).

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.

The increasing penetration of the real world with embedded and globally networked sensors enables the formation of a Web of Things (WoT), where high-level state information derived from sensors is embedded into Web representations of real-world entities (e.g. places, objects, creatures). A key service for the WoT is searching for entities which exhibit a certain dynamic state at the time of the query, which is a challenging problem due to the dynamic nature of the sought state information and due to the potentially huge scale of the WoT. Below we report on our initial efforts to construct such a search engine and the underlying WoT.

By means of so-called Scatternets, Bluetooth provides the ability to construct robust wireless multi-hop networks. In this paper we propose a practical protocol for time synchronization of such Bluetooth multi-hop networks. Our protocol makes use of the internal clock maintained by Bluetooth, requires minimal communication overhead, and provides an accuracy of few milliseconds across multiple hops. The protocol has been implemented and evaluated on BTnodes, an embedded computing platform which uses Bluetooth for ad hoc networking.

Generic role assignment is a programming abstraction that supports the assignment of user-defined roles to sensor nodes such that certain conditions are met. Many common network configuration problems such as coverage (assign roles ON and OFF to sensor nodes such that ON nodes cover a physical area with their sensors), clustering, or in-network data aggregation can be formulated as role assignment problems. Building on our previous work in this area, we propose an extended role specification language that supports the minimization or maximization of the use of a given role. Moreover, we provide a mapping of this language to integer linear programs and implement this mapping. We show how the resulting tool can be used analyze aspects of role specifications such as feasibility and optimality.

Event-driven programming is a popular paradigm for programming sensor nodes. It is based on the specification of actions (also known as event handlers) which are triggered by the occurrence of events. While this approach is both simple and efficient, it suffers from two important limitations. Firstly, the association of events to actions is static-there is no explicit support for adopting this association depending on the program state. Secondly, a program is split up into many distinct actions without explicit support for sharing information among these. These limitations often lead to issues with code modularity, complexity, and correctness. To tackle these issues we propose OSM, a programming model and language for sensor nodes based on finite state machines. OSM extends the event paradigm with states and transitions, such that the invocation of actions becomes a function of both the event and the program state. For removing the second limitation, OSM introduces state attributes that allow sharing of information among actions. They can be considered local variables of a state with support for automatic memory management. OSM specifications can be compiled into sequential C code that requires only minimal runtime support, resulting in efficient and compact systems.

We consider configuration of wireless sensor networks, where certain functions must be automatically assigned to sensor nodes, such that the properties of a sensor node (e.g., remaining energy, network neighbors) match the requirements of the assigned function. Essentially, sensor nodes take on certain roles in the network as a result of configuration. To help developers with such configuration tasks for a variety of applications, we propose generic role assignment as a programming abstraction, where roles and rules for their assignment can be easily specified using a configuration language. We present such a role specification language and distributed algorithms for role assignment according to such specifications. We evaluate our approach and show that efficient and robust generic role assignment is practically feasible for wireless sensor networks.

Smart Dust sensor networks -- consisting of cubic millimeter scale sensor nodes capable of limited computation, sensing, and passive optical communication with a base station -- are envisioned to fulfill complex large scale monitoring tasks in a wide variety of application areas. In many potential Smart Dust applications such as object detection and tracking, fine-grained node localization plays a key role. However, due to the unique characteristics of Smart Dust, traditional localization systems cannot be used. In this paper we present and analyse the Lighthouse location systems, a novel laserbased location system for Smart Dust, which allows tiny dust nodes to autonomously estimate their location with high accuracy without additional infrastructure components besides a modified base station device. Using an early 2D prototype of the system, node locations could be estimated with an average accuracy of about 2% and an average standard deviation of about 0.7% of the node's distance to the base station.

We present our results in the conceptual design and the implementation of ubiquitous computing applications using smart identification technologies. First, we describe such technologies and their potential application areas, followed by an overview of some applications we have developed. Based on the experiences we gained from the development of these systems, we point out design concepts that we find useful for structuring and implementing such applications. Building upon these concepts, we have created two frameworks based on Jini (i.e., distributed Java objects) and Web services to support the development of ubiquitous computing applications that make use of smart identification technology. We describe our prototype frameworks, discuss the underlying concepts and present some lessons learned.

Ubiquitous computing environments are typically based upon ad hoc networks of mobile computing devices. These devices may be equipped with sensor hardware to sense the physical environment and may be attached to real world artifacts to form so-called smart things. The data sensed by various smart things can then be combined to derive knowledge about the environment, which in turn enables the smart things to "react" intelligently to their environment. For this so-called sensor fusion, temporal relationships (X happened before Y) and real-time issues (X and Y happended within a certain time interval) play an important role. Thus physical time and clock synchronization are crucial in such environments. However, due to the characteristics of sparse ad hoc networks, classical clock synchronization algorithms are not applicable in this setting. We present a time synchronization scheme that is appropriate for sparse ad hoc networks