We present Neuro-centric Networks (Neuro-C), a neural network architecture we design to eliminate multiply-accumulate operations for efficient inference on ultra-low-power microcontrollers (MCUs). Although some MCUs include specialized hardware for neural acceleration, many ultra-low-power MCUs do not, requiring neural networks to align with limited compute and memory resources. Rather than compressing existing models or assuming dedicated hardware, Neuro-C integrates hardware constraints directly into the architecture, effectively shaping the network design around the limitations of the target platform. We shift the computational burden from connections to neurons and encode connectivity with a fixed ternary adjacency matrix, overcoming the bottleneck of matrix multiplications and large weight storage. This design enables a specialized inference kernel implementation that reduces memory usage and latency through pointer-based traversal and sparse dynamic memory allocation, complex control flows, and index decoding logic common in sparse or compressed models. Experimental results show that Neuro-C achieves accuracy comparable to or better than standard multilayer perceptrons across multiple datasets, while reducing inference latency and program memory usage by up to 90%. Compared to conventional ternary neural networks, Neuro-C provides improved convergence and accuracy under identical architectural settings, with negligible impact on inference latency.

The need for generating radio signals with a high frequency stability and very low phase noise is a demanding requirement in communication systems. In low-power designs, such high-frequency signal generation is often the main source of power consumption, which necessitates low-power alternatives. Although tunnel diodes can generate high-frequency signals at low power, the generated signal is frequency unstable with high phase noise and unwanted harmonics. State-of-the-art address these limitations through injection locking, which requires an external signal generator that significantly increases the overall system power consumption. We present SharpPeak, a low-power long-range transmitter design that achieves high frequency stability using tunnel diodes without relying on external injection signals. This design lowers both system power and transmitter complexity, while improving frequency stability, phase noise, and frequency drifts. SharpPeak achieves high (370 kbps) data rates and long (1 km) communication range with under 175 μW power consumption at the RF front-end, advancing the state-of-the-art in energy-efficient communication systems. We believe that this work is a significant advancement in the development of a new generation of low-power communication systems.

Analog backscatter communication enables ultra-low-power and low-cost wireless connectivity, making it a promising alternative to traditional RFID systems. Although analog backscatter systems offer simplicity, they are by design less capable compared to their digital backscatter counterparts. A key drawback of existing analog backscatter systems is their single-tag operation, which poses limitations for applications that require multiple tags to function simultaneously. In this paper, we propose a novel low-cost analog backscatter system that allows multiple tags to sense the channel concurrently, thereby enabling signal strength-based localization and tracking of ambient RF sources. The ability to localize RF sources is essential for interference management, security, and optimization of wireless networks, particularly in dynamic environments. We evaluate our system in indoor settings, and demonstrate localization accuracy of RF sources with an average error of 20 cm in a 3m range, without requiring calibration. Our system is also capable of tracking RF sources with an average speed up to 0.6m/s with an average power consumption of 526µW, making it a promising solution for real-time and energy-constrained applications.

Radiometric fingerprinting is a promising passive security measure for low-power IoT devices, that exploits the hardware imperfections of their radio signals. In this paper, we focus on extracting radiometric fingerprints from Bluetooth Low Energy (BLE) devices. We depart from previous work in that we facilitate fingerprinting in embedded network scenarios by extracting features from I/Q samples collected using a widely used BLE System-on-Chip. We introduce a novel approach that leverages instantaneous frequency analysis of signal dynamics to reveal hardware imperfections during symbol transitions in the packet payload. Our objective is to identify the most significant features contributing to device identification. Our experimental evaluation demonstrates that augmenting traditional aggregated FFT-based features with our proposed transition-based features increases identification accuracy from 56% to 74%.

In-body communication is a key enabler for next-generation healthcare applications, allowing seamless networking of implants. Fat tissue, with its lower water content and reduced signal attenuation compared to other body tissues at microwave frequencies, has emerged as a promising medium for radio-based in-body networks. Despite this advantage, signal leakage through the body can compromise privacy, exposing sensitive data and the mere presence of implants to external adversaries. This paper investigates the feasibility of covert communication in fat tissue-based in-body networks by leveraging the previously unexplored signal attenuation properties of human tissue to transmit data undetectable to adversaries, ensuring privacy beyond encryption. We develop a system in which an implanted transmitter communicates discreetly with an implanted receiver, shielded from external passive eavesdroppers. Our theoretical analysis and experimental results demonstrate that the attenuation properties of human tissues enable covert communication at reduced transmit power levels without requiring friendly jamming, unlike over-the-air systems. To further enhance covertness, we explore the use of an external friendly jammer and show its significant benefits. Experimental results show a 500% increase in the maximum channel capacity of covert communication, from 2.86 bps/Hz at -56 dBm transmit power without jamming, to 17 bps/Hz with no bit errors at 0 dBm transmit power with a friendly jammer, using the IEEE 802.15.4 standard for communication in the 2.45 GHz frequency band. These findings highlight that covert communication is achievable in fat tissue-based in-body networks at low data rates without additional infrastructure such as an external jammer. For applications requiring higher data rates, a friendly jammer offers a scalable solution, making this approach practical for a wide range of implant communication scenarios.

Reckless usage of electric (e-) scooters causes many injury accidents, raising critical safety concerns. Despite newly introduced regulations, specifically, speed limits and sidewalk driving prohibitions, the number of accidents increases due to the challenges in enforcement. Therefore, a reliable method to detect safety-threatening illegal behaviors of e-scooters is essential to mitigate this growing problem. In this article, we propose SecureRide, a system that accurately detects illegal e-scooter behaviors, i.e., speeding violation and sidewalk riding, at runtime using only battery information, without the need for additional sensors. To this end, we first design a neural network-based illegal behavior predictor that takes sequences of three battery factors, i.e., voltage, current, and capacity, as inputs. The model architecture is optimized based on time constraints, target accuracy, and resource constraints of the target devices. Next, we devise a runtime detection strategy to achieve both high accuracy and low detection time. SecureRide operates in two modes with different predictors– lightweight-quick and complex-accurate models–depending on the driving situation, ensuring both high accuracy and low detection time. We extensively validate SecureRide based on actual driving experiments. Our results show that SecureRide detects illegal behaviors with an accuracy of up to 99.77% within 1.01 seconds.

Innovative medical applications based on networked implants foster the development of in-body communication technologies. Among the in-body communication technologies that are being considered, fat intra-body communication (Fat-IBC) is a very recent approach. Its main advantage lies in its higher data rate compared to earlier approaches based on capacitive and galvanic coupling. However, Fat-IBC faces privacy-, security-, as well as safety-related attacks. In this paper, we discuss security and privacy concerns about Fat-IBC, as well as corresponding countermeasures. Furthermore, we present our secure protocol stack for Fat-IBC and suggest directions for future research.

We present TaDA, a system architecture enabling efficient execution of Internet of Things (IoT) applications across multiple computing units, powered by ambient energy harvesting. Low-power microcontroller units (MCUs) are increasingly specialized; for example, custom designs feature hardware acceleration of neural network inference, next to designs providing energy-efficient input/output. As application requirements are growingly diverse, we argue that no single MCU can efficiently fulfill them. TaDA allows programmers to assign the execution of different slices of the application logic to the most efficient MCU for the job. We achieve this by decoupling task executions in time and space, using a special-purpose hardware interconnect we design, while providing persistent storage to cross periods of energy unavailability. We compare our prototype performance against the single most efficient computing unit for a given workload. We show that our prototype saves up to 96.7% energy per application round. Given the same energy budget, this yields up to a 68.7x throughput improvement.

The IEEE 802.15.4 radio standard features the possibility for IEEE 802.15.4 nodes to run on batteries for several years. This is made possible by duty-cycling medium access control (MAC) protocols, which allow IEEE 802.15.4 nodes to leave their radios in energy-saving sleep modes most of the time. Yet, duty-cycling MAC protocols usually incur long routing delays since it may take a while until a particular forwarder becomes available for forwarding a packet. Opportunistic routing alleviates this problem by opportunistically using a currently available forwarder, rather than waiting for a particular forwarder. Among all opportunistic routing schemes, so-called dynamic switch-based forwarding (DSF) schemes are most promising from a security and practical perspective, but some security and reliability issues with them persist. In this paper, we propose secure multipath opportunistic routing (SMOR), a DSF scheme that improves on current DSF schemes in three regards. First, SMOR builds on a denial-of-sleep-resilient MAC layer. Current DSF schemes, by comparison, rest on MAC protocols that put the limited energy reserves of battery-powered IEEE 802.15.4 nodes at risk. Second, SMOR operates in a distributed fashion and efficiently supports point-to-point traffic. All current DSF schemes, by contrast, suffer from a single point of failure and focus on convergecast traffic. Third, SMOR duplicates packets on purpose and routes them along disjoint paths. This makes SMOR tolerant of compromises of single IEEE 802.15.4 nodes, whereas current DSF schemes lack intrusion tolerance. We integrated SMOR into the network stack of the Contiki-NG operating system and benchmarked SMOR against the Routing Protocol for Low-Power and Lossy Networks (RPL) with the Cooja network simulator. Indeed, SMOR turns out to improve on RPL’s delays by between 33.51% and 39.84%, depending on the exact configurations and network dynamics. Furthermore, SMOR achieves between 0.16% and 2.03% higher mean packet delivery ratios (PDRs), thereby attaining mean PDRs of 99.999% or better in all simulated scenarios. Beyond that, SMOR has only a fraction of RPL’s memory requirements. SMOR’s intrusion tolerance, on the other hand, increases the mean energy consumption per IEEE 802.15.4 node by between 1.55% and 2.74% compared to RPL in our simulations. SMOR specifically targets IEEE 802.15.4 networks with a network diameter of 2, such as body area networks.

Byzantine-robust federated learning aims at mitigating Byzantine failures during the federated training process, where malicious participants (known as Byzantine clients) may upload arbitrary local updates to the central server in order to degrade the performance of the global model. In recent years, several robust aggregation schemes have been proposed to defend against malicious updates from Byzantine clients and improve the robustness of federated learning. These solutions were claimed to be Byzantine-robust, under certain assumptions. Other than that, new attack strategies are emerging, striving to circumvent the defense schemes. However, there is a lack of systematical comparison and empirical study thereof. In this paper, we conduct an experimental study of Byzantine-robust aggregation schemes under different attacks using two popular algorithms in federated learning,FedSGDandFedAvg. We first survey existing Byzantine attack strategies, as well as Byzantine-robust aggregation schemes that aim to defend against Byzantine attacks. We also propose a new scheme,ClippedClustering, to enhance the robustness of a clustering-based scheme by automatically clipping the updates. Then we provide an experimental evaluation of eight aggregation schemes in the scenario of five different Byzantine attacks. Our experimental results show that these aggregation schemes sustain relatively high accuracy in some cases, but they are not effective in all cases. In particular, our proposedClippedClusteringsuccessfully defends against most attacks under independent and identically distributed (IID) local datasets. However, when the local datasets are Non-IID, the performance of all the aggregation schemes significantly decreases. With Non-IID data, some of these aggregation schemes fail even in the complete absence of Byzantine clients. Based on our experimental study, we conclude that the robustness of all the aggregation schemes is limited, highlighting the need for new defense strategies, in particular for Non-IID datasets.

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed the design and development of various intelligent systems over wide application domains. While most existing machine learning models require large memory and computing power, efforts have been made to deploy some models on resource-constrained devices as well. A majority of the early application systems focused on exploiting the inference capabilities of ML and DL models, where data captured from different mobile and embedded sensing components are processed through these models for application goals such as classification and segmentation. More recently, the concept of exploiting the mobile and embedded computing resources for ML/DL model training has gained attention, as such capabilities allow (i) the training of models via local data without the need to share data over wireless links, thus enabling privacy-preserving computation by design, (ii) model personalization and environment adaptation, and (iii) deployment of accurate models in remote and hardly accessible locations without stable internet connectivity. This work summarizes and analyzes state-of-the-art systems research that allows such on-device model training capabilities and provides a survey of on-device training from a systems perspective.

Microwave communication through the fat tissue in the human body enables a new channel for wearable devices to communicate with each other. The wearable devices can communicate to the external world through a powerful device in their network called central control unit (CU); for example, a smartphone. Some wearable devices may be out of the range of the CU temporarily due to body movements or permanently due to low signal strength, in a fat channel communication network. Such devices can connect to the CU with the help of their neighbor device in the same network. In this paper, we propose a protocol to ensure secure indirect authentication and key establishment between the out-of-range device and the CU in a fat channel communication network, via an untrusted intermediate device in the network. The proposed protocol is lightweight and resistant to denial-of-sleep attacks on the intermediate device. We analyze the security and the computation overhead of the proposed protocol.

We present a machine learning approach that uses a custom Convolutional Neural Network (CNN) for estimating the depth of water pools from multispectral drone imagery. Using drones to obtain this information offers a cheaper, timely, and more accurate solution compared to alternative methods, such as manual inspection. This information, in turn, represents an asset to identify potential breeding sites of mosquito larvae, which grow only in shallow water pools. As a significant part of the world’s population is affected by mosquito-borne viral infections, including Dengue and Zika, identifying mosquito breeding sites is key to control their spread. Experiments with 5-band drone imagery show that our CNN-based approach is able to measure shallow water depths accurately up to a root mean square error of less than 0.5 cm, outperforming state-of-the-art Random Forest methods and empirical approaches.

Ensuring two battery-free devices discover each other to start communication is challenging due to intermittent and unpredictable energy availability. In this abstract, we exploit ultra-low power channel sensing to enable efficient neighbor discovery. Preliminary results show our method is promising in various ambient energy scenarios.

Novel backscatter communication techniques enable battery-free sensor tags to interoperate with unmodified standard IoT devices, extending a sensor network’s capabilities in a scalable manner. Without requiring additional dedicated infrastructure, the battery-free tags harvest energy from the environment, while the IoT devices provide them with the unmodulated carrier they need to communicate. A schedule coordinates the provision of carriers for the communications of battery-free devices with IoT nodes. Optimal carrier scheduling is an NP-hard problem that limits the scalability of network deployments. Thus, existing solutions waste energy and other valuable resources by scheduling the carriers suboptimally. We present DeepGANTT, a deep learning scheduler that leverages graph neural networks to efficiently provide near-optimal carrier scheduling. We train our scheduler with optimal schedules of relatively small networks obtained from a constraint optimization solver, achieving a performance within 3% of the optimum. Without the need to retrain, our scheduler generalizes to networks 6 × larger in the number of nodes and 10 × larger in the number of tags than those used for training. DeepGANTT breaks the scalability limitations of the optimal scheduler and reduces carrier utilization by up to compared to the state-of-the-art heuristic. As a consequence, our scheduler efficiently reduces energy and spectrum utilization in backscatter networks.

Analog backscatter enables sensing and communication at lower power consumption than digital backscatter. In analog tags, a sensor typically changes the resistance or capacitance value that then translates into a frequency change that is backscattered on top of a carrier. However, to read the sensor data with high resolution, a powerful receiver is required. The resolution of sensor data is limited by the FFT size and computation at the receiver. This limits the use of low-cost radio receivers, like RTL-SDR, that are gaining popularity in the RF community. We propose to use higher order harmonic frequencies, that are inherently generated by square wave backscattering at no extra cost, to derive sensor data using such low-cost radio receivers. We present experimental results that demonstrate the viability of our approach.

Medical implants are becoming increasingly widespread, and with that comes a need for networking multiple implants in the human body. This puts new demands on in-body communication, where conventional techniques (such as galvanic coupling) suffer from low bandwidth and data rates that can be insufficient for a network with several medical implants. Radio-based techniques at microwave frequencies can, on the other hand, provide a high-capacity communication channel, with the caveat that wave propagation through bodily materials at such frequencies is associated with significant signal loss, which limits the range. Fat tissue has been shown to have low loss compared to other tissues at frequencies such as 2.45 GHz and 5.8 GHz and could be a good choice of medium for a high-capacity channel. However, a drawback of radio-based in-body communication remains: signals may "leak'' out of the channel to the outside environment. This work investigates the leakage aspects of fat tissue-based in-body communication and explores methods for preserving the privacy of data from implants communicating through fat tissue. Through both simulations and practical experiments, we show that signals are heavily attenuated (on average by about 27 dB) when leaving the fat channel through the skin. Signal attenuation through the skin layer is similar even when the channel is not straight. Additionally, we demonstrate that reducing the transmit power as well as using an external, friendly "jamming'' signal can prevent that an external eavesdropper receives the data packets. In summary, we show that there is indeed RF leakage from in-body communication through fat tissue. However, the skin layer attenuates the signal quite heavily so that reducing the transmit power in combination with external jamming may prevent eavesdroppers outside the body from receiving sensitive in-body data.

We present ReTA (Relative Timing Analysis), a differential timing analysis technique to verify the impact of an update on the execution time of embedded software. Timing analysis is computationally expensive and labor intensive. Software updates render repeating the analysis from scratch a waste of resources and time, because their impact is inherently confined. To determine this boundary, in ReTA we apply a slicing procedure that identifies all relevant code segments and a statement categorization that determines how to analyze each such line of code. We adapt a subset of ReTA for integration into aiT, an industrial timing analysis tool, and also develop a complete implementation in a tool called Delta. Based on staple benchmarks and realistic code updates from official repositories, we test the accuracy by analyzing the worst-case execution time (WCET) before and after an update, comparing the measures with the use of the unmodified aiT as well as real executions on embedded hardware. Delta returns WCET information that ranges from exactly the WCET of real hardware to 148% of the new version's measured WCET. With the same benchmarks, the unmodified aiT estimates are 112% and 149% of the actual executions; therefore, even when Delta is pessimistic, an industry-strength tool such as aiT cannot do better. Crucially, we also show that ReTA decreases aiT's analysis time by 45% and its memory consumption by 8.9%, whereas removing ReTA from Delta, effectively rendering it a regular timing analysis tool, increases its analysis time by 27%.

We present NeRTA ( Ne xt R elease T ime A nalysis), a technique to enable dynamic software updates for low-level control software of mobile robots. Dynamic software updates enable software correction and evolution during system operation. In mobile robotics, they are crucial to resolve software defects without interrupting system operation or to enable on-the-fly extensions. Low-level control software for mobile robots, however, is time sensitive and runs on resource-constrained hardware with no operating system support. To minimize the impact of the update process, NeRTA safely schedules updates during times when the computing unit would otherwise be idle. It does so by utilizing information from the existing scheduling algorithm without impacting its operation. As such, NeRTA works orthogonal to the existing scheduler, retaining the existing platform-specific optimizations and fine-tuning, and may simply operate as a plug-in component. To enable larger dynamic updates, we further conceive an additional mechanism called bounded reactive control and apply mixed-criticality concepts. The former cautiously reduces the overall control frequency, whereas the latter excludes less critical tasks from NeRTA processing. Their use increases the available idle times. We combine real-world experiments on embedded hardware with simulations to evaluate NeRTA. Our experimental evaluation shows that the difference between NeRTA’s estimated idle times and the measured idle times is less than 15% in more than three-quarters of the samples. The combined effect of bounded reactive control and mixed-criticality concepts results in a 150+% increase in available idle times. We also show that the processing overhead of NeRTA and of the additional mechanisms is essentially negligible.

Federated learning (FL) is a promising paradigm to empower on-device intelligence in Industrial Internet of Things (IIoT) due to its capability of training machine learning models across multiple IIoT devices while preserving the privacy of their local data. However, the distributed architecture of FL relies on aggregating the parameter list from the remote devices, which poses potential security risks caused by malicious devices. In this article, we propose a flexible and robust aggregation rule, called auto-weighted geometric median (AutoGM), and analyze the robustness against outliers in the inputs. To obtain the value ofAutoGM, we design an algorithm based on the alternating optimization strategy. UsingAutoGMas aggregation rule, we propose two robust FL solutionsAutoGM_FLandAutoGM_PFL.AutoGM_FLlearns a shared global model using the standard FL paradigm, andAutoGM_PFLlearns a personalized model for each device. We conduct extensive experiments on the FEMNIST and Bosch IIoT datasets. The experimental results show that our solutions are robust against both model poisoning and data poisoning attacks. In particular, our solutions sustain high performance even when 30% of the nodes perform model or 50% of the nodes perform data poisoning attacks.

High-speed visible light communication (VLC) is a complementary technology to conventional radio frequency communication in wireless networks. One of the essential traits of VLC systems is the ability to provide wireless communication and illumination seamlessly. High-speed VLC systems use high-power LEDs as transmitters in their access points (APs). In real-world deployments, overlapping light beams from multiple APs are necessary to avoid unlit areas. However, overlapping light beams could cause interference in each other's communication at the receiver; hence APs must synchronise their communication. This paper presents Harmony, a time synchronisation system for VLC APs to synchronise their transmissions. Internally, Harmony uses synchronous transmissions in the infrared frequency spectrum to reach nodes over multiple hops. The evaluation of a prototype implementation of Harmony on a small-scale testbed shows that it can synchronise nodes up to nine hops with a maximum error of a few 100s of nanoseconds. While the enduring work on high-speed VLC systems primarily focuses on improving the performance of individual APs, Harmony provides an infrastructure for enhancing system-wide performance.

Radiometric fingerprint schemes have been shown effective in identifying wireless devices based on imperfections in their hardware electronics. The robustness of fingerprint systems under complex channel conditions, however, is a critical challenge that makes their application in real-world scenarios difficult. We systematically evaluate the wireless channel's impact on radiometric fingerprints and find that the channel impacts fingerprint features in a very particular way that depends on the channel's properties. Based on the insights, we present RRF, a system that provides a robust identification/authentication service even under complex channel fading disturbance. Our design deploys a hybrid architecture that combines wireless channel simulation, signal processing and machine learning. In this pipeline, RRF first utilizes a series of structured channel simulations to strategically improve system tolerance towards multipath channel interference. On top of that, in the identification phase, RRF relies on noise compensation and a feature denoising filter to augment the system's stability in noisy conditions with weak signals. Our experimental results show that RRF achieves an average accuracy consistently above 99% in empirical scenarios with complex channels, where the baseline approach from previous work rarely exceeds 50%.

Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants without accessing their local data. Standard federated learning techniques that naively minimize an average loss function are vulnerable to data corruptions from outliers, systematic mislabeling, or even adversaries. In this article, we address this challenge by proposing Auto-weighted Robust Federated Learning ( ARFL ), a novel approach that jointly learns the global model and the weights of local updates to provide robustness against corrupted data sources. We prove a learning bound on the expected loss with respect to the predictor and the weights of clients, which guides the definition of the objective for robust federated learning. We present an objective that minimizes the weighted sum of empirical risk of clients with a regularization term, where the weights can be allocated by comparing the empirical risk of each client with the average empirical risk of the best \( p \) clients. This method can downweight the clients with significantly higher losses, thereby lowering their contributions to the global model. We show that this approach achieves robustness when the data of corrupted clients is distributed differently from the benign ones. To optimize the objective function, we propose a communication-efficient algorithm based on the blockwise minimization paradigm. We conduct extensive experiments on multiple benchmark datasets, including CIFAR-10, FEMNIST, and Shakespeare, considering different neural network models. The results show that our solution is robust against different scenarios, including label shuffling, label flipping, and noisy features, and outperforms the state-of-the-art methods in most scenarios.

Wireless low-power, multi-hop networks are exposed to numerous attacks also due to their resource-constraints. While there has been a lot of work on intrusion detection systems for such networks, most of these studies have considered only a few topologies, scenarios and attacks. One of the reasons for this shortcoming is the lack of sufficient data traces that are required to train many machine learning algorithms. In contrast to other wireless networks, multi-hop networks do not contain one entity that can capture all the traffic which makes it more difficult to acquire such traces. In this paper we present Multi-Trace. Multi-Trace extends the Cooja simulator with multi-level tracing facilities that enable data logging at different levels while maintaining a global time. We discuss the opportunities that traces generated by Multi-Trace enable for researchers interested in input for their machine learning algorithms. We present experiments that show the efficiency with which Multi-Trace generates traces. We expect Multi-Trace to be a useful tool for the research community.

The Internet of Things (IoT) is connecting a massive scale of everyday objects to the internet. We need to ensure the secure connectivity and authentication of these devices. Physical (PHY)-layer identification methods can distinguish between different devices by leveraging their unique hardware imperfections. But these methods typically require large quantities of training data which makes them impractical for large deployment scenarios. Also, these methods do not address the PHY-layer identification of new devices joining an IoT network. In this paper, we propose a PHY-layer identification method using one-shot learning that can identify new devices using the network solicitation packet of the devices as reference packets. We show that our method can accurately identify new devices without training, achieving a precision and recall over 80% even in the presence of 10 dBm noise. Furthermore, we show that with minimal retraining using only three packets from each device, we can accurately identify all devices in the IoT network with a precision and recall of 93%.

Though fat channel communication has advantages over earlier intra-body communication (IBC) technologies based on galvanic or capacitive coupling, the development of a protocol stack on top of fat channel communication is still at its infancy. In this paper, we consider Krentz's denial-of-sleep-resilient multi-channel medium access control (MAC) layer for IEEE 802.15.4 networks as a starting point for such a protocol stack. In brief, we conducted the following experiment with a phantom that mimics human tissues. Two devices exchanged IEEE 802.15.4 radio frames in a ping-pong manner on the phantom's fat tissue using Krentz's MAC layer. The data collected from this experiment lends itself to two purposes. First, it can serve to benchmark and tune algorithms for selecting radio channels. Second, it can also serve to benchmark and tune schemes for deriving cryptographic keys from received signal strength indicator (RSSI) readings. We made the data available at https://uppsala.box.com/s/z2a6jpigswpoifd5l73yophokcfwd88b.

This paper presents a low-resolution infrared thermal dataset of people and thermal objects, such as a working laptop, in indoor environments. The dataset was collected by a far infrared thermal camera (32x24 pixels), which can capture the position and shape information of thermal objects without privacy issues that enable trustworthy computer vision applications. The dataset consists of 1770 thermal images with high-quality annotation collected from an indoor room with around 15°C. We implemented a privacy-preserving human detection method and trained a multiple object detection (MOD) model based on the dataset. The human detection method reaches 90.3% accuracy. On the other hand, the MOD model achieved 56.8% mean average precision (mAP). Researchers can implement interesting applications based on our dataset, for example, privacy-preserving people counting systems, occupancy estimation systems for smart buildings, and social distance detectors.

The use of directional antennas for wireless communications brings several benefits, such as increased communication range and reduced interference. One example of directional antennas are electronically switched directional (ESD) antennas that can easily be integrated into Wireless Sensor Networks (WSNs) due to their small size and low cost. However, current literature questions the benefits of using ESD antennas in WSNs due to the increased likelihood of hidden terminals and increased power consumption. This is mainly because earlier studies have used directionality for transmissions but not for reception. In this article, we introduce novel cross-layer optimizations to fully utilize the benefits of using directional antennas. We modify the Medium Access Control (MAC) , routing, and neighbor discovery mechanisms to support directional communication. We focus on convergecast investigating a large number of different network topologies. Our experimental results, both in simulation and with real nodes, show when the traffic is dense, networks with directional antennas can significantly outperform networks with omnidirectional ones in terms of packet delivery rate, energy consumption, and energy per received packet.

The ubiquity of wirelessly connected sensing devices in IoT applications provides the opportunity to enable various types of interaction with our digitally connected environment. Currently, low processing capabilities and high energy costs for communication limit the use of energy-constrained devices for this purpose. In this paper, we address this challenge by exploring the new possibilities highly capable deep neural network classifiers present. To reduce the energy consumption for transferring continuously sampled data, we propose to compress the sensed data and perform classification at the edge. We evaluate several compression methods in the context of a shadow-based hand gesture detection application, where the classification is performed using a convolutional neural network. We show that simple data reduction methods allow us to compress the sensed data into a single IEEE 802.15.4 packet while maintaining a classification accuracy of 93%. We further show the generality of our compression methods in an audio-based interaction scenario.

New battery-free sensor tags that interoperate with unmodified standard IoT devices and protocols can extend a sensor network's capabilities in a scalable and cost-effective manner. The tags achieve battery-free operation through backscatterrelated techniques, while the standard IoT devices avoid additional dedicated infrastructure by providing the unmodulated carrier that tags need to communicate. However, this approach requires coordination between devices transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in the shared spectrum. We present a scheduling mechanism that optimizes the use of carrier generators, minimizing any disruptions to the regular nodes. We employ time slots to coordinate the unmodulated carrier while minimizing latency, energy consumption and overhead radio emissions. We propose an efficient scheduling algorithm that parallelizes communications with battery-free tags when possible and shares carriers among multiple tags concurrently. In our evaluation we demonstrate the feasibility and reliability of our approach in testbed experiments. We find that we can significantly reduce the excess latency and energy consumption caused by the addition of sensor tags when compared to sequential interrogation. We show that the gains tend to improve with the network size and that our solution is close to optimal on average.

New battery-free sensor tags that interoperate with unmodified standard IoT devices can extend a sensor network’s capabilities in a scalable and cost-effective manner. The tags achieve battery-free operation through backscatter-related techniques, while the standard IoT devices can provide the necessary unmodulated carrier, avoiding additional dedicated infrastructure. However, this approach presents multiple challenges: It requires coordination between nodes transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in shared spectrum. We present TagAlong, a medium access mechanism for interoperable sensor tags that, besides coordinating, optimizes the use of carrier generators, minimizing the disruption caused to the operation of the regular nodes. We accomplish this by parallelizing communications with battery-free tags when possible, sharing carriers for multiple tags concurrently and synchronizing communications with tags that share carrier generators. We demonstrate the feasibility of TagAlong in a testbed deployment. In our evaluation we find that it can reduce the duration of the tags’ schedule by 60% while improving the energy and spectrum usage by 30% when compared to sequential interrogation with no difference in reliability.

In the near future more and more people will have multiple implants to handle their diseases. The implants benefit from being connected using in-body sensor networks. We have previously shown that RF communication through human adipose (fat) tissue is feasible. In this poster, we argue why we believe that backscatter communication within this fat channel is possible. As security is of utmost importance for in-body communication, we also discuss how backscatter-based in-body networks can be secured.

Massive deployment of diverse ultra-low power wireless devices in different application areas has given rise to a plethora of heterogeneous architectures and communication protocols. It is challenging to provide convergent access to these miscellaneous collections of communicating devices. In this paper, we propose VGATE, an edge-based virtualized IoT gateway for bringing these devices together in a single framework using SDRs as technology agnostic radioheads. SDR platforms, however, suffer from large unpredictable delays. We design a GNU Radio-based IEEE 802.15.4 experimental setup using LimeSDR, where the data path is time-stamped at various points of interest to get a comprehensive understanding of the characteristics of the delays. Our analysis shows that GNU Radio processing and LimeSDR buffering delays are the major delays. We decrease the LimeSDR buffering delay by decreasing the USB transfer size but show that this comes at the cost of increased processing overhead. We modify the USB transfer packet size to investigate which USB transfer size provides the best balance between buffering delay and processing overhead across two different host computers. Our experiments show that for the best measured configuration the mean and jitter of latency decreases by 37% and 40% respectively for the host computer with higher processing resources. We also show that the throughput is not affected by these modifications.

New battery-free backscatter tags that integrate with unmodified standard IoT devices can extend the latter's sensing capabilities in a scalable and cost effective way. Existing IoT nodes can provide the unmodulated carrier needed by the new nodes, avoiding the need for additional infrastructure. This, however, puts extra energetic demands on constrained IoT nodes while increasing interference and contention in the network. We use a slotted MAC protocol to guarantee synchronization between transmitters, receivers and carrier generators. We then express the slot allocation problem as a Constraint Optimization Problem (COP) that parallelizes interrogations to battery-free tags when they do not collide with each other and reuses carriers for multiple tags looking to minimize the total time and the number of carrier generators needed to interrogate a set of tags. In networks with sufficient battery-free nodes we obtain a 25% reduction in the number of necessary carriers and a 50% decrease in interrogation time in most cases; leading to significant energy savings, reduced collisions and improved latency.

In this paper, we address the support of Time-Slotted Channel Hopping (TSCH) on multiple frequency bands within a single TSCH network. This allows to simultaneously run applications with different requirements on link characteristics and to increase resilience against interference. To this end, we first enable sub-GHz communication in TSCH, which has been primarily defined for the 2.4 GHz band. Thereafter, we propose two designs to support multiple physical layers in TSCH on the same nodes. Our experimental evaluation shows that TSCH is applicable in a wide range of data rates between 1.2 kbps and 1000 kbps. We find that data rates of 50 kbps and below have a long communication range and a nearly perfect link symmetry, but also have a 20x higher channel utilization compared to higher data rates, increasing the risk of collisions. Using these findings, we show the advantages of the multi-band support on the example of synchronization accuracy when exchanging TSCH beacons with a low data rate and application data at a high data rate.

Due to extremely low power consumption, backscatter has become the transmission mechanism of choice for battery-free devices that operate on harvested energy. However, a limitation of recent backscatter systems is that the communication range scales with the strength of the ambient carrier signal~(ACS). This means that to achieve a long-range, a backscatter tag needs to reflect a strong ACS, which in practice means that it needs to be close to an ACS emitter. We present TunnelScatter, a mechanism that overcomes this limitation. TunnelScatter uses a tunnel diode-based radio frequency oscillator to enable transmissions when there is no ACS, and the same oscillator as a reflection amplifier to support backscatter transmissions when the ACS is weak. Our results show that even without an ACS, TunnelScatter is able to transmit through several walls covering a distance of 18m while consuming a peak biasing power of \SI57 \micro\watt. Based on TunnelScatter, we design battery-free sensor tags, called TunnelTags, that can sense physical phenomena and transmit them using the TunnelScatter mechanism.

Wearable devices, such as implantable medical devices and smart wearables, are becoming increasingly popular with applications that vary from casual activity monitoring to critical medical uses. Unsurprisingly, numerous security vulnerabilities have been found in this class of devices. Yet, research on physical measurement-based authentication and key distribution assumes that body-worn devices are benign and uncompromised. Tiek is a novel authentication and key distribution protocol which addresses this issue. We utilize two sources of randomness to perform device authentication and key distribution simultaneously but through separate means. This creates a two-tier authorization scheme that enables devices to join the network while protecting them from each other. We describe Tiek and analyze its security.

Energy efficient sensing is one of the main objectives in the design of networked embedded monitoring systems. However, existing approaches such as duty cycling and ambient energy harvesting face challenges in railway bridge health monitoring applications due to the unpredictability of train passages and insufficient ambient energy around bridges. This paper presents eco-friendly vibration (ECO VIBE), an on-demand sensing system that automatically turns on itself when a train passes on the bridge and adaptively powers itself off after finishing all tasks. After that, it goes into an inactive state with near-zero power dissipation. ECO VIBE achieves these by: first, a novel, fully passive event detection circuit to continuously detect passing trains without consuming any energy. Second, combining train-induced vibration energy harvesting with a transistor-based load switch, a tiny amount of energy is sufficient to keep ECO VIBE active for a long time. Third, a passive adaptive off control circuit is introduced to quickly switch off ECO VIBE. Also this circuit does not consume any energy during inactivity periods. We present the prototype implementation of the proposed system using commercially available components and evaluate its performance in real-world scenarios. Our results show that ECO VIBE is effective in railway bridge health monitoring applications.

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.

We introduce BackVLC, a system to connect battery-free IoT tags using LED bulbs. We make use of bulbs beyond illumination. We send data to the tags with visible light communication (VLC), and retrofit the bulbs with simple circuitry to enable the uplink channel current VLC systems lack, using Radio Frequency (RF) backscatter communication from the tags. Tags process and send data, harvesting energy from light and radio. We present our system design and implementation, evaluate it in preliminary simulation studies and experiments, and discuss the research challenges to develop a complete network architecture. BackVLC is the first work that combines VLC with RF backscatter.

We present the architecture for an 802.15.4 receiver that enables battery-free operation. To reach micro-power consumption, the architecture diverges from that of commodity receivers in the following ways: First, similar to backscatter transmitters, it offloads the power-hungry local oscillator to an external device. Second, we avoid the energy cost of demodulating a phase-modulated signal by treating 802.15.4 as a frequency-modulated one, allowing us to receive with a simple passive detector and an energy-efficient thresholding circuit. We demonstrate an off-the-shelf prototype of our receiver receives 802.15.4 from a distance of 470 cm with the carrier generator 30 cm away. This range is sufficient to integrate with deployed wireless sensor networks (WSNs). We demonstrate this integration by pairing our receiver with a 802.15.4 backscatter transmitter and integrating it with unmodified commodity sensor nodes running the TSCH protocol.

We present the architecture of an 802.15.4 receiver that, for the first time, operates at a few hundred microwatts, enabling new battery-free applications. To reach the required micro-power consumption, the architecture diverges from that of commodity receivers in two important ways. First, it offloads the power-hungry local oscillator to an external device, much like backscatter transmitters do. Second, we avoid the energy cost of demodulating a phase-modulated signal by treating 802.15.4 as a frequency-modulated one, which allows us to receive with a simple passive detector and an energy-efficient thresholding circuit. We describe a prototype that can receive 802.15.4 frames with a power consumption of 361 μW. Our receiver prototype achieves sufficient communication range to integrate with deployed wireless sensor networks (WSNs).We illustrate this integration by pairing the prototype with an 802.15.4 backscatter transmitter and integrating it with unmodified 802.15.4 sensor nodes running the TSCH and Glossy protocols.

Security mechanisms for battery-free devices have to operate under severe energy constraints relying on harvested energy. This is challenging, as the energy harvested from the ambient environment is usually scarce, intermittent and unpredictable. One of the challenges for developing security mechanisms for such settings is the lack of hardware platforms that recreate energy harvesting conditions experienced on a battery-free sensor node. In this demonstration, we present an energy harvesting security (EHS) platform that enables the development of security algorithms for battery-free sensors. Our results demonstrate that our platform is able to harvest sufficient energy from indoor lighting to support several widely used cryptography algorithms.

Radio Tomographic Imaging (RTI) enables novel radio frequency (RF) sensing applications such as intrusion detection systems by observing variations in radio links caused by human actions. RTI applications are, however, severely limited by the requirement to retrofit existing infrastructure with energy-expensive sensors. In this demonstration, we present our ongoing efforts to develop the first battery-free RTI system that operates on minuscule amounts of energy harvested from the ambient environment. Our system eliminates the energy-expensive components employed on state-of-the-art RTI systems achieving two orders of magnitude lower power consumption. Battery-free operation enables a sustainable deployment, as RTI sensors could be deployed for long periods of time with little maintenance effort. Our demonstration showcases an intrusion detection scenario enabled by our system.

A significant number of human and elephant lives have been lost due to the human-elephant conflict in Sri Lanka. To save lives of humans and elephants, it is therefore important to minimize encounters between them. In this paper, we present Eloc, a system that detects the presence of elephants using their infrasonic emissions near human habitats and then localize their positions. The high cost of infrasonic detectors is an important challenge to the real-world deployment of such localization systems, in particular in developing countries where the human-elephant conflict occurs. In order to address this problem, we design a low cost infrasonic detector that can be easily built using commodity off-the-shelf hardware. We present promising results in localizing an artificial infrasonic source and real-world experiments that suggest that we can localize free ranging elephants in the wild using this low cost infrasonic detector with an accuracy of around 10 m at distances of several hundred meters.

Glossy is a flooding-based communication primitive for low-power wireless networks that leverages constructive interference to achieve high reliability. The Low-power Wireless Bus (LWB) uses Glossy to abstract an entire wireless network into a shared bus like topology. As Glossy is not designed as a secure communication protocol, Glossy and hence LWB are vulnerable to unauthorised eavesdropping and packet injection attacks. In this paper, we propose several security mechanisms to protect Glossy and LWB communication and evaluate their effectiveness in real-world settings. The evaluation of the proposed security mechanisms shows that we can confine the effect of the packet injection attacks on Glossy networks into single hop nodes from the attacker.

We present our efforts to design the first Visible Light Sensing (VLS) system that consumes only tens of μ Ws of power to sense and communicate. Our system requires no modification to the existing light infrastructure and uses unmodulated ambient light as sensing medium. We achieve this by designing a sensing mechanism that uses solar cells to achieve sub-μWs of power consumption. Further, we devise an ultra-low power backscatter based transmission mechanism we call Scatterlight that transmits digital readings without incurring the processing and computation overhead of existing sensors. Based on these principles we build a preliminary prototype. Our initial results demonstrate its ability to sense and communicate three hand gestures at 20 μWs of power.

The human-elephant conflict is causing significant damages to the life of human and elephants in Sri Lanka. Minimizing encounters between humans and elephants is hence crucial in alleviating the human-elephant conflict. We have earlier introduced Eloc, a cost-effective localization system that is based on infrasonic emissions from wild elephants. One of the remaining challenges is a resource-efficient method to detect the elephants' infrasonic emissions. We present our efforts on devising a support vector machine (SVM) that is able to detect elephant rumbles on Eloc nodes.

The sensing capabilities of most sensor networks are fixed at the time of deployment. Adding new sensing capabilities to such networks is a costly and cumbersome process. We present Passive Sensor Tags, battery-free sensing devices that could be used to extend the sensing capabilities of an existing network. Sensor tags feature our new 802.15.4 receiver design which is suitable for micro-power operation, making battery-free tags possible. Because our tags can both transmit and receive 802.15.4 frames there is no need for any modification to the deployed hardware. We present preliminary measurements of transmission and reception range.

There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LOREA an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LOREA costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LOREA's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1 m distance from a 28 dBm carrier source while consuming 70 μW at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75 m, a significant improvement over existing RFID readers.

We present LOREA an architecture consisting of a backscatter tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing backscatter systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions. Second, by mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, by decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. LOREA's communication range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4km when the tag is located 1 m from the carrier source while consuming 70 μWs at the backscatter tag. We present various ultra-low power and long-range features of the LoRea architecture.

Visible Light Communication (VLC) is an emerging communication channel serving as a complement to traditional wireless communication. Visible light has many advantages over other communication channels like its inability to penetrate opaque objects. Securely sharing secret keys is a known problem in computer security. Sharing security keys using Radio Frequency (RF) communication is prone to sniffing attacks. In this paper, we introduce a system called BouKey which uses visible light to share keys. This makes BouKey immune to through the wall sniffing attacks. BouKey divides the key and shares it using multiple transmission bulbs allowing the key to be received in a specific area only. Our initial results show that the characteristics and height of the receiver, as well as the transmission angle play a key role in determining the shape and size of this area.

Most existing indoor localization systems are battery-powered and use the changes in Radio Frequency (RF) signals to localize objects. In this paper, we present LocaLight: a battery-free indoor localization system that localizes objects using visible light by tracking the shadow they cast. By sensing a drop in the intensity of ambient light caused by the presence of a shadow, LocaLight localizes the object. Since the position of the shadow can be predicted, it is possible to localize the object in a sensitive area by carefully positioning the light sensors and the overhead lights. Our initial results suggest that LocaLight achieves an accuracy comparable to many of the state-of-the art solutions that use RF.

The sensing capabilities of an Internet-of-Things (IoT) network are usually fixed at deployment. Adding new sensing modalities is a cumbersome process because it requires altering the deployed hardware. We introduce passive sensor tags that allow to easily and seamlessly add new sensors to existing IoT deployments without requiring hardware modifications or additional energy sources. Passive sensor tags employ backscatter communication to generate transmissions that can be decoded by the radio transceivers present in today's IoT devices. Furthermore, unlike recent works, our approach does not require dedicated infrastructure to generate the unmodulated carrier used for backscatter communication.

Visual codes are used to embed digital data in physical objects, or they are shown in video sequences to transfer data over screen/camera links. Existing codes either carry limited data to make them robust against a range of channel conditions (e.g., low camera quality or long distances), or they support a high data capacity but only work over a narrow range of channel conditions. We present FOCUS, a new code design that does not require this explicit trade-off between code capacity and the reader's channel quality. Instead, FOCUS builds on concepts from OFDM to encode data at different levels of spatial detail. This enables each reader to decode as much data from a code as its channel quality allows. We build a prototype of FOCUS devices and evaluate it experimentally. Our results show that FOCUS gracefully adapts to the reader's channel, and that it provides a significant performance improvement over recently proposed designs, including Strata and PixNet.

New Internet of Things (IoT) technologies such as Long Range (LoRa) are emerging which enable power efficient wireless communication over very long distances. Devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. Given the fact that a wide area is covered and that all devices communicate directly to a few sink nodes a large number of nodes have to share the communication medium. LoRa provides for this reason a range of communication options (centre frequency, spreading factor, bandwidth, coding rates) from which a transmitter can choose. Many combination settings are orthogonal and provide simultaneous collision free communications. Nevertheless, there is a limit regarding the number of transmitters a LoRa system can support. In this paper we investigate the capacity limits of LoRa networks. Using experiments we develop models describing LoRa communication behaviour. We use these models to parameterise a LoRa simulation to study scalability. Our experiments show that a typical smart city deployment can support 120 nodes per 3.8 ha, which is not sufficient for future IoT deployments. LoRa networks can scale quite well, however, if they use dynamic communication parameter selection and/or multiple sinks.

Glossy networks make use of concurrent ransmissions to achieve rapid network flooding in wireless networks with high reliability. They are robust against jamming and header injection attacks. We find that Glossy floods can be hijacked by a packet injection attacker to penetrate into the network and cause severe loss. We demonstrate the design of such an attacker by evaluating its effectiveness in a 30-node testbed.

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.

Abstract Comprehensive security mechanisms are required for a successful implementation of the Internet of Things (IoT). Existing solutions focus mainly on securing the communication links between Internet hosts and IoT devices. However, as most IoT devices nowadays provide vast amounts of flash storage space, it is as well required to consider storage security within a comprehensive security framework. Instead of developing independent security solutions for storage and communication, we propose Fusion, a framework that provides coalesced confidential storage and communication. Fusion uses existing secure communication protocols for the IoT such as Internet protocol security (IPsec) and datagram transport layer security (DTLS) and re‐uses the defined communication security mechanisms within the storage component. Thus, trusted mechanisms developed for communication security are extended into the storage space. Notably, this mechanism allows us to transmit requested data directly from the file system without decrypting read data blocks and then re‐encrypting these for transmission. Thus, Fusion provides benefits in terms of processing speed and energy efficiency, which are important aspects for resource‐constrained IoT devices. This paper describes the Fusion architecture and its instantiation for IPsec‐based and DTLS‐based systems. We describe Fusion's implementation and evaluate its storage overheads, communication performance, and energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.

Wireless Sensor Networks performing low-power data collection often suffer from uneven load distribution among nodes. Nodes close to the network root typically face a higher load, see their battery deplete first, and become prematurely unable to operate (both sensing and relaying other nodes' data). We argue that opportunistic routing, by making forwarding decision on a per-packet basis and at the receiver rather than the sender, has the potential to better balance the load across nodes. We extend ORPL, an opportunistic version of the standard routing protocol RPL, with support for load-balancing. In our protocol, ORPL-LB, nodes continuously adapt their wake-up interval in order to adjust their availability and attain a deployment-specific target duty cycle. We implement our protocol in Contikiand present our experimental validation in Indriya, a 93-nodestestbed. Our results show that ORPL-LB reduces significantly(by approximately 40%) the worst node's duty cycle, with little or no impact on packet delivery ratio and latency.

Range-based IEEE 802.15.4 localization systems currently require relatively high anchor density for indoor deployments. It can therefore be beneficial to use external sources of transmission as additional anchors. We present methods for using WiFi beacons to improve localization accuracy of a range-based IEEE 802.15.4 localization system in cases where only two IEEE 802.15.4 anchor nodes are available. We do this by identifying WiFi beacons from RSSI traces that we dynamically sample online, and applying fingerprinting and range-based methods using the RSSI values of the identified beacons. However, because the data of the WiFi traffic is not decodable by the IEEE 802.15.4 devices, these RSSI measurements lack identifiers that can associate them to specific WiFi Access Points (APs). Therefore, novel methods are required for both fingerprinting and range-based approaches to allow for these additional WiFi APs to be used as anchors. We show by using real-world measurements that our beacon identification method gives a false-positive rate of only 3%, and that if the range measurements to the IEEE 802.15.4 anchors are relatively accurate, with a standard deviation of 1 and 3 m, a localization accuracy improvement of 47% and 24% can be gained, respectively.

Radio tomographic imaging (RTI) enables device free localisation of people and objects in many challenging environments and situations. Its basic principle is to detect the changes in the statistics of radio signals due to the radio link obstruction by people or objects. However, the localisation accuracy of RTI suffers from complicated multipath propagation behaviours in radio links. We propose to use inexpensive and energy efficient electronically switched directional (ESD) antennas to improve the quality of radio link behaviour observations, and therefore, the localisation accuracy of RTI. We implement a directional RTI (dRTI) system to understand how directional antennas can be used to improve RTI localisation accuracy. We also study the impact of the choice of antenna directions on the localisation accuracy of dRTI and propose methods to effectively choose informative antenna directions to improve localisation accuracy while reducing overhead. Furthermore, we analyse radio link obstruction performance in both theory and simulation, as well as false positives and false negatives of the obstruction measurements to show the superiority of the directional communication for RTI. We evaluate the performance of dRTI in diverse indoor environments and show that dRTI significantly outperforms the existing RTI localisation methods based on omni-directional antennas.

Cyber-physical systems consist of several wirelessly connected components such as sensors that monitor physical phenomena, computational entities that make decisions based on sensed information and actuators that interact with physical processes. Connecting cyber-physical systems to the Internet using IP protocols increases interoperability by avoiding the need for protocol translation gateways. Unfortunately, in this context TCP has been disregarded since it is known to perform poorly in wireless scenarios as it treats packet loss as an indicator for network congestion rather than poor link quality. In this paper, we use the Low-power Wireless Bus (LWB) as a link layer for TCP/IP, taking advantage of its reliability and its routing-free communication. We design a system that integrates LWB with a low-power IP stack and includes TCP-aware schedulers for LWB. We evaluate our system with experiments on real hardware using uIP, a popular embedded Internet protocol stack. Our results demonstrate high TCP throughput in mobile and static scenarios and, furthermore, show that mobility does not decrease TCP performance.

We advocate the use of property-based testing in the area of sensor networks and present a framework to apply this testing methodology. Our framework provides an expressive high-level language to specify a wide range of properties, starting from properties of individual functions to network-global properties, and infrastructure to automatically test these properties in Cooja, the network simulator of the Contiki operating system. We demonstrate the ease of use and effectiveness of our framework by two case studies. In the first, we test whether the energy consumption of the radio duty-cycle protocol X-MAC is within some specific bound. Property-based testing finds minimal network configurations where a small number of nodes violate the property. Property-based testing also reveals that the same property is not violated when ContikiMAC is used instead, but finds cases where ContikiMAC has higher energy consumption than X-MAC. In the second case study, we test the C API of CONTIKI's TCP socket library and find bugs in its event system that would be very hard to detect with other methods.

Visual codes, such as QR codes, offer a low-cost alternative to RF technology when digital data needs to be embedded in objects in the physical world. However, in order to support receivers with a poor visual channel, e.g. low-resolution cameras, most visual codes are designed for low data capacity and short reading distances. We present our work on Focus, a visual code that avoids earlier work's explicit trade-off between code capacity and channel quality. Rather than encoding the payload directly into individual pixels, Focus encodes the payload over a range of spatial frequencies. As a result, even a receiver with a very poor visual channel (e.g., a low-resolution camera, or a camera experiencing motion blur) can still partly decode a Focus code, because the code's low-frequency components are robust to common channel impairments. A receiver with a good channel can decode all data from the same code.

We present DPT: a wireless sensor network protocol for bulk traffic that uniquely leverages electronically switchable directional (ESD) antennas. Bulk traffic is found in several scenarios and supporting protocols based on standard antenna technology abound. ESD antennas may improve performance in these scenarios; for example, by reducing channel contention as the antenna can steer the radiated energy only towards the intended receivers, and by extending the communication range at no additional energy cost. The corresponding protocol support, however, is largely missing. DPT addresses precisely this issue. First, while the network is quiescent, we collect link metrics across all possible antenna configurations. We use this information to formulate a constraint satisfaction problem (CSP) that allows us to find two multi-hop disjoint paths connecting source and sink, along with the corresponding antenna configurations. Domain-specific heuristics we conceive ameliorate the processing demands in solving the CSP, improving scalability. Second, the routing configuration we obtain is injected back into the network. During the actual bulk transfer, the source funnels data through the two paths by quickly alternating between them. Packet forwarding occurs deterministically at every hop. This allows the source to implicitly "clock" the entire pipeline, sparing the need of proactively synchronizing the transmissions across the two paths. Our results, obtained in a real testbed using 802.15.4-compliant radios and custom ESD antennas we built, indicate that DPT approaches the maximum throughput supported by the link layer, peaking at 214 kbit/s in the settings we test.

Wireless sensor networks are often deployed indoors where artificial lighting is present. Indoor lighting is increasingly being composed of Light Emitting Diodes (LEDs) that offer the ability to precisely control the intensity and the frequency of the light carrier. This can be used to coordinate wireless sensor networks (WSN). The periodic variations in the light intensity can synchronise the clocks on the sensor nodes, while the ability to modulate the light carrier enables the transmission of control information like channel assignment or transmission schedules.We present Guidelight, a simple mechanism that uses controlled fluctuations in the light intensity to coordinate sensor nodes. Guidelight can wake-up or time synchronise sensor nodes or even send small bits of control information to them. All of these have separate dedicated solutions in WSN. Guidelight aims to provide a single solution to all these problems. Our initial experiments demonstrate the ability of Guidelight to trigger sensor nodes. We demonstrate Guidelight is able to trigger sensor nodes selectively at a mean error of 21 μ s.

We study the effect of interference on localization algorithms through the study of the interference effect on signal features that are used for localization. Particularly, the effect of interference on packet-based Received Signal Strength Indicator (RSSI), reported by IEEE 802.11 and IEEE 802.15.4 technologies, and on Time of Flight (ToF), reported by IEEE 802.15.4 technology, is studied using both theoretical discussions and experimental verifications. As for the RSSI values, using an information theoretic formulation, we distinguish three operational regimes and we show that the RSSI values, in dBm, remain unchanged in the noise-limited regime, increase almost linearly with interference power in dBm in the interference- limited regime and cannot be obtained due to packet loss in the collision regime. The maximum observable RSSI variation is dependent on the transmission rate and Signal to Noise Ratio (SNR). We also show that ToF is, interestingly, decreased under interference which is caused in the symbol synchronization procedure at the receiver. After providing the experimental results, we discuss how the localization algorithms are affected by interference.

The past couple of years have seen a heightened interest in the Internet of Things (IoT), transcending industry, academia and government. As with new ideas that hold immense potential, the optimism of IoT has also exaggerated the underlying technologies well before they can mature into a sustainable ecosystem. While 6LoWPAN has emerged as a disruptive technology that brings IP capability to networks of resource constrained devices, a suitable radio technology for this device class is still debatable. In the recent past, Bluetooth Low Energy (LE) — a subset of the Bluetooth v4.0 stack — has surfaced as an appealing alternative that provides a low-power and loosely coupled mechanism for sensor data collection with ubiquitous units (e.g., smartphones and tablets). When Bluetooth 4.0 was first released, it was not targeted for IP-connected devices but for communication between two neighboring peers. However, the latest release of Bluetooth 4.2 offers features that makes Bluetooth LE a competitive candidate among the available low-power communication technologies in the IoT space. In this paper, we discuss the novel features of Bluetooth LE and its applicability in 6LoWPAN networks. We also highlight important research questions and pointers for potential improvement for its greater impact.

Lifetime estimation in Wireless Sensor Networks (WSN) is crucial to ensure that the network will last long enough (low maintenance cost) while not being over-dimensioned (low initial cost). Existing solutions have at least one of the two following limitations: (1) they are based on theoretical models or high-level protocol implementations, overlooking low-level (e.g., hardware, driver, etc.) constraints which we find have a significant impact on lifetime, and (2) they use an ideal battery model which over-estimates lifetime due to its constant voltage and its inability to model the non-linear properties of real batteries. We introduce a method for WSN lifetime estimation that operates on compiled firmware images and models the complex behavior of batteries. We use the MSPSim/Cooja node emulator and network simulator to run the application in a cycle-accurate manner and log all component states. We then feed the log into our lifetime estimation framework, which models the nodes and their batteries based on both technical and experimental specifications. In a case study of a Contiki RPL/6LoWPAN application, we identify and resolve several low-level implementation issues, thereby increasing the predicted network lifetime from 134 to 484 days. We compare our battery model to the ideal battery model and to the lifetime estimation based on the radio duty cycle, and find that there is an average over-estimation of 36% and 76% respectively.

Exploiting multiple radio channels for communication has been long known as a practical way to mitigate interference in wireless settings. In Wireless Sensor Networks, however, multi-channel solutions have not reached their full potential: the MAC layers included in TinyOS or the Contiki OS for example are mostly single-channel. The literature offers a number of interesting solutions, but experimental results were often too few to build confidence. We propose a practical extension of low-power listening, MiCMAC, that performs channel hopping, operates in a distributed way, and is independent of upper layers of the protocol stack. The above properties make it easy to deploy in a variety of scenarios, without any extra configuration/scheduling/channel selection hassle. We implement our solution in Contiki and evaluate it in a 97-node~testbed while running a complete, out-of-the-box low-power IPv6 communication stack (UDP/RPL/6LoWPAN). Our experimental results demonstrate increased resilience to emulated WiFi interference (e.g., data yield kept above 90% when Contiki MAC drops in the 40% range). In noiseless environments, MiCMAC keeps the overhead low in comparison to Contiki MAC, achieving performance as high as 99% data yield along with sub-percent duty cycle and sub-second latency for a 1-minute inter-packet interval data collection.

IP technology for resource-constrained devices enables transparent end-to-end connections between a vast variety of devices and services in the Internet of Things (IoT). To protect these connections, several variants of traditional IP security protocols have recently been proposed for standardization, most notably the DTLS protocol. In this paper, we identify significant resource requirements for the DTLS handshake when employing public-key cryptography for peer authentication and key agreement purposes. These overheads particularly hamper secure communication for memory-constrained devices. To alleviate these limitations, we propose a delegation architecture that offloads the expensive DTLS connection establishment to a delegation server. By handing over the established security context to the constrained device, our delegation architecture significantly reduces the resource requirements of DTLS-protected communication for constrained devices. Additionally, our delegation architecture naturally provides authorization functionality when leveraging the central role of the delegation server in the initial connection establishment. Hence, in this paper, we present a comprehensive, yet compact solution for authentication, authorization, and secure data transmission in the IP-based IoT. The evaluation results show that compared to a public-key-based DTLS handshake our delegation architecture reduces the memory overhead by 64 %, computations by 97 %, network transmissions by 68 %.

ABSTRACT The future Internet is an IPv6 network interconnecting traditional computers and a large number of smart objects. This Internet of Things (IoT) will be the foundation of many services and our daily life will depend on its availability and reliable operation. Therefore, among many other issues, the challenge of implementing secure communication in the IoT must be addressed. In the traditional Internet, IPsec is the established and tested way of securing networks. It is therefore reasonable to explore the option of using IPsec as a security mechanism for the IoT. Smart objects are generally added to the Internet using IPv6 over Low‐power Wireless Personal Area Networks (6LoWPAN), which defines IP communication for resource‐constrained networks. Thus, to provide security for the IoT based on the trusted and tested IPsec mechanism, it is necessary to define an IPsec extension of 6LoWPAN. In this paper, we present such a 6LoWPAN/IPsec extension and show the viability of this approach. We describe our 6LoWPAN/IPsec implementation, which we evaluate and compare with our implementation of IEEE 802.15.4 link‐layer security. We also show that it is possible to reuse crypto hardware within existing IEEE 802.15.4 transceivers for 6LoWPAN/IPsec. The evaluation results show that IPsec is a feasible option for securing the IoT in terms of packet size, energy consumption, memory usage, and processing time. Furthermore, we demonstrate that in contrast to common belief, IPsec scales better than link‐layer security as the data size and the number of hops grow, resulting in time and energy savings. Copyright © 2012 John Wiley & Sons, Ltd.

Energy is one of the most important resources in wireless sensor networks (WSN). Energy directly translates to lifetime which is an important ingredient of performance control in WSNs. We use an idealized mathematical model to study the impact of routing on energy consumption. We find explicit bounds on the minimal and maximal energy routings will consume, and use them to bound the lifetime of the network. We demonstrate the practical relevance of our theoretical results by experimenting with two different MAC layers, GinLITE, a MAC protocol explicitly designed for performance control, and ContikiMAC.

Sensor networks that operate in the unlicensed 2.4~GHz frequency band suffer cross-technology radio interference from a variety of devices, e.g., Bluetooth headsets, laptops using WiFi, or microwave ovens. Such interference has been shown to significantly degrade network performance. We present SoNIC, a system that enables resource-limited sensor nodes to detect the type of interference they are exposed to and select an appropriate mitigation strategy. The key insight underlying SoNIC is that different interferers disrupt individual 802.15.4 packets in characteristic ways that can be detected by sensor nodes. In contrast to existing approaches to interference detection, SoNIC does not rely on active spectrum sampling or additional hardware, making it lightweight and energy-efficient.

In this paper we present a new kind of Denial-of-Service attack against the PHY layer of low power wireless sensor networks. Overcoming the very limited range of jamming-based attacks, this attack can penetrate deep into a target network with high power efficiency. We term this the Droplet attack, as it attains enormous disruption by dropping small, payload-less frame headers to its victim's radio receiver, depriving the latter of bandwidth and sleep time. We demonstrate the Droplet attack's high damage rate to full duty-cycle receivers, and further show that a high frequency version of Droplet can even force nodes running on very low duty-cycle MAC protocols to drop most of their packets.

The future Internet of Things (IoT) may be based on the existing and established Internet Protocol (IP). Many IoT application scenarios will handle sensitive data. However, as security requirements for storage and communication are addressed separately, work such as key management or cryp-tographic processing is duplicated. In this paper we present a framework that allows us to combine secure storage and secure communication in the IP-based IoT. We show how data can be stored securely such that it can be delivered securely upon request without further cryptographic processing. Our prototype implementation shows that combined secure storage and communication can reduce the security-related processing on nodes by up to 71% and energy consumption by up to 32.1%.

We study the benefits of electronically-switched directional (ESD) antennas in wireless sensor networks (WSNs). ESD antennas have proved beneficial in cellular and ad-hoc networks, by increasing the communication range and by alleviating contention in directions other than the destination. The advantages in WSNs are, however, still largely to be quantified. Unlike existing works in this field, we start by characterizing a real-world antenna prototype, and apply this to an existing WSN stack, which we adapt with minimal changes. Our results show that: i) the combination of a low-cost ESD antenna and a mainstream WSN stack already brings significant performance improvements, e.g., nearly halving the radio-on time per delivered packet; ii) the margin of improvement available to alternative clean-slate protocol designs is similarly large and concentrated in the control rather than the data plane; iii) by artificially modifying our antenna's link-layer model, further potential benefits opened by different antenna designs may be available. To the best of our knowledge, this is the first study providing such quantitative insights based on a real ESD antenna prototype and a complete WSN stack.

In wireless networks that operate in those bands where spectrum sharing occurs across a variety of wireless technologies, such as the license-free Industrial Scientific and Medical (ISM) bands, mitigating interference becomes challenging. Addressing interference is an important aspect for the design and development of solutions intended to satisfy the demands of applications requiring QoS guarantees. In this paper, we investigate dynamic radio resource adaptation techniques based on instantaneous spectrum usage. Using a novel metric to quantify the spectrum usage, we address packet size and error correction code overhead optimizations. On one hand, large payloads lead to energy and throughput gains due to the amortization of the transmission overheads, but on the other hand, larger payloads imply larger resource wastage in the event of packet collisions. Using real-world data, we found that payload size in the neighbourhood of 100 bytes leads to near-optimal performance in general in the IEEE 802.15.4 networks. Our data also shows that for very high interference scenarios, erasure codes capable of correcting 10% of the packet payload can provide an equivalent Signal to Interference plus Noise Ratio (SINR) gain of 25 dB with probability greater than 0.6. This is significant for interference management and for increasing spatial re-use by employing lower transmission power. We show that erasure codes drastically improve energy-efficiency and throughput of low-power wireless links. In the heavy interference regime, even though interference doubles the energy-per-usable-bit cost, erasure codes remain cost-effective for very large payload sizes, up-to 1500 bytes. Finally, we discuss interference-dependent dynamic adjustment of the correction capacity of erasure codes.

Routing in battery-operated wireless networks is challenging, posing a tradeoff between energy and latency. Previous work has shown that opportunistic routing can achieve low-latency data collection in duty-cycled networks. However, applications are now considered where nodes are not only periodic data sources, but rather addressable end points generating traffic with arbitrary patterns.

The poor performance of TCP over wireless and lossy links has hindered its use in low power networks. The Low Power Wireless Bus (LWB) offers high reliable and low latency communication in IEEE 802.15.4 networks. In this paper, we argue that TCP can be used to provide reliable connectivity between sensor nodes in an LWB-based sensor network and external Internet hosts.

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.

The global positioning system (GPS) system is a dominant wireless technology that enables reliable location sensing for a diverse range of outdoor mobile applications. Following rising demands for location sensing, low-cost GPS receivers are becoming widely available; but their energy demands are still too high to be useful for many of these applications. For energy efficient GPS sensing, the possibility of offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix is being actively investigated. In an attempt to reduce the energy cost of this data offloading operation, we propose SparseGPS: a lightweight GPS acquisition mechanism based on sparse approximation.

Many sensor network applications generate large amounts of sensed data. These often need to be delivered reliably to the sink node for further processing. In such applications, high communication throughput allows for more data to be sensed. Intra-path interference is a problem in reliable forwarding of data and affects the end-to-end throughput. We show that using antennas that allow directional transmissions and receptions significantly reduces intra-path interference and enables high throughput forwarding of packet bursts over multiple hops using only one wireless channel.

Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

The main task of most deployed wireless sensor networks is data collection. While a number of solutions have been designed for static networks, there are currently no widely used data collection algorithms for mobile sensor networks. In this paper, we concentrate on scenarios where many nodes, both data sources and sinks, move along a certain track in one direction, a scenario that is common in sports events. Rather than designing a new protocol from scratch, we extend an existing data collection protocol with lightweight mechanisms to make it efficient for mobility. Our extensive simulations and results in a test bed that includes mobile robots demonstrate that our solution is able to achieve high packet delivery rates at low energy consumption. For our target scenario, our solution more than doubles packet delivery rates when the network is sparse. Our solution also works well in scenarios with a higher degree of mobility where nodes move according to a more demanding random waypoint model.

Real deployments of the IoT require security. CoAP is being standardized as an application layer protocol for the Internet of Things (IoT). CoAP proposes to use DTLS to provide end-to-end security to protect the IoT. DTLS is a heavyweight protocol and its headers are too long to fit in a single IEEE802.15.4 MTU. 6LoWPAN provides header compression mechanisms to reduce the size of upper layer headers. 6LoWPAN header compression mechanisms can be used to compress the security headers as well. In this paper we propose 6LoWPAN header compression for DTLS. We link our compressed DTLS with the 6LoWPAN standard using standardized mechanisms. We show that our proposed DTLS compression significantly reduces the number of additional security bits. For example, only for the DTLS Record header that is added in every DTLS packet, the number of additional security bits can be reduced by 62%. Our compressed-DTLS is the first lightweight 6LoWPAN extension for DTLS.

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.

Low-power wireless networks must leverage radio duty cycling to reduce energy consumption, but duty cycling drastically increases the risk of radio collisions, resulting in power-expensive retransmissions or data loss. We present Strawman, a contention resolution mechanism designed for low-power duty-cycled networks that experience traffic bursts. Strawman efficiently resolves network contention, mitigates the hidden terminal problem, and has zero overhead unless activated to resolve data collisions. Our testbed experiments show that Strawman instantaneously provides increased network capacity when needed, allocates the available bandwidth evenly among contenders, and increases energy efficiency in multi-hop collection networks compared to the traditionally used random backoff.

We present pTunes, a framework for runtime adaptation of low-power MAC protocol parameters. The MAC operating parameters bear great influence on the system performance, yet their optimal choice is a function of the current network state. Based on application requirements expressed as network lifetime, end-to-end latency, and end-to-end reliability, pTunes automatically determines optimized parameter values to adapt to link, topology, and traffic dynamics. To this end, we introduce a flexible modeling approach, separating protocol-dependent from protocol-independent aspects, which facilitates using pTunes with different MAC protocols, and design an efficient system support that integrates smoothly with the application. To demonstrate its effectiveness, we apply pTunes to X-MAC and LPP. In a 44-node testbed, pTunes achieves up to three-fold lifetime gains over static MAC parameters optimized for peak traffic, the latter being current - and almost unavoidable - practice in real deployments. pTunes promptly reacts to changes in traffic load and link quality, reducing packet loss by 80% during periods of controlled wireless interference. Moreover, pTunes helps the routing protocol recover quickly from critical network changes, reducing packet loss by 70% in a scenario where multiple core routing nodes fail.

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.

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.

Smart antennas are a specific type of directional antenna able to dynamically control the gain as a function of direction. This contrasts with more traditional directional antennas, where the dynamic ability is missing, and with omnidirectional antennas, which are designed to have equal gain in all directions.

The increased industrial interest in wireless sensor networks demands a shift from optimizing protocols for energy-efficient reporting of sporadic events to developing solutions for high-rate real-time data collection and dissemination. We study time-optimal convergecast under the communication constraints of commodity sensor network platforms, and propose a novel convergecast model in which packet copying between the microcontroller and the radio transceiver is separated from packet transmission, thereby improving channel utilization and system throughput. Based on this model, we establish tight lower bound on the number of time slots for convergecast in networks with tree routing topology, and present both centralized and distributed algorithms for generating time-optimal convergecast schedules. Our scheme is also memory-efficient as each node needs to buffer at most one packet at any time. We evaluate our scheme in simulation and on real hardware, and show that our scheme can achieve a throughput of 203 kbit/s (86.4% of the theoretical upper bound) and up to 86.24% improvement compared with traditional TDMA-based convergecast. With optimal routing tree and maximum MAC layer payload, convergecast in a network with 20 sensor nodes can be completed in only 100 ms.

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.

WirelessHART is a secure and reliable communication standard for industrial process automation. The WirelessHART specifications are well organized in all aspects except security: there are no separate specifications of security requirements or features. Rather, security mechanisms are described throughout the documentation. This hinders implementation of the standard and development of applications since it requires profound knowledge of all the core specifications on the part of the developer. In this paper we provide a comprehensive overview of WirelessHART security: we analyze the provided security mechanisms against well known threats in the wireless medium, and propose recommendations to mitigate shortcomings. Furthermore, we elucidate the specifications of the security manager, its placement in the network, and interaction with the network manager.

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.

Power consumption is of utmost concern in sensor networks. Researchers have several ways of measuring the power consumption of a complete sensor network, but they are typically either impractical or inaccurate. To meet the need for practical and scalable measurement of power consumption of sensor networks, we have developed a cycle-accurate simulator, called COOJA/MSPsim, that enables live power estimation of systems running on MSP430 processors. This demonstration shows the ease of use and the power measurement accuracy of COOJA/MSPsim. The demo setup consists of a small sensor network and a laptop. Beside gathering software-based power measurements from the motes, the laptop runs COOJA/MSPsim to simulate the same network.We visualize the power consumption of both the simulated and the real sensor network, and show that the simulator produces matching results.

WirelessHART is the first open standard for wireless sensor networks designed specifically for industrial process automation and control systems. WirelessHART is a secure protocol; however, it relies on a Security Manager for the management of the security keys and the authentication of new devices. The WirelessHART standard does not provide the specification and design of the Security Manager. Also, the security specifications in the standard are not well organized and are dispersed throughout the standard which makes an implementation of the standard more difficult. In this paper we provide the detailed specification and design as well as an implementation of the Security Manager for the WirelessHART standard. We evaluate our security manager against different cryptographic algorithms and measure the latency between the Network Manager and the Security Manager. Our evaluation shows that the proposed security manager meets the WirelessHART requirements. Our analysis shows that the provided Security Manager is capable of securing both the wireless and wired part of the WirelessHART network.

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.

Most of the existing sensor network deployments are convergecast data collection applications that transmit data from multiple sources to a sink. In this paper, we present CoReDac, a self-organizing, collision-free convergecast protocol. In contrast to previous solutions, CoReDac consists not only of a data collection phase but also an efficient and collision-free command phase. This way, the protocol can be used below the application layer for wireless versions of building automation protocols such as BACnet that require both efficient response and command phases. We present experiments that show that CoReDac works as expected and demonstrate its energy-efficiency for low duty cycle command-response applications by comparing it to X-MAC.

Software in deployed sensor networks needs to be updated to introduce new functionality or to fix bugs. Reducing dissemination time is important because the dissemination disturbs the regular operation of the network. We present a method for reducing the dissemination time and energy consumption based on compression of native code modules. Code compression reduces the size of the software update, but the decompression on the sensor nodes requires processing time and energy. We quantify these trade-offs for seven different compression algorithms. Our results show that GZIP has the most favorable trade-offs, saving on average 67% of the dissemination time and 69% of the energy in a multi-hop wireless sensor network.

Building automation systems (BAS) are used to control and improve indoor building climate at reduced costs. By integrating BAS with wireless sensor networks, the need for cabling can be removed, and both installation and operational costs significantly reduced. Furthermore, temporary BAS installations are made possible. By implementing and evaluating the open BAS standard BACnet on resource-constrained sensor nodes we show that integrating existing standard BAS protocols with wireless sensor networks is feasible.

In this paper we present the design and implemen tation of a small-scale marine sensor network. The network monitors the temperature in the Baltic Sea on different heights from the water surface down to the bottom. Unlike many other wireless sensor networks, this network contains both a wired and a wireless part. One of the major challenges is that the network is hard to access after its deployment and hence both hard- and software must be robust and reliable. We also present the design of an advanced buoy system featuring a diving unit that achieves a better vertical resolution and discuss remaining challenges of sensor networking in aquatic environments.

Simulators for wireless sensor networks are a valuable tool for system development. However, current simulators can only simulate a single level of a system at once. This makes system development and evolution difficult since developers cannot use the same simulator for both high-level algorithm development and low-level development such as device-driver implementations. We propose cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels. We present an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system. COOJA allows for simultaneous simulation at the network level, the operating system level, and the machine code instruction set level. With COOJA, we show the feasibility of the cross-level simulation approach

From experience with wireless sensor networks it has become apparent that dynamic reprogramming of the sensor nodes is a useful feature. The resource constraints in terms of energy, memory, and processing power make sensor network reprogramming a challenging task. Many different mechanisms for reprogramming sensor nodes have been developed ranging from full image replacement to virtual machines.We have implemented an in-situ run-time dynamic linker and loader that use the standard ELF object file format. We show that run-time dynamic linking is an effective method for reprogramming even resource constrained wireless sensor nodes. To evaluate our dynamic linking mechanism we have implemented an application-specific virtual machine and a Java virtual machine and compare the energy cost of the different linking and execution models. We measure the energy consumption and execution time overhead on real hardware to quantify the energy costs for dynamic linkin.Our results suggest that while in general the overhead of a virtual machine is high, a combination of native code and virtual machine code provide good energy efficiency. Dynamic run-time linking can be used to update the native code, even in heterogeneous networks.

Event-driven programming is a popular model for writing programs for tiny embedded systems and sensor network nodes. While event-driven programming can keep the memory overhead down, it enforces a state machine programming style which makes many programs difficult to write, maintain, and debug. We present a novel programming abstraction called protothreads that makes it possible to write event-driven programs in a thread-like style, with a memory overhead of only two bytes per protothread. We show that protothreads significantly reduce the complexity of a number of widely used programs previously written with event-driven state machines. For the examined programs the majority of the state machines could be entirely removed. In the other cases the number of states and transitions was drastically decreased. With protothreads the number of lines of code was reduced by one third. The execution time overhead of protothreads is on the order of a few processor cycles.

This paper presents a fuzzy control approach that guarantees absolute delays in Web servers. Previous work has proposed the use of classical PI controllers for delay guarantees. However, a disadvantage of the classical PI controller is that the system model, which is obtained by system identification, mismatches the real system and inevitably degrades the performance of the Web system. In contrast with classical PI controllers, fuzzy controllers are nonlinear and therefore independent of the accurate model of the plant, i.e. the controlled system. Hence, fuzzy controllers seem to be very suitable for Web servers. Our experiments show that fuzzy controllers indeed perform better than PI controllers presented in earlier papers.

Energy conservation plays a crucial in wireless sensor networks since such networks are designed to be placed in hostile and nonaccessible areas. While battery-driven sensors will run out of battery sooner or later, the use of renewable energy sources such as solar power or gravitation may extend the lifetime of a sensor network. We propose to utilize solar power in wireless sensor networks and extend LEACH a well-known cluster-based protocol for sensor networks to become solar-aware. The presented simulation results show that making LEACH solar-aware significantly extends the lifetime of sensor networks.

Wireless sensor networks are composed of large numbers of tiny networked devices that communicate untethered. For large scale networks, it is important to be able to download code into the network dynamically. We present Contiki, a lightweight operating system with support for dynamic loading and replacement of individual programs and services. Contiki is built around an event-driven kernel but provides optional preemptive multithreading that can be applied to individual processes. We show that dynamic loading and unloading is feasible in a resource constrained environment, while keeping the base system lightweight and compact.

Studies of mobile ad-hoc networks are often based on simulation and their underlying, necessarily simplified assumptions of physical reality. In order to analyse the practical problems we built our own hardware and software. The hardware consists of a core Motorola controller and different wired and wireless interfaces like a Bluetooth and a 433/868 MHz RF module. It allows therefore analysing different scenarios: First, the deployment of a pure ad-hoc network using Bluetooth or 433/868 MHz RF modules. Scenarios going beyond this cover the connection of Bluetooth piconets using the complementary RF technology. This overcomes the proximity requirements of Bluetooth scatternets. Finally, hybrid scenarios with some nodes connected to the Internet and providing Web access over a multihop adhoc network can be studied.

Sensor networks are designed especially for deployment in adverse and non-accessible areas without a fixed infrastructure. Therefore, energy conservation plays a crucial role for these networks. We propose to utilize solar power in wireless sensor networks, establishing a topology where, changing over time, some nodes can receive and transmit packets without consuming the limited battery resources. We propose and evaluate two protocols that perform solar-aware routing. The presented simulation results show that both protocols provide significant energy savings when utilizing solar power. The paper shows that incorporating the solar status of nodes in the routing decision is feasible and results in reduced overall battery consumption.

Web servers must be protected from overload since server overload leads to low server throughput and increased response times experienced by the clients. Server overload occurs when one or more server resources are overutilized. We present an adaptive architecture that performs admission control based on the expected resource consumption of requests. By dynamically setting the maximum rate of accepted requests, we avoid overutilization of the critical resources. We also provide mechanisms for service differentiation. We present our admission control architecture and experiments that show that it can sustain low response times and high throughput for premium clients even during high load.