Electric motors account for a substantial portion of electricity usage in industrial sectors. Significant energy savings can be achieved by using high-efficiency motors and Variable Speed Drives (VSDs). However, to maximize energy efficiency, it is crucial to ensure motors are properly sized, as even VSDs cannot compensate for the inefficiencies of incorrectly sized motors. This paper tackles the issue of motor sizing, which requires a detailed evaluation of operational losses and the selection of appropriate motor specifications. To address this, we introduce a framework that integrates various motor loss prediction models, facilitates comprehensive energy efficiency assessment, and offers recommendations for motor specifications that meet the operational requirements. The framework aims to improve energy efficiency, lower operational costs, and promote sustainable operations in industrial environments.

Failures in safety-critical embedded systems can result in loss of life, environmental damage or significant property damage. Examples include control systems for commercial industries, but also protection devices in residential settings. The reliability of such systems depends on their ability to periodically execute safety-related software tasks without interruption. When updating the firmware of such safety-critical embedded systems, execution is suspended until the update is complete. This suspension, or down-time, needs to be minimized or avoided completely in order to mitigate the resulting safety risks. Here, we present the design and implementation of a secure on-the-fly method for firmware updates that minimizes the down-time of safety-critical embedded systems during firmware updates. Design considerations based on the hardware capabilities of typical micro-controllers are highlighted, a software architecture is detailed, and an experimental evaluation of the down-time and memory requirements demonstrate that the proposed approach facilitates secure on-the-fly firmware updates for general safety-critical embedded applications.

Machine-learning-based approaches have emerged as viable solutions for automatic detection of container-related cyber attacks. Choosing the best anomaly detection algorithms to identify such cyber attacks can be difficult in practice, and it becomes even more difficult for zero-day attacks for which no prior attack data has been labeled. In this paper, we aim to address this issue by adopting an ensemble learning strategy: a combination of different base anomaly detectors built using conventional machine learning algorithms. The learning strategy provides a highly accurate zero-day container attack detection. We first architect a testbed to facilitate data collection and storage, model training and inference. We then perform two case studies of cyber attacks. We show that, for both case studies, despite the fact that individual base detector performance varies greatly between model types and model hyperparameters, the ensemble learning can consistently produce detection results that are close to the best base anomaly detectors. Additionally, we demonstrate that the detection performance of the resulting ensemble models is on average comparable to the best-performing deep learning anomaly detection approaches, but with much higher robustness, shorter training time, and much less training data. This makes the ensemble learning approach very appealing for practical real-time cyber attack detection scenarios with limited training data.

Energy-efficient location tracking with battery-powered devices using energy harvesting necessitates duty-cycling of GPS to prolong the system lifetime. We propose an energy and mobility-aware scheduling framework that adapts to real-world dynamics to achieve optimal long-term tracking performance. To forecast energy, the framework uses an exponentially weighted moving average filter to compute a virtual energy budget for the remainder of the forecast period. The virtual energy budget is then used as input for our proposed information-based GPS sampling approach, which estimates the current tracking error through dead-reckoning and schedules a new GPS sample when the error exceeds a given threshold. In order to improve the long-term tracking performance, the threshold is adapted based on the current energy and movement trends to balance the expected information gain from a new GPS sample with its energy cost. We evaluate our approach on empirical traces from wild flying foxes and compare it to strategies that sample GPS using fixed and adaptive duty cycles and by using dead-reckoning with a fixed threshold. Our analysis shows that the proposed information-based GPS sampling strategy reduces the mean tracking error compared to existing methods and approaches the performance of the optimal offline sampling strategy.

State-of-the-art trajectory compression methods usually involve high space-time complexity or yield unsatisfactory compression rates, leading to rapid exhaustion of memory, computation, storage, and energy resources. Their ability is commonly limited when operating in a resource-constrained environment especially when the data volume (even when compressed) far exceeds the storage limit. Hence, we propose a novel online framework for error-bounded trajectory compression and ageing called the Amnesic Bounded Quadrant System (ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family that includes a normal version (BQS), Fast version (FBQS), and a Progressive version (PBQS). ABQS intelligently manages a given storage and compresses the trajectories with different error tolerances subject to their ages. In the experiments, we conduct comprehensive evaluations for the BQS algorithm family and the ABQS framework. Using empirical GPS traces from flying foxes and cars, and synthetic data from simulation, we demonstrate the effectiveness of the standalone BQS algorithms in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 45 percent). We also show that the operational time of the target resource-constrained hardware platform can be prolonged by up to 41 percent. We then verify that with ABQS, given data volumes that are far greater than storage space, ABQS is able to achieve 15 to 400 times smaller errors than the baselines. We also show that the algorithm is robust to extreme trajectory shapes.

Long-term location tracking, where trajectory compression is commonly used, has gained high interest for many applications in transport, ecology, and wearable computing. However, state-of-the-art compression methods involve high space-time complexity or achieve unsatisfactory compression rate, leading to rapid exhaustion of memory, computation, storage and energy resources. We propose a novel online algorithm for error-bounded trajectory compression called the Bounded Quadrant System (BQS), which compresses trajectories with extremely small costs in space and time using convex-hulls. In this algorithm, we build a virtual coordinate system centered at a start point, and establish a rectangular bounding box as well as two bounding lines in each of its quadrants. In each quadrant, the points to be assessed are bounded by the convex-hull formed by the box and lines. Various compression error-bounds are therefore derived to quickly draw compression decisions without expensive error computations. In addition, we also propose a light version of the BQS version that achieves O(1) complexity in both time and space for processing each point to suit the most constrained computation environments. Furthermore, we briefly demonstrate how this algorithm can be naturally extended to the 3-D case. Using empirical GPS traces from flying foxes, cars and simulation, we demonstrate the effectiveness of our algorithm in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 47%). We then show that with this algorithm, the operational time of the target resource-constrained hardware platform can be prolonged by up to 41%.

Clock synchronization is an enabling service for a wide range of applications and protocols in both wired and wireless networks. We study the implications of clock drift and communication latency on the accuracy of clock synchronization when scaling the network diameter. Starting with a theoretical analysis of synchronization protocols, we prove tight bounds on the synchronization error in a model that assumes independently and randomly distributed communication delays and slowly changing drifts. While this model is more optimistic than traditional worst-case analysis, it much better captures the nature of real-world systems such as wireless networks. The bound on the synchronization accuracy, which is roughly the square root of the network diameter, is achieved by the novel PulseSync protocol. Extensive experiments demonstrate that PulseSync is able to meet the predictions from theory and tightly synchronizes large networks. This contrasts against an exponential growth of the skew incurred by the state-of-the-art protocol for wireless sensor networks. Moreover, PulseSync adapts much faster to network dynamics and changing clock drifts than this protocol.

Wireless sensor network platforms share the wireless communication channels with Wi-Fi and Bluetooth based networks, resulting in heavy use of these bands. As a consequence, platforms in wireless sensor networks need to carefully consider external interference to achieve reliable communication. In this paper, we present an experimental analysis of wireless channels for wireless sensor network operating on dual frequency bands. Specifically, we designed a set of detailed experiments aiming to find out correlation patterns in 900 MHz and 2.4 GHz ISM bands. We conducted our experiments on two testbeds and investigated the band correlation between two distinctive radio transceivers in two different office-space environments. From our data samples, we quantified frequency channel and band correlations in parallel experiments that eliminate artifacts stemming from different external activity on the test site. We found that network formed in 900 MHz band has 15% more connectivity than network formed in 2.4 GHz band, even on radio channels that minimize overlap with Wi-Fi networks.

Researchers have proposed many multi-channel and dual-band communication systems to address the limitations of single-channel hardware and software. The most common dual-band communication for sensor network applications use 900 MHz and 2.4 GHz radios. There are now some testbeds such as Flocklab and Twonet that allow experimentation with dual-radio systems. However, there is no widely accepted efficient and comprehensive mechanism to survey all the channels of the dual band systems during networking experiments. In this work, we evaluate two mechanisms that can survey the RF environment in multiple channels in both the bands concurrently. We implement these two mechanisms on Twonet and evaluate them. We find that the channel scanning methodology is generally sound but sometimes static channel assignment may be attractive due to its simplicity.

Long-term outdoor localisation with battery-powered devices remains an unsolved challenge, mainly due to the high energy consumption of GPS modules. The use of inertial sensors and short-range radio can reduce reliance on GPS to prolong the operational lifetime of tracking devices, but they only provide coarse-grained control over GPS activity. In this paper, we introduce our feature-rich lightweight Camazotz platform as an enabler of Multimodal Activity-based Localisation~(MAL), which detects activities of interest by combining multiple sensor streams for fine-grained control of GPS sampling times. Using the case study of long-term flying fox tracking, we characterise the tracking, connectivity, energy, and activity recognition performance of our module under both static and 3-D mobile scenarios. We use Camazotz to collect empirical flying fox data and illustrate the utility of individual and composite sensor modalities in classifying activity. We evaluate MAL for flying foxes through simulations based on retrospective empirical data. The results show that multimodal activity-based localisation reduces the power consumption over periodic GPS and single sensor-triggered GPS by up to 77% and 14% respectively, and provides a richer event type dissociation for fine-grained control of GPS sampling.

Testbeds are indispensable for debugging and evaluating wireless embedded systems. While existing testbeds provide ample opportunities for realistic, large-scale experiments, they are limited in their ability to closely observe and control the distributed operation of resource-constrained nodes - access to the nodes is restricted to the serial port. This paper presents FlockLab, a testbed that overcomes this limitation by allowing multiple services to run simultaneously and synchronously against all nodes under test in addition to the traditional serial port service: tracing of GPIO pins to record logical events occurring on a node, actuation of GPIO pins to trigger actions on a node, and high-resolution power profiling. FlockLab's accurate timing information in the low microsecond range enables logical events to be correlated with power samples, thus providing a previously unattained level of visibility into the distributed behavior of wireless embedded systems. In this paper, we describe FlockLab's design, benchmark its performance, and demonstrate its capabilities through several real-world test cases.

Limited visibility into the global network state renders testing and debugging sensor network applications a challenging task. Existing debugging methods are often non-intrusive and require modifications of the binary image. Hardware based debugging instrumentation such as JTAG has not been widely used beyond a single node, mainly due to its relatively high cost and lack of software support for distributed debugging. This demonstration presents a novel architecture for distributed debugging of wireless sensor networks using a low-cost extension board to access the on-chip debug module of the node's processor. Connecting several of those debug boards using a backbone network provides distributed control and monitoring of the sensor network in test.

The high popularity of wireless sensor networks has led to novel applications with diverse, and sometimes demanding, data communication requirements, for example, streaming camera images in surveillance applications. In response bulk-data transfer protocols were proposed that provide low latency and high throughput communication over multiple hops. However, due to typical hardware platforms only providing a single radio, which implies that forwarding nodes need to serialize send and receive actions, the maximum end-to-end throughput is limited to 1/2 the radio capacity. To bridge this performance gap we present Fast Forward, a connection-oriented multi-hop bulk-data transfer protocol optimized for dual-radio platforms, data packets are sent across a path of alternating radio and frequency channels to exploit parallel transfers and avoid intra-path interference. We implemented Fast Forward in TinyOS to run on the Opal platform equipped with two IEEE 802.15.4 radios. In this paper we show that, with some minor tweaking of the original protocol stack to streamline internal access to the SPI bus, Fast Forward is capable of operating both radios in parallel so packets can be forwarded at full speed. We have evaluated Fast Forward on a 12-node testbed in an office environment. The sustained throughput peaks around 23.7 kBps, or 76 % of the radio capacity while the best single-radio protocol flattens out at 19 %. When introducing artificial packet loss the built-in link-level acknowledgements ensure that Fast Forward manages to deliver packets with high yield (close to 100 %) at the sink across 11 hops.

We present Twonet, a large-scale sensor network testbed with dual-radio nodes. Twonet has 100 Opal nodes with low-power 32-bit ARM CPU and 2.4 GHz and 900 MHz radios. These nodes are managed by a network of 20 Raspberry Pi nodes at tier 2 and a PC server at tier 1. These nodes together provide a robust testbed for public access. Twonet represents a major addition to the collection of wireless sensor network testbeds that are publicly available. We hope Twonet's availability will foster sensor network research based on a modern 32-bit sensor node architecture and multi-channel wireless networking.

Development of wireless sensor network applications remains a challenge, due to lack of visibility into the global network state. Debugging instrumentation using printf-like instructions affects the execution timing and non-intrusive approaches, such as JTAG, have not been used beyond a single node due to their high cost.

While the sun is important for our health, overexposure to sunlight carries significant health risks ranging from sunburn to skin cancer. Although people know about these risks, sunlight related skin damages have increased over the past decades. We have conducted a survey that sheds light on this phenomenon and suggests that the missing natural sense for UV radiation negatively influences people's sun related behavior. To address this issue, we have implemented Sundroid. Sundroid measures the incident UV radiation using a body-worn sensing unit that communicates wirelessly with the user's smartphone. The phone thereby acts as a user interface to present the measured data in an intuitive manner, and to notify the user once a critical amount of sunlight has been reached. Sundroid can also be applied in other contexts, such as behavioral research or medicine. We show that after calibration, errors are within 5% compared to a high-precision reference signal.

Data gathering is one of the most common applications of wireless sensor networks. Such networks are an extremely useful tool for researchers in various domains since they allow for measurements in unaccessible locations, e.g., on mountains, glaciers or in animal habitats. At the same time, the deployment and maintenance of such sites is time consuming and costly. Network reprogramming protocols allow to distribute code updates over the wireless network, which prolongs the interval between human intervention at the deployment site.

Having access to accurate position information is a key requirement for many wireless sensor network applications. We present SpiderBat, an ultrasound-based ranging platform for wireless sensor networks. It is designed to extend existing node platforms with ultrasound ranging capabilities. SpiderBat features four pairs of independently controllable ultrasound transmitters and receivers, which point towards different directions of the compass. Having multiple ultrasound transmitters and receivers provides an increased spatial coverage. Furthermore, our design allows to determine the angle of arrival of an ultrasound wave, which can be exploited for improved accuracy of the positioning.

We present YETI, an Eclipse plug-in providing support for TinyOS development. YETI provides features well-known from development environments for other languages such as syntax highlighting, code completion and error detection. Furthermore, it includes an additional set of tools which are designed to ease the TinyOS development process for both newcomers and experienced developers. The plugin seamlessly integrates with the existing TinyOS toolchain and provides debugging support on the target platform using a JTAG hardware adapter.

Having access to an accurate time is a vital building block in all networks; in wireless sensor networks even more so, because wireless media access or data fusion may depend on it. Starting out with a novel analysis, we show that orthodox clock synchronization algorithms make fundamental mistakes. The state-of-the-art clock synchronization algorithm FTSP exhibits an error that grows exponentially with the size of the network, for instance. Since the involved parameters are small, the error only becomes visible in midsize networks of about 10--20 nodes. In contrast, we present PulseSync, a new clock synchronization algorithm that is asymptotically optimal. We evaluate PulseSync on a Mica2 testbed, and by simulation on larger networks. On a 20 node network, the prototype implementation of PulseSync outperforms FTSP by a factor of 5. Theory and simulation show that for larger networks, PulseSync offers an accuracy which is several orders of magnitude better than FTSP. To round off the presentation, we investigate several optimization issues, e.g. media access and local skew.