Brano Kusy

Opal: A Multiradio Platform for High Throughput Wireless Sensor Networks

Design of current sensor network platforms has favored low power operation at the cost of communication throughput or range, which severely limits support for real-time monitoring applications with high throughput requirements. This letter presents the design of the versatile Opal platform that couples a Cortex M3 MCU with two IEEE 802.15.4 radios for supporting sensing applications with high transfer rates without sacrificing communication range. We present experiments that evaluate Opals throughput and range when operating with one or two radios, and we compare these results with an Iris-based node and TelosB nodes. We introduce the spatial energy cost metric that measures the energy to transfer one bit of information in a unit area for comparing the performance of the platforms. The results show that Opal operating with dual radios increases the throughput compared to single radio platforms with the same data-rate by a factor of 3.7, without sacrificing communication range. Opal operating with one radio can deliver a 460% increase in throughput over other single radio nodes at reduced range. We also analyze the implications of Opals design for multihop communication, showing that the dual radio architecture removes the bandwidth bottleneck in multihop communications that is inherent to single radio platforms.

Bounded Quadrant System: Error-bounded trajectory compression on the go

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%.

A Novel Framework for Online Amnesic Trajectory Compression in Resource-Constrained Environments

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.

Collaborative localization of mobile users with Bluetooth: caching and synchronisation

Location awareness is a key requirement for many pervasive applications. Collaborative localization can improve accuracy and coverage indoors and improve power consumption by duty-cycling GPS outdoors. We use Bluetooth for collaborative localization of mobile personal devices. Specifically, we embed information in Bluetooth device names to improve latency of information exchange between participating nodes. We identify and demonstrate on real hardware two problems in the Bluetooth stack that negatively impact localization accuracy: a) device name caching that introduces significant device-specific delays in transmitting information between nodes, and b) poor accuracy of time synchronization in modern mobile devices. Our solution is to append additional time information to the device name and track time offsets between nodes. We verify experimentally that this helps to both detect outliers and correct for time-synchronization errors and thus mitigate localization errors.

WETX: A weighted expected transmission routing metric for diversity in wireless sensor networks

The expected number of transmission (ETX) metric represents the link quality for links in Wireless Sensor Networks (WSNs) that can vary for different radios. To adapt to these differences, radio diversity is a recent explored solution for wireless sensor networks. In this paper, we show that in a multi radio environment it is not enough to only choose a next hop based on link qualities as with the popular ETX metric. In such an environment, the cost of transmission and reception is radio specific, which needs to be considered when making a routing decision. Instead, we propose a scheme that explores the diversity in ETX over radios and therefore enables a node to choose an energy efficient link by considering a new metric WETX, for weighted ETX. We show by both analysis and simulation that our proposal can improve the energy consumption in a network and extend the network lifetime with up of 60%.

Learning abstract snippet detectors with Temporal embedding in convolutional neural Networks

The prediction of periodical time-series remains challenging due to various types of scaling, misalignments and distortion effects. Here, we propose a novel model called Temporal embedding-enhanced convolutional neural Network (TeNet) to learn repeatedly-occurring-yet-hidden structural elements in periodical time-series, called abstract snippet detectors, to predict future changes. Our model effectively learns a new feature space for a time-series dataset. In the new feature space, distorted time-series that have implicit similarity but substantial differences in value and sequence to regular patterns are re-aligned to the regular patterns in the dataset, and subsequently contribute to a robust prediction mode. The model is robust to various types of distortions and misalignments and demonstrates strong prediction power for periodical time-series. We conduct extensive experiments and discover that the proposed model shows significant and consistent advantages over existing methods on a variety of data modalities ranging from human mobility to household power consumption records, when evaluated under four metrics. The model is also robust to various factors such as number of samples, variance of data, numerical ranges of data etc. The experiments verify that the intuition behind the model can be generalized to multiple data types and applications and promises significant improvement in prediction performance across the datasets studied.

Long-term energy-neutral operation of solar energy-harvesting sensor nodes under time-varying utility: poster abstract

Sensor networks increasingly rely on harvesting energy from the environment to sense, process, and transmit data. Online energy availability forecasting and energy management are critical to ensure long-term energy-neutral operation of battery-powered energy-harvesting sensor nodes. Existing methods focus on applications with time-invariant utility and custom-tailored hardware platforms, which limits their effectiveness across diverse application domains, different platforms, and in the face of aging hardware components. To address these limitations, we formulate an optimisation problem with respect to time-varying utility under the given hardware constraints. We also present PREACT, an online energy-management algorithm that approximates the optimal solution to the optimisation problem by incorporating long-term energy forecasting.