Publications
89 results found
Arteaga JM, Sanchez J, Elsakloul F, et al., 2023, High frequency inductive power transfer through soil for agricultural applications, IEEE Transactions on Power Electronics, Vol: 38, Pages: 13415-13429, ISSN: 0885-8993
This paper presents 13.56 MHz inductive powertransfer (IPT) through soil for sensors in agricultural ap-plications. Two IPT system designs and their prototypes are presented. The first was designed for gathering data and observing the relationship between the performance of the coil driving circuits in response to water content, salinity, organic matter and compaction of the soil. The second prototype was designed as an application demonstrator, featuring IPT to an in-house sensor node enclosure buried 200 mm under the surface of an agricultural field. The results highlight that from the parameters studied, the combination of high salinity and high water content significantly increases the losses of the IPT system.The experiments demonstrate an over 40% rise in the losses from dc source to dc load after a 16% increase in soil water content and high salinity. In the technology demonstrator we mounted an IPT transmitter on a drone to wirelessly power an in-house bank of supercapacitors in the buried sensor-node enclosure. A peak power transfer of 30 W received at over 40% efficiency was achieved from a 22 V power supply on the drone to the energy storage under the ground. The coil separation in these experiments was 250 mm of which 200 mm correspond to the layer of soil. The coupling factor in all the experiments was lower than 5%. This system was trialled in the field for forty days andwireless power was performed five times throughout.
Aloufi R, Haddadi H, Boyle D, 2023, Paralinguistic privacy protection at the edge, ACM Transactions on Privacy and Security, Vol: 26, Pages: 1-27, ISSN: 2471-2566
Voice user interfaces and digital assistants are rapidly entering our lives and becoming singular touch points spanning our devices. These always-on services capture and transmit our audio data to powerful cloud services for further processing and subsequent actions. Our voices and raw audio signals collected through these devices contain a host of sensitive paralinguistic information that is transmitted to service providers regardless of deliberate or false triggers. As our emotional patterns and sensitive attributes like our identity, gender, and well-being are easily inferred using deep acoustic models, we encounter a new generation of privacy risks by using these services. One approach to mitigate the risk of paralinguistic-based privacy breaches is to exploit a combination of cloud-based processing with privacy-preserving, on-device paralinguistic information learning and filtering before transmitting voice data.In this article we introduce EDGY, a configurable, lightweight, disentangled representation learning framework that transforms and filters high-dimensional voice data to identify and contain sensitive attributes at the edge prior to offloading to the cloud. We evaluate EDGY’s on-device performance and explore optimization techniques, including model quantization and knowledge distillation, to enable private, accurate, and efficient representation learning on resource-constrained devices. Our results show that EDGY runs in tens of milliseconds with 0.2% relative improvement in “zero-shot” ABX score or minimal performance penalties of approximately 5.95% word error rate (WER) in learning linguistic representations from raw voice signals, using a CPU and a single-core ARM processor without specialized hardware.
Hong F, Lampret B, Myant C, et al., 2023, 5-axis multi-material 3D printing of curved electrical traces, ADDITIVE MANUFACTURING, Vol: 70, ISSN: 2214-8604
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- Citations: 2
Holmes AS, Yang SKE, Kiziroglou ME, et al., 2022, Miniaturized wet-wet differential pressure sensor, IEEE Sensors Conference, Publisher: IEEE, ISSN: 1930-0395
We report a miniaturized wet-wet differential pressure sensor with applications in pressure and flow sensing in water networks and other harsh environments. The device is similar in concept to a conventional wet-wet differential pressure sensor in that the sensing element is protected from the external environment by oil-filled cavities closed off by corrugated diaphragms. However, with a package envelope of 11.0 x 4.8 x 3.4 mm 3 , corresponding to a volume of only 0.18 cm 3 , the device is considerably smaller than commercially available wet-wet differential pressure sensors. A high degree of miniaturization has been achieved by using micromachining to fabricate the corrugated diaphragms. Preliminary experimental results are presented showing operation of the device as a delta-pressure flow speed sensor in a water flow test rig.
Polonelli T, Magno M, Niculescu V, et al., 2022, An open platform for efficient drone-to-sensor wireless ranging and data harvesting, SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, Vol: 35, ISSN: 2210-5379
Holmes AS, Kiziroglou ME, Yang SKE, et al., 2022, Minimally invasive online water monitor, IEEE Internet of Things Journal, Vol: 9, Pages: 14325-14335, ISSN: 2327-4662
Sensor installation on water infrastructure is challenging due to requirements for service interruption, specialised personnel, regulations and reliability as well as the resultant high costs. Here, a minimally invasive installation method is introduced based on hot-tapping and immersion of a sensor probe. A modular architecture is developed that enables the use of interchangeable multi-sensor probes, non-specialist installation and servicing, low-power operation and configurable sensing and connectivity. A prototype implementation with a temperature, pressure, conductivity and flow multi-sensor probe is presented and tested on an evaluation rig. This paper demonstrates simple installation, reliable and accurate sensing capability as well as remote data acquisition. The demonstrated minimally invasive multi-sensor probes provide an opportunity for the deployment of water quality sensors that typically require immersion such as pH and spectroscopic composition analysis. This design allows dynamic deployment on existing water infrastructure with expandable sensing capability and minimal interruption, which can be key to addressing important sensing parameters such as optimal sensor network density and topology.
Hong F, Tendera L, Myant C, et al., 2022, Vacuum-Formed 3D Printed Electronics: Fabrication of Thin, Rigid and Free-Form Interactive Surfaces, SN Computer Science, Vol: 3, ISSN: 2662-995X
Vacuum-forming is a common manufacturing technique for constructing thin plastic shell products by pressing heated plastic sheets onto a mold using atmospheric pressure. Vacuum-forming is ubiquitous in packaging and casing products in the industry, spanning fast moving consumer goods to connected devices. Integrating advanced functionality, which may include sensing, computation and communication, within thin structures is desirable for various next-generation interactive devices. Hybrid additive manufacturing techniques like thermoforming are becoming popular for prototyping freeform surfaces owing to their design flexibility, speed and cost-effectiveness. This paper presents a new hybrid method for constructing thin, rigid and free-form interconnected surfaces via fused deposition modelling (FDM) 3D printing and vacuum-forming that builds on recent advances in thermoforming circuits. 3D printing the sheet material allows for the embedding of conductive traces within thin layers of the substrate, which can be vacuum-formed but remain conductive and insulated. This is an unexplored fabrication technique within the context of designing and manufacturing connected things. In addition to explaining the method, this paper characterizes the behavior of vacuum-formed 3D printed sheets, analyses the electrical performance of printed traces after vacuum-forming, and showcases a range of sample artefacts constructed using the technique. In addition, the paper describes a new design interface for designing conformal interconnects that allows designers to draw conductive patterns in 3D and export pre-distorted sheet models ready to be printed.
Hong F, Hodges S, Myant C, et al., 2022, Open5x: accessible 5-axis 3D printing and conformal slicing, CHI '22: CHI Conference on Human Factors in Computing Systems, Publisher: ACM
The common layer-by-layer deposition of regular, 3-axis 3D printing simplifies both the fabrication process and the 3D printer’s mechanical design. However, the resulting 3D printed objects have some unfavourable characteristics including visible layers, uneven structural strength and support material. To overcome these, researchers have employed robotic arms and multi-axis CNCs to deposit materials in conformal layers. Conformal deposition improves the quality of the 3D printed parts through support-less printing and curved layer deposition. However, such multi-axis 3D printing is inaccessible to many individuals due to high costs and technical complexities. Furthermore, the limited GUI support for conformal slicers creates an additional barrier for users. To open multi-axis 3D printing up to more makers and researchers, we present a cheap and accessible way to upgrade a regular 3D printer to 5 axes. We have also developed a GUI-based conformal slicer, integrated within a popular CAD package. Together, these deliver an accessible workflow for designing, simulating and creating conformally-printed 3D models.
Zhao Y, Afzal SS, Akbar W, et al., 2022, Towards battery-free machine learning and inference in underwater environments, HotMobile '22: The 23rd International Workshop on Mobile Computing Systems and Applications, Publisher: ACM, Pages: 29-34
This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments? An affirmative answer to this question would have significant implications for a new generation of underwater sensing and monitoring applications for environmental monitoring, scientific exploration, and climate/weather prediction.To answer this question, we explore the feasibility of bridging advances from the past decade in two fields: battery-free networking and low-power machine learning. Our exploration demonstrates that it is indeed possible to enable battery-free inference in underwater environments. We designed a device that can harvest energy from underwater sound, power up an ultra-low-power microcontroller and on-board sensor, perform local inference on sensed measurements using a lightweight Deep Neural Network, and communicate the inference result via backscatter to a receiver. We tested our prototype in an emulated marine bioacoustics application, demonstrating the potential to recognize underwater animal sounds without batteries. Through this exploration, we highlight the challenges and opportunities for making underwater battery-free inference and machine learning ubiquitous.
Aloufi R, Haddadi H, Boyle D, 2021, EDGY: On-device paralinguistic privacy protection, Pages: 3-5
Voice user interfaces and assistants are rapidly entering our lives and becoming singular touchpoints spanning our devices. Raw audio signals collected through these devices contain a host of sensitive paralinguistic information (e.g., emotional patterns) that is transmitted to service providers regardless of deliberate or false triggers. We thus encounter a new generation of privacy risks by using these services. To tackle this issue, we have developed EDGY; a configurable, lightweight, disentangled representation learning framework that transforms and filters high-dimensional voice data to identify and selectively filter sensitive attributes at the edge prior to offloading to the cloud. Our results show that EDGY runs in tens of milliseconds with 0.2% relative improvement in ABX score and minimal performance penalties in learning linguistic representations from raw signals on a CPU and single-core ARM processor with no specialized hardware.
Shaukat-Jali R, Van Zalk N, Boyle DE, 2021, Detecting subclinical social anxiety using physiological data from a wrist-worn wearable: a small-scale feasibility study, JMIR Formative Research, Vol: 5, ISSN: 2561-326X
Background: Subclinical (ie, threshold) social anxiety can greatly affect young people’s lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment, which would be greatly beneficial for persons with social anxiety, society, and health care services. Nevertheless, indicators such as skin temperature measured by wrist-worn sensors have not been used in prior work on physiological social anxiety detection.Objective: This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including heart rate, skin temperature, and electrodermal activity (EDA).Methods: Young adults (N=12) with self-reported subclinical social anxiety (measured using the widely used self-reported version of the Liebowitz Social Anxiety Scale) participated in an impromptu speech task. Physiological data were collected using an E4 Empatica wearable device. Using the preprocessed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbours (KNN) were used to develop models for 3 different contexts. Models were trained to differentiate (1) between baseline and socially anxious states, (2) among baseline, anticipation anxiety, and reactive anxiety states, and (3) social anxiety among individuals with social anxiety of differing severity. The predictive capability of the singular modalities was also explored in each of the 3 supervised learning experiments. The generalizability of the developed models was evaluated using 10-fold cross-validation as a performance index.Results: With modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when differentiating between baseline and socially anxious states. Models
Arteaga JM, O'Keefe J, Boyle DE, et al., 2021, Interrogation and charging of embedded sensors by autonomous vehicles, 2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers), Publisher: IEEE, Pages: 296-299
This paper presents a concept and experimental results for end-to-end energy-autonomous sensor systems using unmanned aerial vehicles (drones) as agents for power delivery to and data gathering from sensing devices. Such systems are particularly useful for delay tolerant monitoring scenarios in which the sensing devices are deployed in remote or harsh conditions, often with sparse connectivity, long life and high reliability requirements. Results presented include miniaturisation of wireless charging hardware for drones of low payload capacity, methods for navigation to and alignment with sensors for efficient power transfer, and some data transfer aspects.
Hong F, Myant C, Boyle D, 2021, Thermoformed Circuit Boards: Fabrication of highly conductive freeform 3D printed circuit boards with heat bending, CHI Conference on Human Factors in Computing Systems, Pages: 1-10
Fabricating 3D printed electronics using desktop printers has become moreaccessible with recent developments in conductive thermoplastic filaments.Because of their high resistance and difficulties in printing traces invertical directions, most applications are restricted to capacitive sensing. Inthis paper, we introduce Thermoformed Circuit Board (TCB), a novel approachthat employs the thermoformability of the 3D printed plastics to constructvarious double-sided, rigid and highly conductive freeform circuit boards thatcan withstand high current applications through copper electroplating. Toillustrate the capability of the TCB, we showcase a range of examples withvarious shapes, electrical characteristics and interaction mechanisms. We alsodemonstrate a new design tool extension to an existing CAD environment thatallows users to parametrically draw the substrate and conductive trace, andexport 3D printable files. TCB is an inexpensive and highly accessiblefabrication technique intended to broaden HCI researcher participation.
Qiuchen Q, Akshayaa P, Boyle D, 2021, Optimal recharge scheduler for drone-to-sensor wireless power transfer, IEEE Access, Vol: 9, Pages: 59301-59312, ISSN: 2169-3536
Wireless recharging by autonomous power delivery vehicles is an attractive maintenance solution for Internet of Things devices. Improving the operating efficiency of power delivery vehicles is challenging due to complex dynamic environments and the need to solve difficult optimization problems to determine the best combination of routes, number of vehicles, and numerous safety thresholds prior to deployment. The optimal recharge scheduling problem considers minimizing discharged energy of drones while maximizing devices’ recharged energy. In this paper, a configurable optimal recharge scheduler is proposed that incorporates several evolutionary and clustering approaches. A modified version of the Black Hole algorithm is presented, which is shown to execute on average 35% faster than the state of the art genetic approach, while delivering comparable performance in simulation across 18 scenarios with varying area and density of sensor nodes deployed under different initialization scenarios.
Aloufi R, Haddadi H, Boyle D, 2021, Configurable privacy-preserving automatic speech recognition, Publisher: arXiv
Voice assistive technologies have given rise to far-reaching privacy andsecurity concerns. In this paper we investigate whether modular automaticspeech recognition (ASR) can improve privacy in voice assistive systems bycombining independently trained separation, recognition, and discretizationmodules to design configurable privacy-preserving ASR systems. We evaluateprivacy concerns and the effects of applying various state-of-the-arttechniques at each stage of the system, and report results using task-specificmetrics (i.e. WER, ABX, and accuracy). We show that overlapping speech inputsto ASR systems present further privacy concerns, and how these may be mitigatedusing speech separation and optimization techniques. Our discretization moduleis shown to minimize paralinguistics privacy leakage from ASR acoustic modelsto levels commensurate with random guessing. We show that voice privacy can beconfigurable, and argue this presents new opportunities for privacy-preservingapplications incorporating ASR.
Chen P-Y, Bhatia L, Kolcun R, et al., 2021, Contact-aware opportunistic data forwarding in disconnected LoRaWAN mobile networks, 40th IEEE International Conference on Distributed Computing Systems, Publisher: IEEE, Pages: 574-583
LoRaWAN is one of the leading Low Power WideArea Network (LPWAN) architectures. It was originally designedfor systems consisting of static sensor or Internet of Things (IoT)devices and static gateways. It was recently updated to introducenew features such as nano-second timestamps which open upapplications to enable LoRaWAN to be adopted for mobile devicetracking and localisation. In such mobile scenarios, devices couldtemporarily lose communication with the gateways because ofinterference from obstacles or deep fading, causing throughputreduction and delays in data transmission. To overcome thisproblem, we propose a new data forwarding scheme. Instead ofholding the data until the next contact with gateways, devices canforward their data to nearby devices that have a higher probabil-ity of being in contact with gateways. We propose a new networkmetric called Real-Time Contact-Aware Expected TransmissionCount (RCA-ETX) to model this contact probability in real-time. Without making any assumption on mobility models, thismetric exploits data transmission delays to model complex devicemobility. We also extend RCA-ETX with a throughput-optimalstochastic backpressure routing scheme and propose Real-TimeOpportunistic Backpressure Collection (ROBC), a protocol tocounter the stochastic behaviours resulting from the dynamicsassociated with mobility. To apply our approaches seamlesslyto LoRaWAN-enabled devices, we further propose two newLaRaWAN classes, namely Modified Class-C and Queue-basedClass-A. Both of them are compatible with LoRaWAN Class-Adevices. Our data-driven experiments, based on the London busnetwork, show that our approaches can reduce data transmissiondelays up to25%and provide a53%throughput improvementin data transfer performance.
Maali E, Boyle D, Haddadi H, 2020, Towards identifying IoT traffic anomalies on the home gateway: Poster abstract, Pages: 735-736
The number of IoT devices continues to grow despite the alarming rate of identification of security and privacy issues. There is widespread concern that development of IoT devices is performed without sufficient attention paid to security and privacy issues. Consequently, networks have a higher probability of incorporating vulnerable IoT devices that may be easy to compromise to launch cyber attacks. Inclusion of IoT devices paves the way for a new category of anomalies to be introduced to networks. Traditional anomaly detection techniques (e.g., semi-supervised and signature-based methods), however, are likely inefficient in detecting IoT-based anomalies. This is because these techniques require static signatures of known attacks, specialized hardware, or full packet inspection. They are also expensive, and may be inaccurate or unscalable. Vulnerable IoT devices can be used to perform destructive attacks or invade privacy. The ability to find anomalies in IoT traffic has the potential to assist with early detection and deployment of countermeasures to thwart such attacks. Thus, new techniques for detecting infected IoT devices are needed to mitigate the associated security and privacy risks. In this research, we investigate the possibility to identify IoT traffic using a combination of behavioural profile, predefined blocklist and device fingerprint. Such a system may be able to detect anomalous and/or malicious devices and/or traffic reliably and quickly. Initial results show that for our implementation of such a system, IoT traffic can be identified using device behaviour profile, fingerprint, and contacted destinations. This work takes the first step towards designing and evaluating iDetector, a framework that can detect anomalous behaviour within IoT networks. In our experiments, iDetector was able to correctly identify 80 - 90% of all captured traffic traversing a home gateway.
Aloufi R, Haddadi H, Boyle D, 2020, Privacy-preserving Voice Analysis via Disentangled Representations, CCSW 2020 - Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop, Pages: 1-14
Voice User Interfaces (VUIs) are increasingly popular and built into smartphones, home assistants, and Internet of Things (IoT) devices. Despite offering an always-on convenient user experience, VUIs raise new security and privacy concerns for their users. In this paper, we focus on attribute inference attacks in the speech domain, demonstrating the potential for an attacker to accurately infer a target user's sensitive and private attributes (e.g. their emotion, sex, or health status) from deep acoustic models. To defend against this class of attacks, we design, implement, and evaluate a user-configurable, privacy-aware framework for optimizing speech-related data sharing mechanisms. Our objective is to enable primary tasks such as speech recognition and user identification, while removing sensitive attributes in the raw speech data before sharing it with a cloud service provider. We leverage disentangled representation learning to explicitly learn independent factors in the raw data. Based on a user's preferences, a supervision signal informs the filtering out of invariant factors while retaining the factors reflected in the selected preference. Our experimental evaluation over five datasets shows that the proposed framework can effectively defend against attribute inference attacks by reducing their success rates to approximately that of guessing at random, while maintaining accuracy in excess of 99% for the tasks of interest. We conclude that negotiable privacy settings enabled by disentangled representations can bring new opportunities for privacy-preserving applications.
Pandiyan A, Boyle D, Kiziroglou M, et al., 2020, Optimal dynamic recharge scheduling for two stage wireless power transfer, IEEE Transactions on Industrial Informatics, Vol: 17, Pages: 5719-5729, ISSN: 1551-3203
Many Industrial Internet of Things applications require autonomous operation and incorporate devices in inaccessible locations. Recent advances in wireless power transfer (WPT) and autonomous vehicle technologies, in combination, have the potential to solve a number of residual problems concerning the maintenance of, and data collection from embedded devices. Equipping inexpensive unmanned aerial vehicles (UAV) and embedded devices with subsystems to facilitate WPT allows a UAV to become a viable mobile power delivery vehicle (PDV) and data collection agent. A key challenge is therefore to ensure that a PDV can optimally schedule power delivery across the network, such that it is as reliable and resource efficient as possible. To achieve this and out-perform naive on-demand recharging strategies, we propose a two-stage wireless power network (WPN) approach in which a large network of devices may be grouped into small clusters, where packets of energy inductively delivered to each cluster by the PDV are acoustically distributed to devices within the cluster. We describe a novel dynamic recharge scheduling algorithm that combines genetic weighted clustering with nearest neighbour search to jointly minimize PDV travel distance and WPT losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces, and the algorithm is shown to achieve 90% throughput for large, dense networks.
Polonelli T, Qin Y, Yeatman E, et al., 2020, A flexible, low-power platform for UAV-based data collection from remote sensors, IEEE Access, Vol: 8, Pages: 164775-164785, ISSN: 2169-3536
This article presents the design and characterisation of a new low-power hardware platform to integrate unmanned aerial vehicle and wireless sensor technologies. In combination, these technologies can overcome data collection and maintenance problems of in situ monitoring in remote and extreme environments. Precision localisation in support of maximum efficiency mid-range inductive power transfer when recharging devices and increased throughput between drone and device are needed for data intensive monitoring applications, and to balance proximity time for devices powered by supercapacitors that recharge in seconds. The platform described in this article incorporates ultra-wideband technology to achieve high-performance ranging and high data throughput. It enables the development of a new localisation system that is experimentally shown to improve accuracy by around two orders of magnitude to 10 cm with respect to GNSS and achieves almost 6 Mbps throughput in both lab and field conditions. These results are supported by extensive modelling and analysis. The platform is designed for application flexibility, and therefore includes a wide range of sensors and expansion possibilities, with source code for two applications made immediately available as part of a open source project to support research and development in this new area.
Shaukat Jali R, Van Zalk N, Boyle D, 2020, Detecting Subclinical Social Anxiety Using Physiological Data from a Wrist-worn Wearable: A Small-Scale Feasibility Study (Preprint), JMIR Preprints
<sec> <title>BACKGROUND</title> <p>Subclinical (i.e., threshold) social anxiety can greatly affect young people’s lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment that would be greatly beneficial for sufferers, society and healthcare services. Nevertheless, indicators such as skin temperature from wrist-worn sensors have not been used in prior work on physiological social anxiety detection.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including Heart Rate (HR), Skin Temperature (ST) and Electrodermal Activity (EDA).</p> </sec> <sec> <title>METHODS</title> <p>Young adults (N = 12) with self-reported subclinical social anxiety (measured by the widely used self-reported version of the Liebowitz Social Anxiety Scale, LSAS-SR) participated in an impromptu speech task. Physiological data was collected using an E4 Empatica wearable device. Using the pre-processed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest and K-Nearest Neighbours (KNN) were used to develop models for three different contexts. Models were trained to (1) classify between baseline and socially anxious states, (2) differentiate between baseline, anticipation anxiety and reactive anxiety states, and (3) classify between social anxiety experienced by individuals with diffe
Pandiyan AYS, Kiziroglou ME, Boyle DE, et al., 2020, Optimal energy management of two stage energy distribution systems using clustering algorithm, 19th International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (Power MEMS), Publisher: IEEE, Pages: 1-4
Motivated by recent developments in Wireless Power Transfer (WPT), this work presents a solution for the optimization of a two-stage energy distribution system combining inductive and acoustic power transfer using a clustering algorithm. A network of immobile wireless sensors equipped with acoustic transceivers, storage capacitors and with known cartesian coordinates in a 2D plane is considered. A power delivery vehicle (PDV) with finite energy storage capacity is used to recharge a sensor node's supercapacitor which then transmits power to neighboring sensors acoustically within range. This work aims to find an optimal charging route for the PDV. The proposed algorithm is a combination of cluster analysis and breadth-first search. A theoretical study was performed, and the simulation results obtained were studied for the long-term failure probability for the proposed energy scheme.
Lan L, Polonelli T, Qin Y, et al., 2020, An Induction-Based Localisation Technique for Wirelessly Charged Drones, IEEE PELS Workshop on Emerging Technologies - Wireless Power Transfer (WoW) / IEEE Wireless Power Week (WPW) / IEEE MTT-S Wireless Power Transfer Conference (WPTC), Publisher: IEEE, Pages: 275-277
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- Citations: 2
Qin Y, Boyle D, Yeatman E, 2019, Efficient and reliable aerial communication with wireless sensors, IEEE Internet of Things Journal, Vol: 6, Pages: 9000-9011, ISSN: 2327-4662
This paper describes the design, implementation and evaluation of a first of its kind cross-layer protocol for wireless communication between flying agents and terrestrial wireless sensors. The protocol is composed of three layers: a new application layer built upon a modified implementation of ContikiMAC over the IEEE 802.15.4 2.4 GHz physical layer. The experimental evaluation shows the protocol to have excellent energy efficiency, low latency and high reliability-approaching 100% for certain parameter settings and operational conditions. The effects of speed, altitude, and direction of approach are also experimentally evaluated, demonstrating that it is of critical importance to take these into account when planning mobile aerial data collection campaigns.
Aloufi R, Haddadi H, Boyle D, 2019, Emotion filtering at the edge, Publisher: arXiv
Voice controlled devices and services have become very popular in theconsumer IoT. Cloud-based speech analysis services extract information fromvoice inputs using speech recognition techniques. Services providers can thusbuild very accurate profiles of users' demographic categories, personalpreferences, emotional states, etc., and may therefore significantly compromisetheir privacy. To address this problem, we have developed a privacy-preservingintermediate layer between users and cloud services to sanitize voice inputdirectly at edge devices. We use CycleGAN-based speech conversion to removesensitive information from raw voice input signals before regeneratingneutralized signals for forwarding. We implement and evaluate our emotionfiltering approach using a relatively cheap Raspberry Pi 4, and show thatperformance accuracy is not compromised at the edge. In fact, signals generatedat the edge differ only slightly (~0.16%) from cloud-based approaches forspeech recognition. Experimental evaluation of generated signals show thatidentification of the emotional state of a speaker can be reduced by ~91%.
Aloufi R, Haddadi H, Boyle D, 2019, Emotionless: privacy-preserving speech analysis for voice assistants, Publisher: arXiv
Voice-enabled interactions provide more human-like experiences in manypopular IoT systems. Cloud-based speech analysis services extract usefulinformation from voice input using speech recognition techniques. The voicesignal is a rich resource that discloses several possible states of a speaker,such as emotional state, confidence and stress levels, physical condition, age,gender, and personal traits. Service providers can build a very accurateprofile of a user's demographic category, personal preferences, and maycompromise privacy. To address this problem, a privacy-preserving intermediatelayer between users and cloud services is proposed to sanitize the voice input.It aims to maintain utility while preserving user privacy. It achieves this bycollecting real time speech data and analyzes the signal to ensure privacyprotection prior to sharing of this data with services providers. Precisely,the sensitive representations are extracted from the raw signal by usingtransformation functions and then wrapped it via voice conversion technology.Experimental evaluation based on emotion recognition to assess the efficacy ofthe proposed method shows that identification of sensitive emotional state ofthe speaker is reduced by ~96 %.
Kiziroglou M, Wright S, Shi M, et al., 2019, Milliwatt power supply by dynamic thermoelectric harvesting, PowerMEMS 2018, Publisher: Institute of Physics (IoP), Pages: 1-4, ISSN: 1742-6588
In this work we demonstrate a power supply that collects thermal energy from temperature fluctuations in time, to provide regulated power in the milliwatt range. It is based on the dynamic thermoelectric energy harvesting concept, in which a phase change material is used to store heat and create spatial heat flow from temperature transients. A simple, cost-effective and reproducible fabrication method is employed, based on 3D printing and off-the-shelf components. The harvester is integrated with a commercial power management module and supercapacitor storage. Output energy up to 2 J is demonstrated from temperature cycles corresponding to avionic applications. The demonstration includes harvesting while powering a 10 kΩ analogue voltmeter directly from the supercapacitor, including during cold-starting.
Tsiatsis V, Karnouskos S, Holler J, et al., 2019, WHY THE INTERNET OF THINGS?, INTERNET OF THINGS: TECHNOLOGIES AND APPLICATIONS FOR A NEW AGE OF INTELLIGENCE, 2ND EDITION, Publisher: ELSEVIER ACADEMIC PRESS INC, Pages: 3-7
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Tsiatsis V, Karnouskos S, Holler J, et al., 2019, ORIGINS AND IOT LANDSCAPE, INTERNET OF THINGS: TECHNOLOGIES AND APPLICATIONS FOR A NEW AGE OF INTELLIGENCE, 2ND EDITION, Publisher: ELSEVIER ACADEMIC PRESS INC, Pages: 9-30
Tsiatsis V, Karnouskos S, Holler J, et al., 2019, ARCHITECTURE REFERENCE MODEL, INTERNET OF THINGS: TECHNOLOGIES AND APPLICATIONS FOR A NEW AGE OF INTELLIGENCE, 2ND EDITION, Publisher: ELSEVIER ACADEMIC PRESS INC, Pages: 181-234
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