Publications
60 results found
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.
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.
Aloufi R, Haddadi H, Boyle D, 2020, Privacy-preserving Voice Analysis via Disentangled Representations, Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop
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 %.
Boyle DE, Wright SW, Kiziroglou ME, et al., 2019, Inductive Power Delivery with Acoustic Distribution to Wireless Sensors, IEEE MTT-S Wireless Power Transfer Conference (WPTC) / IEEE PELS Workshop on Emerging Technologies - Wireless Power (WoW) / Wireless Power Week Conference, Publisher: IEEE, Pages: 202-204
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- Citations: 2
Qin Y, Boyle D, Yeatman E, 2019, Radio Diversity for Heterogeneous Communication with Wireless Sensors, 5th IEEE World Forum on Internet of Things (IEEE WF-IoT), Publisher: IEEE, Pages: 955-960
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- Citations: 4
Aloufi R, Haddadi H, Boyle D, 2019, Poster Abstract: Privacy Preserving Speech Analysis using Emotion Filtering at the Edge, 17th ACM Conference on Embedded Networked Sensor Systems (SenSys), Publisher: ASSOC COMPUTING MACHINERY, Pages: 426-427
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.
Bhatia L, Boyle D, McCann J, 2018, Aerial interactions with wireless sensors, The 16th ACM Conference on Embedded Networked Sensor Systems (SenSys 2018), Pages: 373-374
Sensing systems incorporating unmanned aerial vehicles have the potential to enable a host of hitherto impractical monitoring applications using wireless sensors in remote and extreme environments. Their use as data collection and power delivery agents can overcome challenges such as poor communications reliability in difficult RF environments and maintenance in areas dangerous for human operatives. Aerial interaction with wireless sensors presents some interesting new challenges, including selecting or designing appropriate communications protocols that must account for unique practicalities like the effects of velocity and altitude. This poster presents a practical evaluation of the effects of altitude when collecting sensor data using an unmanned aerial vehicle. We show that for an otherwise disconnected link over a long distance (70m), by increasing altitude (5m) the link is created and its signal strength continues to improve over tens of metres. This has interesting implications for protocol design and optimal aerial route planning.
Mitcheson PD, Kkelis G, Aldhaher S, et al., 2018, Power Electronics for Wireless Power Delivery in Synthetic Sensor Networks, 17th International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS), Publisher: IOP PUBLISHING LTD, ISSN: 1742-6588
Kiziroglou M, Cowell M, Kumaravel BT, et al., 2018, Speed vs efficiency and storage type in portable energy systems, PowerMEMS 2017, Publisher: Institute of Physics (IoP), ISSN: 1742-6588
Portable power management systems must optimise power interfacing, storage androuting, to meet application specific functionality requirements. Two key aspects are reliabilityand efficiency. For reliable operation, it is required that powering on/off the system must occurin a planned manner. For efficient operation, it is desired that the system is powered for anoptimal amount of time. maximizing its useful operational outcome per unit of energy consumed.This can be achieved by optimizing energy usage based on the anticipated energy income andpower demand of duty-cycled power consumers. Both battery and supercapacitor storage can beemployed to meet energy and power density demand, on both sides, and to enable fast transitionfrom cold-starting to active power management. A simplified model is used to calculate thereliability of a simple solar-powered microsystem. The modelling of dynamically configurableinterfacing and storage may enable a new generation of power management, providing reliablepower from irregular and small energy sources.
Qin Y, Boyle D, Yeatman E, 2018, A Novel Protocol for Data Links between Wireless Sensors and UAV Based Sink Nodes, 4th IEEE World Forum on Internet of Things (WF-IoT), Publisher: IEEE, Pages: 371-376
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- Citations: 8
Mitcheson PD, Boyle D, Kkelis G, et al., 2017, Energy-Autonomous Sensing Systems Using Drones, 16th IEEE SENSORS CONFERENCE, Publisher: IEEE, Pages: 648-650, ISSN: 1930-0395
Mitcheson PD, Boyle D, Kkelis G, et al., 2017, Energy-autonomous sensing systems using drones, 2017 IEEE SENSORS, Publisher: IEEE
Magno M, Boyle D, 2017, Wearable Energy Harvesting: From Body to Battery, 12th IEEE International Conference on Design & Technology of Integrated Systems In Nanoscale Era (DTIS), Publisher: IEEE
Kiziroglou M, Boyle D, Wright S, et al., 2017, Acoustic power delivery to pipeline monitoring wireless sensors, Ultrasonics, Vol: 77, Pages: 54-60, ISSN: 1874-9968
The use of energy harvesting for powering wireless sensors is made more challenging in most applications by the requirement for customization to each specific application environment because of specificities of the available energy form, such as precise location, direction and motion frequency, as well as the temporal variation and unpredictability of the energy source. Wireless power transfer from dedicated sources can overcome these difficulties, and in this work, the use of targeted ultrasonic power transfer as a possible method for remote powering of sensor nodes is investigated. A powering system for pipeline monitoring sensors is described and studied experimentally, with a pair of identical, non6inertial piezoelectric transducers used at the transmitter and receiver. Power transmission of 18 mW (Root6Mean6Square) through 1 m of a 118 mm diameter cast iron pipe, with 8 mm wall thickness is demonstrated. By analysis of the delay between transmission and reception, including reflections from the pipeline edges, a transmission speed of 1000 m/s is observed, corresponding to the phase velocity of the L(0,1) axial and F(1,1) radial modes of the pipe structure. A reduction of power delivery with water6filling is observed, yet over 4 mW of delivered power through a fully6filled pipe is demonstrated. The transmitted power and voltage levels exceed the requirements for efficient power management, including rectification at cold6starting conditions, and for the operation of low6power sensor nodes. The proposed powering technique may allow the implementation of energy autonomous wireless sensor systems for monitoring industrial and network pipeline infrastructure.
Kiziroglou M, boyle D, Yeatman E, et al., 2016, Opportunities for sensing systems in mining, IEEE Transactions on Industrial Informatics, Vol: 13, Pages: 278-286, ISSN: 1551-3203
Pervasive sensing - the capability to deploy large numbers of sensors, to link them to communication networks, and to analyze their collective data - is transforming many industries. In mining, networked sensors are already used for remote operation, automation including driverless vehicles, health and safety, and exploration. In this paper, the state-of-the-art sensing and monitoring technologies are assessed as solutions against the main challenges and opportunities in the mining industry. Localization, mapping, remote operation, maintenance and health and safety are identified as the main beneficiaries, from rapidly developing technologies such as 3D visualization, augmented reality, energy autonomous sensor nodes, distributed sensing, smart network protocols and big data analytics. It is shown that the identification and management of ore grade in particular, which transcends each stage of the mining process, may critically benefit from certain arising sensing technologies, where major efficiency improvements are possible in exploration, extraction, haulage and processing activities.
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