224 results found
Heggo M, Bhatia L, McCann J, 2021, Cognisense: A contactless rotation speed measurement system, The 19th ACM Conference on Embedded Networked Sensor Systems, Pages: 341-348
Several engineering applications require reliable rotation speed measurement for their correct functioning. The rotation speed measurements can be used to enhance the machines’ vibration signalanalysis and can also elicit faults undetectable by vibration monitoringalone. The current state of the art sensors for rotation speed measurement are optical, magnetic and mechanical tachometers. Thesesensors require line-of-sight and direct access to the machine which limits their use-cases. In this demo, we showcase Cognisense, an RF-based hardware-software sensing system that uses Orbital AngularMomentum (OAM) waves to accurately measure a machine’s rotation speed. Cognisense uses a novel compact patch antenna in a monostatic radar configuration capable of transmitting and receiving OAM waves in the 5GHz license-exempt band. The demo will show Cognisense working on machines with varied numbers of blades, sizes and materials. We will also present how Cognisense operates reliably in non-line-of-sight scenarios where traditional tachometers fail. We demonstrate how Cognisense works well in high-scattering scenarios and is not impacted by the material of rotor blades. Unlike optical tachometers that require one to face the machine head-on, Our demo will also show Cognisense performing reliably in the presence of a tilt angle between the system and the machine which is not possible with optical tachometers.
Zhao C, Sun X, Yang S, et al., 2021, Exploration across small silos: federated few-shot learning on network edge, IEEE Network: the magazine of global information exchange, ISSN: 0890-8044
Federated Learning (FL) has been drawing significant attention from both academia and industry working on distributed machine learning. In practice, learning over mutually isolated datasets residing at the network edge, also known as silos, FL clients can suffer from a lack of samples, due to many reasons (e.g., expensive annotation), and this has potentially significant negative impact on FL performance. Few-Shot Learning (FSL) has been considered as a promising solution, but unfortunately cannot be directly applied to practical Cross-Silo Federated Learning (CSFL) systems. In this article, as far as we know, we conduct the first systematic discussion of the specific challenges of FSL in CSFL systems. We extract essential design issues found in Federated Few-Shot Learning (FFSL), and develop a new FFSL method based on Model-Agnostic Meta Learning (MAML). Through experiments using real-world federated datasets, we comprehensively demonstrate our method's advantages over existing FL and FSL methods in different practical CSFL scenarios where hitherto FL and FSL methods failed. We also highlight some promising future research directions.
Bhatia L, Chen P-Y, Breza M, et al., 2021, IRONWAN: Increasing Reliability of Overlapping Networks in LoRaWAN, IEEE Internet of Things Journal, ISSN: 2327-4662
Junejo AK, Benkhelifa F, Wong B, et al., 2021, LoRa-LiSK: A Lightweight Shared Secret Key Generation Scheme for LoRa Networks, IEEE Internet of Things Journal, Pages: 1-1
Wong B, McCann J, 2021, Failure detection methods for pipeline networks: from acoustic sensing to cyber-physical systems, Sensors, Vol: 21, ISSN: 1424-8220
Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention. In order to optimise the spatial use of the pipeline infrastructure, pipelines are either buried underground, or located in submarine environments. Due to the continuous expansion of pipeline networks in locations that are inaccessible to maintenance personnel, research efforts have been ongoing to introduce and develop reliable detection methods for pipeline failures, such as blockages, leakages, cracks, corrosion and weld defects. In this paper, a taxonomy of existing pipeline failure detection techniques and technologies was created to comparatively analyse their respective advantages, drawbacks and limitations. This effort has effectively illuminated various unaddressed research challenges that are still present among a wide array of the state-of-the-art detection methods that have been employed in various pipeline domains. These challenges include the extension of the lifetime of a pipeline network for the reduction of maintenance costs, and the prevention of disruptive pipeline failures for the minimisation of downtime. Our taxonomy of various pipeline failure detection methods is also presented in the form of a look-up table to illustrate the suitability, key aspects and data or signal processing techniques of each individual method. We have also quantitatively evaluated the industrial relevance and practicality of each of the methods in the taxonomy in terms of their respective deployability, generality and computational cost. The outcome of the evaluation made in the taxonomy will contribute to our future works involving the utilisation of sensor fusion and data-centric frameworks to develop efficient, accurate and reliable failure detection solutions.
Fu A, McCann JA, 2021, Dynamic Decentralized Periodic Event-Triggered Control for Wireless Cyber-Physical Systems, IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, Vol: 29, Pages: 1783-1790, ISSN: 1063-6536
This brief adds dynamic variables to decentralized periodic event-triggered control (DPETC), denoting dynamic DPETC (DDPETC) for wireless cyber-physical systems whose sensors are distributed and bandwidths are limited, and studies the stability and L 2 -gain. In DDPETC, dynamic variables that require only local information are designed and added to the event-triggered mechanisms (ETMs). This dynamic variable can guarantee that the ETMs still be localized which does not require information from other nodes. For implementations with DDPETC, at each periodic sampling time the ETMs decide events based on new measurements from local sensors. Those measurements that trigger the events are transmitted to controllers, and after network transmission delays, they are received and the controller computes the control actions. The transmissions of these control actions from controllers to actuators are also decided by ETMs. The feasibility of the approach is shown via two examples. Numerical results show that, with the same stability and performance, DDPETC can save more than 40% events, making it more suitable for wireless cyber-physical systems.
Junejo AK, Komninos N, McCann JA, 2021, A secure integrated framework for Fog-assisted Internet-of-Things systems, IEEE Internet of Things Journal, Vol: 8, Pages: 6840-6852, ISSN: 2327-4662
Fog-Assisted Internet of Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this paper proposes a framework featuring attribute-based access control and trust-based behavioural monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities’ performance using a trust model based on a set of QoS and network communication features. Subsequently, trust is embedded as an attribute within SC’s access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely DoS, DDoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework’s design principles. The framework is evaluated on a Raspberry Pi 3 Model B to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices.
Benkhelifa F, Qin Z, McCann JA, 2021, User fairness in energy harvesting-based LoRa networks with imperfect SF orthogonality, IEEE Transactions on Communications, Vol: 69, Pages: 4319-4334, ISSN: 0090-6778
Long range (LoRa) demonstrates high potential in supporting massive Internet-of-Things (IoT) applications. In this paper, we study the resource allocation in energy harvesting (EH)-enabled LoRa networks with imperfect spreading factor (SF) orthogonality. We maximize the user fairness in terms of the minimum time-averaged throughput while jointly optimizing the SF assignment, the EH time duration, and the transmit power of all LoRa users. First, we provide a general expression of the packet collision time between LoRa users which depends on the SFs and EH duration requirements of each user. Then, we develop two SF allocation schemes that either assure fairness or not for the LoRa users. Within this, we optimize the EH time and the power allocation for single and multiple uplink transmission attempts. For the single uplink transmission attempt, the optimal power allocation is obtained using bisection method. For the multiple uplink transmission attempts, the suboptimal power allocation is derived using concave-convex procedure (CCCP). Our results unearth new findings. Firstly, we demonstrate that the unfair SF allocation algorithm outperforms the others in terms of the minimum data rate. Additionally, we observe that co-SF interference is the main limitation in the throughput performance, and not really energy scarcity.
Mijic A, Whyte J, Fisk D, et al., 2021, The Centre for Systems Engineering and Innovation – 2030 vision and 10-year celebration
The 2030 vision of the Centre is to bring Systems Engineering and Innovation to Civil Infrastructure by changing how cross-sector infrastructure challenges are addressedin an integrated way using principles of systems engineering to maximise resilience, safety and sustainability in an increasingly complex world.We want to better understand the environmental and societal impacts of infrastructure interventions under uncertainty. This requires a change in current approaches to infrastructure systems engineering: starting from the natural environmentand its resources, encompassing societaluse of infrastructure and the supporting infrastructure assets and services.We argue for modelling that brings natural as well as built environments within the system boundaries to better understand infrastructure and to better assess sustainability. We seethe work as relevant to both the academic community and to a wide range of industry and policy applications that are working on infrastructure transition pathways towards fair, safe and sustainable society.This vision was developed through discussions between academics in preparation for the Centre for Systems Engineering and Innovation (CSEI) 10 years celebration. These rich discussions about the future of the Centre were inspired by developing themes for a celebration event, through which we have summarised the first 10 years of the Centre’s work and our vision for the future and identified six emerging research areas.
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.
Zhao C, Yang S, McCann JA, 2021, On the data quality in privacy-preserving mobile crowdsensing systems with untruthful reporting, IEEE Transactions on Mobile Computing, Vol: 20, Pages: 647-661, ISSN: 1536-1233
The proliferation of mobile smart devices with ever improving sensing capacities means that human-centric Mobile Crowdsensing Systems (MCSs) can economically provide a large scale and flexible sensing solution. The use of personal mobile devices is a sensitive issue, therefore it is mandatory for practical MCSs to preserve private information (the user's true identity, precise location, etc.) while collecting the required sensing data. However, well intentioned privacy protection techniques also conceal autonomous, or even malicious, behaviors of device owners (termed as self-interested), where the objectivity and accuracy of crowdsensing data can therefore be severely threatened. The issue of data quality due to untruthful reporting in privacy-preserving MCSs has been yet to produce solutions. Bringing together game theory, algorithmic mechanism design, and truth discovery, we develop a mechanism to guarantee and enhance the quality of crowdsensing data without jeopardizing the privacy of MCS participants. Together with solid theoretical justifications, we evaluate the performance of our proposal with extensive real-world MCS trace-driven simulations. Experimental results demonstrate the effectiveness of our mechanism on both enhancing the quality of the crowdsensing data and eliminating the motivation of MCS participants, even when their privacy is well protected, to report untruthfully.
Bouazizi Y, Benkhelifa F, McCann J, 2021, Spatiotemporal modelling of multi-gateway LoRa networks with imperfect SF orthogonality, 2020 IEEE Global Communications Conference (also virtual), Publisher: IEEE, Pages: 1-7
Meticulous modelling and performance analysis ofLow-Power Wide-Area (LPWA) networks are essential for largescale dense Internet-of-Things (IoT) deployments. As Long Range(LoRa) is currently one of the most prominent LPWA tech-nologies, we propose in this paper a stochastic-geometry-basedframework to analyse the uplink transmission performance ofa multi-gateway LoRa network modelled by a Matern ClusterProcess (MCP). The proposed model is first to consider alltogether the multi-cell topology, imperfect spreading factor (SF)orthogonality, random start times, and geometric data arrivalrates. Accounting for all of these factors, we initially develop theSF-dependent collision overlap time function for any start timedistribution. Then, we analyse the Laplace transforms of intra-cluster and inter-cluster interference, and formulate the uplinktransmission success probability. Through simulation results, wehighlight the vulnerability of each SF to interference, illustratethe impact of parameters such as the network density, and thepower allocation scheme on the network performance. Uniquely,our results shed light on when it is better to activate adaptivepower mechanisms, as we show that an SF-based power allocationthat approximates LoRa ADR, negatively impacts nodes nearthe cluster head. Moreover, we show that the interfering SFsdegrading the performance the most depend on the decodingthreshold range and the power allocation scheme.
Bhatia L, Tomic I, Fu A, et al., 2021, Control communication co-design for wide area cyber-physical systems, ACM Transactions on Cyber-Physical Systems, Vol: 5, Pages: 1-27, ISSN: 2378-962X
Wide Area Cyber-Physical Systems (WA-CPSs) are a class of control systems that integrate low-powered sensors, heterogeneous actuators and computer controllers into large infrastructure that span multi-kilometre distances. Current wireless communication technologies are incapable of meeting the communication requirements of range and bounded delays needed for the control of WA-CPSs. To solve this problem, we use a Control-Communication Co-design approach for WA-CPSs, that we refer to as the C3 approach, to design a novel Low-Power Wide Area (LPWA) MAC protocol called Ctrl-MAC and its associated event-triggered controller that can guarantee the closed-loop stability of a WA-CPS. This is the first paper to show that LPWA wireless communication technologies can support the control of WA-CPSs. LPWA technologies are designed to support one-way communication for monitoring and are not appropriate for control. We present this work using an example of a water distribution network application which we evaluate both through a co-simulator(modelling both physical and cyber subsystems) and test bed deployments. Our evaluation demonstrates full control stability, with up to 50% better packet delivery ratios and 80% less average end-to-end delays when compared to a state of the art LPWA technology. We also evaluate our scheme against an idealised, wired,centralised, control architecture and show that the controller maintains stability and the overshoots remain within bounds.
Bhatia L, Breza M, Marfievici R, et al., 2020, Dataset: LoED: The LoRaWAN at the Edge dataset, The 3rd International SenSys+BuildSys Workshop on Data: Acquisition to Analysis, Publisher: ACM, Pages: 7-8
This paper presents the LoRaWAN at the Edge Dataset (LoED), an open LoRaWAN packet dataset collected at gateways. Real-world LoRaWAN datasets are important for repeatable sensor-network and communications research and evaluation as, if carefully collected, they provide realistic working assumptions. LoED data is collected from nine gateways over a four month period in a dense urban environment. The dataset contains packet header information and all physical layer properties reported by gateways such as the CRC, RSSI, SNR and spreading factor. Files are provided to analyse the data and get aggregated statistics. The dataset is available at: doi.org/10.5281/zenodo.4121430
Altherwy YN, McCann JA, 2020, SING: free space SensING of grape moisture using RF shadowing, IEEE Transactions on Instrumentation and Measurement, Vol: 70, Pages: 1-12, ISSN: 0018-9456
Convenient, non-obtrusive, low-cost, and accurate sensing of fruit moisture content is crucial for the scientific studies of Pomology and Viticulture and their associated agriculture. It can provide early indicators of yield estimation and crop health as well as providing data for food production and precision farming systems. With a focus on grapes, we introduce SING, a scheme that senses grape moisture content by utilizing RF signals but without physical contact with the fruit. In this paper, we extend the investigation of the theoretical relationship between the dielectric properties and the moisture content of agricultural products to establish a sensing model in the 5 GHz band. To make the work practical, we are first to measure the dielectric properties of grape bunches (not individually as that would be destructive), presenting a unique measurement challenge as internal grapes are hidden. In doing so, we demonstrate that our technique precisely estimates moisture content to a high degree of accuracy (90Current RF sensing models to estimate moisture are destructive; they require samples to be constrained in containers. Our work is first to dispense with such impracticalities, and, without contact with the object, accurately measures non-uniform grape clusters in open space. We demonstrate that SING is superior to existing work in its ability to accurately measure the dielectric properties of non-uniform fruit objects and test this through both lab-based experimentation and preliminary outdoor vineyard tests.
Webster M, Breza M, Dixon C, et al., 2020, Exploring the effects of environmental conditions and design choices on IoT systems using formal methods, Journal of Computational Science, Vol: 45, ISSN: 1877-7503
Wireless communication protocols are often used in critical applications, e.g., urban water supply networks or healthcare monitoring within the Internet of Things. It is essential that control software and protocols for such systems are verified to be both robust and reliable. The effects on the hardware caused by environmental conditions and the choice of parameters used by the protocol are among the largest obstacles to robustness and reliability in wireless systems. In this paper we use formal verification to verify that a wireless sensor network synchronization and dissemination protocol is not adversely affected by these factors.
Wang H, Zhou G, Bhatia L, et al., 2020, Energy-neutral and QoS-aware protocol in wireless sensor networks for health monitoring of hoisting systems, IEEE Transactions on Industrial Informatics, Vol: 16, Pages: 5543-5553, ISSN: 1551-3203
Hoisting equipment is core to many industrial systems and therefore their state of health significantly affects production lines and personnel safety; this is especially important in environments such as coal mines. The health of the hoisting system, can be estimated by deploying energy harvesting wireless sensor nodes that monitor the drum surface stress. In this network of sensor devices, it is very costly to send highly sampled data as it causes radio congestion and consumes energy. However, from our experience of sensing hoist systems, we note that the data observed at the upper surface of the hoist is significantly more indicative of the state of health of the whole system, compared with data sensed at the lower surface. Therefore, we need to take advantage of this to optimise the communications of sensor nodes. However, scarce energy can be collected for these devices from the hoist itself, along with the prioritised Quality of Service (QoS) requirements (throughput, delay) of monitoring signals, raises important challenges for energy management. In this paper, we use Lyapunov optimisation techniques and propose an Energy-neutral and QoS-aware Protocol (EQP), including duty cycling and network scheduling to solve it. Extensive simulations show that EQP helps sensor nodes realize consecutive monitoring, and achieve more than 38% utility gain compared with existing strategies.
Wei Z, Pagani A, Fu G, et al., 2020, Optimal sampling of water distribution network dynamics using graph fourier transform, IEEE Transactions on Network Science and Engineering, Vol: 7, Pages: 1570-1582, ISSN: 2327-4697
Water distribution networks are critical infrastructures under threat from the accidental or intentional release of contaminants. Large-scale data collection is vital for digital twin modelling, but remains challenging in underground spaces over vast areas. Therefore, inferring the contaminant spread process with minimal sensor data is important. Existing sensor deployment optimisation approaches use scenario-based numerical optimisation, but suffer from scalability issues and lack performance guarantees. Analytical graph theoretic approaches link complex network topology (e.g. Laplacian spectra) to optimal sensing locations, but neglect the complex fluid dynamics. Alternative data-driven approaches such as compressed sensing offer limited sample node reduction. In this work, we introduce a novel data-driven Graph Fourier Transform that exploits the low-rank property of networked dynamics to optimally sample WDNs. The proposed GFT guarantees error free recovery of network dynamics and offers attractive compression and scaling improvements over existing numerical optimisation, compressed sensing, and graph theoretic approaches. By testing on 100 different contaminant propagation data sets, the proposed scheme shows that, on average, with nearly 30% of the junctions monitored, we are able to fully recover the networked dynamics. The framework is useful for other monitoring applications of WDNs and can be applied to a variety of infrastructure sensing for digital twin modelling.
Wu D, Lambrinos L, Przepiorka T, et al., 2020, Enabling Efficient Offline Mobile Access to Online Social Media on Urban Underground Metro Systems, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol: 21, Pages: 2750-2764, ISSN: 1524-9050
Wu D, Nie X, Asmare E, et al., 2020, Towards distributed SDN: mobility management and flow scheduling in software defined urban IoT, IEEE Transactions on Parallel and Distributed Systems, Vol: 31, Pages: 1400-1418, ISSN: 1045-9219
IEEE The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist. Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. We present UbiFlow that adopts multiple controllers to divide urban-scale SDN into different geographic partitions and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Our experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g. 67.21% throughput improvement, 72.99% reduced delay, and 69.59% jitter improvements, compared with alternative SDN systems.
Wang H, Zhou G, Xue R, et al., 2020, A driving-behavior-based SoC prediction method for light urban vehicles powered by supercapacitors, IEEE Transactions on Intelligent Transportation Systems, Vol: 21, Pages: 2090-2099, ISSN: 1524-9050
Range anxiety is one of the problems that hinder the large-scale application of electric vehicles (EVs). We propose a driving-behavior-based State-of-Charge (SoC) prediction (DBSP) algorithm to overcome this problem. This algorithm can determine whether drivers can reach their destinations while also predicting the SoC if drivers were to return the trip. First, two supercapacitor equivalent circuit models are established with one based on the historical average power and the other based on the equivalent current, which is proposed in this algorithm. Then, based on the equivalent transformation of the two models, an analytical expression relating the historical average power and the predicted SoC is derived by using the equivalent current as a “bridge.” Therefore, the predicted SoC can be dynamically adjusted in response to recorded historical data, including the output power, speed, and distance of EVs powered by supercapacitors. The simulation results demonstrate that the total prediction error is less than 0.5% of the real SoC at different initial SoC and temperature, which represents idealized behavior-based driving. In contrast, in actual driving experiments, the total prediction error is less than 3% of the real SoC at different initial SoC and temperature.
Tomic I, Breza M, McCann J, 2020, Jamming-resilient control and communication framework for cyber physical systems, Living in the Internet of Things 2019, Publisher: IET
The control and monitoring of large infrastructure installations is becoming smarter, cheaper to run and easier to managethrough the use of wireless sensor and actuator networks (WSANs). Cyber Physical Systems (CPSs)are the combination of cyber sensing via WSANs and physical control. The problem withthe use of WSANs in CPSs is that they make the whole system being controlled exposed to the world andvulnerable to theft or cyber-attacks.In this article we examine the failure of CPS infrastructure due to intelligent radio jamming.The intelligent jammer employs a protocol-aware jamming strategy to learn the transmission period of a sensor device.It then broadcasts noise to disrupt the wireless communication and destabilise the CPS.Wepresent a CPS control and communication approachto counter thethreat of intelligentradio jamming. The approach exploitsthe properties of the event-based controlstrategy combined with a reservation-based communication protocolthat employs obfuscation. We usea physical model of a water distribution networkto demonstrate that the approachis resilient to an intelligent jamming attack, it is able to continue normaloperationof the systemand maintain the desired level of performance while achieving low overheads.
Li K, Benkhelifa F, McCann J, 2020, Resource allocation for non-orthogonal multiple access (NOMA) enabled LPWA networks, IEEE GLOBECOM 2019, Publisher: IEEE, Pages: 1-6
In this paper, we investigate the resource allocation for uplink non-orthogonal multiple access (NOMA) enabled low-power wide-area (LPWA) networks to support the massive connectivity of users/nodes. Here, LPWA nodes communicate with a central gateway through resource blocks like channels, transmission times, bandwidths, etc. The nodes sharing the same resource blocks suffer from intra-cluster interference and possibly inter-cluster interference, which makes current LPWA networks unable to support the massive connectivity. Using the minimum transmission rate metric to highlight the interference reduction that results from the addition of NOMA, and while assuring user throughput fairness, we decompose the minimum rate maximization optimization problem into three sub- problems. First, a low-complexity sub-optimal nodes clustering scheme is proposed assigning nodes to channels based on their normalized channel gains. Then, two types of transmission time allocation algorithms are proposed that either assure fair or unfair transmission time allocation between LPWA nodes sharing the same channel. For a given channel and transmission time allocation, we further propose an optimal power allocation scheme. Simulation evaluations demonstrate approximately 100dB improvement of the selected metric for a single network with 4000 active nodes.
Benkhelifa F, ElSawy H, McCann JA, et al., 2020, Recycling cellular energy for self-sustainable IoT networks: a spatiotemporal study, IEEE Transactions on Wireless Communications, Vol: 19, Pages: 2699-2712, ISSN: 1536-1276
This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modelled via a two-dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of the data buffers and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission success probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which define the network parameters that ensure stable network operation and finite packet delay. Furthermore, the spatially averaged network performance, in terms of transmission success probability, average queueing delay, and average queue size are investigated. For self-sustainable networks, the results quantify the required buffer size and packet delay, which are crucial for the design of IoT devices and time critical IoT applications.
Spina A, Breza M, Dulay N, et al., 2020, XPC: fast and reliable synchronous transmission protocols for 2-phase commit and 3-phase commit, The 2020 International Conference on Embedded Wireless Systems and Networks, Publisher: ACM
The improvement of software abstractions and frame-works for programmers is one of the major challenges forthe engineering of reliable and efficient wireless sensing sys-tems. We address this challenge with X Process Commit(XPC), an atomic commit protocol framework, andHybrid,a Synchronous Transmission (ST) communication approach.Hybridexploits the reliability of Glossy and the speed ofChaos, two Synchronous Transmission primitives, to getlower latency and higher reliability than either on their own.Hybridis a general approach that can provide reliable com-munication for any round based protocol. We use XPC andHybridto build the classical 2-phase and 3-phase commitprotocols. Through extensive experimentation, we comparethe performance of the 2-phase and 3-phase commit proto-cols when they useHybrid, Glossy, and Chaos for commu-nication. Our results show thatHybridis more robust thanChaos to radio interference, with almost 100% reliability in anetwork of nodes suffering from moderate radio interference,13% to 50% faster than Glossy, and has comparable over-heads to other state of the art ST atomic commit approachesA2/Synchrotron.
Yu W, McCann J, Zhang C, 2019, Efficient pairwise penetrating-rank similarity retrieval, ACM Transactions on the Web, Vol: 13, Pages: 1-52, ISSN: 1559-1131
Many web applications demand a measure of similarity between two entities, such as collaborative filtering, web document ranking, linkage prediction, and anomaly detection. P-Rank (Penetrating-Rank) has been accepted as a promising graph-based similarity measure, as it provides a comprehensive way of encoding both incoming and outgoing links into assessment. However, the existing method to compute P-Rank is iterative in nature and rather cost-inhibitive. Moreover, the accuracy estimate and stability issues for P-Rank computation have not been addressed. In this article, we consider the optimization techniques for P-Rank search that encompasses its accuracy, stability, and computational efficiency. (1) The accuracy estimation is provided for P-Rank iterations, with the aim to find out the number of iterations, k, required to guarantee a desired accuracy. (2) A rigorous bound on the condition number of P-Rank is obtained for stability analysis. Based on this bound, it can be shown that P-Rank is stable and well-conditioned when the damping factors are chosen to be suitably small. (3) Two matrix-based algorithms, applicable to digraphs and undirected graphs, are, respectively, devised for efficient P-Rank computation, which improves the computational time from O(kn3) to O(υ n2+υ6) for digraphs, and to O(υn2) for undirected graphs, where n is the number of vertices in the graph, and υ (≪ n) is the target rank of the graph. Moreover, our proposed algorithms can significantly reduce the memory space of P-Rank computations from O(n2) to O(υn+υ4) for digraphs, and to O(υ n) for undirected graphs, respectively. Finally, extensive experiments on real-world and synthetic datasets demonstrate the usefulness and efficiency of the proposed techniques for P-Rank similarity assessment on various networks.
Mcgrane SJ, Acuto M, Artioli F, et al., 2019, Scaling the nexus: Towards integrated frameworks for analysing water, energy and food, Geographical Journal, Vol: 185, Pages: 419-431, ISSN: 0016-7398
The emergence of the water-energy-food (WEF) nexus has resulted in changes to the way we perceive our natural resources. Stressors such as climate change and population growth have highlighted the fragility of our WEF systems, necessitating integrated solutions across multiple scales. While a number of frameworks and analytical tools have been developed since 2011, a comprehensive WEF nexus tool remains elusive, hindered in part by our limited data and understanding of the interdependencies and connections across the WEF systems. To achieve this, the community of academics, practitioners and policy-makers invested in WEF nexus research are addressing several critical areas that currently remain as barriers. First, the plurality of scales (e.g., spatial, temporal, institutional, jurisdictional) necessitates a more comprehensive effort to assess interdependencies between water, energy and food, from household to institutional and national levels. Second, and closely related to scale, a lack of available data often hinders our ability to quantify physical stocks and flows of resources. Overcoming these barriers necessitates engaging multiple stakeholders, and using experiences and local insights to better understand nexus dynamics in particular locations or scenarios, and we exemplify this with the inclusion of a UK-based case study on exploring the nexus in a particular geographical area. We elucidate many challenges that have arisen across nexus research, including the impact of multiple scales in operation, and concomitantly, what impact these scales have on data accessibility. We assess some of the critical frameworks and tools that are applied by nexus researchers and articulate some of the steps required to develop from nexus thinking to an operationalisable concept, with a consistent focus on scale and data availability.
Altherwy YN, Elmallah ES, McCann JA, 2019, Two-terminal connectivity in UWSN probabilistic graphs: A polynomial time algorithm: Poster abstract, Pages: 444-445
We investigate the likelihood that two nodes are connected in an Underwater Wireless Sensor Network (UWSN) where nodes are floating freely with the underwater currents and the location of nodes at any given time can only be determined in a probabilistic fashion. This problem is #P-hard, thus, we propose HB-Conn2, an algorithm that returns an exact solution in polynomial time when applied on a set of node-disjoint (s, t)-paths.
Fu A, Tomic I, McCann J, 2019, Asynchronous sampling for decentralized periodic event-triggered control, 2019 American Control Conference, Publisher: IEEE, Pages: 145-150, ISSN: 2378-5861
Decentralized periodic event-triggered control(DPETC) strategies are an attractive solution for wireless cyber-physical systems where resources such as network bandwidthand sensor power are scarce. This is because these strategieshave the advantage of preventing unnecessary data transmis-sions and therefore reduce bandwidth and energy requirements,however the sensor sampling regime remains synchronous.Typically the action of sampling leads almost immediately toa transmission on an event being detected. If the sampling issynchronous, multiple transmission requests may be raised atthe same time which further leads to bursty traffic patterns.Bursty traffic patterns are critical to the DPETC systemsperformance as the probability of collisions and the amount ofrequested bandwidth resources become high ultimately causingdelays. In this paper, we propose an asynchronous samplingscheme for DPETC. The scheme ensures that at each samplingtime, no more than one transmission request can be generatedwhich prevents the occurrence of network traffic collision.At the same time, for the DPETC system with asynchronoussampling a pre-designed global exponential stability andL2-gain performance can still be guaranteed. We illustrate theeffectiveness of the approach through a numerical example.
In this paper, we investigate the uplink transmission performance of low-power wide-area (LPWA) networks with regards to coexisting radio modules. We adopt the long-range (LoRa) radio technique as an example of the network of focus, even though our analysis can be easily extended to other situations. We exploit a new topology to model the network, where the node locations of LoRa follow a Poisson cluster process while other coexisting radio modules follow a Poisson point process. Unlike most of the performance analysis based on stochastic geometry, we take noise into consideration. More specifically, two models, with a fixed and a random number of active LoRa nodes in each cluster, respectively, are considered. To obtain insights, both the exact and simple approximated expressions for coverage probability are derived. Based on them, area spectral efficiency and energy efficiency are obtained. From our analysis, we show how the performance of LPWA networks can be enhanced by adjusting the density of LoRa nodes around each LoRa receiver. Moreover, the simulation results unveil that the optimal number of active LoRa nodes in each cluster exists to maximize the area spectral efficiency.
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