Continuous tracking is often thought to require specialised, actively powered sensors. Yet energy harvesters already embedded in commercial devices, such as Garmin solar-powered smartwatches, generate energy signals that inherently carry continuous variations linked to user motion and environment. Prior studies have shown that these signals are sufficient for classification tasks such as human activity and gesture recognition by exploiting class-distinguishing cues. However, whether they can support continuous trajectory tracking has remained an open question-until now.In this paper, we present the first fundamental study of continuous hand trajectory tracking with wearable solar harvesters. Through a novel radiometric model, we analytically link photovoltaic (PV) power to the solar cell’s geometric configuration, exposing both the promise of energy signals for tracking and their core limitations: the positional ambiguities and distortions that arise when power is used directly for positioning. To resolve this ambiguity, we propose SolarTrack, a framework that embeds a radiometric model as a physical backbone within a sequence-learning pipeline, enforcing cycle consistency between data-driven predictions and physical feasibility. This yields the first standalone solar-based tracker capable of estimating continuous hand motion directly from harvested energy signals.To validate this, we built a wearable prototype with a solar panel and IMU and collected the first dataset pairing motion-capture ground truth with harvested energy from 15 participants (700k samples). Results show that solar signals alone achieve subdecimeter tracking accuracy, outperforming purely data-driven baselines and only 1.6 cm worse compared to the IMU tracker despite having access to only 1D power signal. Furthermore, when fused with IMU, it further boosts IMU performance by 13%.

Post-Quantum Cryptography (PQC) creates payloads that strain the timing and energy budgets of Personal Area Networks. In post-quantum key exchange (PQKE), this causes severe fragmentation, prolonged radio activity, and high transmission overhead on low-power wireless devices. Prior work optimizes cryptographic computation but largely ignores communication cost. This paper separates computation and communication costs using Bluetooth Low Energy as a representative platform and validates them on real hardware. Results show communication often dominates PQKE energy, exceeding cryptographic cost. Efficient quantum-resilient pairing therefore requires coordinated protocol configuration and lower-layer optimization. This work provides developers a practical way to reason about PQC energy trade-offs and informs the evolution of PAN standards toward quantum-safe operation.

In the current rapidly shifting tech environment, energy management systems evolves at a brisk pace. Vehicles are no longer for transportation but have turned into important actors in modern energy grids, where they produce, store and exchange power through innovations like Vehicle-to-Grid (V2G) and Vehicle-to-Vehicle (V2V) schemes. Yet, these new breakthroughs also introduce a unique set of challenges. Traditional setups still rely on centralized trust models, resulting in inefficiencies and security gaps owing to limited transparency and auditability. Blockchain introduce decentralization and trust, keeping transaction records unchangeable, and enabling automated agreements through smart contracts. While this makes blockchain an attractive option for boosting the security, and transparency of energy trading, it does not inherently protect user privacy. This paper systematically surveys blockchain-based energy trading in vehicular networks, aiming to reconcile transparency and decentralization with strong privacy, security, and scalability. We develop a unified taxonomy that organizes the space across trading frameworks, core components (consensus, privacy/confidentiality, security including mutual authentication and Sybil resistance), and system design features (algorithmic strategies, incentive mechanisms, and network architectures). Through a structured review, we compare and categorize existing schemes, highlighting their strengths and weaknesses, and crucially, identify concrete gaps with suggesting future directions to make these technologies work better together.

Multi-class medical image classification using DL continues to face major challenges, including managing multi-modal data, adapting to new tasks, handling distributed datasets, and operating under limited computational resources. Existing approaches fail to address these issues simultaneously, restricting the clinical scalability of AI in healthcare. To overcome these limitations, this paper introduces BigOrthoATD.Net, a unified, serverless, and decentralized learning framework that redefines scalability, adaptability, and efficiency in orthopedic image analysis. Designed to operate across distributed clinical nodes, BigOrthoATD.Net enables privacy-preserving knowledge fusion and multimodal integration across X-ray and CT imaging modalities. The framework supports progressive scalability for new tasks and institutions, achieving continual learning without retraining or performance degradation. Comprehensive experiments conducted across 13 simulated decentralized nodes and 50 orthopedic classes demonstrated that BigOrthoATD.Net achieved a state-of-the-art accuracy of 97.0%, outperforming swarm learning (70.8%) and centralized learning (84.8%), while federated learning failed to converge beyond moderate scale under identical resource-constrained conditions. BigOrthoATD.Net establishes a new benchmark for decentralized medical imaging by surpassing both centralized and decentralized frameworks in accuracy, scalability, and class diversity, while operating efficiently in low-resourced settings.

Malicious Python packages make software supply chains vulnerable by exploiting trust in open-source repositories like Python Package Index (PyPI). Lack of real-time behavioral monitoring makes metadata inspection and static code analysis inadequate against advanced attack strategies such as typosquatting, covert remote access activation, and dynamic payload generation. To address these challenges, we introduce DySec, a machine learning (ML)-based dynamic analysis framework for PyPI that uses eBPF kernel and user-level probes to monitor behaviors during package installation. By capturing 36 real-time features–including system calls, network traffic, resource usage, directory access, and installation patterns–DySec detects threats like typosquatting, covert remote access activation, dynamic payload generation, and multiphase attack malware. We developed a comprehensive dataset of 14,271 Python packages, including 7,127 malicious sample traces, by executing them in a controlled isolated environment. Experimental results demonstrate that DySec achieves 96% detection accuracy with an ML inference latency of <0.5s after dynamic feature extraction, reducing false negatives by 78.65% compared to static analysis and 82.24% compared to metadata analysis. During the evaluation, DySec flagged eleven packages that PyPI classified as benign. A manual analysis, including installation behavior inspection, confirmed six of them as malicious. These findings were reported to PyPI maintainers, resulting in the removal of four packages. DySec bridges the gap between reactive traditional methods and proactive, scalable threat mitigation in open-source ecosystems by uniquely detecting malicious install-time behaviors.

Model pruning has gained traction as a promising defense strategy against backdoor attacks in deep learning. However, existing pruning-based approaches often fall short in accurately identifying and removing the specific parameters responsible for inducing backdoor behaviors. Despite the dominance of fine-tuning-based defenses in recent literature, largely due to their superior performance, pruning remains a compelling alternative, offering greater interpretability and improved robustness in low-data regimes. In this paper, we propose a novel pruning approach featuring a learned \emph{selection} mechanism to identify parameters critical to both main and backdoor tasks, along with an \emph{invertible} pruning mask designed to simultaneously achieve two complementary goals: eliminating the backdoor task while preserving it through the inverse mask. We formulate this as a bi-level optimization problem that jointly learns selection variables, a sparse invertible mask, and sample-specific backdoor perturbations derived from clean data. The inner problem synthesizes candidate triggers using the inverse mask, while the outer problem refines the mask to suppress backdoor behavior without impairing clean-task accuracy. Extensive experiments demonstrate that our approach outperforms existing pruning-based backdoor mitigation approaches, maintains strong performance under limited data conditions, and achieves competitive results compared to state-of-the-art fine-tuning approaches. Notably, the proposed approach is particularly effective in restoring correct predictions for compromised samples after successful backdoor mitigation.

Securing software supply chains is a growing challenge due to the inadequacy of existing datasets in capturing the complexity of next-gen attacks, such as multiphase malware execution, remote access activation, and dynamic payload generation. Existing datasets, which rely on metadata inspection and static code analysis, are inadequate for detecting such attacks. This creates a critical gap because these datasets do not capture what happens during and after a package is installed. To address this gap, we present QUT-DV25, a dynamic analysis dataset specifically designed to support and advance research on detecting and mitigating supply chain attacks within the Python Package Index (PyPI) ecosystem. This dataset captures install and post-install-time traces from 14,271 Python packages, of which 7,127 are malicious. The packages are executed in an isolated sandbox environment using an extended Berkeley Packet Filter (eBPF) kernel and user-level probes. It captures 36 real-time features, that includes system calls, network traffic, resource usages, directory access patterns, dependency logs, and installation behaviors, enabling the study of next-gen attack vectors. ML analysis using the QUT-DV25 dataset identified four malicious PyPI packages previously labeled as benign, each with thousands of downloads. These packages deployed covert remote access and multi-phase payloads, were reported to PyPI maintainers, and subsequently removed. This highlights the practical value of QUT-DV25, as it outperforms reactive, metadata, and static datasets, offering a robust foundation for developing and benchmarking advanced threat detection within the evolving software supply chain ecosystem.

Federated Learning (FL) has emerged as a promising paradigm for secure data sharing in Industrial Internet of Things (IIoT), enabling collaborative model training without direct exchange of raw data. However, recent studies have shown that FL still suffers from privacy vulnerabilities, where adversaries can reconstruct sensitive information by analyzing shared model parameters. Although several privacy-preserving FL (PPFL) schemes have been proposed to address these challenges, they primarily focus on protecting local model privacy, with limited attention to protecting global model confidentiality during aggregation. Additionally, their reliance on centralized aggregation servers introduces risks of single points of failure. To address these challenges, we propose a novel privacy-preserving blockchain-based FL framework (PBFL) that integrates blockchain, homomorphic encryption (HE), and a single masking. Specifically, PBFL employs HE to enable secure model training within the ciphertext domain, ensuring global model confidentiality. The single masking technique allows clients to apply unique random masks to their encrypted local model updates, enabling secure aggregation while preserving local privacy. Additionally, PBFL leverages blockchain for decentralized aggregation and encrypted model storage, effectively mitigating the risks associated with centralized servers. Experimental results demonstrate that PBFL achieves comparable model accuracy to state-of-the-art solutions while providing enhanced privacy protection. Furthermore, even with a client dropout rate of up to 30%, PBFL outperforms other blockchain-based PPFL methods in terms of computational and communication efficiency.

Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative model training in the Industrial Internet of Things (IIoT). By leveraging the decentralization, immutability, and transparency of blockchain technology, Blockchain-enabled FL (BFL) has gained significant attention for enhancing FL’s security and reliability. However, BFL still faces challenges in motivating client participation. While several incentive mechanisms have been proposed, most primarily focus on short-term rewards and overlook the long-term influence of individual contributions on global model performance. To address these challenges, we propose a novel BFL framework that integrates model training with blockchain mining on the client side. Specifically, we design a long-term incentive mechanism based on repeated game theory, where the interactions between participants and the task publisher (TP) are modeled as an infinitely repeated game. We formally prove the existence of a Subgame Perfect Nash Equilibrium, providing theoretical guarantees for stable long-term cooperation. Furthermore, we introduce a hybrid reward scheme that jointly considers contributions to both training and mining tasks, encouraging sustained engagement and attracting new participants. Extensive experiments on MNIST and CIFAR-10 validate that the proposed mechanism enhances the robustness of FL and effectively promotes long-term client participation.

Blockchain is an immutable and distributed ledger managed by all participants, enhancing data transparency and safety. Immutability is a crucial factor in ensuring data transparency and safety. However, there is a significant demand for redaction of the ledger due to security and privacy concerns. In this paper, we propose a redactable blockchain solution based on meta-transactions using zk-SNARK to improve anonymity in a decentralised manner. A one-time cryptographic key generation scheme, designed for a signature generation scheme, produces different keys for each transaction to enhance security and privacy by preventing identity tracing. We also employ zk-SNARK to hide the information of cryptographic keys and signatures. The modification history for each transaction is linked together, and the verification time is significantly short, around 10 msec, even when transactions have multiple modifications. Furthermore, we introduce a redaction fee scheme for transaction owners to maintain concise modification histories encouraging removal instead of modification to minimise the performance overhead associated with this redaction approach.

Integrating Distributed Energy Resources (DERs) into smart grids presents challenges in privacy and transparency for Demand Response (DR) programs. Blockchain offers a secure, but transparent solution, risking privacy. ‘CypherChain’ is introduced as a novel framework for these programs, utilizing Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), and Hypergraph Coloring via CHAIN and CYPHER protocols. These ensure private data aggregation and encrypted processing, balancing privacy with transparency. Tested on real-world smart building data, CypherChain improved data aggregation speed by 40% and cut computational costs by 30% against existing systems, showcasing its potential to revolutionize privacy in smart grids and address DR programs’ privacy-transparency issues.

Web applications use Universal Resource Identifiers (URIs), interchangeably referred to as Uniform Resource Locators (URLs), to locate resources such as files and web pages on the Internet. Messaging services, firewalls, content distribution frameworks, event logs, databases and datasets store countless URIs. Due to the proliferation of the Internet, the number of URIs has increased rapidly, demanding significant storage. Several compression schemes are present in the literature for efficiently storing files on hard disks. However, existing compression schemes are designed for generic content, resulting in sub-optimal storage efficiency for standalone URIs. This paper presents a compression scheme specifically designed for URIs. Our contribution is three-fold: a) an empirical analysis of existing compression schemes for storing URIs, b) a design for a novel URI-focused compression scheme that improves on existing schemes and c) an adaptation of the well-known Huffman coding scheme to URIs using Natural Language Processing (NLP) to create a custom compression dictionary. Evaluation results using five million standalone URI strings show that our novel compression scheme improves storage efficiency by 18%. Furthermore, our customized Huffman coding compression scheme outperforms the standard content-agnostic Huffman technique. Our compression scheme reduces the storage space of a single instance of all URIs in existence – estimated to be more than 130 trillion – by more than 1.2 petabytes (PB) compared to the standard Huffman coding technique. Considering only unique instances of URIs, this bare minimum of 1.2 PB of hard disk savings worth approximately USD$24,000 can be saved, however, in practice, many orders of magnitude more may be possible.

There has been growing attention to the realm of Self-Sovereign Identities (SSI) in the past few years, with significant effort being put into the development of standards and specifications, such as Decentralized Identifiers (DIDs), to be in conformity with essential identity prerequisites such as decentralization, privacy and interoperability. However, the portability of DIDs is still an under-researched topic, even though it is a requirement of great importance in order to guarantee user autonomy amidst the numerous implementations being developed by the industry. In this paper, we explore the concept of portability of DIDs, highlighting how the current standards and protocols don't fully address this major point. We also define key requirements for addressing this feature, and discuss some major concerns on security and privacy that may emerge with the development of DID portability schemes.

As we approach the physical limits of storage density, digital storage prices are no longer plummeting, despite the lingering belief that they still are. Meanwhile, data production continues to grow, making it harder to securely manage the data we produce. Typical digital storage media is often consumed by a small number of large files that are widely available on the web. If the availability of files on the web could be predicted, the choice between consuming local storage resources or simply redownloading the file in the future could be automated, thus increasing the efficiency of backup and encryption workflows. Through a large-scale analysis of hundreds of billions of crawl URLs spanning 8 years, as well as over 60 million HTTP header request responses from web servers, we explore the requirements and design of a framework for such predictions. It includes a data structure for efficiently representing the lateral/longitudinal availability of files and an extensible mathematical model for fast and adaptable prediction calculations. Additionally, we contribute novel observations about file availability on the web, including the identification of a period of initial volatility in their lifespans. Analysis indicates that a pool of 2,500TB of distributed, popular files is freely and predictably available to users, offering opportunities to reduce the storage and computational costs of both backup and encryption.

Web users often follow hyperlinks hastily, expecting them to be correctly programmed. However, it is possible those links contain typos or other mistakes. By discovering active but erroneous hyperlinks, a malicious actor can spoof awebsite or service, impersonating the expected content and phishing private information. In typosquatting, misspellings of common domains are registered to exploit errors when users mistype a web address. Yet, no prior research has been dedicated to situations where the linking errors of web publishers (i.e. developers and content contributors) propagate to users. We hypothesize that these hijackable hyperlinks exist in large quantities with the potential to generate substantial traffic. Analyzing largescale crawls of the web using high-performance computing, we show the web currently contains active links to more than 572 000 dot-com domains that have never been registered, what we term phantom domains. Registering 51 of these, we see 88% of phantom domains exceeding the traffic of a control domain, with up to 10 times more visits. Our analysis shows that these links exist due to 17 common publisher error modes, with the phantom domains they point to free for anyone to purchase and exploit for under $20, representing a low barrier to entry for potential attackers.

Blockchain full nodes are pivotal for transaction availability, as they store the entire ledger, but verifying their storage integrity faces challenges from malicious remote storage attacks such as Sybil, outsourcing, and generation attacks. However, there is no suitable proof-of-storage solution for blockchain full nodes to ensure a healthy number of replicas of the ledger. Existing proof-of-storage solutions are designed for general-purpose settings where a data owner uses secret information to verify storage, rendering them unsuitable for blockchain where proof-of-storage must be fast, publicly verifiable, and data owner-agnostic. This paper introduces a decentralised and quantum-resistant solution named Non-interactive Practical Proof of Storage (nPPoS) with an asymmetric encoding and decoding scheme, for fast and secure PoStorage, and Zero-Knowledge Scalable Transparent Arguments of Knowledge (zk-STARKs), for public variability in blockchain full nodes. The algorithm with asymmetric times for encoding and decoding creates unique block replicas and corresponding proofs for each storage node to mitigate malicious remote attacks and minimise performance degradation. The intentional resource-intensive encoding deters attacks, while faster decoding minimises performance overhead. Through zk-STARKs, nPPoS achieves public verifiability enabling one-to-many verification for scalability, quantum resistance and decentralisation. It also introduces a two-phase randomisation technique and a time-weighted trustworthiness measurement for scalability and adaptability.

The emergence of the Internet of Things (IoT), Industry 5.0 applications and associated services have caused a powerful transition in the cyber threat landscape. As a result, organisations require new ways to proactively manage the risks associated with their infrastructure. In response, a significant amount of research has focused on developing efficient Cyber Threat Intelligence (CTI) sharing. However, in many cases, CTI contains sensitive information that has the potential to leak valuable information or cause reputational damage to the sharing organisation. While a number of existing CTI sharing approaches have utilised blockchain to facilitate privacy, it can be highlighted that a comprehensive approach that enables dynamic trust-based decision-making, facilitates decentralised trust evaluation and provides CTI producers with highly granular sharing of CTI is lacking. Subsequently, in this paper, we propose a blockchain-based CTI sharing framework, called Priv-Share, as a promising solution towards this challenge. In particular, we highlight that the integration of differential sharing, trustless delegation, democratic group managers and incentives as part of Priv-Share ensures that it can satisfy these criteria. The results of an analytical evaluation of the proposed framework using both queuing and game theory demonstrate its ability to provide scalable CTI sharing in a trustless manner. Moreover, a quantitative evaluation of an Ethereum proof-of-concept prototype demonstrates that applying the proposed framework within real-world contexts is feasible.

While UbiComp research has steadily improved the performance of localisation systems, the analysis of such datasets remains largely unaddressed. In this paper, we present a tool to facilitate querying and analysis of localisation time-series with a focus on semantic localisation. Drawing on well-established models to represent movement and mobility, we first develop a query language for localisation datasets. We then develop a software library in R that implements this querying. We use case studies to demonstrate how our programming tool can be used to query localisation datasets. Our work addresses an important gap in localisation research, by providing a flexible tool that can model and analyse localisation data programmatically and in real time.

By 2050, global sales of electric vehicles (EVs) are predicted to account for approximately 70% of all vehicle sales. However, whilst transitioning from combustion engine vehicles to EVs would result in reduced carbon dioxide emissions, it would place significant strain on energy generation, and grid infrastructure. Many EV studies investigated routing or charge station management, while research on predicting energy demand at a specific location was lacking. To address this, our study focused on predicting EV’s next charge location. We developed a localised onboard Convolutional Neural Network (CNN) model that achieved accuracies up to 95%. Our proposal used community area Distributed Energy Resource Management Systems (DERMS) to train EV models during charge transactions, while predictions were made onboard each EV. To address the lack of EV mobility charge data, we created a hybrid dataset using empirical Chicago city taxi mobility data adding synthetic EV charging event states. We conducted multiple experiments over various battery charge levels to understand how far ahead in time next charge location could be predicted, achieving reliable predictions up to 3 days before requiring next charge. Finally, this study laid a foundation for future EV mobility research by providing a novel EV mobility charge dataset.

CTI sharing is increasingly used by organisations to strengthen security. The sensitivity of CTI has led to research on trust-based sharing, yet most existing CTI sharing approaches only support static trust-based decisions or centralised trust evaluation, limiting their scalability and lead to centralised risk. This paper proposes a blockchain-based CTI sharing framework that relies on trustless delegates for dynamic trust-based decision-making and decentralised trust evaluation. To facilitate trustless delegation, our proposal allows CTI producers to intentionally inject false data on a periodic basis into the system to audit the behaviour of delegates. Moreover, unlike existing approaches, delegates within our framework facilitate sharing of CTI directly with consumers such that scalable CTI sharing occurs. The results of a qualitative evaluation of the proposed framework's security show that it is resilient to common privacy and trust concerns. Moreover, a quantitative evaluation of a proof-of-concept prototype using Ethereum show that the proposed framework is scalable and cost-effective.

Blockchain is a distributed and immutable ledger managed by all participants. The full nodes which store the entire ledger play an essential role in managing it in a transparent and decentralised manner. However, it is difficult to verify that full nodes store the entire ledger in their dedicated storage due to Sybil, outsourcing, or generation attacks. Existing work on proving storage for cloud computing and remote data storage applications has high latency for decryption, and its impact on decentralisation is unclear, rendering it impractical for use in blockchain. In this paper, we propose a decentralised Practical Proof of Storage (PPoS) solution for blockchain full nodes with asymmetric latencies for encryption and decryption, which introduces a chained encryption and decryption architecture. To generate a unique replica of a block, each full node performs encryption with its own address and a previously encrypted block, storing the unique block in its dedicated storage. In PPoS, encryption is expensive and time consuming, enabling it to detect outsourcing and generation attacks and to deter Sybil attacks. Simultaneously, decryption is about 25 times faster than encryption, resulting in minimal performance overhead. The proof process is also decentralised by randomly selecting provers, verifiers, and encrypted blocks. Our experiments use up to 720 real BitCoin blocks to evaluate the performance and quantify the decentralisation of PPoS. Our results show that PPoS's asymmetric design reduces decryption time 25-fold over existing approaches, while maintaining a high degree of decentralisation, confirming its suitability for blockchain full nodes.

Blockchain technology has the potential to revolutionize the energy sector by enabling peer-to-peer energy trading, demand-side flexibility trading, and renewable energy certificate trading, among other decentralised energy trading use cases. However, the lack of interoperability between blockchain networks and platforms is a significant challenge that leads to data and information silos. To address this challenge, a Blockchain Agnostic Interoperability Framework (BAILIF) is proposed, which provides a decentralized notary service and a cross-chain attestation and verification protocol. BAILIF adheres to the core principles of blockchain, such as decentralization, transparency, and trust, and can be adopted in other decentralized ecosystems where blockchain interoperability is required. A proof of concept for a distributed energy trading application demonstrates the solution's feasibility. The results showed that BAILIF could achieve a throughput of up to 666 transactions per second, indicating its potential to enable seamless data sharing across blockchain platforms and promote the adoption of renewable energy sources.

6G-enabled IoT demands effectively utilising scarce resources to provide massive scale in network capacity. While blockchain-based resource sharing schemes have been proposed to enable effective resource allocation, they alone cannot ascertain the trust in the participating nodes, as they do not monitor node activities during the resource sharing. Trust and Reputation Management (TRM) can potentially solve these trust issues. However, changeable keys employed in blockchains to improve privacy preservation may render the TRM unusable, as the same node is no longer identifiable by a single key to which the trust and reputation scores are bound. This paper proposes a privacy-preserving TRM for blockchain-based resource sharing in 6G-enabled IoT networks. Our solution employs interconnected public-private blockchains, namely Isolated Identity Chain and Main Resource-sharing Chain to protect nodes' identity. Our TRM framework allows the nodes to use changeable keys in each transaction, making it impossible to trace the sharing history. The experimental results on a proof-of-concept implementation indicate the feasibility of our framework as it only incurs minimal overheads.

Many real-world applications employ web service frameworks to provide application programming interface (API) services to businesses and end-consumers, following a client-server architecture. Service providers typically run a web server to deliver services to consumers. Here, service providers and consumers often belong to different organisations in applications such as supply chain management and logistics. In multi-stakeholder safety-critical and mission-critical applications, the centralised web server delivers services by executing computations upon receiving consumers' API requests. Such computations may fail due to crash or byzantine failures. The former happens because of hardware or infrastructure faults, while the latter happens because of a malicious actor. Note that the organisation that runs the web server may act dishonestly by running computations incorrectly for financial benefits, or an external attacker may compromise the web server without the knowledge of the infrastructure owner. As a result of these failures, the centralised web server is susceptible to single-point-of-failure issues. The organisation that runs the web server must be blindly trusted and does not provide transparency and auditability to its clients. We propose DeWS, a Decentralised and Byzantine Fault-tolerant Web Service framework, which overcomes these single-point-of-failure issues while delivering transparency and auditability through a blockchain-based ledger. The proof-of-concept implementation of DeWS using the Tendermint blockchain platform shows that our framework can tolerate byzantine failures at the cost of high latency. DeWS is the first Byzantine Fault-tolerant web service framework. It can support a shift towards more decentralised web services to provide safety assurances for safety-critical and mission-critical applications.

In this paper, we propose to utilise diffusion models for data augmentation in speech emotion recognition (SER). In particular, we present an effective approach to utilise improved denoising diffusion probabilistic models (IDDPM) to generate synthetic emotional data. We condition the IDDPM with the textual embedding from bidirectional encoder representations from transformers (BERT) to generate high-quality synthetic emotional samples in different speakers' voices. We implement a series of experiments and show that better quality synthetic data helps improve SER performance. We compare results with generative adversarial networks (GANs) and show that the proposed model generates better-quality synthetic samples that can considerably improve the performance of SER when augmented with synthetic data.

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual effort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitateddeep representation learningwhere hierarchical representations are automatically learned in a data-driven manner. This article presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER.

Despite the recent advancement in speech emotion recognition (SER) within a single corpus setting, the performance of these SER systems degrades significantly for cross-corpus and cross-language scenarios. The key reason is the lack of generalisation in SER systems towards unseen conditions, which causes them to perform poorly in cross-corpus and cross-language settings. Recent studies focus on utilising adversarial methods to learn domain generalised representation for improving cross-corpus and cross-language SER to address this issue. However, many of these methods only focus on cross-corpus SER without addressing the cross-language SER performance degradation due to a larger domain gap between source and target language data. This contribution proposes an adversarial dual discriminator (ADDi) network that uses the three-players adversarial game to learn generalised representations without requiring any target data labels. We also introduce a self-supervised ADDi (sADDi) network that utilises self-supervised pre-training with unlabelled data. We propose synthetic data generation as a pretext task in sADDi, enabling the network to produce emotionally discriminative and domain invariant representations and providing complementary synthetic data to augment the system. The proposed model is rigorously evaluated using five publicly available datasets in three languages and compared with multiple studies on cross-corpus and cross-language SER. Experimental results demonstrate that the proposed model achieves improved performance compared to the state-of-the-art methods.

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems lack generalisation across different conditions. A key underlying reason for poor generalisation is the scarcity of emotion datasets, which is a significant roadblock to designing robust machine learning (ML) models. Recent works in SER focus on utilising multitask learning (MTL) methods to improve generalisation by learning shared representations. However, most of these studies propose MTL solutions with the requirement of meta labels for auxiliary tasks, which limits the training of SER systems. This paper proposes an MTL framework (MTL-AUG) that learns generalised representations from augmented data. We utilise augmentation-type classification and unsupervised reconstruction as auxiliary tasks, which allow training SER systems on augmented data without requiring any meta labels for auxiliary tasks. The semi-supervised nature of MTL-AUG allows for the exploitation of the abundant unlabelled data to further boost the performance of SER. We comprehensively evaluate the proposed framework in the following settings: (1) within corpus, (2) cross-corpus and cross-language, (3) noisy speech, (4) and adversarial attacks. Our evaluations using the widely used IEMOCAP, MSP-IMPROV, and EMODB datasets show improved results compared to existing state-of-the-art methods.

In cryptocurrencies, privacy of users is preserved using pseudonymity . However, it has been shown that pseudonymity does not result in anonymity if a user's transactions are linkable. This makes cryptocurrencies vulnerable to deanonymization attacks. The current solutions proposed in the literature suffer from at least one of the following issues: (1) requiring a trusted third–party entity, (2) poor performance, and (3) incompatible with the standard structure of cryptocurrencies. In this article, we propose Unlinkable Coin (UCoin), a secure mix–based approach to address these issues. In UCoin, the link between the input (payer) and output (payee) addresses in a transaction is broken. This is done by mixing the transactions of multiple users into a single aggregated transaction in which the output addresses have been secretly shuffled. In our protocol design, we first develop HDC–net, a secure shuffling protocol that enables a group of users to anonymously publish their data. Then, we deploy the proposed HDC–net protocol in the UCoin architecture (as a mixing unit) to generate the aggregate transactions. We show that UCoin (1) does not rely on a trusted third–party, (2) can mix 50 transactions in 6.3 seconds that is 18% faster than the current solutions, and (3) is fully compatible with the architecture of cryptocurrencies.

Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, and consent management. We introduce a decentralized data governance framework based on blockchain technology and proxy re-encryption to let data owners control and track their data through privacy-enhancing and consent management mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We have used Digital e-prescription as the use case since it has multiple stakeholders and sensitive data while enabling the medical fraternity to manage patients’ prescription data, involving patients as data owners, doctors, and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm and pyUmbral PRE show that the proposed decentralized system guarantees transparency, privacy, and trust with minimal overhead.

Blockchain has attracted considerable attention as a solution to the challenges of privacy, security and decentralisation for many applications. However, these characteristics of the blockchain result in ever growing ledger size, which is one of the major barriers to blockchain adoption in large-scale networks such as the Internet of Things (IoT). In this paper, we propose Multi-Level Distributed Caching (MLDC) for blockchain storage optimisation which reduces data replication based on data access pattern. MLDC divides nodes into storage classes (SCs) by their node availability, and assigns each SC a different Access Frequency (AF) to remove data from the local storage. Over time, each node only stores frequently accessed data, so MLDC can reduce the total storage cost by 83% compared to conventional blockchain systems, while maintaining blockchain consistency and data availability with a slight increase in network overhead and data query delay.

Cyber Threat Intelligence (CTI) sharing has become a significant issue with the increasing number of cyberattacks. In CTI sharing, one entity (e.g., an organisation or a user) intends to share specific threat information to another entity that might otherwise be unavailable to another entity. However, this process needs to address many challenges, including privacy, trust, and accountability. In this paper, we propose a novel blockchain-based architecture that facilitates the secure dissemination of CTI data. The motivation for this study is to provide a solution that can efficiently address privacy, trust, and accountability when sharing CTI among organisations as well as maintaining an intelligence-based informed decisions. We discuss the current problems within the domain of CTI sharing using blockchain, and our proposal leverages the salient properties of the blockchain, e.g., decentralised, cryptographic keys, immutability, etc., to address those issues. We discuss the detailed design of the proposed architecture. We demonstrate that our approach offers a more effective and efficient way of CTI sharing that has the potential to overcome the trust barriers, data privacy, and accountability issues inherent in this domain.

Energy-positive activity recognition classifies human activities, including walking, running, and sitting, while harvesting kinetic energy from such activities. In this setting, the device's lifetime de-pends on the user's activity profile and the resources needed to run inference to classify activities. Thus, the selection of machine learning classification models for energy-positive activity recognition must consider both model's classification accuracy and energy con-sumption compared to the harvested energy from human activities. In this paper, we study the trade-off between accuracy and resource usage of a neural network model when different feature extraction techniques are used. Our results indicate that an on-board sched-uling algorithm can be used to dynamically switch between the optimal feature input tuned for accuracy and energy consumption.

Cyber Threat Intelligence (CTI) is the knowledge of cyber and physical threats that help mitigate potential cyber attacks. The rapid evolution of the current threat landscape has seen many organisations share CTI to strengthen their security posture for mutual benefit. However, in many cases, CTI data contains attributes (e.g., software versions) that have the potential to leak sensitive information or cause reputational damage to the sharing organisation. While current approaches allow restricting CTI sharing to trusted organisations, they lack solutions where the shared data can be verified and disseminated ‘differentially’ (i.e., selective information sharing) with policies and metrics flexibly defined by an organisation. In this paper, we propose a blockchain-based CTI sharing framework that allows organisations to share sensitive CTI data in a trusted, verifiable and differential manner. We discuss the limitations associated with existing approaches and highlight the advantages of the proposed CTI sharing framework. We further present a detailed proof of concept using the Ethereum blockchain network. Our experimental results show that the proposed framework can facilitate the exchange of CTI without creating significant additional overheads.

In this paper, we present a WItness based Data priority mEchanism (WIDE) for vehicles in the vicinity of an accident to facilitate liability decisions. WIDE evaluates the integrity of data generated by these vehicles, called witnesses, in the event of an accident to assure the reliability of data to be used for making liability decisions and ensure that such data are received from credible witnesses. To achieve this, WIDE introduces a two-level integrity assessment to achieve end-to-end integrity by initially ascertaining the integrity of data-producing sensors, and validating that data generated have not been altered on transit by compromised road-side units (RSUs) by executing a practical byzantine fault tolerance (pBFT) protocol to reach consensus on data reliability. Furthermore, WIDE utilises a blockchain based reputation management system (BRMS) to ensure that only data from highly reputable witnesses are utilised as contributing evidence for facilitating liability decisions. Finally, we formally verify the proposed framework against data integrity requirements using the Automated Verification of Internet Security Protocols and Applications (AVISPA) with High-Level Protocol Specification Language (HLPSL). Qualitative arguments show that our proposed framework is secured against identified security attacks and assures the reliability of data utilised for making liability decisions, while quantitative evaluations demonstrate that our proposal is practical for fully autonomous vehicle forensics.

In recent years, blockchain technology has received tremendous attention. Blockchain users are known by a changeable public key (PK) that introduces a level of anonymity; however, studies have shown that anonymized transactions can be linked to deanonymize the users. Most of the existing studies on user deanonymization focus on monetary applications; however, the blockchain has received extensive attention in nonmonetary applications such as the Internet of Things (IoT). In this article, we study the impact of deanonymization on the IoT-based blockchain. We populate a blockchain with data of smart home devices and then apply machine learning algorithms in an attempt to classify the transactions to a particular device that, in turn, risks the privacy of the users. Two types of attack models are defined: 1) informed attacks: where attackers know the type of devices installed in a smart home and 2) blind attacks: where attackers do not have this information. We show that machine learning algorithms can successful classify the transactions with 90% accuracy. To enhance the anonymity of the users, we introduce multiple obfuscation methods which include combining multiple packets into a transaction, merging ledgers of multiple devices, and delaying transactions. The implementation results show that these obfuscation methods significantly reduce the attack success rates to 20%–30% and, thus, enhance the user privacy.

The emergence of the Internet of Things (IoT) and distributed energy resources (DERs), has given rise to collaborative communities that manage their energy production and consumption load through peer-to-peer decentralized energy trading (P2P DET). To address the issue of distributed trust in these communities, blockchain technology is widely considered as a promising solution due to its ability to provide records provenance and visibility. However, the current blockchain-based platforms are known to compromise on scalability and privacy in favor of trustless interactions and often do not support interoperability. In this work, we propose a hypergraph-based adaptive consortium blockchain (HARB) framework, which coordinates DERs through high-order relationships rather than P2P pairwise relationships. HARB is presented in a three-layered network architecture to address the aforementioned challenges. The bottom layer (Underlay) addresses the issue of scalability, by exploiting the rich representation ability of hypergraphs to describe complex relationships among DERs and end users. We use the described complex relations to form scalable network clusters with intra- and inter-community energy trading relationships. The middle layer (Overlay) presents a blockchain service model to describe adaptive blockchain modules that support interoperability between the network clusters. To preserve privacy, we present a data tagging and anonymization model in the top layer (Contract), which attributes transactions to specific network clusters. To evaluate our framework, we modeled various IoT devices with different computing resource profiles to simulate a distributed energy trading (DET) environment. The analyzed results have shown that our proposed framework can effectively improve the performance of blockchain-based DET systems.

Inspite the emerging importance of Speech Emotion Recognition (SER), the state-of-the-art accuracy is quite low and needs improvement to make commercial applications of SER viable. A key underlying reason for the low accuracy is the scarcity of emotion datasets, which is a challenge for developing any robust machine learning model in general. In this article, we propose a solution to this problem: a multi-task learning framework that uses auxiliary tasks for which data is abundantly available. We show that utilisation of this additional data can improve the primary task of SER for which only limited labelled data is available. In particular, we use gender identifications and speaker recognition as auxiliary tasks, which allow the use of very large datasets, e. g., speaker classification datasets. To maximise the benefit of multi-task learning, we further use an adversarial autoencoder (AAE) within our framework, which has a strong capability to learn powerful and discriminative features. Furthermore, the unsupervised AAE in combination with the supervised classification networks enables semi-supervised learning which incorporates a discriminative component in the AAE unsupervised training pipeline. This semi-supervised learning essentially helps to improve generalisation of our framework and thus leads to improvements in SER performance. The proposed model is rigorously evaluated for categorical and dimensional emotion, and cross-corpus scenarios. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on two publicly available datasets.

Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, consent management, and selective disclosure. We introduce a decentralized data governance framework based on blockchain technology, proxy re-encryption, and Boneh, Boyen, and Shacham (BBS) signatures to let data owners control, selectively share and track their data through privacy-enhancing, consent management, and selective disclosure mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We use Digital medical e-prescription as the use case since it handles sensitive data in a multi-stakeholder environment while showing how the medical community can manage patients’ sensitive prescription data, involving patients as data owners, and doctors, and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm, Hyperledger Besu, Ethereum, pyUmbral PRE, and BBS signatures show that the proposed decentralized system is platform-agnostic, scalable and guarantees a higher degree of transparency, privacy, and trust with minimal overhead.

Distribution, security, and immutability have led to the great success of blockchain in many applications, while contributing to major increases in ledger size. The storage challenge is one of the major barriers to the adoption of blockchain in the Internet of Things (IoT), which consists of many resource constrained devices. In this paper, we propose Multi-Level Distributed Caching (MLDC) for blockchain storage optimisation which reduces data replication based on data access patterns in a decentralised manner. For storage optimisation of data-centric blockchains, MLDC introduces a hierarchical storage class (SC), in which every node is assigned to an SC with its own Access Frequency (AF) threshold based on node availability. To reduce the number of replications shared among participant nodes, each node in a SC continues to remove unaccessed data from local storage based on a threshold time determined by the AF threshold of the SC, while maintaining all block hashes for consistency. Eventually, all nodes in MLDC store the most frequently accessed data in their local storage, so MLDC effectively reduces the storage and query costs while minimising network overhead. We also analyse the security of MLDC and quantitatively evaluate its performance for both the uniform access and exponentially decaying access patterns. The evaluation was carried out on a representative blockchain simulator with 15 storage nodes. Our results from 11 hours of experiments producing 6667 blocks and 39997 transactions show good performance for MLDC. The results of the experimentation for the exponentially decaying assess pattern show that MLDC can reduce the total storage cost by 83% compared to conventional blockchain systems, while maintaining blockchain consistency and data availability with a slight increase in network overhead and query cost.

The recent outbreak of coronavirus disease has demonstrated that physical human interactions and modern movement paradigms are the principle drivers for the rapid spatial spread of infectious diseases. Modelling the impact of human mobility is crucial to understand the underlying dynamics of disease spread and consequently to develop effective containment and control strategies. While previous studies have investigated the impact of specific mobility profiles on the spreading dynamics of infectious diseases, they used either highly aggregated spatio-temporal data or portions of datasets that span a short period of time. These limitations do not allow to study how the influence of different mobility aspects on the spread changes as a disease outbreak progresses. In this paper we use large-scale comprehensive human mobility traces to study the impact of the latent period on the spreading dynamics of diseases. In addition, we provide a detailed analysis of how the spreading power of different mobility profiles changes over time. We propose an approach that analyses the behaviour of the individuals' spreading power as time progresses. Through extensive disease spread simulations we uncover a population influence homogeneity threshold, defined by a percentage of the population at which the identified mobility groups become equally influential to the spread.

The high power consumption of inertial activity sensors limits the battery lifetime of today's wearable devices. Recent studies promise to extend the lifetime of wearable devices by translating kinetic energy from human movements into electrical energy while using the harvesting signal to replace conventional activity sensors. However, in human-centric applications, the amount of harvested kinetic energy is not enough to power a real-time activity recognition algorithm and run the wearable device perpetually. In this paper, we propose Solar based human Activity Recognition (SolAR), which uses solar cells simultaneously as an activity sensor as well as an energy source. Our key observation is that the power available from a wrist-worn solar cell changes dynamically while a person moves, encoding information about the underlying activity. We collect empirical solar energy data to explore its activity sensing potential and implement the activity recognition pipeline on an ultra low-power micro-controller unit to evaluate the end-to-end power consumption of the system. Our analysis reveals that SolAR improves activity recognition accuracy by up to 8.3% and harvests more than one order of magnitude higher power compared to its kinetic counterpart. This enables SolAR to generate more energy than required for the entire activity recognition pipeline, which we term as energy positive activity recognition, achieving uninterrupted, autonomous, self-powered and real-time operation.

Blockchain technology can provide immutability, provenance and traceability in supply chains. To utilize Blockchain's full potential, it is important to link supply chain events to the relevant entities for traceability and accountability purposes. Authorized participation is realised through consortium of various organisations. Transactions are verified by peer nodes pertaining to the consortium. Hence, privacy preservation of trade sensitive information such as trade flows and locations of production, storage and retail sites cannot be ascertained. In this work, we propose a privacy-preservation framework, TradeChain, which decouples the trade events of participants using decentralised identities. TradeChain adopts the Self-Sovereign Identity (SSI) principles and makes the following novel contributions: a) it incorporates two separate ledgers: a public permissioned blockchain for maintaining identities and the permissioned blockchain for recording trade flows, b) it uses Zero Knowledge Proofs (ZKPs) on traders' private credentials to prove multiple identities on trade ledger and c) allows data owners to define dynamic access rules for verifying traceability information from the trade ledger using access tokens and Ciphertext Policy Attribute-Based Encryption (CP-ABE). A proof of concept implementation of TradeChain is presented on Hyperledger Indy and Fabric and an extensive evaluation of execution time, latency and throughput reveals minimal overheads.

The Internet of Things (IoT) has important applications in our daily lives, including health and fitness tracking, environmental monitoring, and transportation. However, sensor nodes in IoT suffer from the limited lifetime of batteries resulting from their finite energy availability. A promising solution is to harvest energy from environmental sources, such as solar, kinetic, thermal, and radio-frequency (RF) waves, for perpetual and continuous operation of IoT sensor nodes. In addition to energy generation, recently energy harvesters have been used for context detection, eliminating the need for conventional activity sensors (e.g., accelerometers), saving space, cost, and energy consumption. Using energy harvesters for simultaneous sensing and energy harvesting enables energy positive sensing-an important and emerging class of sensors, which harvest more energy than required for context detection and the additional energy can be used to power other components of the system. Although simultaneous sensing and energy harvesting is an important step forward toward autonomous self-powered sensor nodes, the energy and information availability can be still intermittent, unpredictable, and temporally misaligned with various computational tasks on the sensor node. This article provides a comprehensive survey on task scheduling algorithms for the emerging class of energy harvesting-based sensors (i.e., energy positive sensors) to achieve the sustainable operation of IoT. We discuss inherent differences between conventional sensing and energy positive sensing and provide an extensive critical analysis for devising revised task scheduling algorithms incorporating this new class of sensors. Finally, we outline future research directions toward the implementation of autonomous and self-powered IoT.

Authorization or access control limits the actions a user may perform on a computer system, based on predetermined access control policies, thus preventing access by illegitimate actors. Access control for the Internet of Things (IoT) should be tailored to take inherent IoT network scale and device resource constraints into consideration. However, common authorization systems in IoT employ conventional schemes, which suffer from overheads and centralization. Recent research trends suggest that blockchain has the potential to tackle the issues of access control in IoT. However, proposed solutions overlook the importance of building dynamic and flexible access control mechanisms. In this paper, we design a decentralized attribute-based access control mechanism with an auxiliary Trust and Reputation System (TRS) for IoT authorization. Our system progressively quantifies the trust and reputation scores of each node in the network and incorporates the scores into the access control mechanism to achieve dynamic and flexible access control. We design our system to run on a public blockchain, but we separate the storage of sensitive information, such as user’s attributes, to private sidechains for privacy preservation. We implement our solution in a public Rinkeby Ethereum test-network interconnected with a lab-scale testbed. Our evaluations consider various performance metrics to highlight the applicability of our solution for IoT contexts.

Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) data. In this work, we propose a framework that utilises the mixup data augmentation scheme to augment the GAN in feature learning and generation. To show the effectiveness of the proposed framework, we present results for SER on (i) synthetic feature vectors, (ii) augmentation of the training data with synthetic features, (iii) encoded features in compressed representation. Our results show that the proposed framework can effectively learn compressed emotional representations as well as it can generate synthetic samples that help improve performance in within-corpus and cross-corpus evaluation.

Speech emotion recognition systems (SER) can achieve high accuracy when the training and test data are identically distributed, but this assumption is frequently violated in practice and the performance of SER systems plummet against unforeseen data shifts. The design of robust models for accurate SER is challenging, which limits its use in practical applications. In this paper we propose a deeper neural network architecture wherein we fuse DenseNet, LSTM and Highway Network to learn powerful discriminative features which are robust to noise. We also propose data augmentation with our network architecture to further improve the robustness. We comprehensively evaluate the architecture coupled with data augmentation against (1) noise, (2) adversarial attacks and (3) cross-corpus settings. Our evaluations on the widely used IEMOCAP and MSP-IMPROV datasets show promising results when compared with existing studies and state-of-the-art models.

Privacy concerns are considered one of the major challenges in the applications of speech emotion recognition (SER) as it involves the complete sharing of speech data, which can bring threatening consequences to people’s lives. Federated learning is an effective technique to avoid privacy infringement by involving multiple participants to collaboratively learn a shared model without revealing their local data. In this work, we evaluated federated learning for SER using a publicly available dataset. Our preliminary results show that speech emotion recognition can benefit from federated learning by not exporting sensitive user data to central servers, while achieving promising results compared to the state-of-the-art.

Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive validator selection and consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger.

Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer information-rich sensing signals while the energy from the harvester is used to power a load. We systematically analyze multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes.

Personal IoT data is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Typically, marketplaces are centralized systems that raise concerns of privacy, single point of failure, little transparency and involve trusted intermediaries to be fair. Furthermore, the battery-operated IoT devices limit the amount of IoT data to be traded in real-time that affects buyer/seller satisfaction and hence, impacting the sustainability and usability of such a marketplace. This work proposes to utilize blockchain technology to realize a trusted and transparent decentralized marketplace for contract compliance for trading IoT data streams generated by battery-operated IoT devices in real-time. The contribution of this paper is two-fold: (1) we propose an autonomous blockchain-based marketplace equipped with essential functionalities such as agreement framework, pricing model and rating mechanism to create an effective marketplace framework without involving a mediator, (2) we propose a mechanism for selection and allocation of buyers' demands on seller's devices under quality and battery constraints. We present a proof-of-concept implementation in Ethereum to demonstrate the feasibility of the framework. We investigated the impact of buyer's demand on the battery drainage of the IoT devices under different scenarios through extensive simulations. Our results show that this approach is viable and benefits the seller and buyer for creating a sustainable marketplace model for trading IoT data in real-time from battery-powered IoT devices.

Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. However, analysis of multiple security data could provide more comprehensive and system-wide anomaly detection in industrial networks. In this paper, we present an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWaT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors. However, multi-source analysis is more robust because it can detect anomalies from multiple sources simultaneously, while achieving comparable precision for each of the sources.

The recent outbreak of a novel coronavirus and its rapid spread underlines the importance of understanding human mobility. Enclosed spaces, such as public transport vehicles (e.g. buses and trains), offer a suitable environment for infections to spread widely and quickly. Investigating the movement patterns and the physical encounters of individuals on public transit systems is thus critical to understand the drivers of infectious disease outbreaks. For instance, previous work has explored the impact of recurring patterns inherent in human mobility on disease spread, but has not considered other dimensions such as the distance travelled or the number of encounters. Here, we consider multiple mobility dimensions simultaneously to uncover critical information for the design of effective intervention strategies. We use one month of citywide smart card travel data collected in Sydney, Australia to classify bus passengers along three dimensions, namely the degree of exploration, the distance travelled and the number of encounters. Additionally, wes imulate disease spread on the transport network and trace the infection paths. We investigate in detail the transmissions between the classified groups while varying the infection probability and the suspension time of pathogens. Our results show that characterizing individuals along multiple dimensions simultaneously uncovers a complex infection interplay between the different groups of passengers, that would remain hidden when considering only a single dimension. We also identify groups that are more influential than others given specific disease characteristics, which can guide containment and vaccination efforts.

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

Long range wide area networks (LoRaWAN) technology provides a simple solution to enable low-cost services for low power internet-of-things (IoT) networks in various applications. The current evaluation of LoRaWAN networks relies on simulations or early testing, which are typically time consuming and prevent effective exploration of the design space. This paper proposes an analytical model to calculate the delay and energy consumed for reliable Uplink (UL) data delivery in Class A LoRaWAN. The analytical model is evaluated using a real network test-bed as well as simulation experiments based on the ns-3 LoRaWAN module. The resulting comparison confirms that the model accurately estimates the delay and energy consumed in the considered environment. The value of the model is demonstrated via its application to evaluate the impact of the number of end-devices and the maximum number of data frame retransmissions on delay and energy consumed for the confirmed UL data delivery in LoRaWAN networks. The model can be used to optimize different transmission parameters in future LoRaWAN networks.

Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically crafted to echo human perception of speech signals. However, a filter bank that is designed from perceptual evidence is not always guaranteed to be the best in a statistical modelling framework where the end goal is for example emotion classification. This has fuelled the emerging trend of learning representations from raw speech especially using deep learning neural networks. In particular, a combination of Convolution Neural Networks (CNNs) and Long Short Term Memory (LSTM) have gained great traction for the intrinsic property of LSTM in learning contextual information crucial for emotion recognition; and CNNs been used for its ability to overcome the scalability problem of regular neural networks. In this paper, we show that there are still opportunities to improve the performance of emotion recognition from the raw speech by exploiting the properties of CNN in modelling contextual information. We propose the use of parallel convolutional layers to harness multiple temporal resolutions in the feature extraction block that is jointly trained with the LSTM based classification network for the emotion recognition task. Our results suggest that the proposed model can reach the performance of CNN trained with hand-engineered features from both IEMOCAP and MSP-IMPROV datasets.

Careful energy management is a prerequisite for long-term, unattended operation of solar-harvesting sensing systems. We observe that in many applications the utility of sensed data varies over time, but current energy-management algorithms do not exploit prior knowledge of these variations for making better decisions. This paper presents PreAct, the first energy-management algorithm that exploits time-varying utility to optimize application performance. PreAct's design combines strategic long-term planning of future energy utilization with feedback control to compensate for deviations from the expected conditions. We implement PreAct on a low-power microcontroller and compare it against the state of the art on multiple years of real-world data. Our results demonstrate that PreAct is up to 53 % more effective in utilizing harvested solar energy and significantly more robust to uncertainties and inefficiencies of practical systems. These gains translate into an improvement of 28% in the end-to-end performance of a real-world application we investigate when using PreAct.

Blockchain has received tremendous attention as a distributed platform to enhance the security of Internet of Things (IoT). The history of communications is stored in blockchain which introduces auditability. On the flip side, new privacy risks are introduced as the entire history of IoT device communication is exposed to participants. We study the likelihood of classifying IoT devices by analyzing the temporal patterns of their transactions, which to the best of our knowledge, is the first work of its kind. We apply machine learning algorithms on blockchain data to analyze the success rate of device classification. Our results demonstrate success rates over 90% in classifying devices. We propose three timestamp obfuscation methods, namely combining multiple packets into a single transaction, merging ledgers of multiple devices, and randomly delaying transactions, to reduce the success rate in classifying devices which reduce the classification success rates to as low as 24%.

Blockchain is a promising technology for establishing trust in IoT networks, where network nodes do not necessarily trust each other. Cryptographic hash links and distributed consensus mechanisms ensure that the data stored on an immutable blockchain can not be altered or deleted. However, blockchain mechanisms do not guarantee the trustworthiness of data at the origin. We propose a layered architecture for improving the end-to-end trust that can be applied to a diverse range of blockchain-based IoT applications. Our architecture evaluates the trustworthiness of sensor observations at the data layer and adapts block verification at the blockchain layer through the proposed data trust and gateway reputation modules. We present the performance evaluation of the data trust module using a simulated indoor target localization and the gateway reputation module using an end-to-end blockchain implementation, together with a qualitative security analysis for the architecture.

Outbreaks of infectious diseases present a global threat to human health and are considered a major healthcare challenge. One major driver for the rapid spatial spread of diseases is human mobility. In particular, the travel patterns of individuals determine their spreading potential to a great extent. These travel behaviors can be captured and modelled using novel location-based data sources, e.g., smart travel cards, social media, etc. Previous studies have shown that individuals who cannot be characterized by their most frequently visited locations spread diseases farther and faster; however, these studies are based on GPS data and mobile call records which have position uncertainty and do not capture explicit contacts. It is unclear if the same conclusions hold for large scale real-world transport networks. In this paper, we investigate how mobility patterns impact disease spread in a large-scale public transit network of empirical data traces. In contrast to previous findings, our results reveal that individuals with mobility patterns characterized by their most frequently visited locations and who typically travel large distances pose the highest spreading risk.

We consider the problem of data collection from a network of energy harvesting sensors, applied to tracking mobile assets in rural environments. Our application constraints favor a fair and energy-aware solution, with heavily duty-cycled sensor nodes communicating with powered base stations. We study a novel scheduling optimization problem for energy harvesting mobile sensor network, that maximizes the amount of collected data under the constraints of radio link quality and energy harvesting efficiency, while ensuring a fair data reception. We show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. Moreover, our algorithm is flexible in handling progressive energy harvesting events, such as with solar panels, or opportunistic and bursty events, such as with Wireless Power Transfer. We use empirical link quality data, solar energy, and WPT efficiency to evaluate the proposed algorithm in extensive simulations and compare its performance to state-of-the-art. We show that our algorithm achieves high data reception rates, under different fairness and node lifetime constraints.

Lambda platform is a new concept based on an event-driven server-less computation that empowers application developers to build scalable enterprise software in a virtualized environment without provisioning or managing any physical servers (a server-less solution). In reality, however, devising an effective consolidation method to host multiple Lambda functions into a single machine is challenging. The existing simple resource allocation algorithms, such as the round-robin policy used in many commercial server-less systems, suffer from lack of responsiveness to a sudden surge in the incoming workload. This will result in an unsatisfactory performance degradation that is directly experienced by the end-user of a Lambda application. In this paper, we address the problem of CPU cap management in a Lambda platform for ensuring different QoS enforcement levels in a platform with shared resources, in case of fluctuations and sudden surges in the incoming workload requests. To this end, we present a closed-loop (feedback-based) CPU cap controller, which fulfills the QoS levels enforced by the application owners. The controller adjusts the number of working threads per QoS class and dispatches the outstanding Lambda functions along with the associated events to the most appropriate working thread. The proposed solution reduces the QoS violations by an average of 6.36 times compared to the round-robin policy. It can also maintain the end-to-end response time of applications belonging to the highest priority QoS class close to the target set-point while decreasing the overall response time by up to 52%.

Indoor localization using radio signals is challenging. A recently proposed algorithm based on WiFi channel state information is an effective solution. However, it relies on a computationally expensive grid search. We propose a new algorithm based on a modified matrix pencil method that reduces the computational complexity by two orders of magnitude without any loss of accuracy.

This work presents a variation of a data collection problem referred as TSP-Data Collection (TSP-DC). Previously we have demonstrated that a two-stage algorithm using Linear-Programming Optimization and Gradient-Descent Optimization (LPO-GDO) is able to solve TSP-DC for minimum tour time. This poster abstract shows that LPO-GDO is also able to solve TSP-DC for an objective function of minimum energy, giving different solutions for different relative energy costs of data transmission and sink movement, and for high or low data loads at sensor nodes.

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

There is increased interest in smart vehicles acting as both data consumers and producers in smart cities. Vehicles can use smart city data for decision-making, such as dynamic routing based on traffic conditions. Moreover, the multitude of embedded sensors in vehicles can collectively produce a rich data set of the urban landscape that can be used to provide a range of services. Key to the success of this vision is a scalable and private architecture for trusted data sharing. This paper proposes a framework called SpeedyChain, that leverages blockchain technology to allow smart vehicles to share their data while maintaining privacy, integrity, resilience, and non-repudiation in a decentralized and tamper-resistant manner. Differently from traditional blockchain usage (e.g., Bitcoin and Ethereum), the proposed framework uses a blockchain design that decouples the data stored in the transactions from the block header, thus allowing fast addition of data to the blocks. Furthermore, an expiration time for each block is proposed to avoid large sized blocks. This paper also presents an evaluation of the proposed framework in a network emulator to demonstrate its benefits.

An increased incidence of food mislabeling and handling in recent years has led to consumers demanding transparency in how food items are produced and handled. The current traceability solutions suffer from issues such as scattering of information across multiple silos and susceptibility in recording erroneous data and thus are often unable to produce reliable farm to fork stories of products. Blockchain (BC) is a promising technology that could play an important role in providing data transparency and integrity due to its salient features which include decentralisation, immutability and auditability. In this paper, we propose a permissioned blockchain framework which is governed by a consortium of key Food Supply Chain (FSC) entities including government and regulatory bodies to promote food provenance. We propose to use a sharded, three-tiered architecture which ensures availability of data to consumers, limits access to competitive partners and provides scalability for handling transaction load. We also propose a transaction vocabulary and access rights to manage read and write privileges to BC supported by the consortium. The framework, ProductChain, ensures that trade flows are kept confidential when provenance information is retrieved by consumers and stakeholders. Simulation results show that query time for a product ledger is of the order of a few milliseconds even when the information is collated from multiple shards. ProductChain is generalised and applicable to supply chains in diverse industries.

We consider the problem of self-localization by a resource-constrained mobile node given perturbed anchor position information and distance estimates from the anchor nodes. We consider normally-distributed noise in anchor position information. The distance estimates are based on the log-normal shadowing path-loss model for the RSSI measurements. The available solutions to this problem are based on complex and iterative optimization techniques such as semidefinite programming or second-order cone programming, which are not suitable for resource-constrained environments. In this paper, we propose a closed-form weighted least-squares solution. We calculate the weights by taking into account the statistical properties of the perturbations in both RSSI and anchor position information. We also estimate the bias of the proposed solution and subtract it from the proposed solution. We evaluate the performance of the proposed algorithm considering a set of arbitrary network topologies in comparison to an existing algorithm that is based on a similar approach but only accounts for perturbations in the RSSI measurements. We also compare the results with the corresponding Cramer-Rao lower bound. Our experimental evaluation shows that the proposed algorithm can substantially improve the localization performance in terms of both root mean square error and bias.

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

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

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

Increasing human population, economical development, and industrialization of our society lead to disturbance of natural ecosystems. To prevent long-term damage of the environment, the affected ecosystems need to be rehabilitated to a sustainable form after industrial operations cease. Environment rehabilitation is a long-term process that is complex and costly as it includes restoration of soil, water bodies, and reintroduction of plant, insect, and animal species. We present a wireless sensor network for evidence-based rehabilitation of disturbed natural ecosystems. Drawing on our case study of an open-cut surface mine in rural Australia, we show that microclimate data can provide insights into the efficiency of specific rehabilitation processes, disentangle the impact of microclimate on the rehabilitation success, and provide early indicators into potential rehabilitation problems. We worked with ecologists to provide domain-based advice on addressing a range of rehabilitation problems and developed a system that periodically generates reports on the rehabilitation status of areas of interest. The report incorporates data from expert surveys from the field and microclimate data from sensors, and provides recommendations to improve rehabilitation in under-performing areas. Such evidence-based assessment of rehabilitation is an important step towards ensuring compliance with set rehabilitation objectives, potentially leading to both more successful and less costly environment rehabilitation.

Delay Tolerant Networks (DTN) are characterized by the absence of continuous connectivity resulting in high delivery delays that may exceed the acceptable limit for practical applications. In this paper, we address this issue by introducing Augur. Augur is a new routing protocol for DTNs targeted to minimize delays of message delivery. The routing scheme benefits from the spatiotemporal history data of the nodes to route message s only through gateways having less expected delay to deliver a message to its destination. We demonstrate through a comparative evaluation that Augur outperforms the state of the art DTN protocols in terms of delivery probability, overhead ratio and latency. We found that at low traffic rates Augur reduces the overhead ratio by up to 94%, and by up to 88% at high traffic. We also observed that the improvement in latency was reduced by up to half over the existing protocols in both traffic rates while still improving the delivery probability of messages.

Long-term sensor network deployments demand careful power management. While managing power requires understanding the amount of energy harvestable from the local environment, current solar prediction methods rely only on recent local history, which makes them susceptible to high variability. In this article, we present a model and algorithms for distributed solar current prediction based on multiple linear regression to predict future solar current based on local, in situ climatic and solar measurements. These algorithms leverage spatial information from neighbors and adapt to the changing local conditions not captured by global climatic information. We implement these algorithms on our Fleck platform and run a 7-week-long experiment validating our work. In analyzing our results from this experiment, we determined that computing our model requires an increased energy expenditure of 4.5mJ over simpler models (on the order of 10 -7 % of the harvested energy) to gain a prediction improvement of 39.7%.

Low-power compact sensor nodes are being increasingly used to collect trajectory data from moving objects such as wildlife. The size of this data can easily overwhelm the data storage available on these nodes. Moreover, the transmission of this extensive data over the wireless channel may prove to be difficult. The memory and energy constraints of these platforms underscores the need for lightweight online trajectory compression albeit without seriously affecting the accuracy of the mobility data. In this paper, we present a novel online Polygon Based Approximation (PBA) algorithm that uses regular polygons, the size of which is determined by the allowed spatial error, as the smallest spatial unit for approximating the raw GPS samples. PBA only stores the first GPS sample as a reference. Each subsequent point is approximated to the centre of the polygon containing the point. Furthermore, a coding scheme is proposed that encodes the relative position (distance and direction) of each polygon with respect to the preceding polygon in the trajectory. The resulting trajectory is thus a series of bit codes, that have pair-wise dependencies at the reference point. It is thus possible to easily reconstruct an approximation of the original trajectory by decoding the chain of codes starting with the first reference point. Encoding a single GPS sample is an O (1) operation, with an overall complexity of O (n). Moreover, PBA only requires the storage of two raw GPS samples in memory at any given time. The low complexity and small memory footprint of PBA make it particularly attractive for low-power sensor nodes. PBA is evaluated using GPS traces that capture the actual mobility of flying foxes in the wild. Our results demonstrate that PBA can achieve up to nine-fold memory savings as compared to Douglas-Peucker line simplification heuristic. While we present PBA in the context of low-power devices, it can be equally useful for other GPS-enabled devices such smartphones and car navigation units.

An intriguing approach to increasing throughput, lowering latency, extending network coverage and reducing load on infrastructure is wireless content sharing via mobile ad hoc networks (MANETs). The potential efficacy of multi-hop MANET content diffusion is heavily influenced by small-scale patterns of mobility and content demand; a topic about which relatively little is known. We infer device encounters from a large multi-site wireless access point association trace to analyze the impact of; time of day; day of week; site; and number of content sources on universal diffusion potential. In addition, we draw upon an empirical trace of application usage from a popular mobile campus maps application to model realistic content demand. Using trace-driven simulations we find that universal diffusion potential varies widely over the analyzed parameters, favoring larger more active sites and weekdays over weekends. More content sources proves beneficial, though mostly over the short-term. Our analysis of content demand for campus maps suggests that up to a third of application requests could be served from the MANET over a 12-hour period and again that weekdays are more amenable to content diffusion than weekends.

Radio connectivity in wireless sensor networks is highly intermittent due to unpredictable and time-varying noise and interference patterns in the environment. Because link qualities are not predictable prior to deployment, current deterministic solutions to unreliable links, such as increasing network density or transmission power, require overprovisioning of network resources and do not always improve reliability. We propose a new dual-radio network architecture to improve communication reliability in wireless sensor networks. Specifically, we show that radio transceivers operating at well-separated frequencies and spatially separated antennas offer robust communication, high link diversity, and better interference mitigation. We derive the optimal parameters for the dual-transceiver setup from frequency and space diversity in theory. We observe that frequency diversity holds the most benefits as long as the antennas are sufficiently separated to prevent coupling. Our experiments on an indoor/outdoor testbed confirm the theoretical predictions and show that radio diversity can significantly improve end-to-end delivery rates and network stability at only a small increase in energy cost over a single radio. Simulation experiments further validate the improvements in multiple topology configurations, but also reveal that the benefits of radio diversity are coupled to the number of available routing paths to the destination.

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

GPS is a commonly used and convenient technology for determining absolute position in outdoor environments, but its high power consumption leads to rapid battery depletion in mobile devices. An obvious solution is to duty cycle the GPS module, which prolongs the device lifetime at the cost of increased position uncertainty while the GPS is off. This article addresses the trade-off between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty while GPS is off. Empirical GPS and radio contact data from a large-scale animal tracking deployment is used to model node mobility, radio performance, and GPS. Because GPS takes a considerable, and variable, time after powering up before it delivers a good position measurement, we model the GPS behavior through empirical measurements of two GPS modules. These models are then used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose strategies that use RSSI ranging and GPS back-offs to further reduce energy consumption. Results show that our combined strategies can cut node energy consumption by one third while still meeting application-specific positioning criteria.

Low power listening (LPL) has been widely adopted to save energy in wireless sensor networks. However, LPL is ineffective in adapting to dynamic networks with asymmetric traffic patterns, as it sets a network-wide check interval. As a result, nodes with low data traffic waste significant energy resources doing idle listening. This problem is particularly exacerbated in multi-radio networks where majority of data comes through the most reliable radio and the duty cycles of other radios could be reduced. We address this issue in AutoSync, a protocol that combines synchronous LPL with automatic selection of check intervals to reduce energy consumption in both single and multi-radio networks. We first present the justification for AutoSync's design, and we then discuss our implementation of AutoSync in TinyOS. We compare AutoSync against existing protocols in both simulations and empirical experiments. Results show that AutoSync attains a substantial increase in the operational lifetime and mean power consumption over existing protocols in single radio networks and even more in dual radio networks.

Abstract Sensor network monitoring and control are currently addressed separately through specialized tools. However, the high degree of coupling of network state to the physical environment in which the network is deployed demands that users can monitor the network and respond to network state changes continuously. This paper presents the open‐source Octopus visualization and control tool. Octopus is a protocol‐independent tool that provides live information about the network topology and sensor data in order to enable live debugging of deployed sensor networks. It enables operators to reconfigure the network behavior, such as switching between time‐driven, event‐driven, and query‐driven modes or between awake and sleep modes of one, many, or all nodes through its graphical interface. Octopus also supports changing duty cycles of nodes, data reporting period, or sensing thresholds in event‐driven networks. Reconfiguration of nodes is achieved through short request messages that support typical reconfiguration options without the overhead of epidemically sending new program images over the air. Our empirical tests showcase Octopus's capacity to debug application behavior and to characterize heterogeneous network performance under multiple settings, as a step toward establishing a rules database that relates data delivery to network‐level parameters, and toward enabling autonomous network reconfiguration. Copyright © 2009 John Wiley & Sons, Ltd.

International audience

We describe our current work towards a framework that establishes a hierarchy of devices (sensors and actuators) within a wireless multimedia node and uses frequent sampling of cheaper devices to trigger the activation of more energy-hungry devices. Within this framework, we consider the suitability of servos for Wireless Multimedia Sensor Networks (WMSNs) by examining their functional characteristics and energy consumption [2].

Wireless Sensor Actuator Networks (WSANs) extend wireless sensor networks through actuation capability. Designing robust logic for WSANs however is challenging since nodes can affect their environment which is already inherently complex and dynamic. Fixed (offline) logic does not have the ability to adapt to significant environmental changes and can fail under changed conditions. To address this challenge, we present In situ Distributed Genetic Programming (IDGP) as a framework for evolving logic post-deployment (online) and implement this framework on a physically deployed WSAN. To demonstrate the features of the framework including individual, cooperative and heterogeneous evolution, we apply it to two simple optimisation problems requiring sensing, communications and actuation. The experiments confirm that IDGP can evolve code to achieve a system wide objective function and is resilient to unexpected environmental changes.

This paper addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. We use empirical GPS and radio contact data from a large-scale animal tracking deployment to model node mobility, GPS and radio performance. These models are used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose a versatile contact logging strategy that relies on RSSI ranging and GPS lock back-offs for reducing the node energy consumption relative to GPS duty cycling. Results show that our strategy can cut the node energy consumption by half while meeting application-specific positioning criteria.

This paper demonstrates the implementation of TinyTune, a collaborative musical instrument using sensor motes. The system implementation is distributed across multiple nodes and supports the basic elements of a musical instrument, such as pitch and octave selection. The communication design for realizing a collaborative musical instrument and the available user configuration options are then presented. Other topics of discussion include: the underlying system architecture, covering the advantages of our design choices; and the extensibility of the concept, which discusses how the nodes are configured in multi-instrument environments. 1.

This paper is concerned with the application of wireless sensor network (WSN) technology to long-duration and large-scale environmental monitoring. The holy grail is a system that can be deployed and operated by domain specialists not engineers, but this remains some distance into the future. We present our views as to why this field has progressed less quickly than many envisaged it would over a decade ago. We use real examples taken from our own work in this field to illustrate the technological difficulties and challenges that are entailed in meeting end-user requirements for information gathering systems. Reliability and productivity are key concerns and influence the design choices for system hardware and software. We conclude with a discussion of long-term challenges for WSN technology in environmental monitoring and outline our vision of the future.

Energy efficiency is a central challenge in sensor networks, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low-power radio mode for putting the radio to sleep. Fixed low-power modes involve an inherent trade-off: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low-power sleep modes based on current traffic conditions in the network. It first introduces a comprehensive node energy model, which includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded microcontroller energy component for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFID impulse protocol enhanced with adaptive low-power modes, and comparing it against BMAC and IEEE 802.15.4, for both MicaZ and TelosB platforms under varying data rates. The comparative analysis confirms that RFID impulse with adaptive low-power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low-power modes on the basis of data rates for each node platform, and provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low-power mode, and node platform for a given set of traffic requirements of a sensor network application.

This paper considers the use of servo-mechanisms as part of a tightly integrated homogeneous wireless multimedia sensor network (WMSN). We describe the design of our second generation WMSN node platform, which has increased image resolution, in-built audio sensors, PIR sensors, and servomechanisms. These devices have a wide disparity in their energy consumption and in the information quality they return. As a result, we propose a framework that establishes a hierarchy of devices (sensors and actuators) within the node and uses frequent sampling of cheaper devices to trigger the activation of more energy-hungry devices. Within this framework, we consider the suitability of servos for WMSNs by examining the functional characteristics and by measuring the energy consumption of 2 analog and 2 digital servos, in order to determine their impact on overall node energy cost. We also implement a simple version of our hierarchical sampling framework to evaluate the energy consumption of servos relative to other node components. The evaluation results show that: (1) the energy consumption of servos is small relative to audio/image signal processing energy cost in WMSN nodes; (2) digital servos do not necessarily consume as much energy as is currently believed; and (3) the energy cost per degree panning is lower for larger panning angles.

The efficient management of scarce network resources, including energy and bandwidth, represents a central challenge for wireless sensor networks. The current trend in resource management relies on the introduction of control mechanisms, such as control message exchanges, node-specific addressing, and storage of partial network state information. These mechanisms typically incur communication and processing overhead that does not scale well for larger or denser networks. Instead of introducing control mechanisms for network resource management, this article proposes and evaluates a Directed Broadcast with Overhearing (DBO) approach for sensor networks that combines directed broadcast at the network layer with CSMA and packet overhearing at the MAC layer. Through avoidance of control messaging and exchange of network state information, DBO trades off limited packet duplication overhead for control messaging overhead. This article introduces an analytical model that provides the basis for DBO evaluation and for analysis of the approach's transient packet retransmissions, route convergence, and energy consumption in the average and worst cases. We also present the model implementation details and the simulation experiments that explore the suitability of DBO for networks of different sizes with three different radio models that vary the width of grey regions, and we compare DBO's energy consumption against conventional unicast beacon-based and snooping-based routing protocols. The results indicate that that DBO's route convergence requires an average of five hops for ideal radio reception, seven hops for narrow grey regions, and twelve hops for wide grey regions. These results confirm that DBO shifts energy consumption from critical nodes near the base station to nodes near the source. The overall energy consumption of limited packet duplication overhead with DBO compared to unicast routing shrinks for medium- to large-size networks, rendering it more favorable than conventional communication approaches for large and dense sensor networks.

Energy efficiency is a central challenge in battery- operated sensor networks. Current energy-efficient mechanisms employ either duty cycling, which reduces idle listening but does not eliminate it, or low power wake-up radio, which adds complexity and cost to the sensor platform. In this paper, we propose a novel mechanism called RFIDImpulse that uses RFID technology as an out-of-band wake-up channel for sensor networks. RFIDImpulse is an on-demand mechanism that enables nodes to sleep until they have to send or receive packets. It relies on IEEE 802.15.4 radio to emulate an RFID reader at a sender node, and on an off-the-shelf RFID tag attached to the external interrupt pin of each sensor node. The sender can simply activate the receiver's tag before sending it data packets. This setup enables both radio and microcontroller to go into deep sleep mode until they need to be active. We develop an analytical model to evaluate the energy tradeoffs of RFIDImpulse, and then evaluate the mechanism against BMAC and IEEE 802.15.4 in high and low traffic scenarios. The results confirm that RFIDImpulse reduces the energy consumption relative to both protocols for low and medium traffic scenarios, and they reveal the thresholds for adaptive activation of RFIDImpulse based on traffic load.

Abstract—Energy-efficient performance is a central challenge in sensor network deployments, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low power radio mode for putting the radio to sleep. Fixed low power modes involve an inherent tradeoff: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low power sleep modes based on current traffic conditions in the network, as an enhancement to our recent RFIDImpulse low power wake-up mechanism. The paper also introduces a comprehensive node energy model, that includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded micro-controller energy component to evaluate energy performance for both MicaZ and TelosB platforms, which use different MCU’s. We then use the model for comparing the energy-related performance of RFIDImpulse enhanced with adaptive low power modes with BMAC and IEEE 802.15.4 for the two node platforms under varying data rates. The comparative analysis confirms that RFIDImpulse with adaptive low power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low power modes on the basis of data rates for each node platform, and it provides guidelines for the selection of appropriate MAC protocol, low power mode, and node platform for a given set of traffic requirements of a sensor network application. I.

Symbol synchronization in traditional hardware- driven communication systems has relied on the transmission of training sequences of symbols just before the beginning of the frame symbols. The use of training sequences is not suitable for software-driven communication systems, such as lightweight acoustic underwater sensor networks [4,5], in which the high symbol loss rate may cause the loss of training symbols, preventing accurate symbol synchronization. Software-driven communication networks require symbol synchronization that is resilient to a high loss environment, that does not represent large communication or processing overhead, and that is tunable to the noise profile of different environments. These requirements are emphasized for mote-based acoustic underwater sensor networks in which the bandwidth and processing capability are sparse. This paper proposes the use of a short signature synchronization symbol (S4) as both a preamble and post-amble to enable receiver synchronization in mote-based acoustic communication systems that rely on software modems. To synchronize to an incoming signal, the receiver performs cross-correlation of N reference signature symbols with the incoming signal to identify the beginning of the preamble and post-amble. The output of the cross-correlation yields 2N peak values, from which the receiver chooses the sharpest and most symmetric for synchronization to the beginning of the frame. Empirical experiments confirm a synchronization accuracy within 5 ms in air within a range of 10.5 m, and 11 ms in water within a range of 15 m.

Most sensor networks require application-specific network-wide performance guarantees, suggesting the need for global and flexible network optimization. The dynamic and nonuniform local states of individual nodes in sensor networks complicate global optimization. Here, we present a cross-layer framework for optimizing global power consumption and balancing the load in sensor networks through greedy local decisions. Our framework enables each node to use its local and neighborhood state information to adapt its routing and MAC layer behavior. The framework employs a flexible cost function at the routing layer and adaptive duty cycles at the MAC layer in order to adapt a node's behavior to its local state. We identify three state aspects that impact energy consumption: 1) number of descendants in the routing tree, 2) radio duty cycle, and 3) role. We conduct experiments on a test-bed of 14 mica2 sensor nodes to compare the state representations and to evaluate the framework's energy benefits. The experiments show that the degree of load balancing increases for expanded state representations. The experiments also reveal that all state representations in our framework reduce global power consumption in the range of one-third for a time-driven monitoring network and in the range of one-fifth for an event-driven target tracking network.

Rapid growth in the number of wireless enabled devices has led to an increased interest in location-aware applications. The backbone of such applications is provided by a location system. In this paper we present Beep, an indoor location system that senses audible sound. The use of audible sound makes our system cheap and easily deplorable to most existing roaming devices. Unlike positioning systems using ultrasound and infrared signals, Beep does not require the user to carry any kind of specialized hardware. Our system is based on standard 3D multilateration algorithms. However, the requirement of being able to locate existing devices, whose sound cards were not designed for high-precision signaling, introduces additional challenges to the location problem. This paper describes how those problems were solved and presents experimental results. Beep works with an accuracy of about 2 feet in more than 97% cases. The paper also describes a sensor deployment strategy that requires low sensor density and consequently low installation costs.

Abstract Ultra wide band (UWB) technology has received increasing recognition in recent years for its potential applications beyond radar technology to communication networks. UWB is a spread spectrum technology that requires careful coordination among communicating nodes to jointly control link power and transmission rates. Here, we present ultra wide band MAC (U‐MAC), an adaptive medium access control (MAC) protocol for UWB in which nodes periodically declare their current state, so that neighbors can proactively assign power and rate values for new links locally in order to optimize global network performance. Simulations comparing U‐MAC to the reactive approach confirm that U‐MAC lowers link setup latency and control overhead, doubles the throughput and adapts better to high network loads. Simulations also reveal that the basic form of U‐MAC favors nodes that are closer to the receiver. As a result, we also introduce novel mechanisms that control the radius around a receiver within which nodes can have fair access to it. We show through simulations the effect of the mechanisms on the tradeoff between network throughput and fair access. Copyright © 2005 John Wiley & Sons, Ltd.