Graph data is increasingly prevalent across domains, offering analytical value but raising significant privacy concerns. Edges may encode sensitive relationships, while node attributes may contain sensitive entity or personal data. Differential Privacy (DP) has gained traction for its strong guarantees, yet applying DP to graphs is challenging because of their complex relational structure, leading to trade-offs between privacy and utility. Existing methods vary in privacy definitions, utility goals, and contextual settings, complicating comparison. For practitioners, this is compounded by DP's interpretability issues, contributing to misleading protection claims.

As digital ecosystems become more complex with decentralized technologies like the Internet of Things (IoT) and blockchain, traditional access control models fail to meet the security needs of dynamic, high-risk environments. The need for dynamic, fine-grained access control mechanisms has become critical, particularly in environments where trust must be continuously evaluated, and access decisions must adapt to real-time conditions. Traditional models often rely on static identity management and centralized trust assumptions, which are inadequate for modern, decentralized, and highly dynamic environments such as IoT ecosystems. Consequently, existing solutions lack fine-grained identity management, flexible delegation, and continuous trust evaluation, highlighting the need for a more robust, adaptive, and decentralized access control architecture. To address these gaps, this paper presents a novel access control architecture that integrates self-sovereign identity (SSI) and decentralized identifier (DID)-based access control with zero trust principles, enhanced by a flexible capability-based access control (CapBAC) approach. Leveraging SSI and DID allows entities to manage their identities without relying on a central authority, aligning with zero-trust principles. The integration of CapBAC ensures flexible, context-aware, and attribute-based access control, where access rights are dynamically granted based on the requester's capabilities. This enables fine-grained delegation of access rights, allowing trusted entities to delegate specific privileges to others without compromising overall security. Continuous trust evaluation is employed to assess the authenticity of access requests, mitigating the risks posed by compromised devices or users. The proposed architecture also incorporates blockchain technology to ensure transparent, immutable, and secure management of access logs, providing traceability and accountability for all access events. We demonstrate the feasibility and effectiveness of this solution through performance evaluations and comparisons with existing access control schemes, showing its superior security, scalability, and adaptability in real-world scenarios. Our work demonstrates a comprehensive, decentralized, and scalable solution for secure access control delegation using zero trust-driven principles.

Despite their significant added value in the context of consumer-oriented e-commerce, reputation systems have seen limited adoption in other business settings and models these days. Yet, reliable reputation scores are essential in such settings for easing the establishment of new business relationships—an aspect that is particularly crucial in dynamic supply chain environments, where business partners change frequently. Existing approaches, however, usually target other application domains and fall short in addressing the specific challenges of dynamic supply chains—–especially with respect to reliability (incl. availability) and privacy preservation (incl. confidentiality). To close this research gap and to support novel directions in this important research area, we propose PRepChain, our highly-configurable approach that leverages fully homomorphic encryption and distributed competences to provide businesses with a versatile reputation-enriched ecosystem. PRepChain is specifically designed to operate in dynamic environments by also offering a trade-off between data availability and confidentiality guarantees. We make contributions in four primary directions: (i) It offers performant privacy preservation even in large-scale settings, (ii) ensures availability of computed reputation scores, (iii) seamlessly integrates with existing supply chain information systems, and (iv) in addition to subjective reputation scores, it also supports reliably-calculated, i.e., objective, ones, thereby strengthening the reliability of third-party-sourced information. Our evaluation of PRepChain documents its performance—based on a real-world use case—, security, and privacy preservation, hence, its applicability. We conclude that it is indeed destined for practical deployments in modern supply networks.

WiFi devices are ubiquitous in modern environments, from smartphones and laptops to IoT sensors and AR/VR headsets. Identifying device types/models within these populations enables crowd analysis, network optimization, and detection of unusual devices. Current identification methods struggle with MAC address randomization, require large training datasets, and perform poorly in real-world deployments. This paper introduces a device identification method based on Information Element (IE) attributes extracted from WiFi probe requests. We evaluate the approach using probe requests captured in the 2.4 GHz band. Evaluation across 70+ device types yields 99% precision, 98% recall, and 99% F1 score, exceeding deep learning approaches (92% F1 score) under similar training conditions. Our approach maintains accuracy despite MAC randomization and requires minimal training data. We demonstrate practical applicability through an operational dashboard tested in real-world scenarios for urban planning and network management. Case studies across diverse environments confirm the effectiveness of the method for operational use.

The Domain Name System (DNS) translates human-readable domain names into machine-readable IP addresses. This critical internet infrastructure faces increasing exploitation through various malicious activities collectively known as DNS abuse. While DNS abuse has been extensively researched globally, the unique patterns and vulnerabilities within the Australian domain space remain understudied. This paper provides a comprehensive empirical analysis of DNS abuse within Australian domain names. Our thorough data collection and analysis allow us to characterize the specific nature of abuse trends within the Australian domain space. We document how malicious actors strategically employ methods to evade detection systems, and highlight the concentration of abuse among specific domain registrations. These research findings provide stakeholders with actionable, data-driven insights, emphasizing the necessity of industry-specific defensive measures and tailored state-level threat mitigation strategies. We have laid the foundation for building a more refined, risk-based Australian digital infrastructure security system.

Online services often require users to agree to lengthy and obscure Terms of Service (ToS), leading to information asymmetry and legal risks. This paper proposes TOSense—a Chrome extension that allows users to ask questions about ToS in natural language and get concise answers in real time. The system combines (i) a crawler "tos-crawl" that automatically extracts ToS content, and (ii) a lightweight large language model pipeline: MiniLM for semantic retrieval and BART-encoder for answer relevance verification. To avoid expensive manual annotation, we present a novel Question Answering Evaluation Pipeline (QEP) that generates synthetic questions and verifies the correctness of answers using clustered topic matching. Experiments on five major platforms, Apple, Google, X (formerly Twitter), Microsoft, and Netflix, show the effectiveness of TOSense (with up to 44.5% accuracy) across varying number of topic clusters. During the demonstration, we will showcase TOSense in action. Attendees will be able to experience seamless extraction, interactive question answering, and instant indexing of new sites.

Recommendation Systems (RS) play an important role in our everyday life in this data-driven digital era by providing users with the convenience of navigating the plethora of available choices. An RS collects user behavioural data to provide them with valuable suggestions. The growing privacy concerns regarding private data collection have led to the use of Federated Learning (FL) to implement RS. However, many research works have exposed the privacy leakages in FL gradient sharing. The embedding gradients shared by FL users during the RS model training can be used to infer the items that users have interacted with. Existing defences, such as random noise injection or pseudo-interaction sampling to obfuscate the privacysensitive information reflected by the shared gradients. However, these techniques provide limited protection and often result in substantial degradation of recommendation performance, leading to an unfavourable privacy–utility trade-off. In this paper, we propose FedSIG (Federated Synthetic Interaction Generation), a defence mechanism that mitigates useritem interaction inference in federated recommendation systems by generating synthetic interaction data using generative models. The generated items are selectively used to replace or augment real user interactions, thereby obfuscating sensitive data while preserving user preference signals. To further enhance utility, we design an item selection module based on an attention mechanism to identify less contributive interactions for replacement. Extensive experiments conducted on five real-world datasets and two state-of-the-art recommendation models demonstrate that FedSIG achieves a significantly improved privacy–utility balance compared to existing approaches, effectively reducing inference success rates while maintaining competitive recommendation accuracy.

An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, in real-life scenarios, this assumption rarely holds. For example, medical diagnosis result distributions change over time and across locations; fraud detection models must adapt as patterns of fraudulent activity shift; the category distribution of social media posts changes based on trending topics and user demographics. In the task of label shift estimation, the goal is to estimate the changing label distribution pt(y) in the testing set, assuming the likelihood p(x|y) does not change, implying no concept drift. In this paper, we propose a new approach for post-hoc label shift estimation, unlike previous methods that perform moment matching with confusion matrix estimated from a validation set or maximize the likelihood of the new data with an expectation-maximization algorithm. We aim to incrementally update the prior on each sample, adjusting each posterior for more accurate label shift estimation. The proposed method is based on intuitive assumptions on classifiers that are generally true for modern probabilistic classifiers. The proposed method relies on a weaker notion of calibration compared to other methods. As a post-hoc approach for label shift estimation, the proposed method is versatile and can be applied to any black-box probabilistic classifier. Experiments on CIFAR-10 and MNIST show that the proposed method consistently outperforms the current state-of-the-art maximum likelihood-based methods under different calibrations and varying intensities of label shift.

Smart environments are increasingly equipped with interconnected digital systems to manage access and physical security. However, traditional authentication methods, typically restricted to static checkpoints, fail to provide persistent assurance once entry is granted, leaving facilities vulnerable to credential misuse, tailgating, and unauthorised movement. This paper presents the Continuous Authentication Platform (CAP), a modular, multi-modal framework developed within the RAAISE project to enable continuous and context-aware verification across dynamic facility zones. CAP integrates heterogeneous off-the-shelf sensors, including NFC, RFID, biometric, motion, and WiFi positioning units, which collectively support persistent user tracking and real-time access enforcement. The platform’s architecture couples distributed sensing and edge processing with a centralised intelligence layer for event correlation and policy-driven decision-making. A live testbed deployment at Deakin University was used to evaluate CAP’s performance under realistic operational conditions. Results from functional trials demonstrate CAP’s ability to detect credential misuse, prevent tailgating, and maintain authentication continuity with sub-second responsiveness. These findings underscore CAP’s potential as a scalable, privacy-aligned foundation for next-generation smart facility security systems.

Deep neural networks often produce miscalibrated probability estimates, leading to overconfident predictions. A common approach for calibration is fitting a post-hoc calibration map on unseen validation data that transforms predicted probabilities. A key desirable property of the calibration map is instance-wise monotonicity (i.e., preserving the ranking of probability outputs). However, most existing post-hoc calibration methods do not guarantee monotonicity. Previous monotonic approaches either use an under-parameterized calibration map with limited expressive ability or rely on black-box neural networks, which lack interpretability and robustness. In this paper, we propose a family of novel monotonic post-hoc calibration methods, which employs a constrained calibration map parameterized linearly with respect to the number of classes. Our proposed approach ensures expressiveness, robustness, and interpretability while preserving the relative ordering of the probability output by formulating the proposed calibration map as a constrained optimization problem. Our proposed methods achieve state-of-the-art performance across datasets with different deep neural network models, outperforming existing calibration methods while being data and computation-efficient. Our code is available at https://github.com/YunruiZhang/Calibration-by-Constrained-Transformation

Recent cyberattacks have increasingly targeted distributed networking environments like IoT networks. To detect these attacks, hidden under network traffic encryption, many centralized Machine Learning (ML) based solutions have been introduced, which are not well suited for IoT networks. This work proposes PIFL a practical approach to secure IoT networks by combining federated learning, in-network ML using P4-enabled devices, software-defined networks, and binarized neural networks. PIFL detects compromised edge devices and isolates them into separate network slices based on trust parameters derived from their behavior. We demonstrate the feasibility of PIFL using an experimental testbed with three intelligent network devices and seven IoT devices implemented on Raspberry Pi devices.

Deceptive files, often called honeyfiles, have become an established tool in cyber security. Advances in machine learning (ML) models for content generation now allow the synthesis of deceptive material automatically and at scale. Metrics to quantify honeyfile attributes are thus essential to creating and evaluating effective deceptions. The two critical aspects of honeyfiles for which metrics are useful are enticement and realism . Enticement is the ability to attract the attention of intruders or users with malicious intent. Realism measures the similarity of deceptive artefacts to the objects they mimic. In the honeyfile literature, metrics for these attributes have been proposed: the Common Token Count (CTC) [ 1 ], and Topic Semantic Matching (TSM) [ 2 ] scores for enticement, and coherence and cohesion [ 3 ] for realism. In this study, we compare these metrics to the perceptions of human users exposed to text samples in a simulated data breach scenario on a crowd-sourcing platform. We recruited participants to judge the realism and enticement of honeyfile text generated using several techniques. The main findings are: (i) for the enticement metrics, TSM is aligned with the perceived enticement (p-value<0.001), while for the CTC score, we find inconsistent and inconclusive results, and (ii) for the realism metrics, cohesion and coherence, do not consistently align with perceived realism.

Decentralized machine learning, such as Federated Learning (FL), is widely adopted in many application domains. Especially in domains like recommendation systems, sharing gradients instead of private data has recently caught the research community’s attention. Personalized travel route recommendation utilizes users’ location data to recommend optimal travel routes. Location data is extremely privacy sensitive, presenting increased risks of exposing behavioral patterns and demographic attributes. FL for route recommendation can mitigate the sharing of location data. However, this article shows that an adversary can recover the user trajectories used to train the federated recommendation models with high proximity accuracy. To this effect, we propose a novel attack called DeepSneak, which uses shared gradients obtained from global model training in FL to reconstruct private user trajectories. We formulate the attack as a regression problem and train a generative model by minimizing the distance between gradients. We validate the success of DeepSneak on two real-world trajectory datasets. The results show that we can recover the location trajectories of users with reasonable spatial and semantic accuracy.

Vibration energy harvesting (VEH) has emerged as a viable option for mobile devices that serves the dual purpose of generating power and sensing ambient vibrations. This paper highlights the location privacy leakage resulting from unrestricted access to seemingly innocuous VEH data on mobile devices. We present VEH-Attack, a side-channel attack that exploits an inference model and VEH data patterns generated from train vibrations, enabling precise tracking of train passengers. VEH-Attack achieves an accuracy of 97% and 83.13% for VEH derived data and actual VEH data, respectively, for trip length of 6 stations with the accuracy reaching 100% for longer trip lengths.

In many real-world applications, from robotics to pedestrian trajectory prediction, there is a need to predict multiple real-valued outputs to represent several potential scenarios. Current deep learning techniques to address multiple-output problems are based on two main methodologies: (1) mixture density networks, which suffer from poor stability at high dimensions, or (2) multiple choice learning (MCL), an approach that uses M single-output functions, each only producing a point estimate hypothesis. This paper presents a Mixture of Multiple-Output functions (MoM) approach using a novel variant of dropout, Multiple Hypothesis Dropout. Unlike traditional MCL-based approaches, each multiple-output function not only estimates the mean but also the variance for its hypothesis. This is achieved through a novel stochastic winner-take-all loss which allows each multiple-output function to estimate variance through the spread of its subnetwork predictions. Experiments on supervised learning problems illustrate that our approach outperforms existing solutions for reconstructing multimodal output distributions. Additional studies on unsupervised learning problems show that estimating the parameters of latent posterior distributions within a discrete autoencoder significantly improves codebook efficiency, sample quality, precision and recall.

Recent works have shown that Federated Learning (FL) is vulnerable to backdoor attacks. Existing defenses cluster submitted updates from clients and select the best cluster for aggregation. However, they often rely on unrealistic assumptions regarding client submissions and sampled clients population while choosing the best cluster. We show that in realistic FL settings, state-of-the-art (SOTA) defenses struggle to perform well against backdoor attacks in FL. To address this, we highlight that backdoored submissions are adversarially biased and overconfident compared to clean submissions. We, therefore, propose an Adversarially Guided Stateful Defense (AGSD) against backdoor attacks on Deep Neural Networks (DNNs) in FL scenarios. AGSD employs adversarial perturbations to a small held-out dataset to compute a novel metric, called the trust index, that guides the cluster selection without relying on any unrealistic assumptions regarding client submissions. Moreover, AGSD maintains a trust state history of each client that adaptively penalizes backdoored clients and rewards clean clients. In realistic FL settings, where SOTA defenses mostly fail to resist attacks, AGSD mostly outperforms all SOTA defenses with minimal drop in clean accuracy (5% in the worst-case compared to best accuracy) even when (a) given a very small held-out dataset—typically AGSD assumes 50 samples (≤ 0.1% of the training data) and (b) no held-out dataset is available, and out-of-distribution data is used instead. For reproducibility, our code will be openly available at: https://github.com/hassanalikhatim/AGSD.

Deep learning system components are vulnerable to backdoor attacks. Detectors are no exception. Detectors, in contrast to classifiers, possess unique characteristics, architecturally and in task execution; often operating in challenging conditions, for instance, detecting traffic signs in autonomous cars. But, our knowledge dominates attacks against classifiers and tests in the "digital domain".To address this critical gap, we conducted an extensive empirical study targeting multiple detector architectures and two challenging detection tasks in real-world settings: traffic signs and vehicles. Using diverse, methodically collected videos captured from driving cars and flying drones, incorporating physical object trigger deployments in authentic scenes, we investigated the viability of physical object-triggered backdoor attacks in application settings.Our findings revealed 7 key insights. Importantly, the prevalent "digital" data poisoning method for injecting backdoors into models does not lead to effective attacks against detectors in the real world, although proven effective in classification tasks. We construct a new, cost-efficient attack method, dubbed Morphing, incorporating the unique nature of detection tasks; ours is remarkably successful in injecting physical object-triggered backdoors, even capable of poisoning triggers with clean label annotations or invisible triggers without diminishing the success of physical object triggered backdoors. We discovered that the defenses curated are ill-equipped to safeguard detectors against such attacks. To underscore the severity of the threat and foster further research, we, for the first time, release an extensive video test set of real-world backdoor attacks. Our study not only establishes the credibility and seriousness of this threat but also serves as a clarion call to the research community to advance backdoor defenses in the context of object detection. Our dataset—DriveByFlyBy—release, demo videos and code is at https://BackdoorDetectors.github.io.

The rapid spread of false information and persistent manipulation attacks on online social networks (OSNs), often for political, ideological, or financial gain, has affected the openness of OSNs. While researchers from various disciplines have investigated different manipulation-triggering elements of OSNs (such as understanding information diffusion on OSNs or detecting automated behavior of accounts), these works have not been consolidated to present a comprehensive overview of the interconnections among these elements. Notably, user psychology, the prevalence of bots, and their tactics concerning false information detection have been overlooked in previous research.

Personalized location recommendation allows users to enjoy a seamless travel experience by suggesting the optimal travel locations/routes based on user preferences. Most service providers collect users' location data centrally to develop accurate route recommendation applications. Federated learning (FL) can be used as an inherent privacy-preserving mechanism in these applications to prevent users from sharing private data. However, recent research shows that FL is still vulnerable to privacy leakages. Therefore, many FL-based recommendation systems use Local Differential Privacy (LDP) to defend against such attacks. In this paper, we propose the Visited Location Inference Attack (VLIA), a novel attack for federated location recommendation systems through the lens of Membership Inference Attack (MIA). Specifically, we focus on inferring user behaviour data (visited locations) even when the federated recommendation system is protected with LDP. We design and implement VLIA leveraging both embedding and proximity information of locations, making the inference more accurate. Our extensive experiments with two state-of-the-art personalized route recommendation (PRR) systems implemented in the FL setting and two real-world trajectory datasets showcase the effectiveness of the VLIA attack. Our results show that LDP cannot defend VLIA unless the recommendation performance is significantly compromised.

Fuzzing is an industry-standard software testing technique that uncovers bugs in a target program by executing it with mutated inputs. Over the lifecycle of a fuzzing campaign, the fuzzer accumulates inputs inducing new and interesting target behaviors, drawing from these inputs for further mutation and generation of new inputs. This rapidly results in a large pool of inputs to select from, making it challenging to quickly determine the "most promising" input for mutation. Reinforcement learning (RL) provides a natural solution to this seed scheduling problem---a fuzzer can dynamically adapt its selection strategy by learning from past results. However, existing RL approaches are (a) computationally expensive (reducing fuzzer throughput), and/or (b) require hyperparameter tuning (reducing generality across targets and input types). To this end, we propose T-Scheduler, a seed scheduler built upon multi-armed bandit theory to automatically adapt to the target. Notably, our formulation does not require the user to select or tune hyperparameters and is therefore easily generalizable across different targets. We evaluate T-Scheduler over 35 CPU-yr fuzzing effort, comparing it to 11 state-of-the-art schedulers. Our results show that T-Scheduler improves on these 11 schedulers on both bug-finding and coverage-expansion abilities.

Federated Learning (FL) is a privacy-preserving, collaborative machine learning technique where multiple clients train a shared model on their private datasets without sharing the data. While offering advantages, FL is susceptible to backdoor attacks, where attackers insert malicious model updates into the model aggregation process. Compromised models predict attacker-chosen targets when presented with specific attacker-defined inputs. Backdoor defences generally rely on anomaly detection techniques based on Differential Privacy (DP) or require legitimate clean test examples at the server. Anomaly detection-based defences can be defeated by stealth techniques and generally require inspection of client-submitted model updates. DP-based approaches tend to degrade the performance of the trained model due to excessive noise addition during training. Methods that require legitimate clean data on the server require strong assumptions about the task and may not be applicable in real-world settings. In this work, we view the question of backdoor attack robustness through the lens of loss function optimal points to build a defence that overcomes these limitations. We propose Mode Connectivity Based Federated Learning (MCFL), which leverages the recently discovered property of neural network loss surfaces, mode connectivity. We simulate backdoor attack scenarios using computer vision benchmark datasets, including CIFAR10, Fashion MNIST, MNIST, and Federated EMNIST. Our findings show that MCFL converges to high-quality models and effectively mitigates backdoor attacks relative to baseline defences from the literature without requiring inspection of client model updates or assuming clean data at the server.

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.

We propose CredAct, a user activity verification designed with data minimisation to protect privacy. Many Benefits Schemes, such as discount offers, loyalty programs, and incentive systems, require verification of user activity (e.g., buying healthy food, step counts) in their business processes. These service providers can collect a large amount of users’ personal information, and often users do not have fine-grained control over the scope of data disclosure. In CredAct, we propose a Self-Sovereign Identity based framework implemented on blockchain that enables users participating in a benefits scheme to minimise data sharing during the submission and verification of data. We use a smart contract-based function along with a Zero-Knowledge Proof cryptographic commitment scheme, that forces the entities involved in the business process to collect or disclose only the required (minimum) data to fulfill the intended purpose. The evaluation shows that the system is feasible with minimal operational overheads compared to traditional cryptographic techniques. We also perform a qualitative privacy and security analysis considering relevant threats to CredAct.

WiFi has emerged as the standard method for local connectivity across various devices, including smart assistants, IoT devices, smart TVs, and AR/VR devices. Identifying WiFi devices in neighborhoods has implications for law enforcement, urban planning, and socio-economic analysis. This paper introduces a novel approach to constructing WiFi device-type signatures using Information Element attributes from wildcard WiFi probe requests. Our method accurately identifies device types even when dealing with randomized MAC addresses and requires minimal training data, thus addressing limitations of existing machine learning and deep learning approaches. We evaluate our approach using a dataset of 51,726 probe requests across 50 device types, achieving an average F1 score of 99%, precision of 99%, and recall of 98% in device-type identification. Importantly, our method outperforms deep learning methods with significantly less training data, achieving a 92% F1 score with only one training sample per device type.

Location trajectories provide valuable insights for applications from urban planning to pandemic control. However, mobility data can also reveal sensitive information about individuals, such as political opinions, religious beliefs, or sexual orientations. Existing privacy-preserving approaches for publishing this data face a significant utility-privacy trade-off. Releasing synthetic trajectory data generated through deep learning offers a promising solution. Due to the trajectories' sequential nature, most existing models are based on recurrent neural networks (RNNs). However, research in generative adversarial networks (GANs) largely employs convolutional neural networks (CNNs) for image generation. This discrepancy raises the question of whether advances in computer vision can be applied to trajectory generation. In this work, we introduce a Reversible Trajectory-to-CNN Transformation (RTCT) that adapts trajectories into a format suitable for CNN-based models. We integrated this transformation with the well-known DCGAN in a proof-of-concept (PoC) and evaluated its performance against an RNN-based trajectory GAN using four metrics across two datasets. The PoC was superior in capturing spatial distributions compared to the RNN model but had difficulty replicating sequential and temporal properties. Although the PoC's utility is not sufficient for practical applications, the results demonstrate the transformation's potential to facilitate the use of CNNs for trajectory generation, opening up avenues for future research. To support continued research, all source code has been made available under an open-source license.

As the adoption of Internet of Things (IoT) devices continues to rise in enterprise environments, the need for effective and efficient security measures becomes increasingly critical. This paper presents a cost-efficient platform to facilitate the pre-deployment security checks of IoT devices by predicting potential weaknesses and associated attack patterns. The platform employs a Bidirectional Long Short-Term Memory (Bi-LSTM) network to analyse device-related textual data and predict weaknesses. At the same time, a Gradient Boosting Machine (GBM) model predicts likely attack patterns that could exploit these weaknesses. When evaluated on a dataset curated from the National Vulnerability Database (NVD) and publicly accessible IoT data sources, the system demonstrates high accuracy and reliability. The dataset created for this solution is publicly accessible.

Once analysed, location trajectories can provide valuable insights beneficial to various applications, including urban planning, market analysis, and public health surveillance. However, such data is also highly sensitive, rendering them susceptible to privacy risks in the event of mismanagement, for example, revealing an individual's identity, home address, or political affiliations. Hence, ensuring that privacy is preserved for this data is a priority. One commonly taken measure to mitigate this concern is aggregation. Previous work by Xu et al. in [Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data (2017)] shows that trajectories are still recoverable from anonymised and aggregated datasets. However, the study lacks implementation details, obfuscating the mechanisms of the attack. Additionally, the attack was evaluated on commercial non-public datasets, rendering the results and subsequent claims unverifiable. This study reimplements the trajectory recovery attack from scratch and evaluates it on two open-source datasets, detailing the preprocessing steps and implementation. Results confirm that privacy leakage still exists despite common anonymisation and aggregation methods but also indicate that the initial accuracy claims may have been overly ambitious. We release all code as open-source to ensure the results are entirely reproducible and, therefore, verifiable. Moreover, we propose a stronger attack by designing a series of enhancements to the baseline attack. These enhancements yield higher accura-cies by up to 16%, providing an improved benchmark for future research in trajectory recovery methods. Our improvements also enable online execution of the attack, allowing partial attacks on larger datasets previously considered unprocessable, thereby furthering the extent of privacy leakage. The findings emphasise the importance of using strong privacy-preserving mechanisms when releasing aggregated mobility data and not solely relying on aggregation as a means of anonymisation.

In Internet of Things (IoT) deployments, sensing applications have emerged as critical tools. They combine data streams from heterogeneous, untrusted sensors to provide valuable insights or make automated decisions. This paper shows that such systems can be easily manipulated by only compromising a single sensor and perturbing the data from specific time slots rather than entire data streams in grey-box and black-box settings -attack scenarios not considered in traditional machine learning literature. Drawing from two datasets related to behavioural biometrics of smart headsets, we demonstrate that by altering just 6.2% of the data, an attacker can significantly reduce the system’s accuracy—achieving drops of 85% in grey-box scenarios and 74.5% in black-box settings. Next, we show that while adversarial training can mitigate such attacks, an attacker can overcome such defences by increasing the perturbation only in specific time steps. To this end, we propose a two-step defence where we detect more significant perturbations in IoT sensor readings using anomaly detection and mitigate more minor perturbations through adversarial training. Overall, our proposed method can limit the accuracy drop to a maximum of 9.59% across all magnitudes of perturbations, thus protecting against adversarial attacks on multi-sensor systems.

While location trajectories represent a valuable data source for analyses and location-based services, they can reveal sensitive information, such as political and religious preferences. Differentially private publication mechanisms have been proposed to allow for analyses under rigorous privacy guarantees. However, the traditional protection schemes suffer from a limiting privacy-utility trade-off and are vulnerable to correlation and reconstruction attacks. Synthetic trajectory data generation and release represent a promising alternative to protection algorithms. While initial proposals achieve remarkable utility, they fail to provide rigorous privacy guarantees. This paper proposes a framework for designing a privacy-preserving trajectory publication approach by defining five design goals, particularly stressing the importance of choosing an appropriate Unit of Privacy. Based on this framework, we briefly discuss the existing trajectory protection approaches, emphasising their shortcomings. This work focuses on the systematisation of the state-of-the-art generative models for trajectories in the context of the proposed framework. We find that no existing solution satisfies all requirements. Thus, we perform an experimental study evaluating the applicability of six sequential generative models to the trajectory domain. Finally, we conclude that a generative trajectory model providing semantic guarantees remains an open research question and propose concrete next steps for future research.

Digital identity is evolving from centralized systems to a decentralized approach known as Self-Sovereign Identity (SSI). SSI empowers individuals to control their digital identities, eliminating reliance on third-party data custodians and reducing the risk of data breaches. However, the concept of trust in SSI remains complex and fragmented. This paper systematically analyzes trust in SSI in light of its components and threats posed by various actors in the system. As a result, we derive three distinct trust models that capture the threats and mitigations identified across SSI literature and implementations. Our work provides a foundational framework for future SSI research and development, including a comprehensive catalogue of SSI components and design requirements for trust, shortcomings in existing SSI systems and areas for further exploration.

Federated learning (FL) is a distributed, privacy-preserving learning paradigm where a joint model is trained on private data stored on client devices. Data owners (clients) train models locally and then submit them to an aggregation server for incorporation into the joint model. Malicious clients can apply training time attacks, e.g., backdoor attacks, by submitting maliciously trained models. Prior work has shown that Differential Privacy (DP) can provide certified robustness to backdoor attacks; however, there are limited studies regarding DP parameter selection as a function of the model architecture. In this work, we show empirically that larger models (i.e., with more parameters) require stronger DP parameter settings to mitigate backdoor attacks. Furthermore, we present a framework that alters the FL training algorithm to preserve certified accuracy round-by-round and show empirically that it is superior to a model trainer selecting DP parameters ahead of time before training begins and with incomplete information about the attacker. Although tools from DP are used in our proposed framework, it is focused on backdoor attack mitigation and does not provide privacy guarantees.

The location information of cars contains great value, but the uncontrollable characteristics of public data and the difficulty in distributing benefits derived from the potential value of the data greatly reduces the enthusiasm for data owners to share their data. In addition, the current selective disclosure schemes based on merkle tree still require large costs when there are many data items. To solve these problems, a blockchain-based framework for sharing cars’ location information applicable to the online car hailing industry is proposed in this paper, enabling the sharing of cars’ location information while protecting passengers’ privacy through selective disclosure. The combination of homomorphic encryption and probabilistic verification enables a faster batch data verification compared to other blockchain-based data sharing schemes, as well as ensures the authenticity of the data uploaded to the blockchain. The experimental results show that the proposed selective disclosure mechanism based on hash exclusive or tree has lower costs than the baseline for cases with many data items. Moreover, the proposed framework meets both security and feasibility requirements. Specifically speaking, under the constraint of 128-bits security level, the costs of time and space on the location information during one drive are at microsecond level and kilobyte level, respectively. Finally, the scheme is suitable for scenarios with higher throughput.

We present a new algorithm to train a robust malware detector. Malware is a prolific problem and malware detectors are a front-line defense. Modern detectors rely on machine learning algorithms. Now, the adversarial objective is to devise alterations to the malware code to decrease the chance of being detected whilst preserving the functionality and realism of the malware. Adversarial learning is effective in improving robustness but generating functional and realistic adversarial malware samples is non-trivial. Because: i) in contrast to tasks capable of using gradient-based feedback, adversarial learning in a domain without a differentiable mapping function from the problem space (malware code inputs) to the feature space is hard; and ii) it is difficult to ensure the adversarial malware is realistic and functional. This presents a challenge for developing scalable adversarial machine learning algorithms for large datasets at a production or commercial scale to realize robust malware detectors. We propose an alternative; perform adversarial learning in the feature space in contrast to the problem space. We prove the projection of perturbed, yet valid malware, in the problem space into feature space will always be a subset of adversarials generated in the feature space. Hence, by generating a robust network against feature-space adversarial examples, we inherently achieve robustness against problem-space adversarial examples. We formulate a Bayesian adversarial learning objective that captures the distribution of models for improved robustness. To explain the robustness of the Bayesian adversarial learning algorithm, we prove that our learning method bounds the difference between the adversarial risk and empirical risk and improves robustness. We show that Bayesian neural networks (BNNs) achieve state-of-the-art results; especially in the False Positive Rate (FPR) regime. Adversarially trained BNNs achieve state-of-the-art robustness. Notably, adversarially trained BNNs are robust against stronger attacks with larger attack budgets by a margin of up to 15% on a recent production-scale malware dataset of more than 20 million samples. Importantly, our efforts create a benchmark for future defenses in the malware domain.

With a rising frequency and scale, Distributed Denial-of-Service (DDoS) attacks persist as a critical cybersecurity issue. While shared attack fingerprints aid many intrusion detection systems in identifying threats, their application for DDoS attacks remains limited due to their distinct nature. However, fingerprints observed from multiple locations can provide valuable insights. This paper presents Reassembler, a novel platform for achieving a global DDoS attack analysis using attack fingerprints recorded from various locations. Reassembler consolidates these fingerprints into a unified view allowing to obtain a global overview of DDoS attacks. The evaluation, conducted on four simulated scenarios, demonstrates Reassembler's ability to extract novel properties, such as the count of intermediate nodes and the estimated percentage of spoofed IPs.

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.

Automated accounts or bots on Online Social Networks (OSNs) play a significant role in disseminating information, including false news, which may instigate cyber propaganda. The existing research on fake news detection does not account for the existence of bots. Also, they only focus on identifying fake news in “the articles shared in posts” rather than the post’s (textual) content and use manually labeled limited datasets. In this research, we overcome the challenge of data scarcity by proposing an automated approach for labeling data using verified fact-checked statements on OSNs such as Twitter. Moreover, we analyze the presence and impact of bots and show that bots change their behavior over time. Our experiments focus on COVID-19, collect 10.22 million COVID-19-re1ated tweets, and use our annotation model to build an extensive ground truth dataset for classification purposes. We evaluated our automatic annotation model on two existing COVID-19-re1ated misinformation datasets and achieved a ~ 2% increase in precision compared to the existing annotation models. In addition, our best classification model achieves 83% precision, 96% recall, and a ~ 4% false positive rate on our annotated dataset, outperforming existing techniques.

With the improvement of Internet of Things (IoT) technologies, services, and applications, there is a proliferation of access to smart devices in everyday life. However, granting access and controlling access rights for each resource is challenging in highly dynamic and large-scale IoT deployments. In particular, multiple access information may need to be provided to an entity when granting access rights to several resources. The situation becomes more complex when an entity is required to share its identity attribute to receive the access information. These raise the question of what identity information an entity needs to provide to obtain the required access to a particular resource and, subsequently, what access information needs to be provided when accessing that resource. That said, there is a need for a flexible approach where an entity can share a distinct identity and access attributes for accessing a resource without revealing additional information. Such flexibility in sharing information is significant given the privacy risk of an entity’s identity. This paper presents an architecture that delivers access rights to an entity with selective disclosure of information. Our approach ensures the minimum exchange of information (identity and access attribute) to enhance an entity’s privacy when granting access rights to an entity. We use blockchain to provide data authenticity (i.e., tamper-proof), transparency and automatic execution of access rights based on shared attributes using smart contracts. We implement a proof of concept of the proposed system using Hyperledger fabric as a permissioned blockchain network. Our results demonstrate the feasibility of the proposed system showing efficiency in granting access rights.

Existing integrity verification approaches for deep models are designed for private verification (i.e., assuming the service provider is honest, with white-box access to model parameters). However, private verification approaches do not allow model users to verify the model at run-time. Instead, they must trust the service provider, who may tamper with the verification results. In contrast, a public verification approach that considers the possibility of dishonest service providers can benefit a wider range of users. In this paper, we propose PublicCheck, a practical public integrity verification solution for services of run-time deep models. PublicCheck considers dishonest service providers, and overcomes public verification challenges of being lightweight, providing anti-counterfeiting protection, and having fingerprinting samples that appear smooth. To capture and fingerprint the inherent prediction behaviors of a run-time model, PublicCheck generates smoothly transformed and augmented encysted samples that are enclosed around the model's decision boundary while ensuring that the verification queries are indistinguishable from normal queries. PublicCheck is also applicable when knowledge of the target model is limited (e.g., with no knowledge of gradients or model parameters). A thorough evaluation of PublicCheck demonstrates the strong capability for model integrity breach detection (100% detection accuracy with less than 10 black-box API queries) against various model integrity attacks and model compression attacks. PublicCheck also demonstrates the smooth appearance, feasibility, and efficiency of generating a plethora of encysted samples for fingerprinting.

Abstract Recommender system for the IoT (RSIoT) has attracted considerable attention. By leveraging emerging technologies such as the Internet of Things (IoT), artificial intelligence, and blockchain, RSIoT improves various indicators of residents' life. However, data integrity threats may affect the accuracy and consistency of the data particularly in the IoT environment where most devices are inherently dynamic and have limited resources that could fail in ensuring the quality of data transmission. Prior work has focused on processing big data and ensuring their integrity by considering cloud storage service as the popular way. In this article, we address integrity of data leveraging blockchain capabilities to ensure the integrity of the critical data. We adapted the Ethereum blockchain to our RCS for ensuring integrity of data during sharing them between doctor and patient without handling their data by third party. We build four smart contracts that enable our system of gaining more advantage of blockchain. We evaluated the performance of our smart contracts in Kovan and Rinkeby test networks. The preliminary results show the feasibility and effectiveness of the proposed solution.

The Internet of Things (IoT) has revolutionized the manufacturing process in the industry. It has created a new ecosystem allowing a diversified set of devices to be controlled remotely with minimal human intervention. Today, with the advances in intelligence, processing, storage, communication, and networking capabilities of IoT devices, we are one step closer to realizing the vision of Industry 4.0. Cyber-physical systems (CPS) are now significantly more intelligent and automated with the aid of advances in Machine Learning (ML). Intelligent IoT (IIoT), Digital Twins (DT) and the advances in mobile networks are now paving the path towards decentralized self-managed CPS in the industry. DT permits mobile networks to provide adaptive and dynamic configurations for cooperative CPS. Moreover, trustworthy cooperation may be realized with blockchain. In this article, we present a blockchain-assisted hierarchical federated learning (FL)-enabled platform (HFL) for Industry 4.0. The solution integrates DT into CPS to accurately capture the characteristics of industrial IoT devices and assist in the HFL process. A two-stage FL algorithm is used that groups Internet-enabled factory machinery and their DTs into groups in accordance with their organizational structure. A global model is created for the groups from the averaged local models and the DT model in the first stage. During the second stage, federated aggregation is used to create a global model from the first-stage models. Blockchain is used to cross-verify and validate newly added blocks with the support of validator nodes. Numerical analysis is performed to compare between the presented DT-enabled and blockchain-assisted HFL solution and benchmark solutions in terms of network overhead, block optimization, and accuracy.

Researchers nowadays are particularly focusing on the interpretation of EEG signals to understand and exploit the information they provide for brain activities. Deep learning architectures performing sleep staging have recently grown to their full potential with their ability to learn and interpret highly complex mathematical contexts. This has been catered to owing to the increasing availability of large EEG data sets. In this paper, we describe how sleep staging differs from microsleep prediction. We also provide a fresh methodology for the microsleep classification job that works with even less training data. Our proposed model exploits the attention-based mechanism that clubs the advantages available in Wavelet transform with Short Time Fourier Transform(STFT) Spectrogram. We also put forward a robust deep learning model that contains separate “time-dependent” and “time-independent” parts, which can record contexts from the sequence of features and simultaneously learn intra-epoch relations. A single-electrode EEG signal was employed for our analysis to accommodate such procedures’ social acceptance. For the task of microsleep detection on the MWT dataset, our model achieves fairly high accuracy rates (92% training and 89.9% testing accuracy), and an overall improvement in the kappa value by ≈ 42%, as compared to prior novel approaches.

Location trajectories collected by smartphones and other devices represent a valuable data source for applications such as location-based services. Likewise, trajectories have the potential to reveal sensitive information about individuals, e.g., religious beliefs or sexual orientations. Accordingly, trajectory datasets require appropriate sanitization. Due to their strong theoretical privacy guarantees, differential private publication mechanisms receive much attention. However, the large amount of noise required to achieve differential privacy yields structural differences, e.g., ship trajectories passing over land. We propose a deep learning-based Reconstruction Attack on Protected Trajectories (RAoPT), that leverages the mentioned differences to partly reconstruct the original trajectory from a differential private release. The evaluation shows that our RAoPT model can reduce the Euclidean and Hausdorff distances between the released and original trajectories by over 68 % on two real-world datasets under protection with ε ≤ 1. In this setting, the attack increases the average Jaccard index of the trajectories’ convex hulls, representing a user’s activity space, by over 180 %. Trained on the GeoLife dataset, the model still reduces the Euclidean and Hausdorff distances by over 60 % for T-Drive trajectories protected with a state-of-the-art mechanism (ε = 0.1). This work highlights shortcomings of current trajectory publication mechanisms, and thus motivates further research on privacy-preserving publication schemes.

Blockchain has the potential to enhance supply chain management systems by providing stronger assurance in transparency and traceability of traded commodities. However, blockchain does not overcome the inherent issues of data trust in IoT enabled supply chains. Recent proposals attempt to tackle these issues by incorporating generic trust and reputation management methods, which do not entirely address the complex challenges of supply chain operations and suffers from significant drawbacks. In this paper, we propose DeTRM, a decentralised trust and reputation management solution for supply chains, which considers complex supply chain operations, such as splitting or merging of product lots, to provide a coherent trust management solution. We resolve data trust by correlating empirical data from adjacent sensor nodes, using which the authenticity of data can be assessed. We design a consortium blockchain, where smart contracts play a significant role in quantifying trustworthiness as a numerical score from different perspectives. A proof-of-concept implementation in Hyperledger Fabric shows that DeTRM is feasible and only incurs relatively small overheads compared to the baseline.

Recent research has shown the efficacy of machine learning-based IoT network traffic analysis to infer attributes such as IoT device type, IoT device activity state and even user behaviours in smart home environments. Therefore, various traffic obfuscation techniques have been proposed to reduce the classification performance of these machine learning algorithms. However, most of the proposed traffic obfuscation techniques can only alter traffic originating from the IoT device, with the incoming traffic from the communicating servers largely unaffected. We show that IoT device activity can still be successfully inferred by only using incoming network traffic for analysis. Therefore, this research emphasizes the need for obfuscation techniques, which can alter network traffic in both directions between the IoT devices and their communicating servers.

Graph Neural Networks (GNNs) have achieved tremendous success in many graph mining tasks benefitting from the message passing strategy that fuses the local structure and node features for better graph representation learning. Despite the success of GNNs, and similar to other types of deep neural networks, GNNs are found to be vulnerable to unnoticeable perturbations on both graph structure and node features. Many adversarial attacks have been proposed to disclose the fragility of GNNs under different perturbation strategies to create adversarial examples. However, vulnerability of GNNs to successful backdoor attacks was only shown recently.

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.

Abstract Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.

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.

Buildings are required to follow standard operational procedures during emergency evacuation. In addition to people evacuating the building, one of the recommended steps during a fire evacuation is to shut down the air handling units (AHUs) of the heating, ventilation, and air conditioning (HVAC) system to prevent smoke from spreading in the building via the air ducts. Shutting down the AHU will inevitably cut-off cooling, resulting in internal temperatures rising steeply particularly on hot days. This phenomenon imposes considerable power demand on the HVAC to rapidly cool the building down during reoccupation. In this article, we study the energy implications of post-evacuation scenarios. Our contributions are threefold: 1) we quantify power excursion caused in 43 evacuation events across 14 buildings of a university campus using a data-driven building thermal model. We show evacuations during summer season can result in power consumption up to 150% above the power demand threshold; 2) we develop a method to reschedule planned evacuations in order to eliminate the power excursions while adhering to building evacuation standards; and 3) we develop a formal optimization framework to minimize the energy costs during planned and emergency evacuations without compromising the desired thermal comfort temperatures by intelligently cooling the building post evacuation. This is the first study to understand and reduce the HVAC power consumption associated with building evacuation events.

Transportation is undergoing significant change due to advances in automotive technologies, such as electric and autonomous cars and transportation paradigms, such as car and ridesharing. Coupled with the rapid prevalence of IoT devices, this provides an opportunity for many organizations with large on-premise parking spaces, to better utilize this space, reduce energy footprint, and monetize data generated by IoT systems. This article outlines our efforts to instrument our University’s multistorey parking lot with IoT sensors to monitor real-time usage, and develop a novel dynamic space allocation framework that allows campus manager to redimension the car park to accommodate both car sharing and existing private car users. Our first contribution describes experiences and challenges in measuring car park usage on the university campus and removing noise in the collected data. Our second contribution analyzes data collected during 15 months and draws insights into usage patterns. Our third contribution employs machine learning algorithms to forecast future car park demand in terms of arrival and departure rates, with a mean absolute error of 4.58 cars per hour for a 5-day prediction horizon. Finally, our fourth contribution develops an optimal method for partitioning car park space that aids campus managers in generating revenue from shared cars with minimal impact on private car users.

Blockchain technology is becoming the cornerstone for the development and deployment of other technologies like Federated Learning (FL) and the Internet of Things (IoT), as it plays a critical role in data sharing and incentives. Blockchains supports decentralization, data-privacy protection, security, and reliability. Assuring secure data sharing in mobile computing and FL is challenging because of untrustworthy participants and unknown data quality. Blockchain provides trust in decentralized environments without requiring trusted third parties. By using smart contracts, blockchain has been able to supporting rich decentralized applications. However, the scalability of blockchain is a challenge that prevents its wide adoption by high-performance applications. To address the blockchain scalability issue, various blockchain sharding technologies and off-chain solutions have been proposed. To improve the network throughput, blockchain sharding divides the entire network into several smaller parallel groups and exploits fast consensus algorithms in blockchain shards. Off-chain solutions, such as payment channel networks (PCNs), transfer the slow on-chain transactions to the off-chain environment, in which transactions can be accelerated. Without consensus and on-chain expensive operations, off-chain scalable solutions significantly reduce transaction costs and increase transaction throughput. This special issue aims to provide a forum for the presentation of state-of-the-art research approaches that advance the construction of intelligent blockchain systems. A total of 27 articles were accepted after a two-round rigorous review process. Based on their topics, we have grouped the accepted articles into four categories: blockchain-based federated learning systems, blockchain and the IoT, blockchain scalability, and high-performance blockchains. In what follows, we introduce these articles and their contributions.

With the rapid adoption of new technologies and applications, e.g., the Internet of Things, 5G/6G communication networks, big data analytics, and artificial intelligence, a deluge of devices are being connected to the network, thus generating a large amount of data. The collection, processing, and analysis of this vast amount of data are essential to help people and enterprises gain valuable information, make sensible decisions, and subsequently improve the quality of people’s lives. However, the underlying communication networks are thus facing a new number of unprecedented challenges. Managing these large numbers of devices in a scalable and secure manner is bringing significant challenges to the infrastructure construction, maintenance, and management of the communication networks. Recurring data privacy breaches and the lack of control make Internet users and enterprises less willing to provide valuable data for processing and analysis.

Computing robust and accurate quality scores for users and items in online review systems is critical, since scores directly reflect the community-wide belief about their quality. A broad range of methods have been proposed to compute rating scores, including simple aggregation, weighted aggregation, and iterative techniques, where the latter provides relatively accurate results. However, there are still serious challenges to address, especially in terms of time complexity, accuracy, and robustness against manipulation. In this article, we propose an adaptive quality assessment framework that computes dependable and accurate quality scores for users and items. The proposed method is a semi-iterative weighted aggregation technique in which, a novel approach is used to assign weights to received reviews. The weight depends on two parameters: similarity of reviews, and review prediction. In review prediction, we utilize a combination of online machine learning and collaborative filtering to predict the review expected from the user. The intuition behind using online learning is its ability to obtain lower time complexity in comparison with batch learning. We evaluate our proposed model using a real-word dataset, and compare it with two related approaches. Results show the superiority of our proposed approach, in terms of accuracy and robustness against manipulation.

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.

Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite the advantages of UAV-BSs, their dependence on the on-board, limited-capacity battery hinders their service continuity. Shorter trajectories can save flying energy, however UAV-BSs must also serve nodes based on their service priority since nodes' service requirements are not always the same. In this paper, we present an energy-efficient trajectory optimization for a UAV assisted IoT system in which the UAV-BS considers the IoT nodes' service priorities in making its movement decisions. We solve the trajectory optimization problem using Double Q- Learning algorithm. Simulation results reveal that the Q-Learning based optimized trajectory outperforms a benchmark algorithm, namely Greedily served algorithm, in terms of reducing the average energy consumption of the UAV-BS as well as the service delay for high priority nodes.

Physical layer key generation has attracted considerable attention in the past decade since it provides an alternative solution for the key establishment in wireless networks using channel reciprocity. In this paper we explore the possibility of physical layer key generation for emerging Low Power Wide Area Networks (LPWAN) such as LoRa (Long Range). However, due to the lower transmission rates of LPWANs compared to Wi-Fi and Zigbee, the channel reciprocity is relatively low, which makes timely key generation challenging. To address this problem, we propose a novel information-theoretic key generation scheme that can operate at all data rate settings, featuring a model-based key generation method. Furthermore, we derive an optimal window size to calculate the parameters of the channel model based on a random waypoint model to balance the channel reciprocity and entropy. Extensive evaluations on a campus testbed show that our method can achieve up to 13.8 bps key generation rate. Compared to state-of-the-art methods, the proposed method improves key generation rate by 3x to 5x. We also analyzed the security of the proposed approach and demonstrated it to be resilient to eavesdropping attacks.

Designing visually appealing layouts for multimedia documents containing text, graphs and images requires a form of creative intelligence. Modelling the generation of layouts has recently gained attention due to its importance in aesthetics and communication style. In contrast to standard prediction tasks, there are a range of acceptable layouts which depend on user preferences. For example, a poster designer may prefer logos on the top-left while another prefers logos on the bottom-right. Both are correct choices yet existing machine learning models treat layouts as a single choice prediction problem. In such situations, these models would simply average over all possible choices given the same input forming a degenerate sample. In the above example, this would form an unacceptable layout with a logo in the centre.

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.

Although Electronic Health Record (EHR) systems are widely used in health care organisations, they still face security and privacy problems. Blockchain is considered a promising technology that could help overcome many of these problems. However, efforts to incorporate blockchain into EHR systems so far have shown heavy overhead, unsuitable software architectural structures and inefficient consensus protocols. We present ME Chain, a multi-layer blockchain structure, which aims to solve the adoption, storage and consensus problems when implementing blockchain in EHR systems. The multi-layer structure is better suited for the operational hierarchy in health organisations and is supported by the following novel features: (i) a consensus implementation optimised for the multi-layer operations, which solves the byzantine failure in both layers, hence guarantees consistency, it also reduces request confirmation latency and overhead and provides every layer with the ability to correct the faults, (ii) a data synchronisation method for some nodes that does not receive the correct information or simply get offline to catch up with the system, data verification and retrieve mechanisms to protect data integrity. Experiments are conducted to evaluate ME Chain in terms of security, performance and storage cost. The results show that ME Chain can establish a secure EHR with high performance and acceptable storage cost.

Edge of Things (EoT) enables the seamless transfer of services, storage, and data processing from the cloud layer to edge devices in a large-scale distributed Internet of Things (IoT) ecosystems (e.g., Industrial systems). This transition raises the privacy and security concerns in the EoT paradigm distributed at different layers. Intrusion detection systems (IDSs) are implemented in EoT ecosystems to protect the underlying resources from attackers. However, the current IDSs are not intelligent enough to control the false alarms, which significantly lower the reliability and add to the analysis burden on the IDSs. In this article, we present a Dew Computing as a Service (DaaS) for intelligent intrusion detection in EoT ecosystems. In DaaS, a deep learning-based classifier is used to design an intelligent alarm filtration mechanism. In this mechanism, the filtration accuracy is improved (or sustained) by using deep belief networks. In the past, the cloud-based techniques have been applied for offloading the EoT tasks, which increases the middle layer burden and raises the communication delay. Here, we introduce the dew computing features that are used to design the smart false alarm reduction system. DaaS, when experimented in a simulated environment, reflects lower response time to process the data in the EoT ecosystem. The revamped DBN model achieved the classification accuracy up to 95%. Moreover, it depicts a 60% improvement in the latency and 35% workload reduction of the cloud servers as compared to edge IDS.

Research has shown the potential of device-free WiFi sensing for human identification. Each and every human has a unique gait and prior works suggest WiFi devices are able to capture the unique signature of a person's gait. In this article, we show for the first time that the monitored gait could be inconsistent and have mirror-like perturbations when individuals walk through WiFi devices in different directions, provided that the WiFi antenna array is horizontal to the walking path. Such inconsistent mirrored patterns are to negatively affect the uniqueness of gait and accuracy of human identification. Therefore, we propose a system called Gate-ID for accurately identifying individuals' identities irrespective of different walking directions. Gate-ID employs theoretical communication model and real measurements to demonstrate that antenna array orientations and walking directions contribute to the mirror-like patterns in WiFi signals. A novel heuristic algorithm is proposed to infer individual's walking directions. A set of methods are employed to extract and augment the representative spatial-temporal features of gait and enable the system performing irrespective of walking directions. We further propose a novel attention-based deep learning model that fuses various weighted features and ignores ineffective noises to uniquely identify individuals. We implement Gate-ID on commercial off-the-shelf devices. Extensive experiments demonstrate that our system can uniquely identify people with average accuracy of 90.7%-75.7% from a group of 6-20 people, respectively, and improve the accuracy by 12.5%-43.5% compared with baselines.

Data is central to the Internet of Things (IoT) ecosystem. With billions of devices connected, most of the current IoT systems are using centralized cloud-based data sharing systems, which will be difficult to scale up to meet the demands of future IoT systems. The involvement of such a third-party service provider requires also trust from both the sensor owner and sensor data user. Moreover, fees need to be paid for their services. To tackle both the scalability and trust issues and to automatize the payments, this paper presents a blockchain-based marketplace for sharing of the IoT data. We also use a proxy re-encryption scheme for transferring the data securely and anonymously, from data producer to the consumer. The system stores the IoT data in cloud storage after encryption. To share the collected IoT data, the system establishes runtime dynamic smart contracts between the sensor and data consumer without the involvement of a trusted third-party. It also uses a very efficient proxy re-encryption scheme which allows that the data is only visible by the owner and the person present in the smart contract. This novel combination of smart contracts with proxy re-encryption provides an efficient, fast and secure platform for storing, trading and managing sensor data. The proposed system is implemented using off-the-shelf IoT sensors and computer devices. We also analyze the performance of our hybrid system by using the permission-less Ethereum blockchain and compare it to the IBM Hyperledger Fabric, a permissioned blockchain.

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.

With the rapid development of mobile devices, people are generating huge volumes of images data every day for sharing on social media, which draws much research attention to understanding the contents of images. Image clustering plays an important role in image understanding systems. Often, most of the existing image clustering algorithms flatten digital images that are originally represented by matrices into 1D vectors as the image representation for the subsequent learning. The drawbacks of vector-based algorithms include limited consideration of spatial relationship between pixels and computational complexity, both of which blame to the simple vectorized representation. To overcome the drawbacks, we propose a novel image clustering framework that can work directly on matrices of images instead of flattened vectors. Specifically, the proposed algorithm simultaneously learn the clustering results and preserve the original correlation information within the image matrix. To solve the challenging objective function, we propose a fast iterative solution. Extensive experiments have been conducted on various benchmark datasets. The experimental results confirm the superiority of the proposed algorithm.

Network communications, despite being encrypted, leak crucial information via side channels. WiFi networks are more prone to such side-channel attacks since any attacker within the network’s range can passively eavesdrop the channel. With the increasing number of smart home devices and sensors connecting to private WiFi networks, it is essential to understand the inadvertent information leakage through WiFi side-channels. Our work demonstrates how fine-granular information on the activities happening inside a house can be inferred by passively monitoring WiFi network traffic. In particular, we were able to correctly classify various user interactions with simple IoT devices such as smart bulbs or power sockets as well as advanced voice-based intelligent assistants.

Taking the advantages of reciprocity and randomness of wireless fading channels, key generation via physical layer is attracting more attention. It becomes a remarkable solution for wireless communication in recent years. However, the feasibility under long-range and low data rate scenarios of narrow band low power wide area network (LPWAN) lacks proper studies. In this poster, we introduce a novel modeling method for Long Range Wide Area Network (LoRaWAN) key generation. The approach combines several signal processing techniques and using measured real-time Received Signal Strength Indicator (RSSI) to improve the applicability of key generation as well as increasing key generation rate (KGR) significantly.

Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes with potentially varying QoS requirements. However, the dependence on the on-board, limited-capacity battery of the UAV-BS limits their service continuity. While conserving energy is important, meeting the QoS requirements of the ground nodes is equally important. We present an energy-efficient trajectory optimization for the UAV-BS while satisfying QoS requirements. We model the trajectory optimization as an MDP problem and solve it using Q-Learning. Simulation results reveal that our proposed algorithm decreases the average energy consumption by nearly 55% compared to a randomly-served algorithm.

IoT devices are the target of choice for attackers, and one of the most devastating threats involving compromised IoT devices has been their exploitation as part of botnets. Here, we propose c-Shield, as a distributed and extensible solution designed to detect and respond to IoT-based bots in an enterprise network. c-Shield passively inspects network traffic associated with IoT devices over a range of different protocols and systematically analyses the URLs extracted. Compared with the existing solutions, c-Shield is designed to be capable of detecting bots using advanced evasion techniques such as Domain Name Generation Algorithms (DGA) with a high accuracy rate.

Mobile Crowdsensing (MCS) systems take advantage of the ubiquity and sensing power of smartphones in data gathering. Reverse auction is a popular incentive mechanism framework for MCS wherein, the participants can determine their expected rewards for their contributions. In this paper, for the first time, we consider a multi-task location-dependent reverse auction based MCS setting wherein, each task requires a specific amount of contribution to be fulfilled, participants may need to move to the task locations in order to participate in them, and the goal is to assign each participant to at most one task in a way such that the cumulative contribution of the fulfilled tasks are maximised while not exceeding a limited budget. We show that this is an NP-hard optimization problem and propose Ant Colony Optimisation-based Multi-Task Assignment (ACOMTA) as an approximation algorithm for it. We uncover an issue with the basic instantiation of ACO and propose an approach called Valid Random Path Generator (VRPG) to avoid lack of or premature convergence of the algorithm. Through extensive experiments, we show that the proposed algorithm outperforms a greedy approach as well as a random-based solution.

The rapidly increasing mobile traffic across the globe has proliferated the deployment of cellular base stations, which has, in turn, led to an increase in the power consumption and carbon footprint of the telecommunications industry. In recent times, solar-powered base stations (SPBSs) have gained much popularity in the telecom sector due to their ability to make operations more sustainable. However, some potential energy benefits rendered by the SPBSs have not yet been realized. In areas with dense base station deployment or low mobile traffic, SPBSs store surplus energy, which, in most instances, gets lost due to limited charge storage capacity of the batteries. To limit the wastage of energy, an appropriate mechanism enabling the utilization of excess energy produced by these base stations can be adopted. To this end, we model a Base Station-to-Grid (BS2G) network in which the grid can utilize surplus energy spared by the SPBSs. To overcome challenges in regards to scalability, robustness, and cost-optimization, we propose using the blockchain technology to create the BS2G network. Blockchain is a distributed ledger designed to record transactions in a transparent, lightweight, and tamper-proof manner. To make energy trade between base stations and the grid cost-effective, a game-theoretical approach has also been adopted in this paper. The proposed model simplifies the process of energy trading while also making it cost-optimal.

Internet of Things (IoT) devices are being deployed in huge numbers around the world, and often present serious vulnerabilities. Accordingly, delivering regular software updates is critical to secure IoT devices. Manufactures face two predominant challenges in providing software updates to IoT devices: 1) scalability of the current client-server model and 2) integrity of the distributed updates - exacerbated due to the devices' computing power and lightweight cryptographic primitives. Motivated by these limitations, we propose CrowdPatching, a blockchain-based decentralized protocol, allowing manufacturers to delegate the delivery of software updates to self-interested distributors in exchange for cryptocurrency. Manufacturers announce updates by deploying a smart contract (SC), which in turn will issue cryptocurrency payments to any distributor who provides an unforgeable proof-of-delivery. The latter is provided by IoT devices authorizing the SC to issue payment to a distributor when the required conditions are met. These conditions include the requirement for a distributor to generate a zero-knowledge proof, generated with a novel proving system called zk-SNARKs. Compared with related work, CrowdPatching protocol offers three main advantages. First, the number of distributors can scale indefinitely by enabling the addition of new distributors at any time after the initial distribution by manufacturers (i.e., redistribution among the distributor network). The latter is not possible in existing protocols and is not account for. Secondly, we leverage the recent common integration of gateway or Hub in IoT deployments in our protocol to make CrowdPatching feasible even for the more constraint IoT devices. Thirdly, the trustworthiness of distributors is considered in our protocol, rewarding the honest distributors' engagements. We provide both informal and formal security analysis of CrowdPatching using Tamarin Prover.

Human Activity Recognition (HAR) plays an irreplaceable role in various applications such as security, gaming, and assisted living. Recent studies introduce deep learning to mitigate the manual feature extraction (i.e., data representation) efforts and achieve high accuracy. However, there are still challenges in learning accurate representations for sensory data due to the weakness of representation modules and the subject variances. We propose a scheme called Distance-based HAR from Ensembled spatial-temporal Representations (DHARER) to address above challenges. The idea behind DHARER is straightforward—the same activities should have similar representations. We first learn representations of the input sensory segments and latent prototype representations of each class, using a Convolution Neural Network (CNN)-based dual-stream representation module; then the learned representations are projected to activity types by measuring their similarity to the learned prototypes. We have conducted extensive experiments under a strict subject-independent setting on three large-scale datasets to evaluate the proposed scheme, and our experimental results demonstrate superior performance of DHARER to several state-of-the-art methods.

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.

Sharing credentials could raise privacy concerns. For digital credentials to be widely accepted, there is a need for an end-to-end system that provides (i) secure verification of the participant identities and credentials to increase trust, and (ii) a data minimisation mechanism to reduce the risk of oversharing the credential data. This paper proposes CredChain, a blockchain-based Self-Sovereign Identity (SSI) platform architecture that allows secure creation, sharing and verification of credentials. Beyond the verification of identities and credentials, a flexible selective disclosure solution is proposed using redactable signatures. The credentials are managed through a decentralised application/wallet which allows users to store their credential data privately under their full control and re-use as necessary. Our evaluation results show that CredChain architecture is feasible, secure and exhibits the level of performance that is within the expected benchmarks of the well-known blockchain platform, Parity Ethereum.

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.

In recent years, we have witnessed the rapid development of the long range (LoRa) technology, together with extensive studies trying to understand its performance in various application settings. In contrast to measurements performed in large outdoor areas, a limited number of attempts have been made to understand the characterization and performance of the LoRa technology in indoor environments. In this article, we present a comprehensive study of the LoRa technology in multifloor buildings. Specifically, we investigate the large-scale fading characteristic, temporal fading characteristic, coverage, and energy consumption of the LoRa technology in four different types of buildings. Moreover, we find that the energy consumption using different parameter settings can vary up to 145 times. These results indicate the importance of parameter selection and enabling the LoRa adaptive data rate feature in energy-limited applications. We hope the results in this article can help both academia and industry understand the performance of the LoRa technology in multifloor buildings to facilitate developing practical indoor applications.

Object detection is important in real-world applications. Existing methods mainly focus on object detection with sufficient labelled training data or zero-shot object detection with only concept names. In this paper, we address the challenging problem of zero-shot object detection with natural language description, which aims to simultaneously detect and recognize novel concept instances with textual descriptions. We propose a novel deep learning framework to jointly learn visual units, visual-unit attention and word-level attention, which are combined to achieve word-proposal affinity by an element-wise multiplication. To the best of our knowledge, this is the first work on zero-shot object detection with textual descriptions. Since there is no directly related work in the literature, we investigate plausible solutions based on existing zero-shot object detection for a fair comparison. We conduct extensive experiments on three challenging benchmark datasets. The extensive experimental results confirm the superiority of the proposed model.

Online ride-sharing platforms have become a critical part of the urban transportation system. Accurately recommending hotspots to drivers in such platforms is essential to help drivers find passengers and improve users' experience, which calls for efficient passenger demand prediction strategy. However, predicting multi-step passenger demand is challenging due to its high dynamicity, complex dependencies along spatial and temporal dimensions, and sensitivity to external factors (meteorological data and time meta). We propose an end-to-end deep learning framework to address the above problems. Our model comprises three components in pipeline: 1) a cascade graph convolutional recurrent neural network to accurately extract the spatial-temporal correlations within citywide historical passenger demand data; 2) two multi-layer LSTM networks to represent the external meteorological data and time meta, respectively; 3) an encoder-decoder module to fuse the above two parts and decode the representation to predict over multi-steps into the future. The experimental results on three real-world datasets demonstrate that our model can achieve accurate prediction and outperform the most discriminative state-of-the-art methods.

Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.

This paper considers that a legitimate UAV tracks suspicious UAVs' flight for preventing intended crimes and terror attacks. To enhance tracking accuracy, the legitimate UAV proactively eavesdrops suspicious UAVs' communication via sending jamming signals. A tracking algorithm is developed for the legitimate UAV to track the suspicious flight by comprehensively utilizing eavesdropped packets, angle-of-arrival and received signal strength of the suspicious transmitter's signal. A new co-simulation framework is implemented to combine the complementary features of optimization toolbox with channel modeling (in Matlab) and discrete event-driven mobility tracking (in NS3). Moreover, numerical results validate the proposed algorithms in terms of tracking accuracy of the suspicious UAVs' trajectory.

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.

The Internet of Things (IoT) is transforming our physical world into a complex and dynamic system of connected devices on an unprecedented scale. Connecting everyday physical objects is creating new business models, improving processes and reducing costs and risks. Recently, blockchain technology has received a lot of attention from the community as a possible solution to overcome security issues in IoT. However, traditional blockchains (such as the ones used in Bitcoin and Ethereum) are not well suited to the resource-constrained nature of IoT devices and also with the large volume of information that is expected to be generated from typical IoT deployments. To overcome these issues, several researchers have presented lightweight instances of blockchains tailored for IoT. For example, proposing novel data structures based on blocks with decoupled and appendable data. However, these researchers did not discuss how the consensus algorithm would impact their solutions, i.e., the decision of which consensus algorithm would be better suited was left as an open issue. In this paper, we improved an appendable-block blockchain framework to support different consensus algorithms through a modular design. We evaluated the performance of this improved version in different emulated scenarios and studied the impact of varying the number of devices and transactions and employing different consensus algorithms. Even adopting different consensus algorithms, results indicate that the latency to append a new block is less than 161ms (in the more demanding scenario) and the delay for processing a new transaction is less than 7ms, suggesting that our improved version of the appendable-block blockchain is efficient and scalable, and thus well suited for IoT scenarios.

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.

Transportation is undergoing significant change due to the growth of ride-sharing, electric cars, car-sharing, and self-driving cars. Organizations that have significant real-estate dedicated to on-premise employee car parking are therefore looking to adapt the use of this space, motivated by the opportunity to become greener, improve sharing, and pursue new revenue opportunities. In this paper, we outline our experiences from instrumenting, measuring, and analyzing car-park usage in our University's multi-storey parking lot, and building a model that explores its use for multiple purposes in the near future. Our specific contributions are as follows: (1)We begin by describing experiences and challenges in measuring car-park usage on our campus and cleaning the collected data; (2)We analyze data collected over 23 weeks (covering teaching and non-teaching periods)and draw insights into the usage patterns, including occupancy patterns by times-of-day and days-of-week, and identifying various user groups based on attributes such as arrival time and duration of stay; (3)We develop a queuing model to optimize the use of parking space for generating revenue from shared cars with minimal impact on private car users. We believe our study guides campus managers wanting to generate more value from their existing parking resources.

Data quality, or sometimes referred to as data credibility, is a critical issue in mobile crowd sensing (MCS) and more generally Internet of Things (IoT). While candidate solutions, such as incentive mechanisms and data mining have been well explored in the literature, the power of crowds has been largely overlooked or under-exploited. In this paper, we propose a cross validation approach which seeks a validating crowd to ratify the contributing crowd in terms of the sensor data contributed by the latter, and uses the validation result to reshape data into a more credible posterior belief of the ground truth. This approach consists of a framework and a mechanism, where the framework outlines a four-step procedure and the mechanism implements it with specific technical components, including a weighted random oversampling (WRoS) technique and a privacy-aware trust-oriented probabilistic push (PATOP2) algorithm. Unlike most prior work, our proposed approach augments rather than redesigning existing MCS systems, and requires minimal effort from the crowd, making it conducive to practical adoption. We evaluate our proposed mechanism using a real-world MCS IoT dataset and demonstrate remarkable (up to 475%) improvement of data quality. In particular, it offers a unified solution to reconciling two disparate needs: reinforcing obscure (weakly recognizable) ground truths and discovering hidden (unrecognized) ground truths.

Increasing demand for university education is putting pressure on campuses to make better use of their real-estate resources. Evidence indicates that enrollments are rising, yet attendance is falling due to diverse demands on student time and easy access to online content. This paper outlines our efforts to address classroom under-utilization in a real university campus arising from the gap between enrollment and attendance. We do so by instrumenting classrooms with Internet of Things (IoT) sensors to measure real-time usage, using AI to predict attendance, and performing optimal allocation of rooms to courses so as to minimize space wastage. Our first contribution undertakes an evaluation of several IoT sensing approaches for measuring class occupancy, and comparing them in terms of cost, accuracy, privacy, and ease of deployment/operation. Our second contribution instruments nine lecture halls of varying capacity across campus, collects and cleans live occupancy data spanning about 250 courses over two sessions, and draws insights into attendance patterns, including identification of canceled lectures and class tests, while also releasing our data openly to the public. Our third contribution is to use AI techniques for predicting classroom attendance, applying them to real data, and accurately predicting future attendance with an root-mean-square error as low as 0.16. Our final contribution is to develop an optimal allocation of classes to rooms based on predicting attendance rather than enrollment, resulting in over 10% savings in room costs with very low risk of room overflows.

There is an increasing interest in Internet of Things (IoT) enabled smart buildings over the past decades. However, the development of smart buildings is impeded by the high installation/maintenance cost and the difficulty of large-scale evaluation in the wild. In this paper, we report the design, implementation, and deployment of an emergency light-based smart building solution. The key advantage of the system is that it is built on the top of the existing facilities in the building (i.e., emergency light). As a case study, we have implemented and deployed our system in nine production smart buildings of different types including residential, commercial office, and warehouse of multiple level building complexes. Using real data from four typical buildings, we show the proposed system can achieve >97% average packet delivery rate. Evaluation results also demonstrate the stability and robustness of the system to environmental changes. The results of this paper provide practical insights to facilitate the development of smart building systems.

A brain-computer interface (BCI) acquires brain signals, analyzes, and translates them into commands that are relayed to actuation devices for carrying out desired actions. With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), BCI can empower individuals to directly control objects such as smart home appliances or assistive robots, directly via their thoughts. However, realization of this vision is faced with a number of challenges, most importantly being the issue of accurately interpreting the intent of the individual from the raw brain signals that are often of low fidelity and subject to noise. Moreover, preprocessing brain signals and the subsequent feature engineering are both time-consuming and highly reliant on human domain expertise. To address the aforementioned issues, in this paper, we propose a unified deep learning-based framework that enables effective human-thing cognitive interactivity in order to bridge individuals and IoT objects. We design a reinforcement learning-based selective attention mechanism (SAM) to discover the distinctive features from the input brain signals. In addition, we propose a modified long short-term memory to distinguish the interdimensional information forwarded from the SAM. To evaluate the efficiency of the proposed framework, we conduct extensive real-world experiments and demonstrate that our model outperforms a number of competitive state-of-the-art baselines. Two practical real-time human-thing cognitive interaction applications are presented to validate the feasibility of our approach.

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.

Twitter is an important source of information that people frequently contribute to and rely on for emerging topics, public opinions, and event awareness. Crisis-relevant tweets can potentially avail a magnitude of applications such as helping authorities and governments become aware of situations and thus offer better responses. One major challenge toward crisis-awareness in Twitter is to identify those tweets that are relevant to unseen crises. In this article, we propose an automatic labeling approach to distinguishing crisis-relevant tweets while differentiating source types (e.g., government or personal accounts) simultaneously. We first analyze and identify tweet-specific linguistic, sentimental, and emotional features based on statistical topic modeling. Then, we design a novel correlative convolutional neural network which uses a shared hidden layer to learn effective representations of the multi-faceted features. The model can discover salient information while being robust to the variations and noises in tweets and sources. To obtain a bird’s-eye view of a crisis event, we further develop an approach to automatically summarize key information of identified tweets. Empirical evaluation on a real Twitter dataset demonstrates the feasibility of discerning relevant tweets for an unseen crisis. The applicability of our proposed approach is further demonstrated with a crisis aider system.

Data aggregation in Wireless Sensor Networks (WSNs) can effectively reduce communication overheads and reduce the energy consumption of sensor nodes. A WSN needs to be not only energy efficient but also secure. Various attacks may make data aggregation unsecure. We investigate the reliable and secure end-to-end data aggregation problem considering selective forwarding attacks and modification attacks in homogeneous WSNs, and propose two data aggregation approaches. Our approaches, namely Sign-Share and Sham-Share, use secret sharing and signatures to allow aggregators to aggregate the data without understanding the contents of messages and the base station to verify the aggregated data and retrieve the raw data from the aggregated data. To the best of our knowledge, this is the first lightweight en-routing malicious node detection in concealed data aggregation. We have performed an extensive simulation to compare our approaches and the two state-of-the-art approaches PIP and RCDA-HOMO. The simulation results show that both Sign-Share and Sham-Share consume a reasonable amount of time in processing and aggregating the data. The simulation results show that our first approach achieved an average network lifetime of 102.33% over PIP and average aggregation energy consumption of 74.93%. In addition, it achieved an average aggregation processing time and sensor data processing time of 95.4% and 90.34% over PIP and 98.7% and 92.07% over RCDA-HOMO, respectively, and it achieved an average network delay of 71.95% over PIP. Although RCDA-HOMO is completely a different technique, a comparison was performed to measure the computational overhead.

This demo presents our web-tool to access and visualize student attendance data obtained from instrumenting a pilot set of classrooms with people counting sensors in a large university campus in Sydney, Australia. We showcase two aspects: (1) how to access and process our open data-set containing time-stamped occupancy counts for 9 lecture rooms of varying size in which over 250 courses are conducted over a 12-week semester; and (2) visualizing occupancy at multiple spatial (per-room and per-course) and temporal (over a day, week, or semester) granularities, enabling new insights into student attendance and room usage patterns.

Student enrollments world-wide are increasing each year, while lecture attendance continues to fall, due to diverse demands on student time and easy access to online content. The resulting underutilization of classrooms entails cost penalties, especially in campuses where real-estate is at a premium. This paper outlines our efforts to instrument a University campus with sensors to measure classroom attendance, in a cost-effective and scalable manner without endangering student privacy. We begin by undertaking a lab evaluation of several approaches to measuring class occupancy, and compare them in terms of cost, accuracy, and ease of deployment and operation. We then instrument 9 lecture halls of varying capacity across campus, collect and clean live data on occupancy spanning about 250 courses over 12 weeks during session, and draw insights into attendance patterns, including identification of canceled lectures and class tests; our occupancy data is released openly to the public. Lastly, we show how classroom allocation can be optimized based on attendance rather than enrollments, resulting in potential savings of 52% in room costs.

With the widespread adoption of Internet of Things (IoT), billions of everyday objects are being connected to the Internet. Effective management of these devices to support reliable, secure and high quality applications becomes challenging due to the scale. As one of the key cornerstones of IoT device management, automatic cross-device classification aims to identify the semantic type of a device by analyzing its network traffic. It has the potential to underpin a broad range of novel features such as enhanced security (by imposing the appropriate rules for constraining the communications of certain types of devices) or context-awareness (by the utilization and interoperability of IoT devices and their high-level semantics) of IoT applications. We propose an automatic IoT device classification method to identify new and unseen devices. The method uses the rich information carried by the traffic flows of IoT networks to characterize the attributes of various devices. We first specify a set of discriminating features from raw network traffic flows, and then propose a LSTM-CNN cascade model to automatically identify the semantic type of a device. Our experimental results using a real-world IoT dataset demonstrate that our proposed method is capable of delivering satisfactory performance. We also present interesting insights and discuss the potential extensions and applications.

Anytime, Anywhere' data access model has become a widespread IT policy in organizations making insider attacks even more complicated to model, predict and deter. Here, we propose Gargoyle, a network-based insider attack resilient framework against the most complex insider threats within a pervasive computing context. Compared to existing solutions, Gargoyle evaluates the trustworthiness of an access request context through a new set of contextual attributes called Network Context Attribute (NCA). NCAs are extracted from the network traffic and include information such as the user's device capabilities, security-level, current and prior interactions with other devices, network connection status, and suspicious online activities. Retrieving such information from the user's device and its integrated sensors are challenging in terms of device performance overheads, sensor costs, availability, reliability and trustworthiness. To address these issues, Gargoyle leverages the capabilities of Software-Defined Network (SDN) for both policy enforcement and implementation. In fact, Gargoyle's SDN App can interact with the network controller to create a 'defence-in-depth' protection system. For instance, Gargoyle can automatically quarantine a suspicious data requestor in the enterprise network for further investigation or filter out an access request before engaging a data provider. Finally, instead of employing simplistic binary rules in access authorizations, Gargoyle incorporates Function-based Access Control (FBAC) and supports the customization of access policies into a set of functions (e.g., disabling copy, allowing print) depending on the perceived trustworthiness of the context. Our extensive evaluation results prove the practicality of Gargoyle with better performance metrics compared to existing solutions.

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.

Most two-factor authentication (2FA) implementations rely on the user possessing and interacting with a secondary device (e.g. mobile phone) which has contributed to the lack of widespread uptake. We present a 2FA system, called Wi-Sign that does not rely on a secondary device for establishing the second factor. The user is required to sign at a designated place on the primary device with his finger following a successful first step of authentication (i.e. username + password). Wi-Sign captures the unique perturbations in the WiFi signals incurred due to the hand motion while signing and uses these to establish the second factor. Wi-Sign detects these perturbations by measuring the fine-grained Channel State Information (CSI) of the ambient WiFi signals at the device from which log-in attempt is being made. The logic is that, the user's hand geometry and the way he moves his hand while signing cause unique perturbations in CSI time-series. After filtering noise from the CSI data, principal component analysis is employed for compressing the CSI data. For segmentation of sign related perturbations, Wi-Sign utilizes the thresholding approach based on the variance of the first-order difference of the selected principal component. Finally, the authentication decision is made by feeding scrupulously selected features to a One-Class SVM classifier. We implement Wi-Sign using commodity off-the-shelf 802.11n devices and evaluate its performance by recruiting 14 volunteers. Our evaluation shows that Wi-Sign can on average achieve 79% TPR. Moreover, Wi-Sign can detect attacks with an average TNR of 86%.

Mobile Crowdsensing (MCS) applications take advantage of the ubiquity and sensing power of smartphones in data gathering. Designing an incentive mechanism for motivating the individuals to participate in such systems is vital. Reverse Auction (RA) is a popular framework in which the participants bid their expected returns for their contributions, and a task creator selects a subset of them with a view to maximise the cumulative contribution within a prescribed budget. In RA, the participants are not aware of their winning probability before the auction is closed. If the participants are given some statistical information about the returns associated with their bid, they may reduce their bid in order to increase their returns. In this paper, we propose Bid-Revisable Reverse Auction (BRRA), as well as an enhancement called BRRA with Virtual Contribution (BRRA-VC), wherein the participants are allowed to revise their bids during the auction, based on the feedback they receive about the winning probability of their submitted bids. Through conducting extensive experiments, we show that in comparison to RA, the BRRA schemes not only benefit the task creator by increasing the return on investment (i.e., the total contribution for the same budget) and also by decreasing the participant dropout ratio, but also profit the participants who are open to revise their bids by increasing their received rewards as well as their winning chances.

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.

A Software-Defined Network (SDN) controller (aka. Network Operating System or NOS) is regarded as the brain of the network and is the single most critical element responsible to manage an SDN. Complimentary to existing solutions that aim to protect a NOS, we propose an intrusion protection system designed to protect an SDN against a controller that has been successfully compromised. Gwardar maintains a virtual replica of the data plane by intercepting the OpenFlow messages exchanged between the control and data plane. By observing the long-term flow of the packets, Gwardar learns the normal set of trajectories in the data plane for distinct packet headers. Upon detecting an unexpected packet trajectory, it starts by verifying the data plane forwarding devices by comparing the actual packet trajectories with the expected ones computed over the virtual replica. If the anomalous trajectories match the NOS instructions, Gwardar inspects the NOS itself. For this, it submits policies matching the normal set of trajectories and verifies whether the controller submits matching flow rules to the data plane and whether the network view provided to the application plane reflects the changes. Our evaluation results prove the practicality of Gwardar with a high detection accuracy in a reasonable time-frame.

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subject's active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large-scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the-art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.

Cash-less payment via a variety of credit, debit or prepaid cards is pervasive in our interconnected society, but not so ubiquitous in remote rural regions where network connectivity is intermittent. We proposed a cash-less payment scheme for remote villages based on blockchains that allow maintaining a record of verifiable transactions in a distributed manner. We overcome the limitations of intermittent network connectivity by solely relying on blockchain mining nodes in the village for transaction processing and verification. The bank joins as a peer and monitors node behaviors, rewards miners and processes currency exchanges whenever the connectivity is available. We take advantage of the Ethereum network to develop our solution and demonstrate the feasibility of the proposed system on off-the-shelf computing devices. We emulate a remote village scenario with intermittent network connectivity and show the robustness and reliability of the proposed system.

We consider a wireless information surveillance in UAV network, where a legitimate unmanned aerial vehicle (UAV) proactively eavesdrops communication between two suspicious UAVs. However, challenges arise due to lossy airborne channels and limited power of the UAV. In this paper, we study an emerging legitimate eavesdropping paradigm that the legitimate UAV improves the eavesdropping performance via jamming the suspicious communication. Moreover, a power efficient legitimate eavesdropping scheme, PELE, is proposed to maximize the number of eavesdropped packets from the legitimate UAV while maintaining a target signal to interference plus noise ratio at the suspicious link. Numerical results are shown to validate the performance of PELE. Additionally, four typical fading channel models are applied to the network so as to investigate their impact on PELE.

We propose a privacy preserving mobile advertising system for in-app ad placement, that enables user profiling and targeted ads without revealing user interests to the mobile advertising companies. Our proposal relies on device-based user profiles, derived from app activity, on the use of Private Information Retrieval (PIR) to query ads database(s) for matching (to profile) ads, without the database(s) learning the content or the result of queries. We implement a Proof of Concept (POC) solution comprising critical system components for Android devices, including the profile builder and the PIR mechanism based on Percy++ library (ported to Android). We evaluate the practicality of selected PIR techniques in a mobile ads system using measured real world parameters. Overall, we show that a mobile PIR client can be effectively used for private advertising: for a single client connecting to a desktop PIR server, the Information theoretic (IT) and Hybrid PIR mechanisms allow close to real time ad retrieval. E.g., when querying a 1GB ad database for a block of 4 ads (total of 64KB), the ads are retrieved with a delay of around 2.5sec and utilising (for IT PIR) 1.25MB of data. The selected Computational PIR mechanism, however, introduces unacceptable overheads (the delay is of the order of 1300sec and 9.4GB of data is exchanged between the Android client and server for the same ad block). Further multi-client scalability tests indicate that, for all schemes, the server side is a performance bottleneck and, in addition to using commercial grade equipment, implementation enhancements including parallel processing would be necessary to have close to real time system responsiveness.

Mobile Crowdsensing (MCS) systems take advantage of the ubiquity and sensing power of smartphones in data gathering. Designing an incentive mechanism for motivating the individuals to participate in such systems is vital. Reverse auction is a popular incentive framework in which the users bid their expected returns for their contributions, and the mechanism then selects a number of them as the participants based on their value for the system. In this paper, we consider the goal of participant selection as maximising the total contribution within a budget constraint where the user contributions may be disparate and coverage overlap is possible. We propose a genetic algorithm approximation solution for this optimisation problem. We call the mechanism as Genetic Algorithm based Incentive Mechanism (GAIM). We also propose an enhanced version of this approach (EGAIM) in which an improved parent selection strategy is utilised to overcome two limitations of GAIM which arise in situations where the budget is limited. We compare EGAIM with GAIM and a greedy algorithm under two real-world scenarios, and show that using EGAIM can save up to 55% of budget for achieving at the same level of contribution.

While second factor authentication (2FA) is now widely available, user adoption is still very low, as most of 2FA implementations require significant interaction from the user. In this paper, we present a novel 2FA system, called Wi-Auth that requires minimal participation from the user. A user after confirming her credentials with an online service, simply has to place a pre-registered secondary device in close proximity (< 2.5 inches) of the primary device from which the login attempt is being made. Wi-Auth detects the proximity of these two devices by comparing the fine-grained Channel State Information (CSI) of the ambient WiFi signals measured at the two devices. The logic being that two devices that are in such close proximity will exhibit very similar CSI characteristics. Wi-Auth uses a lightweight two-step matching algorithm to compare the two CSI measurements. We also address (for the first time in literature) the issue of targeted attacks where an attacker may be co-located with the victim. We implement Wi-Auth using commodity off-the-shelf 802.11n devices and evaluate its performance in three different practical settings including an open office, an apartment and a large meeting space. Our experiments performed at 90 different location reveal that Wi-Auth can on average achieve 94% authentication accuracy with 5% false positives and 6% false negatives. Moreover, Wi-Auth is very robust in preventing co-located attacks with a 95% attack detection accuracy.

Respiratory conditions significantly impact the health of individuals in the modern society. Long-term breath monitoring is critical for diagnosing the onset of various chronic respiratory diseases. Traditional breathing monitoring methods rely on wearable devices (e.q. face masks or chest bands) which are intrusive and uncomfortable. Recent research has demonstrated that it is possible to use device-free WiFi sensing to monitor breathing. However, these approaches only work when the monitored individual is stationary, i.e., sleeping or sitting perfectly still. In this paper, we propose WiCare, a system that employs the off-the-shelf WiFi devices and is able to monitor in-situ breathing rate in a natural setting where the individual can perform actions such as reading, writing, using phone, etc, which we refer to as micro motions. WiCare exploits Channel State Information (CSI) of WiFi data and can effectively distinguish breathing from the micro motions performed by the monitored individuals. The key idea is that certain specific subcarriers carry strong imprints of breathing motions because of the multipath effect and frequency and spacial diversity of MIMO systems. We model breathing signals as periodical sinusoidal waves and use curve fitting realised by interior point non-linear optimisation to identify breath in time series of each subcarrier. The goodness of fit measured by Dynamic Time Warping is exploited to select subcarriers that effectively capture breathing. Independent component analysis is used to precisely isolate the breathing signals. We recruit five participants to perform 9 common micro motions. Our extensive experiments show WiCare can accurately distinguish breathing from the micro motions and estimate breath rate with an average accuracy of over 90%. WiCare also outperforms the state-of-the-art breath rate estimation methods by up to 80%. WiCare represents a first and important step towards in-situ breath monitoring in natural settings.

This paper presents VeinDeep, a system for using vein patterns to secure smartphones from opportunistic access, e.g. a device left unattended. VeinDeep takes advantage of infrared depth sensors, which at the time of writing have recently started to appear in smartphones for 3D indoor mapping and localisation. We find these sensors can be re-purposed to capture images of the unique vein patterns on the back of each person's hand. We simulate a depth sensor equipped smartphone by developing VeinDeep on a low power Compute Stick. We use Kinect V2 depth sensor to collect 240 recordings from 40 hands belonging to 20 test subjects. Then use this data to compare VeinDeep to one older but popular vein pattern recognition algorithm which uses Hausdorff distance and one recently developed algorithm which uses Kernel distance. We achieve a precision of 0.98, compared to 0.9 for Kernel distance and 0.5 for Hausdorff distance when recall is approximately at 0.83. In addition VeinDeep does all this while taking an average of 6 MiB of memory and 466 milliseconds per comparison. This is an average of 1/6 run time, 2/3 the memory of Hausdorff distance and 1/3 the run time, 1/2 the memory of Kernel distance.

Data aggregation in WSNs (Wireless Sensor Networks) can effectively reduce communication overheads and the energy consumption of sensor nodes. A WSN needs to be not only energy efficient, but also secure. Various attacks may make data aggregation unsecure. We investigate the reliable and secure endto- end data aggregation problem considering selective forwarding attacks and modification attacks in homogeneous cluster-based WSNs, and propose two data aggregation approaches. Our approaches, namely, Sign-Share and Sham-Share, use secret sharing and signatures to allow aggregators to aggregate the data without understanding the contents of messages and the base station to verify the aggregated data and retrieve the raw data from the aggregated data. We have performed extensive simulations to compare our approaches with the two state-of-theart approaches PIP and RCDA-HOMO. The simulation results show both Sign-Share and Sham-Share are faster in processing and aggregating data.

Prior research has shown the potential of device-free WiFi sensing for human activity recognition. In this paper, we show for the first time WiFi signals can also be used to uniquely identify people. There is strong evidence that suggests that all humans have a unique gait. An individual's gait will thus create unique perturbations in the WiFi spectrum. We propose a system called WiFi-ID that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow us to uniquely identify that person. We implement WiFi-ID on commercial off-the-shelf devices. We conduct extensive experiments to demonstrate that our system can uniquely identify people with average accuracy of 93% to 77% from a group of 2 to 6 people, respectively. We envisage that this technology can find many applications in small office or smart home settings.

In HTTP-based adaptive streaming systems, media server simply stores video content segmented into a series of small chunks coded in different qualities and sizes. The decision for next chunk's quality level to achieve a high quality viewing experience is left to the client which is a challenging task, especially in mobile environment due to unexpected changes in network bandwidth. Using computer simulations, previous work has demonstrated that Markov decision process (MDP) is very effective for such decision making and that it can reduce video freezing or re-buffering events drastically compared to other methods of adaptation. However, to date there has been no practical implementation and evaluation of MDP-based DASH players. In this work, we extend a publicly available DASH player recently released by DASH industry forum to realise a real DASH player that implements MDP-based video adaptation. We implement two alternative MDP optimisation algorithms, value iteration and Q learning and evaluate their performances in real driving conditions under 300 minutes of video streaming. Our results show that value iteration and Q learning reduce video freezing by a factor of 8 and 11, respectively, compared to the default decision making algorithm implemented in the public DASH player.

Bandwidth fluctuation in mobile networks severely effects the quality of service (QoS) of bandwidth-sensitive applications such as video streaming. Using bandwidth statistics it is possible to predict the network behaviour and take proactive actions to counter network fluctuations, which in turn can improve the QoS. In this paper, we present comprehensive bandwidth datasets from extensive measurement campaigns conducted in Sydney on both 3G and 4G networks under vehicular driving conditions. A particularly distinguishing feature of our dataset is that we have collected data from repeated trips along a few routes. Thus our data can be useful to obtain statistically significant results on network performance in an urban setting. We outline the measurement methodology and present key insights obtained from the collected traces. We have made our dataset available to the wider research community.

Personnel working in health services and food preparation industries maintain hand hygiene by washing their hands. Often this follows the WHO hand hygiene guidelines. Systems exist to monitor and detect compliance with all stages of the guidelines. However, the critical step of verifying whether the subject has correctly lathered soap on the hands can only be monitored using wrist worn sensors. This comes with several disadvantages: the wearable sensors may become contaminated, require battery, the user forgets to wear or misplaces the device. We address these problems by proposing WashInDepth, a system which uses a fixed contactless depth sensor mounted above the wash basin and is activated using a wireless trigger. As the system is fixed in position and contactless, there is reduced chance of contamination, the option to not require a battery and no possibility of forgetting or misplacement of the device. This system provides the potential ability to warn a person, after they have washed their hand, if improper application of soap was detected based on their hand gestures. We evaluate the gesture detection accuracy with 15 subjects and achieve 94% gesture detection accuracy. We deploy the system on a low power Compute Stick and demonstrate that it can keep track of hand gestures in real-time when video is recorded at 20 FPS.

Pervasive and mobile computing technologies can make our everyday living environments and our cities "smart", i.e., capable of reaching awareness of physical and social processes and of dynamically affecting them in a purposeful way. In general, living in a smart environment and being made part of its activities somehow make us - as individuals - smarter as well, by increasing our perceptory and social capabilities. However, a potential risk could be to start delegating too much to the environment itself, losing in critical attention, abandoning individual decision making for relying on collective computational governance of our activity, and in the end also losing awareness of environmental and social processes. The panel intends to discuss the above issues with the help of relevant researchers in the area of pervasive computing, smart environments, collective intelligence.

Summary Ubiquitous computing is predicted to be the next major paradigm shift in computing since the personal computing revolution. The past 5–10 years has seen the development and adoption of fast mobile devices and widely available data networks, enabling a new generation user and device interaction. However, without understanding the context in which they operate, devices cannot truly adapt to humans or the environment in which they operate. We present a real‐time, context‐aware platform that promotes information‐centric, natural interactions between many different personal computing devices. Our system allows devices to intelligently interpret the context they operate in to better serve the user. Moreover, our platform supports a rapidly emerging trend in computing, wherein a single user simultaneously uses multiple devices in unison (computer, smartphone and tablet) to complete a particular task. Our platform can use current device context to sense collaboration between devices and users, automatically annotating information to improve future retrieval. Through removing the perceived borders between devices, our system aims to create a seamless information sharing experience in a multi‐device, multiuser environment. Our reference implementation supports Apple iOS devices and OSX computers. Moreover, we present extensive evaluations to demonstrate its efficacy. Copyright © 2014 John Wiley & Sons, Ltd.

Airborne relaying can extend wireless sensor networks (WSNs) to remote human-unfriendly terrains. However, lossy airborne channels and limited battery of unmanned aerial vehicles (UAVs) are critical issues, adversely affecting success rate and network lifetime, especially in real-time applications. We propose an energy-efficient cooperative relaying scheme which extends network lifetime while guaranteeing the success rate. The optimal transmission schedule of the UAVs is formulated to minimize the maximum (min-max) energy consumption under guaranteed bit error rates, and can be judiciously reformulated and solved using standard optimisation techniques. We also propose a computationally efficient suboptimal algorithm to reduce the scheduling complexity, where energy balancing and rate adaptation are decoupled and carried out in a recursive alternating manner. Simulation results confirm that the suboptimal algorithm cuts off the complexity by orders of magnitude with marginal loss of the optimal network yield (throughput) and lifetime. The proposed suboptimal algorithm can also save energy by 50 percent, increase network yield by 15 percent, and extend network lifetime by 33 percent, compared to the prior art.

Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers' types (e.g., abilities or costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions each for a potential winner. We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, called strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interesting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property.

Airborne relaying is of potential to extend wireless sensor networks (WSN) to human-unfriendly terrains. Challenges arise due to lossy airborne channels and limited battery of unmanned aerial vehicles (UAVs). We propose an energy-efficient relaying scheme to overcome the challenges. A swarm of UAVs are deployed to listen to remote sensors from distributed locations, improving packet reception over lossy channels. UAVs report their reception qualities to the base station where the optimal schedule with guaranteed success rates and balanced energy consumption can be generated. Such scheduling is an NP-hard binary integer programming. We develop a suboptimal solution by decoupling the processes of energy balancing and data rate adjustment. Simulations confirm that, in terms of network yield, our method is indistinguishable to the NP-hard optimal solution, 15% higher than greedy algorithms. Our method can reduce the complexity by orders of magnitude, and extend network lifetime by 33%.

Incentive mechanisms for crowdsourcing have been extensively studied under the framework of all-pay auctions. Along a distinct line, this paper proposes to use Tullock contests as an alternative tool to design incentive mechanisms for crowdsourcing. We are inspired by the conduciveness of Tullock contests to attracting user entry (yet not necessarily a higher revenue) in other domains. In this paper, we explore a new dimension in optimal Tullock contest design, by superseding the contest prize - which is fixed in conventional Tullock contests - with a prize function that is dependent on the (unknown) winner's contribution, in order to maximize the crowdsourcer's utility. We show that this approach leads to attractive practical advantages: (a) it is well-suited for rapid prototyping in fully distributed web agents and smartphone apps; (b) it overcomes the disincentive to participate caused by players' antagonism to an increasing number of rivals. Furthermore, we optimize conventional, fixed-prize Tullock contests to construct the most superior benchmark to compare against our mechanism. Through extensive evaluations, we show that our mechanism significantly outperforms the optimal benchmark, by over three folds on the crowdsourcer's utility cum profit and up to nine folds on the players' social welfare.

Point-to-point traffic has emerged as a widely used communications paradigm for cyber-physical systems and wireless sensor networks in industrial settings. However, existing point-to-point communication protocols often entail substantial overhead to find and maintain reliable routes. In recent research, protocols that rely on the phenomenon of constructive interference have thus emerged. They allow to quickly, efficiently, and reliably flood packets to the entire network. As all nodes in the network need to (re-)broadcast all packets in such protocols by design, substantial energy is consumed by nodes that do not even contribute to the actual point-to-point transmission. We propose a novel point-to-point communication protocol, called RFT, which attempts to discover the most reliable route between a source and a destination. To achieve this objective, RFT selects the minimum number of participating nodes required to ensure reliable communications while allowing all other devices in the network to sleep. During data transmissions, the nodes on the direct route as well as all helper nodes broadcast the data packets and exploit the benefits of constructive interference in order to reduce end-to-end latency.

The technological development of inexpensive GPS receivers has enabled a new realm of applications for embedded sensing systems. The availability of location information allows these sensing system to study the motion trajectories of humans, animals, and objects. The storage of the collected trajectory data, however, represents a challenge for constrained devices with limited memory. In fact, external memory is often required, which incurs an additional cost for the storage component, enlarges the physical dimensions of the device, and also results in a measurable increase of the node's energy expenditure. In this paper, we present a clustering approach for GPS location information that is specifically tailored to resource-constrained sensing platforms. While our approach can be generalised to wide variety of applications, we focus on wireless animal tracking as an illustrative example. Our two-stage clustering process only records areas in which the animal has spent an extended period of time, in order to reduce the storage requirement while ensuring a low memory foot-print and processing requirements. We evaluate our solution using real-world animal GPS traces and show that our scheme achieves 90% improvement in location accuracy while also reducing the memory footprint by up to 99% in comparison with the state-of-the-art.

Hypertext transfer protocol (HTTP) is the fundamental mechanics supporting web browsing on the Internet. An HTTP server stores large volumes of contents and delivers specific pieces to the clients when requested. There is a recent move to use HTTP for video streaming as well, which promises seamless integration of video delivery to existing HTTP-based server platforms. This is achieved by segmenting the video into many small chunks and storing these chunks as separate files on the server. For adaptive streaming, the server stores different quality versions of the same chunk in different files to allow real-time quality adaptation of the video due to network bandwidth variation experienced by a client. For each chunk of the video, which quality version to download, therefore, becomes a major decision-making challenge for the streaming client, especially in vehicular environment with significant uncertainty in mobile bandwidth. In this paper, we demonstrate that for such decision making, the Markov decision process (MDP) is superior to previously proposed non-MDP solutions. Using publicly available video and bandwidth datasets, we show that the MDP achieves up to a 15x reduction in playback deadline miss compared to a well-known non-MDP solution when the MDP has the prior knowledge of the bandwidth model. We also consider a model-free MDP implementation that uses Q-learning to gradually learn the optimal decisions by continuously observing the outcome of its decision making. We find that the MDP with Q-learning significantly outperforms the MDP that uses bandwidth models.

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.

The ongoing success of smartphones and tablet computers, combined with the widespread deployment of cellular network infrastructure, has paved the way for ubiquitous Internet access. Access to mobile services has become a commodity for many commuters on public transport vehicles. On their daily trips to work and back, however, people often experience varying throughput rates due to the different capacities of network cells and the channel quality to the cell site. Links with reduced or no throughput are clearly unfavorable when users need to download large files or engage in synchronous communication activities. We thus introduce the notion of opportunistic personal bandwidth maps (OPBMs) in this paper. OPBMs allow the user to schedule activities with high throughput demand to parts of their journey where the bandwidth requirements are likely to be met. Users create their own OPBM by means of opportunistically monitoring their throughput during access to the cellular network and consolidating these individual measurements. Due to the opportunistic nature of our approach, no additional data transfers are required. Our measurements for more than 70 commutes show that the achievable throughput for road segments is highly variable across different trips. Still, the availability of OPBMs allows users to make decisions (e.g. to download a large file) when traveling along the segment with highest expected throughput.

Incentive is key to the success of crowd sourcing which heavily depends on the level of user participation. This paper designs an incentive mechanism to motivate a heterogeneous crowd of users to actively participate in crowd sourcing campaigns. We cast the problem in a new, asymmetric all-pay contest model with incomplete information, where an arbitrary n of users exert irrevocable effort to compete for a prize tuple. The prize tuple is an array of prize functions as opposed to a single constant prize typically used by conventional contests. We design an optimal contest that (a) induces the maximum profit -- total user effort minus the prize payout -- for the crowdsourcer, and (b) ensures users to strictly have incentive to participate. In stark contrast to intuition and prior related work, our mechanism induces an equilibrium in which heterogeneous users behave independently of one another as if they were in a homogeneous setting. This newly discovered property, which we coin as strategy autonomy (SA), is of practical significance: it (a) reduces computational and storage complexity by n-fold for each user, (b) increases the crowdsourcer's revenue by counteracting an effort reservation effect existing in asymmetric contests, and (c) neutralizes the (almost universal) law of diminishing marginal returns (DMR). Through an extensive numerical case study, we demonstrate and scrutinize the superior profitability of our mechanism, as well as draw insights into the SA property.

The Internet of Things will encompass a rich variety of sensing systems including mobile phones, embedded sensor and actuator platforms, and even smart electricity meters. Through their collaborative operation, billions of such devices will realize the vision of smart homes, smart cities, and beyond

Two crucial issues to the success of participatory sensing are (a) how to incentivize the large crowd of mobile users to participate and (b) how to ensure the sensing data to be trustworthy. While they are traditionally being studied separately in the literature, this paper proposes a Simple Endorsement Web (SEW) to address both issues in a synergistic manner. The key idea is (a) introducing a social concept called nepotism into participatory sensing, by linking mobile users into a social “web of participants” with endorsement relations, and (b) overlaying this network with investment-like economic implications. The social and economic layers are interleaved to provision and enhance incentives and trustworthiness. We elaborate the social implications of SEW, and analyze the economic implications under a Stackelberg game framework. We derive the optimal design parameter that maximizes the utility of the sensing campaign organizer, while ensuring participants to strictly have incentive to participate. We also design algorithms for participants to optimally “sew” SEW, namely to manipulate the endorsement links of SEW such that their economic benefits are maximized and social constrains are satisfied. Finally, we provide two numerical examples for an intuitive understanding.

Aerial wireless sensor and actor networks are composed of multiple unmanned aerial vehicles. An actor node in the network has the capabilities of both acting on the environment and also performing networking functionalities for sensor nodes. Thus, positioning of actors is critical for the efficient data collection. In this paper, we propose an actor positioning strategy, which utilizes a hybrid antenna model that combines the complimentary features of an isotropic omni radio and directional antennas. We present a distributed algorithm for fast neighbor discovery with the hybrid antenna. The omni module of the hybrid antenna is used to form a self organizing network and the directional module is used for reliable data transmission. Extensive simulations show that our protocol improves the packet reception ratio by up to 50% compared to omnidirectional antenna. Moreover, the network reorganization delay is also reduced. The tradeoff between coverage and reorganization delay is also illustrated.

With the emergence of high speed mobile Internet access in smart devices, providing a specific quality of service (QoS) for video delivery is a challenging task due to the dynamic nature of the wireless channels. In this paper, we propose an auction based mechanism that facilitates cooperation among mobile nodes, in order to increase the level of QoS satisfaction in the network. In the proposed mechanism, users with excess resources that are surplus to their QoS requirement (sellers) are motivated to provide a relaying service to users with low QoS (buyers), in exchange for monetary compensation. Our mechanism is entirely distributed and does not require a coordination by a centralized auctioneer, which helps to minimize the implementation overhead and allows it to be applied regularly with short period times. The efficiency of the auction mechanism is verified using simulation over a range of typical practical scenarios.

The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.

Characterization of communication links in Aerial Wireless Sensor Networks (AWSN) is of paramount importance for achieving acceptable network performance. Protocols based on an arbitrary link performance threshold may exhibit inconsistent behavior due to link behavior not considered during the design stage. It is thus necessary to account for factors that affect the link performance in real deployments. This paper details observations from an extensive set of experiments designed to characterize the behavior of communication links in AWSN. We employ the widely used TelosB sensor platform for these experiments. The experimental results highlight the fact that apart from the usual outdoor environmental factors affecting the link performance, two major contributors to the link degradation in AWSN are the antenna orientation, and the multi-path fading effect due to ground reflections. Based on these observations, we propose a Link Aware Protocol for AWSN (LAAWN) that takes into account the effect of these potential sources of performance degradation. This paper details the design and performance evaluation of our proposed LAAWN protocol. We evaluated the LAAWN protocol in two real-world use cases namely delay-tolerant and real-time AWSN. The simulation results show that on average, LAAWN improves the overall network performance by reducing the percentage of dropped packets from about 34% to less than 4% for an AWSN that requires real-time data transfer.

In geographic routing, nodes need to maintain up-to-date positions of their immediate neighbors for making effective forwarding decisions. Periodic broadcasting of beacon packets that contain the geographic location coordinates of the nodes is a popular method used by most geographic routing protocols to maintain neighbor positions. We contend and demonstrate that periodic beaconing regardless of the node mobility and traffic patterns in the network is not attractive from both update cost and routing performance points of view. We propose the Adaptive Position Update (APU) strategy for geographic routing, which dynamically adjusts the frequency of position updates based on the mobility dynamics of the nodes and the forwarding patterns in the network. APU is based on two simple principles: 1) nodes whose movements are harder to predict update their positions more frequently (and vice versa), and (ii) nodes closer to forwarding paths update their positions more frequently (and vice versa). Our theoretical analysis, which is validated by NS2 simulations of a well-known geographic routing protocol, Greedy Perimeter Stateless Routing Protocol (GPSR), shows that APU can significantly reduce the update cost and improve the routing performance in terms of packet delivery ratio and average end-to-end delay in comparison with periodic beaconing and other recently proposed updating schemes. The benefits of APU are further confirmed by undertaking evaluations in realistic network scenarios, which account for localization error, realistic radio propagation, and sparse network.

ABSTRACT This paper presents an efficient hub‐based message forwarding scheme for people‐centric DTN, wherein hubs (i.e. nodes with high degree distribution) are utilized as message relays. This scheme reduces redundant message transmissions without penalizing delivery ratio, since hubs collectively form a virtual data conduit which reaches most of the nodes. Further, in order to utilize bandwidth efficiently, this approach leverages linear network coding for exchanging messages among hubs. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT In multihop wireless networks, delivering a packet to all nodes within a specified geographic distance from the source is a packet forwarding primitive (geography‐limited broadcasting), which has a wide range of applications including disaster recovery, environment monitoring, intelligent transportation, battlefield communications, and location‐based services. Geography‐limited broadcasting, however, relies on all nodes having continuous access to precise location information, which may not be always achievable. In this paper, we consider achieving geography‐limited broadcasting by means of the time‐to‐live (TTL) forwarding, which limits the propagation of a packet within a specified number of hops from the source. Because TTL operation does not require location information, it can be used universally under all conditions. Our analytical results, which are validated by simulations, confirm that TTL‐based forwarding can match the performance of the traditional location‐based geography‐limited broadcasting in terms of the area coverage as well as the broadcasting overhead. It is shown that the TTL‐based approach provides a practical trade‐off between geographic coverage and broadcast overhead. By not delivering the packet to a tiny fraction of the total node population, all of which are located near the boundary of the target area, TTL‐based approach reduces the broadcast overhead significantly. This coverage‐overhead trade‐off is useful if the significance of packet delivery reduces proportionally to the distance from the source. Copyright © 2011 John Wiley & Sons, Ltd.

Participatory sensing is a revolutionary paradigm in which volunteers collect and share information from their local environment using mobile phones. The design of a successful participatory sensing application is met with two challenges - (1) user privacy and (2) data trustworthiness. Addressing these challenges concurrently is a non-trivial task since they result in conflicting system requirements. User privacy is often achieved by removing the links between successive user contributions while such links are essential in establishing trust. In this work, we present a way to transfer reputation values (which is a proxy for assessing trustworthiness) between anonymous contributions. We also propose a reputation anonymization scheme that prevents the inadvertent leakage of privacy due to the inherent relationship between reputation information. We conduct extensive simulations using real-world mobility traces and practical application. The results show that our solution reduces the probabilities of users being tracked via successive contributions by as much as 80%. Moreover, this improvement has no discernible impact on the normal operation of the application.

An AWSN composed of bird-sized Unmanned Aerial Vehicles (UAVs) equipped with sensors and wireless radio, enables low cost high granularity three-dimensional sensing of the physical world. The sensed data is relayed in real-time over a multi-hop wireless communication network to ground stations. The following characteristics of an AWSN make effective multi-hop communication challenging - (i) frequent link disconnections due to the inherent dynamism (ii) significant inter-node interference (iii) three dimensional motion of the UAVs. In this paper, we investigate the use of a hybrid antenna to accomplish efficient neighbor discovery and reliable communication in AWSNs. We propose the design of a hybrid Omni Bidirectional ESPAR (O-BESPAR) antenna, which combines the complimentary features of an isotropic omni radio (360 degree coverage) and directional ESPAR antennas (beamforming and reduced interference). Control and data messages are transmitted separately over the omni and directional modules of the antenna, respectively. Moreover, a communication protocol is presented to perform fast neighbor discovery and beam steering. We present results from extensive simulations then consider three different real-world AWSN application scenarios and empirical aerial link characterization and show that the proposed antenna design and protocol reduces the packet loss rate, as compared to a single omni or ESPAR antenna.

Reputation systems rate the contributions to participatory sensing campaigns from each user by associating a reputation score. The reputation scores are used to weed out incorrect sensor readings. However, an adversary can deanonmyize the users even when they use pseudonyms by linking the reputation scores associated with multiple contributions. Since the contributed readings are usually annotated with spatiotemporal information, this poses a serious breach of privacy for the users. In this paper, we address this privacy threat by proposing a framework called IncogniSense. Our system utilizes periodic pseudonyms generated using blind signature and relies on reputation transfer between these pseudonyms. The reputation transfer process has an inherent trade-off between anonymity protection and loss in reputation. We investigate by means of extensive simulations several reputation cloaking schemes that address this tradeoff in different ways. Our system is robust against reputation corruption and a prototype implementation demonstrates that the associated overheads are minimal.

It is widely evidenced that location has a significant influence on the actual bandwidth that can be expected from Wireless Wide Area Networks (WWANs), e.g., 3G. Because a fast-moving vehicle continuously changes its location, vehicular mobile computing is confronted with the possibility of significant variations in available network bandwidth. While it is difficult for providers to eliminate bandwidth disparity over a large service area, it may be possible to map network bandwidth to the road network through repeated measurements. In this paper, we report results of an extensive measurement campaign to demonstrate the viability of such bandwidth maps. We show how bandwidth maps can be interfaced with adaptive multimedia servers and the emerging vehicular communication systems that use on-board mobile routers to deliver Internet services to the passengers. Using simulation experiments driven by our measurement data, we quantify the improvement in Quality of Service (QoS) that can be achieved by taking advantage of the geographical knowledge of bandwidth provided by the bandwidth maps. We find that our approach reduces the frequency of disruptions in perceived QoS for both audio and video applications in high-speed vehicular mobility by several orders of magnitude.

This article presents a comprehensive summary and recommendations towards the use of IREEL, an e-learning platform designed for network studies in CSE courses, based on our hands-on experience in a large hybrid undergraduate/postgraduate course at the UNSW. We found that the tool was well received by the students for understanding key concepts, especially when compared to legacy tools used in labs. Furthermore we show that our tool was able to handle a very large number of experiments in a relatively short amount of time.

The presence of multimodal sensors on current mobile phones enables a broad range of novel mobile applications including, e.g., monitoring noise pollution or traffic and road conditions in urban environments. Data of unprecedented quantity and quality can be collected and reported by a possible user base of billions of mobile phone subscribers worldwide. The collection of detailed sensor and location data may however compromise user privacy. In this paper, we present a decentralized mechanism to preserve location privacy during the collection of sensor readings. As most sensor readings are geotagged, we propose to exchange them between users in physical proximity in order to jumble the paths followed by the users. We evaluate different strategies to exchange and report the sensor readings to the application using real-world GPS traces of mobile users. The results demonstrate the feasibility and efficacy of our proposed scheme, which can obfuscate up to 100% of the visited locations in the best instances.

The recent wave of sensor-rich, Internet-enabled, smart mobile devices such as the Apple iPhone has opened the door for a novel paradigm for monitoring the urban landscape known as participatory sensing. Using this paradigm, ordinary citizens can collect multi-modal data streams from the surrounding environment using their mobile devices and share the same using existing communication infrastructure (e.g., 3G service or WiFi access points). The data contributed from multiple participants can be combined to build a spatiotemporal view of the phenomenon of interest and also to extract important community statistics. Given the ubiquity of mobile phones and the high density of people in metropolitan areas, participatory sensing can achieve an unprecedented level of coverage in both space and time for observing events of interest in urban spaces. Several exciting participatory sensing applications have emerged in recent years. For example, GPS traces uploaded by drivers and passengers can be used to generate real time traffic statistics. Similarly, street-level audio samples collected by pedestrians can be aggregated to create a citywide noise map. In this advanced seminar, we will provide a comprehensive overview of this new and exciting paradigm and outline the major research challenges.

Spatial packet loss correlation is important for error control protocols in wireless multicast and broadcast communications. This paper quantitatively studies the spatial packet loss correlation in 802.11 wireless networks using a series of experiments conducted in different environments with different impact of multipath fading on wireless links. It is found that environments with more multipath opportunities exhibit less spatial correlation. It is also observed that spatial correlation is strongly dependent on the packet reception rate. Based on the experimental data, an empirical model is proposed to estimate the level of spatial loss correlation as a function of packet reception rate. It is shown that the empirical model yields accurate estimates of spatial packet loss correlation for different environments.

While mobile broadband performance measured from moving vehicles in metropolitan areas has drawn significant attentions in recent studies, similar investigations have not been conducted for regional areas. Compared to metropolitan cities, regional suburbs are often serviced by wireless technologies with significantly lower data rates and less dense deployments. Conversely, vehicle speeds are usually much higher in the regional areas. In this paper, we seek to provide some insights to user experience of mobile broadband in terms of TCP throughput when travelling in a regional train. We find that (1) using a single broadband provider may lead to a large number of blackouts, which could be reduced drastically by simultaneously subscribing to multiple providers (provider blackouts are not highly correlated), (2) the choice of train route may have a more significant effect on broadband experience than the time-of-day of a particular trip, and (3) the speed of the train itself has no deterministic effect on TCP throughput.

Abstract Success of Wireless Sensor Networks (WSN) largely depends on whether the deployed network can provide desired area coverage with acceptable network lifetime. This paper seeks to address the problem of determining the current coverage achieved by the non‐deterministic deployment of static sensor nodes and subsequently enhancing the coverage using mobile sensors. We identify three key elements that are critical for ensuring effective area coverage in Hybrid WSN: (i) determining the boundary of the target region and evaluating the area coverage (ii) locating coverage holes and maneuvering mobile nodes to fill these voids, and (iii) maintaining the desired coverage over the entire operational lifetime of the network. We propose a comprehensive solution that addresses all of the aforementioned aspects of the area coverage, called MAPC (mobility assisted probabilistic coverage). MAPC is a distributed protocol that operates in three distinct phases. The first phase identifies the boundary nodes using the geometric right‐hand rule. Next, the static nodes calculate the area coverage and identify coverage holes using a novel probabilistic coverage algorithm (PCA). PCA incorporates realistic sensing coverage model for range‐based sensors. The second phase of MAPC is responsible for navigating the mobile nodes to plug the coverage holes. We propose a set of coverage and energy‐aware variants of the basic virtual force algorithm (VFA). Finally, the third phase addresses the problem of coverage loss due to faulty and energy depleted nodes. We formulate this problem as an Integer Linear Program (ILP) and propose practical heuristic solutions that achieve similar performance as that of the optimal ILP solution. A guiding principle in our design process has been to ensure that the MAPC can be readily implemented in real‐world applications. We implemented the boundary detection and PCA algorithm (i.e., Phase I) of the MAPC protocol on off‐the‐shelf sensor nodes and results show that the MAPC can successfully identify boundary nodes and accurately determine the area coverage in the presence of real radio irregularities observed during the experiments. Extensive simulations were carried out to evaluate the complete MAPC protocol and the results demonstrate that MAPC can enhance and maintain the area coverage, while reducing the total energy consumption by up to 70% as compared with the basic VFA. Copyright © 2010 John Wiley & Sons, Ltd.

Performance of a deployed Wireless Sensor Network (WSN) is greatly influenced by the interference it is subject to during operation. Degradation happens due to interference resulting in packet drops, retransmissions, link instability and inconsistent protocol behavior. These potential sources of interference must be accounted for during the design stage of a WSN in order to achieve acceptable network performance. Based on these observations, we have proposed a multi-hop routing protocol for ZigBee based WSN that takes into account interference caused by WiFi networks in operation in the vicinity and uses multiple channels at different frequencies to increase the network throughput.

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps.

In this paper, we present a thorough characterization of the spatio-temporal communication graph of a large-scale real-world vehicle-based Delay Tolerant Network (DTN). Unlike previous studies which either use synthetic mobility traces or data from small networks (< 50 nodes), our analysis is based on the mobility patterns of a large scale (∼ 1200 nodes) real-world public transport network. In particular, we examine the node degree distribution, encounter patterns, periodicity and clustering behavior, properties that are particularly relevant in the context of data forwarding in DTN. Moreover, we also study the impact of different radio propagation models on these properties. Our extensive study demonstrates that public transport networks exhibit repetitive patterns and corroborates the existence of few highly connected nodes, termed as hubs, in these networks. We have also found that the degree distribution of nodes and the inter-contact durations follow the properties of power-law distributions. We provide insights on how these properties can be leveraged to design effective communication protocols for such large-scale DTN.

Performance of a deployed Wireless Sensor Network (WSN) is greatly influenced by the interference it is subject to during operation. Degradation happens due to interference resulting in packet drops, retransmissions, link instability and inconsistent protocol behavior. We have conducted experiments that highlight the fact that interference caused by WiFi and co-channel contention significantly degrades the network performance of protocols. These potential sources of interference must therefore be accounted for during the design stage of a WSN in order to achieve acceptable network performance. Based on these observations, we have proposed a multi-hop multi-channel topology control protocol RMMTC for WSN that takes into account interference caused by WiFi networks in operation in the vicinity and uses multiple channels at different frequencies to mitigate the effect of co-channel interference. This paper details the design and performance evaluation of our proposed RMMTC protocol using both simulations and empirical experiments. In addition, we have formulated the multiple channel assignment problem as an Integer linear program (ILP) and compared the performance of our distributed protocol with the centralized ILP solution. The simulation results show that RMMTC performs close to the optimal centralized ILP and achieves a nine-fold reduction in the percentage of dropped packets when a dense network is subjected to interference from WiFi and co-channel contention.

Participatory sensing is a revolutionary new paradigm in which volunteers collect and share information from their local environment using mobile phones. The inherent openness of this platform makes it easy to contribute corrupted data. This paper proposes a novel reputation system that employs the Gompertz function for computing device reputation score as a reflection of the trustworthiness of the contributed data. We implement this system in the context of a participatory noise monitoring application and conduct extensive real-world experiments using Apple iPhones. Experimental results demonstrate that our scheme achieves three-fold improvement in comparison with the state-of-the-art Beta reputation scheme.

Routing in delay tolerant networks (DTN) can benefit from the fact that most real life DTN, especially in the context of people-centric networks (e.g. Pocket Switching Networks (PSN)), exhibit some sort of periodicity in their mobility patterns. For example, public transportation networks follow periodic schedules. Even most individuals have fairly repetitive movement patterns, for example, driving to and from work at approximately the same time everyday. This paper proposes a Bayesian classifier based DTN routing framework that adopts a methodical approach for computing the routing metrics by utilizing the network parameters (e.g. spatial and temporal information at the time of packet forwarding) that capture the periodic behavior of DTN nodes. After the calculation of routing metrics, different routing instantiations are possible based on this framework. We simulate a real-world vehicular DTN network using mobility traces from a metropolitan public transportation bus network and demonstrate that even a simplistic single-copy forwarding scheme based on our framework outperforms existing gradient-based single copy schemes by 25% in terms of delivery ratio. To the best of our knowledge this work is one of the first studies that adopts Bayesian inference in the context of DTN routing.

Adaptive video is a popular technique to continuously deliver a video stream to a user in the best quality possible when the underlying network bandwidth cannot be guaranteed. As such, quality of adaptive video depends critically on the agility of the rate adaptation algorithms in tracking the varying bandwidth. In this paper, we investigate the performance of a popular rate adaptation algorithm, namely, TCP-friendly rate control (TFRC), in vehicular environments. Our results show that TFRC cannot cope well with the pattern of bandwidth changes faced by a user travelling in a fast moving vehicle, resulting in poor viewing experience. Motivated by the observation that bandwidth changes in vehicular environment is significantly influenced by the rapid change of user's geographic location, we propose Geo-TFRC, which empowers TFRC with a street-level bandwidth map that holds summary of past bandwidth observations for each segment of the street. We conduct simulation experiments which are driven by the real High-Speed Downlink Packet Access (HSDPA) bandwidth traces collected from a vehicle traveling along a route in Sydney. Our results reveal that Geo-TFRC can track the bandwidth changes much more effectively, which in turn improves the quality of the mobile video. We find our proactive approach can significantly reduce the time that a user suffers from pixelated viewing experience by up to five folds as compared to TFRC.

The work reported in this paper investigates the performance of the Particle Filter (PF) algorithm for tracking a moving object using a wireless sensor network (WSN). It is well known that the PF is particularly well suited for use in target tracking applications. However, a comprehensive analysis on the effect of various design and calibration parameters on the accuracy of the PF has been overlooked. This paper outlines the results from such a study. In particular, we evaluate the effect of various design parameters (such as the number of deployed nodes, number of generated particles, and sampling interval) and calibration parameters (such as the gain, path loss factor, noise variations, and nonlinearity constant) on the tracking accuracy and computation time of the particle-filter-based tracking system. Based on our analysis, we present recommendations on suitable values for these parameters, which provide a reasonable trade-off between accuracy and complexity. We also analyze the theoretical Cramér-Rao Bound as the benchmark for the best possible tracking performance and demonstrate that the results from our simulations closely match the theoretical bound. In this paper, we also propose a novel technique for calibrating off-the-shelf sensor devices. We implement the tracking system on a real sensor network and demonstrate its accuracy in detecting and tracking a moving object in a variety of scenarios. To the best of our knowledge, this is the first time that empirical results from a PF-based tracking system with off-the-shelf WSN devices have been reported. Finally, we also present simple albeit important building blocks that are essential for field deployment of such a system.

The Fourth International IEEE Workshop on Practical Issues in Building Sensor Networks Applications (SenseApp 2009) was held in Zurich, Switzerland, in conjunction with the 34thIEEE Conference on Local Computer Networks (LCN 2009).

In a typical wireless mesh network (WMN), the interfering links can broadly be classified as coordinated and non-coordinated links, depending upon the geometric relationship. It is known that compared to coordinated interference, the non-coordinated interference result in significantly lower throughput and an unfair capacity distribution amongst the links. However, identification of non-coordinated interference relationships requires that each node is aware of the precise location of its neighbours, which is impractical. In this paper, we propose a novel two-phase cluster-based channel assignment scheme (CCAS) that minimizes both non-coordinated as well as coordinated interference without requiring the nodes to be aware of the location of its neighbours. CCAS logically partitions the network into non-overlapping clusters. The links within each cluster operate on a common channel which is orthogonal to that used in neighbouring clusters, thus eliminating non-coordinated interference. The inter-cluster links are assigned channels such that any non-coordinated interference that they introduce is minimized. The second phase of CCAS minimizes the coordinated interference by exploiting the channel diversity to sub-divide each cluster into multiple interference domains, thereby increasing the capacity of individual links. Simulation-based evaluations demonstrate that CCAS can achieve twice the aggregate network goodput as compared to existing channel assignment schemes, while ensuring a fair distribution of capacity amongst the links.

Most people-centric delay tolerant networks have been shown to exhibit power-law behavior. Analysis of the temporal connectivity graph of such networks reveals the existence of Hubs, a fraction of the nodes, which are collectively connected to the rest of the nodes. In this paper, we propose a novel forwarding strategy called HubCode, which seeks to use the hubs as message relays. The hubs employ random linear network coding to encode multiple messages addressed to the same destination, reducing the forwarding overheads. Further, the use of the hubs as relays, ensures that most messages are delivered to the destinations. Two versions of HubCode are presented, with each scheme exhibiting contrasting behavior in terms of the computational costs and routing overheads. We simulate a large-scale vehicular DTN using empirically collected movement traces of a city-wide public transport network and demonstrate the efficacy of our solutions in comparison with other forwarding schemes.

Noise map can provide useful information to control noise pollution. We propose a people-centric noise collection system called the Ear-Phone. Due to the voluntary participation of people, the number and location of samples cannot be guaranteed. We propose and study two methods, based on compressive sensing, to reconstruct the missing samples.

The energy expended by sensor nodes in data communication makes up a significant quantum of their total energy consumption. Consequently, a mathematical model that can accurately predict the communication traffic load of a sensor node is critical for designing efficient sensor network protocols. In this paper, we present an analytical model for estimating the per-node traffic load in a multi-hop wireless sensor network. We consider a typical scenario wherein, the sensor nodes periodically sense the environment and forward the collected samples to a sink using greedy geographic routing. The analysis incorporates the idealistic circular coverage radio model as well as a realistic model, log-normal shadowing. Our results confirm that irrespective of the radio model, the traffic load generally increases as a function of the node's proximity to the sink. However, in the immediate vicinity of the sink, the two radio models yield quite contrasting results. The ideal radio model reveals the existence of a volcano region near the sink, where the traffic load drops significantly. On the contrary, with the log-normal shadowing model, the opposite effect is observed, wherein the traffic load actually increases at a much higher rate as one approaches the sink, resulting in the formation of a mountain peak. The results from our analysis are validated by extensive simulations.

In this paper, we present a comprehensive analysis of the performance of a wireless sensor network based target tracking system using the particle filter. In particular, we evaluate the effect of various network design parameters such as the number of nodes, number of generated particles, and sampling interval on the tracking accuracy and computation time of the tracking system. Based on our analysis, we also present recommendations on suitable values for the relevant network design parameters, which provide a reasonable tradeoff between accuracy and computational expense for this problem. In addition, we also analyse the theoretical Cramer-Rao bound as the benchmark for the best possible tracking performance. We demonstrate that the results from our simulations closely match the theoretical bounds. We also present initial results from experiments comprising of a 25 node wireless sensor network. Initial experimental results are promising and show that the PF based estimation is suitable for detection and tracking using inexpensive wireless sensor network devices.

Packet forwarding in Public Transport Networks is particularly challenging due to the high mobility, rapidly changing topology and intermittent connectivity observed in these networks. Though clustering of nodes can aid forwarding decision in these delay tolerant networks (DTNs), the clustering process is extremely costly in a large network. In this paper, we introduce a generic efficient clustering method which is suitable for grouping the nodes of large networks. We also demonstrated how encounter frequencies of public transport networks can be fed to that clustering algorithm in order to build clusters of nodes. And finally, our large scale extensive simulation study on real bus traces shows the efficacy of clustering in packet forwarding.

The aggregate capacity of wireless mesh networks can be improved significantly by equipping each node with multiple interfaces and by using multiple channels in order to reduce the effect of interference. Efficient channel assignment is required to ensure the optimal use of the limited channels in the radio spectrum. In this paper, a cluster-based multipath topology control and channel assignment scheme (CoMTaC), is proposed, which explicitly creates a separation between the channel assignment and topology control functions, thus minimizing flow disruptions. A cluster-based approach is employed to ensure basic network connectivity. Intrinsic support for broadcasting with minimal overheads is also provided. CoMTaC also takes advantage of the inherent multiple paths that exist in a typical WMN by constructing a spanner of the network graph and using the additional node interfaces. The second phase of CoMTaC proposes a dynamic distributed channel assignment algorithm, which employs a novel interference estimation mechanism based on the average link-layer queue length within the interference domain. Partially overlapping channels are also included in the channel assignment process to enhance the network capacity. Extensive simulation based experiments have been conducted to test various parameters and the effectiveness of the proposed scheme. The experimental results show that the proposed scheme outperforms existing dynamic channel assignment schemes by a minimum of a factor of 2.

Research in Wireless Sensor Networks (WSN) is widespread and pervasive in many disciplines because of the potential to embed tiny, inexpensive,

The concept of providing mobile Internet connectivity for passengers in public transport vehicles, where users connect to a local network that attaches to the Internet via a mobile router and a wireless link, has become increasingly popular in recent years, as evidenced by the growing amount of commercially available systems and associated research and standardization activities. The challenge of providing wireless connectivity to networks in motion is compounded by the highly dynamic nature of the user population and the strict quality-of-service (QoS) requirements of many applications typical of such environments. As a result, several protocols extending Internet QoS support approaches to on-board mobile networks have been proposed in the past. In this paper, we focus on modeling and performance evaluation of periodical aggregation of resource reservation messages, which forms the basis of the on-board RSVP protocol. We present a model consisting of a discrete-time, multiple-server and finite-capacity queueing system with bulk arrivals and departures, conduct a detailed analysis of the model, and use it to evaluate the performance of the resource reservation aggregation scheme in a practical scenario. The validity of our model is also backed by extensive simulation results.

Geocasting uses location information to disseminate messages within a specified geographic area. However, in some applications, it is not feasible for the nodes to be able to determine their location coordinates. In this paper, we propose a novel distanced-based approach for local geocasting to address this problem. In local geocasting, source node is interested in spread messages within a local area around itself. We exploit the relationship between radius of local area and the expected hop count in a multi-hop wireless network with uniformly distributed nodes. We estimate the minimum number of hops required to cover all of the nodes within the given local geocasting area. The hop count is then used as hop limit to restrict flooding. We theoretically analyze the average number of rebroadcast messages in the proposed approach and the analytical model is validated by the statistic results. We further conduct a simulation-based comparison between distance-based local geocasting and traditional local geocasting. The results show that our approach can achieve a similar performance as that of traditional local geocasting.

In order to fully exploit the aggregate bandwidth available in the radio spectrum, future wireless mesh networks (WMN) are expected to take advantage of multiple orthogonal channels, where the nodes have the ability to communicate with multiple neighbours simultaneously using multiple radios (NICs) over orthogonal channels. Dynamic channel assignment is critical for ensuring effective utilization of the non-overlapping channels. Several algorithms have been proposed in recent years, which aim at achieving this. However, all these schemes inherently assume that the mesh nodes are well-behaved without any malicious intentions. A recent work has exposed the vulnerabilities in channel assignment algorithms. In this paper, a mechanism is proposed to secure the channel assignment algorithms, addressing the security vulnerabilities in the existing algorithms. The proposed mechanism successfully prevents the WMN from the recently exposed attacks. The simulation based experiments show the effectiveness of the proposed solution. The experiments also show that the incurred overhead because of security is negligible.

Over the past decade, the problem of achieving fair bandwidth allocation on a link shared by multiple traffic flows has been extensively researched. However, as these flows traverse a computer network, they share many different kinds of resources, such as links, buffers, and router CPU. The ultimate goal should hence be overall fairness in the allocation of multiple resources rather than a single specific resource such as link bandwidth. In this paper, we present a novel scheduler, called prediction-based composite fair queuing (PCFQ), which jointly allocates the fair share of the link bandwidth and processing resources to all competing flows. We derive the worst-case delay bound, the work complexity, and the relative fairness bound for the PCFQ scheduler and show that it outperforms a system consisting of separate bandwidth and CPU schedulers. We further present simulation results which illustrate the improved performance characteristics achieved by PCFQ. We also demonstrate that our composite scheduler can be easily implemented on an off-the-shelf network processor such as the Intel IXP 2400. Experimental results from the IXP 2400 implementation highlight the effectiveness and high performance of this algorithm in a real-world system.

Over the past decade, the problem of fair bandwidth allocation among contending traffic flows on a link has been extensively researched. However, as these flows traverse a computer network, they share different kinds of resources (e.g., links, buffers, router CPU). The ultimate goal should hence be overall fairness in the allocation of multiple resources rather than a specific resource. Moreover, conventional resource scheduling algorithms depend strongly upon the assumption of prior knowledge of network parameters and cannot handle variations or lack of information about these parameters. In this paper, we present a novel scheduler called the composite bandwidth and CPU scheduler (CBCS), which jointly allocates the fair share of the link bandwidth as well as processing resource to all competing flows. CBCS also uses a simple and adaptive online prediction scheme for reliably estimating the processing times of the incoming data packets. Analytically, we prove that CBCS is efficient, with a per-packet work complexity of O(1). Finally, we present simulation results and experimental outcomes from a real-world implementation of CBCS on an Intel IXP 2400 network processor. Our results highlight the improved performance achieved by CBCS and demonstrate the ease with which it can be implemented on off-the-shelf hardware

In order to fully exploit the aggregate bandwidth available in the radio spectrum, future wireless mesh networks (WMN) are expected to take advantage of multiple orthogonal channels, with nodes having the ability to communicate with multiple neighbors simultaneously using multiple radios (NICs) over orthogonal channels. Dynamic channel assignment is critical for ensuring effective utilization of the non-overlapping channels. Several algorithms have been proposed in recent years, which aim at achieving this. However, all these schemes inherently assume that the mesh nodes are well-behaved without any malicious intentions. In this paper, we expose the vulnerabilities in channel assignment algorithms and unveil three new security attacks: Network Endo-Parasite Attack (NEPA), Channel Ecto-parasite Attack (CEPA) and low-cost ripple effect attack (LORA). These attacks can be launched with relative ease by a malicious node and can cause significant degradation in the network performance. We also evaluate the effectiveness of these attacks through simulation based experiments and briefly discuss possible solutions to counter these new threats.

In geographic routing, nodes need to maintain up-to-date positions of their immediate neighbours for making effective forwarding decisions. Periodic broadcasting of beacon packets that contain the geographic location coordinates of the nodes is a popular method used by most geographic routing protocols to maintain neighbour positions. We contend that periodic beaconing regardless of network mobility and traffic pattern does not make optimal ulilisation of the wireless medium and node energy. For example, if the beacon interval is too small compared to the rate at which a node changes its current position, periodic beaconing will create many redundant position updates. Similarly, when only a few nodes in a large network are involved in data forwarding, resources spent by all other nodes in maintaining their neighbour positions are greatly wasted. To address these problems, we propose the Adaptive Position Update (APU) strategy for geographic routing. Based on mobility prediction, APU enables nodes to update their position adaptively to the node mobility and traffic pattern. We embed APU into the well known Greedy Perimeter Stateless Routing Protocol (GPSR), and compare it with original GPSR in the ns-2 simulation platform. We conducted several experiments with randomly generated network topologies and mobility patterns. The results confirm that APU significantly reduces beacon overhead without having any noticeable impact on the data throughput of the network. This result is further validated through a trace driven simulation of a practical vehicular ad-hoc network topology that exhibits realistic movement patterns of public transport buses in a metropolitan city.

Inter-vehicular communication is fast emerging as a popular application for mobile ad-hoc networks. Content distribution in Vehicular Ad-Hoc Networks (VANET) is particularly challenging due to the high mobility, rapidly changing topology and intermittent connectivity observed in these networks. Effective mechanisms are needed to enable rapid sharing of real-time such as traffic warnings and multimedia-rich files. In this paper, we propose a novel network coding based co-operative content distribution scheme called VANETCODE. The randomization introduced by the coding scheme makes distribution efficient. Our scheme also leverages on the broadcast nature of the wireless medium to expedite the dissemination of the encoded blocks amongst the one-hop neighbors and is entirely independent of routing. We have carried out extensive simulations to demonstrate that VANETCODE effectively enhances cooperative content sharing in VANETs without introducing additional overhead.

The extension of Internet services to public transport passengers is slowly becoming inevitable. Several architectures for providing Internet access to moving vehicles have been evaluated in the past. However, most of these studies have focused on using static gateways. In this paper, we study the feasibility of an architecture that involves deploying mobile gateways on a selected subset of public transport vehicles for providing Internet connectivity to the entire fleet. The vehicles organize as dynamic clusters and connect to the Internet by communicating with the gateways via multi-hop paths. We evaluate the underlying connectivity characteristics and the coverage achieved by employing an optimal gateway placement strategy. In our analysis, we use realistic movement patterns of public transport buses in a metropolitan city. We also propose a prediction based enhancement, which takes advantage of the known mobility patterns of the buses to improve the performance of the multi-hop routing protocols employed within each cluster.

We address the deployment issues in a hybrid sensor network consisting of both static and mobile sensor nodes. Existing deployment schemes often assume either known regular boundaries of the region, or that mobile sensors are able to detect the region boundary. This is overly idealistic especially for unknown, outdoor environments. In our proposed two-phase deployment scheme, following their initial random deployment, the static sensors estimate the boundary of the unknown region by using the right-hand rule. This phase results in the identification of static boundary nodes, B-nodes. The mobile sensors are assumed concentrated at one or more points within the target area. In phase II, mobile sensor nodes spread in the target area in a distributed manner using one of the proposed variations of the virtual force algorithm. Neighboring B-nodes form a Virtual Boundary and exerts repulsive forces on mobile nodes to keep them in the target area. Using simulations, we demonstrate the effectiveness of our proposed scheme in uniformly deploying mobile sensor nodes in a hybrid sensor network

In this paper, we propose a novel approach for reducing the download time of large files over the Internet. Our approach, known as Parallelized File Transport Protocol (P-FTP), proposes simultaneous downloads of disjoint file portions from multiple file servers. P-FTP server selects file servers for the requesting client on the basis of a variety of QoS parameters, such as available bandwidth and server utilization. The sensitivity analysis of our file server selection technique shows that it performs significantly better than random selection. During the file transfer, P-FTP client monitors the file transfer flows to detect slow servers and congested links and adjusts the file distributions accordingly. P-FTP is evaluated with simulations and real-world implementation. The results show at least 50 percent reduction in download time when compared to the traditional file-transfer approach. Moreover, we have also carried out a simulation-based study to investigate the issues related to large scale deployment of our approach on the Internet. Our results demonstrate that a large number of P-FTP users has no adverse effect on the performance perceived by non-P-FTP users. In addition, the file servers and network are not significantly affected by large scale deployment of P-FTP.

The parallelized file transfer protocol (P-FTP) is a novel network resource aware parallel technique for improving file transfer performance on the Internet. Before starling the parallel file transfer sessions, it considers the available resources in the network (available bandwidth) and at the file servers (memory and CPU utilization). The client dynamically changes the file portions being downloaded from different file servers by monitoring the FTP flows and detecting slow servers and congested links. Early experimentation on Planet-Lab (2004) for a single P-FTP client suggests that the download time can be reduced by more than 50% for large files. In this paper, our goal is to evaluate whether P-FTP can be widely adopted within the Internet, To this end, we have carried out a simulation-based study to investigate the performance of P-FTP when it is adopted by a large user base. We find that, by virtue of its self-tuning capability, P-FTP continues to exhibit improved performance even with many simultaneous clients. Our results also demonstrate that introducing a large number of P-FTP users has no adverse effect on the performance perceived by users of the traditional single server file transfer. We attribute this improvement to the fact that P-FTP dynamically adapts the parallel sessions in response to changes in network state and server resources. This illustrates that P-FTP is highly scalable and is hence suitable for widespread deployment in the Internet.

The sensing capabilities of networked sensors are affected by environmental factors in real deployment and it is imperative to have practical considerations at the design stage in order to anticipate this sensing behavior. We investigate the coverage issues in wireless sensor networks based on probabilistic coverage and propose a distributed probabilistic coverage algorithm (PCA) to evaluate the degree of confidence in detection probability provided by a randomly deployed sensor network. The probabilistic approach is a deviation from the idealistic assumption of uniform circular disc for sensing coverage used in the binary detection model. Simulation results show that area coverage calculated by using PCA is more accurate than the idealistic binary detection model

Currently, denial of service (DoS) attacks remain amongst the most critical threats to Internet applications. The goal of the attacker in a DoS attack is to overwhelm a shared resource by sending a large amount of traffic thus, rendering the resource unavailable to other legitimate users. In this paper, we expose a novel contrasting category of attacks that is aimed at exploiting the adaptive behavior exhibited by several network and system protocols such as TCP. The goal of the attacker in this case is not to entirely disable the service but to inflict sufficient degradation to the service quality experienced by legitimate users. An important property of these attacks is the fact that the desired adversarial impact can be achieved by using an non-suspicious low-rate attack stream, which can easily evade detection. Further by tuning various parameters of the attack traffic stream, the attacker can inflict varying degrees of service degradation and at the same time making it extremely difficult for the victim to detect attacker presence. Our simulation based experiments validate our observations and demonstrate that an attacker can significantly degrade the performance of the TCP flows by inducing lowrate attack traffic which is co-ordinated to exploit the congestion control behavior of TCP

An 'Internet protocol' (IP) network established over Bluetooth affords higher practical throughput compared to the 'synchronous connection oriented' (SCO) physical link allowing the usage of high quality audio codecs such as the 'Motion Picture Expert Group-1 Layer 3' (MP3) and 'Ogg Vorbis' audio codecs. The Bluetooth IP network by default sets the 'logical link control and adaptation protocol' (L2CAP) layer to infinite retransmissions effecting a trade-off between audio quality and audio playback continuity. This paper studies the attributes of the Bluetooth IP network for streaming stored high quality audio.

The problem of achieving fairness in the allocation of the bandwidth resource on a link shared by multiple flows of traffic has been extensively researched over the last decade. However, as these flows traverse a computer network, they share many different kinds of resources such as links, processor cycles, buffers and battery power, a critical resource in mobile devices. The ultimate goal should hence be overall fairness in the allocation of multiple resources rather than a single specific resource such as link bandwidth. In our earlier work we have presented a novel scheduler called prediction-based composite fair queueing (PCFQ), which jointly allocates the fair share of the link bandwidth as well as processing resource to all competing flows. Our scheme also uses a simple and adaptive online prediction scheme for reliably estimating the execution times of the incoming data packets. We have demonstrated via simulation experiments that PCFQ can provide much improved quality of service (QoS) guarantees as compared to separate bandwidth and processor schedulers. With the rapid increase in the capacity of transmission links, the ease with which a scheduler can be implemented in real hardware systems gains paramount importance. In this paper we concentrate on the design and implementation of the PCFQ scheduler in a programmable router. We demonstrate that our scheduler can be easily implemented on an off-the-shelf network processor such as the Intel IXP 2400 board. We also validate our design by carrying out extensive experiments and demonstrate the improved performance achieved by the PCFQ scheduler. The experimental results from the IXP 2400 implementation highlight the effectiveness and high performance of this algorithm in a real world system

The emerging high-speed broadband packet-switched networks are expected to simultaneously support a variety of services with different quality-of-service (QoS) requirements over the same physical infrastructure. Fair packet scheduling algorithms used in switches and routers play a critical role in providing these QoS guarantees. Deficit round robin (DRR), a popular fair scheduling discipline, is very efficient with an O(l) dequeuing complexity. Using the concept of latency-rate (/spl Lscr//spl Rscr/) servers introduced by Stiliadis and Varma (1996), we obtain an upper bound on the latency of DRR and prove that our bound is tight. Our upper bound is lower than the previously known upper bound. This illustrates that the DRR scheduler has better performance characteristics than previously believed, especially for real-time applications where the latency plays a role in the size of the playback buffer required.

In the emerging high-speed packet-switched networks, packet scheduling algorithms used in the switches and routers will play a critical role in satisfying the quality of service (QoS) requirements of various applications. The latency bound of a scheduling discipline is an important QoS parameter, especially for real-time playback applications. Frame-based schedulers such as deficit round robin (DRR), though extremely efficient with an O(1) dequeuing complexity, lead to high latencies due to bursty transmissions of each flow's traffic. In a previous work by, Tsao and Lin (see Computer Networks, vol.35, no.2-3, p.287-305, 2001), the authors propose pre-order deficit round robin, a novel scheme that overcomes this limitation of DRR while still achieving a low work complexity. In pre-order DRR, a priority queue module is appended to the original DRR scheduler which re-orders the packet transmission sequence in DRR to distribute the output more evenly among flows and thus reduce burstiness and improve the latency. We employ a novel approach to analytically derive the latency bound of pre-order DRR and show that our bound is a tight one. Our latency bound is significantly lower than the bound derived by Tsao and Lin, demonstrating that pre-order DRR has even better performance characteristics than previously argued by its own authors.

Parallel systems are increasingly being used in multiuser environments with the interconnection network shared by several users at the same time. Fairness is an intuitively desirable property in the allocation of bandwidth available on a link among traffic flows of different users that share the link. Strict fairness in traffic scheduling can improve the isolation between users, offer a more predictable performance and improve performance by eliminating some bottlenecks. This paper presents a simple, fair, efficient, and easily implementable scheduling discipline, called Elastic Round Robin (ERR), designed to satisfy the unique needs of wormhole switching, which is popular in interconnection networks of parallel systems. In spite of the constraints of wormhole switching imposed on the design, ERR is also suitable for use in Internet routers and has better fairness and performance characteristics than previously known scheduling algorithms of comparable efficiency, including Deficit Round Robin and Surplus Round Robin. In this paper, we prove that ERR is efficient, with a per-packet work complexity of O(1). We analytically derive the relative fairness bound of ERR, a popular metric used to measure fairness. We also derive the bound on the start-up latency experienced by a new flow that arrives at an ERR scheduler. Finally, this paper presents simulation results comparing the fairness and performance characteristics of ERR with other scheduling disciplines of comparable efficiency.

Most switch architectures for parallel systems are designed to eliminate only the worst kinds of unfairness such as starvation scenarios in which packets belonging to one traffic flow may not make forward progress for an indefinite period of time. However stricter fairness can lead to a more predictable and better performance, in addition to improving isolation between traffic belonging to different users. This paper presents a new easily implementable scheduling discipline, called Elastic Round Robin (ERR), for the unique requirements of wormhole switching, popular in interconnection networks for parallel systems. Despite the constraints of wormhole switching imposed on the design, our scheduling discipline is at least as efficient as other scheduling disciplines, and more fair than scheduling disciplines of comparable efficiency proposed for any other kind of network, including the Internet. We prove that the work complexity of ERR is O(1) with respect to the number of flows. We analytically prove the fairness properties of ERR, and show that its relative fairness measure has an upper bound of 3 m, where m is the size of the largest packet that actually arrives during an execution of ERR. Finally, we present simulation results comparing the fairness and performance characteristics of ERR with other scheduling disciplines of comparable efficiency.