Fake news detection is essential for safeguarding social media users, as the rapid spread of unverified information on these platforms facilitates the easy dissemination of fake news, creating significant risks to society. This underscores the urgent need for automated fake news detection systems that leverage advanced technologies. Current multimodal fake news detection approaches encounter challenges including inconsistent decision-making, prioritizing shared subspace information fusion over view-specific data extraction, and insufficient attention given to analyzing the sentimental elements of the news. To this end, we introduce sentiment-aware fake news detection with view-specific feature extraction (SentiView), which integrates multimodal and view-specific features and authors’ sentiments into a unified framework. The proposed framework comprises four modules: a) modal-specific encoder, which generates encodings from text and image modalities; b) multimodal consistency learning that captures the interrelationships to recognize the inherent ambiguity between the different modalities; c) view-aware feature extractor that captures information from different views within the image modality, integrating an orthogonal constraint within the shared subspace, thus facilitating the utilization of distinctive discriminative details; and d) sentiment extractor that captures the sentiments reflected to extract the underlying intention and potential bias of the news author. Extensive experiments on three publicly available datasets X, Weibo, and GossipCop show that SentiView outperforms state-of-the-art fake news detection approaches with an accuracy of 93.8%, 94.5%, and 87.3%, on the datasets respectively.

The integration of sensing capabilities into emerging wireless standards, such as 802.11 bf, presents an increasing threat to public privacy. Recent studies have demonstrated that even minor activities, such as finger movements on a keyboard, can be detected by exploiting Wi-Fi Channel State Information (CSI). To mitigate the privacy risks associated with Wi-Fi sensing, prior research has explored methods to disrupt the CSI measurement process by injecting interference into the wireless channel. However, current techniques often inundate the channel with excessive interference, resulting in a significant degradation of the wireless communication link. This paper proposes a spectrally efficient approach by investigating the sensitivity of Wi-Fi sensing to the CSI measurement rate, and designing interference that reduces sensing accuracy while preserving the integrity of the communication link. First, we quantify the accuracy of Wi-Fi-based keystroke recognition in relation to the CSI data rate. We then present theoretical justifications for the use of interference and demonstrate its impact on CSI data rates. Then, a method of adversarial interference is applied, reducing Wi-Fi sensing accuracy by 70 % in a keystroke detection scenario. Finally, a comprehensive trade-off study is conducted to demonstrate how interference can be optimized to protect privacy with savings of up to 21 % in bandwidth, ensuring minimal degradation of service quality.

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.

Network traffic analysis (NTA) remains a central research area, underpinning advances in both security and performance optimization. Recent years have seen a surge of machine learning-based approaches for NTA, supported by widely used public datasets, such as ISCX-VPN, ISCX-ToR and USTCTFC. While these benchmarks provide reproducibility, many were collected prior to 2018, thus, fail to reflect contemporary protocols, such as TLS 1.3 and HTTP/3 over QUIC. By reviewing NTA studies published in 2024 and 2025 across premier venues in security, networking, and artificial intelligence, we find that 13 out of 15 studies continue to utilize datasets collected before 2018, underscoring a persistent misalignment between academic practice and today’s network traffic. This reliance on outdated datasets risks producing models that do not generalize, embed invalid assumptions, and report misleading performance. We call for the adoption and public release of up-to-date datasets and outline a research agenda that emphasizes evaluating classifiers across diverse downstream tasks using modern traffic traces.

The adoption of modern encryption protocols such as TLS 1.3 has significantly challenged traditional network traffic classification (NTC) methods. As a consequence, researchers are increasingly turning to machine learning (ML) approaches to overcome these obstacles. This paper analyses ML-based NTC studies by developing a taxonomy of their design choices, benchmarking suites, and prevalent assumptions impacting classifier performance. Through this systematization, we demonstrate widespread reliance on outdated datasets, oversights in design choices, and the consequences of unsubstantiated assumptions. Our evaluation reveals that the majority of proposed encrypted traffic classifiers have mistakenly utilized unencrypted traffic due to the use of legacy datasets. Furthermore, by conducting 348 feature occlusion experiments on state-of-the-art classifiers, we show how oversights in NTC design choices lead to overfitting and validate or refute prevailing assumptions with empirical evidence. By highlighting lessons learned, we offer strategic insights, identify emerging research directions, and recommend best practices to support the development of real-world applicable NTC methodologies.

The past decade has witnessed the wide adoption of AI code generators, such as GitHub Copilot, AskCodi, and OpenAI Codex. They offer intelligent solution code for code completion to achieve faster development, cleaner code, and a significant boost in overall productivity. However, such significant productivity advantages also inadvertently lead to the generation of insecure solution code because most AI code generators derive their knowledge from existing projects, which typically prioritize functionality over security. Although numerous tools have been developed to integrate with the code generators for identifying vulnerabilities, the inconsistency in syntactic features and variability in coding rules make the detection task challenging across different programming languages. To address the challenges, we devise a prompt-enhancing approach, PECKER. It examines textual prompts provided by users to identify risky prompts that could lead to insecure code generation. Given the risky prompts, PECKER conducts security-centric rewriting to strengthen the "potentially insecure" descriptions, thereby guiding AI code generators in mitigating vulnerabilities during code generation. We integrated PECKER with one of the most prevalent AI code generators, GitHub Copilot for evaluation. Among 509 risky prompts, PECKER successfully identified and rectified 471 risky prompts.

Given the increasing number and severity of cyber attacks, there has been a surge in cybersecurity information across various mediums such as posts, news articles, reports, and other resources. Cyber Threat Intelligence (CTI) involves processing data from these cybersecurity sources, enabling professionals and organizations to gain valuable insights. However, with the rapid dissemination of cybersecurity information, the inclusion of fake CTI can lead to severe consequences, including data poisoning attacks. To address this challenge, we have implemented a three-step strategy: generating synthetic CTI, evaluating the quality of the generated CTI, and detecting fake CTI. Unlike other subdomains, such as fake COVID news detection, there is currently no publicly available dataset specifically tailored for fake CTI detection research. To address this gap, we first establish a reliable groundtruth dataset by utilizing domain-specific cybersecurity data to fine-tune a Large Language Model (LLM) for synthetic CTI generation. We then employ crowdsourcing techniques and advanced synthetic data verification methods to evaluate the quality of the generated dataset, introducing a novel evaluation methodology that combines quantitative and qualitative approaches. Our comprehensive evaluation reveals that the generated CTI cannot be distinguished from genuine CTI by human annotators, regardless of their computer science background, demonstrating the effectiveness of our generation approach. We benchmark various misinformation detection techniques against our groundtruth dataset to establish baseline performance metrics for identifying fake CTI. By leveraging existing techniques and adapting them to the context of fake CTI detection, we provide a foundation for future research in this critical field. To facilitate further research, we make our code, dataset, and experimental results publicly available on GitHub .

The rise of Wi-Fi sensing applications leveraging Channel State Information (CSI) from ambient wireless signals has opened up extensive opportunities for human activity and identity recognition. However, this advancement raises serious privacy concerns, as sensitive personal data can be inferred by applying advanced Machine Learning (ML) algorithms to CSI data. In response, researchers have explored adversarial techniques to degrade Wi-Fi sensing accuracy and protect privacy, often by interfering with or corrupting CSI. This paper introduces Wi-Spoof, a novel approach for spoofing CSI to deceive Wi-Fi-based Human Activity Recognition (HAR) systems. Wi-Spoof manipulates Wi-Fi transmission power to inject noise into the CSI and employs a pseudo-Pulse Width Modulation (PWM) scheme to generate controlled, adversarial CSI. Using commercially available hardware, we experimentally demonstrate that Wi-Spoof can achieve targeted misclassification in a state-of-the-art HAR system with a 93% success rate. Our approach is validated on a widely recognised public dataset and further supported by extensive local experiments, underscoring Wi-Spoof’s effectiveness in steering HAR predictions to specified outcomes.

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.

This paper introduces GLOBE, a high-quality English corpus with worldwide accents, specifically designed to address the limitations of current zero-shot speaker adaptive Text-to-Speech (TTS) systems that exhibit poor generalizability in adapting to speakers with accents.Compared to commonly used English corpora, such as LibriTTS and VCTK, GLOBE is unique in its inclusion of utterances from 23,519 speakers and covers 164 accents worldwide, along with detailed metadata for these speakers.Compared to its original corpus, i.e., Common Voice, GLOBE significantly improves the quality of the speech data through rigorous filtering and enhancement processes, while also populating all missing speaker metadata.The final curated GLOBE corpus includes 535 hours of speech data at a 24 kHz sampling rate.Our benchmark results indicate that the speaker adaptive TTS model trained on the GLOBE corpus can synthesize speech with better speaker similarity and comparable naturalness than that trained on other popular corpora.We will release GLOBE publicly after acceptance.The GLOBE dataset is available at https://globecorpus.github.io/.

Internet of Things (IoT) devices have grown in popularity since they can directly interact with the real world. Home automation systems automate these interactions. IoT events are crucial to these systems’ decision-making but are often unreliable. Security vulnerabilities allow attackers to impersonate events. Using statistical machine learning, IoT event fingerprints from deployed sensors have been used to detect spoofed events. Multivariate temporal data from these sensors has structural and temporal properties that statistical machine learning cannot learn. These schemes’ accuracy depends on the knowledge base; the larger, the more accurate. However, the lack of huge datasets with enough samples of each IoT event in the nascent field of IoT can be a bottleneck. In this work, we deployed advanced machine learning to detect event-spoofing assaults. The temporal nature of sensor data lets us discover important patterns with fewer events. Our rigorous investigation of a publicly available real-world dataset indicates that our time-series-based solution technique learns temporal features from sensor data faster than earlier work, even with a 100- or 500-fold smaller training sample, making it a realistic IoT solution.

The Internet of Things (IoT) involves complex, interconnected systems and devices that depend on contextsharing platforms for interoperability and information exchange. These platforms are, therefore, critical components of real-world IoT deployments, making their security essential to ensure the resilience and reliability of these “systems of systems.” In this paper, we take the first steps toward systematically and comprehensively addressing the security of IoT context-sharing platforms. We propose a framework for threat modelling and security analysis of a generic IoT context-sharing solution, employing the MITRE ATT&CK framework. Through an evaluation of various industry-funded projects and academic research, we identify significant security challenges in the design of IoT context-sharing platforms. Our threat modelling provides an in-depth analysis of the techniques and sub-techniques adversaries may use to exploit these systems, offering valuable insights for future research aimed at developing resilient solutions. Additionally, we have developed an open-source threat analysis tool that incorporates our detailed threat modelling, which can be used to evaluate and enhance the security of existing context-sharing platforms.

IoT context-sharing platforms are an essential component of today's interconnected IoT deployments with their security affecting the entire deployment and the critical in-frastructure adopting IoT. We report on a lack of systematic approach to the security of IoT context-sharing platforms and propose the need for a methodological and systematic alternative to evaluate the existing solutions and develop ‘secure-by-design’ solutions. We have identified the key components of a generic IoT context-sharing platform and propose using MITRE ATT&CK for threat modelling of such platforms.

Conventional text-to-speech (TTS) research has predominantly focused on enhancing the quality of synthesized speech for speakers in the training dataset. The challenge of synthesizing lifelike speech for unseen, out-of-dataset speakers, especially those with limited reference data, remains a significant and unresolved problem. While zero-shot or few-shot speakeradaptive TTS approaches have been explored, they have many limitations. Zero-shot approaches tend to suffer from insufficient generalization performance to reproduce the voice of speakers with heavy accents. While few-shot methods can reproduce highly varying accents, they bring a significant storage burden and the risk of overfitting and catastrophic forgetting. In addition, prior approaches only provide either zero-shot or few-shot adaptation, constraining their utility across varied real-world scenarios with different demands. Besides, most current evaluations of speakeradaptive TTS are conducted only on datasets of native speakers, inadvertently neglecting a vast portion of non-native speakers with diverse accents. Our proposed framework unifies both zeroshot and few-shot speaker adaptation strategies, which we term as “instant” and “fine-grained” adaptations, respectively, based on their merits. To alleviate the insufficient generalization performance observed in zero-shot speaker adaptation, we designed two innovative discriminators and introduced a memory mechanism for the speech decoder. To prevent catastrophic forgetting and reduce storage implications for few-shot speaker adaptation, we designed two adapters and a unique adaptation procedure. Additionally, we introduce a new TTS dataset that encompasses 44,000 English utterances from 134 non-native speakers, capturing a wide array of non-native English accents. This dataset is intended to enhance holistic evaluations of adaptive TTS capabilities. Through comprehensive experiments on multiple datasets comprising both native and non-native speakers, our approach outperforms contemporary methodologies across various subjective and objective metrics.

The exponential rise in popularity of distributed energy resources (DERs) is attributed to their numerous benefits within the power sector. However, the risks that new DERs pose to the power grid have not yet been closely assessed, exposing a gap in the literature. This article addresses this gap by presenting a comprehensive threat model of the DER architecture, combining the MITRE ATT&CK catalogue for industrial control systems (ICS), and the IDDIL/ATC threat model, to create a hybrid approach. Our first contribution is to propose criteria derived from seven metrics to evaluate and compare the efficacy and usability of threat modelling frameworks for DER systems, allowing more informed framework selection. Our second contribution is to develop a comprehensive hybrid threat modelling approach based on IDDIL/ATC and MITRE ATT&CK and organise attack paths chronologically using the cyber kill chain methodology to categorise attacker techniques. Our third contribution is to perform a comprehensive DER architecture system decomposition, elaborating assets, trust levels, entry points, data, protocols, and entity relations to identify the threat landscape. Our final contribution is to apply the proposed approach to the distribution system operator (DSO), mapping potential attacker techniques and illustrating a ransomware attack chain on the DSO's Energy Management System, with proposed mitigations.

Amidst the rapid technological advancements of today, privacy and anonymity are facing increasing threats. Tor, one of the most widely used anonymity networks, enables users to browse the Internet without their activities being tracked. Extensive research has been conducted on both attacking and defending the anonymity of Tor users. Website Fingerprinting (WF) is one of the popular de-anonymisation techniques employed against Tor users. This paper presents two novel WF techniques based on Graph Neural Networks (GNNs) to explore two relatively understudied avenues of WF: the fingerprintability of Decentralised Applications (DApps) and the impact of reload traffic on WF. Due to the lack of publicly available datasets for DApp traffic and reload traffic suitable for WF, we collected five new datasets for our experiments. Our findings reveal that GNN-based techniques surpass the performance of state-of-the-art WF techniques when reload traffic is used. Meanwhile, certain high-performing state-of-the-art techniques exhibit a significant reduction in accuracy, more than 40%, when reload traffic is used instead of homepage traffic. Additionally, we identify that DApps are less susceptible to fingerprinting than conventional websites, leading to a 25% decrease in accuracy in some state-of-the-art WF techniques. While confirming prior research findings that GNN-based techniques can outperform existing techniques when accessing DApps via Chrome, we further demonstrate that using Tor to access DApps makes them even more difficult to fingerprint. Finally, we expect our datasets, four of which lack publicly available alternatives, will prove invaluable for future research.

The widespread integration of the Internet of Things (IoT) is crucial in advancing sustainable development. IoT service providers actively collect user data for analysis using sophisticated Deep Learning (DL) algorithms. This enables the extraction of valuable insights for business intelligence and improving service quality. However, as these datasets contain sensitive personal information, there is a risk of privacy breaches when DL models are employed. This vulnerability may result in Membership Inference Attacks (MIA), potentially leading to the unauthorized disclosure of highly sensitive data. Therefore, developing an efficient and privacy-preserving data analysis system for IoT is imperative. Recent research has highlighted the effectiveness of utilizing Bloom Filter (BF)-encoding in conjunction with Differential Privacy (DP) for safeguarding privacy during data analysis. Given its attributes of low complexity and high utility, this approach proves effective, particularly in resource-constrained IoT domains. With this in mind, we propose a novel framework for privacy-preserving IoT data analysis based on BF-encoded data. Our research introduces an innovative BF-encoding technique combined with Local Differential Privacy (LDP), capable of efficiently encoding various types of IoT data (such as facial images and smart-meter data) while maintaining privacy when integrated into DL algorithms for downstream analysis. Experimental results demonstrate that our BF-encoded data surpasses the utility of standard BF-encoded data when utilized in DL algorithms for downstream tasks, showcasing an approximate 30% improvement in classification accuracy. Furthermore, we assess the privacy of these DL models against MIA, revealing that attackers can only make random guesses with an accuracy of approximately 50%.

WiFi sensing has gained tremendous traction due to its inherent advantages in terms of privacy and ubiquity. Recent work has shown the ability to sense physical environments, such as counting the number of human occupants. These results have traditionally been achieved using statistical features on WiFi Channel State Information (CSI) amplitude, however more recently there has been interest in exploiting Image based Machine Learning (ML) techniques to achieve better outcomes. In this work, we produce Probability Mass Function (PMF) Images on WiFi CSI, to create spectral maps which clearly distinguish between different human occupancies. We validate our PMF images with common default CNN architectures such as GoogleNet, ResNet and ShuffleNet. By changing the filter size and training parameters, we improve the performance of ShuffleNet from 84% to 98%. Furthermore, we demonstrate how the PMF images can be optimised for sensing outcomes, by controlling the image resolution.

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.

We propose an end-to-end lecture video generation system that can generate realistic and complete lecture videos directly from annotated slides, instructor’s reference voice and instructor’s reference portrait video. Our system is primarily composed of a speech synthesis module with few-shot speaker adaptation and an adversarial learning-based talking-head generation module. It is capable of not only reducing instructors’ workload but also changing the language and accent which can help the students follow the lecture more easily and enable a wider dissemination of lecture contents. Our experimental results show that the proposed model outperforms other current approaches in terms of authenticity, naturalness and accuracy. Here is a video demonstration of how our system works, and the outcomes of the evaluation and comparison: https://youtu.be/cY6TYkI0cog.

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.

Passwords are pervasively used to authenticate users’ identities in mobile apps. To secure passwords against attacks, protection is applied to the password authentication protocol (PAP). The implementation of the protection scheme becomes an important factor in protecting PAP against attacks. We focus on two basic protection in Android, i.e., SSL/TLS-based PAP and timestamp-based PAP. Previously, we proposed an automated tool,GLACIATE, to detect authentication flaws. We were curious whether orchestration (i.e., involving manual-effort) works better than automation. To answer this question, we propose an orchestrated approach,AuthExploitand compare its effectivenessGLACIATE. We study requirements for correct implementation of PAP and then applyGLACIATEto identify protection enhancements automatically. Through dependency analysis,GLACIATEmatches the implementations against the abstracted flaws to recognise defective apps. To evaluateAuthExploit, we collected 1,200 Android apps from Google Play. We comparedAuthExploitwith the automation tool,GLACIATE, and two other orchestration tools,${\sf MalloDroid}$and${\sf SMV-Hunter}$. The results demonstrated that orchestration tools detect flaws more precisely although the F1 score ofGLACIATEis higher thanAuthExploit. Further analysis of the results reveals that highly popular apps and e-commerce apps are not more secure than other apps.

In this paper, we propose a feature-free method for detecting phishing websites using the Normalized Compression Distance (NCD), a parameter-free similarity measure which computes the similarity of two websites by compressing them, thus eliminating the need to perform any feature extraction. It also removes any dependence on a specific set of website features. This method examines the HTML of webpages and computes their similarity with known phishing websites, in order to classify them. We use the Furthest Point First algorithm to perform phishing prototype extractions, in order to select instances that are representative of a cluster of phishing webpages. We also introduce the use of an incremental learning algorithm as a framework for continuous and adaptive detection without extracting new features when concept drift occurs. On a large dataset, our proposed method significantly outperforms previous methods in detecting phishing websites, with an AUC score of 98.68%, a high true positive rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of 0.58%. Our approach uses prototypes, eliminating the need to retain long term data in the future, and is feasible to deploy in real systems with a processing time of roughly 0.3 seconds.

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.

Localization is important for a large number of Internet of Things (IoT) endpoint devices connected by LoRaWAN. Due to the bandwidth limitations of LoRaWAN, existing localization methods without specialized hardware (e.g., GPS) produce poor performance. To increase the localization accuracy, we propose a super-resolution localization method, called Seirios, which features a novel algorithm to synchronize multiple non-overlapped communication channels by exploiting the unique features of the radio physical layer to increase the overall bandwidth. By exploiting both the original and the conjugate of the physical layer, Seirios can resolve the direct path from multiple reflectors in both indoor and outdoor environments. We design a Seirios prototype and evaluate its performance in an outdoor area of 100 m × 60 m, and an indoor area of 25 m × 15 m, which shows that Seirios can achieve a median error of 4.4 m outdoors (80% samples < 6.4 m), and 2.4 m indoors (80% samples < 6.1 m), respectively. The results show that Seirios produces 42% less localization error than the baseline approach. Our evaluation also shows that, different to previous studies in Wi-Fi localization systems that have wider bandwidth, time-of-fight (ToF) estimation is less effective for LoRaWAN localization systems with narrowband radio signals.

Internet of Things (IoT) is flourishing and has penetrated deeply into people's daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the Cloud. The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this work, we propose Kryptein, a compressive-sensing-based lightweight encryption scheme for Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Kryptein supports random compressed encryption, statistical computation over cipher, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption. The performance of Kryptein is also measured on off-the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices. After comparing with other state-of-the-art lightweight ciphers on IoT (Simon and Speck), IoT system with Kryptein is expected to have a much more longevity with about 35 percent extended lifetime. Further, experiments illustrated IoT data variance will not affect Kryptein's accuracy in a long term usage, and Krpytein is also able to support basic analytics tasks like machine learning (e.g., classification).

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.

We propose Nephelai, a Compressive Sensing-based Cloud Radio Access Network (C-RAN), to reduce the uplink bit rate of the physical layer (PHY) between the gateways and the cloud server for multi-channel LPWANs. Recent research shows that single-channel LPWANs suffer from scalability issues. While multiple channels improve these issues, data transmission is expensive. Furthermore, recent research has shown that jointly decoding raw physical layers that are offloaded by LPWAN gateways in the cloud can improve the signal-to-noise ratio (SNR) of week radio signals. However, when it comes to multiple channels, this approach requires high bandwidth of network infrastructure to transport a large amount of PHY samples from gateways to the cloud server, which results in network congestion and high cost due to Internet data usage. In order to reduce the operation's bandwidth, we propose a novel LPWAN packet acquisition mechanism based on Compressive Sensing with a custom design dictionary that exploits the structure of LPWAN packets, reduces the bit rate of samples on each gateway, and demodulates PHY in the cloud with (joint) sparse approximation. Moreover, we propose an adaptive compression method that takes the Spreading Factor (SF) and SNR into account. Our empirical evaluation shows that up to 93.7% PHY samples can be reduced by Nephelai when SF = 9 and SNR is high without degradation in the packet reception rate (PRR). With four gateways, 1.7x PRR can be achieved with 87.5% PHY samples compressed, which can extend the battery lifetime of embedded IoT devices to 1.7.

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.

A great quantity of user passwords nowadays has been leaked through security breaches of user accounts. To enhance the security of the Password Authentication Protocol (PAP) in such circumstance, Android app developers often implement a complementary One-Time Password (OTP) authentication by utilizing the short message service (SMS). Unfortunately, SMS is not specially designed as a secure service and thus an SMS One-Time Password is vulnerable to many attacks. To check whether a wide variety of currently used SMS OTP authentication protocols in Android apps are properly implemented, this paper presents an empirical study against them. We first derive a set of rules from RFC documents as the guide to implement secure SMS OTP authentication protocol. Then we implement an automated analysis system, AUTH-EYE, to check whether a real-world OTP authentication scheme violates any of these rules. Without accessing server source code, AUTH-EYE executes Android apps to trigger the OTP-relevant functionalities and then analyzes the OTP implementations including those proprietary ones. By only analyzing SMS responses, AUTH-EYE is able to assess the conformance of those implementations to our recommended rules and identify the potentially insecure apps. In our empirical study, AUTH-EYE analyzed 3,303 popular Android apps and found that 544 of them adopt SMS OTP authentication. The further analysis of AUTH-EYE demonstrated a far-from-optimistic status: the implementations of 536 (98.5%) out of the 544 apps violate at least one of our defined rules. The results indicate that Android app developers should seriously consider our discussed security rules and violations so as to implement SMS OTP properly.

Summary In this paper, we explore the problem of providing selective read/write access to the outsourced data for clients using mobile devices in an environment that supports users from multiple domains and where attributes are generated by multiple authorities. We consider Ciphertext‐Policy Attribute‐based Encryption (CP‐ABE) scheme as it can provide access control on encrypted outsourced data. One limitation of CP‐ABE is that the users can modify the access policy specified by the data owner if write operations are introduced in the scheme. We propose a protocol for providing different levels of access to outsourced data that permits the authorized users to perform write operation without altering the access policy specified by the data owner. Our scheme provides fine‐grained read/write access to the users, accompanied with a light weight signature scheme and computationally inexpensive user revocation mechanism suitable for resource‐constrained mobile devices. We provide a theoretical analysis of the security of the proposed protocol and the experimental results measured from a real‐world testbed.

Physical layer key generation that exploits reciprocity and randomness of wireless fading channels has attracted considerable attention in recent years. Despite much research efforts in this field, the problem of wireless key generation at long distance and low data rate remains unknown and has not been studied. In this paper, we conduct extensive experiments and analysis in real indoor and outdoor environments to explore the feasibility of wireless key generation for long range (LoRa)-based network. Our experimental results show that: 1) the low transmission rate will lead to low channel reciprocity which makes wireless key generation significantly challenging and 2) when the requirement of high reciprocity is fulfilled, two nodes can generate the same secret key even when they are far away from each other (a few kilometers). Building on the strengths of existing secret key extraction approaches, we present LoRa-Key, the first complete key establishment protocol for LoRa network by exploring the shared randomness extracted from measured received signal strength indicator. LoRa-Key employs a number of signal processing techniques to improve key generation rate significantly. Moreover, we propose a novel compressive sensing-based reconciliation framework to reduce mismatch rate. Experimental results show that LoRa-Key can achieve key establishment rates of 18 bit/s in stationary scenario and 31 bit/s in mobile scenario. To the best of our knowledge, this is the first work that studies key generation protocol for LoRa network.

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.

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.

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.

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.

The capability to securely (re)program embedded devices over-the-air is a fundamental functionality for the emerging Internet of Things (IoT). Current approaches work efficiently by exploiting the homogeneity within sensing devices, where all nodes require the update and participate in the process. Due to the heterogeneity of IoT deployments, where the type of devices and program images vary, existing solutions suffer from severe performance degradation. We address this shortcoming with our system SEDA, which leverages a secure multicast approach. SEDA outperforms existing systems since the program image is securely delivered and processed on targeted nodes only, hence reducing the overhead for not involved nodes. In order to enable the multicast approach, we introduce an asymmetric broadcast encryption primitive, which we have optimized towards constrained nodes to reduce the communication/computation overhead as compared to existing approaches. With an extensive experimental study on a public testbed in several practical settings, we show SEDA's efficient performance compared to state-of-the-art approaches. Finally, our theoretical security analysis shows SEDA's security against identified adversary models.

Outsourcing data to the cloud for computation and storage has been on the rise in recent years. In this paper we investigate the problem of supporting write operation on the outsourced data for clients using mobile devices. We consider the Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme as it is well suited to support access control in outsourced cloud environments. One shortcoming of CP-ABE is that users can modify the access policy specified by the data owner if write operations are incorporated in the scheme. We propose a protocol for collaborative processing of outsourced data that enables the authorized users to perform write operation without being able to alter the access policy specified by the data owner. Our scheme is accompanied with a light weight signature scheme and simple, inexpensive user revocation mechanism to make it suitable for processing on resource-constrained mobile devices. The implementation and detailed performance analysis of the scheme indicate the suitability of the proposed scheme for real mobile applications. Moreover, the security analysis demonstrates that the security properties of the system are not compromised.

Formal security analysis has proven to be a useful tool for tracking modifications in communication protocols in an automated manner, where full security analysis of revisions requires minimum efforts. In this paper, we formally analysed prominent IoT protocols and uncovered many critical challenges in practical IoT settings. We address these challenges by using formal symbolic modelling of such protocols under various adversaries and security goals. Furthermore, this paper extends formal analysis to cryptographic Denial-of-Service (DoS) attacks and demonstrates that a vast majority of IoT protocols are vulnerable to such resource exhaustion attacks. We present a cryptographic DoS attack countermeasure that can be generally used in many IoT protocols. Our study of prominent IoT protocols such as CoAP and MQTT shows the benefits of our approach.

Networks are vulnerable to disruptions caused by malicious forwarding devices. The situation is likely to worsen in Software Defined Networks (SDNs) with the incompatibility of existing solutions, use of programmable soft switches and the potential of bringing down an entire network through compromised forwarding devices. In this paper, we present WedgeTail, an Intrusion Prevention System (IPS) designed to secure the SDN data plane. WedgeTail regards forwarding devices as points within a geometric space and stores the path packets take when traversing the network as trajectories. To be efficient, it prioritizes forwarding devices before inspection using an unsupervised trajectory-based sampling mechanism. For each of the forwarding device, WedgeTail computes the expected and actual trajectories of packets and 'hunts' for any forwarding device not processing packets as expected. Compared to related work, WedgeTail is also capable of distinguishing between malicious actions such as packet drop and generation. Moreover, WedgeTail employs a radically different methodology that enables detecting threats autonomously. In fact, it has no reliance on pre-defined rules by an administrator and may be easily imported to protect SDN networks with different setups, forwarding devices, and controllers. We have evaluated WedgeTail in simulated environments, and it has been capable of detecting and responding to all implanted malicious forwarding devices within a reasonable time-frame. We report on the design, implementation, and evaluation of WedgeTail in this manuscript.

Nowadays cloud services have become a cost-effective platform for sharing data and performing large scale computations. As outsourcing data to a third party involves security and privacy concerns, data should be sent to the cloud in encrypted form. To handle access control on encrypted cloud data, Ciphertext-Policy Attribute-based Encryption (CP-ABE) has become very popular. In this scheme data can be encrypted once and can be read multiple times by the authorized users. However, CP-ABE currently has no mechanism to protect against a user of data being able to alter the access policy originally specified by the owner of the data when s/he re-encrypts the modified data (write operation). In this work, we propose a scheme that extends CP-ABE without compromising the security to preserve data owner defined access policy when multiple write operations are performed along with a user revocation mechanism in a setting where multiple authorities generate decryption keys.

With the proliferation of Android-based devices, malicious apps have increasingly found their way to user devices. Many solutions for Android malware detection rely on machine learning; although effective, these are vulnerable to attacks from adversaries who wish to subvert these algorithms and allow malicious apps to evade detection. In this work, we present a statistical analysis of the impact of adversarial evasion attacks on various linear and non-linear classifiers, using a recently proposed Android malware classifier as a case study. We systematically explore the complete space of possible attacks varying in the adversary's knowledge about the classifier; our results show that it is possible to subvert linear classifiers (Support Vector Machines and Logistic Regression) by perturbing only a few features of malicious apps, with more knowledgeable adversaries degrading the classifier's detection rate from 100% to 0% and a completely blind adversary able to lower it to 12%. We show non-linear classifiers (Random Forest and Neural Network) to be more resilient to these attacks. We conclude our study with recommendations for designing classifiers to be more robust to the attacks presented in our work.

Attribute-based encryption schemes provide read access to data based on users' attributes. In these schemes, user privacy is compromised as the access policies are visible. This privacy issue has been addressed in literature by enabling the data owner to obfuscate the policy in a setting where a single authority generates decryption keys. However, a single authority can figure out the hidden access policy which violates user privacy. We present PPDAS, a scheme which overcomes these limitations and makes two contributions. Firstly, we present a mechanism which supports fine-grained read and write operations in a setting where decryption keys are generated by multiple attribute authorities, and the access policy is hidden from all unauthorized entities including the attribute authorities. Our scheme is also accompanied with a user revocation mechanism. Secondly, we show that it is possible to adapt the scheme for accessing data through resource-constrained devices such as smart watches and IoT devices through extensive experimental evaluations.

The Internet of Things (IoT) is an emerging paradigm, where the ubiquitous devices can form the networks and connect to Internet. Security and management of devices remain open challenges for the IoT. We adopt the management framework of industry consortium THREAD, where a group of devices cooperating to accomplish the same task (called policy) are administrated by a designated device called commissioner and together they form a policy group. All these policy groups are further managed by a centralized server. In this hierarchical network structure, the secure distribution of the policy information, access control, and group key from the centralized server to commissioner and its peers become challenging given the pervasive, complex and heterogeneous properties of devices. To solve this, we propose protocols/mechanisms along with a variant of Broadcast Encryption called Secure Identity-Based Broadcast Encryption (SIBBE) and demonstrate the feasibility for secure distribution of information to the IoT devices from centralized server. Most of the related work is based on the Attribute-based Encryption (ABE) for IoT devices, which has scalability issues with the number of attributes. Our experimental and simulation evaluations show that our scheme outperforms the existing schemes in terms of scalability, latency, and communication overhead.

The increased usage of smart wearables in various applications, specifically in health-care, emphasizes the need for secure communication to transmit sensitive health-data. In a practical scenario, where multiple devices are carried by a person, a common secret key is essential for secure group communication. Group key generation and sharing among wearables have received very little attention in the literature due to the underlying challenges: 1) difficulty in obtaining a good source of randomness to generate strong cryptographic keys, and 2) finding a common feature among all the devices to share the key. In this paper, we present a novel solution to generate and distribute group secret keys by exploiting on-board accelerometer sensor and the unique walking style of the user, i.e., gait. We propose a method to identify the suitable samples of accelerometer data during all routine activities of a subject to generate the keys with high entropy. In our scheme, the smartphone placed on waist employs fuzzy vault, a cryptographic construct, and utilizes the acceleration due to gait, a common characteristic extracted on all wearable devices to share the secret key. We implement our solution on commercially available off-the-shelf smart wearables, measure the system performance, and conduct experiments with multiple subjects. Our results demonstrate that the proposed solution has a bit rate of 750 b/s, low system overhead, distributes the key securely and quickly to all legitimate devices, and is suitable for practical applications.

Cross-correlation is a popular signal processing technique used in numerous location tracking systems for obtaining reliablerangeinformation. However, its efficient design and practical implementation has not yet been achieved on mote platforms that are typical in wireless sensor network due to resource constrains. In this paper, we proposeStructS-XCorr: cross-correlation via structured sparse representation, a new computing framework for ranging based on$\ell _1$-norm minimization[1]and structured sparsity. The key idea is to compress the ranging signal samples on the mote by efficient random projections and transfer them to a central device; where a convex optimization process estimates the range by exploiting the sparse signal structure in the proposedcorrelationdictionary. Through theoretical validation, extensive empirical studies and experiments on anend-to-endacoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework can achieve up totwo orders of magnitudebetter performance compared to other approaches such as working on DCT domain and downsampling. Compared to the standard cross-correlation, it is able to obtain range estimates with a bias of 2-6 cm with 30 percent and approximately 100 cm with 5 percent compressed measurements. Its structured sparsity model is able to improve the ranging accuracy by 40 percent under challenging recovery conditions (such as high compression factor and low signal-to-noise ratio) by overcoming limitations due to dictionary coherence.

Wireless mesh networks (WMNs) connect to the Internet via access gateways. This paper studies multi-source video multicast in Internet-connected WMNs. The focus is on the design of a shareable integrated multicast that allows the multicasts of video sources to employ common Internet shortcuts or WMN paths to avoid potentially high WMN overheads and excessive Internet usage. Several algorithms are described that together form a video multicast framework running a controlled number of shareable multicasts under the constraint of Internet availability. These algorithms are the resource-efficient source group algorithm, the efficient integrated architecture algorithm, and the interference-controlled multicasting tree algorithm. These algorithms represent different approaches to overcoming various costs arising from multi-source video multicast, enabling multiple video sources to distribute delay, and throughput-guaranteed videos to receivers across large-scale areas. Simulation results are presented that quantify the performance gains that can be achieved.

Botnet threats include a plethora of possible attacks ranging from distributed denial of service (DDoS), to drive-by-download malware distribution and spam. While for over two decades, techniques have been proposed for either improving accuracy or speeding up the detection of attacks, much of the damage is done by the time attacks are contained. In this work we take a new direction which aims to predict forthcoming attacks (i.e. before they occur), providing early warnings to network administrators who can then prepare to contain them as soon as they manifest or simply quarantine hosts. Our approach is based on modelling the Botnet infection sequence as a Markov chain with the objective of identifying behaviour that is likely to lead to attacks. We present the results of applying a Markov model to real world Botnets' data, and show that with this approach we are successfully able to predict more than 98% of attacks from a variety of Botnet families with a very low false alarm rate.

In recent years, the proliferation of wireless devices has contributed to the emergence of new set of applications termed as Location Based Services (LBS). LBS provide privileges to mobile users based on their proximity to a facility. In order to gain benefits, users may lie or falsely claim their location. Hence, it is essential to verify the legitimacy of users. In this paper, we propose our novel solution for generating location proof for mobile users and verification of the location claim by application services. Our protocol exploits unique Wi-Fi signal characteristics and employs an information theoretically secure fuzzy vault scheme. We provide a detailed theoretical and experimental evaluation of our protocol. Our solution is faster by an order of magnitude, and the performance of our scheme is independent of the location tag size and distance between the mobile user and location proof provider compared to the state-of-the-art.

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.

The existing secret key generation schemes for body-worn devices using wireless channel characteristics, e.g., received signal strength indicator (RSSI) are dependent on the node mobility and have very low bit rate. In this work, we propose a novel mobility independent RSSI based secret key generation protocol - iARC, which induces artificial randomness in the channel by employing dual antennas and dynamic frequency hopping effectively.

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%.

Trust and reputation systems are widely employed in WSNs to help decision making processes by assessing trustworthiness of sensor nodes in a data aggregation process. However, in unattended and hostile environments, more sophisticated malicious attacks, such as collusion attacks, can distort the computed trust scores and lead to low quality or deceptive service as well as undermine the aggregation results. In this paper we propose a novel, local, collaborative-based trust framework for WSNs that is based on the concept of credibility propagation which we introduce. In our approach, trustworthiness of a sensor node depends on the amount of credibility that such a node receives from other nodes. In the process we also obtain an estimates of sensors' variances which allows us to estimate the true value of the signal using the Maximum Likelihood Estimation. Extensive experiments using both real-world and synthetic datasets demonstrate the efficiency and effectiveness of our approach.

Outsourcing data to the cloud for computation and storage has been on rise in recent years. In this paper we investigate the problem of supporting write operation on the outsourced data for clients using mobile devices. We consider the Attribute-based Encryption (ABE) scheme as it is well suited to support access control in outsourced cloud environment. Currently there is a gap in the literature on providing write access on the data encrypted with ABE. Moreover, since ABE is computationally expensive, it imposes processing burden on resource constrained mobile devices. Our work has two fold advantages. Firstly, we extend the single authority Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme to support write operations. Secondly, in achieving this goal, we move some of the expensive computations to a manager and remote cloud server by exploiting their high-end computational power. Our security analysis demonstrates that the security properties of system are not compromised.

Exploiting unique wireless channel characteristics like signal strength for secret key generation has been recently studied by researchers. These schemes are lightweight and suitable for resource constrained wearable devices. However, a major drawback of existing schemes is that the successive channel samples with small sampling interval will have high correlation in time. This reduces the entropy and bit rate of keys. In this paper, we present dual-link based Radio Frequency fingerprinting solution - DLINK, which dynamically identifies the suitable multipath link to generate secret keys with improved entropy and bit rate in fast as well as slow fading channel conditions. We conduct an extensive set of experiments with real sensor devices mounted on subjects in multiple indoor environments. Our results show that, DLINK reduces the correlation of successive channel samples by 67%, and has 5 times higher bit rate, and improved entropy in all channel conditions compared to existing solutions.

In recent years, Location Based Services (LBS) and related applications are gaining popularity for providing access to the resources. LBS can either increase or decrease the access privileges of the users based on their location. Current mobile devices lack the intelligence to prove their location when requested. In this paper, we propose our novel solution for generating location proof for mobile users leveraging unique wireless characteristics and verification of the location claim by application services.

Current Wireless Sensor Networks (WSNs) approaches do not provide an efficient and secure code dissemination function due to emerging issues of IoT applications. In this work, we adopt a multicast approach instead of the existing end-to-end or epidemic approaches. In order to enable the multicast approach, we propose an efficient/robust group key distribution scheme. We will evaluate and quantify the performance of our prototype implementation in a public testbed, while emulating several practical IoT settings, and show our security measures against known attack models.

We consider the problem of data collection from a large-scale energy harvesting sensors in smart city. We study a novel scheduling optimisation model for WSN with Wireless Power Transfer (WPT), that maximises the amount of collected data under constraints of radio link quality and WPT efficiency, while ensuring a fair data reception. A WPT-WSN testbed is built and preliminary results show that the WPT efficiency is jointly affected by distance between WPT transmitter and receiver, and their antenna orientation.

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.

Given the limited resources and computational power of current embedded sensor devices, memory protection is difficult to achieve and generally unavailable. Hence, the software run-time buffer overflow that is used by the worm attacks in the Internet could be easily exploited to inject malicious codes into wireless sensor networks (WSNs). As a result, the remote attestation on the application codes installed in WSNs is the first important step to detect any unauthorized changes through the buffer overflow attack. Previous software-based remote code verification approaches such as SoftWare-based ATTestation and Secure Code Update By Attestation have been shown difficult to deploy in recent work. In this paper, we propose and implement a remote attestation protocol for detecting unauthorized tampering in the application codes running on sensor nodes with the assistance of Trusted Platform Modules (TPMs), a tiny, cost-effective and tamper-proof cryptographic micro-controller. In our design, each sensor node is equipped with a TPM, and the firmware running on the node could be verified by the other sensor nodes in a WSN, including the sink. Specifically, we present a hardware-based remote attestation protocol, discuss the potential attacks an adversary could launch against the protocol and provide comprehensive system performance results of the protocol in a multi-hop sensor network testbed. The experimental results demonstrated that our scheme is able to attest the application codes running in sensor node with small delay (less than 25 s for single-hop), considerable network lifetime and reasonable communication and energy overhead. Copyright © 2015 John Wiley & Sons, Ltd.

Due to limited computational power and energy resources, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However such aggregation is known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor nodes is crucial for WSN. As the performance of very low power processors dramatically improves, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, thus making WSN less vulnerable. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that several existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging.

Detection of high risk hosts and flows continues to be a significant problem in security monitoring of high throughput networks. A comprehensive risk assessment method should consider the risk propagation among risky hosts and flows. In this paper, this is achieved by introducing two novel concepts. First, an interdependency relationship among the risk scores of a network flow and its source and destination hosts. On the one hand, the risk score of a host depends on risky flows initiated by or terminated at the host. On the other hand, the risk score of a flow depends on the risk scores of its source and destination hosts. Second, which we call flow provenance, represents risk propagation among network flows which considers the likelihood that a particular flow is caused by the other flows. Based on these two concepts, we develop an iterative algorithm for computing the risk score of hosts and network flows. We give a rigorous proof that our algorithm rapidly converges to unique risk estimates, and provide its extensive empirical evaluation using two real-world data sets. Our evaluation shows that our method is effective in detecting high risk hosts and flows and is sufficiently efficient to be deployed in the high throughput networks.

This paper presents a cross-layer approach for enabling high-throughput reliable multicast in multi-hop wireless mesh networks. The building block of our approach is a multicast routing metric, called the expected multicast transmission count (EMTX). EMTX is designed to capture the combined effects of MAC-layer retransmission-based reliability, wireless broadcast advantage, and link quality awareness. The EMTX of single-hop transmission of a multicast packet from a sender is the expected number of multicast transmissions (including retransmissions) required for its next-hop recipients to receive the packet successfully. We formulate the EMTX-based multicast problem with the objective of minimizing the sum of EMTX over all forwarding nodes in the multicast tree, aiming to reduce network bandwidth consumption while ensure high end-to-end packet delivery ratio for the multicast traffic. We provide rigorous mathematical formulations and methods to find near-optimal solutions of the problem computationally efficiently. We present centralized and distributed algorithms, and demonstrate their effectiveness in tackling the EMTX-based multicast problem with a combination of theoretical and numerical results. Simulation experiments show that, in comparison with two baseline approaches, EMTX-based multicast routing reduces the number of hop-by-hop transmissions per packet by up to 40 percent and yet improves the multicast throughput by up to 24 percent.

Software Defined Networking (SDN) is rapidly emerging as a new paradigm for managing and controlling the operation of networks ranging from the data center to the core, enterprise, and home. The logical centralization of network intelligence presents exciting challenges and opportunities to enhance security in such networks, including new ways to prevent, detect, and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. In this paper, we undertake a comprehensive survey of recent works that apply SDN to security, and identify promising future directions that can be addressed by such research.

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.

Wi-Fi based fingerprinting systems, mostly utilize the Received Signal Strength Indicator (RSSI), which is known to be unreliable due to environmental and hardware effects. In this paper, we present a novel Wi-Fi fingerprinting system, exploiting the fine-grained information known as Channel State Information (CSI). The frequency diversity of CSI can be effectively utilized to represent a location in both frequency and spatial domain resulting in more accurate indoor localization. We propose a novel location signature CSI-MIMO that incorporates Multiple Input Multiple Output (MIMO) information and use both the magnitude and the phase of CSI of each sub-carrier. We experimentally evaluate the performance of CSI-MIMO fingerprinting using the k-nearest neighbor and the Bayes algorithm. The accuracy of the proposed CSI-MIMO is compared with Finegrained Indoor Fingerprinting System (FIFS) and a simple CSI-based system. The experimental result shows an accuracy improvement of 57% over FIFS with an accuracy of 0.95 meters.

Detection of high risk network flows and high risk hosts is becoming ever more important and more challenging. In order to selectively apply deep packet inspection (DPI) one has to isolate in real time high risk network activities within a huge number of monitored network flows. To help address this problem, we propose an iterative methodology for a simultaneous assessment of risk scores for both hosts and network flows. The proposed approach measures the risk scores of hosts and flows in an interdependent manner; thus, the risk score of a flow influences the risk score of its source and destination hosts, and also the risk score of a host is evaluated by taking into account the risk scores of flows initiated by or terminated at the host. Our experimental results show that such an approach not only effective in detecting high risk hosts and flows but, when deployed in high throughput networks, is also more efficient than PageRank based algorithms.

Virtual Multiple-Input Multiple-Output (MIMO) is a promising uplink technology that can meet the throughput demand of Long-Term Evolution-Advanced (LTE-A) systems. However, the complexity of scheduling virtual MIMO is a challenge; existing virtual MIMO is therefore limited to best effort applications. We investigate the resource allocation and scheduling problem in a heterogeneous virtual MIMO system where delay sensitive applications are present. The goal is to maximize the system throughput while maintaining delay bound for delay sensitive traffic. To tackle the complexity challenge, we propose two low-complexity suboptimal algorithms, where the key idea is to reduce the search space and iteratively minimize the rate loss respectively. Simulation results show that the rate loss minimization based heuristic algorithm converges to within 99% of the optimal throughput on average and maintains delay bound for delay sensitive users. It also achieves almost the same fairness performance as the optimal solution.

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.

Wireless bodyworn sensing devices are fast becoming popular for fitness, sports training, and personalized healthcare applications. Securing data generated by these devices is essential if they are to be integrated into the current health infrastructure and employed in medical applications. In this paper, we propose a mechanism to secure the data provenance for these devices by exploiting spatio-temporal characteristics of the wireless channel that these devices use for communication. Our solution enables two parties to generate closely matching link fingerprints, which uniquely associate a data session with a wireless link such that a third party can later verify the details of the transaction, particularly the wireless link on which the data was transmitted. These fingerprints are very hard for an eavesdropper to forge; they are lightweight compared with traditional provenance mechanisms and enable interesting security properties such as accountability, nonrepudiation, and resist man-in-the-middle attacks. We validate our technique with experiments using bodyworn sensors in scenarios approximating actual device deployment and present some extensions, which reduce energy consumption. We believe this is a promising first step toward using wireless-link characteristics for the data provenance in body area networks.

Bandwidth-demanding mobile video applications are becoming increasingly popular in wireless networks, leading to a relentless growth in the demand for wireless throughput and quality of service (QoS). Multiuser Multiple-Input Multiple-Output (MIMO) has great potential to meet the growth of wireless throughput. However, this advancement in physical-layer technologies does not necessarily translate into better QoS for the applications, unless the design principles and operating protocols at the higher layers of the networking stack are adapted accordingly to fully capture this potential. We propose a new scheduling algorithm, which selects mobile users to form multiuser MIMO based on the priorities we carefully design to leverage the demands of wireless throughput and video quality. We also develop a new computationally efficient parallel technique to calculate the priorities precisely, which allows the users to be selected in a computationally effective way. Analyses and simulations show that the proposed scheme allows video applications to achieve close to the throughput upper bound of multiuser MIMO. Our scheme also improves the video quality by reducing the loss of video enhancement packets by an order of magnitude and by reducing the delay by 35%, compared to the state of the art.

Discovering high risk network flows and hosts in a high throughput network is a challenging task of network monitoring. Emerging complicated attack scenarios such as DDoS attacks increase the complexity of tracking malicious and high risk network activities within a huge number of monitored network flows. To address this problem, we propose an iterative framework for assessing risk scores for hosts and network flows. To obtain risk scores of flows, we take into account two properties, flow attributes and flow provenance. Also, our iterative risk assessment measures the risk scores of hosts and flows based on an interdependency property where the risk score of a flow influences the risk of its source and destination hosts, and the risk score of a host is evaluated by risk scores of flows initiated by or terminated at the host. Moreover, the update mechanism in our framework allows flows to keep streaming into the system while our risk assessment method performs an online monitoring task. The experimental results show that our approach is effective in detecting high risk hosts and flows as well as sufficiently efficient to be deployed in high throughput networks compared to other algorithms.

Existing mobile video applications are continuously driving up the demand for throughput and better quality of service (QoS) for future Long Term Evolution-Advanced (LTE-A) networks. Multi-User Multiple-Input Multiple-Output (MU-MIMO) is one of the most promising technologies that would meet the throughput demand. Unfortunately, existing MU-MIMO schemes do not consider metrics such as delay, and therefore, cannot meet the QoS requirement of delay sensitive applications, such as mobile video. We propose a new cross-layer MU-MIMO scheduling algorithm, which is referred to as joint channel and delay aware user scheduling (CDAUS), satisfies both the throughput and delay requirements. The key idea of the CDAUS algorithm is to select users to form MU-MIMO based on the delay requirements of individual users, as well as their channel correlations. The priority of the users is carefully designed to leverage their delay and throughput. Simulation results show that the proposed CDAUS algorithm is able to reduce the average delay by up to 30% with a marginal 2% sacrifice of throughput, compared to previous work. It also reduces delay variations and improves fairness among the users.

Received signal strength based fingerprinting approaches have been widely exploited for localization. The received signal strength (RSS) plays a very crucial role in determining the nature and characteristics of location fingerprints stored in a radio-map. The received signal strength is a function of distance between the transmitter and receiving device, which varies due to various in-path interferences. A detailed analysis of factors affecting the received signal for indoor localization is presented in this paper. The paper discusses the effect of factors such as spatial, temporal, environmental, hardware and human presence on the received signal strength through extensive measurements in a typical IEEE 802.11b/g/n network. It also presents the statistical analysis of the measured data that defines the reliability of RSS-based location fingerprints for indoor localization.

The IEEE 802.11 MAC protocol performance degrades severely as the number of stations in the collision domain increases in a saturated network. Employing cooperative retransmission at the MAC layer provides a way to work around the performance limitations by exploiting the broadcast nature of wireless communication. This paper presents a new routing metric, termed Expected Cooperative Forwarding Delay (ECFD), with the aim of selecting a minimum end-to-end delay path for multi-hop multi-rate wireless mesh networks. Different from previously proposed cooperative-aware routing metrics such as ORETT, the ECFD metric is based not only on expected cooperative transmission time but accounts for queueing and backoff delays in the path selection process. We describe the design and implementation of ECFD, and conduct extensive simulation experiments in random topologies showing that, under various realistic network traffic patterns, the use of ECFD instead of ORETT and ETT significantly improves both end-to-end delay and throughput.

Wireless bodyworn sensing devices are becoming popular for fitness, sports training and personalized healthcare applications. In this paper, we demonstrate a mechanism to secure data provenance for these devices by exploiting symmetric spatio-temporal characteristics of the wireless link between two communicating parties. Our solution enables both parties to generate closely matching 'link' fingerprints which uniquely associate a data session with a wireless link such that a third party, at a later date, can verify the links the data was communicated on. These fingerprints are unique, they are very hard for an eavesdropper to forge, lightweight compared to traditional provenance mechanisms, and allow for certain interesting security properties such as system accountability and non-repudiation.

In this paper we introduce a novel sophisticated collusion attack scenario against a number of existing iterative filtering algorithms. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging.

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.

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments, where, typically, there is a need for a higher degree of accuracy. In this article, we focus on robust range estimation , an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation, and evaluation of acoustic (both ultrasound and audible) ranging systems. We distill the limitations of acoustic ranging and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler's effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20m (outdoor) and an average accuracy ≈2cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance.

Cross-correlation is a popular signal processing technique used in numerous localization and tracking systems for obtaining reliable range information. However, a practical efficient implementation has not yet been achieved on resource constrained wireless sensor network platforms. We propose cross-correlation via sparse representation: a new framework for ranging based on l1-minimization. The key idea is to compress the signal samples on the mote platform by efficient random projections and transfer them to a central device, where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain. Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework, together with the proposed correlation domain achieved up to two order of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling. Furthermore, compared to cross-correlation results, 30-40% measurements are sufficient to obtain precise range estimates with an additional bias of only 2-6cm for high accuracy application requirements, while 5% measurements are adequate to achieve approximately 100cm precision for lower accuracy applications.

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.

This paper studies an efficient resource allocation scheme for multicast in OFDMA based wireless networks. Apart from the conventional resource allocation schemes for multicast which allocate exactly the same subcarriers to the users in a multicast group, this paper proposes a more flexible scheme to divide the multicast group members into different subgroups by utilising the diversity of channel coefficient of different users. We first formulate an optimisation problem to maximise the overall transmission rate. Given the NP-hardness of the problem, we design a low-complexity heuristic, Flexible Resource Allocation with Geometric programming (FRAG). FRAG is a two-step heuristic to subdivide the multicast groups and allocate resource to corresponding subgroups. In the first step, we propose a greedy algorithm to subdivide groups and allocate subcarriers given the assumption of even power distribution. Then we use geometric programming (GP) to solve the optimal power allocation problem. Numerical results show that FRAG is able to allocate subcarriers and power efficiently and effectively, and it achieves up to 33% improvement in aggregated throughput.

This paper proposes an opportunistic rate-adaptive expected transmission time (ORETT) metric, which exploits cooperative (relay-aided) retransmissions to achieve high-throughput routing in multi-rate wireless mesh networks. This metric captures the combined effects of MAC-layer cooperative retransmission by neighbor nodes with transmission rate diversity (and rate-dependent link quality). In our approach, a relay node is selected among one-hop neighbors to assist with packet retransmissions and minimize the expected retransmission time. The paper describes the design and implementation of the ORETT routing metric using the DSR routing protocol. Our extensive simulation on the Qualnet platform confirms that multi-rate routing using ORETT significantly reduces the overall transmission time while yielding higher packet delivery ratio compared to single rate unicast or non-cooperative ETT based routing.

Cross-correlation is a popular signal processing technique used for obtaining reliable range information. Recently, a practical and efficient implementation of cross-correlation (via sparse approximation) was demonstrated on resource constrained wireless sensor network platforms, where the key idea was to compress the received signal samples, and transfer them to central device where the range information was retrieved by l1-minimization. Although, this mechanism yields accurate ranging results, its applicability is limited due to its slow execution speed and inaccurate recovery of the correlation peak magnitude, which implicitly provides the useful measure of signal-to-noise ratio. In this work, we propose Fast Gradient Projection (F-GP), a new l1-minimization algorithm, which overcomes the existing limitations, and provides fast and accurate ranging.

We present a cross-layer approach for enabling high-throughput routing in multi-hop wireless networks. This approach builds on a MAC-layer cooperative retransmission mechanism, which is explicitly designed to exploit the benefits of MAC-layer retransmission-based reliability, cooperative communications, and link-quality awareness. Based on this mechanism, we devise a routing metric, called the expected cooperative transmission count (ECTX), to capture the combined effects of MAC-layer cooperative retransmission and per-link estimates of packet delivery ratios. We show that, compared to conventional approaches using the well-known expected transmission count (ETX) metric, our approach has two advantages in principle: 1) with the cooperative retransmission mechanism, one can effectively reduce the expected total number of packet transmissions (including retransmissions) on the same path found by ETX; 2) with the ECTX metric, one may indeed identify a more effective path and further reduce the transmission count. We discuss modifications to IEEE 802.11 MAC to incorporate the cooperative retransmission mechanism, and implement ECTX-based routing using the DSR protocol. Extensive simulations on QualNet demonstrate that ECTX-based routing is significantly more efficient than ETX-based routing, in many instances reducing the transmission count by more than 30% and yet improving the network throughput by up to 80%.

This paper studies video multicasting in large-scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy and low-capacity multihop wireless paths. A set of heuristic-based algorithms is described that together aim to maximize reliable network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link-controlled routing tree algorithm, and the dynamic group management algorithm. These algorithms use different approaches to arrange nodes involved in video multicasting into a clustered and two-tier integrated architecture in which network protocols can make use of multiple gateways to improve system throughput. Simulation results are presented, showing that our multicasting algorithms can achieve up to 40 percent more throughput than other related published approaches.

We propose a routing metric for enabling high-throughput reliable multicast in multi-rate wireless mesh networks. This new multicast routing metric, called expected multicast transmission time (EMTT), captures the combined effects of 1) MAC-layer retransmission-based reliability, 2) transmission rate diversity, 3) wireless broadcast advantage, and 4) link quality awareness. The EMTT of one-hop transmission of a multicast packet minimizes the amount of expected transmission time (including that required for retransmissions). This is achieved by allowing the sender to adapt its bit-rate for each ongoing transmission/retransmission, optimized exclusively for its next-hop receivers that have not yet received the multicast packet. We model the rate adaptation process as a Markov decision process (MDP) and derive an efficient procedure for computing EMTT from the theory of MDP. We present receiver-initiated algorithms and describe protocol implementation for the EMTT-based multicast routing problem. Numerical results are presented to demonstrate the accuracy of the proposed algorithms against optimal solutions to the multicast routing problem. Simulation experiments confirm that, in comparison with single-rate multicast, multi-rate multicast using the EMTT metric effectively reduces the overall multicast transmission time while yielding higher packet delivery ratio and lower end-to-end latency.

Fine-grained location information at long range can benefit many applications of embedded sensor networks and robotics. In this paper, we focus on range estimation - an important prerequisite for fine-grained localization - in the ultrasonic domain for both indoor and outdoor environments, and make three contributions. First, we evaluate the characteristics of broadband signals, and provide useful statistics in their design and engineering to achieve a good trade-off between range and accuracy. Second, to overcome the inaccuracy due to correlation sidelobes, we propose a signal detection technique that estimates the envelope of the correlated pulse using a simple least-square approximation approach, and undertake a simulation study to verify its ranging efficiency on linear chirps. Third, leveraging on the insights obtained from our initial study, we present the design and implementation of TWEET: a mote-based ultrasonic broadband ranging system based on linear chirps using the CSIRO Audio nodes, which comprises of a Fleck-3z mote along with audio codecs and a Blackfin DSP. Our evaluation results indicate that the system is precise enough to support source localization applications: a reliable operational range of 20m (outdoor) and an average accuracy of < 2 cm with a 95% confidence interval of 2 cm.

We consider the downlink transmit power allocation problems in multi-user MIMO wireless networks using zeroforcing beamforming. Traditionally such problems are solved by water-filling algorithm under the assumption of perfect channel knowledge. However when channel information is not known a priori or time varying the water-filling solution is shown to be unstable. We use the sliding mode control theory to synthesize the transmit powers so that the target SINR requirements of all users are met. We synthesize the sliding mode controller for the case of zero-forcing beamforming. The synthesis problem is solved under time varying Rayleigh fading channel conditions. Our solutions and simulation results show that our sliding mode controller is stable and delivers better quality of service under practical channel conditions.

Location awareness is of benefit to a rich set of applications in indoor environments such as asset tracking, resource discovery, interactive virtual games, location-aware sensor networking, navigation support for humans and robots, etc. There exists a vast array of location sensing systems, but they mostly operate under dense indoor deployment due to the limited range and coverage of the sensing device. In this paper, we focus on improving coverage range - a critical prerequisite for localization. Our experiments using the existing Cricket system reveals many of its limitations. We overcome these hurdles, and present the design, implementation and evaluation of a custom designed omni-directional ultrasonic receiver unit integrated with the existing Cricket motes. The modified Cricket system improves the coverage range by ≈20% in comparison to the original Cricket. The lessons and experiences also provide an analytical and system-level understanding of how various factors affect the ranging characteristics.

Current network programming protocols provide an efficient way to update program images running on sensor nodes without having physical access to them. Securing these updates, however, remains a challenging and important issue, given the open environment where sensor nodes are often deployed. Several approaches addressing these issues have been reported, but their use of cryptographically strong protocols means that their computational costs (and hence, power consumption and communication costs) are relatively high. In this paper, we propose a novel scheme to secure a multihop network programming protocol through the use of multiple one-way hash chains. The scheme is shown to be lower in computational, power consumption, and communication costs yet still able to secure multihop propagation of program images. We demonstrate the use of this scheme and provide some results using the popular network programming protocol, Deluge. In addition, we include a performance evaluation of our scheme, implemented in TinyOS, in terms of latency and energy consumption.

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.

The performance of various localisation schemes in sensor networks can be improved by long-range detection. However, its performance deteriorates under noisy environmental conditions. This work presents a system implementation of an acoustic ranging mechanism based on wideband ultrasonic linear chirps and time-reversal technique to counter the negative influence of channel multipath. We investigated the performance of our technique through experimentation with chirps of length 500 milliseconds. The proposed scheme provided distance ranging up to 10 meters, and showed an improvement in peak detection with respect to conventional techniques.

Given the limited resources and computational power of current embedded sensor devices memory protection is difficult to achieve and generally unavailable. Hence, the buffer overflow that is used by the worm attacks in the Internet can be easily exploited to inject malicious code into Wireless Sensor Networks (WSNs). We designed a hardware-based remote attestation protocol to counter the buffer overflow attack. In our attestation protocol, each sensor node is equipped with a Trusted Platform Module (TPM) board. The TPM is responsible for content verification of the program flash. To the best of our knowledge, it is the first remote attestation protocol in WSNs with each sensor node equipped with TPM.

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.

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.

This article presents the design and implementation of a trusted sensor node that provides Internet-grade security at low system cost. We describe trustedFleck, which uses a commodity Trusted Platform Module (TPM) chip to extend the capabilities of a standard wireless sensor node to provide security services such as message integrity, confidentiality, authenticity , and system integrity based on RSA public-key and XTEA-based symmetric-key cryptography. In addition trustedFleck provides secure storage of private keys and provides platform configuration registers (PCRs) to store system configurations and detect code tampering. We analyze system performance using metrics that are important for WSN applications such as computation time, memory size, energy consumption and cost. Our results show that trustedFleck significantly outperforms previous approaches (e.g., TinyECC) in terms of these metrics while providing stronger security levels. Finally, we describe a number of examples, built on trustedFleck, of symmetric key management, secure RPC, secure software update, and remote attestation .

The increasing number of independent IEEE 802.11 WLANs owned and managed by autonomous users has led to increased interference, resulting in performance degradation and unfairness. Performance can be improved by allowing these networks to operate on different channels. Due to the autonomous nature of the networks, a suitable channel selection scheme should be distributed, adaptive and require no explicit coordination. In this paper, we model the channel selection of WLANs as a non-cooperative game in a learning setting. Using a novel method of acquiring a disruption factor value, we propose a class of socially conscious channel selection schemes based on game-theoretic learning. These schemes are distributed, adaptive and are able to improve fairness without explicit inter-network communication. These features allow the WLANs to coexist in an interference-limited but non-cooperative environment. They also have the advantage of not requiring any modification to the existing 802.11 standards. Simulations show improved fairness and aggregate throughput compared with two existing schemes.

Recent experimental studies in wireless sensor networks (WSNs) have confirmed that asymmetry in the wireless links has a significant effect on the performance of WSN network protocols. Protocols which work in simulation studies often fail when link asymmetry is encountered in real deployments. Characterization of link asymmetry is thus of paramount importance for the design and operation of re-silient WSN protocols in real scenarios. This paper details an empirical study to characterize link asymmetry in WSNs.There are several factors that contribute to link asymmetry in WSNs, the major ones being environmental effects and hardware performance. In this work, we used a systematic approach to measure the effects of hardware performance, i.e. transmitter, receiver and antenna characteristics, on link asymmetry using off-the-shelf WSN devices such as Xbow Mica2 and MicaZ motes. We conducted experiments to study the variations in spectrum utilization in these WSN devices, for both wired and wireless connections, and to ascertain the effects of any frequency and amplitude mismatches on link symmetry between nodes in transmission and reception.

Ultrasound (US) based Cricket indoor location system has limited range when the transmitter and receiver motes are not in the line-of-sight (LOS) positions. It uses narrowband US transducers which are unidirectional and require tilting of the motes in order to improve the signal reception quality. The main focus of this work is to improve the ranging distance of the ultrasound based sensor motes with an objective of successfully deploying in harsh environments where the prime challenge is that of gracefully sustaining the signal characteristics for the purpose of distance estimation. We implement and provide an analysis of two different techniques with respect to the original Cricket: 1) Omni-directional receiver: A dodecahedron arrangement of an array of 3 US transducers. 2) Chirp spread spectrum based broadband transmission scheme and signal correlation technique.

Code dissemination protocols provide a convenient way to update program images via wireless communication. Due to the open environment in which Wireless Sensor Networks (WSNs) are typically deployed, it is important that a code dissemination protocol ensures that a program image update can be authenticated as coming from a trusted source. In some applications it is also required that the data be kept confidential in spite of the possibility of message interception. Authentication and confidentiality are implemented through cryptographic operations which may be expensive in power consumption, making a protocol with these features vulnerable to attack by an adversary who transmits forged data, forcing nodes to waste energy in identifying it as invalid i.e., a signature-based DoS attack). Additionally, in multi-hop dissemination protocols, each sensor node is required to broadcast its program image when requested by its neighbors. An adversary could repeatedly send spurious program image requests to its neighbors, making them exhaust their energy reserves i.e., request-based DoS attack). In this paper, we present a new approach to achieve confidentiality in multi-hop code dissemination. We propose counter-measures against both types of DoS attacks mentioned above. To our knowledge, we are the first to integrate confidentiality and DoS-attack-resistance in a multi-hop code dissemination protocol. Our approach is based on Deluge, an open source, state-of-the-art code dissemination protocol for WSNs. In addition, We provide a performance evaluation in terms of latency and energy consumption in our scheme, compared with the original Deluge and the existing secure Deluge.

This article investigates a wireless acoustic sensor network application—monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing, wide area sensing coverage and long-lived unattended operation. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to ninth frog species found in northern Australia. Our first prototype consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates four algorithms designed to account for the sampling, processing, energy, and communication bottlenecks of the Mica2s (1) high frequency sampling, (2) thresholding and noise reduction, to reduce data transmission by up to 90%, (3) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate, and (4) harvesting-aware energy management, which exploits sensor energy harvesting capabilities to extend the system lifetime. Our evaluation shows the performance of our systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

A common application of unattended sensor networks (WSN) is low data rate streaming from many scattered sensors to one or more sink nodes. To meet the stringent requirement of prolonged WSN lifetime, we introduce a new notion of statistical reliability for data streaming applications and propose several variants of stop-and-wait hop-by-hop ARQ with explicit and implicit ACKs. The energy-efficiency of the protocols are mathematically analyzed and compared. The analysis reveals that implicit ACKs should be applied with caution to prevent an "avalanche" of implicit ACK transmissions. It is further shown that a simple combined implicit/explicit ACK resolves the "avalanche" problem. Our proposal is further validated by simulation.

This paper studies video multicasting in large scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy multi-hop wireless paths with low capacity. A set of heuristic-based algorithms are described that together aim to maximize network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link-controlled routing tree algorithm, and the alternative channel assignment algorithm. These algorithms use different approaches to arrange multicast group members into a clustered and two-tier integrated architecture in which network protocols can make use of multiple gateways to improve system throughput. Simulation results are used to determine the performance of the different approaches.

Two of the fundamental challenges associated with data gathering in sensor networks are data classification and data aggregation. This paper provides a solution to classify and aggregate sensor readings. We leverage our previous experience[7] and use Competitive Learning Neural Network (CLNN) as the data classification mechanism. We then propose and evaluate Graph Theory Based Aggregation (GTBA) which combines outputs of CLNN across the network. We have evaluated two main interpretations of GTBA on real data sets produced by the WSN and on a testbed consisting of MicaZ motes. We demonstrate its ability to deduce an accurate representation of the data and distinguish the noise free data with a high probability.

In this paper, we describe the design and the implementation of a management system called SRM for controlling data reliability in Wireless Sensor Networks. SRM is based on a hierarchical management architecture and policy-based network management paradigm. SRM consists of four modules: a user policy specification module, an evaluation module, a decision making module and an action module. The interaction among these modules ensures that the network provides adequate information to the users while reducing energy consumption. To demonstrate the effectiveness of the management framework, we design a policy for balancing energy consumption and data reliability. Our experimental results show that SRM can meet the reliability requirements, and reduces energy consumption by up to 50% compared to the case of no management.

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.

To support large-scale Internet-based broadcast of live streaming video efficiently in content delivery networks (CDNs), it is essential to implement a cost-effective overlay multicast mechanism by exploiting peer-to-peer distribution capabilities among end hosts. This way, the access bandwidth demand on CDN servers in the multicast overlay backbone can be largely reduced. Such a streaming infrastructure gives rise to an interesting host-aware routing problem (HARP). For a live streaming video broadcast event, each participating CDN server is made aware of the largest delay from it to end hosts within its service area. The problem is to optimize routing among CDN servers in the multicast overlay backbone such that the de facto maximal end-to-end latency from the origin server to all end hosts is minimized subject to access bandwidth constraints on CDN servers. In this paper, we frame HARP as a constrained spanning tree problem which is shown to be NP-hard. We present a distributed algorithm for HARP. Simulation experiments confirm that our proposed algorithm converges to good quality solutions that are close to the optimum.

We derive two new energy efficient reliable data transport protocols for overcoming the negative impact of asymmetric radio links in wireless sensor networks. The energy efficiency of these algorithms is explicitly derived using our theoretical model, and validated by results obtained from simulations and field trials. The analytical, simulation and field trials demonstrate that our proposed protocols perform well in networks with asymmetric links and can save energy of up to 27% compared to conventional ARQ schemes.

We propose an efficient reliable data collection(eRDC) algorithm. The eRDC is designed for energy-constraint wireless sensor networks (WSN) to balance reliability and energy consumption. We derive energy efficiencies of the proposed reliability schemes, and evaluate their performances. These analyses provide a guideline to determine the number of retransmissions for reliable data delivery. Dynamic programming concept is used to find the optimal solution. We present a distributed eRDC implementation to dynamically control the maximum number of retransmissions based on the guideline provided. Discrete event simulations and field trials with wireless sensor nodes confirmed our results.

Current network programming protocols provide an efficient way to update the program image running on sensor nodes without physical access to them. However, given the open environment in which sensor nodes are deployed, securing network programming is a challenging task. Existing work addressing this issue either lack consideration of securing multi-hop network programming protocols, or are not cost-efficient. To our knowledge, none of them have evaluated the power consumption. In this paper, we propose a novel scheme to secure multi-hop network programming protocols using multiple one-way hash chains. This scheme is resilient to malicious program image injection by the compromised nodes and it secures multi-hop propagation of program images for sensor nodes. Based on the most popular network programming protocol, Deluge, an overhead analysis on this schemes is given. In addition, our scheme is implemented in TinyOS and a performance evaluation in terms of latency and energy consumption is presented.

This paper studies probabilistically reliable multicast in wireless mesh networks (WMNs), utilizing MAC layer re-transmission and wireless broadcast advantage to improve both the multicast throughput and the delivery rate. We first present a new multicast routing metric which we call the expected multicast transmissions (EMT). EMT captures the effect of link packet delivery ratio, MAC layer retransmission and wireless broadcast advantage at the same time. The EMT of a MAC layer multicast transmission is the expected number of data transmissions (including retransmissions) required for a packet to reach all the recipients. The EMT of a multicast tree is the sum over the EMT of each forwarding node. Then, we propose a probabilistically reliable on-demand (PROD) multicast protocol with the objective of minimizing the EMT of the multicast tree. Simulation results show that, in comparison with existing approaches, PROD reduces the end-to-end packet loss ratio by up to 30% and improves the multicast throughput by up to 25%. In addition, it reduces the number of transmissions per packet by up to 40% and thus significantly reduces the network overhead of the multicast session.

Placing multicast proxies on the Internet can largely reduce the delivery delay of Internet live media streaming using overlay multicast mechanisms. In this paper, we propose a new approach to address the issue of proxy placement in an overlay multicast network. The task is to decide an optimal placement of multicast proxies in the overlay multicast network so as to minimize the average end-to-end delay of the overlay skeleton tree subject to out-degree balancing constraint and maximum delivery delay bound. We present two heuristic methods for this proxy placement problem. Experimental results demonstrate that proxy placement due to our proposed approach can greatly improve the overlay multicast routing performance. In comparison with an existing approach commonly used for replica placement in content distribution networks, our proposed approach significantly improves the end-to-end latency performance of overlay multicast networks, and is not much sensitive to network dynamics.

In this paper, we proposed a probabilistically reliable multicast routing (PRMR) which uses link packet delivery rate as the link-quality-based metric to find paths from source to destinations. In order to increase the reliability of the multicast session, we use a pair of disjoint paths to connect the source to each destination. We reduce the total number of forwarding nodes by exploiting the wireless broadcast advantage. Our experiment results show that PRMR can increase the average reliability by up to 25% and the node with the lowest reliability by up to 50% in comparison with ODMRP.

As more wireless networks are being deployed in a given geographic area, the problem of interference and coexistence of these independently operated networks is becoming an increasing problem. This paper looks at the coexistence of independent multihop wireless mesh networks (WMNs). We argue that cooperation is difficult in such scenarios. We define a coexistence game model and apply it to study channel assignment in co-located WMNs. In addition, we propose using no-regret learning algorithms that allow WMNs to iteratively arrive at Nash equilibrium outcomes. Simulation results show that the informed no-regret learning algorithms we have tested converge to a set of Nash equilibrium strategy profiles. We also show that network information is not critical for games with large numbers of players.

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.

In this paper, we propose an anchor free localization mechanism for wireless sensor networks. Our algorithm is based on a graph drawing approach and uses inter-node distances to localize sensor nodes in a local coordinate system up to a global translation, rotation and reflection without any absolute reference positions such as GPS or other anchor nodes. We show that it is possible to avoid folds and flips in the localized network layout by introducing long range constraints among non adjacent nodes which can be derived from inter-node distance measurements between adjacent nodes. We evaluate the effect of different parameters like network shape, scale, average neighbors and ranging noise on our algorithm and compare it with an anchor based and a number of anchor free schemes. We also present experimental results from an actual sensor network showing the accuracy of our approach.

Wireless sensor networks need broadcast for operations such as software updates, network queries, and command dissemination. Alongside ensuring authenticity of the source and data, keeping the broadcast data secret is vital in certain applications such as battlefield control, emergency response, and natural resource management. In this paper we propose and prototype a mechanism for ensuring confidentiality and authenticity of broadcast data in single-hop networks, and discuss possible extensions to multi-hop settings. Our scheme uses known low-complexity symmetric encryption techniques for confidentiality, while changing the encryption key on a per-packet basis in a verifiable but non-forgeable way to ensure authenticity. Message integrity, freshness, and semantic security are also provided, and the broadcast data can be dynamic and incrementally processed. We incorporate our security scheme into Deluge, the de facto network programming protocol in TinyOS, and quantify the cost in terms of broadcast data transfer time and node memory space on a TelosB mote based platform.

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

A wireless mesh network (WMN) that utilizes multiple interfaces and multiple channels has been shown to improve network performance by reducing the interference and increasing the available bandwidth. However, the contention delay experienced by a frame along every hop of the WMN can still limit the performance. In addition, cross-layer delay occurs when a frame has to travel up and down the protocol stack to access different interfaces. This paper motivates the need for a MAC in a multi-interface backhaul WMN and proposed a cut-through MAC that is able to reduce the end-to-end delay of data frames in the network. Preliminary simulation results show that this MAC scheme gives higher good put and lower end-to-end delay in a chain topology, when compared to IEEE 802.11 DCF MAC. The paper also highlights key challenges that need to be addressed in the design of a cut-through MAC for multihop wireless networks.

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

Hybrid medium access control (MAC) protocols combine the strength of random and schedule based MAC schemes. From random MAC schemes, hybrid MAC protocols borrow flexibility and ease of operation while also incorporating the scalability and high capacity performance of schedule based schemes. A number of hybrid approaches exist. The most effective of those, as shown in (I. Chlamtac, 2000), can dynamically adapt to the contention level in the medium. They achieve this adaptability by combining TDMA (schedule-based) and CSMA (random-based). Whereas the combination of TDMA and CSMA enhance contentions resolution, the existing solutions do not allocate slots in proportion to the bandwidth requirements of the individual nodes. This affects the performance adversely. In this paper we propose an algorithm to optimize slot allocations during the schedule-based phase of the hybrid MAC protocol. Through simulations, we evaluate the performance of our algorithm in both single-hop and multihop networks. Our results show an improvement of up to 40% in some cases

This paper investigates the problem of how to improve TCP performance in multi-rate ad-hoc networks with congested links. To improve network performance, different rate adaptation algorithms, such as automatic rate fallback (ARF) and receiver-based autorate (RBAR), have been proposed to adapt the data rate according to the current channel quality. Opportunistic auto rate (OAR) protocol is an optimisation for any existing rate adaptation algorithm which leads to a significant performance gain by providing temporal fairness. We analyze the reasons for the high performance gain obtained using OAR, and show that the OAR protocol does not work well for TCP communications in ad-hoc networks where nodes use different data rates to communicate with each other (heterogeneous). Based on these analysis, we propose a congestion reactive opportunistic auto rate (CROAR) protocol, which is a new rate adaption enhancement tailored to improve TCP performance in heterogeneous multi-hop ad-hoc networks. Extensive simulations show that CROAR, compared to OAR and RBAR, produces significant throughput and end-to-end transmission latency improvements while only marginally relaxing temporal fairness

To support reliable multicast routing in wireless mesh networks, it is important to protect multicast sessions against link or node failures. The issue of protecting multicast sessions in wireless mesh networks is a new problem to the best of our knowledge. In this paper, we propose a resilient forwarding mesh approach for protecting a multicast session in wireless mesh networks. Utilizing the wireless broadcast advantage, a resilient forwarding mesh effectively establishes two node disjoint paths for each source-destination pair. This allows a multicast session to be immune from any single link or intermediate node failure. We introduce four heuristic algorithms to obtain approximate solutions that seek to minimize the number of required broadcast transmissions. We evaluate the performance of these heuristic algorithms against the optimal resilient forwarding mesh (ORFM) obtained by solving an integer linear programming (ILP) formulation of the problem. Experimental results demonstrate that one of these heuristic algorithms, which we call the minimal disjoint mesh algorithm (MDM), performs sufficiently close to ORFM. Besides, we find that the resilient forwarding mesh approach provides efficient 1+ 1 protection (Pioro and Medhi, 2004) to the multicast session without incurring much additional overhead on a single minimal cost multicast tree

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

Most ad hoc and sensor network research assumes idealized radio propagation models without considering fading and shadowing effects. Experimental results have shown that many well-designed protocols will fail simply because of fading and shadowing experienced in a realistic wireless environment. While fading and shadowing for radio propagation are well understood in wireless communication community, they are rarely studied in network level research for wireless sensor networks. This paper studies the fading and shadowing effects on the performance of different MAC protocols for wireless sensor networks. We show that fading and shadowing can have a significant influence on network performance. We study and compare network performance for three different systems: 1) a multi-channel CDMA system; 2) a pure CDMA system; 3) a contention based system. Through discrete event simulation (using ns-2), we show that the multi-channel CDMA system outperforms the pure CDMA system as well as the contention based system under fading and shadowing environments

Ad hoc wireless sensor networks derive much of their promise from their potential for autonomously monitoring remote or physically inaccessible locations. As we begin to deploy sensor networks in real world applications, concerns are being raised about the fidelity and integrity of the sensor network data. In this paper, we motivate and propose an online algorithm that leverages competitive learning neural network for characterization of a dynamic, unknown environment. Based on the proposed characterization sensor networks can autonomously construct multimodal views of their environments and derive the conditions for verifying data integrity over time

The availability of simulators and emulators that are tailored for wireless sensor networks (WSNs) is a necessary step in allowing accurate evaluation, but simply having the right tools is not sufficient to ensure that the results of experiments actually correspond to what might be expected in realistic deployments. The critical issue is what models are available in these tools, and if these are used in an appropriate manner. In this paper we review the current approaches to WSN experimentation and identify a serious shortcoming in common simulation methodology, specifically in regard to the choice of network topologies and traffic models. We hypothesize that this mismatch has an important impact on the sensitivity of published sensor network simulations. We support our hypothesis using an analysis of the directed diffusion protocol

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.

This paper investigates a wireless, acoustic sensor network application-monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to 9 frog species found in northern Australia. Our first prototype is simple and consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates three algorithms designed to account for the sampling, processing and communication bottlenecks of the Mica2s (i) high frequency sampling, (ii) compression and noise reduction, to reduce data transmission by up to 90%, and (iii) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate. We evaluate the performance of both systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

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

In this paper we consider dynamic routing of restorable quality of service (QoS) connections in multi-protocol label switched (MPLS) networks under a single link failure model. To route a restorable QoS connection, two link disjoint label switched paths from the ingress to the egress node need to be computed such that both paths comply with the QoS constraints. When looking at QoS constraints like bandwidth guarantee and end-to-end delay bound, the current approach described in the literature converts the end-to-end delay bound into an effective bandwidth in a pre-processing step before computing two link disjoint bandwidth guaranteed paths. We argue that the current approach results in poor network performance and suggest a new approach that exploits the dependency between the end-to-end delay, chosen path and provisioned bandwidth. Thus, the two link disjoint paths are computed dynamically with their respective bandwidths so that they meet both the bandwidth guarantee and the end-to-end delay bound. In this paper, we present a new generic algorithm and two new linear programming formulations that implement the new approach. The two linear programming formulations are not intended to be used in a production environment due to their extensive running times, up to 2.5 minutes on average per request, but rather to benchmark approximation algorithms that in turn can be used in production. Furthermore our experiments show that the network performance improvement when exploiting the dependency between end-to-end delay, chosen path and provisioned bandwidth is substantial.

Guaranteed levels of network resilience are essential for many emerging and future Internet services. Currently, the only network model that can support stringent resilience requirements is a provider-based overlay network. In such an environment, resilience can be implemented either in the overlay networks or in the physical network. In this paper we look at how the network provider revenue is affected by where resilience is implemented. The idea is to measure the revenue in the two implementation scenarios and compare the generated revenues to find out if the network provider has something to gain by implementing resilience in the physical network and if so under what circumstances. The results of our linear programming based experiments show that network providers can potentially double its revenue when resilience is implemented in the physical network. The customers benefit as well since the overlay networks are significantly less complex and can be built at the same or lower cost than when resilience is implemented in the overlay networks.

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

This demonstration shows a wireless, acoustic sensor network application--- monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our system uses automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the Cane Toad (see Fig. 1). This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage [2]. Our prototype consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica motes operating in concert. The Mica motes are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks.

The performance of direct sequence code division multiple access (DS-CDMA) sensor networks is limited by multiple access interference (MAI). The paper proposes using frequency division to reduce the MAI in a DS-CDMA sensor network. We provide theoretical characterization of the mean MAI at a given node and show that a small number of frequency channels can reduce the MAI significantly. In addition, we provide a comparison of our proposed system to systems which do not use frequency division or which employ contention based protocols. Our study found that, by using only a small number of frequency channels, our system has less channel contention, lower packet latency, higher packet delivery ratio and lower energy consumption.

We present a novel scheme for node localization in a delay-tolerant sensor network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a robust extended Kalman filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1 m in a large indoor setting.

Expanding ring search (ERS) is a widely used technique to reduce broadcast overhead in multi-hop wireless networks (e.g., ad-hoc and sensor networks). ERS works by searching successively larger areas in the network centred around the source of broadcast. Network-wide broadcast is initiated only if L successive searches fail. This paper explores if there exists an optimal L that would minimise the broadcast cost of ERS. A theoretical model is developed to analyse the expected broadcast cost as a function of L. Using this model, we show that an optimal L exists for any random network topology. The analytical results are validated through extensive numerical experiments that consider a large number of random network topologies of varying sizes and hop lengths. By tuning the parameter L to the optimum value, broadcast cost can be reduced up to 52% depending on the topology.

This paper presents a new approach called the backup path cost estimation (BPCE) for calculating the service (working) path in the online restoration problem. The objective of BPCE is to increase bandwidth efficiency and reduce the number of blocked requests in limited capacity networks. We propose two new algorithms that combine the BPCE approach with an existing restoration algorithm called shortest path local optimization (SPLO). One of the algorithms called BPCE-SPLO aims to reduce the total bandwidth requirement of the service path and the backup paths. The second algorithm called biased-SPLO aims to reduce the backup bandwidth requirement only. Both BPCE-SPLO and biased-SPLO have polynomial-time complexity. A comparative analysis is used to evaluate the performance of BPCE-SPLO and biased-SPLO. For the comparison, we include another known polynomial-time algorithm. Results have shown that BPCE-SPLO has a slightly lower total bandwidth requirement and has a significantly lower number of blocked requests in limited capacity networks than the other algorithms.

Location management refers to the problem of updating and searching the current locations of mobile nodes in a wireless network. To make it efficient, the sum of update/lookup costs of a location database must be minimized. Previous work relying on fixed location databases is unable to fully exploit the knowledge of user mobility patients in the system so as to achieve this minimization. We present a novel location management architecture that can adapt dynamically to user pattern changes. The key idea is to use a hierarchy of agents that act as mobile location databases that rapidly replicate to other base-stations in response to a change in user patterns. We provide an analytical cost model for location management and through simulations demonstrate our algorithm's ability to handle location updates and call finding.

Service and application requirements on network resilience have increased over the past few years. New on-line services such as e-commerce and connection-oriented interactive real-time services require higher network resilience than the more traditional off-line services. Programmable virtual networks promise fast and easy provisioning of new services but no consideration to meet the diverse resilience requirements has been made. This paper discusses issues related to resilience-differentiation in programmable virtual networks. A set of general guidelines is presented that apply to resilience-differentiation in programmable virtual network architectures. A case study is used to illustrate how the proposed guidelines can be met by extending an existing programmable virtual network architecture.

In today's Internet, packet processing may be accomplished in the routers within the data path. For efficient resource allocation in such routers, the packet scheduling schemes should consider multiple resources such as CPU and memory in addition to the bandwidth to improve overall performance. The dynamic nature of network load and the inherent unpredictability of processing times of data packets pose a significant challenge for CPU scheduling, which in turn also limits the application of well-known bandwidth scheduling algorithms. This paper presents a new composite scheduling algorithm called prediction-based composite fair queueing (PCFQ), which is designed to schedule both bandwidth and CPU resources adaptively, fairly, and efficiently among all the competing flows. PCFQ is the first scheduler to have the following properties: (1) a very new idea of combining both CPU and bandwidth scheduling functionalities under a single scheduling scheme that can provide better QoS guarantees to the data flows competing for both processing and bandwidth resources; and (2) a simple and adaptive online prediction scheme for the scheduling algorithm for reliably estimating the execution times of the incoming data packets. Through simulation and analysis we show that PCFQ can provide significantly better delay guarantees than those achievable through separate CPU and bandwidth schedulers.

We propose two location-aware select optimal neighbor (SON) algorithms that are suitable for CSMA/CA based MAC protocols for wireless ad hoc networks. Both algorithms optimize the energy efficiency by reducing the effective number of neighbors and thus reduce the transmission power as well as the overhearing power consumption at irrelevant receivers. NS-2 simulations show that our algorithms can achieve about 28% and 38% average energy savings per node compared to CSMA/CA based MAC protocols such as IEEE 802.11.

This paper proposes CSMAC (CDMA Sensor MAC), a novel self-organizing, location-aware media-access control (MAC) protocol for DS-CDMA sensor networks to support applications that feature high bandwidth traffic and stringent latency requirements. Previously proposed MAC protocols for sensor networks such as SMAC (W. Ye et al, IEEE Proc. Infocom, pp. 1567-1576, 2002) primarily prioritize energy efficiency over latency. Our protocol design balances the considerations of energy efficiency, latency, accuracy, and fault-tolerance in sensor networks. CSMAC uses a combination of DS-CDMA and frequency diversity in channel allocation to reduce channel interference and consequently message latency in the network. It features two algorithms that exploit the location awareness of sensor nodes to enable intelligent and efficient network formation for collaborative sensing tasks: turn off redundant node (TORN) and select minimum neighbor (SMN).

A new polynomial-time approximation algorithm called service path local optimization (SPLO) is presented. SPLO is shown to perform competitively with existing non-polynomial approximations based on the failure-oriented approach. SPLO is designed for online computation where only one request is computed at a time, and the decision making does not depend on future requests. The polynomial-time and online nature of the algorithm makes SPLO suitable for use in real-time on-demand path request applications. In addition, a non-polynomial approximation algorithm based on SPLO called nSPLO is proposed. Result shows that spare capacity is reduced significantly but at the cost of substantially higher computation run-time. The paper also introduces a new concept called path intermix where the service path's allocated bandwidth can be used by the backup paths protecting that particular service path. The result shows that path intermix can reduce spare capacity by up to 5% for single node failure.

The paper investigates an anycast communication paradigm for a hybrid sensor/actuator network, consisting of both resource-rich and resource-impoverished devices. The key idea is to exploit the capabilities of resource-rich devices (called micro-servers) to reduce the communication burden on smaller sensor nodes which are energy, bandwidth and memory constrained. The goal is to deliver sensor data to the nearest micro-server, which can (i) store it, (ii) forward it to other micro-servers using out-of-band communication, or (iii) perform the desired actuation. Our approach is to construct an anycast tree rooted at each potential event source, which micro-servers can dynamically join and leave. Our anycast mechanism is self-organizing, distributed, robust, scalable, and incurs very little overhead. Simulations using ns-2 show that our anycast mechanism can reduce network energy consumption by more than 50%, both the mean end-to-end latency of the transmission and the mean number of transmissions by more than 50%, and achieves 99% data delivery rate for low and moderate micro-server mobility rate.

Localization of small wireless sensor devices, with the deployment of the minimal infrastructure or hardware, has been the topic of significant research over the past few years. We have developed the Robust Extended Kalman Filter (REKF) localization system [1], which enables a mobile, data gathering robot to localize static sensor devices, by combining the RSSI data received from the motes, with estimates of its trajectory. The REKF localization system is particularly well suited to delay-tolerant sensor networks, where node positions need not be known in real time. We have observed accuracies ranging from approximately 30cm to 1m in practice.

In this article, a new polynomial-time approximation algorithm called Service Path Local Optimization (SPLO) is proposed for the online restoration problem. SPLO is shown to perform competitively with existing offline heuristics algorithm in terms of spare capacity. SPLO is designed for online computation where only one request is computed at any one time, and the decision making does not depend on future requests. The polynomial-time and online nature of the algorithm makes SPLO suitable for use in real-time on-demand path request applications. SPLO can be combined with a non-polynomial post-processing component that re-optimizes the backup paths. Significant reductions in spare capacity requirements are achievable at the expense of higher computation time. Further, the potential for SPLO as an algorithm in traffic engineering applications is investigated by looking at the performance impact when source-destination-based traffic aggregation is applied. We also introduce a new concept called path intermix where the service path's allocated bandwidth can be used by the backup paths protecting that particular service path.

The current trend of increasing accessibility and reachability of the Internet has resulted in many new services at the application layer. The potential growth of services on the Internet is only restricted by the network technologies that realize the Internet. In particular, layer 3 technologies are conventionally inflexible and do not adapt well to rapid changes in the Internet environment. Service-oriented networks should be more user-focused, which includes providing mechanisms that show value to the network providers, service providers, and clients. The next generation networking technologies must not only excel in performance, but also in flexibility, control, and scalability. This paper introduces a new network architecture called active protocol label switching (APLS), which establishes a foundation offering the same level of performance and scalability as current label-switching architectures lack. All existing label-switching architectures position the label as a shim layer between layers 2 and 3. The major reason behind this is to make the architecture network protocol independent. However, in designing APLS we investigated the merit of a new concept: label switching over IP. Several other new concepts are introduced: virtual label space, micro-instruction architecture, and micro-policy based forwarding. We will also focus on how APLS can be combined with active programmable networks to offer services at an unprecedented level.

In this paper, we propose and analyze a timer-based location caching scheme to reduce the signalling cost of on-demand location management in the context of a light-weight cellular wireless network architecture, called a cell-hopping network. The timer value is the most important control parameter of the proposed caching scheme. We derive a closed-form expression as a function of the message-to-mobility ratio (MMR), for the optimum timer value that minimizes signalling cost. Using numerical examples, we show that the signalling cost can be reduced by as much as 80% for reasonable MMR values, if the caching timer is set to the optimum value. Conversely, incorrectly selected timer values can nullify the use of the location cache.

Enterprise-wide distributed computing systems are inherently stochastic and the performance management of distributed applications running on the top of DCS is a complex and computationally hard task, in this paper, we define the enterprise distributed application and provide queueing analysis on the relationship between the application level performance parameters and the network and computer system parameters. We also provide a binary integer programming (BIP) model and a case study on how to allocate application components across an enterprise local area network.

VPN services have been available for decades but not widely used until recently. A combination of new VPN technologies, network technologies and business communication requirements is the driving force behind the take-up of VPN services. This paper gives an overview of recent VPN solutions and analyses their scalability and flexibility. VPN solutions are categorized and related to potential customer segments. Furthermore, measurements based on a large north-American carrier's backbone are presented. The results show that provision of a moderate number of large VPNs puts higher constraints on the provider network than a large number of moderately sized VPNs.

In active and programmable networks, packet processing could be accomplished in the router within the data path. For efficient resource allocation in such networks, the packet scheduling schemes should consider multiple resources such as CPU and memory in addition to the bandwidth to improve overall performance. The inherent unpredictability of processing times of active packets poses a significant challenge in CPU scheduling. It has been identified that unlike bandwidth scheduling, prior estimation of CPU requirements of a packet is very difficult since it is platform dependent and it also depends on processing load at the time of execution and operating system scheduling etc. This paper presents an adaptive solution for estimating the processing requirements of active flows efficiently and accurately. The estimation process has been used in our composite scheduling algorithm called CBCS/sup WFQ/ to estimate processing requirement. The performances of the estimation process for our composite scheduler have been analyzed through simulation works.

Parallelized FTP (P-FTP) approach, attempts to solve the problem of slow downloads of large multimedia files while optimizing the utilization of mirror servers. The approach presented in this paper downloads a single file from multiple mirror servers simultaneously, where each mirror server transfers a portion of the file. P-FTP server calculates the optimum division of the file for efficient transfer. The dynamic monitoring ability of P-FTP maintains the file transfer process at the optimized level no matter how abruptly network and mirror server characteristics change.

This paper provides mobility estimation and prediction for a variant of GSM network which resembles an adhoc wireless mobile network where base stations and users are both mobile. We propose using Robust Extended Kalman Filter (REKF)as a location heading altitude estimator of mobile user for next node (mobile-base station)in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm can successfully track the mobile users with less system complexity as it requires either one or two closest mobile-basestation measurements. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model. Through simulation, we show the accuracy and simplicity in implementation of our prediction algorithm.

This paper examines the issues and problems in providing QoS in wireless environments. The design and implementation of the mobile wireless control load protocol is presented in this paper. The results from experimentation with our Free-BSD prototype implementation of MWCLP demonstrate that utilizing the available best effort bandwidth whilst preserving the privilege flow requirements can be achieved, at the same time finding an efficient solution to communicate to all terminals in the wireless subnet to avoid the shadow terminals problem associated with wireless networks, without resorting to the inefficient RTS-CTS handshaking available in wireless networks. Through experiments we have shown that the protocol can interwork with host mobility in heterogeneous wireless subnets.

The recent auctioning of wireless bandwidth in various countries indicates that the service provided by Telcos over these networks will be very expensive. A new type of architecture is currently evolving as a low cost alternative cellular wireless service by allowing the users to play the role of a wireless network operator. Some users of the current Internet may install a low cost base station based on their needs and interconnection between these base stations will form a grid of wireless Internet. This paper provides a survey on related work that either uses ad-hoc mobile networking or a combination of fixed and wireless networking. Finally we present an alternate architecture and describe new issues that arise from this new type of architecture.

We describe a Java implementation of a policy based bandwidth management system using the standard policy protocols and an interface to the Linux DiffServ implementation and demonstrate the capability of our implementation in supporting policy based dynamic resource allocations in enterprise networks.

This paper proposes an extension to the standard subnet bandwidth manager (SBM). The extension controls the best effort traffic on the subnet to minimize its impact on the QoS of the RSVP (resource reservation protocol) flows. The extension is achieved by adding extra messages to the standard RSVP that enables the SBM to signal the hosts about the available bandwidth. The hosts then control the rate of best effort traffic accordingly to avoid excessive collisions on the subnet. The implementation of the proposed extension on a shared Ethernet LAN demonstrates the benefits of this extension.

In absence of any link layer traffic controls or priority-queuing mechanism in LAN infrastructure (such as shared media LAN), the subnet bandwidth management based approach of managing bandwidth is limited to only the total amount of traffic load imposed by RSVP associated flows. In such cases no mechanism is available to separate RSVP flows from best effort traffic. This brings the usefulness of the subnet bandwidth manager into question. This uses a combination of integrated services of a specific link layer model based on RSVP and an IP rate-control approach for best effort traffic to manage intra-domain traffic in a differentiated services network.

The term lip synchronisation applies to synchronising audio with the movement of a person's lips. If the data is out of synchronisation then human perception tends to identify the presentation as artificial, strange and annoying. We propose metrics to evaluate the degree of synchronisation for interactive multimedia communications. Through experimental results we demonstrate that such metrics are quite useful in providing quality of service (QoS) feedback to end users. A quantitative evaluation of the degree of synchronisation between loosely coupled multimedia tools is performed based on mean absolute deviation calculations and a subjective evaluation.

Most research on network management concentrates on technical issues. We discuss the possibility of using a network management system as an integrated environment for capacity planning of local area networks. The paper gives an overview of network management systems and relevance to capacity planning, and this also looks into the capabilities of the current network management frameworks of Internet and OSI and their suitability for the capacity planning task.>

The maximum likelihood estimator is the optimal estimator of the direction of sources, but it requires the minimization of a complex, multimodal, multidimensional cost function. A neural optimization procedure is presented that does not require an initial estimate of the direction of the sources and offers the potential of real-time solutions to the direction of arrival problem by utilizing the fast relaxation properties of the Hopfield network. A modification based on an iterated descent procedure is introduced into the Hopfield model dynamic equation to increase the probability of convergence to the global optimum. The algorithms are implemented on an array of closely coupled transputers that perform the random asynchronous neural updates in parallel. The mapping is achieved using a technique called chaotic relaxation. Simulation results are presented to characterize the performance of the neural approach in terms of the variance of the estimates of source directions and the time required for the computation of the estimates.>

The bearing estimation problem is mapped onto the Liapunov energy function of the Hopfield model neural network. However, the Hopfield model implements a gradient descent algorithm, and, in common with all such algorithms, it is liable to find a local minimum rather than the desired global minimum. To overcome this problem three modifications, gain annealing, iterated descent, and stochastic networks, have been proposed. The modifications to the neural algorithm are outlined and simulated, and results are presented to show their convergence properties in the context of the bearing estimation problem.>

Two modifications to the neural-network algorithm originally proposed by J.J. Hopfield (1982), gain annealing and iterated descent, are proposed that yield better convergence to the global minimum. Simulation results are presented to illustrate the performance of the proposed algorithm for bearing estimation.>