Unmanned Aerial Vehicles (UAVs) play an increasingly critical role in Intelligence, Surveillance, and Reconnaissance (ISR) missions such as border patrolling and criminal detection due to their ability to access remote areas and transmit real-time imagery to servers. However, UAVs face limitations in payload, power, and communication bandwidth, necessitating selective data transmission strategies. While traditional methods strive to preserve maximal information in transferred video frames, missing the fact that only certain parts of images/video frames are relevant for Object Detection and Tracking (OD/OT) in ISR missions. This paper adopts a different perspective and offers an alternative AI-driven scheduling policy that prioritizes selecting regions of the image that significantly contribute to the mission objective. The key idea is tiling the image into small patches and developing a Deep Reinforcement Learning (DRL) framework that assigns higher transmission probabilities to patches that present higher overlaps with the detected object of interest while penalizing sharp transitions over consecutive frames to promote smooth scheduling shifts. Although we used YOLOv8 object detection and UDP transmission protocols as a benchmark testing scenario, the idea is general and applicable to different transmission protocols and OD/OT methods. To further boost the system's performance and avoid OD errors for cluttered image patches, we integrate it with inter-frame interpolations. With this method, we achieved about 45% improvement in terms of OD accuracy for the proposed method (F1 score:98%) compared to random selection (F1 score: 53%) when the transmission budget is 50% (we afford sending half of the image patches). Under an extremely constrained transmission budget (5%), this gain can be as high as 87%. The only cost for such improvement is a feedback channel from the ground server to drones.

Both supervised contrastive learning and traditional cross entropy based supervised learning methods have demonstrated strong performance in classification tasks. The combination of those two methods has the potential to achieve better performance for classification tasks. In this paper, we introduce Supervised Contrastive Disentanglement for Classification (CoDiC), a novel method that combines supervised contrastive learning and cross entropy to explicitly disentangle latent features into class logits and instance-specific embeddings. This disentanglement enables CoDiC to isolate class information while suppressing spurious or instance-dependent variations, leading to enhanced performance. We evaluate CoDiC across a diverse set of datasets, including CIFAR-10, CIFAR-100, and the histopathology dataset CAMELYON16. CoDiC consistently outperforms baseline methods, including cross-entropy, SimCLR, and SupCon. Notably, on CIFAR-100, CoDiC achieves a top-1 accuracy of 80.1%, outperforming the next-best method by 3.6%, and demonstrating the effectiveness of disentanglement for robust classification in different domains.

Large language models (LLMs) are fast becoming ubiquitous and have shown impressive performance in various natural language processing (NLP) tasks. Annotating data for downstream applications is a resource-intensive task in NLP. Recently, the use of LLMs as a cost-effective data annotator for annotating data used to train other models or as an assistive tool has been explored. Yet, little is known regarding the societal implications of using LLMs for data annotation. In this work, focusing on hate speech detection, we investigate how using LLMs such as GPT-4 and Llama-3 for hate speech detection can lead to different performances for different text dialects and racial bias in online hate detection classifiers. We used LLMs to predict hate speech in seven hate speech datasets and trained classifiers on the LLM annotations of each dataset. Using tweets written in African-American English (AAE) and Standard American English (SAE), we show that classifiers trained on LLM annotations assign tweets written in AAE to negative classes (e.g., hate, offensive, abuse, racism, etc.) at a higher rate than tweets written in SAE and that the classifiers have a higher false positive rate towards AAE tweets. We explore the effect of incorporating dialect priming in the prompting techniques used in prediction, showing that introducing dialect increases the rate at which AAE tweets are assigned to negative classes.

The Black Lives Matter (BLM) movement seeks to spread awareness and fight against social and racial injustice. In 2020, BLM-related discussions surged on social media after the death of George Floyd and the protests that followed. Previous works have qualitatively analyzed the scaling, dynamics, and topics of BLM discussions on social media. However, very few works have studied the offensive content, the emotions expressed, and the topics of offensive discussions in BLM-related discussions. In this measurement study, to examine offensive language and emotion, we conduct a largescale study of BLM discussions on Twitter. We first develop a classifier that uses sentiment representation to aid offensive language detection. We then develop an emotion classifier based on deep attention fusion with sentiment features to classify emotions. We further use topic modeling to analyze the topics of offensive tweets. Our analysis of over 20 million tweets revealed that offensive tweets peeked in the weeks following George Floyd’s death and rapidly decreased but remained stable. The analysis further revealed that negative emotions were the most expressed emotions. Offensive reply network analysis reveals that most offensive replies are unidirectional. Our contribution in this work is five-fold: (1) We identify offensive content during BLM protests; (2) we identify online emotions that were significant in the offensive and non-offensive content during the protests; (3) we assess the characteristics of users who replied offensively and those who are the recipients of offensive content; (4) we assess emotion dynamics across offenders and recipients; (5) we identify the hot topics that most drove the offensive content on Twitter. Our work offers important implications for content moderation and the conscious and unconscious attitudes towards the black/African American community.

Abstract Despite the widespread adoption of Web services in modern computing applications, there remains a lack of a systematic approach that can guide service developers in creating appealing services. This article addresses this gap by presenting findings from a comprehensive study of RapidAPI web services, the largest service marketplace, and their integration into GitHub-hosted applications. We collected data on over 16K RapidAPI services and 19K corresponding GitHub repositories invoking these services, evaluating each service based on metrics such as latency, reliability, pricing, followers, aggregate ratings community support, and provider support. Our analysis examines how these metrics influence service popularity and usage patterns on GitHub. We manually analyzed 800 GitHub repositories and identified developers’ service selection preferences and integration patterns, considering alternative services and their features. We then classified GitHub developers based on proficiency levels to understand how developers’ levels of proficiency impact their service selection and integration strategies. Moreover, we examined the metrics influence for matured set of repositories by excluding those intended solely for practice purposes. Our findings offer insights for service marketplaces to recommend integration-friendly services and for service developers to create offerings tailored to real-world application needs.

Reconstructing keyboard input through side-channel attacks has posed significant threats to user security. While conventional keystroke eavesdropping attacks have demonstrated effectiveness using side channels such as acoustic signals, they are usually shorter in range and can be significantly affected by environmental noises. In this paper, we proposeLaserKey, a novel keystroke eavesdropping technique that leverages the long-range and noise-resistant nature of lasers to achieve a more stealthy side-channel attack. We utilize laser sensors to accurately capture the subtle vibrations induced on laptop screens by keystrokes, and innovatively design a laser-driven deep learning-based keystroke recognition model with the inputs being the Mel-frequency Cepstral Coefficien (MFCC), Time Difference of Arrival (TDoA), and amplitude features extracted from such vibration signals. Through systematic experiments, we demonstrate thatLaserKeyachieves a 92.2% single-key recognition accuracy. By combining multiple single-key recognition capabilities based on this, we then realize the end-to-end word-level recognition. Moreover, to mitigate the recognition errors caused by the changes in keystroke positions, we introduce a meta-learning based domain generalization approach for achieving robust laser position calibration. Results show thatLaserKeyachieves as low as 3% character error rate (CER) for word-level recognition, proving its effectiveness for long-range and high-accuracy keystroke eavesdropping, and highlighting the necessity for countermeasures in the future.

Service registry, a key component of the service-oriented architecture (SOA), aids software developers in discovering services that meet specific functionality requirements. Recent years have witnessed the transition from the traditional service registries to its successor, the Service Marketplaces, which involves deeper engagement in the SOA software lifecycle and offers additional features, such as service request delegation and monitoring of services' Quality of Service (QoS). However, by analyzing developers' questions posted on online Q&A forums, we found that many developers struggle with such transition, leading to development inefficiencies and even security vulnera-bilities. This paper presents the first empirical study aimed at uncovering the issues developers face with marketplaces, particularly those arising from the transition. Through a meticulous process of manually labeling and analyzing developers' questions, we develop a taxonomy of these issues, summarize the impacts caused by the transition, and provide actionable suggestions to App developers, service providers, and marketplaces. Utilizing the labeled questions and our insights, we fine-tune a Large Language Model (LLM) for providing answers to similar questions raised by developers and helping service providers and marketplaces extract useful information from these questions, such as service outages and key leakages. Our evaluation of the model's performance in answering and extracting pertinent information from a set of real-world questions demonstrates its effectiveness: it accurately classified 85 % of the queries and successfully identified 88 % of service names and 77 % of key leakages. As the first empirical study in this domain, this work not only aids developers in navigating the transition more effectively but also sheds light on the under explored issue of service registry evolution, offering valuable insights for researchers.

Voice Personal Assistants (VPA) are becoming popular entry points to control connected devices in an IoT environment, e.g., by invoking Amazon Alexa voice-apps (called skills) to turn on/off lights through voice commands. Amazon Alexa platform allows third-party developers to build skills and publish them to marketplaces, which greatly extends the functionalities of VPA. Despite the many convenient features, there are increasing security and safety concerns about VPA-controlled IoT systems. Previous research demonstrated the prevalence of potentially malicious or problematic skills in the marketplace. However, existing works mainly focus on non-IoT skills (e.g., skills under the Kids and Health categories). The security and safety risks of IoT skills are largely under-explored.

With the emergence of the open API ecosystem, third-party developers can publish their APIs on the API marketplace, significantly facilitating the development of cutting-edge features and services. The RapidAPI platform is currently the largest API marketplace and it provides over 40,000 APIs, which have been used by more than 4 million developers. However, such open API also raises security and privacy concerns associated with APIs hosted on the platform. In this work, we perform the first large-scale analysis of 32,089 APIs on the RapidAPI platform. By searching in the GitHub code and Android apps, we find that 3,533 RapidAPI keys, which are important and used in API request authorization, have been leaked in the wild. These keys can be exploited to launch various attacks, such as Resource Exhaustion Running, Theft of Service, Data Manipulation, and User Data Breach attacks. We also explore risks in API metadata that can be abused by adversaries. Due to the lack of a strict certification system, adversaries can manipulate the API metadata to perform typosquatting attacks on API URLs, impersonate other developers or renowned companies, and publish spamming APIs on the platform. Lastly, we analyze the privacy non-compliance of APIs and applications, e.g., Android apps, that call these APIs with data collection. We find that 1,709 APIs collect sensitive data and 94% of them dont provide a complete privacy policy. For the Android apps that call these APIs, 50% of them in our study have privacy non-compliance issues.

As the advent of autonomous vehicle (AV) technology revolutionizes transportation, it simultaneously introduces new vulnerabilities to cyber-attacks, posing significant challenges to vehicle safety and security. The complexity of these systems, coupled with their increasing reliance on advanced computer vision and machine learning algorithms, makes them susceptible to sophisticated AV attacks. This paper explores the potential of Large Multimodal Models (LMMs) in identifying Natural Denoising Diffusion (NDD) attacks on traffic signs. Our comparative analysis show the superior performance of LMMs in detecting NDD samples with an average accuracy of 82.52% across the selected models compared to 37.75% for state-of-the-art deep learning models. We further discuss the integration of LMMs within the resource-constrained computational environments to mimic typical autonomous vehicles and assess their practicality through latency benchmarks. Results show substantial superiority of GPT models in achieving lower latency, down to 4.5 seconds per image for both computation time and network latency (RTT), suggesting a viable path towards real-world deployability. Lastly, we extend our analysis to LMMs’ applicability against a wider spectrum of AV attacks, particularly focusing on the Automated Lane Centering systems, emphasizing the potential of LMMs to enhance vehicular cybersecurity.

Memory Electromagnetic Radiation (EMR) allows attackers to manipulate the DRAM of infiltrated systems to leak sensitive secret information. Although most of the existing works have demonstrated its feasibility, practical concerns, such as the ideal electromagnetic environment and stationary attacking layout, make the covert channel attack less convincing, especially in vulnerable sites such as offices and data centers. This work removes the above impractical assumptions to uncover the potential of memory EMR by proposing the first parallel EMR covert communication protocol. Our design reshapes the current "1-to-1" covert communication mode to "n-to-1" mode via a novel pattern-based 2-dimensional symbol encoding scheme, allowing multiple victim computers to simultaneously perform data exfiltration to one attacker (the receiver) without mutual interference. Meanwhile, this novel scheme design also enables the very first mobile attacker, i.e., a smartphone connected to a software-defined radio (SDR) dongle, to capture parallel memory EMR signals in a volatile environment. Extensive experiments are conducted to verify the performance in a volatile environment with different parameter configurations, distances, motion modes, shielding materials, orientations, hardware configurations, and SDR platforms. Our experimental results demonstrate that FreeEM can support up to 4 parallel memory EMR transmissions to achieve an overall throughput of 625Kbps and a decoding accuracy of 96.88%. The maximum communication distance can reach up to 20 meters.

Online hate is an escalating problem that negatively impacts the lives of Internet users, and is also subject to rapid changes due to evolving events, resulting in new waves of online hate that pose a critical threat. Detecting and mitigating these new waves present two key challenges: it demands reasoning-based complex decision-making to determine the presence of hateful content, and the limited availability of training samples hinders updating the detection model. To address this critical issue, we present a novel framework called HateGuard for effectively moderating new waves of online hate. HateGuard employs a reasoning-based approach that leverages the recently introduced chain-of-thought (CoT) prompting technique, harnessing the capabilities of large language models (LLMs). HateGuard further achieves prompt-based zero-shot detection by automatically generating and updating detection prompts with new derogatory terms and targets in new wave samples to effectively address new waves of online hate. To demonstrate the effectiveness of our approach, we compile a new dataset consisting of tweets related to three recently witnessed new waves: the 2022 Russian invasion of Ukraine, the 2021 insurrection of the US Capitol, and the COVID-19 pandemic. Our studies reveal crucial longitudinal patterns in these new waves concerning the evolution of events and the pressing need for techniques to rapidly update existing moderation tools to counteract them. Comparative evaluations against state-of-the-art approaches illustrate the superiority of our framework, showcasing a substantial 10.59% to 88% improvement in detecting the three new waves of online hate. Our work highlights the severe threat posed by the emergence of new waves of online hate and represents a paradigm shift in addressing this threat practically.

Amazon Alexa is one of the largest Voice Personal Assistant (VPA) platforms and it allows third-party developers to publish their voice apps, named skills, to the Alexa skill store. To satisfy the needs of European users, Amazon Alexa has established multiple skill marketplaces in Europe and allows developers to publish skills in their native languages. Skills in European marketplaces are required to comply with GDPR (General Data Protection Regulation), which imposes strict obligations on data collection and processing. Skills that involve data collection should provide a privacy policy to disclose the data practice to users and meet GDPR requirements.

Mobile crowdsensing (MCS) can promote data acquisition and sharing among mobile devices. Traditional MCS platforms are based on a triangular structure consisting of three roles: data requester, worker (i.e. , sensory data provider) and MCS platform. However, this centralized architecture suffers from poor reliability and difficulties in guaranteeing data quality and privacy, even provides unfair incentives for users. In this paper, we propose a blockchain-based MCS platform, namely BlockSense, to replace the traditional triangular architecture of MCS models by a decentralized paradigm. To achieve the goal of trustworthiness of BlockSense, we present a novel consensus protocol, namely Proof-of-Data (PoD), which leverages miners to conduct useful data quality validation work instead of “useless” hash calculation. Meanwhile, in order to preserve the privacy of the sensory data, we design a homomorphic data perturbation scheme, through which miners can verify data quality without knowing the contents of the data. We have implemented a prototype of BlockSense and conducted case studies on campus, collecting over 7,000 data from workers' mobile phones. Both simulations and real-world experiments show that BlockSense can not only improve system security, preserve data privacy and guarantee incentives fairness, but also achieve at least 5.6x faster than Ethereum smart contracts in verification efficiency.

Recent studies regarding the COVID-19 pandemic have revealed the widespread propagation of hateful content during this period. While significant research has focused on COVID-19-related online hate in text (e.g., text-based tweets), the role of memes in propagating online hate during the pandemic has been largely overlooked. Memes are a popular mechanism used by Internet users to convey their thoughts and opinions on a variety of topics. However, memes have emerged as an important mechanism through which ideologically potent and hateful content spreads on social media platforms. In this work, we focus on investigating the role of memes in the propagation of online hate during the COVID-19 pandemic. We first collect a novel dataset of 4,001 COVID-19-related hateful memes and their replies over a 3-year period from Twitter. Then, we carry out the first large-scale investigation into the impact of these memes on Twitter users, by studying the psychological reactions of Twitter users to these memes using various text analysis methods. We find that COVID-19-related hateful memes have a significantly greater negative impact on Twitter users in comparison to text-based hateful tweets, and increasing negativity towards such memes over the 3-year period. Our new dataset of COVID-19-related hateful memes and findings from our work pave the way for studying the dissemination and moderation of COVID-19-related online hate through the medium of memes.

Emergent Cyber-Physical Systems (CPSs) like VANETs and UAV swarms are expected to fulfill essential roles in critical infrastructure domains. This increasing utility and the present nurturing economic conditions that enable their cost-effective deployment herald a period of significant growth and adoption. In addition, these systems are increasingly required to support complex data-intensive and QoS-sensitive applications in challenging operating conditions. However, the growth of these systems is limited by current Internet protocols that do not comprehensively meet the communication requirements of these systems. In this work, we present the design and evaluation of Software-Defined NAmed-data enabled Publish-subscribe (SNAP) communication framework that can effectively meet the communication requirements of demanding applications in emergent CPSs.

Given the widespread use of Python and its sustaining impact, the security and reliability of the Python runtime system is highly and broadly critical. Yet with real-world bugs in Python runtimes being continuously and increasingly reported, technique/tool support for automated detection of such bugs is still largely lacking. In this paper, we present PyRTFuzz, a novel fuzzing technique/tool for holistically testing Python runtimes including the language interpreter and its runtime libraries. PyRTFuzz combines generationand mutation-based fuzzing at the compiler- and application-testing level, respectively, as enabled by static/dynamic analysis for extracting runtime API descriptions, a declarative, specification language for valid and diverse Python code generation, and a custom type-guided mutation strategy for format/structure-aware application input generation. We implemented PyRTFuzz for the primary Python implementation (CPython) and applied it to three versions of the runtime. Our experiments revealed 61 new, demonstrably exploitable bugs including those in the interpreter and most in the runtime libraries. Our results also demonstrated the promising scalability and cost-effectiveness of PyRTFuzz and its great potential for further bug discovery. The two-level collaborative fuzzing methodology instantiated in PyRTFuzz may also apply to other language runtimes especially those of interpreted languages.

The Amazon Alexa marketplace is the largest Voice Personal Assistant (VPA) platform with over 100,000 voice applications (i.e., skills) published to the skills store. In an effort to maintain the quality and trustworthiness of voice-apps, Amazon Alexa has implemented a set of policy requirements to be adhered to by third-party skill developers. However, recent works reveal the prevalence of policy-violating skills in the current skills store. To understand the causes of policy violations in skills, we first conduct a user study with 34 third-party skill developers focusing on whether they are aware of the various policy requirements defined by the Amazon Alexa platform. Our user study results show that there is a notable gap between VPA's policy requirements and skill developers' practices. As a result, it is inevitable that policy-violating skills will be published.

In recent years, the field of Natural Language Processing (NLP) has witnessed a groundbreaking transformation with the emergence of large language models (LLMs). ChatGPT stands out as an example among these LLM models captivating considerable public interest due to its impressive language generation capabilities. Researchers have been exploring the potential of using ChatGPT for data annotation tasks, aiming to discover more timesaving and cost-effective approaches. In this paper, we present a comprehensive evaluation of ChatGPT's data annotation capabilities across ten diverse datasets covering various subject areas and varied number of classes. To ensure the quality of our evaluation, we leveraged datasets that were previously annotated by human experts, providing a reliable benchmark for comparison. Through rigorous experimentation, we assessed the impact of different prompt strategies and model configurations on the annotation performance. Our findings emphasize the capability of ChatGPT in handling most data annotation tasks achieving average accuracy of 78.2% across various tasks. The banking queries dataset stands out with an impressive 95.9% accuracy, while emotions classification presents challenges, yielding an accuracy of 57.5%. Our evaluation also highlights the impact of prompt strategies on annotation performance and reveals significant performance differences between GPT models, with “gpt-4” achieving higher accuracy 79.2% on average compared to “gpt-3.5” of 74.6%. Our research provides valuable insights into the capabilities and limitations of ChatGPT in automating data annotation tasks.

During the global COVID-19 pandemic, people utilized social media platforms, especially Twitter, to spread and express opinions about the pandemic. Such discussions also drove the rise in COVID-related offensive speech. In this work, focusing on Twitter, we present a comprehensive analysis of COVID-related offensive tweets and their targets. We collected a COVID-19 dataset with over 747 million tweets for 30 months and fine-tuned a BERT classifier to detect offensive tweets. Our offensive tweets analysis shows that the ebb and flow of COVID-related offensive tweets potentially reflect events in the physical world. We then studied the targets of these offensive tweets. There was a large number of offensive tweets with abusive words, which could negatively affect the targeted groups or individuals. We also conducted a user network analysis, and found that offensive users interact more with other offensive users and that the pandemic had a lasting impact on some offensive users. Our study offers novel insights into the persistence and evolution of COVID-related offensive tweets during the pandemic

The rocketed population, poor security, and 24/7 online properties make Linux-based Internet of Things (IoT) devices ideal targets for attackers. However, due to the budget constraints and an enormous number of vulnerabilities on such devices, protecting them against attacks is very challenging. Therefore, understanding and detecting IoT malware remote infection, which is before the compromised IoT devices are monetized by adversaries, is crucial to mitigate damages and financial loss caused by IoT malware. In this paper, we conduct an empirical study on a large-scale dataset covering 403,464 samples collected from VirusShare and a large group of IoT honeypots to gain a deep insight into the characteristics of IoT malware remote infection. We share detailed statistics of shell commands found in our dataset, highlight malicious behaviors performed through those commands, investigate current states of fingerprinting methods of those commands, and offer a taxonomy of shell commands by introducing the notion of infection capability. To demonstrate the usefulness of the knowledge gained from our study, we develop an approach to detect ongoing remote infection activities based on infection capabilities. Our evaluation shows that our detection approach can achieve a 99.22% detection rate for remote infections in the wild and introduce small performance overhead.

A large number of photos shared online often contain private user information, which can cause serious privacy breaches when viewed by unauthorized users. Thus, there is a need for more efficient privacy control that requires automatic detection of users' private photos. However, the automatic detection of users' private photos is a challenging task, since different users may have different privacy concerns and a generalized one-size-fits-all approach for private photo detection would not be suitable for most users. User-specific detection of private photos should, therefore, be investigated. Furthermore, for effective privacy control, the exact sensitive regions in private photos need to be pinpointed, so that sensitive content can be protected via different privacy control methods. In this paper, we propose a novel system, AutoPri, to enable automatic and user-specific content-based photo privacy control in online social networks. We collect a large dataset of 31, 566 private and public photos from real-world users and present important observations on photo privacy concerns. Our system can automatically detect private photos in a user-specific manner using a detection model based on a multimodal variational autoencoder and pinpoint sensitive regions in private photos with an explainable deep learning-based approach. Our evaluations show that AutoPri can effectively determine user-specific private photos with high accuracy (94.32%) and pinpoint exact sensitive regions in them to enable effective privacy control in user-centered online social networks.

Pre-trained multilingual language models play an important role in cross-lingual natural language understanding tasks.However, existing methods did not focus on learning the semantic structure of representation, and thus could not optimize their performance.In this paper, we propose Multi-level Multilingual Knowledge Distillation (MMKD), a novel method for improving multilingual language models.Specifically, we employ a teacher-student framework to adopt rich semantic representation knowledge in English BERT.We propose token-, word-, sentence-, and structure-level alignment objectives to encourage multiple levels of consistency between source-target pairs and correlation similarity between teacher and student models.We conduct experiments on crosslingual evaluation benchmarks including XNLI, PAWS-X, and XQuAD.Experimental results show that MMKD outperforms other baseline models of similar size on XNLI and XQuAD and obtains comparable performance on PAWS-X.Especially, MMKD obtains significant performance gains on low-resource languages.

We live in a world where social and economic disparity has led to many 'disconnects' that collectively provide unfair bias towards the top 1 % of of the wealthiest individuals and elite industry behemoths. This bias can be seen in the technical world leading to ‘disconnects' including limited access to broadband access and advanced technology. In this paper we identify hidden 'disconnects' that we believe are stifling innovation. We introduce an abstraction called 'application systems' (APPSYS) which, with more development, could move our Nation's disparate forms of large scale technology use (Facebook, Tik- Tok, and ‘the Internet’) to a technology fabric that includes current Internet applications but supplemented with an overlay of APPSYSs forming a ‘tech-fabric’ that can benefit all citizens, and that promotes citizens to contribute to the evolution of the concept. The ‘fabric’ is potentially a merging of our Nation's Critical Infrastructure with commercial innovation. These two worlds historically have been isolated however there are clearly overlaps and synergies that can no longer go unchecked. As the Nation enters the ‘Age of Machines‘, this fabric would provide the unique architectural model required to facilitate more secure, dependable Critical Infrastructure in a manner that reflects Internet-like attributes such as transparency, and policies/protocols for joining individual Autonomous Systems to form a unified system. Critical to the concept is an APPSYS which will have incentives to ‘contribute to the greater good’. In this paper, we summarize the APPSYS concept and identify necessary incremental mandates by the FCC to broaden access to advanced technology for all citizens primarily by shifting the FCC's bias away from current policies that promote the disconnect between economic success centered on the allocation of wealth that has seen the allocation move from 80- 20 (80 % of the wealth is owned by 20 % of the population to the current 90–10 allocation split.

Hate speech datasets contain bias which machine learning models propagate. When these models classify tweets written in African American English (AAE), they predict AAE tweets as hate/abusive at a higher rate than tweets written in Standard American English (SAE). This paper assesses bias in language models fine-tuned for hate speech detection and the effectiveness of adversarial learning in reducing such bias. We introduce AAEBERT, a pre-trained language model for African American English obtained by re-training BERT-base on AAE tweets. AAEBERT is used to extract the representation of each tweet in the various hate speech datasets and to classify tweets into two classes - AAE dialect and non-AAE dialect. A three-layer feedforward neural network that takes the representation from AAEBERT and a dialect label as input is used as the adversarial network for debiasing. We evaluate bias in language models fine-tuned for hate speech detection. Then assess the effectiveness of adversarial debiasing in these models by comparing results before and after adversarial debiasing is applied. Analysis reveals that the fine-tuned models are biased towards AAE, and adversarial debiasing is effective in reducing bias.

As the COVID-19 pandemic scattered businesses and their workforces into new scales of remote work, vital security concerns arose surrounding remote access. Bring Your Own Device (BYOD) also plays a growing role in the ability of companies to support remote workforces. As more enterprises embrace concepts of zero trust in their network security posture, access control policy management problems become a more significant concern as it relates to BYOD security enforcement. This BYOD security policy must enable work from home, but enterprises have a vested interest in maintaining the security of their assets. Therefore, the BYOD security policy must strike a balance between access, security, and privacy, given the personal device use. This paper explores the challenges and opportunities of enabling zero trust in BYOD use cases. We present a BYOD policy specification to enable the zero trust access control known as BYOZ. Accompanying this policy specification, we have designed a network architecture to support enterprise zero trust BYOD use cases through the novel incorporation of continuous authentication & authorization enforcement. We evaluate our architecture through a demo implementation of BYOZ and demonstrate how it can meet the needs of existing enterprise networks using BYOD.

Network measurement provides operators an efficient tool for many network management tasks such as performance diagnosis, traffic engineering and intrusion prevention. However, with the rapid and continuous growth of traffic speed, it needs more computing and memory resources to monitor traffic in per-flow or per-packet granularity. Sample-based measurement systems (e.g., NetFlow, sFlow) have been developed to perform coarse-grained measurement, but they may miss part of records, especially for mice flows, which are important for some network management tasks (e.g., anomaly detection, performance diagnosis). To address these issues, data streaming algorithms such as hash tables and sketches have been introduced to balance the trade-off among accuracy, speed, and memory usage. In this article, we present a systematic survey of various data structures, algorithms and systems which have been proposed in recent years to perform fine-grained measurement for high-speed networks. We organize these methods and systems from a software-defined perspective. In particular, we abstract fine-grained network measurement into three-layer architecture. We introduce the responsibility of each layer and categorize existing state-of-the-art works into this architecture. Finally, we conclude the article and discuss the future directions of fine-grained network measurement.

Convergecast is a fundamental operation in wireless sensor networks (WSNs). To support long-term deployment of WSNs, sensor nodes normally operate at low-duty-cycles. However, the low-duty-cycle operation significantly reduces the communication chance between nodes. Consequently, the risk of data collisions significantly increases when multiple senders transmit packets to a receiver during its very short active period. This problem further causes not only wasted packet retransmissions, but also a large delivery latency. Under such conditions, collision-free medium access is more appealing than recovering after collision for low-duty-cycle WSNs. In this work, we propose anincast-collision-free convergecast protocol, named iCore, to address the many-to-one collision problem in low-duty-cycle WSNs. iCore employs the dynamic forwarding technique, establishes a non-conflicting schedule for efficient convergecast, and improves the channel utilization by allowing senders to opportunistically transmit packets once detecting unused slots. Specifically, we design efficient forwarder assignment and forwarding optimization algorithms that ensure low end-to-end latency under diverse data traffic types. Through comprehensive performance evaluations, we demonstrate that, compared with the baseline protocol, iCore effectively minimizes the end-to-end delay by 25% ~ 57% and maintains high delivery ratio and energy efficiency for different many-to-one convergecast scenarios.

Binary code similarity detection is a fundamental technique for many security applications such as vulnerability search, patch analysis, and malware detection. There is an increasing need to detect similar code for vulnerability search across architectures with the increase of critical vulnerabilities in IoT devices. The variety of IoT hardware architectures and software platforms requires to capture semantic equivalence of code fragments in the similarity detection. However, existing approaches are insufficient in capturing the semantic similarity. We notice that the abstract syntax tree (AST) of a function contains rich semantic information. Inspired by successful applications of natural language processing technologies in sentence semantic understanding, we propose a deep learning-based AST-encoding method, named ASTERIA, to measure the semantic equivalence of functions in different platforms. Our method leverages the Tree-LSTM network to learn the semantic representation of a function from its AST. Then the similarity detection can be conducted efficiently and accurately by measuring the similarity between two representation vectors. We have implemented an open-source prototype of ASTERIA. The Tree-LSTM model is trained on a dataset with 1,022,616 function pairs and evaluated on a dataset with 95,078 function pairs. Evaluation results show that our method outperforms the AST-based tool Diaphora and the-state-of-art method Gemini by large margins with respect to the binary similarity detection. And our method is several orders of magnitude faster than Diaphora and Gemini for the similarity calculation. In the application of vulnerability search, our tool successfully identified 75 vulnerable functions in 5,979 IoT firmware images.

With the dramatic growth of hate speech on social media during the COVID-19 pandemic, there is an urgent need to detect various hate speech effectively. Existing methods only achieve high performance when the training and testing data come from the same data distribution. The models trained on the traditional hateful dataset cannot fit well on COVID-19 related dataset. Meanwhile, manually annotating the hate speech dataset for supervised learning is time-consuming. Here, we propose COVID-HateBERT, a pre-trained language model to detect hate speech on English Tweets to address this problem. We collect 200M English tweets based on COVID-19 related hateful keywords and hashtags. Then, we use a classifier to extract the 1.27M potential hateful tweets to re-train BERT-base. We evaluate our COVID-HateBERT on four benchmark datasets. The COVID-HateBERT achieves a 14.8%-23.8% higher macro average F1 score on traditional hate speech detection comparing to baseline methods and a 2.6%-6.73% higher macro average F1 score on COVID-19 related hate speech detection comparing to classifiers using BERT and BERTweet, which shows that COVID-HateBERT can generalize well on different datasets.

Emerging Internet of Things (IoT) systems leverage connected devices to enable intelligent and automated functionalities. Despite the benefits, there exist privacy risks of network traffic, which have been studied by the previous research. However, with the current privacy inference remaining at the event-level, potential privacy risks are underestimated, which, as our study shows, can be much higher than previously reported through app-level traffic analysis. A key observation of our research is that IoT event-triggered traffic is generated by apps, which often adopt an if-trigger-then-action (trigger-action) programming paradigm. We utilize this feature to develop fingerprints to differentiate running apps and learn context-rich privacy-sensitive information from apps. In this article, we present a privacy leakage analysis called ALTA to infer running apps in smart home IoT environments. First, ALTA identifies app fingerprints through static analysis and extracts sensitive information from app descriptions and input prompts. Then, through dynamic traffic profiling, it learns traffic fingerprints of apps. Finally, ALTA matches the fingerprints of app and traffic, and thus is able to pinpoint which app is running from IoT traffic at runtime. To demonstrate the feasibility of our approach, we analyze 254 SmartThings applications via program and natural language processing (NLP) analysis. We also perform the app inference evaluation on 31 apps executed in a simulated smart home. The results suggest that ALTA can effectively infer running apps from IoT traffic and learn context-rich information (e.g., health conditions, daily routines, and user activities) from apps with high accuracy.

Securing cyber-physical systems (CPS) against malicious attacks is of paramount importance because these attacks may cause irreparable damages to physical systems. Recent studies have revealed that control programs running on CPS devices suffer from both control-oriented attacks (e.g., code-injection or code-reuse attacks) and data-oriented attacks (e.g., non-control data attacks). Unfortunately, existing detection mechanisms are insufficient to detect runtime data-oriented exploits, due to the lack of runtime execution semantics checking. In this work, we propose Orpheus, a new security methodology for defending against data-oriented attacks by enforcing cyber-physical execution semantics. We first present a general method for reasoning cyber-physical execution semantics of a control program (i.e., causal dependencies between the physical context/event and program control flows), including the event identification and dependence analysis. As an instantiation of Orpheus, we then present a new program behavior model, i.e., the event-aware finite-state automaton (eFSA). eFSA takes advantage of the event-driven nature of CPS control programs and incorporates event checking in anomaly detection. It detects data-oriented exploits if a specific physical event is missing along with the corresponding event dependent state transition. We evaluate our prototype's performance by conducting case studies under data-oriented attacks. Results show that eFSA can successfully detect different runtime attacks. Our prototype on Raspberry Pi incurs a low overhead, taking 0.0001s for each state transition integrity checking, and 0.063s~0.211s for the cyber-physical contextual consistency checking.

Data-oriented attacks manipulate non-control data to alter a program’s benign behavior without violating its control-flow integrity. It has been shown that such attacks can cause significant damage even in the presence of control-flow defense mechanisms. However, these threats have not been adequately addressed. In this survey article, we first map data-oriented exploits, including Data-Oriented Programming (DOP) and Block-Oriented Programming (BOP) attacks, to their assumptions/requirements and attack capabilities. Then, we compare known defenses against these attacks, in terms of approach, detection capabilities, overhead, and compatibility. It is generally believed that control flows may not be useful for data-oriented security. However, data-oriented attacks (especially DOP attacks) may generate side effects on control-flow behaviors in multiple dimensions (i.e., incompatible branch behaviors and frequency anomalies). We also characterize control-flow anomalies caused by data-oriented attacks. In the end, we discuss challenges for building deployable data-oriented defenses and open research questions.

Voice Assistants (VA) such as Amazon Alexa and Google Assistant are quickly and seamlessly integrating into people’s daily lives. The increased reliance on VA services raises privacy concerns such as the leakage of private conversations and sensitive information. Privacy policies play an important role in addressing users’ privacy concerns and informing them about the data collection, storage, and sharing practices. VA platforms (both Amazon Alexa and Google Assistant) allow third-party developers to build new voice-apps and publish them to app stores. Voice-app developers are required to provide privacy policies to disclose their apps’ data practices. However, little is known whether these privacy policies are informative and trustworthy or not on emerging VA platforms. On the other hand, many users invoke voice-apps through voice and thus there exists a usability challenge for users to access these privacy policies.

With the emergence of the voice personal assistant (VPA) ecosystem, third-party developers are allowed to build new voice-apps are called skills in the Amazon Alexa platform and actions in the Google Assistant platform, respectively. For the sake of brevity, we use the term skills to describe voice-apps including Amazon skills and Google actions, unless we need to distinguish them for different VPA platforms. and publish them to the skills store, which greatly extends the functionalities of VPAs. Before a new skill becomes publicly available, that skill must pass a certification process, which verifies that it meets the necessary content and privacy policies. The trustworthiness of skill certification is of significant importance to platform providers, developers, and end users. Yet, little is known about how difficult it is for a policy-violating skill to get certified and published in VPA platforms. In this work, we study the trustworthiness of the skill certification in Amazon Alexa and Google Assistant platforms to answer three key questions: 1) Whether the skill certification process is trustworthy in terms of catching policy violations in third-party skills. 2) Whether there exist policy-violating skills published in their skills stores. 3) What are VPA users' perspectives on the skill certification and their vulnerable usage behavior when interacting with VPA devices? Over a span of 15 months, we crafted and submitted for certification 234 Amazon Alexa skills and 381 Google Assistant actions that intentionally violate content and privacy policies specified by VPA platforms. Surprisingly, we successfully got 234 (100%) policy-violating Alexa skills certified and 148 (39%) policy-violating Google actions certified. Our analysis demonstrates that policy-violating skills exist in the current skills stores, and thus users (children, in particular) are at risk when using VPA services. We conducted a user study with 203 participants to understand users' misplaced trust on VPA platforms. Unfortunately, user expectations are not being met by the skill certification in leading VPA platforms.

The vulnerability of Internet of Things (IoT) devices to malware attacks poses huge challenges to current Internet security. The IoT malware attacks are usually composed of three stages: intrusion, infection and monetization. Existing approaches for IoT malware detection cannot effectively identify the executed malicious activities at intrusion and infection stages, and thus cannot help stop potential attacks timely. In this paper, we present DeepPower, a non-intrusive approach to infer malicious activities of IoT malware via analyzing power side-channel signals using deep learning. DeepPower first filters raw power signals of IoT devices to obtain suspicious signals, and then performs a fine-grained analysis on these signals to infer corresponding executed activities inside the devices. DeepPower determines whether there exists an ongoing malware infection by conducting a correlation analysis on these identified activities. We implement a prototype of DeepPower leveraging low-cost sensors and devices and evaluate the effectiveness of DeepPower against real-world IoT malware using commodity IoT devices. Our experimental results demonstrate that DeepPower is able to detect infection activities of different IoT malware with a high accuracy without any changes to the monitored devices.

Widely deployed IoT devices have raised serious concerns for the spectrum shortage and the cost of multi-protocol gateway deployment. Recent emerging Cross-Technology Communication (CTC) technique can alleviate this issue by enabling direct communication among heterogeneous wireless devices, such as WiFi, Bluetooth, and ZigBee on 2.4 GHz. However, this new paradigm also brings security risks, where an attacker can use CTC to launch wireless attacks against IoT devices. Due to limited computational capability and different wireless protocols being used, many IoT devices are unable to use computationally-intensive cryptographic approaches for security enhancement. Therefore, without proper detection methods, IoT devices cannot distinguish signal sources before executing command signals. In this paper, we first demonstrate a new defined physical layer attack in the CTC scenario, named as waveform emulation attack, where a WiFi device can overhear and emulate the ZigBee waveform to attack ZigBee IoT devices. Then, to defend against this new attack, we propose a physical layer defensive mechanism, named as AuthCTC, to verify the legitimacy of CTC signals. Specifically, at the sender side, an authorization code is embedded into the packet preamble by leveraging the dynamically changed cyclic prefix. A WiFi-based detector is used to verify the authorization code at the receiver side. Extensive simulations and experiments using off-the-shelf devices are conducted to demonstrate both the feasibility of the attack and the effectiveness of our defensive mechanism.

The emergence of COVID-19 has engendered a new wave of online hate speech in social media platforms such as Twitter. Its widespread effects range from acts of cyber-harassment towards certain ethnic communities (e.g., the Asian community), to targeting older people belonging to age groups correlated with higher mortality rates (termed infamously as "Boomer Remover"). Thus, an urgent need arises for a timely mitigation of this new wave of online hate speech. In this work, we aim to discover the hate-related keywords linked to COVID-19 in hateful tweets posted on Twitter so that users posting such keywords can be asked to reconsider posting them. We first collect a new dataset of tweets targeting older people supplementing with a dataset targeting the Asian community. Then, we develop an approach to analyze the datasets with BERT (a transformer-based model) attention mechanism and discover 186 novel keywords targeting the Asian community and 100 keywords targeting older people. Based on our study, we then propose a control mechanism wherein a user can be asked to reconsider using certain sensitive words identified by our approach. We further perform an exploratory analysis of BERT attention mechanism and find that the most high-impact, long distance attentions are learned in the earlier or later layers of the model depending on the underlying data distribution. Our study indicates that the BERT model in some cases uses a hate keyword and an associated group or individual to make predictions, a finding that is inline with existing hate-speech research, which suggests that hate-speech is often aimed at certain groups or individuals.

LoRa, which is considered as an appealing wireless technique for Low-Power Wide-Area Networks (LPWANs), has found wide applications in fields such as smart cities, intelligent agriculture. Despite its popularity, there exists a growing concern about secure communications mainly due to the free frequency band and minimalist design specified in LoRa communications. For example, an attacker can forge messages to launch spoofing attack. To mitigate the threat, an authentication mechanism is needed. In this paper, we propose a lightweight node authentication scheme named SLoRa for LoRa networks by leveraging two physical layer features-Carrier Frequency Offset (CFO) and spatial-temporal link signature. In particular, we propose a novel CFO compensation algorithm, and identify slight CFO variations by adopting linear fitting for received upchirps to mitigate the noise's randomness on fine-grained CFO estimation. Besides, we can obtain fine-grained link signatures without the conventional de-convolution operation based on the theoretical analysis. Then, we show how these two physical-layer features complement each other to conquer the drift challenge brought by weather and environment variations. Combining these two features, SLoRa can distinguish whether the received signal is conveyed from a legitimate LoRa node or not. Experiments covering indoor and outdoor scenarios are conducted to demonstrate a high accuracy for node authentication in SLoRa, which is around 97% indoors and 90% outdoors.

The smart factory is a representative element reshaping conventional computer-aided industry to data-driven smart industry, while it is nontrivial to achieve cost effectiveness, reliability, mobility, and scalability of smart industrial systems. Data-driven industrial systems mainly rely on sensory data collected from statically deployed sensors. However, the spatial coverage of industrial sensor networks is constrained due to the high deployment and maintenance cost. Recently, mobile crowd sensing (MCS) has become a new sensing paradigm owing to its merits, such as cost effectiveness, mobility, and scalability. Nevertheless, traditional MCS systems are vulnerable to malicious attacks and single point of failure due to the centralized architecture. To this end, in this article we integrate MCS with industrial systems without introducing any additional dedicated devices. To overcome the drawbacks of traditional MCS systems, we propose a blockchain-based MCS system (BMCS). In particular, we exploit miners to verify the sensory data and design a dynamic reward ranking incentive mechanism to mitigate the imbalance of multiple sensing tasks. Meanwhile, we also develop a sensory data quality detection scheme to identify and mitigate the data anomaly. We implement a prototype of the BMCS on top of Ethereum and conduct extensive experiments on a realistic factory workroom. Both experimental results and security analysis demonstrate that the BMCS can secure industrial systems and improve the system reliability.

Despite many existing research on data forwarding in low-duty-cycle wireless sensor networks (WSNs), relatively little work has been done on energy-efficient data forwarding with probabilistic delay bounds. Probabilistic delay guarantees (i.e., delay bounded data delivery with reliability constraints) are of increasing importance for many delay-constrained applications, since deterministic delay bounds are prohibitively expensive to guarantee in WSNs. However, radio duty-cycling and unreliable wireless links pose challenges for achieving the probabilistic delay guarantee in WSNs. In this paper, we propose EEAF, a novel energy-efficient adaptive forwarding technique tailored for low-duty-cycle WSNs with unreliable wireless links. We show the existence of path diversity in low-duty-cycle WSNs, where delay-optimal routing and energy-optimal routing are likely following different paths. The key idea of EEAF is to exploit the intrinsic path diversity to provide probabilistic delay guarantees while minimizing transmission cost. In EEAF, an early arriving packet will be adaptively switched to the energy-optimal path for energy conservation. Delay quantiles are derived at each node in a distributed manner and are used as the guidelines in the adaptive forwarding decision making. Extensive testbed experiment and large-scale simulation show that EEAF effectively reduces the transmission cost by 12%~25% with probabilistic delay guarantees under various network settings. In addition, we extend the EEAF technique with data aggregation for event-based traffic scenarios. Evaluation using publicly available WSN event traffic traces yields very encouraging results with up to 40% energy saving in probabilistic delay bounded data delivery.

Internet of Things (IoT) plays an indispensable role in our daily life, in many cases, IoT systems are implemented following the client-server paradigm, which are vulnerable to single point of failures and malicious attacks. Due to the resilience and security promise of blockchain, the idea of combining blockchain and IoT has gained considerable attention in recent years. However, blockchains are power-intensive and low-throughput, which may not suitable for power-constrained IoT devices. To tackle these challenges, we present B-IoT, a blockchain based IoT system with credit-based consensus mechanism. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which enhances security and improves transaction efficiency simultaneously. In order to protect the confidentiality of sensitive IoT data, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on a directed acyclic graph (DAG)-structured blockchain, which is more efficient than the satoshi-style blockchain. We implement a prototype of B-IoT on Raspberry Pi, and conduct case studies of a smart factory. Extensive evaluation and analysis results demonstrate that the proposed credit-based PoW mechanism and data access control are practical for IoT.

Wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. However, cameras inside are generating a large amount of data, which brings challenges to the transmission through resource-constrained wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames can dramatically relieve the transmission pressure. Additionally, real-world environment may bring shielding or blind areas in videos, which notoriously affects the accuracy of frame analysis. The collaboration between cameras facing at different angles can compensate for such accuracy loss.

Smart devices (e.g., smartphones or tablets) have become an indispensable part of our daily lives for conducting mobile payment transactions, and storing both corporate and personal sensitive data. As a result, unauthorized access to smart devices can result in a catastrophic security breach. Lock screen provides the first line of defense against unauthorized access to smart devices, where users typically use the PIN, the pattern of drawing, or biometric to unlock their devices. Unfortunately, recent studies have revealed that individual unlocking methods are insufficient to prevent unauthorized access to smart devices. In this paper, we aim to increase the security barrier of smart device unlocking. We present the CP3, a combined unlocking framework to achieve highly secure and usable authentication for commodity smart devices. We address several challenges of combing unlocking methods from different modalities, such as the high reliability and low latency. We implement a prototype of our approach based on the Android platform, which selects the fingerPrint authentication, bluetooth transmission Power authentication and facial Pattern verification as our typical Combination for the secure unlocking. We have made the source code of our implementation public. Real-world experiments demonstrate the effectiveness of our solution. CP3achieves 88 % accuracy and 2.88s operation latency, which guarantees both good user experience and high security level compared with existing methods.

Radio frequency identification (RFID) technology, with its advantages such as battery-free tags, low cost, and scalability, has been playing an important role in many application domains, such as large-scale storage systems, supermarkets, construction sites, etc. Many of those application scenarios also require indoor positioning technologies, for example, warehouse goods positioning, item positioning in production assembly lines, and worker positioning in construction sites. However,indoor positioning using RFID faces accuracy degradation in dynamic environments, especially when tracking randomly moving targets. In this paper, we proposePSOTrack, a continuous RFID-based tracking system for random moving targets in unconstrained indoor environments. InPSOTrack, a data preprocessed, and an optimized particle swarm optimization algorithm is applied to determine the initial position, after that a dynamic correction method for trajectory prediction is proposed for continuous tracking. Results show that the proposed algorithm effectively improves the positioning accuracy and is able to achieve 1 m localization accuracy in dynamic indoor environments, which makes it a promising technology to support future pervasive RFID-based tracking applications.

WiFi-fingerprint localization is recognized as a promising indoor localization technique. However, it suffers from high implementation overhead such as heavy initial training and fingerprint map maintenance overtime. In this paper, we present the design, implementation, and evaluation of AP-Sequence. It is a fingerprint-based localization system that achieves extremely low overhead in fingerprint map construction and maintenance. AP-Sequence achieves this by treating a scan from any reference locations as an input to adjust a large portion of the fingerprint map. The power of AP-Sequence comes from dynamic region partitioning mechanism generating a fingerprint based on relative RSS values. AP-Sequence offers several advantages over existing methods with respect to robustness against environment noises, ability to handle dynamic power control, and mobile device heterogeneity. We have implemented AP-Sequence on an Android platform. Experiment results with over one month of evaluation demonstrate that our design achieves an average localization accuracy of 4-7.6 m over an extended time period with low-overhead in fingerprint map construction and maintenance.

Recent studies have revealed that control programs running on embedded devices suffer from both control-oriented attacks (e.g., code-injection or code-reuse attacks) and data-oriented attacks (e.g., non-control data attacks). Unfortunately, existing detection mechanisms are insufficient to detect runtime data-oriented exploits, due to the lack of runtime execution semantics checking. In this work, we propose Orpheus, a security methodology for defending against data-oriented attacks by enforcing cyber-physical execution semantics. We address several challenges in reasoning cyber-physical execution semantics of a control program, including the event identification and dependence analysis. As an instantiation of Orpheus, we present a new program behavior model, i.e., the event-aware finite-state automaton (eFSA). eFSA takes advantage of the event-driven nature of control programs and incorporates event checking in anomaly detection. It detects data-oriented exploits if physical events and eFSA's state transitions are inconsistent. We evaluate our prototype's performance by conducting case studies under data-oriented attacks. Results show that eFSA can successfully detect different runtime attacks. Our prototype on Raspberry Pi incurs a low overhead, taking 0.0001s for each state transition integrity checking, and 0.063s~0.211s for the cyber-physical contextual consistency checking.

In this work, we present a new program behavior model, i.e., the event-aware finite-state automaton ( eFSA ), which takes advantage of the event-driven nature of control programs in cyber-physical systems (CPS) and incorporates event checking in anomaly detection. eFSA provides new detection capabilities to detect data-oriented attacks in CPS control programs, including attacks on control intensity (i.e., hijacked for/while-loops) and attacks on control branch (i.e., conditional branches). We implement a prototype of our approach on Raspberry Pi and evaluate eFSA 's performance by conducting CPS case studies. Results show that it is able to effectively detect different CPS attacks in our experiments.

Abstract Group signature schemes enable anonymous-yet-accountable communications. Such a capability is extremely useful for applications, such as smartphone-based crowdsensing and citizen science. However, the performance of modern group signature schemes is still inadequate to manage large dynamic groups. In this paper, we design the first provably secure verifier-local revocation (VLR) - based group signature scheme that supports sublinear revocation, namedSublinear Revocation with Backward unlinkability and Exculpability(SRBE). To achieve this performance gain, SRBE introducestime bound pseudonymsfor the signer. By introducing low-cost short-lived pseudonyms with sublinear revocation checking, SRBE drastically improves the efficiency of the group-signature primitive. The backward-unlinkable anonymity of SRBE guarantees that even after the revocation of a signer, her previously generated signatures remain unlinkable across epochs. This behavior favors the dynamic nature of real-world crowdsensing settings. We prove its security and discuss parameters that influence its scalability. Using SRBE, we also implement a prototype named GroupSensefor anonymous-yet-accountable crowdsensing, where our experimental findings confirm GroupSense’s scalability. We point out the open problems remaining in this space.

IP traceback plays an important role in cyber investigation processes, where the sources and the traversed paths of packets need to be identified. It has a wide range of applications, including network forensics, security auditing, network fault diagnosis, and performance testing. Despite a plethora of research on IP traceback, the Internet is yet to see a large-scale practical deployment of traceback. Some of the major challenges that still impede an Internet-scale traceback solution are, concern of disclosing Internet Service Provider (ISP's) internal network topologies (in other words, concern of privacy leak), poor incremental deployment, and lack of incentives for ISPs to provide traceback services. In this paper, we argue that cloud services offer better options for the practical deployment of an IP traceback system. We first present a novel cloud-based traceback architecture, which possesses several favorable properties encouraging ISPs to deploy traceback services on their networks. While this makes the traceback service more accessible, regulating access to traceback service in a cloud-based architecture becomes an important issue. Consequently, we address the access control problem in cloud-based traceback. Our design objective is to prevent illegitimate users from requesting traceback information for malicious intentions (such as ISPs topology discovery). To this end, we propose a temporal token-based authentication framework, called FACT, for authenticating traceback service queries. FACT embeds temporal access tokens in traffic flows, and then delivers them to end-hosts in an efficient manner. The proposed solution ensures that the entity requesting for traceback service is an actual recipient of the packets to be traced. Finally, we analyze and validate the proposed design using real-world Internet data sets.

Passive indoor localization for smartphones requires no explicit cooperation of the smartphone and enables a new spectrum of applications such as passive user tracking, mobility monitoring, social pattern analysis, etc. However, existing passive localization methods either achieve coarse-grained localization accuracy or require expensive infrastructure support. In this paper, we present Pallas, a self-bootstrapping system for fine-grained passive indoor localization using non-intrusive WiFi monitors. Pallas uses off-the-shelf access point hardware to opportunistically capture WiFi packets to infer the location of smartphones in the indoor environment. The key novelty of Pallas lies in that the passive fingerprint database for localization is automatically constructed and updated without any active participation of WiFi devices or manual calibration. To achieve this, Pallas first identifies passive landmarks that are present in WiFi RSS traces. Given the knowledge of the indoor floor plan and the location of WiFi monitors, Pallas statistically maps the collected RSS traces to specific indoor pathways. With sufficient mapping opportunistically detected, Pallas is able to bootstrap a fine-grained passive fingerprint database and build Gaussian processes for localization automatically without requiring any additional calibration effort.

Many-to-one data collection is a fundamental operation in wireless sensor networks (WSNs). To support long-term deployment of WSNs, sensor nodes normally operate at low-duty-cycles. However, the low-duty-cycle operation significantly reduces the communication chance between nodes. Consequently, the risk of data collisions significantly increases when multiple senders transmit packets to a receiver during its very short active period. Data collision not only results in wasted packet transmissions, but also incurs a large delivery latency. Under such conditions, collision-free medium access is more appealing than recovering after collision for low-duty-cycle WSNs. In this work, we propose an incast-collision-free data collection protocol, named iCore, to address the many-to-one collision problem in low-duty-cycle WSNs. iCore employs the dynamic forwarding technique and establishes a non-conflicting schedule for delay reduction. Specifically, we design efficient forwarder assignment and forwarding optimization algorithms that ensure low end-to-end latency under diverse data traffic types. Through comprehensive performance evaluations, we demonstrate that, compared with the state-of-the-art protocol, iCore effectively minimizes the end-to-end delay by 25% ∼ 57% and maintains high delivery ratio and energy efficiency for different many-to-one convergecast scenarios.

IP traceback is a solution for attributing cyber attacks, and it is also useful for accounting user traffic and network diagnosis. Marking-based traceback (MBT) has been considered a promising traceback approach, and has received considerable attention. However, we find that the traceback message delivery problem in MBT, which is important to the successful completion of a traceback, has not been adequately studied in the literature. To address this issue, we present the design, analysis, and evaluation of opportunistic piggyback marking (OPM) for IP traceback in this paper. The OPM distinguishes itself from the existing works by decoupling the traceback message content encoding and delivery functions in MBT, and efficiently achieves expedited and robust traceback message delivery by exploiting piggyback marking opportunities. Based on the proposed OPM scheme, we then present the flexible marking-based traceback framework, which is a novel design paradigm for IP traceback and has several favorable features for practical deployment of IP traceback. Through the numerical analysis and the comprehensive simulation evaluations, we demonstrate that our design effectively reduces the traceback completion delay and router processing overhead, and increases the message delivery ratio compared with other baseline approaches.

Reliable flooding in wireless sensor networks (WSNs) is desirable for a broad range of applications and network operations. However, relatively little work has been done for reliable flooding in low-duty-cycle WSNs with unreliable wireless links. It is a challenging problem to efficiently ensure 100% flooding coverage considering the combined effects of low-duty-cycle operation and unreliable wireless transmission. In this paper, we propose a novel dynamic switching-based reliable flooding (DSRF) framework, which is designed as an enhancement layer to provide efficient and reliable delivery for a variety of existing flooding tree structures in low-duty-cycle WSNs. The key novelty of DSRF lies in the dynamic switching decision making when encountering a transmission failure, where a flooding tree structure is dynamically adjusted based on the packet reception results for energy saving and delay reduction. DSRF distinguishes itself from the existing works in that it explores both poor links and good links on demand. In addition, we define the optimal wakeup schedule-ranking problem in order to maximize the switching gain in DSRF. We prove the NP-completeness of this problem and present a heuristic algorithm with a low computational complexity. Through comprehensive performance comparisons, including the simulation of large-scale scenarios and small-scale experiments on a WSN testbed, we demonstrate that compared with the flooding protocol without DSRF enhancement, the DSRF effectively reduces the flooding delay and the total number of packet transmission by 12%' 25% and 10%' 15%, respectively. Remarkably, the achieved performance is close to the theoretical lower bound.

WiFi fingerprint-based indoor localization techniques have been proposed and widely used in recent years. Most solutions need a site survey to collect fingerprints from interested locations to construct the fingerprint database. However, the site survey is labor-intensive and time-consuming. To overcome this shortcoming, we record user motions as well as WiFi signals without the active participation of the users to construct the fingerprint database, in place of the previous site survey. In this paper, we develop an indoor localization system called WicLoc, which is based on WiFi fingerprinting and crowdsourcing. We design a fingerprint model to form fingerprints of each location of interest after fingerprint collection. We propose a weighted KNN (K-Nearest Neighbor) algorithm to assign different weights to APs and achieve room-level localization. To obtain the absolute coordinate of users, we design a novel MDS (Multi-Dimensional Scaling) algorithm called MDS-C (Multi-Dimensional Scaling with Calibrations) to calculate coordinates of interested locations in the corridor and rooms, where anchor points are used to calibrate absolute coordinates of users. Experimental results show that our system can achieve a competitive localization accuracy compared with state-of-the-art WiFi fingerprint-based methods while avoiding the labor-intensive site survey.

Participatory sensing enables to collect a vast amount of data from the crowd by allowing a wide variety of sources to contribute data. However, the openness of participatory sensing exposes the system to malicious and erroneous participations, inevitably resulting in poor data quality. This brings forth the important issues of false data detection and correction in participatory sensing. Furthermore, data collected by participants normally include considerable missing values, which poses challenges for accurate false data detection. In this work, we propose DECO, a general framework to detect false values for participatory sensing in the presence of missing data. By applying a tailored spatio-temporal compressive sensing technique, DECO is able to accurately detect the false data and estimate both false and missing values for data correction. We validate our design through an experimental case study.

Radio duty cycling is a commonly employed mechanism to support long-term sustainable operations of WSNs. Combined with the effect of unreliable wireless links, many challenges arise for ensuring delay bounded data delivery with reliability constraint. However, research on energy-efficient data forwarding with statistical delay bound in duty-cycled WSNs still remains unaddressed. This paper proposes EDGE, a novel opportunistic forwarding technique tailored for duty-cycled WSNs with unreliable wireless links. The key idea is to exploit the available path diversity to minimize the transmission cost while providing statistical delay guarantees. Delay quantiles are derived at each node in a distributed manner and are used as the guidelines in forwarding decision making, so that an early arriving packet will be opportunistically switched to the energy-optimal path for communication cost minimization. Comprehensive evaluation results show that EDGE effectively reduces the transmission cost with statistical delay guarantees under various network settings.

Indoor environment inference is of great importance to mobile and pervasive computing. As high-level metadata of indoor environment, floor maps contain rich information and are widely required in many pervasive systems. However, despite significant research progress, automatic inference of indoor maps has been less studied. In this paper, we present iMap, a smartphone-based opportunistic sensing system that automatically constructs the indoor maps by merging crowdsourced walking trajectories from smart-phone users. Most importantly, indoor semantics, such as stairs, escalators, elevators and doors are also automatically detected and annotated to the constructed map in the same inference process. The evaluation result shows that iMap can accurately detect different indoor semantics and be applied to different indoor environments. With the capability of generating semantic-annotated indoor maps without requiring any prior knowledge of the indoor environment, iMap has the potential to be widely deployed in practice.

Environment reconstruction is to rebuild the physical environment in the cyberspace using the sensory data collected by sensor networks, which is a fundamental method for human to understand the physical world in depth. A lot of basic scientific work such as nature discovery and organic evolution heavily relies on the environment reconstruction. However, gathering large amount of environmental data costs huge energy and storage space. The shortage of energy and storage resources has become a major problem in sensor networks for environment reconstruction applications. Motivated by exploiting the inherent feature of environmental data, in this paper, we design a novel data gathering protocol based on compressive sensing theory and time series analysis to further improve the resource efficiency. This protocol adapts the duty cycle and sensing probability of every sensor node according to the dynamic environment, which cannot only guarantee the reconstruction accuracy, but also save energy and storage resources. We implement the proposed protocol on a 51-node testbed and conduct the simulations based on three real datasets from Intel Indoor, GreenOrbs and Ocean Sense projects. Both the experiment and simulation performances demonstrate that our method significantly outperforms the conventional methods in terms of resource efficiency and reconstruction accuracy.

Proliferation of mobile phones over the last decade has led to innovative methods for studying social behavior and friendship patterns. Our present study combined advanced mobile phone technologies, such as Bluetooth scanning and automated pushing surveys, with self-reported questionnaires to examine the social structure and psychological well-being of college students. We distributed smartphones to 35 first-year undergraduate students to use everyday over one academic term (three months) and asked them to complete questionnaires on language background, loneliness, adaptation to college life, feelings of community cohesion, and friendship ties. Results suggested that behavioral networks in physical co-location, obtained using our mobile phone sensing techniques, reliably inferred real reported friendships. In addition, mobile phone usage, such as calling and SMS, were significantly correlated with psychological well-being in terms of feelings of loneliness, sense of community cohesion and adaptation to college life. Finally, results revealed that activities in the mobile phone social network, in particular SMS, may influence students' language use, such that students tended to adapt their language behavior to match that of their SMS partners.

Link correlation in wireless sensor networks has recently attracted a considerable amount of attention in the research community. Various pioneer works have empirically demonstrated the existence of link correlations and designed novel network protocols to exploit such link correlations. While all existing works focus on the correlated receptions at multiple receivers from a single sender, in this work we empirically demonstrate another type of link correlation, called sender-based link correlation. For sender-based link correlation, we observe wireless links from multiple senders to a single receiver are also correlated. Based on this observation, we design a two-tiered data forwarding scheme for improving the energy efficiency of unicast in the network. At the micro-level, individual nodes reduce their transmission energy consumption by temporarily switching to a new forwarder or suppressing the current transmission with the knowledge of link correlations. At the macro-level, we schedule the ordering of transmission times among neigh boring nodes so that the gains from all link correlation information in the network is maximized. Through trace-driven emulations and large scale simulations, we demonstrate that our design reduces data retransmissions by an average of 12% when compared with already highly energy efficient ETX-based protocols.

Providing reliable and efficient communication under fading channels is one of the major technical challenges in wireless sensor networks (WSNs), especially in industrial WSNs (IWSNs) with dynamic and harsh environments. In this work, we present the Reliable Reactive Routing Enhancement (R3E) to increase the resilience to link dynamics for WSNs/IWSNs. R3E is designed to enhance existing reactive routing protocols to provide reliable and energy-efficient packet delivery against the unreliable wireless links by utilizing the local path diversity. Specifically, we introduce a biased backoff scheme during the route-discovery phase to find a robust guide path, which can provide more cooperative forwarding opportunities. Along this guide path, data packets are greedily progressed toward the destination through nodes' cooperation without utilizing the location information. Through extensive simulations, we demonstrate that compared to other protocols, R3E remarkably improves the packet delivery ratio, while maintaining high energy efficiency and low delivery latency.

As a key approach to achieve energy efficiency in sensor networks, sensing coverage has been studied extensively in the literature. Researchers have designed many coverage protocols to provide various kinds of service guarantees on the network lifetime, coverage ratio and detection delay. While these protocols are effective, they are not flexible enough to meet multiple design goals simultaneously. In this paper, we propose a unified sensing coverage architecture for duty cycled wireless sensor networks, called uSense, which features three novel ideas: Asymmetric Architecture, Generic Switching and Global Scheduling. We propose asymmetric architecture based on the conceptual separation of switching from scheduling. Switching is efficiently supported in sensor nodes, while scheduling is done in a separated computational entity, where multiple scheduling algorithms are supported. As an instance, we propose a two-level global coverage algorithm, called uScan. At the first level, coverage is scheduled to activate different portions of an area. We propose an optimal scheduling algorithm to minimize area breach. At the second level, sets of nodes are selected to cover active portions. Importantly, we show the feasibility to obtain optimal set-cover results in linear time if the layout of areas satisfies certain conditions. Through extensive testbed and simulation evaluations, we demonstrate that uSense is a promising architecture to support flexible and efficient coverage in sensor networks.

QoS routing is an important research issue in wireless sensor networks (WSNs), especially for mission-critical monitoring and surveillance systems which requires timely and reliable data delivery. Existing work exploits multipath routing to guarantee both reliability and delay QoS constraints in WSNs. However, the multipath routing approach suffers from a significant energy cost. In this work, we exploit the geographic opportunistic routing (GOR) for QoS provisioning with both end-to-end reliability and delay constraints in WSNs. Existing GOR protocols are not efficient for QoS provisioning in WSNs, in terms of the energy efficiency and computation delay at each hop. To improve the efficiency of QoS routing in WSNs, we define the problem of efficient GOR for multiconstrained QoS provisioning in WSNs, which can be formulated as a multiobjective multiconstraint optimization problem. Based on the analysis and observations of different routing metrics in GOR, we then propose an Efficient QoS-aware GOR (EQGOR) protocol for QoS provisioning in WSNs. EQGOR selects and prioritizes the forwarding candidate set in an efficient manner, which is suitable for WSNs in respect of energy efficiency, latency, and time complexity. We comprehensively evaluate EQGOR by comparing it with the multipath routing approach and other baseline protocols through ns-2 simulation and evaluate its time complexity through measurement on the MicaZ node. Evaluation results demonstrate the effectiveness of the GOR approach for QoS provisioning in WSNs. EQGOR significantly improves both the end-to-end energy efficiency and latency, and it is characterized by the low time complexity.

Reliable flooding in wireless sensor networks (WSNs) is desirable for a broad range of applications and network operations, and has been extensively investigated. However, relatively little work has been done for reliable flooding in lowduty-cycle WSNs with unreliable wireless links. It is a challenging problem to efficiently ensure 100% flooding coverage considering the combined effects of low-duty-cycle operation and unreliable wireless transmission. In this work, we propose a novel dynamic switching-based reliable flooding (DSRF) framework, which is designed as an enhancement layer to provide efficient and reliable delivery for a variety of existing flooding tree structures in lowduty-cycle WSNs. The key novelty of DSRF lies in the dynamic switching decision making when encountering a transmission failure, where a flooding tree structure is dynamically adjusted based on the packet reception results for energy saving and delay reduction. DSRF is distinctive from existing works in that it explores both poor links and good links on demand. Through comprehensive performance comparisons, we demonstrate that, compared with the flooding protocol without DSRF enhancement, DSRF effectively reduces the flooding delay and the total number of packet transmission by 12% 25% and 10% 15%, respectively. Remarkably, the achieved performance is close to the theoretical lower bound.

Location-based services, such as targeted advertisement, geo-social networking and emergency services, are becoming increasingly popular for mobile applications. While GPS provides accurate outdoor locations, accurate indoor localization schemes still require either additional infrastructure support (e.g., ranging devices) or extensive training before system deployment (e.g., WiFi signal fingerprinting). In order to help existing localization systems to overcome their limitations or to further improve their accuracy, we propose Social-Loc, a middleware that takes the potential locations for individual users, which is estimated by any underlying indoor localization system as input and exploits both social encounter and non-encounter events to cooperatively calibrate the estimation errors. We have fully implemented Social-Loc on the Android platform and demonstrated its performance on two underlying indoor localization systems: Dead-reckoning and WiFi fingerprint. Experiment results show that Social-Loc improves user's localization accuracy of WiFi fingerprint and dead-reckoning by at least 22% and 37%, respectively. Large-scale simulation results indicate Social-Loc is scalable, provides good accuracy for a long duration of time, and is robust against measurement errors.

A tree-based topology is often used to flood packets from the sink node in wireless sensor networks (WSNs). Therefore, flooding tree construction is an important and fundamental problem in WSNs, and has been extensively investigated in the literature. However, we note that the flooding tree construction problem in asynchronous low-duty-cycle WSNs has not been sufficiently investigated in existing work. In this work, we focus our investigation on minimum-delay and energy-efficient flooding tree construction considering the duty-cycle operation and unreliable wireless links. We formulate the problem as a undetermined-delay-constrained minimum spanning tree (UDC-MST) problem, where the delay constraint is known a posteriori. We design a distributed heuristic algorithm, named MDET, to solve the problem. Through extensive simulations, we demonstrate that MDET achieves a very good balance between flooding delay and energy efficiency.

Indoor localization is essential to enable location-based services in wireless pervasive computing environment. In recent years, WiFi fingerprint-based localization has received considerable attention due to its deployment practicability. In order to achieve on-the-fly localization, WiFi receivers (e.g., mobile phones or laptops) being located need to scan WiFi signals continuously. Since they are normally battery driven, energy efficiency is a very important consideration in WiFi fingerprinting localization systems. Motivated by the fact that IEEE 802.11 (WiFi) and 802.15.4 (ZigBee) channels overlap in the 2.4GHz ISM band, in this work, we develop a WiFi fingerprint-based localization system using ZigBee radio, called ZiLoc. We first present a novel RSS-location fingerprint model to identify the features of surrounding APs. We then propose a simple yet effective method to compute the similarity of two RSS fingerprints. Experimental results demonstrate that ZiLoc can achieve an average of 85% room-level localization accuracy and reduce more than 60% energy consumption compared with the method using WiFi interfaces to collect RSS fingerprints.

Reliable flooding in wireless sensor networks (WSNs) is desirable for a broad range of applications and network operations. However, it is a challenging problem to ensure 100% flooding coverage efficiently considering the combined effects of low-duty-cycle operation and unreliable wireless transmission. In this work, we propose a novel dynamic switching-based reliable flooding (DSRF) framework, which is designed as an enhancement layer to provide efficient and reliable flooding over a variety of existing flooding tree structures in low-duty-cycle WSNs. Through comprehensive simulations, we demonstrate that DSRF can effectively improve both flooding energy efficiency and latency.

In this paper, we propose Social-Loc, which uniquely utilizes social interactions in addition to common on-board sensors such as accelerometer and gyroscope on modern smartphones, to localize indoor mobile users. Specifically, Social-Loc takes the potential locations for individual users estimated by a novel particle filter tailored for indoor localization as input, and exploits both social encounter and non-encounter events to further improve the localization accuracy. We have implemented Social-Loc on the Android platform and extensively evaluated its performance. The simulation results demonstrate that Social-Loc improves the accuracy of the particle-filter-only scheme by as much as 560% on average and is able to achieve accuracy of few meters without any external ranging device or system training.

Wireless ad hoc networks can experience significant performance degradation under fading channels. In this work, we present a robust forwarding extension (RFE) for reactive routing protocols in wireless ad hoc networks. RFE is designed to enhance existing reactive routing protocols to provide reliable and energy-efficient packet delivery against the unreliable wireless links. Specifically, we introduce a biased backoff scheme during the route discovery phase to find a robust virtual path, which can provide more cooperative forwarding opportunities. Along this virtual path, data packets are greedily progressed toward the destination through nodes cooperation. We extend the widely used AODV routing protocol with RFE to study its performance. Through extensive simulations, we demonstrate that AODV-RFE effectively improves the reliability, end-to-end energy efficiency and latency.

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It has been shown that sink mobility provides an energy-efficient approach to data delivery in wireless sensor networks (WSNs). Most of the approaches targeted to WSNs with mobile sinks (MSs) addressed the problem of data delivery where only a few messages are reported during a long time frame. However, transmitting streaming data is becoming relevant in WSNs, as more and more multimedia sensor nodes - equipped with image, audio, and video capabilities - are being used to characterize the sensing environment. In this scenario, a sequence of messages propagates into the network, hence the problem of finding an effective routing path for delivering data to MSs becomes even more challenging, since the communication overhead for reaching the MS might also be significant. In this paper, we present an energy-efficient streaming data delivery (SDD) protocol for cluster-based WSNs with MSs. Different from existing works, we focus on the mobility support for the delivery of streaming data in hierarchical WSNs. By introducing a cross-cluster handover mechanism and a path redirection scheme, SDD maintains the end-to-end connectivity between the source and the MS, while avoiding the constant transmission of the MS location as it moves across multiple clusters. We evaluate the performance of the proposed SDD protocol, and compare it with a hierarchical cluster-based data dissemination protocol. Simulation results demonstrate its effectiveness, in terms of both end-to-end delivery delay and energy-efficiency.

The availability and diffusion of wireless sensor nodes, personal communication devices (e.g., smartphones), as well as application-specific devices (e.g., surveillance cameras) has changed the typical sensing application scenarios where data are collected from the environment for the purpose of monitoring a phenomenon and detecting events. The combination of highly heterogeneous devices, in terms of sensing, processing, and communication capabilities, has become a key feature to collaborative, distributed, and multimodal sensing applications. However, the heterogeneity of devices also raises a number of challenges for the application developers. In this paper, we present a general software framework for heterogeneous and multimedia wireless sensor networks. The framework abstracts from the individual sensing devices and platforms, and enables collaborative and distributed sensing applications. We present a reference application scenario represented by Assisted Living Environments (ALEs).We show the potential of our proposed framework by a preliminary testbed implementation consisting in a multimodal application for fall detection of elderly people.

Contention-based geographic forwarding (CGF) is a state-free communication paradigm for data delivery in multihop wireless sensor networks. CGF is robust to frequent topology changes, scalable to large-scale node deployment and applicable to data-centric applications and resource constrained networks. However, CGF may experience significant performance degradation under unreliable links. In this work, we present the intelligent CGF (ICGF) to combat the channel variation. IGCF combines the advantages of both cooperative and contention-based forwarding, involving multiple neighbours of the sender into the local forwarding to improve the transmission reliability. ICGF differs from existing work in that it extends the cooperation scope intelligently, by sending one additional control message on demand. For this reason, the probability of cooperation void in ICGF is decreased and the single-hop packet progress is increased. The authors conduct extensive simulations to study the performance of the proposed ICGF compared with existing protocols. Simulation results demonstrate that ICGF improves the end-to-end data delivery delay, energy efficiency and data delivery ratio.

Usually, sensor data needs to be disseminated from the source sensors to data collectors (e.g., sink nodes), making those spatially distributed sensor data available for applications to access. The widespread and ubiquitous nature of mobile devices, e.g., PDAs and cell phones around the world, makes them attractive to be used as mobile data collectors (MDCs) to collect and deliver the sensor data. The goal of this work is to design a dissemination protocol that leads to efficient data delivery from the source sensors to ubiquitous MDCs. We propose the Wait-Focus-Spray (WFS) scheme for wireless sensor networks with ubiquitous MDCs. The main objective of WFS is to balance the data delivery latency and transmission overhead when considering the existence of ubiquitous MDCs. In WFS, we also propose a corresponding mechanism-probabilistic scattered binary spraying (PSBS), to reduce the spatial redundancy when spraying data copies, which can increase the probability of meeting a MDC. Through extensive simulations, we demonstrate that our WFS scheme reduces the transmission cost per message while provides comparable delivery delay compared with the existing work.

Energy efficient multicast routing is one of the fundamental problems in wireless sensor networks (WSNs). Previous work has shown that when the goal is to find multicast trees with minimum transmission cost, the problem becomes NP-complete. In this work, we present a heuristic distributed minimum transmission multicast routing protocol (MTMRP) for WSNs. By introducing the biased backoff scheme and taking advantage of the broadcast nature of wireless communication, MTMRP chooses the forwarding routes which can connect more multicast receivers. Moreover, MTMRP introduces a path handover scheme, which can prune redundant routes for multicast routing. As a result, the multicast transmission cost is reduced in a distributed manner. We conduct extensive evaluations to study the performance of the proposed MTMRP compared with existing protocols. Simulation results demonstrate that our scheme effectively improves the multicast routing energy efficiency.

Cooperative forwarding has been considered as an effective strategy for improving the geographic routing performance in wireless sensor networks (WSNs). However, we observe that existing works do not fully utilize the forwarding opportunities provided by available neighboring nodes. In this paper, we redesign the cooperative forwarding process and present a novel cooperative contention-based forwarding (CCBF) protocol for WSNs. CCBF extends the scope of cooperation and attains the full potential of cooperative forwarding at the expense of sending one additional control message on demand. We conduct extensive simulations to study the performance of the proposed CCBF compared with existing protocols. Simulation results demonstrate that CCBF decreases the end-to-end hop counts, hence, further improves the end-to-end energy efficiency and latency. Remarkably, it provides up to 50% improvement for the packet loss ratio when the retransmission mechanism is not adopted.

In a pervasive computing environment, the devices are embedded in the physical world, providing services and interconnected by a communication network. Composition of these services is important issue of pervasive applications which integrate the physical and cyber worlds. Most existing research on service composition in pervasive computing relies on the existence of one or more entities that maintain the global service information. However, such an approach is not always practical due to dynamicity of the environment. In this paper, we propose a fully decentralized approach to service composition. We first model the service composition problem as finding an overlay of the communication network that matches the composition graph. The problem is proved to be NP-complete. We propose an algorithm for the devices to cooperatively construct the requested services through localized interactions. For the purpose of reducing redundant broadcast we propose the service composition backbone built in a fully localized way. We have carried out extensive simulations. to evaluate the performance of our algorithm. Compared with existing pull-based centralized techniques our decentralized service composition algorithm on the service composition backbone is more efficient in terms of response delay and message overhead, while achieving similar quality of composed service.

In this paper, we exploit the geographic opportunistic routing (GOR) for QoS provisioning with both end-to-end reliability and delay constraints in wireless sensor networks (WSNs). Recent work exploits multipath routing to guarantee both reliability and delay QoS constraints in WSNs. However, the multipath routing approach suffers from a significant energy cost. We also find that existing GOR protocol may not be suitable for QoS provisioning in WSNs, due to the large computation delay at each hop. To improve the efficiency of QoS routing in WSNs, we study the problem of efficient GOR for multiconstrained QoS provisioning in WSNs, which can be formulated as a multiobjective multiconstraint optimization problem. We look in depth at the properties of the multiple objectives. Based on the analysis and observations, we then propose a heuristic efficient GOR (EGOR) algorithm for QoS provisioning in WSNs. We evaluate EGOR by comparing it with the multipath routing approach through ns-2 simulation and evaluate its time complexity through measurement on the MicaZ node. Evaluation results demonstrate that EGOR can significantly improve both the end-to-end energy efficiency and latency for multiconstrainted QoS provisioning in WSNs, and that EGOR is characterized by its low time complexity.

Considering sensor nodes deployed densely and uniformly a mobile sink moving through the sensing field queries a specific area of interest for monitoring information. The Query packet, injected by the mobile sink, is routed to the specific area and the corresponding Response packet is expected to return via multi-hop communication. In this paper, we analyze such a network model to address the problem of efficient data collection for mobile wireless monitoring applications. We first propose a meeting position-aware routing (MPAR) protocol for routing the Response packet efficiently and then propose an efficient query-based data collection scheme (QBDCS) for mobile wireless monitoring applications based on the MPAR. In order to minimize the energy consumption and packet delivery latency, the QBDCS chooses the optimal query time of injecting the Query packet and tailors the routing mechanism for sensor nodes forwarding packets. Simulation study has verified the analysis and demonstrated that the QBDCS can significantly reduce the energy consumption and end to end delivery latency.

Utilizing mobile devices such as PDAs, cell phones to collect and deliver sensor data is considered as a promising application in future ubiquitous computing environment. In such application scenarios, the human factor is becoming increasingly important. In this paper, we propose a group-level incentive scheme targeted for encouraging mobile users to collect sensor data. Such idea of widening the scope of incentive from device- level to group-level is inspired by the incentive scheme of blood donation in real life. Mobile users earn credits by collecting and delivering sensor data with their mobile devices and they may consume the credits to access the services provided by wireless sensor networks applications. The main idea of the group-level incentive scheme is to group mobile users into social groups and share credits so that credits earned by one user can be consumed by another user in the same group. Through theoretical analysis and simulation we illustrate the potential of the proposed solution.

Considering sensor nodes deployed densely and uniformly in the sensing field, we focus on a scenario that a mobile sink moving through the sensing field queries a specific area or a point of interest for data collection. A Query packet is injected by the mobile sink and routed to the specific area, then the corresponding Response packet is returned to the mobile sink via multi-hop communication. Due to the mobility of the sink, the Query and Response should have different routes. We analyze such a network model to address the problem of efficient data collection in wireless sensor networks and propose an efficient Query-Based Data Collection Scheme (QBDCS). In order to minimize the energy consumption and packet delivery latency, QBDCS chooses the optimal time to send the Query packet and tailors the routing mechanism for partial sensor nodes forwarding packets. Simulation results demonstrate that QBDCS completes a query-based data collection cycle with minimum energy consumption and delivery latency.

An application scenario embracing the area of Intelligent Transportation Systems (ITS) is studied in this paper. The infrastructure of the application scenario is wireless sensor networks with mobile sinks. The WSN is query-based and with multi-hop transmission for data sensing. The application-specific network architecture was introduced and exploited. The detailed routing algorithm as well as its initialization phase were developed. Analysis was given on the end-to-end delivery latency, aiming to optimize the routing in both real-time ability and energy efficiency. Compared with the existing routing mechanism for the same application scenario, the proposed algorithm demonstrates less delivery latency and consequently lower energy consumption. Extensive simulations were conducted to evaluate the performance of the routing mechanism as well as to compare it with existing method numerically.

In this paper, meeting position aware routing (MPAR) is proposed for query-based mobile enabled wireless sensor network (mWSN). We consider mWSNs with dense sensor nodes deployed in the sensing field. A mobile sink (MS) moving through the sensing field with random speed queries a specific area or a point of interest for information from physical world. Queried data over a area is aggregated to a source node and delivered to the mobile sink via multi-hop communication. The feature of MPAR is that the mWSNs can predict the position of the mobile sink at the time it meets the data packet. The queried data packet is delivered towards this predicted position from the source node during the propagation. We compared MPAR with the existing routing protocol for the similar scenarios. Analysis was conducted to discover the conditions under which MPAR demonstrates the optimal performance. Extensive simulations were also conducted to evaluate the performance of the routing mechanism as well as to compare it with existing method. Evaluation results show that MPAR outperforms the existing mechanism in that it provides less end-to-end delivery latency, and consequently reduces the energy consumption. The robustness to variation of MS velocity is also improved.