The Long-Range (LoRa) system for the Internet of Things (IoT) has proved effective in infrastructure, energy consumption, resource reduction, and pollution control. However, in terms of security, LoRa faces several challenges. LoRa has a centralised architecture where all sensor data is transmitted to a fog (edge) server via LoRa gateways and processed at the fog server. This exposes sensitive sensor data to some major security threats, such as data tampering, injection of fake data and data loss. LoRa gateways in such architecture simply forward packets to the server. We present a blockchain-based open-source solution for LoRa Gateways that addresses the security vulnerabilities of this architecture and utilises the computing and storage resources of LoRa Gateways. The main challenges in implementing blockchain at LoRa gateways are limited processing, storage capabilities and battery power. The number of gateway nodes improves security and reduces the workload on the fog server. With the introduction of packet processing at the gateway, unnecessary packets are dropped at the gateway itself. Simulation results depict a reduced CPU usage at the fog server for up to 2 % when 16 end devices are being used. Further, our work reduces the overall packet handling time by up to 17% compared to existing works.

Obesity is a global health crisis projected to affect one billion people worldwide by 2030. Previous research has emphasized the role of food cues in print media and television as contributing factors to the obesity epidemic. However, the influence of food cues shared by general users on social media platforms, particularly Snapchat, remains largely unexplored. To this end, this paper presents a comprehensive, large-scale study employing a multi-method approach to assess, measure, and mitigate the influence of food cues shared on Snapchat in three different countries with diverse cultural backgrounds: Saudi Arabia, the United States, and France. Our analysis of over 350K collected snaps reveals that food cues are prevalent among Snapchat users, with food content comprising approximately 20% of all collected snaps. Subsequently, we assessed the impact of exposure to Snapchat food content on appetite and examined whether it might inadvertently exacerbate cravings. Our experimental study, involving 37 participants, yields a significant finding: exposure to food snaps leads to a substantial increase in caloric intake. In light of these findings and as an effort to mitigate this impact, this paper sheds light on Snapchat users' perceptions of a proposed intervention design idea, which would allow them to customize their feed and hide food content to potentially reduce their exposure to it. According to our survey of 813 Snapchat users, the majority (57%) indicated they would be willing to hide food-related content from their feeds. In particular, being male and having a high body mass index (BMI) were both associated with a higher willingness to block food content from their Snapchat feed. While food snaps are intended to disappear after 24 hours, our results suggest that their impact can have a long-lasting effect on health and wellness.

Planned disruptions such as highway constructions are commonplace nowadays and the communities living near these disruptions generally tend to be environmental justice communities---low socio-economic status with disproportionately high and adverse human health and environmental effects. A major concern is that such activities negatively impact people's well-being by disrupting their daily commutes via frequent road closures and increased dust & air pollution. This paper addresses this concern by developing a personalized navigation service called PureNav to mitigate the negative impacts of disruptions in daily commutes on people's well-being. PureNav has been designed using active engagement with four environmental justice communities affected by major highway construction. It has been deployed in the real world among the members of the four communities, and a detailed analysis of the data collected from this deployment as well as surveys show that PureNav is potentially useful in improving people's wellbeing. The paper describes the design, implementation, and evaluation of PureNav, and offers suggestions for further improving its efficacy.

Cloud-native technologies consisting of containers, microservices, and service meshes bring the traditional advantages of Cloud Computing like scalability, composability, and rapid deployability to the IoT Edge. An application built on the microservices architecture relies on a collection of individual containerized components offering modular services via REST or gRPC interfaces over the network. Compared to a monolithic application, the magnitude of data and control exchange between the components of a microservices application is several orders higher. Studies have shown that overheads caused by such inter-container communication are a significant hurdle in achieving the sub-50ms latencies required for 5G enabled network Edges comprised of a much smaller compute cluster, unlike the Cloud. In this paper, we present Shimmy - a shared memory-based communication interface for containers that is cleanly integrated into the Kubernetes orchestration architecture while offering significant acceleration for microservices. Results have shown a consistent 3-4x latency improvement over UDP and TCP, as much as 20x latency improvement over RabbitMQ, while significantly reducing memory and CPU usage in large data transfers as well as real-time video streaming.

Smart agriculture is one of the most promising areas where IoT-enabled technologies have the potential to substantially improve the quality and quantity of the crops and reduce the operational cost. However, building a smart agriculture system presents several challenges, including high latency and bandwidth consumption associated with cloud computing, Internet disconnections in rural areas, and the need to keep costs low for farmers. To address these issues, this paper proposes a fog-based smart agriculture infrastructure with edge computing and LoRa communication. We address the top concern of farmers - animals intruding - by proposing a solution that detects animal intrusion using low-cost PIR sensors, cameras, and computer vision and predicts animal locations using a novel algorithm. Our system can detect animals before the intrusion, identify them, predict their future locations, and alert farmers promptly. The paper proposes three sensor layouts, and experiments confirm the system’s effectiveness and lower cost compared to state-of-the-art systems.

This paper presents a system architecture for building digital twins at the edge. In particular, it addresses two major challenges: system scalability and complexity. It introduces two novel architectural components: a Context Aware Communication Component (CACC) that addresses the scalability issue in communication between physical and virtual environments, and second, an application-agnostic methodology, a service registry component for integrating digital twin with EdgeAI (DT-EdgeAI integration) at the edge. The paper describes this architecture in detail and provides a preliminary prototype implementation.

Online reviews have become essential for users to make informed decisions in everyday tasks ranging from planning summer vacations to purchasing groceries and making financial investments. A key problem in using online reviews is the overabundance of online that overwhelms the users. As a result, recommendation systems for providing helpfulness of reviews are being developed. This paper argues that cultural background is an important feature that impacts the nature of a review written by the user, and must be considered as a feature in assessing the helpfulness of online reviews. The paper provides an in-depth study of differences in online reviews written by users from different cultural backgrounds and how incorporating culture as a feature can lead to better review helpfulness recommendations. In particular, we analyze online reviews originating from two distinct cultural spheres, namely Arabic and Western cultures, for two different products, hotels and books. Our analysis demonstrates that the nature of reviews written by users differs based on their cultural backgrounds and that this difference varies based on the specific product being reviewed. Finally, we have developed six different review helpfulness recommendation models that demonstrate that taking culture into account leads to better recommendations.

The unprecedented health situation in the year 2020 and to some extent 2021 has forced most businesses to operate online with people working from home (WFH). Like almost all countries in the world, Saudi Arabia has suffered from the shocking unstable health situation facing the COVID-19 pandemic. This study investigates different WFH impressions and behaviors that Saudi employees have built during the pandemic and how that has changed over time. We have conducted surveys in two different phases among Saudi employees that have come from varied personal and job-related demographics, including different gender, marital status, cities, managerial roles, job sectors and company sizes. Our data provides a good comprehensive coverage along different demographics. Key findings includes that for 75% of the people it was a brand new experience especially for big companies employees, people's performance and satisfaction depended on the sector that they work for and their marital status, while life work split was the top challenge and flexibility was the top advantage.

Growing evidence suggests that YouTube's recommendation algorithm plays a role in online radicalization via surfacing extreme content. Radical Islamist groups, in particular, have been profiting from the global appeal of YouTube to disseminate hate and jihadist propaganda. In this quantitative, data-driven study, we investigate the prevalence of religiously intolerant Arabic YouTube videos, the tendency of the platform to recommend such videos, and how these recommendations are affected by demographics and watch history. Based on our deep learning classifier developed to detect hateful videos and a large-scale dataset of over 350K videos, we find that Arabic videos targeting religious minorities are particularly prevalent in search results (30%) and first-level recommendations (21%), and that 15% of overall captured recommendations point to hateful videos. Our personalized audit experiments suggest that gender and religious identity can substantially affect the extent of exposure to hateful content. Our results contribute vital insights into the phenomenon of online radicalization and facilitate curbing online harmful content.

Churn prediction is an important topic in the free online game industry. Reducing the churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Playtime-related features are some of the widely used universal features for most churn prediction models. In this article, we consider developing new universal features for churn predictions for long-term players based on playtime. In particular, we measure playtime regularity using the notion ofentropyandcross-entropyfrom information theory. After computing playtime regularity of players from the datasets of six free online games of different types, we leverage information from the playtime regularity in the form of universal features for churn prediction. Experiments show that the proposed features are better at predicting churners compared to the baseline features, implying that the proposed features could utilize the information extracted from playtime more effectively than the related baseline playtime features.

Social media platforms have been exploited to disseminate misinformation in recent years. The widespread online misinformation has been shown to affect users' beliefs and is connected to social impact such as polarization. In this work, we focus on misinformation's impact on specific user behavior and aim to understand whether general Twitter users changed their behavior after being exposed to misinformation. We compare the before and after behavior of exposed users to determine whether the frequency of the tweets they posted, or the sentiment of their tweets underwent any significant change. Our results indicate that users overall exhibited statistically significant changes in behavior across some of these metrics. Through language distance analysis, we show that exposed users were already different from baseline users before the exposure. We also study the characteristics of two specific user groups, multi-exposure and extreme change groups, which were potentially highly impacted. Finally, we study if the changes in the behavior of the users after exposure to misinformation tweets vary based on the number of their followers or the number of followers of the tweet authors, and find that their behavioral changes are all similar.

Social media platforms have been exploited to conduct election interference in recent years. In particular, the Russian-backed Internet Research Agency (IRA) has been identified as a key source of misinformation spread on Twitter prior to the 2016 U.S. presidential election. The goal of this research is to understand whether general Twitter users changed their behavior in the year following first contact from an IRA account. We compare the before and after behavior of contacted users to determine whether there were differences in their mean tweet count, the sentiment of their tweets, and the frequency and sentiment of tweets mentioning @realDonaldTrump or @HillaryClinton. Our results indicate that users overall exhibited statistically significant changes in behavior across most of these metrics, and that those users that engaged with the IRA generally showed greater changes in behavior.

YouTube strives to moderate its content by censoring, demonetizing or removing videos that allegedly violate their community guidelines. Such strategies, especially if seen as unjust by the affected users, could be met with resentment, anger, and in some cases, violence. In addition to YouTube removing videos, uploaders sometimes delete their videos for a variety of reasons such as paraphrasing or preserving online self-image. In this paper, we provide a detailed analysis of deleted/removed videos on YouTube. To do this, we tracked over 73,000 recent YouTube videos for one week and identified those that got deleted or removed. We have then conducted a large-scale analysis of this data and reported on the most informative features that distinguish deleted/removed videos from the ones that remain available. Based on our analysis, we have developed machine learning prediction models that predict videos that will get deleted/removed at different stages of a video's lifetime, viz., at the time of posting, and after up to seven days have elapsed. Our findings indicate that we can predict video deletion/removal with high accuracy even at the time of posting-a strategy that could help users perceive the removal of their videos as fair as well as reduce public and moderators exposure to problematic videos.

Internet users are routinely exposed to fake news in their social media feeds. The main goal of this paper is to identify the factors readers consider important in discriminating against fake news from true news when reading an online news article. We design and conduct three surveys using Amazon Mechanical Turk to identify the top factors and rate them under diverse scenarios. Our results suggest that people perceive news Source and Content to be the most important factors, in general, to distinguish fake news from true news, however, their importance reduces in practice when people actually read a news article. Furthermore, the importance of different factors in the credibility determination of a news article varies with people's political leanings. Our work is the first of its kind and offers new insights into how people determine the legitimacy of online news articles.

Personality inference could create useful information, especially for the game industry. In this paper, we investigate whether and how the player personality is revealed in the player social interactions in a massively multiplayer online strategy game. We conduct an online survey with 50,319 participants to collect the player personality traits. We then develop proper features to capture the player in-game social interactions, and analyze correlations between personality and social interactions using these features. Our results show that the in-game social interactions are correlated with personality, and reveal how personality can affect social interactions in massively multiplayer online strategy games. Then we further discuss the implication of our results.

In the free online game industry, in-game purchase prediction is an important research topic. On the one hand, knowing the players who are likely or unlikely to pay for the game can help the development of corresponding marketing strategies and incentives. On the other hand, the result of purchase prediction indicates the status of the game operation status and helps the game company allocate a reasonable amount of resources for the game. In this paper, we focus on predicting the purchase probability of paying players and finding important features. We consider two types of features related to players’ playing and purchase behaviors. Then, based on these features we proposed, we use two different models to predict the survival probability for players and analyze the feature importance.

In the free online game industry, churn prediction is an important research topic. Reducing churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Most churn prediction models are based on game-specific features, which limits their applicability to other games that do not share those features. In this paper, we consider developing universal features for churn predictions for long-term players. In particular, we mine player time spending regularity from data sets of two free online games. We leverage information from players’ in-game time spending regularity in the form of universal features for churn prediction. Experiments show that our developed features are better at predicting churners, compared to the baseline features. The performance of our developed features is satisfactory even without game-specific features.

Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.

Religious hate speech in the Arabic Twittersphere is a notable problem that requires developing automated tools to detect messages that use inflammatory sectarian language to promote hatred and violence against people on the basis of religious affiliation. Distinguishing hate speech from other profane and vulgar language is quite a challenging task that requires deep linguistic analysis. The richness of the Arabic morphology and the limited available resources for the Arabic language make this task even more challenging. To the best of our knowledge, this paper is the first to address the problem of identifying speech promoting religious hatred in the Arabic Twitter. In this work, we describe how we created the first publicly available Arabic dataset annotated for the task of religious hate speech detection and the first Arabic lexicon consisting of terms commonly found in religious discussions along with scores representing their polarity and strength. We then developed various classification models using lexicon-based, n-gram-based, and deep-learning-based approaches. A detailed comparison of the performance of different models on a completely new unseen dataset is then presented. We find that a simple Recurrent Neural Network (RNN) architecture with Gated Recurrent Units (GRU) and pre-trained word embeddings can adequately detect religious hate speech with 0.84 Area Under the Receiver Operating Characteristic curve (AUROC).

User emotions are important contextual features in building context-aware pervasive applications. In this paper, we explore the question of whether it is possible to predict user emotions from their smartphone activities. To get the ground truth data, we have built an Android app that collects user emotions along with a number of features including their current location, activity they are engaged in, and smartphones apps they are currently running. We deployed this app for over a period of three months and collected a large amount of useful user data. We describe the details of this data in terms of statistics and user behaviors, provide a detailed analysis in terms of correlations between user emotions and other features, and finally build classifiers to predict user emotions. Performance of these classifiers is quite promising with high accuracy. We describe the details of these classifiers along with the results.

Current COTS WiFi based work on wireless motion sensing extracts human movements such as keystroking and hand motion mainly from amplitude training to classify different types of motions, as obtaining meaningful phase values is very challenging due to time-varying phase noises occurred with the movement. However, the methods based only on amplitude training are not very practical since their accuracy is not environment and location independent. This paper proposes an effective phase noise calibration technique which can be broadly applicable to COTS WiFi based motion sensing. We leverage the fact that multi-path for indoor environment contains certain static paths, such as reflections from wall or static furniture, as well as dynamic paths due to human hand and arm movements. When a hand moves, the phase value of the signal from the hand rotates as the path length changes and causes the superposition of signals over static and dynamic paths in antenna and frequency domain. To evaluate the effectiveness of the proposed technique, we experiment with a prototype system that can track hand gestures in a non-intrusive manner, i.e. users are not equipped with any device, using COTS WiFi devices. Our evaluation shows that calibrated phase values provide much rich, yet robust information on motion tracking -- 80th percentile angle estimation error up to 14 degrees, 80th percentile tracking error up to 15 cm, and its robustness to the environment and the speed of movement.

Cyberbullying is a major problem affecting more than half of all American teens. Prior work has largely focused on detecting cyberbullying after the fact. In this paper, we investigate the prediction of cyberbullying incidents in Instagram, a popular media-based social network. The novelty of this work is building a predictor that can anticipate the occurrence of cyberbullying incidents before they happen. The Instagram media-based social network is well-suited to such prediction since there is an initial posting of an image typically with an associated text caption, followed later by the text comments that form the basis of a specific cyberbullying incident. We extract several important features from the initial posting data for automated cyberbullying prediction, including profanity and linguistic content of the text caption, image content, as well as social graph parameters and temporal content behavior. Evaluations using a real-world Instagram dataset demonstrate that our method achieves high performance in predicting the occurrence of cyberbullying incidents.

The theme of cybersafety is an important emerging research topic on the Internet that manifests itself daily as users navigate the Web and networked applications. Examples of cybersafety issues include cyberbullying, cyberthreats, recruiting minors via Internet services for nefarious purposes, using deceptive means to dupe vulnerable populations, exhibiting misbehaving behaviors such as using profanity or flashing in online video chats, and many others. These issues have a direct negative impact on the social, psychological and in some cases physical well-being of the end users. An important characteristic of these issues is that they fall in a grey legal area, where perpetrators may claim freedom of speech or rights to free expression despite causing harm. The main goal of this inaugural workshop on cybersafety is to bring together the researchers and practitioners from academia, industry, government and research labs working in the area of cybersafety to discuss the unique challenges in addressing various cybersafety issues and to share experiences, solutions, tools, and techniques. The focus is on the detection, prevention and mitigation of various cybersafety issues, as well as education and promoting safe practices.

As online social networks have grown in popularity, teenage users have become increasingly exposed to the threats of cyberbullying. The primary goal of this research paper is to investigate cyberbullying behaviors in Vine, a mobile based video-sharing online social network, and design novel approaches to automatically detect instances of cyberbullying over Vine media sessions. We first collect a set of Vine video sessions and use CrowdFlower, a crowd-sourced website, to label the media sessions for cyberbullying and cyberaggression. We then perform a detailed analysis of cyberbullying behavior in Vine. Based on the labeled data, we design a classifier to detect instances of cyberbullying and evaluate the performance of that classifier.

Usage of social networks has exploded over the past decade or so. Users now routinely share their thought, opinions, feelings as well as their daily activities on various social networks. An interesting consequence of this explosive usage of social networks is that it is possible to glean the current mood and emotion of a user from his or her social network postings. A question that arises in this context is: Can we use any differentiating features exhibited by people on their online social activities to build appropriate classifiers that can identify the positivity or negativity of users with high accuracy and low false positive and negative rates?

Cyberbullying is a major problem affecting more than half of all American teens, and has been attributed to suicidal behavior among teens. Instagram, a media-based mobile social network, is one of the most popular social networks used for cyberbullying. In this paper, we describe the development of classifiers to detect cyberbullying in Instagram. We identify systems issues that need to be considered in the design of a cyberbullying detection system.

Augmented reality (AR) applications have recently become popular on modern smartphones. We explore the effectiveness of this mobile AR technology in the context of grocery shopping, in particular as a means to assist shoppers in making healthier decisions as they decide which grocery products to buy. We construct an AR-assisted mobile grocery-shopping application that makes real-time, customized recommendations of healthy products to users and also highlights products to avoid for various types of health concerns, such as allergies to milk or nut products, low-sodium or low-fat diets, and general caloric intake. We have implemented a prototype of this AR-assisted mobile grocery shopping application and evaluated its effectiveness in grocery store aisles. Our application's evaluation with typical grocery shoppers demonstrates that AR overlay tagging of products reduces the search time to find healthy food items, and that coloring the tags helps to improve the user's ability to quickly and easily identify recommended products, as well as products to avoid. We have evaluated our application's functionality by analyzing the data we collected from 15 in-person actual grocery-shopping subjects and 104 online application survey participants.

Cyberbullying has emerged as an important and growing social problem, wherein people use online social networks and mobile phones to bully victims with offensive text, images, audio and video on a 24/7 basis. This paper studies negative user behavior in the Ask.fm social network, a popular new site that has led to many cases of cyberbullying, some leading to suicidal behavior.We examine the occurrence of negative words in Ask.fm's question+answer profiles along with the social network of “likes” of questions+answers. We also examine properties of users with “cutting” behavior in this social network.

Effect of mood and emotion on a person's behavior and his/her interactions with other people has been studied for a long time. Positivity and negativity of a person are two important attributes of emotion and mood. Social media is a very important platform from which we can glean the positivity and negativity attributes of a user based on his/her message postings and interactions with other users. In this paper, we study and analyze a Twitter dataset of more than 130,000 users to understand the nature of their positivity and negativity attributes. We measure behavioral attributes by sentiment analysis relating to social personal concern and psychological process. We observe that social media contains useful behavioral cues to classify users into positive and negative groups based on network density and degree of social activity either in information sharing or emotional interaction and social awareness. We believe that our findings will be useful in developing tools for predicting positive and negative users and help provide the best recommendation towards helping negative users through online social media.

This paper investigates the development of accurate and efficient classifiers to identify misbehaving users (i.e., “flashers”) in a mobile video chat application. Our analysis is based on video session data collected from a mobile client that we built that connects to a popular random video chat ser

There is a growing number of viruses, malware, and other threats designed to gain access to system resources and information stored on smartphones. This paper examines the effectiveness of three techniques for detecting malicious code based on individual power consumption profiles with a focus on time and location. This project used Google's Android platform for testing and data collection. The paper describes the design, implementation, and evaluation of each of these techniques.

With the expansion of the cloud computing usage over a wide range of areas and different kinds of users, the cloud providers are taking full advantage of all their resources as much as they can. Memory is the most expensive resource in terms of oversubscribing and this has resulted in high price to the end user. Furthermore, performing swapping in Virtual Machines (VM) is expensive, so the cloud provider usually do not offer any swapping space for its systems. As a consequence, when a VM runs out of memory, user processes are killed. This scenario in the cloud environment is especially critical, since the user loses all of his/her execution time and, by extension, the money invested in this computation. This paper addresses this critical problem by providing a kernel extension that monitors the memory requirements of a VM and prevents the out of memory state by creating swapping space dynamically. The paper describes the design and implementation of a preliminary prototype of this kernel extensions and evaluates its performance.

Online video chat services such as Chatroulette and Omegle randomly match users in video chat sessions and have become increasingly popular, with tens of thousands of users online at anytime during a day. Our interest is in examining user behavior in the growing domain of mobile video, and in particular how users behave in such video chat services as they are extended onto mobile clients. To date, over four thousand people have downloaded and used our Android-based mobile client, which was developed to be compatible with an existing video chat service. The paper provides a first-ever detailed large scale study of mobile user behavior in a random video chat service over a three week period. This study identifies major characteristics such as mobile user session durations, time of use, demographic distribution and the large number of brief sessions that users click through to find good matches. Through content analysis of video and audio, as well as analysis of texting and clicking behavior, we discover key correlations among these characteristics, e.g., normal mobile users are highly correlated with using the front camera and with the presence of a face, whereas misbehaving mobile users have a high negative correlation with the presence of a face.

Online video chat services such as Chatroulette, Omegle, and vChatter that randomly match pairs of users in video chat sessions are quickly becoming very popular, with over a million users per month in the case of Chatroulette. A key problem encountered in such systems is the presence of flashers and obscene content. This problem is especially acute given the presence of underage minors in such systems. This article presents SafeVchat, a novel solution to the problem of flasher detection that employs an array of image detection algorithms. A key contribution of the article concerns how the results of the individual detectors are fused together into an overall decision classifying a user as misbehaving or not, based on Dempster-Shafer theory. The article introduces a novel, motion-based skin detection method that achieves significantly higher recall and better precision. The proposed methods have been evaluated over real-world data and image traces obtained from Chatroulette.com. SafeVchat has been deployed in Chatroulette. A combination of SafeVchat with human moderation has resulted in banning as many as 50,000 inappropriate users per day on Chatoulette. Furthermore, offensive content on Chatoulette has dropped significantly from 33.08% (before SafeVchat installation) to 3.49% (after SafeVchat installation).

Mobile phones are increasingly equipped with features that allow them to generate, store, and make use of a large amount of digital content. Such content can be associated with attributes obtained via context and/or user annotation. With the increased trend of information sharing and the success of peer-to-peer (P2P) in the wired world, the ability for users to search and share content on their mobile devices is engaging. However, infrastructure-based networks may not always be available or affordable, and attribute- based content sharing in mobile adhoc networks is desirable. This paper investigates the feasibility of such solutions and proposes a system design that incorporates efficient content discovery, robust content distribution, and adaptive energy management techniques. Detailed performance evaluation of a prototype system demonstrates that attribute-based content sharing can be effectively supported in mobile adhoc networks of smartphones over WiFi.

The need for highly scalable and accurate detection and filtering of misbehaving users and obscene content in online video chat services has grown as the popularity of these services has exploded in popularity. This is a challenging problem because processing large amounts of video is compute intensive, decisions about whether a user is misbehaving or not must be made online and quickly, and moreover these video chats are characterized by low quality video, poorly lit scenes, diversity of users and their behaviors, diversity of the content, and typically short sessions. This paper presents EMeralD, a highly scalable system for accurately detecting and filtering misbehaving users in online video chat applications. EMeralD substantially improves upon the state-of-the-art filtering mechanisms by achieving much lower computational cost and higher accuracy. We demonstrate EMeralD's improvement via experimental evaluations on real-world data sets obtained from Chatroulette.com.

Online video chat services such as Chatroulette [1] and Omegle [2] that randomly match pairs of users in video chat sessions have become increasingly popular, with over twenty thousand online users at anytime during a day. A key problem encountered in such systems is the presence of misbehaving users ("flashers") and obscene content. Our previous works [3] [4] prove that using some image recognition methods (skin-detection, dense SIFT) and machine learning algorithms could achieve significantly higher recall and better precision for flasher detection. Nowadays, with the rapid development of advanced mobile phones with both front and back cameras, we expect mobile video chat to become a popular extension of online video chat services. However, because of the computation-intensive features used by our previous solutions and mobile phones' hardware limitations such as memory size and CPU capacity, it is difficult to directly apply our previous works to mobile platforms. As smartphones are increasingly equipped with diverse sensing capabilities, we plan to utilize this multi-dimensional sensor information to extend flasher detection on mobile platform. This project explores how we can mine accelerometer and other mobile sensor data to infer some clues to optimize flasher detection accuracy while reducing the computation demands of flasher detection on the mobile device.

Online video chat services, such as Chatroulette, Omegle, and vChatter are becoming increasingly popular and have attracted millions of users. One critical problem encountered in such applications is the presence of misbehaving users ("flashers") and obscene content. Automatically filtering out obscene content from these systems in an efficient manner poses a difficult challenge. This paper presents a novel Fine-Grained Cascaded (FGC) classification solution that significantly speeds up the compute-intensive process of classifying misbehaving users by dividing image feature extraction into multiple stages and filtering out easily classified images in earlier stages, thus saving unnecessary computation costs of feature extraction in later stages. Our work is further enhanced by integrating new webcam-related contextual information (illumination and color) into the classification process, and a 2-stage soft margin SVM algorithm for combining multiple features. Evaluation results using real-world data set obtained from Chatroulette show that the proposed FGC based classification solution significantly outperforms state-of-the-art techniques.

A personal computing environment is comprised of a set of components that include processors, storage devices, networking support, and peripheral devices. This paper identifies the key functionalities needed to build a loosely- coupled, anywhere, anytime personal computing environment and then describes the design of a middleware service called CID3 (Coordinated Integration of Dynamically Discovered Devices) that provides system-level support for implementing those functionalities. CID3 has been designed to run on a variety of computing devices ranging from small mobile devices such as cell phones and PDAs to larger, resource-rich devices such as laptops and desktops. A prototype of CID3 has been implemented, and the paper reports on an evaluation of this prototype.

WiFi access points that provide Internet access to users have been steadily increasing in urban areas. Different access points differ from one another in terms of services that they provide, including available upstream and downstream bandwidths, overall network capacity, open/blocked ports, security features, and so on. However, there is no reliable service available at present that can aid a user in selecting an access point from the many that are available. The primary research challenge is how to accurately estimate the current backhaul bandwidth of different access points in an efficient manner without requiring any installation of special software on the access points, and not burdening the WiFi subscribers to perform any communication or computation intensive task. This paper presents a new highly scalable bandwidth estimation technique that is suitable for efficiently estimating the backhaul bandwidth of a large number of APs. This technique has been extensively evaluated via a prototype implementation in an indoor testbed and in the Amazon EC2 platform. The evaluation demonstrates that the proposed technique exhibits high measurement accuracy, low latency, high scalability, and minimal intrusiveness.

Online video chat services such as Chatroulette, Omegle, and vChatter that randomly match pairs of users in video chat sessions are fast becoming very popular, with over a million users per month in the case of Chatroulette. A key problem encountered in such systems is the presence of flashers and obscene content. This problem is especially acute given the presence of underage minors in such systems. This paper presents SafeVchat, a novel solution to the problem of flasher detection that employs an array of image detection algorithms. A key contribution of the paper concerns how the results of the individual detectors are fused together into an overall decision classifying the user as misbehaving or not, based on Dempster-Shafer Theory. The paper introduces a novel, motion-based skin detection method that achieves significantly higher recall and better precision. The proposed methods have been evaluated over real-world data and image traces obtained from Chatroulette.com.

Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people’s social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance. In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.

With 802.11 WiFi networks becoming popular in homes, it is common for an end-user to have access to multiple WiFi access points (APs) from residents next door. In general, wireless networks have much higher bandwidth than residential Internet (DSL or Cable) connections. This provides an incentive for an end-user to simultaneously harness bandwidths from multiple APs. This paper introduces ARBOR, an 802.11 driver that aggregates broadband connections in a neighborhood and maximizes Internet access bandwidth in a secure manner. ARBOR has four important characteristics. First, ARBOR can sustain a much longer switching cycle without losing packets queued at different APs. Second, it can schedule traffic loads and (in)directly aggregate AP backhaul bandwidths. Third, ARBOR designs and implements a light-weight authentication mechanism that provides sufficient amount of security, and at the same time, ensures fast switching time. Finally, ARBOR is transparent to the upper layers of the network stack. A prototype of ARBOR has been implemented and extensively evaluated. Experiment results show that ARBOR provides significantly better throughput gains and lower Internet access delays.

Nowadays, tariffs and packages on offer for broadband worldwide gives a general story of increased speed and reduced prices. As a complementary to fixed-line broadband access, WiFi networks are undoubtedly taking off in many American households. Due to the combination of wireless technology and fixed-line broadband access, prior available bandwidth measurement tools may not be an appropriate solution for a common broadband subscriber. In this paper, we utilize abget in combination with Multi-Router Traffic Grapher (MRTG) to target the tight link of the Internet. Based on the observation that the tight link of the Internet in the context of an in-home wireless broadband access is usually on the edge of the Internet, we then introduce ABODE, a single-end, light-weight tool for estimating available bandwidth in the context of an in-home wireless bandwidth environment. ABODE harnesses ICMP echo request messages to generate stable, rate-controlled traffic flows. Based on the timestamp of ICMP echo reply messages, ABODE performs available bandwidth estimation by calculating the drift of the time centroid over a traffic flow. To verify the accuracy of ABODE, we use MRTG data to compare with the estimation results of ABODE. The measurement shows that ABODE is capable of efficiently estimating available bandwidth in the context of an in-home wireless broadband environment.

This paper describes an approach to provide intrusion tolerance and privacy to the replies from a state machine replication system. This new voting architecture provides three important functionalities. First, it filters out any incorrect replies from compromised servers. Second, it prevents leakage of replies to an adversary despite server or voter compromise. Third, it provides support for nondeterminism in the replies by matching semantically similar replies even when they are not identical. A prototype of the proposed architecture has been implemented and its performance has been measured using a microbenchmark. The result demonstrates its feasibility and practicality.

Interactive applications such as email, calendar, and maps are migrating from local desktop machines to data centers due to the many advantages offered by such a computing environment. Furthermore, this trend is creating a marked increase in the deployment of servers at data centers. To ride the price/performance curves for CPU, memory and other hardware, inexpensive commodity machines are the most cost effective choices for a data center. However, due to low availability numbers of these machines, the probability of server failures is relatively high. Server failures can in turn cause service outages, degrade user experience and eventually result in lost revenue for businesses. We propose a TCP splice-based Web server architecture that seamlessly tolerates both Web proxy and backend server failures. The client TCP connection and sessions are preserved, and failover to alternate servers in case of server failures is fast and client transparent. The architecture provides support for both deterministic and non-deterministic server applications. A prototype of this architecture has been implemented in Linux, and the paper presents detailed performance results for a PHP-based Webmail application deployed over this architecture.

This paper describes the design, implementation and performance of a security and privacy framework for SenSearch, which is a sensor-based search and rescue system for people in emergency situation in wilderness areas. A key obstacle in a wide deployment of search and rescue systems such as SenSearch is vulnerability to users' security and privacy. Support for security and privacy for SenSearch has been carefully built by employing a combination of both symmetric and asymmetric key cryptography to meet the constraints of resource-limited devices and short time intervals during which most security operations have to be performed.

Tuple spaces have been used to build a wide variety of parallel and distributed applications. Two popular systems for building tuple space applications at present are JavaSpaces and Rinda. JavaSpaces uses Java RMI for distributed communication, while Rinda uses Ruby's DRb. Despite an abundance of applications built using JavaSpaces and Rinda, there hasn't been any comparative evaluation of these two systems. This paper provides a detailed ex perimental evaluation of these two systems under several different networking configurations and operating scenarios.

This paper presents a secure and lightweight protocol for reliable data transfer through moderate bandwidth covert channels. Though data transfer through covert channels is not unprecedented, existing covert channels have been restricted to covert transmission of only small amounts of data. This paper demonstrates that it is possible to transmit large amounts of data covertly with sophisticated support such as security and reliability. The proposed protocol exploits ICMP Echo Request as covert medium, and uses OS finger-printing techniques to simulate real TCP/IP stack behavior for further security enhancements.

Remote reprogramming of in situ wireless sensor networks (WSNs) via the wireless link is an important capability. Securing the process of reprogramming allows each sensor node to authenticate each received code image. Due to the resource constraints of WSNs, public key schemes must be used sparingly. This paper introduces a mechanism for secure and efficient code distribution that employs public key cryptography only to sign the root of a combined structure consisting of both hash chains and hash trees. The chain based scheme works best when packets are received in the order they are sent with very few losses. Our hash tree based scheme allows nodes to authenticate packets and verify their integrity quickly, even when the packets may arrive out of order, but can result in too many public key operations. Integrating hash chains and hash trees produces a mechanism that is both resilient to losses and lightweight in terms of reducing memory consumption and the number of public key operations that a node has to perform. Simulation shows that the proposed secure reprogramming schemes add only a modest amount of overhead to a conventional non-secure reprogramming scheme, namely Deluge, and are therefore feasible and practical in a wireless sensor network.

This paper describes three enhancements to the TCP splicing mechanism: (1) Enable a TCP connection to be simultaneously spliced through multiple machines for higher scalability; (2) Make a spliced connection fault-tolerant to proxy failures; and (3) Provide flexibility of splitting a TCP splice between a proxy and a backend server for further increasing the scalability of a Web server system. A Web server architecture based on this enhanced TCP splicing is proposed. This architecture provides a highly scalable, seamless service to the users with minimal disruption during server failures. In addition to the traditional Web services in which users download Web pages, multimedia files and other types of data from a Web server, the proposed architecture supports newly emerging Web services that are highly interactive, and involve relatively longer, stateful client-server sessions. A prototype of this architecture has been implemented as a Linux 2.6 kernel module, and the paper presents important performance results measured from this implementation

ST-TCP (server fault-tolerant TCP) is an extension of TCP to tolerate TCP server failures. Server fault tolerance is provided by using an active-backup server that keeps track of the state of a TCP connection. The backup server takes over the TCP connection if the primary server fails. This take-over is fast, seamless, and completely transparent to the client. This paper provides a system demonstration of a new ST-TCP prototype. The new prototype incorporates a performance-enhanced architecture and addresses application failure scenarios. Five experiments using this prototype are proposed to demonstrate the following useful features: (1) client-transparent, seamless failover; (2) insignificant performance overhead of ST-TCP during failure-free periods; and (3) an ability to tolerate all single crash failures at the hardware and operating system levels, and, most crash failures at the application level.

Establishing secure links between pairs of directly connected sensor nodes is an important primitive for building secure wireless sensor networks. This paper systematically identifies two important security requirements of pairwise key setup in wireless sensor networks, namely opaqueness and inoculation. Transitory master key schemes, such as the LEAP protocol, can satisfy both requirements if the master key has not been compromised. However, if the master key is compromised, every key in the network is exposed to an adversary. To prevent the master key from becoming a single point failure of the whole system, we propose a new opaque transitory master key (OTMK) scheme for pairwise key setup in sensor networks. In OTMK, even if the master key is compromised, an adversary can only exploit a small number of keys nearby the compromised node, while other keys in the network remain safe. To further investigate key establishment schemes, we experimented with a way to compromise a sensor node, and tested our key establishment time in a real sensor network environment.

Wireless sensor networks are highly vulnerable to the failure of base stations. An adversary can render a wireless sensor network useless by launching remote, softwarebased attacks or physical attacks on the base stations. This paper addresses the problem of defending a base station against physical attacks by concealing the geographic location of a base station. Typical packet traffic in a sensor network reveals pronounced patterns that allow an adversary analyzing packet traffic to deduce the location of a base station. The paper investigates several countermeasures against traffic analysis techniques aimed at disguising the location of a base station. First, a degree of randomness is introduced in the multi-hop path a packet takes from a sensor node to a base station. Second, random fake paths are introduced to confuse an adversary from tracking a packet as it moves towards a base station. Finally, multiple, random areas of high communication activity are created to deceive an adversary as to the true location of the base station. The paper evaluates these techniques analytically and via simulation using three evaluation criteria: total entropy of the network, total energy consumed, and the ability to guard against heuristic-based techniques to locate a base station.

This paper describes the design, implementation and evaluation of a search and rescue system called CenWits. CenWits uses several small, commonly-available RF-based sensors, and a small number of storage and processing devices. It is designed for search and rescue of people in emergency situations in wilderness areas. A key feature of CenWits is that it does not require a continuously connected sensor network for its operation. It is designed for an intermittently connected network that provides only occasional connectivity. It makes a judicious use of the combined storage capability of sensors to filter, organize and store important information, combined battery power of sensors to ensure that the system remains operational for longer time periods, and intermittent network connectivity to propagate information to a processing center. A prototype of CenWits has been implemented using Berkeley Mica2 motes. The paper describes this implementation and reports on the performance measured from it.

Wireless sensor networks face acute security concerns in applications such as battlefield monitoring. A central point of failure in a sensor network is the base station, which acts as a collection point of sensor data. In this paper, we investigate two attacks that can lead to isolation or failure of the base station. In one set of attacks, the base station is isolated by blocking communication between sensor nodes and the base station, e.g. by DOS attacks. In the second attack, the location of the base station is deduced by analyzing data traffic towards the base station, which can lead to jamming and/or discovery and destruction of the base station. To defend against these attacks, two secure strategies are proposed. First, secure multi-path routing to multiple destination base stations is designed to provide intrusion tolerance against isolation of a base station. Second, anti-traffic analysis strategies are proposed to help disguise the location of the base station from eavesdroppers. A performance evaluation is provided for a simulated sensor network, as well as measurements of cryptographic overhead on real sensor nodes.

This paper describes the support provided for mobility and fault tolerance in Mykil, which is a key distribution protocol for large, secure group multicast. Mykil is based on a combination of group-based hierarchy and key-based hierarchy systems. Important advantages of Mykil include a fast and efficient rekeying operation for large group sizes, continuous availability of the key management service in a disconnected network environment, an ability to map group structure to the underlying network infrastructure, fault tolerance, and support for member mobility and smaller hand-held devices.

This paper describes the design, implementation, and performance evaluation of ST-TCP (Server fault-Tolerant TCP), which is an extension of TCP to tolerate TCP server failures. This is done by using an active backup server that keeps track of the state of the TCP connection and takes over the TCP connection whenever the primary fails. This migration of the TCP connection to the backup is completely transparent to the client. Because no changes are required on the client machine, any TCP client can access a ST-TCP server. The performance overhead of ST-TCP over standard TCP is minimal, and during normal operation its behavior is the same as that of a regular TCP. In addition, ST-TCP provides a fast and seamless failover whenever the primary server fails. This is verified by a prototype implementation of ST-TCP in the Linux operating system, and experiments with a number of simulated applications which have different communication characteristics.

This paper describes the design, implementation, and evaluation of Mykil, which is a new key distribution protocol for secure group multicast. Mykil has been designed to be scalable to large group sizes. It is based on a combination of group-based hierarchy and key-based hierarchy systems for group key management. Important advantages of Mykil include a fast rekeying operation for large group sizes, continuous availability of the key management service in a disconnected network environment, an ability to map the group structure to the underlying network infrastructure, robustness, support for user mobility, and support for smaller hand-held devices.

This paper describes the design, implementation, and evaluation of interagent communication and synchronization models in the DaAgent mobile-agent based computing system. Based on the requirements of some sample Internet computing applications, eight system-level models of interagent communication and synchronization are proposed. A new synchronization mechanism called location synchronization that is relevant for interacting mobile agents is also proposed. This paper evaluates the eight models based on their utility, performance, level of communication and synchronization support, and applicability in the Internet computing environment. A prototype implementation and detailed performance evaluation of these models based on two interacting, multiagent applications are also presented.

Describes the timewheel group communication system, which has been designed for a timed asynchronous distributed system model. All protocols in the timewheel group communication system have been designed to be fail-aware in the sense that a process can detect, at any point in time, whether any of its properties is violated. Although these protocols have been designed to operate in an asynchronous distributed computing environment, they provide timeliness properties. The timewheel group communication system provides nine group communication semantics that a user can dynamically choose from while broadcasting an update. This system provides high throughput, fast delivery and stability times, uses a small number of messages per update broadcast, and evenly distributes the processing load among group members.

This paper describes the design and implementation of interagent communication and synchronization models in the DaAgent mobile agent-based computing system. Eight models of interagent communication and synchronization are proposed. The paper evaluates these models based on their utility, performance, and applicability in an Internet computing environment.

Current group communication services have mostly been implemented on a homogeneous, distributed computing environment. This limits their applicability because most modern distributed computing environment are heterogeneous in nature. This paper describes the design, implementation, and performance evaluation of a CORBA group communication service. Using CORBA to implement a group communication service enables that group communication service to operate in a heterogeneous, distributed computing environment. To evaluate the effect of CORBA on the performance of a group communication service, this paper provides a detailed comparison of the performance measured from three implementations of an atomic broadcast protocol and a group membership protocol. Two of these implementations use CORBA, while the third uses UDP sockets for interprocess communication. The main conclusion is that heterogeneity can be achieved in group communication services by implementing them using CORBA, but there is a substantial performance cost. This performance cost can be reduced to a certain extent by carefully choosing a design and tuning various protocol parameters such as buffer sizes and timer values.

Group communication services have been successfully used to construct applications with high availability, dependability, and real-time responsiveness requirements. Flow control techniques enable group members to manage their local buffers, which they use to temporarily store multicast updates. Despite buffer overflow being one of the main causes of process failures, flow control has not been studied much in the literature. We study different flow control techniques used in some of the group communication services and present two generic flow control techniques: a conservative and an optimistic technique. All existing flow control techniques for group communication can be classified as either conservative or optimistic. We then present discrete event simulation results that compare the effect of these two generic flow control techniques on the performance of two, different atomic multicast protocols, a positive acknowledgment protocol and a negative acknowledgment protocol, under several different operating conditions. Based on the study of differed existing flow control techniques for group communication and the results obtained from the simulation experiment, we provide some design guidelines for the design and implementation of a suitable flow control technique for a given group communication service.

This paper describes the design, implementation, and performance of a stable-storage service that has been implemented on top of the Unix operating system. This service allows servers to create, access, and delete persistent memory that survives server crashes. We describe its functionality and exported operations, discuss the experiences and performance of its implementation, and offer concrete examples of its use in implementing some real fault-tolerant distributed protocols.

We discuss two asynchronous atomic broadcast protocols that provide fast delivery and stability times, use a small number of messages to accomplish a broadcast, distribute evenly the processing load, use efficient flow control techniques, and provide gracefully degraded performance in the presence of communication failures. In a prototype implementation on top of UDP and Ethernet, for a group of three broadcast servers, these protocols achieve a throughput of up to a thousand independent broadcasts per second and measure average delivery and stability times of 2.9 and 4.7 msec.>

We develop a framework that helps in developing understanding of a fault-tolerant distributed system and so helps in designing such systems. We define a unit of computation in such systems, referred to as a molecule, that has a well defined interface with other molecules, i.e. has minimal dependence on other molecules. The smallest such unit-an indivisible molecule-is termed as an atom. We show that any execution of a fault-tolerant distributed computation can be seen as an execution of molecules/atoms in a partial order, and such a view provides insights into understanding the computation, particularly for a fault tolerant system where it is important to guarantee that a unit of computation is either completely executed or not at all and system designers need to reason about the states after execution of such units. We prove different properties satisfied by molecules and atoms, and present algorithms to detect atoms in an ongoing computation and to force the completion of a molecule. We illustrate the uses of the developed work in application areas such as debugging, checkpointing, and reasoning about stable properties.>

Consul is a system designed to support the use of fault-tolerance techniques in the construction of fault-tolerant distributed systems structured according to the state machine approach. The way in which modularity has been used in the design and implementation of Consul is described. The approach makes it easy to configure a system customized to the needs of a specific application and facilitates the development of the individual components that make up Consul.>

Abstract The use of modularity in the design and implementation of complex software simplifies the development process, as well as facilitating the construction of customized configurations. This paper describes our experience using modularity in Consul, a communication substrate used for constructing fault‐tolerant distributed programs. First, Consul is presented as a case study of how modularity is feasible in both the design and the implementation of such systems. Secondly, general lessons about modularity in fault‐tolerant systems based on our experience with Consul are given. Issues that are addressed include deciding how the system is divided into various modules, dealing with problems that result when protocols are combined, and ensuring that the underlying object infrastructure provides adequate support. The key observation is that the modularization process is most affected by dependencies between modules, both direct dependencies caused by one module explicitly using another's operation and indirect dependencies where one module is affected by another without direct interaction. Although our observations are based on designing and implementing Consul, the lessons are applicable to any fault‐tolerant distributed system.

Psync is an IPC protocol that explicitly preserves the partial order of messages exchanged among a set of processes. A description is given of how Psync can be used to implement replicated objects in the presence of network and host failures. Unlike conventional algorithms that depend on an underlying mechanism that totally orders messages for implementing replicated objects, the authors' approach exploits the partial order provided by Psync to achieve additional concurrency.>