A fundamental problem in crowd localization using computer vision techniques stems from intrinsic scale shifts. Scale shifts occur when the crowd density within an image is uneven and chaotic, a feature common in dense crowds. At locations nearer to the camera, crowd density is lower than those farther away. Consequently, there is a significant change in the number of pixels representing a person across locations in an image depending on the camera’s position. Existing crowd localization methods do not effectively handle scale shifts, resulting in relatively poor performance in dense crowd images. In this paper, we explicitly address this challenge. Our method, called Gaussian Loss Transformers (GLT), directly incorporates scale variants in crowds by adapting loss functions to handle them in the end-to-end training pipeline. To inform the model about the scale variants within the crowd, we utilize a Gaussian mixture model (GMM) for pre-processing the ground truths into non-overlapping clusters. This cluster information is utilized as a weighting factor while computing the localization loss for that cluster. Extensive experiments on state-of-the-art datasets and computer vision models reveal that our method improves localization performance in dense crowd images. We also analyze the effect of multiple parameters in our technique and report findings on their impact on crowd localization performance.

Docker is a vital tool in modern development, enabling the creation, deployment, and execution of applications using containers, thereby ensuring consistency across various environments. However, developers often face challenges, particularly with filesystem complexities and performance bottlenecks when working directly within Docker containers. This is where Mutagen comes into play, significantly enhancing the Docker experience by offering efficient network file synchronization, reducing latency in file operations, and improving overall data transfer rates in containerized environments. By exploring Docker’s architecture, examining Mutagen’s role, and evaluating their combined performance impacts, particularly in terms of file operation speeds and development workflow efficiencies, this research provides a deep understanding of these technologies and their potential to streamline development processes in networked and distributed environments.

In this work we present SensiTrain - a data collection system designed with the goal of curating a dataset for understanding how differences in cultural backgrounds, sexual orientation, gender, race and national origin affect the way one perceives statements made on social media as being either benign or hurtful and sometimes even hateful and aggressive. SensiTrain displays social media posts to users and asks them to categorize the posts as being either benign or hurtful while providing a reason for such classification. We plan to use the data thus collected to build a public dataset repository and use the same to train a baseline system for identifying potentially hurtful statements before they are posted on social media platforms. The resulting system would be able to explain why a given statement could be perceived as hurtful by one or more groups thus making the inference more trustworthy to the users of the system. We hope that through this endeavor we will be able to mitigate unintentional harm caused due to lack of understanding of different cultures and backgrounds, thus making the world a kinder place.

By analyzing data gathered through Online Learning (OL) systems, data mining can be used to unearth hidden relationships between topics and trends in student performance. Here, in this paper, we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system module. In our implementation, we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%. By focusing on marks below this threshold, we aim to identify and establish associations based on the patterns of weakness observed in the past data. Additionally, we leverage K-means clustering to provide instructors with visual representations of the generated associations. This strategy aids instructors in better comprehending the information and associations produced by the algorithms. K-means clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights, enabling them to support the verification of the relationship between topics. This can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their pedagogy. This paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

Since massive numbers of images are now being communicated from, and stored in different cloud systems, faster retrieval has become extremely important. This is more relevant, especially after COVID-19 in bandwidth-constrained environments. However, to the best of our knowledge, a coherent solution to overcome this problem is yet to be investigated in the literature. In this article, by customizing the Progressive JPEG method, we propose a new Scan Script to ensure Faster Image Retrieval. Furthermore, we also propose a new lossy PJPEG architecture to reduce the file size as a solution to overcome our Scan Script's drawback. In order to achieve an orchestration between them, we improve the scanning of Progressive JPEG's picture payloads to ensure Faster Image Retrieval using the change in bit pixels of distinct Luma and Chroma components ($Y$,$C_{b}$, and$C_{r}$). The orchestration improves user experience even in bandwidth-constrained cases. We evaluate our proposed orchestration in a real-world setting across two continents encompassing a private cloud. Compared to existing alternatives, our proposed orchestration can improve user waiting time by up to 54% and decrease image size by up to 27%. Our proposed work is tested in cutting-edge cloud apps, ensuring up to 69% quicker loading time.

Epidemics and pandemics have been affecting human lives since time, and have sometimes altered the course of history. At this very moment, Coronavirus (COVID-19) pandemic has been the defining global health crisis. Now, perhaps for the first time in history, humanity as a whole has undergone major disruptions to life and some form of lockdown. New policies need to be forged by policy-makers for various sectors such as trading, banking, education, etc., to lessen losses and to heal quickly. For efficient policy-making, in turn, some prerequisites needed are historical trend analysis on the pandemic spread, future forecasting, the correlation between the spread of the disease and various socio-economic and environmental factors, etc. Besides, all of these need to be presented in an integrated manner in real-time to facilitate efficient policy-making. Therefore, in this work, we developed a web-based integrated real-time operational dashboard as a one-stop decision support system for COVID-19. In our study, we conducted a detailed data-driven analysis based on available data from multiple authenticated sources to predict the upcoming consequences of the pandemic through rigorous modeling and statistical analyses. We also explored the correlations between disease spread and diverse socio-economic as well as environmental factors. Furthermore, we presented how the outcomes of our work can facilitate both contemporary and future policy-making.

Jamshidbek Mirzakhalov, Anoop Babu, Duygu Ataman, Sherzod Kariev, Francis Tyers, Otabek Abduraufov, Mammad Hajili, Sardana Ivanova, Abror Khaytbaev, Antonio Laverghetta Jr., Bekhzodbek Moydinboyev, Esra Onal, Shaxnoza Pulatova, Ahsan Wahab, Orhan Firat, Sriram Chellappan. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.

Bangladesh (a low-income country) has a significant number of people dependent on alms for daily survival. These people, who we address as extremely impoverished people (EIP) are deprived of even basic healthcare. Their extreme levels of poverty, coupled with low literacy skills, and complete lack of access to technology means that they are unaware of existing low-cost/free healthcare services (as arranged by local hospitals) available for EIPs. In this paper, we address this gap by means of a carefully-crafted solution, Dakter Bari (a term in Bengali that translates to "Doctor's Home'' in English), that is contextually tailored to enable healthcare access to impoverished people. Extracting critical insights from our field study with (N=70) EIPs, we create a pathway for availing lower-cost healthcare solutions using intermediaries for information dissemination. These intermediaries are small businesses that impoverished people visit often. We also conduct field studies with (N=71) intermediary partners and (N=10) hospitals to identify challenges and realities of such intermediary-based solutions. Based on our findings, we design, iteratively develop, deploy, and user-test our system in real cases and collect feedback from related stakeholders. Preliminary analysis on usage of our system (deployed at intermediaries) revealed 255 healthcare requests made by EIPs via our system in six months. We connect our finding to the broader interests of CSCW around contextualized intermediation, inclusive healthcare, and sustainability of deployed systems.

A recent development emanating from RFID technology is Near Field Communication (NFC). Basically, NFC is a popular short range (<10 cm) wireless communication technology with applications in areas sensitive to security and privacy concerns such as contactless payment. Since NFC communications require very close proximity between two communicating devices (e.g., a smartcard and a terminal), it is generally believed that Man-in-the-Middle (MITM) attacks are practically infeasible here. Contrasting this belief, in this paper, we successfully establish MITM attack in NFC communications between a passive tag and an active terminal. We carefully present physical fundamentals of the attack, our engineering design, and results of successful attack implementation. Subsequently, we present the practical applicability of our MITM attack that exploits a potential vulnerability in EMV based contactless payment protocol, which arises due to separation between card authentication and transaction authorization phases. We demonstrate how an attacker can compromise the integrity of a contactless payment using a malicious MITM card, and also present multiple attack/victim scenarios to analyze different types of impacts of our attack. Further, we conduct rigorous experimental studies to analyze both hardware and practical ramifications of our attack. Finally, we propose a mechanism to detect the MITM attack based on experimental analysis that demands no additional hardware.

Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25, 867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are especially deadly vectors) is amongst the highest. We also present important lessons learned and practical impact of our techniques in this paper.

Securing information transmission is critical today. However, with rapidly developing powerful quantum technologies, conventional cryptography techniques are becoming more prone to attacks each day. New techniques in the realm of quantum cryptography to preserve security against powerful attacks are slowly emerging. What is important though now is the fidelity of the cryptography, because security with massive processing power is not worth much if it is not correct. Focusing on this issue, we propose a method to enhance the fidelity of quantum cryptography using maximally entangled qubit pairs. For doing so, we created a graph state along a path consisting of all the qubits of ibmqx4 and ibmq_16_melbourne respectively and we measure the strength of the entanglement using negativity measurement of the qubit pairs. Then, using the qubits with maximal entanglement, we send the modified encryption key to the receiver. The key is modified by permutation and superdense coding before transmission. The receiver reverts the process and gets the actual key. We carried out the complete experiment in the IBM Quantum Experience project. Our result shows a 15% to 20% higher fidelity of encryption and decryption than a random selection of qubits.

Social media platforms are widely used by people to report, access, and share information during outbreaks and epidemics. Although government agencies and healthcare institutions in developed regions are increasingly relying on social media to develop epidemic forecasts and outbreak response, there is a limited understanding of how people in developing regions interact on social media during outbreaks and what useful insights this dataset could offer during public health crises. In this work, we examined 28,688 tweets to identify public health issues during dengue epidemic in Bangladesh and found several insights, such as irregularities in dengue diagnosis and treatment, shortage of blood supply for Rh negative blood groups, and high local transmission of dengue during Eid-ul-Adha, that impact disease preparedness and outbreak response. We discuss the opportunities and challenges in analyzing tweets and outline how government agencies and healthcare institutions can use social media health data to inform policy making during public health crises.

Demand for fast data sharing among smart devices is rapidly increasing. This trend creates challenges towards ensuring essential security for online shared data while maintaining the resource usage at a reasonable level. Existing research studies attempt to leverage compression based encryption for enabling such secure and fast data transmission replacing the traditional resource-heavy encryption schemes. Current compression-based encryption methods mainly focus on error insensitive digital data formats and prone to be vulnerable to different attacks. Therefore, in this paper, we propose and implement a new Huffman compression based Encryption scheme using lightweight dynamic Order Statistic tree (HEliOS) for digital data transmission. The core idea of HEliOS involves around finding a secure encoding method based on a novel notion of Huffman coding, which compresses the given digital data using a small sized "secret" (called as secret_intelligence in our study). HEliOS does this in such a way that, without the possession of the secret intelligence, an attacker will not be able to decode the encoded compressed data. Hence, by encrypting only the small-sized intelligence, we can secure the whole compressed data. Moreover, our rigorous real experimental evaluation for downloading and uploading digital data to and from a personal cloud storage Dropbox server validates efficacy and lightweight nature of HEliOS.

Lying is a (practically) unavoidable component of our day to day interactions with other people, and it includes both oral and textual communications (e.g. text entered via smartphones). Detecting when a person is lying has important applications, especially with the ubiquity of messaging via smart-phones, coupled with rampant increases in (intentional) spread of mis-information today. In this paper, we design a technique to detect whether or not a person's textual inputs when typed via a smartphone indicate lying. To do so, first, we judiciously develop a smartphone based survey that guarantees any participant to provide a mix of true and false responses. While the participant is texting out responses to each question, the smartphone measures readings from its inbuilt inertial sensors, and then computes features like shaking, acceleration, tilt angle, typing speed etc. experienced by it. Subsequently, for each participant (47 in total), we glean the true and false responses using our own experiences with them, and also via informal discussions with each participant. By comparing the responses of each participant, along with the corresponding motion features computed by the smartphone, we implement several machine learning algorithms to detect when a participant is lying, and our accuracy is around 70% in the most stringent leave-one-out evaluation strategy. Later, utilizing findings of our analysis, we develop an architecture for real-time lie detection using smartphones. Yet another user evaluation of our lie detection system yields 84%-90% accuracy in detecting false responses.

Abuse in cyber space is a problem requiring immediate attention. Unfortunately, despite advances in Natural Language Processing techniques, there are clear limitations in detecting instances of cyber abuse today. Challenges arising due to different languages that teens communicate with today, and usage of codes along with code mixing and code switching make the design of a comprehensive approach very hard. Existing NLP based approaches for detecting cyber abuse thus suffer from a high degree of false negatives and positives. In this paper, we investigate a new approach to detect instances of cyber abuse. Our approach is motivated by the premise that abusers tend to have unique facial expressions while engaging in an actual abuse episode, and if we are successful, such an approach will be language-agnostic. Here, using only four carefully identified facial features without any language processing, and realistic experiments with 15 users, our system proposed in this paper achieves 98% accuracy for same-user evaluation and up to 74% accuracy for cross-user evaluation in detecting instances of cyber abuse.

Chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF) are leading chronic health concerns among the aging population today. They are both typically characterized by episodes of cough that share similarities. In this paper, we design TussisWatch, a smart-phone-based system to record and process cough episodes for early identification of COPD or CHF. In our technique, for each cough episode, we do the following: 1) filter noise; 2) use domain expertise to partition each cough episode into multiple segments, indicative of disease or otherwise; 3) identify a limited number of audio features for each cough segment; 4) remove inherent biases as a result of sample size differences; and 5) design a two-level classification scheme, based on the idea of Random Forests, to process a recorded cough segment. Our classifier, at the first-level, identifies whether or not a given cough segment indicates a disease. If yes, the second-level classifier identifies the cough segment as symptomatic of COPD or CHF. Testing with a cohort of 9 COPD, 9 CHF, and 18 CONTROLS subjects spread across both the genders, races, and ages, our system achieves good performance in terms of Sensitivity, Specificity, Accuracy, and Area under ROC curve. The proposed system has the potential to aid early access to healthcare, and may be also used to educate patients on self-care at home.

In this paper, we explore the feasibility of leveraging the accelerometer and gyroscope sensors in modern smartphones to detect instances of distracted driving activities (e.g., calling, texting, and reading while driving). To do so, we conducted an experiment with 16 subjects on a realistic driving simulator. As discussed later, the simulator is equipped with a realistic steering wheel, acceleration/braking pedals, and a wide screen to visualize background vehicular traffic. It is also programmed to simulate multiple environmental conditions like daytime, nighttime, fog, and rain/snow. The subjects were instructed to drive the simulator while performing a randomized sequence of activities that included texting, calling, and reading from a phone while they were driving, during which the accelerometer and gyroscope in the phone were logging sensory data. By extracting features from this sensory data, we then implemented a machine learning technique based on random forests to detect distracted driving. Our technique achieves very good precision, recall, and F -measure across all environmental conditions we tested. We believe that our contributions in this paper can have a significant impact on enhancing road safety.

Current state-of-the-art systems in the literature using wearables are not capable of distinguishing a large number of fine-grained and/or complex human activities, which may appear similar but with vital differences in context, such as lying on floor versus lying on bed versus lying on sofa. This paper fills the gap by proposing a novel system, called HuMAn, that recognizes and classifies complex at-home activities of humans with wearable sensing. Specifically, HuMAn makes such classifications feasible by leveraging selective multi-modal sensor suites from wearable devices, and enhances the richness of sensed information for activity classification by carefully leveraging placement of the wearable devices across multiple positions on the human body. The HuMAn system consists of the following components: (a) a practical feature set extraction method from selected multi-modal sensor suites; and (b) a novel two-level structured classification algorithm that improves accuracy by leveraging sensors in multiple body positions; and (c) improved refinement in classification of complex activities with minimal external infrastructure support (e.g., only a few Bluetooth beacons used for location context). The proposed system is evaluated with 10 users in real home environments. Experimental results demonstrate that the HuMAn system can detect 21 complex at-home activities with high degree of accuracy. For same-user evaluation strategy, the average activity classification accuracy is as high as 95 percent over all of the 21 activities. For the case of 10-fold cross-validation evaluation strategy, the average classification accuracy is 92 percent, and for the case of leave-one-out cross-validation strategy, the average classification accuracy is 75 percent.

In this paper, we address an important and practical problem - namely to forecast the duration and volume of Internet usage of a subject based on pervasive and unobtrusive of past history. Unfortunately though, profiling users can have privacy ramifications. In this paper, we present a statistical framework to forecast duration and volume of Internet usage of subjects via processing NetFlow logs from routers. Briefly, NetFlow logs are network level information of IP packets as they traverse a router, but they do not contain the packet payload. In our experimental study, Internet traffic logs of octets and durations of 66 subjects in a college campus were collected (via privacy-preserving NetFlow records) in a pervasive and unobtrusive manner for a month. By applying times series forecasting techniques, we demonstrate that predictions on duration and volume of usage at future times can be made based on past usage, with very good precision. Furthermore, our results also show that with more historical data, prediction accuracy improves further. We believe that our problem in this paper has not been addressed in the literature. We also believe that our contributions in this paper have important consequences in enabling privacy preserving techniques to manage network resources for administrators, cyber security via behavioral based authentication, and smarter advertising.

Nowadays, Internet-based technologies possess immense processing power, capacity, flexibility, and are gradually moving towards a service- oriented functionality in order to build new distributed storage systems in the cloud. Several distributed systems are currently running in different geographically located data centers for successful deployment of modern web and social services such as Facebook, Twitter, ringID, etc. Both cache and backend servers in such distributed systems must be functional and reliable for incoming workloads by means of efficient allocation of capacity along with proper configuration and tuning of multiple system resources. To address these challenges, in this paper, we propose a test-driven automated architecture for load testing, named as 'svLoad' to compare the performance of cache and backend servers. Here, we designed test cases considering diversified real scenarios such as different protocol types, same or different URLs, with or without load, cache hit or miss, etc. using tools namely JMeter, Ansible, and some custom utility bash scripts. To validate the efficacy of our proposed methodology, we conduct a set of experiments by running these test cases over a real private cloud development setup using two open source projects - Varnish as the cache server and OpenStack Swift as the backend server. Our focus is also to find out bottlenecks of Varnish and Swift by executing load requests, and then tune the system based on our load test analysis. After successfully tuning the Swift, Varnish, and network system, based on our test analysis, we were able to improve the response time by up to 80%.

Mosquito borne diseases continue to pose grave dangers to global health. An important step in combating their spread in any area is to identify the type of species prevalent there. To do so, trained personnel lay local mosquito traps, and after collecting trapped specimens, they visually inspect each specimen to identify the species type and log their counts. This process takes hours and is cognitively very demanding. In this paper, we design a smart-phone based system that allows anyone to take images of a still mosquito that is either alive or dead (but still retaining its physical form) and automatically classifies the species type. Our system integrates image processing, feature selection, unsupervised clustering, and an SVM based machine learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species (that were captured from a real outdoor trap in Tampa, Florida) demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. With our system, the currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Secondly, ordinary citizens can use our system in their own homes for self-awareness and information sharing with public health agencies.

In a 2012 survey, in the United States alone, there were more than 35 000 reported suicides with approximately 1800 of being psychiatric inpatients. Recent Centers for Disease Control and Prevention (CDC) reports indicate an upward trend in these numbers. In psychiatric facilities, staff perform intermittent or continuous observation of patients manually in order to prevent such tragedies, but studies show that they are insufficient, and also consume staff time and resources. In this paper, we present the Watch-Dog system, to address the problem of detecting self-harming activities when attempted by in-patients in clinical settings. Watch-Dog comprises of three key components-Data sensed by tiny accelerometer sensors worn on wrists of subjects; an efficient algorithm to classify whether a user is active versus dormant (i.e., performing a physical activity versus not performing any activity); and a novel decision selection algorithm based on random forests and continuity indices for fine grained activity classification. With data acquired from 11 subjects performing a series of activities (both self-harming and otherwise), Watch-Dog achieves a classification accuracy of , , and for same-user 10-fold cross-validation, cross-user 10-fold cross-validation, and cross-user leave-one-out evaluation, respectively. We believe that the problem addressed in this paper is practical, important, and timely. We also believe that our proposed system is practically deployable, and related discussions are provided in this paper.

In a heterogeneous computing cluster, cluster objectives are conflicting to each other. Selecting a right combination of machines is necessary to enhance cluster performance, and to optimize all the cluster objectives. In this paper, we perform empirical performance analyses of a real cluster with our year-long collected data, formulate a new many-objective optimization problem for clusters, and integrate a greedy approach with the existing NSGA-III algorithm to solve this problem. From our experimental results, we find our approach performs better than existing optimization approaches.

A recent development emanating from the widely used RFID technology is Near Field Communication (NFC). Basically, NFC is a popular short range (<;10cm) wireless communication technology with applications in areas sensitive to security and privacy concerns including contact-less payment. Since NFC communications require very close proximity between two communicating devices (for example a smartcard and a reader), it is generally believed that Man-in-the-Middle (MITM) attacks are practically infeasible here. On the contrary to this general belief, in this paper, we successfully establish MITM attacks in NFC communications between a passive tag and an active reader. We present physical fundamentals of the attack, our engineering design, and results of our successful implementation. We also present practical impacts of the attack from the perspective of how a malicious user can leverage our MITM attack to compromise integrity of contact-less payment transactions. Finally, we present insights to combat the MITM attack in NFC communications towards the end of the paper.

In emergency scenarios such as earthquakes, fires, avalanches, or building collapses, it is necessary to discover people trapped under debris or anyway hidden from eyesight. In this paper, we propose RescuePal, an energy-efficient smartphone-based system that does not require any interaction by the victim and does not use energy-expensive GPS. RescuePal leverages a wake-up system based on sounds that activates the WiFi interface of the victim's smartphone only when the rescuer is close, to save energy. After presenting the system, we mathematically formulate an optimization problem so as to find the sound frequency and power level that minimizes WiFi false activations and yet guarantees high discovery efficiency. RescuePal has been implemented on off-the-shelf Android-based devices, and its performance has been evaluated on a realistic use-case scenario of victims inside a building. Finally, the energy consumption of RescuePal has been calculated using the Power Monitor hardware tool. Results demonstrate that RescuePal is highly effective and saves more than 60% of energy with respect to an approach based only on WiFi.

In this paper, we investigate the feasibility of leveraging the accelerometer in modern smartphones to detect the triggering of explosion events. By emplacing a static smartphone and a state-of-the-art seismometer in the vicinity of real explosion blasts (conducted at an Explosives Research Lab in a university setting), and comparing their detected event readings, we make several insightful contributions. We find that readings from events in the smartphone and the seismometer are highly correlated in the temporal and frequency domain. We then demonstrate the feasibility of designing an algorithm in the smartphone (executing as an app) to detect the triggering of an explosion based on comparing short term sudden spikes in vibrations due to an explosion event, and long-term dormancy in vibration readings (in the absence of an explosion). To the best of our knowledge, ours is the first work that demonstrates the feasibility of leveraging smartphones for detecting explosion events.

In this paper, we address the problem of ranging explosion events from sensing corresponding accelerometer readings from stationary smartphones. First, we statically emplaced a number of smartphones with built-in accelerometers at various locations in the vicinity of real explosions (conducted at a university training facility). An app was installed in 4 off-the-shelf smartphones to collect accelerometer readings continuously, and effectively retaining only those readings that correspond to an explosion event (while filtering out the rest). As a result, a total of 52 data-sets from 4 individual explosion blast-experiments (with Dynamite acting as the explosive charge) were collected. Using these data-sets, we developed a non linear regression model to estimate the distance of the source of an explosion event, and the intensity of the explosion (measured in terms of charge weight of the explosive material) based on extracting a number of statistical features from the accelerometer sensor readings in three dimensions (lateral (x), longitudinal (y), and vertical (z) directions) from smartphones. We are able to range the explosion event, with an average case error of 12.86% in our experiments. We were also able to estimate the intensity of the explosion event with a high accuracy, with an average case error of 11.26%. To the best of our knowledge, this is the first work that attempts to range explosion events leveraging sensor readings from smartphones.

Modeling and characterizing Internet traffic has been a widely studied problem since the conception of the Internet. The self-similar, bursty nature of the traffic has led to a number of conventional statistical models that unfortunately provide relatively weak modeling power. Recently, fractal analysis techniques have emerged to better characterize and model Internet traffic data. However, past research studies have focused on describing and quantifying the fractal nature of Internet traffic on user groups, instead of a single user. In this paper the authors investigate the issue of individual users exhibiting fractal (self-similar behavior) behavior across multiple application types. Using real Internet traffic traces (collected via Net-Flow logs) collected at a college campus for 30 days, our investigations reveal that in a number of application categories (http, chatting, p2p, email etc.) at least one user exhibits long-range correlations typical of fractal behavior. Of the 10 application groups, 7 had over 80% of users demonstrating self-similar behavior with 3 of those groups having > 98%. Potential benefits of our study in the realm of smart health and network security, by reducing the dimensionality of large Internet traffic datasets, are discussed.

Road Transportation is a crucial component of today's society, which drives several facets of our lives. The goal of intelligent transportation systems (ITS) is to improve the effectiveness, efficiency, and safety of the transportation system. Traffic signals are an elementary component of all road transportation systems. In order to maximize the productivity of a city, traffic signals must be able to efficiently control the flow of vehicles. Traditionally, current traffic signal optimization is based on traffic arrival rates, either estimated or forecasted. In this paper, we illustrate that arrival time based solutions can outperform arrival rate based approaches. To the best of our knowledge, this is the first work that exploits arrival times of vehicles to improve traffic signal efficiency in order to reduce stopped delays and fuel consumptions, thus in turn reducing greenhouse gases and emissions. We show that arrival time knowledge can be utilized in realizing drastic gains in sparse load scenarios and significant gains in moderate load scenarios. The performance improvement translates to reducing stopped delays by over 40,000 hours daily and in reducing fuel consumption by over 650 gallons/signal/day.

Cyber-Physical Systems (CPS) consist of computational components interconnected by computer networks that monitor and control switched physical entities interconnected by physical infrastructures. A fundamental challenge in the design and analysis of CPS is the lack of common semantics across the components. We address this challenge by employing a novel approach that composes the correctness of various components instead of their functionality using a conjunction of non-interfering logical invariants. We present a distributed algorithm that uses this approach to adaptively schedule power transfers between nodes in a smart power grid in such a way that the stability of both the computer network and the physical system are maintained. Simulation results demonstrate the necessity and usefulness of our approach in maintaining overall system stability in the presence of uncertainties in the computer network and with limited information about the global state of the system.

Stress and Anxiety negatively affect mental health and can lead a number of debilitating impacts to overall health and well being. In the recent past, adolescents are becoming increasingly afflicted with Stress and Anxiety. In this paper, we report our findings on a six-week study of 70 students at a college campus on associations between Stress and Anxiety with respect to Internet usage of students. Using Cisco NetFlow records, on-campus Internet usage of students was collected continuously and unobtrusively in a privacy-preserving manner. Using the Depression Anxiety and Stress Scale (DASS), students were separated based on normal scores and high scores separately for both Anxiety and Stress. Mann-Whitney U-tests revealed that there exists statistically significant differences in the mean values between the groups from the perspective of several Internet usage features. Students with high stress scores exhibit decreased chat (octets, packets, duration), total duration, and streaming duration compared to the students with normal stress scores. Students with high anxiety scores showed an increased mail duration and decreased peer-to-peer duration compared with students with normal anxiety scores. The methods and results of this paper provide a framework for conducting similar studies at universities with the goal of aiding student mental health.

A Sybil attack is one where an adversary assumes multiple identities with the purpose of defeating the trust of an existing reputation system. When Sybil attacks are launched in vehicular networks, an added challenge in detecting malicious nodes is mobility that makes it increasingly difficult to tie a node to the location of attacks. In this paper, we present an innovative protocol for Sybil detection in vehicular networks. Considering that vehicular networks are cyber-physical systems integrating cyber and physical components, our technique exploits well grounded results in the physical (i.e., transportation) domain to tackle the Sybil problem in the cyber domain. Compared to existing works that rely on additional cyber hardware support, or complex cryptographic primitives for Sybil detection, the key innovation in our protocol is leverage the theory of platoon dispersion that models the physics of naturally occurring dispersion in roads. Specifically, our technique employs a certain number of roadside units that periodically collect reports from vehicles regarding their physical neighborhood as they move in roads. Leveraging from existing models of platoon dispersion, we design a protocol to detect anomalously close neighborhoods that are reflective of Sybil attacks. To the best of our knowledge, this is the first work integrating a well established theory in transportation engineering for detecting cyber space attacks in vehicular networks. The resulting protocol is naturally simple, efficient and performs very well.

In this paper, we address content retrieval in Mobile Peer-to-Peer (P2P) Networks. We design a multi-tiered architecture for content retrieval, where at Tier 1, we design a protocol for content similarity governed by a parameter α that trades accuracy with search overhead. At Tier 2, we introduce a novel concept called Chained Bloom Filters and design a protocol where popular search items are linked with popular content at each node in an efficient manner for subsequent retrieval. Extensive analysis and numerical simulations demonstrate the effectiveness of our techniques.

Many key predistribution (KP) protocols have been proposed and are well accepted in randomly deployed wireless sensor networks (WSNs). Being distributed and localized, they are perceived to be scalable as node density and network dimension increase. While it is true in terms of communication/computation overhead, their scalability in terms of security performance is unclear. In this paper, we conduct a detailed study on this issue. In particular, we define a new metric called Resilient Connectivity (RC) to quantify security performance in WSNs. We then conduct a detailed analytical investigation on how KP protocols scale with respect to node density and network dimension in terms of RC in randomly deployed WSNs. Based on our theoretical analysis, we state two scaling laws of KP protocols. Our first scaling law states that KP protocols are not scalable in terms of RC with respect to node density. Our second scaling law states that KP protocols are not scalable in terms of RC with respect to network dimension. In order to deal with the unscalability of the above two scaling laws, we further propose logical and physical group deployment, respectively. We validate our findings further using extensive theoretical analysis and simulations.

In many Wireless Sensor Networks (WSNs), providing end to end secure communications between sensors and the sink is important for secure network management. While there have been many works devoted to hop by hop secure communications, the issue of end to end secure communications is largely ignored. In this paper, we design an end to end secure communication protocol in randomly deployed WSNs. Specifically, our protocol is based on a methodology called differentiated key pre-distribution. The core idea is to distribute different number of keys to different sensors to enhance the resilience of certain links. This feature is leveraged during routing, where nodes route through those links with higher resilience. Using rigorous theoretical analysis, we derive an expression for the quality of end to end secure communications, and use it to determine optimum protocol parameters. Extensive performance evaluation illustrates that our solutions can provide highly secure communications between sensor nodes and the sink in randomly deployed WSNs. We also provide detailed discussion on a potential attack (i.e. biased node capturing attack) to our solutions, and propose several countermeasures to this attack.

In this paper, we study the issue of defending against adversarial localization in wireless sensor networks. Adversarial localization refers to attacks where an adversary attempts to disclose physical locations of sensors in the network. The adversary accomplishes this by physically moving in the network while eavesdropping on communication messages exchanged by sensors, and measuring raw physical properties of messages like Angle of Arrival, Signal Strength of the detected signal. In this paper, we aim to defend sensor networks against such kinds of adversarial localization. The core challenge comes from the sensors performing two conflicting objectives simultaneously: localize the adversary, and hide from the adversary. The principle of our approach and the subsequent defense protocol is to allow sensors intelligently predict their own importance as a measure of these two conflicting requirements. Only a few important sensors will participate in any message exchanges. This ensures high degree of adversary localization, while also protecting location privacy of many sensors. Extensive simulations are conducted to demonstrate the performance of our protocol.

Traffic signals are an elementary component of all urban road networks and play a critical role in controlling the flow of vehicles. However, current road transportation systems and traffic signal implementations are very inefficient. The objective of this research is to evaluate optimal phase ordering within a signal cycles to minimize the average waiting delay and thus in turn minimizing fuel consumption and greenhouse gas (GHG) emissions. Through extensive simulation analysis, we show that by choosing optimal phase ordering, the stopped delay can be reduced by 40% per car at each signal resulting in a saving of up to 100 gallons of fuel per traffic signal each day.

Securely authenticating a human user without assistance from any auxiliary device in the presence of powerful passive adversaries is an important and challenging problem. Passive adversaries are those that can passively monitor, intercept, and analyze every part of the authentication procedure, except for an initial secret shared between the user and the server. In this paper, we propose a new secure authentication scheme called predicate-based authentication service (PAS). In this scheme, for the first time, the concept of a predicate is introduced for authentication. We conduct analysis on the proposed scheme and implement its prototype system. Our analytical data and experimental data illustrate that the PAS scheme can simultaneously achieve a desired level of security and user friendliness.

Distributed denial of service attacks continue to pose major threats to the Internet. In order to traceback attack sources (i.e., IP addresses), a well studied approach is probabilistic packet marking (PPM), where each intermediate router of a packet marks it with a certain probability, enabling a victim host to traceback the attack source. In a recent study, we showed how attackers can take advantage of probabilistic nature of packet markings in existing PPM schemes to create spoofed marks, hence compromising traceback. In this paper, we propose a new PPM scheme called TTL-based PPM (TPM) scheme, where each packet is marked with a probability inversely proportional to the distance traversed by the packet so far. Thus, packets that have to traverse longer distances are marked with higher probability, compared to those that have to traverse shorter distances. This ensures that a packet is marked with much higher probability by intermediate routers than by traditional mechanisms, hence reducing the effectiveness of spoofed packets reaching victims. Using formal analysis and simulations using real Internet topology maps, we show how our TPM scheme can effectively trace DDoS attackers even in presence of spoofing when compared to existing schemes.

In this paper, we study the issue of sensor network deployment using limited mobility sensors. By limited mobility, we mean that the maximum distance that sensors are capable of moving to is limited. Given an initial deployment of limited mobility sensors in a field clustered into multiple regions, our deployment problem is to determine a movement plan for the sensors to minimize the variance in number of sensors among the regions and simultaneously minimize the sensor movements. Our methodology to solve this problem is to transfer the nonlinear variance/movement minimization problem into a linear optimization problem through appropriate weight assignments to regions. In this methodology, the regions are assigned weights corresponding to the number of sensors needed. During sensor movements across regions, larger weight regions are given higher priority compared to smaller weight regions, while simultaneously ensuring a minimum number of sensor movements. Following the above methodology, we propose a set of algorithms to our deployment problem. Our first algorithm is the optimal maximum flow-based (OMF) centralized algorithm. Here, the optimal movement plan for sensors is obtained based on determining the minimum cost maximum weighted flow to the regions in the network. We then propose the simple peak-pit-based distributed (SPP) algorithm that uses local requests and responses for sensor movements. Using extensive simulations, we demonstrate the effectiveness of our algorithms from the perspective of variance minimization, number of sensor movements, and messaging overhead under different initial deployment scenarios.

An important phase of sensor networks operation is deployment of sensors in the field of interest. Critical goals during sensor networks deployment include coverage, connectivity, load balancing, etc. A class of work has recently appeared, where mobility in sensors is leveraged to meet deployment objectives. In this paper, we study deployment of sensor networks using mobile sensors. The distinguishing feature of our work is that the sensors in our model have limited mobilities. More specifically, the mobility in the sensors we consider is restricted to a flip, where the distance of the flip is bounded. We call such sensors as flip-based sensors. Given an initial deployment of flip-based sensors in a field, our problem is to determine a movement plan for the sensors in order to maximize the sensor network coverage and minimize the number of flips. We propose a minimum-cost maximum-flow-based solution to this problem. We prove that our solution optimizes both the coverage and the number of flips. We also study the sensitivity of coverage and the number of flips to flip distance under different initial deployment distributions of sensors. We observe that increased flip distance achieves better coverage and reduces the number of flips required per unit increase in coverage. However, such improvements are constrained by initial deployment distributions of sensors due to the limitations on sensor mobility

Abstract—Many wireless sensor network (WSN) applications demand secure communications. The random key predistribution ðRKPÞ protocol has been well accepted in achieving secure communications in WSNs. A host of key management protocols have been proposed based on the RKP protocol. However, due to the randomness in key distribution and strong constraint in key path construction, the RKP-based protocols can only be applied in highly dense networks, which are not always feasible in practice. In this paper, we propose a methodology called network decoupling to address this problem. With this methodology, a WSN is decoupled into a logical keysharing network and a physical neighborhood network, which significantly releases the constraint in key path construction of the RKP protocol. We design two new key management protocols, that is, RKP-DE and RKP-DEA, as well as a set of link and path dependency elimination rules in decoupled sensor networks. Our analytical and simulation data demonstrate the performance enhancement of our solutions from the perspective of connectivity and resilience and its applicability in nonhighly dense sensor networks. Index Terms—Wireless sensor networks, random key predistribution, network decoupling. 1

Secure communications are highly demanded by many wireless sensor network (WSN) applications. The random key pre-distribution (RKP) scheme has become well accepted in achieving secure communications in WSNs. However, due to its randomness in key distribution and strong constraint in key path construction, the RKP scheme can only be applied in highly dense networks, which are not always feasible in practice. In this paper, we propose a methodology called network decoupling to solve this problem. With this methodology, a wireless sensor network is decoupled into a logical key-sharing network and a physical neighborhood network, which significantly releases the constraint in key path construction of the RKP scheme. We design a secure neighbor establishment protocol (called RKP-DE) as well as a set of link and path dependency elimination rules in decoupled wireless sensor networks. Our analytical and simulation data demonstrate the performance enhancement of our solution and its applicability in non-highly dense wireless sensor networks

Sensor nodes being small in size and distributively deployed, are vulnerable to physical attacks that attempt to physically destroy sensors in the sensor network. Generally speaking, physical attacks in sensor networks can be classified into two types: blind physical attacks and search-based physical attacks. In blind attacks, sensors are destroyed using brute-force approaches (like bombs/grenades etc.). The advantage here is the rapidness in destroying sensors. The downside however, is the fact that the deployment field also suffers significant casualties. If the attacker wishes to preserve the deployment field, the attacker will conduct search-based attacks by searching for sensors in the field and destroying only the sensors. While this preserves the deployment field, the attack process is slow. In this paper, we present policy-driven physical attacks, where the bias between the twin objectives of the attacker (rapidly destroying sensors, and preserving the deployment field) is modeled as a policy for the attacker. In policy-driven physical attacks, the attacker walks through the sensor network deployment field using signal detecting equipment to locate active sensors. Depending on the attacker's policy, the attacker takes different actions during the attack process. Based on detailed performance measurement, we observe that the policy has impacts on the network performance and destruction in the deployment field, demonstrating that the attacker can achieve desired bias in its objectives under policy-driven physical attacks

In the framework of a set of clients communicating with a critical server over the Internet, a recent approach to protect communication from distributed denial of service (DDoS) attacks involves the usage of overlay systems. SOS, MAYDAY, and I3 are such systems. The architecture of these systems consists of a set of overlay nodes that serve as intermediate forwarders between the clients and the server, thereby controlling access to the server. Although such systems perform well under random DDoS attacks, it is questionable whether they are resilient to intelligent DDoS attacks which aim to infer architectures of the systems to launch more efficient attacks. In this paper, we define several intelligent DDoS attack models and develop analytical/simulation approaches to study the impacts of architectural design features of such, overlay systems on the system performance in terms of path availability between clients and the server under attacks. Our data clearly demonstrate that the system performance is indeed sensitive to the architectural features and the different features interact with each other to impact overall system performance under intelligent DDoS attacks. Our observations provide important guidelines in the design of such secure overlay forwarding systems.

Recent active worm propagation events show that active worms can spread in an automated fashion and flood the Internet in a very short period of time. Our previous results show that P2P systems with large number of hosts can be a potential vehicle for the active worm attacker to achieve fast worm propagation in the Internet. In this paper, we propose a region-based active immunization defense strategy in P2P systems to fight against P2P-based active worm attacks. We develop an analytical approach to evaluate the efficiency of our proposed defense strategy. Our numerical analysis results show that: although P2P-based attacks can significantly improve the attack performance by attacking vulnerable P2P systems, our proposed defense strategy can effectively slow down the worm propagation. We also observe that defense parameters such as defense list size, worm detection success ratio, and immunization rate have significant impacts on the performance of our defense strategy.

Abstract—In this paper, we address a Resource Constrained Lifetime Problem in sensor networks in an operating environment subject to physical node destructions. Specifically, given a limited number of sensors, our goal is to maximize the network lifetime and derive the deployment plan of the nodes to maximize the lifetime under physical node destructions. The problem of physical destructions due to hostile environmnets and small size of the sensors is a potent threat and severely constrains the practical lifetime of sensor networks. The lifetime problem we define is representative, practical and encompasses other versions of similar problems. We also define a representative physical attack model under which we study and solve the lifetime problem. Our solutions take into account both the energy minimization and node constraints. We make several observations in this realm of which an important one is the high sensitivity of lifetime to physical attacks highlighting the importance of our study. Our work has broad and immediate impacts to system designers during network deployments in hostile environments.

Recent active worm propagation events show that active worms can spread in an automated fashion and flood the Internet in a very short period of time. Due to the recent surge of peer-to-peer (P2P) systems with large numbers of users, P2P systems can be a potential vehicle for the active worms to achieve fast worm propagation in the Internet. In this paper, we address the issue of the impacts of active worm propagation on top of P2P systems. In particular: (1) we define a P2P system based active worm attack model and study two attack strategies (an off-line and on-line strategy) under the defined model; (2) we develop an analytical approach to analyze the propagation of active worms under the defined attack models and conduct an extensive study to the impacts of P2P system parameters, such as size, topology degree, and the structured/unstructured properties on active worm propagation. Based on numerical results, we observe that a P2P-based attack can significantly worsen attack effects (improve attack performance), and we observe that the speed of worm propagation is very sensitive to P2P system parameters. We believe that our work can provide important guidelines in design and control of P2P systems as well as overall active worm defense.

The small form factor of the sensors, coupled with the unattended and distributed nature of their deployment expose sensors to physical attacks that physically destroy sensors in the network. In this paper, we study the modeling and analysis of search-based physical attacks in sensor networks. We define a search-based physical attack model, where the attacker walks through the sensor network using signal detecting equipment to locate active sensors, and then destroys them. We consider both flat and hierarchical sensor networks. The attacker in our model uses a weighted random selection based approach to discriminate multiple target choices (normal sensors and cluster-heads) to enhance sensor network performance degradation. Our performance metric in this paper is accumulative coverage (AC), which effectively captures coverage and lifetime of the sensor network. We then conduct detailed evaluations on the impacts of search-based physic attacks on sensor network performance. Our performance data clearly show that search-based physical attacks significantly reduce sensor network performance. We observe that attack related parameters, namely attacker movement speed, detection range and accuracy have significant impacts on the attack effectiveness. We also observe that the attack effectiveness is significantly impacted by sensor network parameters, namely the frequency of communication and frequency of cluster-head rotation. We believe that our work in this paper on modeling and analyzing search-based physical attacks is an important first step in understanding their overall impacts, and effectively defending against them in the future.

In this paper, we study the issue of mobility based sensor networks deployment. The distinguishing feature of our work is that the sensors in our model have limited mobilities. More specifically, the mobility in the sensors we consider is restricted to a flip, where the distance of the flip is bounded. Given an initial deployment of sensors in a field, our problem is to determine a movement plan for the sensors in order to maximize the sensor network coverage, and minimize the number of flips. We propose a minimum-cost maximum-flow based solution to this problem. We prove that our solution optimizes both the coverage and the number of flips. We also study the sensitivity of coverage and the number of flips to flip distance under different initial deployment distributions of sensors. We observe that increased flip distance achieves better coverage, and reduces the number of flips required per unit increase in coverage. However, such improvements are constrained by initial deployment distributions of sensors, due to the limitations on sensor mobility

In this paper we study the defense of sensor networks against search-based physical attacks. We define search-based physical attacks as those, where an attacker detects sensors using signal detecting equipment and then physically destroys the detected sensors. In this paper, we propose a sacrificial node-assisted approach to defend against search-based physical attacks. The core principle of our defense is to trade short term local coverage for long term global coverage through the sacrificial node-assisted attack notification and states switching of sensors. The performance metric we use is accumulative coverage (AC), which effectively captures coverage and lifetime of the sensor networks to measure sensor network performance. Our simulation results clearly demonstrate that our defense approach can significantly decrease losses in AC even under intense search-based physical attacks.

Distributed denial of service (DDoS) attacks are currently major threats to communication in the Internet. A secure overlay services (SOS) architecture has been proposed to provide reliable communication between clients and a target under DDoS attacks. The SOS architecture employs a set of overlay nodes arranged in three hierarchical layers that controls access to the target. Although the architecture is novel and works well under simple congestion based attacks, we observe that it is vulnerable under more intelligent attacks. We generalize the SOS architecture by introducing more flexibility in layering to the original architecture. We define two intelligent DDoS attack models and develop an analytical approach to study the impacts of the number of layers, number of neighbors per node and the node distribution per layer on the system performance under these two attack models. Our data clearly demonstrate that performance is indeed sensitive to the design features and the different design features interact with each other to impact overall system performance.