Collision hazard detection in industrial work zones faces challenges from signal instability, mobility-induced fluctuations, and non-line-of-sight (NLOS) conditions. While Bluetooth Low Energy (BLE) offers cost-effective proximity sensing, its RSSI variability—fluctuating by ±10dBm even at fixed distances— limits reliability in safety-critical applications. This paper presents AlertBLE, a hybrid BLE-based hazard detection system that combines Extended Kalman Filter (EKF) and Adaptive Moving Average (AMA) algorithms to achieve up to 94% RSSI variance reduction in static NLOS conditions. The system introduces speed-aware safety thresholds based on reaction time and braking distance models, dynamically expanding hazard zones from 5m (static) to 8.19m (at 10 km/h), ensuring adequate safety margins across operational speeds. AlertBLE employs K-means clustering to identify LOS/NLOS propagation environments, integrating this context as features for supervised learning. Among four evaluated classifiers (KNN, SVM, Random Forest, XGBoost), KNN demonstrates optimal performance with an efficiency score of 12.8, balancing 82.74% recall with minimal computational requirements (156KB memory, 1.01 ms inference). Field evaluation using 44,774 samples across diverse outdoor conditions demonstrates 88.63% overall detection accuracy with 63 ms system latency—well below the 250 ms safety threshold. The multi-layered error mitigation framework, incorporating temporal smoothing, confidence thresholding, and state machine logic, achieves 75.6% reduction in combined false positives and negatives. Despite 170.8% average RSSI degradation under severe NLOS conditions, AlertBLE maintains 82% detection accuracy within the critical 5-meter zone. The paper also presents a comprehensive security framework addressing BLE vulnerabilities, providing a roadmap for production deployment enhancements.

Facial Recognition Systems (FRS) have become one of the most viable biometric identity authentication approaches in supervised and unsupervised applications. However, FRSs are known to be vulnerable to adversarial attacks such as identity theft and presentation attacks. The master face dictionary attacks (MFDA) leveraging multiple enrolled face templates have posed a notable threat to FRS. Federated learning-based FRS deployed on edge or mobile devices are particularly vulnerable to MFDA due to the absence of robust MF detectors. To mitigate the MFDA risks, we propose a trustworthy authentication system against visual MFDA (Trauma). Trauma leverages the analysis of specular highlights on diverse facial components and physiological characteristics inherent to human faces, exploiting the inability of existing MFDAs to replicate reflective elements accurately. We have developed a feature extractor network that employs a lightweight and low-latency vision transformer architecture to discern inconsistencies among specular highlights and physiological features in facial imagery. Extensive experimentation has been conducted to assess Trauma’s efficacy, utilizing public GAN-face detection datasets and mobile devices. Empirical findings demonstrate that Trauma achieves high detection accuracy, ranging from $97.83 \%$ to $99.56 \%$, coupled with rapid detection speeds (less than 11 ms on mobile devices), even when confronted with state-of-the-art MFDA techniques.

This paper presents a novel Reflection and Environment-Aware DeepFake (READFake) detection technique. Using reflections on various body parts (e.g., eyes, nose, cheeks, etc.) and environmental factors, we validate the hypothesis that the existing DeepFake creation methods, including reenactment, replacement, and synthesis, fail to coordinate their counterfeits with the reflective components along with the given environmental mapping. We detect various features from the specular highlight images, including color components, shapes, and textures, to check the coordination with the surrounding environmental factors, such as indoor/outdoor, bright/dark backgrounds, and light strength. We have conducted extensive experiments to evaluate the performance of READFake using various input parameters and advanced Deep Neural Network (DNN) architectures on multiple public DeepFake datasets. The empirical results show that READFake achieves high accuracy (99.00%) in detecting sophisticated DeepFake images.

DeepFake has accomplished notable advancement with the AI-leveraged production and manipulation techniques of fictitious human facial images. Despite many benign and fun applications, the generated fake images can negatively influence the authenticity of online information by originating deception, manipulation, persecution, and seduction, defying societal quality and human rights, which becomes critical security and privacy threat in social networks. Hence, real-time DeepFake detection and limitation technologies on the mobile platform are essential to building a controlled, harmless DeepFake ecosystem. This paper presents a real-time, cloudless, lightweight mobile app for human visual DeepFake detection using machine learning technologies named MobiDeep (Mobile DeepFake Detection through Machine Learning-based Corneal-Specular Backseat-tering). MobiDeep stems from a hypothesis that the existing DeepFake creation methods, including replacement, editing, and synthesis, lack the ensemble with the reflective objects. Focusing on the most reflective area of a human face, corneal-specular backscatter images of eyes, we seek the similarity and consistency with multiple surrounding environment features, including color components, shapes, and textures. We have implemented a cross-platform mobile application to evaluate the performance using various input parameters and lightweight Deep Neural Network (DNN) architectures. The empirical results show that MobiDeep achieves high accuracy (98.7%) and rapid detection speed (less than 200 ms) in detecting sophisticated DeepFake images within a subsecond.

The demand for contactless biometric authentication has significantly increased during the COVID-19 pandemic and beyond to prevent the spread of Coronavirus. The global pandemic unexpectedly affords a greater opportunity for contactless authentication, but iris and facial recognition biometrics have many usability, security, and privacy challenges, including mask-wearing and presentation attacks (PAs). Mainly, liveness detection against spoofing is notably a challenging task as various biometric authentication methods cannot efficiently assess the real user’s physical presence in unsupervised environments. Although several face anti-spoofing methods have been proposed using add-on sensors, dynamic facial texture features, and 3-D mapping, most of them require expensive sensors and substantial computational resources, or fail to detect sophisticated 3-D face spoofing. This article presents a software-based facial liveness detection method named Apple in My Eyes (AIME). AIME is intended to detect the liveness against spoofing for mobile device security using challenge-response testing. AIME generates various screen patterns as authentication challenges, then passively detects corneal-specular reflection responses from human eyes using a frontal camera and analyzes the detected reflections using lightweight machine learning techniques. AIME system components include challenge and pattern detection, feature extraction and classification, and data augmentation and training. We have implemented AIME as a cross-platform application compatible with Android, iOS, and the Web. Our comprehensive experimental results reveal that AIME detects liveness with high accuracy at around 200-ms against different types of sophisticated PAs. AIME can also efficiently detect liveness in multiple contactless biometric authentications without any costly extra sensors nor involving users’ active responses.

Scalability is one of the main issues of blockchains that limits its applications. Blockchain throughput (i.e., transactions per second) is constrained by its block size, and block generation rate is restricted by consensus time and the network size. Despite many scaling approaches available, most of them are either not applicable to existing blockchains, or oblivious to blockchain applications. We propose a novel blockchain network scalability mechanism, Advance Block Body Agreement (ABBA), that uses pre-agreement on an upcoming block body to reduce consensus time without changing the consensus algorithm itself. A block size of a blockchain is limited since a large block size causes higher consensus time and unnecessary transient forks, resulting in an unstable network. By reducing consensus time, ABBA allows increasing a blockchain's block size that leads to a higher throughput. ABBA also reduces transient forks in blockchain networks reducing unnecessary network overhead. ABBA can be used with any consensus protocol without changing consensus or its application layer. It can also be used with other existing scalability approaches complementarily. We have built an efficient blockchain network simulator and conducted extensive evaluations under various network conditions. The results show that an ABBA-enabled blockchain network has a significantly higher throughput and lower transient forks than an original base blockchain network.

Senior citizens, young children, and people with age-related diseases, often find it hard to express themselves. They are not fully aware of their need for help, or how to ask for assistance. This lack of awareness decreases the quality of life, and even endangers those individuals.IC-SAFE (Intelligent Connected Sensing Approaches for the Elderly) tracks the safety of the elderly by using various connected smart wearable sensors. IC-SAFE collects motion data, including walking gaits, arm and leg tremors, and long lounging positions, from many lightweight body sensors to identify the safety status (both physical and emotional) of dementia patients. Feasibility tests have been performed using IMU (Inertial Measurement Unit) sensors in various positions and data from these experiments has been gathered. We have proposed efficient real-time algorithms using analytical learning methods and identified several safety target scenarios by analyzing the corresponding gait data.

The virtualization and software-definition of network functions, controls, and applications enhance performance and operation costs while bringing new value to the infrastructures. However, due to the stateless, gossipy, centralized, periodic, and tardy nature, the network control and management approaches in Software-Defined Networking (SDN), such as the current OpenFlow Discovery Protocol (OFDP), pose scalability, latency, and reliability challenges. This paper designs a novel Centrality-Aware Multitemporal (CAMEL) discovery protocol for SDN to enhance the centralized discovery mechanism’s scalability and latency issues. We facilitate multiple discovery timers for each target according to the significance instead of using a single timer for the entire network. CAMEL generalizes the significance measurement for various network topologies by using the centrality models. We combine the normalized degree and betweenness centrality values to find an unbiased impact factor of each node. Applying the identified significance to a multitemporal discovery mechanism, CAMEL reduces network impact by decreasing the discovery delay to the significant nodes and enhances control message efficiency by lowering the discovery frequency to the less significant targets. We have implemented CAMEL on the RYU controller. The experimental results validate that CAMEL improves discovery message efficiency, makes the control traffic less bursty, and enhances the network service quality by reducing discovery delay to the significant nodes.

In this paper, we present a novel software-based face Presentation Attack Detection (PAD) method named "Apple in My Eyes (AIME)" using screen display as a challenge and corneal specular reflections as a response for authenticating the liveness against presentation. To detect face liveness, AIME creates multiple image patterns on the authentication screen as a challenge, then captures meaningful corneal specular reflection responses from user's eyes using the front camera, and analyzes the reflective pattern images using various lightweight Machine Learning (ML) techniques under a subsecond level delay (200 ms). We demonstrate that AIME can detect various attacks, including digital images displayed on the phone or tablet, printed paper images, 2D paper masks, videos, 3D silicon masks, and 3D facial models using VR. AIME liveness detection can be applied for various contactless biometric authentication accurately and efficiently without any costly extra sensors.

As society increasingly relies on digital technologies in many different aspects, those who lack relevant access and skills are lagging increasingly behind. Among the underserved groups disproportionately affected by the digital divide are women who are transitioning from incarceration and seeking to reenter the workforce outside the carceral system (women-in-transition). Women-in-transition rarely have been exposed to sound technology education, as they have generally been isolated from the digital environment while in incarceration. Furthermore, while women have become the fastest-growing segment of the incarcerated population in the United States in recent decades, prison education and reentry programs are still not well adjusted for them. Most programs are mainly designed for the dominant male population. Consequently, women-in-transition face significant post-incarceration challenges in accessing and using relevant digital technologies and thus have added difficulties in entering or reentering the workforce. Against this backdrop, our multi-disciplinary research team has conducted empirical research as part of technology education offered to women-in-transition in the Midwest. In this article, we report results from our interviews with 75 women-in-transition in the Midwest that were conducted to develop a tailored technology education program for the women. More than half of the participants in our study are women of color and face precarious housing and financial situations. Then, we discuss principles that we adopted in developing our education program for the marginalized women and participants’ feedback on the program. Our team launched in-person sessions with women-in-reentry at public libraries in February 2020 and had to move the sessions online in March due to COVID-19. Our research-informed educational program is designed primarily to support the women in enhancing their knowledge and comfort with technology and nurturing computational thinking. Our study shows that low self-efficacy and mental health challenges, as well as lack of resources for technology access and use, are some of the major issues that need to be addressed in supporting technology learning among women-in-transition. This research offers scholarly and practical implications for computing education for women-in-transition and other marginalized populations.

Edge computing extends the realm of information technology beyond the boundaries defined by cloud computing. Performing computation near the sensors, edge computing is promising to address the challenges in many bandwidth-and delay-sensitive applications. Although recently many smart video surveillance approaches based on Machine Learning (ML) algorithms become available, it is still challenging to efficiently migrate those smart algorithms to edge. In this paper, we propose an intelligent Surveillance as an Edge Network Service (iSENSE), which explores the feasibility of moving ML to the edge by testing two popular human-object detection schemes. Besides, a lightweight Convolutional Neural Network (L-CNN) is introduced to improve computational execution by leveraging the depth-wise separable convolution. To enhance performance on edge, we propose a hybrid lightweight tracking algorithm, Kerman (Kernelized Kalman filter), which is a decision tree based hybrid Kernelized Correlation Filter algorithm designed for human-object tracking. We have implemented both Kerman and L-CNN algorithms on edge by using different types of single board computers. The proposed iSENSE system was validated using both real-world campus surveillance video and open image sets. The experimental results present that the proposed algorithms can track the human objects in real-time with a good accuracy with limited resource in edge devices.

The Software-Defined Networking (SDN) technologies enhance the performance, reliability, and cost of managing the functions, controls, and services of the wireless and mobile network infrastructures (i.e., Internet of Things). However, the current OpenFlow Discovery Protocol (OFDP) in SDN poses substantial scalability, accuracy, and latency challenges due to its gossipy, centralized, periodic, and tardy protocol nature. Furthermore, the problems are aggravated in the wireless, and mobile SDN due to the dynamic topology churns and the lack of link-layer discovery methods.In this paper, we design and build a novel Traffic-aware Discovery Protocol (TaDPole) for wireless and mobile SDN. We facilitate multiple discovery frequency timers for each target instead of using a uniform discovery timer for the entire network. TaDPole calculates the significance of each discovery target according to the recent network usage by assuming that the higher traffic node has more impact on the network service. It lessens discovery delay by increasing the discovery frequency to the more critical nodes. Also, it enhances the control message efficiency by reducing the discovery frequency to the less significant targets. Besides, it supports the port-neutral broadcast-based discovery method instead of using port-specific request and response approaches. We have implemented TaDPole on the RYU controller. Extensive Mininet experiment results validate that TaDPole improves discovery message efficiency by two times and makes the control traffic less bursty than OFDP with a uniform timer. It reduces the network status discovery delay by three times without increasing the control overhead.

Edge computing pushes the cloud computing boundaries beyond uncertain network resource by leveraging computational processes close to the source and target of data. Time-sensitive and data-intensive video surveillance applications benefit from on-site or near-site data mining. In recent years, many smart video surveillance approaches are proposed for object detection and tracking by using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. However, it is still hard to migrate those computing and data-intensive tasks from Cloud to Edge due to the high computational requirement. In this paper, we envision to achieve intelligent surveillance as an edge service by proposing a hybrid lightweight tracking algorithm named Kerman (Kernelized Kalman filter). Kerman is a decision tree based hybrid Kernelized Correlation Filter (KCF) algorithm proposed for human object tracking, which is coupled with a lightweight Convolutional Neural Network (L-CNN) for high performance. The proposed Kerman algorithm has been implemented on a couple of single board computers (SBC) as edge devices and validated using real-world surveillance video streams. The experimental results are promising that the Kerman algorithm is able to track the object of interest with a decent accuracy at a resource consumption affordable by edge devices.

Securing data transferred over a WSN is required to protect data from being compromised by attackers. Sensors in the WSN must share keys that are utilized to protect data transmitted between sensor nodes. There are several approaches introduced in the literature for key establishment in WSNs. Designing a key distribution/establishment scheme in WSNs is a challenging task due to the limited resources of sensor nodes. Polynomial-based key distribution schemes have been proposed in WSNs to provide a lightweight solution for resource-constraint devices. More importantly, polynomial-based schemes guarantee that a pairwise key exists between two sensors in the WSNs. However, one problem associated with all polynomial-based approaches in WSNs is that they are vulnerable to sensor capture attacks. Specifically, the attacker can compromise the security of the entire network by capturing a fixed number of sensors. In this paper, we propose a novel polynomial-based scheme with a probabilistic security feature that effectively reduces the security risk of sensor-captured attacks and requires minimal memory and computation overhead. Furthermore, our design can be extended to provide hierarchical key management to support data aggregation in WSNs.

Allowing computation to be performed at the edge of a network, edge computing has been recognized as a promising approach to address some challenges in the cloud computing paradigm, particularly to the delay-sensitive and mission-critical applications like real-time surveillance. Prevalence of networked cameras and smart mobile devices enable video analytics at the network edge. However, human objects detection and tracking are still conducted at cloud centers, as real-time, online tracking is computationally expensive. In this paper, we investigated the feasibility of processing surveillance video streaming at the network edge for real-time, uninterrupted moving human objects tracking. Moving human detection based on Histogram of Oriented Gradients (HOG) and linear Support Vector Machine (SVM) is illustrated for features extraction, and an efficient multi-object tracking algorithm based on Kernelized Correlation Filters (KCF) is proposed. Implemented and tested on Raspberry Pi 3, our experimental results are very encouraging, which validated the feasibility of the proposed approach toward a real-time surveillance solution at the edge of networks.

The recent federation of novel softwareization and virtualization architectures as well as Internet of Things (IoT) technologies complicates management of the network and services. In order to cope with expensive and slow network problem detection, isolation, and root cause analysis based on the SNMP driven pull model management, this paper proposes push based open source streaming network traffic analytics technologies by using P4 (Programming Protocol-Independent Packet Processors) INT (Inband Network Telemetry). Real-time information fusion algorithms on the intelligent edge that correlates multi-source micro and macro streaming telemetry data are proposed. And its proof-of-concept implementation with performance evaluation.

The recent networking paradigm shifts towards the virtualization and softwarization of network functions, controls, and applications that are promising; they optimize costs and processes while bringing new value to the infrastructures. However, the centralized reliability management in softwarization architecture poses both scalability and latency challenges. In this paper, we design and build a novel topology-aware network reliability management framework that enhances the efficiency of network discovery (Link Layer Discovery Protocol (LLDP)) mechanisms and introduces a three-tier-based algorithm that the controller utilizes to calculate the LLDP-discovery frequency. A novel impact based per target LLDP-discovery approach enables fast failure detection and recovery for the important targets. A prototype is implemented on Cisco's OpenDayLight (ODL). Extensive Mininet experiment results validate that the tier-based LLDP message algorithm enables effective decision making tools for the network reliability management framework.

The virtualization and softwareization of network functions, controls, and applications are promising as they optimize costs and processes while bringing new value to the network infrastructures. However, the centralized reliability management over the complex softwareized network poses both scalability and latency challenges on the failure recovery process. In this paper, we propose a Network Architecture-aware Reliability Management Schemes (NetAware) to efficiently orchestrate different reliability monitoring mechanisms over SDN network architecture and synchronize the control messages among different controllers and applications. NetAware facilitates many common reliability monitoring factors for the registered applications by analyzing both off-line and on-line network architecture information such as network topologies, virtualization, and protocols. NetAware consists of a High Availability Registration Platform (HARP) and a Topology Aware Reliability (TAR) discovery facilities. A prototype is implemented on Cisco's OpenDayLight (ODL).

Software-defined networking (SDN) is a softwarization technology of networks that can optimize processes and operation costs and bring new values to infrastructures. The issue of reliability, however, becomes more complex in SDN due to new and multi-lateral network domains, and poses many critical challenges on the existing network reliability mechanisms in order to achieve the same reliability services. In this paper, we first identify and illustrate reliability challenges in a control path network that lies between a control plane network and a data plane network to connect them through either an in-band SDN or an out-of-band traditional network. We then observe a number of distinctive control path reliability problems. Accordingly, we propose and develop a control path management framework to enhance SDN reliability addressing the observed issues. It includes several control path reliability algorithms that enhance performance, network protocols that simplify management of control path reliability, as well as a novel control message classification and prioritization system that serves as a fundamental approach to improve scalability and then reliability for SDN. Recognizing the control path as a network and understanding its potential and practical reliability problems enable us to provide effective solutions that prior approaches fall short of. We validate our proposed management framework through extensive experiments through a real network system as well as numerical analyses.

Despite tremendous software quality assurance efforts made by network vendors, chastising software bugs is a difficult problem especially, for the network systems in operation. Recent trends towards softwarization and opensourcing of network functions, protocols, controls, and applications tend to cause more software bug problems and pose many critical challenges to handle them. Although many traditional redundancy recovery mechanisms are adopted to the softwarized systems, software bugs cannot be resolved with them due to unexpected failure behavior. Furthermore, they are often bounded by common mode failure and common dependencies (CMFD). In this paper, we propose an online software bug detection, debugging, and isolation (BuDDI) middlebox architecture for software-defined network controllers. The BuDDI architecture consists of a shadow-controller based online debugging facility and a CMFD mitigation module in support of a seamless heterogeneous controller failover. Our proof-of-concept implementation of BuDDI is on the top of OpenVirtex by using Ryu and Pox controllers and verifies that the heterogeneous controller switchover does not cause any additional performance overhead.

With the recent rapid advancements in the Internet-of-Things (IoT), one of the applications being developed is that of smart door lock (SDL) systems. SDL are intended to offer high security, easy access and easy sharing. Unlike existing SDL solutions that mostly use biometrics or crunched RF spectrum, we uniquely propose to use Infrared (IR) optical wireless signal (OWS) using IR light emitting diode (LED) of smartphones. We designed and developed a complete system of Android smartphone app including physical layer encoding, a cloud server and programmable hardware prototypes using Arduino as well as Raspberry Pi. Optlock includes multi-level security schemes including user registration, authentication and authorization using one-time-password (OTP). Our extensive experiments show 100% accuracy with 1.33 kbps of average data rate is achieved up to 20 meters of distance between a smartphone and a lock. It allows convenient remote access, easy access control and sharing as well as high security.

In order to tackle the issues of mounting deployments and large energy consumption of base stations, it is integral to devise schemes to improve energy efficiency in cellular networks. We propose a virtualized network function of cell management on an SDN architecture. We develop a cell management algorithm on the architecture that can effectively control the sleep and awake modes of base stations and perform handover operations in a cellular network. It provides significant benefits over current cellular networks that suffer from inflexible management and complex control. Our extensive trace-driven evaluation results show that the proposed control architecture and the cell management algorithm achieve significant energy savings, and incur less control message exchanges, more cells in a sleep mode for longer durations, and less cell status changes than existing energy saving approaches for cellular networks.

As recent smartphone technologies in software and hardware keep on improving, many smartphone users envision to perform various mission critical applications on their smart-phones that were previously accomplished by using PCs. Hence, smartphone authentication has become one of the most critical security issues. Due to the relatively small smartphone form factor, the traditional user id and password typed authentication is considered as an inconvenient and time-taking approach. Taking advantage of various sensor technologies of smartphones, alternative authentication methods such as pattern, gesture, finger print, and face recognition have been actively researched. However, those authentication methods still pose one of speed, reliability, and usability issues. They are especially not suitable for the users in rugged conditions and with physical challenges. In this paper, we evaluate existing alternative smartphone authentication approaches in various usage scenarios to propose ambient light sensor based authentication for smartphones. We have designed and prototyped a challenge-based programmable Fast, Inexpensive, Reliable, and Easy-to-use (FIRE) hardware authentication token. FIRE token uses an onboard LED to transmit passwords via an Optical Wireless Signal (OWS) to the smartphone that captures, and interprets it via its ambient light sensor. FIRE token is a part of the challenge-response technique in the Inverse Dual Signature (IDS) that we designed to facilitate a multi-factor authentication for the mission critical smartphone applications. Our experiments validate that FIRE can authenticate a user on a smartphone in a simple, fast, and reliable way without compromising the security quality and user experience.

With the rapid proliferation of data centers, their energy consumption and green house gas emissions have significantly increased. Some efforts have been made to control and lower energy consumption of data centers such as proportional energy consuming hardware, dynamic provisioning and virtual-ization machine techniques. However, it is still common that many servers and network resources are often underutilized, and idle servers spend a large portion of their peak power consumption. We first built a novel model of a network virtualization in order to minimize energy usage in data centers for both computing and network resources by taking practical factors into consideration. Due to the NP-hardness of the proposed model, we have developed a heuristic algorithm for virtual network scheduling and mapping, considering expected energy consumption at different times, a data center architecture, and virtual network migration, as well as operation costs. Our evaluation results show that our algorithm could reduce energy consumption up to 40%, and take up to 57% higher number of virtual network requests over other existing virtual mapping schemes.

As smartphones gain their popularity, vulnerable road users (VRUs) are increasingly distracted by activities with their devices such as listening to music, watching videos, texting or making calls while walking or bicycling on the road. In spite of the development of various high-tech Car-to-Car (C2C) and Car-to-Infrastructure (C2I) communications for enhancing the traffic safety, protecting such VRUs from vehicles still relies heavily on traditional sound warning methods. Furthermore, as smartphones continue to become highly ubiquitous, VRUs are increasingly oblivious to safety related warning sounds. A traffic accident study shows the number of headphone-wearing VRUs involved in roadside accidents has increased by 300% in the last 10 years. Although recently a few Car2Pedestrian-communication methods have been proposed by various car manufacturers, their practical usage is limited, as they mostly require special communication devices to cope with the wide range of mobility, and also assume VRUs' active attention to the communication while walking. We propose a smartphone-based Car2X-communication system, named WiFi-Honk, which can alert the potential collisions to both VRUs and vehicles in order to especially protect the distracted VRUs. WiFi-Honk provides a practical safety means for the distracted VRUs without requiring any special device using WiFi of smartphone. WiFi-Honk removes the WiFi association overhead using the beacon stuffed WiFi communication with the geographic location, speed, and direction information of the smartphone replacing its SSID while operating in WiFi Direct/Hotspot mode, and also provides an efficient collision estimation algorithm to issue appropriate warnings. Our experimental and simulation studies validate that WiFi-Honk can successfully alert VRUs within a sufficient reaction time frame, even in high mobility environments.

Multicast routing is essential for various one-to-many Wireless Sensor Network (WSN) applications. While many existing multicast protocols in WSNs suffer from the overhead of the intermediate nodes' states, packet header size, computation time, and energy consumption, branch-based multicast protocol achieves an optimal trade off among problems by maintaining the membership information only on the branch nodes that are created by a bottom-up membership join from the member nodes to a source node. However, the multicast path discovered by the simple bottom-up join may incur an inefficient multicast path, and the inefficiency becomes worse as nodes further join the WSNs. In this paper, we propose an Adaptive Path Control Protocol (APCP) for efficient branch-based multicast routing in a WSN. We leverage a source initiated efficient path control approach (top-down) for building an initial branch-based multicast path. However, to overcome the expensive maintenance cost of the top-down approach, the APCP adaptively runs the top-down and bottom-up join schemes using a formula to measure the multicast path quality and overhead, named the Branch Quality Factor (BQF). Our evaluation results show that the proposed APCP reduces a multicast path length by 20% less than the existing bottom-up join schemes with 10 times lower overhead than using only top-down join that builds up the most efficient multicast path.

Software-Defined Network (SDN) enables agile network traffic control and configuration as well as shortens the network function deployment time. Despite the projected benefits of SDN, the abstractions toward the remote and centralized control tend to impose excessive control traffic overhead in order for the controller to acquire the global network visibility as well as to extend the legacy network's inaccurate and unreliable management problems into the control plane. In addition, a complex combination of multiple and heterogeneous management channels further aggravates the scalability problem. In this paper, to address the above management problems. We propose an intelligent management middlebox called Software-defined Unified Monitoring Agent (SUMA). SUMA builds a hybrid SDN architecture by providing intelligent control, management abstraction, and a filtering layer, that eventually will serve as an essential component for the reliable, scalable, and secure SDN deployment. In this paper, we present design, implementation, deployment, and evaluation results of a SUMA.

Despite various high-tech Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications that have been developed for enhancing traffic safety, protecting vulnerable road users (VRU), such as pedestrians and bicyclists from the vehicles and trains still heavily relies on the traditional sound warning method. Furthermore, as smart devices continue to become highly ubiquitous, the more VRU are shutting out the safety related warning sounds. A traffic accident study shows the number of headphone-wearing VRU involved in roadside accidents has tripled since 2004. Although recently a few Car2Pedestrian communication methods have been proposed by various car makers, their practical usage is still in question. They assume VRUs' active attention to the communication while walking, and also mostly require special communication devices to cope with the wide range of mobility. In this paper, we propose a smartphone based Car2X communication system, named WiFiHonk that can alert of potential collisions to both VRU and vehicles in order to especially protect the distracted VRU. WiFiHonk provides a practical safety means for the distracted VRU without requiring any special device using the WiFi of a smart device. It is novel in that it removes the WiFi association overhead using the beacon stuffed WiFi communication and also provides an efficient collision estimation algorithm to issue appropriate warnings. Our experimental and simulation studies validate that WiFiHonk can successfully alert the VRU within a sufficient reaction time frame.

While many security schemes protect the content of messages in the Distributed Sensing Systems (DSS), the contextual information, such as communication patterns, is left vulnerable and can be utilized by attackers to identify critical information such as the locations of event sources and message sinks. Existing solutions for location anonymity are mostly designed to protect source or sink location anonymity individually against limited eavesdroppers on a small region at a time. However, they can be easily defeated by highly motivated global eavesdroppers that can monitor entire communication events on the DSS. To grapple with these challenges, we propose a mechanism for Preserving Anonymity of Sources and Sinks against Global Eavesdroppers (PASSAGES). PASSAGES uses a small number of stealthy permeability tunnels such as wormholes and message ferries to scatter and hide the communication patterns. Unlike prior schemes, PASSAGES effectively achieves a high anonymity level for both source and sink locations, without incurring extra communication overheads. We quantify the location anonymity level and evaluate the effectiveness of PASSAGES via analysis as well as extensive simulations. We also perform evaluations on the synergistic effect when PASSAGES is combined with other traditional solutions.

The fast growing popularity of smartphones and tablets enables us the use of various intelligent mobile applications. As many of those applications require position information, a smart mobile device provides positioning methods such as GPS, WiFi, or Cell-ID based positioning services. However, those positioning methods have different characteristics of energy efficiency, accuracy, and service availability. In this paper, we present an Energy- Efficient Cooperative and Opportunistic Positioning System (ECOPS) for heterogeneous mobile devices. ECOPS facilitates mobile devices with estimated locations using WiFi in cooperation with a few available GPS broadcasting devices, in order to achieve high energy efficiency and accuracy within available budget constraints. ECOPS estimates the location using heterogeneous positioning services and the combination methods including a received signal strength indicator, 2D trilateration, and available power measurement of mobile devices. The evaluation shows that ECOPS significantly reduces energy consumption and achieves the good accuracy of a location.

Preserving privacy is one of the most challenging yet essential issues in many mission critical WSN applications. As most of the existing privacy solutions additionally inject fake traffic assuming limited local adversary models, they can be easily defeated by highly motivated global attackers that monitor the entire network communications. We propose a scheme against a global adversary model, named Source Traceability Elimination for Privacy (STEP) using heterogeneous links. The STEP uses wormhole pairs (WHPs) to hide the communication of an original source location and scatter it to a remote location. Unlike the existing privacy mechanisms, the STEP provides privacy without incurring any additional communication overhead. We quantify a source location privacy level, evaluate the STEP with various parameters, and discuss its effect when used with other privacy techniques simultaneously.

Shared-memory based parallel programming with OpenMP and Posix-thread APIs is becoming more common to fully take advantage of multiprocessor computing environments. One of the critical risks in the multithreaded programming is data races which are hard to debug and greatly damaging to parallel applications if they are uncaughted. Although ample effort has been made in building specialized data race detection techniques, the state of art tools such as Intel thread checker still have various functionality and performance problems. In this paper, we present an efficient data race detection mechanism named ADAT (Adaptive Dynamic Analysis Tool). ADAT analyzes target program models to categorize the race engines (RDC: Race-Detection Classification) and then selects adequate engines to detect races automatically based upon the RDC (ECPS: Engine Code Property Selector). ADAT constructs an emperically optimal set of race engines in the aspect of labeling, filtering, and detection. In addition to RDC and ECPS, we have implemented an OpenMP parser and a source instrument or in ADAT to support OpenMP programs. The functionality and efficiency of ADAT are compared with those of Intel thread checker by using a set of OpenMP based kernel programs. The experimental results show that ADAT can detect data races with more challenging target program models and can achieve a couple of orders of magnitude faster processing time than Intel thread checker.

We propose a novel multicast protocol, named Multicast Routing with Branch Information Nodes (MR.BIN) for wireless sensor networks (WSNs). Addressing the various overhead issues of existing WSN multicast protocols, MR. BIN uses a hybrid approach of geographic unicast routing and state-based multicast routing. It achieves an optimal tradeoff among the overhead of the intermediate nodes' states, packet header size, computation time, and energy consumption and balance. Through both analysis and extensive simulations, we validate that MR.BIN outperforms existing multicast WSN protocols in the aforementioned aspects.

A holistic approach should be made for a wider adoption of a cross-layer approach. A cross-layer design on a wireless network assumed with a certain network condition, for instance, can have a limited usage in heterogeneous environments with diverse access network technologies and time varying network performance. The first step toward a cross-layer approach is an automatic detection of the underlying access network type, so that appropriate schemes can be applied without manual configurations. To address the issue, we investigate the characteristics of round-trip time (RTT) on wireless and wired networks. We conduct extensive experiments from diverse network environments and perform quantitative analyses on RTT variability. We show that RTT variability on a wireless network exhibits greatly larger mean, standard deviation, and min-to-high percentiles at least 10 ms bigger than those of wired networks due to the MAC layer retransmissions. We also find that the impact of packet size on wireless channel is particularly significant. Thus through a simple set of testing, one can accurately classify whether or not there has been a wireless network involved. We then propose effective adaptive cross-layer schemes for multimedia delivery over error-prone links. They include limiting the MAC layer retransmissions, controlling the application layer forward error correction (FEC) level, and selecting an optimal packet size. We conduct an analysis on the interplay of those adaptive parameters given a network condition. It enables us to find optimal cross-layer adaptive parameters when they are used concurrently.

Understanding outage and failure characteristics of a network is important to assess the availability of the network, determine failure source for trouble-shooting, and identify weak areas for network availability improvement. However, there has been virtually no failure measurement and analysis on access networks. In this paper, we carry out an in-depth outage and failure analysis of a university campus network using a rich set of both node outage and link failure data. We investigate the aspects of spatial and temporal localities of failures and outages, the relation of link failure and node outage, and the impact of the hierarchical and redundant network design on outage. We find most of link failure events are not caused by node failures; frequent link up-down events may not lead to the corresponding node's outage; for access layer switches that connect to end hosts, their link up-down events exhibit periodic patterns.

Outage measurement is an integral part of high-availability network operations to assess and report the availability of router components and, in turn, the availability of the network. The paper presents a novel approach to outage measurement, called component outage on-line (COOL) measurement. COOL provides an autonomous real-time outage measurement within the router. It automates the outage measurement process and makes it more accurate, reliable, scalable, and cost-effective. The paper describes COOL's measurement methodology with respect to outage model, measurement metrics, architectural framework, methods for measuring hardware and software outages and planned and unplanned outages, and outage MIB (management information base) design. COOL is being implemented in various network routers. Experiment results on COOL runtime performance are presented.

We present a novel approach, named Fault Recovery Port-Based Fast Spanning Tree Algorithm (FRP-FAST), of the Fault-Tolerant Ethernet (FTE) extension method to the arbitrary switched network topology with providing a significant improvement of failure detection and the spanning tree rebuilding time on the switched Ethernet. We provide a mechanism that expedites failure detection time using peer-based hello message algorithm and eliminates the chance of any transient loop creation during the spanning tree reconstruction using a pre-configured recovery port. As a result, unlike IEEE 802.1D, the scheme does not block data transmission on unaffected data path during the spanning tree discovery phase. The FRP-FAST algorithm has been implemented in the kernel mode of Windows NT-based PC using 3 NICs (3 port switch). The measured failure detection and recovery time meets control industry's 2 seconds requirement.

Providing fault-tolerant Ethernet capability on the large control network systems becomes very important issue. In this paper, we present two efficient scalable fault-tolerant network architecture designs: the "FTE protocol-independent multi-domain approach" and the "layer 2 switch/router-based multi-domain approach", which can efficiently integrate the layer-2-based FTE protocol and the existing standard router fault-tolerant protocols such as Virtual Router Redundancy Protocol (VRRP), Hot Standby Router Protocol (HSRP), and Open Shortest Path First (OSPF). The network designs take into consideration minimizing the control overheads in supporting large network systems, meeting the detection and recovery time requirements of the application regardless of the network size, using COTS redundancy protocols without overall network performance degradation, and justifying the solution cost. The feasibility and performance of our designs are demonstrated through experiment and analysis.

We present an efficient middleware-based fault-tolerant Ethernet (FTE) prototype developed for process control networks. This unique approach requires no change of commercial-off-the-shelf (COTS) hardware (switch, hub, Ethernet physical link and network interface card (NIC)) and software (Ethernet driver and protocol), yet it is transparent to application software. The FTE performs failure detection and recovery for handling multiple points of network failures and supports communications with non FTE-native devices. In this demonstration, we focus on presenting the failure detection and recovery behavior under various failure modes and scenarios. Further, multiple failure handling, node departure and non FTE-native node and FTE node communication scenarios will be presented. The FTE protocol status will be displayed using an FTE user interface on a COTS-based network system.

IP multicast is an important enabling service for the current and future Internet. With the explosive growth of the Internet, a challenging issue facing IP multicast is scalability, in particular, the problem of multicast forwarding state and control explosion. In this paper, we propose a new methodology to address the multicast scalability problem for backbone domains-multicast tunneling with branch filtering (MTBF). This multicast group aggregation scheme is designed on top of the inter-domain protocol architecture such as MASC/BGMF, and is independent of any underlying intra-domain multicast protocols. It aggregates multicast groups by constructing bolder router (BR)-based multicast routing trees and forwards data by using an encapsulation technique called multicast tunneling (MT). The feasibility and performance of our scheme is demonstrated through analysis and simulations.

We present an efficient middleware-based fault-tolerant Ethernet (FTE) developed for process control networks. Our approach is unique and practical in the sense that it requires no change to commercial off-the-shelf hardware (switch, hub, Ethernet physical link, and network interface card) and software (commercial Ethernet NIC card driver and standard protocol such as TCP/IP) yet it is transparent to IP-based applications. The FTE performs failure detection and recovery for handling multiple points of network faults and supports communications with non-FTE-capable devices. Our experimentation shows that FTE performs efficiently, achieving less than 1-ms end-to-end swap time and less than 2-sec failover time, regardless of the concurrent application and system loads. In this paper, we describe the FTE architecture, the challenging technical issues addressed, our performance evaluation results, and the lessons learned in design and development of such an open-network-based fault-tolerant network.

Presented is an open solution based approach to fault tolerant Ethernet for process control networks. This unique approach provides fault tolerance capability that requires no change of vendor hardware (Ethernet physical link and Network Interface Card) and software (Ethernet driver and protocol), yet it is transparent to control applications. The open fault tolerant Ethernet (OFTE) developed based on this approach performs failure detection and recovery for handling single point of network failure and serves regular IP traffic. Our experimentation shows that OFTE performs efficiently, achieving less than 1 ms end to end LAN swapping time and less than 2 sec failover time, and that concurrent application and system loads have little impact on the performance of failure detection and recovery operations.

IP Multicast is an important enabling service for the current and future Internet. With the explosive, growth of the Internet, a challenging issue facing IP multicast is scalability, in particular, the problem of multicast forwarding state and control explosion. In this paper, we propose a new methodology to address the multicast scalability problem for backbone domains Multicast Tunneling with Branch Filtering (MTBF). This multicast group aggregation scheme is designed on top of the inter-domain protocol architecture such as MASC/BGMP, and is independent of any underlying intra-domain multicast protocols. It aggregates multicast groups by constructing Border Router (BR)-based multicast routing trees and forwards data by using an encapsulation technique called Multicast Tunneling (MT). To minimize excess traffic due to aggregate multicast address based data forwarding, an efficient Dynamic Filtering Point Selection (DFPS) algorithm is used. The feasibility and performance of our scheme is demonstrated through analysis and simulations.