This work-in-progress (research-to-practice) paper describes our approach to designing a General Education (GE) Math course, aimed at enabling students not majoring in Computer Science (CS) to learn and apply computer programming in their own fields of study. Research has shown that underrep-resented groups face barriers such as economic and educational disparities, and cultural biases, which limit their access to Data Science (DS) and Artificial Intelligence (AI) education. Given the rapid growth of these technologies, the critical need for a diverse and inclusive workforce is increasingly evident. To address this, educational opportunities tailored for these groups are essential. Our course, Python for Everyone, is a Mathematics/Quantitative Reasoning GE offering and a core requirement for our BS-DS program, designed to meet these needs. It welcomes students from all disciplines, integrating programming skills with mathematical concepts, thereby democratizing access to DS and AI skills, and fostering interdisciplinary collaboration. The course design ensures students from various majors can recognize the relevance and application of computer programming in their fields. This paper delves into the curriculum's theoretical foun-dations, its structure, and the outcomes. Notable achievements include enhanced gender and diversity representation: 39% of the participants were female, surpassing the 25% in traditional CS majors, and the enrollment of underrepresented minority (URM) students reached 19%, compared to 7% in typical CS courses. These outcomes demonstrate the course's effectiveness in fostering diversity, inclusion, and accessibility in DS education. Python for Everyone serves as a pioneering model, guiding students, particularly from underrepresented backgrounds, and providing a pathway to successful careers in DS and AI. Our ongoing efforts involve conducting more surveys and assessments to refine our course, focusing on incorporating more real-world applications to highlight the relevance of DS across disciplines, aiming for a more inclusive workforce, and assessing the impact of this course in enhancing educational and career opportunities for URM students. We believe this paper is a valuable resource for educators dedicated to enhancing GE quantitative reasoning courses and broadening career opportunities in CS, DS, and AI for students from underrepresented groups.

This innovative practice WIP paper considers the remarkable growth of Artificial Intelligence (AI), Machine Learning (ML) and Data Science (DS) in recent years. This surge has sparked an urgent need for skilled workers in these highly-related areas, subject to growing calls for a diverse, inclusive technology workforce. This WIP paper presents an innovative approach in the rapid design and development of interdisciplinary undergraduate degree programs that are computing-based, with heavy emphasis in AI, ML, and DS while providing foundations for applications in specific domains. Located at the center of Silicon Valley, the Department of Computer Science (CS) at SJSU (San Jose State University) has been constantly bombarded by the speedy changes of technology and the corresponding need for skilled tech workers, and more recently, calls for a diverse tech workforce that matches the demographic of our community. As both an HSI (Hispanic Serving Institution) and AANAPISI (Asian American and Native American Pacific Islander Serving Institution), we understand that underrepresented groups face significant challenges. Prevailing stereotypes about computer scientists tend to reinforce the notion that women and minority do not have a place in this field. Trying to break through these stereotypes, we have reached out and develop interdisciplinary programs with majors that traditionally attract more women and minorities (such as majors in biology or the humanities). This paper first describes the success of two existing interdis-ciplinary, joint master degree programs: MS in Bioinformatics and MS in Data Science. Then, it illustrates how we rapidly developed a computing-based BS in Data Science (in one year), and concurrently a BS in Computer Science and Linguistics (in another year) that is jointly offered with the Department of Linguistics and Language Development (LLD). The paper presents the curriculum development approaches, ethical and social awareness components of the curricula, and uniqueness and success factors. Preliminary data show a significant increase in enrollment of women and low-income students in the first major core course of BS-DS. We believe that this paper will serve as a model initiative for rapidly developing a diverse technology workforce that meets the needs of emerging AI, ML and DS industries.

Accurately analyzing dialogue in multi-party meetings is crucial for enhancing communication and decision-making. The informal and dynamic nature of these discussions introduces challenges for computational analysis, including non-standard language, interruptions, and rapid topic changes. To address these challenges, we developed two methods: Argument Classification and Relation Classification. Argument Classification employs machine learning models like Gradient Boosting to identify and categorize key points made by participants. This method improves conversation traceability and insight extraction, achieving an F1 score of 0.79, a 28% improvement over previous methods. Relation Classification uses advanced neural network architectures, such as BERT, to decode interactions and map relationships within discussions. This method captures conversational nuances, achieving an overall accuracy of 0.86 and an F1 score of 0.83. Our models effectively transform raw meeting transcripts into structured data, enhancing the understanding and analysis of dialogues. This approach not only clarifies discussions but also lays a foundation for better decision-making processes. The results demonstrate the potential for deploying these models in various domains requiring nuanced dialogue analysis.

Deduplication is the process of removing replicated data content from storage facilities like online databases, cloud datastore, local file systems, etc. It is commonly performed as part of data preprocessing to eliminate redundant data that requires extra storage spaces and computing power and is crucial for data storage management in cloud computing. Deduplication is essential for file backup systems since duplicated files will presumably consume more storage space, especially with a short backup period such as daily. A common technique in this field involves splitting files into chunks whose hashes can be compared using data structures or techniques like clustering. This paper explores the possibility of performing such file chunk deduplication leveraging an innovative reinforcement learning approach to achieve a high deduplication ratio. The proposed system is named SegDup, which achieves 13% higher deduplication ratio than Extreme Binning, a state-of-the art deduplication algorithm.

As society grows increasingly more online with each passing year, the problem of cyberbullying becomes more and more prominent, with such incidents having the capacity to negatively impact mental health in a major way, especially among children and teenagers. The proposed approach builds on our previous work that established multi-modal detection of cyberbullying on Twitter, and restructures the multi-modal approach by incorporating social media features such as time-related features and social network information. As a result, the new models reach a classification accuracy between 94.4% and 94.6%, from the previous accuracy of 93%. The proposed new approach affirms the use of context-based data in addition to more directly-related features when analyzing cyberbullying and other interactions with promising improvements. We believe that this work contributes significantly to the study of cyberbullying detection, which is an imminent problem with growing importance in the post-COVID society.

Speech separation is a special form of blind source separation in which the objective is to decouple two or more sources such that they are distinct. The need for such an ability grows as speech activated device usage increases in our every day life. These systems, however, are susceptible to malicious actors. In this work, we repurpose proven adversarial attacks and leverage them against a combination speech separation and speech recognition system. The attack adds adversarial noise to a mixture of two voices such that the two outputs of the speech separation system are similarly transcribed by the speech recognition system despite hearing clear differences in the speech. Against ConvTasNet, degradation of separation remains low at 0.34 decibels, allowing the speech recognition system to still work. When testing against automatic speech recognition, the attack achieves a 64.07% word error rate (WER) against Wav2Vec2, compared to 4.22% for unmodified samples. Against Speech2Text, the WER is 84.55%, compared to 10% WER for unmodified samples. For similarity to the target transcript, the attack achieves 24.77% character error rate (CER), reduced from 113% CER. This indicates relatively high similarity between the target transcription and the resulting transcription.

With recent advancements in the field of artificial intelligence, deep learning has created a niche in the technology space and is being actively used in autonomous and IoT systems globally. Unfortunately, these deep learning models have become susceptible to adversarial attacks that can severely impact its integrity. Research has shown that many state-of-the-art models are vulnerable to attacks by well- crafted adversarial examples. These adversarial examples are perturbed versions of clean data with a small amount of noise added to it. These adversarial samples are imperceptible to the human eye yet they can easily fool the targeted model. The exposed vulnerabilities of these models raise the question of their usability in safety-critical real-world applications such as autonomous driving and medical applications. In this work, we have documented the effectiveness of six different gradient-based adversarial attacks on ResNet image recognition model. Defending against these adversaries is challenging. Adversarial re-training has been one of the widely used defense technique. It aims at training a more robust model capable of handling the adversarial examples attack by itself. We showcase the limitations of traditional adversarial-retraining techniques that could be effective against some adversaries but does not protect against more sophisticated attacks. We present a new ensemble defense strategy using adversarial retraining technique that is capable of withstanding six adversarial attacks on cifar10 dataset with a minimum accuracy of 89.31%.

CAPTCHA is a web-based authentication method used by websites to distinguish between humans (valid users) and bots (attackers). Audio captcha is an accessible captcha meant for the visually disabled section of users such as color-blind, blind, near-sighted users. Firstly, this paper analyzes how secure current audio captchas are from attacks using machine learning (ML) and deep learning (DL) models. Each audio captcha is made up of five, seven or ten random digits[0-9] spoken one after the other along with varying background noise throughout the length of the audio. If the ML or DL model is able to correctly identify all spoken digits and in the correct order of occurance in a single audio captcha, we consider that captcha to be broken and the attack to be successful. Throughout the paper, accuracy refers to the attack model's success at breaking audio captchas. The higher the attack accuracy, the more unsecure the audio captchas are. In our baseline experiments, we found that attack models could break audio captchas that had no background noise or medium background noise with any number of spoken digits with nearly 99% to 100% accuracy. Whereas, audio captchas with high background noise were relatively more secure with attack accuracy of 85%. Secondly, we propose that the concepts of adversarial examples algorithms can be used to create a new kind of audio captcha that is more resilient towards attacks. We found that even after retraining the models on the new adversarial audio data, the attack accuracy remained as low as 25% to 36% only. Lastly, we explore the benefits of creating adversarial audio captcha through different algorithms such as Basic Iterative Method (BIM) and deepFool. We found that as long as the attacker has less than 45% sample from each kinds of adversarial audio datasets, the defense will be successful at preventing attacks.

Computer systems utilize logging to record events of interest. These logs are a rich source of information, and can be analyzed to detect attacks, failures, and many other issues. Due to the automated generation of logs by computer processes, the volume and throughput of logs can be extremely large, limiting the effectiveness of manual analysis. Rule-based systems were introduced to automatically detect issues based on rules written by experts. However, these systems can only detect known issues for which related rules exist in the rule-set. On the other hand, anomaly detection (AD) approaches can detect unknown issues. This is achieved by looking for unusual behaviors significantly different from the norm. In this paper, we target the problem of semi-supervised log anomaly detection, where the only training data available are normal logs from a baseline period. We propose a novel hybrid model called "CausalConvLSTM" for modeling log sequences that takes advantage of Convolutional Neural Network's (CNN) ability to efficiently extract spatial features in a parallel fashion, and Long Short-Term Memory (LSTM) network's superior ability to capture sequential relationships. Another major challenge faced by anomaly detection systems is concept drift, which is the change in normal system behavior over time. We proposed and evaluated concrete strategies for retraining neural-network (NN) anomaly detection systems to adapt to concept drift.

Given the great amounts of data being transmitted between devices in the 21st century, existing channels of wireless communication are getting congested. In the wireless space, the focus up to now has been on the microwave frequency range. An alternative for high-speed medium- and long-range communication is the millimeter wave spectrum, which is most effectively used through point-to-point links. In this paper, we develop and compare methods for verifying the Line of Sight (LOS) constraint between two points in a city. To be useful for online wireless network planning systems, the methods must be able to process terabytes of 3D city geolocation data and provide answers in milliseconds. We evaluate our methods using data for the city of San Jose, a major metropolitan area in Silicon Valley, California. Our results indicate that our Hierarchical Polygon Aggregation (HPA) method is able to achieve millisecond-level query times with very little loss of precision.

Music is a complicated form of communication, where creators and cultures communicate and expose their individualities. Thanks to music digitalization, recommendation systems and other online services have become indispensable in the field of Music Information Retrieval (MIR). Classification of music is essential for music recommendation systems. In this paper, we propose an approach for finding similarity between music. Our approach is based on mid-level attributes like pitch, midi value, interval, contour, and duration, and applying text-based classification techniques. Performance evaluation has been done using the accuracy score of scikit-learn. As a preliminary study, our system first predicted jazz, metal, and ragtime for western music. The genre prediction system has been tested on 476 music files with a maximum accuracy of 95.8% across different n-grams. Then, we have analyzed and classified the Indian classical Carnatic music based on their raga. Our system has predicted Sankarabharam, Mohanam, and Sindhubhairavi ragas. The raga prediction system was tested on 68 music files with a maximum accuracy of 90.14% across different n-grams.

Despite clinical trials by pharmaceutical companies as well as current FDA reporting systems, there are still drug side effects that have not been caught. To find a larger sample of reports, a possible way is to mine online social media. With its current widespread use, social media such as Twitter has given rise to massive amounts of data, which can be used as reports for drug side effects. To process these large datasets, Apache Spark has become popular for fast, distributed batch processing. In this work, we have improved on previous pipelines in sentimental analysis-based mining, processing, and extracting tweets with drug-caused side effects. We have also added a new ensemble classifier using a combination of sentiment analysis features to increase the accuracy of identifying drug-caused side effects. In addition, the frequency count for the side effects is also provided. Furthermore, we have also implemented the same pipeline in Apache Spark to improve the speed of processing of tweets by 2.5 times, as well as to support the process of large tweet datasets. As the frequency count of drug side effects opens a wide door for further analysis, we present a preliminary study on this issue, including the side effects of simultaneously using two drugs, and the potential danger of using less-common combination of drugs. We believe the pipeline design and the results present in this work would have great implication on studying drug side effects and on big data analysis in general.

Cloud Radio Access Networks (CRAN) has been proposed for 5G networks for better flexibility, scalability, and performance. CRAN aims to apply cloud-like architecture on RAN. This paper focuses on cache management and load balance in the software architecture of CRAN, intends to provide fault mitigation and carrier-grade real-time services. First, a new cache management algorithm based on event frequency and QoS level of User Equipment (UE) is proposed, utilizing Exponential Decay scoring function and Analytic Hierarchy Process. Next, load balance (LB) function is added, and five LB algorithms are evaluated. The performance evaluation is based on real-life UE event characteristics and RAN system values provided by Nokia Research. Results show the cache hit rate, delay and network traffic; as well as the queue-length analysis of LB algorithms.

Extensive clinical trials are required before a drug is placed on the market; yet it is difficult to discover all the side effects for any approved drugs. The United States Food and Drug Administration actively monitors approved medications to identify adverse events. The FDA Adverse Event Reporting System contains a database of adverse drug events (ADE) reported by the healthcare providers and consumers. The pervasive online social networks, such as Twitter, can provide additional information ADE. Concurrently, advancements in social media technology have resulted in the booming of massive public data; the availability of these huge datasets offers numerous research opportunities for extracting ADEs. Towards this purpose, in this paper a simple, effective computation pipeline is proposed, which uses simple drug-related classification and sentiment analysis to extract ADEs on Twitter. The pipeline is described in detail, and is implemented into an automatic process. Experiments are carried out based on 4-months of Twitter data collected. Comparing with an existing pipeline, the new design is able to successfully capture 5 times more valid ADEs, among them 20% are new ADEs. The proposed method may be applied to other areas such as food, beverages, and other daily consumer products for identifying side effects and user opinions.

As social media has become increasingly popular in the modern world, people are using these platforms to express their opinions about products, businesses, and services. The need for categorizing these consumer reviews has been prominent. One effective solution is sentiment analysis (SA), which has been an active research topic. The goal of SA is to automatically extracting and classifying user opinions. Pervious research works however have not shown satisfied results. In this paper, a multilayer architecture is proposed to increase the performance of multiclass classification. The framework includes data-preprocessing, feature extraction and selection, and classifier building. The framework is a two-layer classification, choosing from Naïve Bayes, Support Vector Machine, Random Forest, and Logistic Regression as base models, and using a voting scheme to obtain the final predicted class. The proposed model is applied to more than 1.3 million restaurant reviews from the Yelp Challenge dataset. We have achieved a high accuracy of 86% for cross validation, and using real-world online review data as test data, we have achieved an accuracy of 80%. The results show that the proposed framework has greatly improved classification accuracy while comparing with those using single-layer architectures. We believe that the proposed method may be applied to, and would have significant contributions to other areas of opinion mining.

Mobile messaging has become a trend in our daily lives. The current schema for messaging is to route all the messages between mobile users through a centralized server. This scheme, though reliable, creates very heavy load on the server. It is possible for users to communicate through peer-to-peer (P2P) connection, especially over urban networks characterized by heavy user traffic and dense network connectivity. P2P connections however do not provide the best user experience, as they are sometimes unreliable due to network coverage fluctuation. We propose an intelligent messaging framework based on reinforcement learning to strike a balance between reducing server load and improving user experience. The system learns and adapts in real-time to user mobility and messaging patterns. The adaptive system dynamically chooses between routing through the server and routing via P2P connection. As it does not rely on user location information, user privacy is thus preserved. Performance evaluation through simulation of user movement and messaging patterns demonstrates that the system is able to find the best messaging policy for users, achieving a well balance between heavy server load and unreliable communication, and provides a fine user messaging experience while reducing server load. We believe that this work is significant for future smart environments and urban networking where mobile messaging will be prominent among mobile users as well as mobile smart objects.

The electrical power grid forms the functional foundation of our modern societies, but in the near future our aging electrical infrastructure will not be able to keep pace with our demands. As a result, nations worldwide have started to convert their power grids into smart grids that will have improved communication and control systems. A smart grid will be better able to incorporate new forms of energy generation as well as be self-healing and more reliable. This paper investigates a threat to wireless communication networks from a fully realized quantum computer, and provides a means to avoid this problem in smart grid domains. We discuss and compare the security aspects, the complexities and the performance of authentication using public-key cryptography and using Merkel Trees. As a result, we argue for the use of Merkle Trees as opposed to public key encryption for authentication of devices in wireless mesh networks (WMN) used in smart grid applications.

Modern web-based services increasingly have a cloud-based component, which only in recent years has been studied in an academic manner. Many cloud-computing courses focus on fundamental concepts that, while universal to cloud computing understanding, may not provide students with enough background to actually deploy an application to the cloud. To that end we present a cloud computing class that through the use of labs, presentations, and research projects, integrates practical hands-on experiences with academic research. The labs not only deploy to multiple public cloud environments, but also walk students through the process of creating, configuring, and maintaining their own private clouds. We maintain that this juxtaposition of practice and theory leads to not only better learning outcomes in the classroom in the form of refereed publications and further study, but also leads to practical skills which better prepare students for joining the workforce.

We developed software that performs priority-based power management and downtime reduction for virtual machines running in data centers. The software deals with power management at the processor level. The software automatically performs load distribution among servers in data centers to save power. In addition, it reduces downtimes for critical virtual machines. Our experiments have achieved an energy saving of up to 35%. We therefore confirm that the proposed scheme has significantly decreased energy consumption while maintaining high runtime availability for the mission critical applications.

One major challenge in broadband wireless access networks is to provide fast, reliable services to time-sensitive communications. We propose an enhanced hybrid automatic repeat request (HARQ) scheme for the worldwide interoperability for microwave access (WiMAX). The new scheme follows the multi-channel stop-and-wait chase combining HARQ adopted by WiMAX, yet it enables the base station (BS) to proactively react to poor channel conditions. The BS would send multiple copies of the same data burst to the subscriber station (SS) over the multiple HARQ channels available to the SS. We design a novel method which would determine the number of multiple copies needed based on the channel feedback. The new HARQ scheme effectively reduces the total time needed for a successful transmission. Simulation results show that, comparing with the original WiMAX HARQ, the new scheme significantly reduces the waiting time for a successful transmission by up to 45%, while maintaining a comparable level of throughput. This work is most significant for time-critical applications that need fast, reliable services, such as VoIP and other interactive uses.

Wireless sensor network (WSN) is a technology that will become an integral part of everyday life. It has countless potential applications in ubiquitous and pervasive computing, including those in biomedical technology and healthcare industry. As the number and variety of wireless sensor device types increase, a middleware layer capable of linking various implementations into a single programming abstraction will be needed. This paper aims to build a platform that allows users to develop WSN applications with ease, and is robust enough to support complex system development. A flexible platform based on Java and JADE (Java Agent Based Development) is proposed to handle sensor network integration and application development. Design methodology is carefully presented. A prototype implementation is described, and illustrated by an application in the healthcare domain. We believe that this work would help bridging the gaps existed among WSN researchers, application developers, and users, and is significant in realizing the full potential of WSN.

Transmission control protocol (TCP) plays an important role in defining a network's performance. Its use in wireless networks has exposed several inadequacies in its operation. We propose an improvement over TCP for communication between fixed host (FH) senders and wireless, mobile host (MH) receivers that are interlinked by base stations (BS). We believe that this is becoming the main infrastructure supporting most mobile Internet traffic. Our new scheme is called TCP-ECN (explicit congestion notification). It aims to address the challenges of high bit error rate in wireless links and long disconnections due to mobility. It keeps the modification in BS and MH minimal without requiring any changes to FH. The BS uses one bit (ECN bit) to enable the MH in distinguishing between congestion and wireless losses, and thus, suppressing unnecessary duplicate acknowledgments (dupACK) due to wireless losses. In addition, zero window acknowledgment and triplicate dupACK schemes are adopted as part of the handoff procedure in BS. Both simulation and network test-bed results show that TCP-ECN performs significantly better than TCP-Reno and Snoop in the face of high wireless loss, high or low congestion loss, and mobility. Performance improvement is more significant when more wireless receivers are supported. We believe this work is significant to wireless carriers and vendors when designing protocols and devices that support mobile Internet access.

Wireless mesh network is a promising new direction resulting from the recent rapid progress made in wireless communication technologies. While multiple radio, multiple channel technologies have offered a great potential for increasing network throughput, they also create new research challenges. In this paper, we propose RingMesh, a token ring-based protocol for multi-channel routing. By applying delay-guaranteed rules for joining a ring and for creating new rings, the multiple-ring protocol bounds the end-to-end packet delay within the mesh network, from the source node to a gateway that connects with the wired network. The RingMesh protocol is described as a state machine; the analytical results on bounded delay are presented. We believe that both the protocol and analysis may be applied to other areas of delay-guaranteed wireless network research

This paper investigates the effectiveness of data gathering protocols for in-body biomedical sensor networks. We studied the performance of representatives from each of three major protocol categories: (1) low energy adaptive clustering hierarchy (LEACH), a cluster-based protocol, (2) power efficient gathering for sensory information systems (PEGASIS), a chain-based protocol, and (3) hybrid indirect transmissions (HIT), a hybrid of chains and clusters. First, the ability of each protocol to perform in-network source separation was judged. We consider a human-machine interaction application in which implanted bio-sensors communicate motor unit actions of human muscles to a remote computer. Motor unit action potentials (MUAPs) were modeled, and source separation and recovery at each sensor was simulated. We compare the performance of HIT and LEACH in terms of signal distortion ratios and the energy costs of fusion and communication. Second, we report on the efficiency of each protocol for in-body data collection, using Gupta et al's propagation loss model for biomedical applications (PMBA) - an accurate model of power loss due to signal absorption by the human body. We investigate the effectiveness of HIT, LEACH, and PEGASIS under this model, and compare their performance in terms of energy efficiency and network lifetime

Group mutual exclusion (GME) was first introduced by Joung in 1998 as a generalization of the n-process mutual exclusion problem, and subsequently modeled as the Congenial Talking Philosophers problem. GME allows processes requesting for the same resource to concurrently access the shared resource. However, a process requesting a resource that is different from the one currently being used will not be able to access the requested resource at the same time. At any time, only one resource may be in use.There have been two solutions to the GME problem for a ring network. The first solution proposed by Wu and Joung [2] had an unbounded message size problem, while the second solution proposed by Cantarell et. al. [1] attempted to provide an upper-bound for the message size. However, in doing so, the second solution generated an unbounded number of messages. Hence, in this work we present a GME algorithm for a token-passing network that solves both of the problems found in the two previous solutions by providing an upper bound both to the number of messages and to message size.

One major challenge in sensor networks is to maximize network life under the constraint of extremely limited power supply. Thus, an important design issue of routing and data-gathering protocols is minimizing energy. This paper investigates the energy efficiency of two data gathering protocols, based on distributed versions of Shortest Path Tree (SPT) and Maximum Leaf Tree (MLT) algorithms [1]. The two have been extended to be dynamic by applying localized tree-reconstruction mechanism [2] to handle joining and leaving (death) of sensor nodes. Accurate energy consumption has been carefully modeled for both leaf-nodes and intermediate nodes, when sending and receiving data. Performance is evaluated through detailed simulation, including exchange of control messages among sensor nodes. Simulation results have shown that SPT, due to its simplicity and smaller number of control messages, achieves better energy efficiency and less delay in tree constructions, data transmissions, and dynamic tree reconstructions.

In-network data gathering and data fusion are essential for the efficient operation of wireless sensor networks. While most existing data gathering routing protocols addressed the issue of energy efficiency, few of them, however, have considered the quality of the implied data aggregation process. In this work, an information model for sensed data is first formulated. A new metric for evaluating data aggregation process, data aggregation quality (DAQ), is formally derived. DAQ does not assume any prior knowledge on values or on statistical distributions of sensing data, and may be applied to most data gathering protocols. Next, two new protocols are proposed: the enhanced LEACH and the clustered PEGASIS, enhanced from two major existing protocols: the cluster-based LEACH and the chain-based PEGASIS. By carefully accounting for listening energy, energy efficiency of all four protocols is evaluated. In addition, DAQ is applied to evaluate their data aggregation process. It is found that, while chain-based protocols are more energy efficient than cluster-based protocols, they however suffer from poor data aggregation quality. DAQ may be readily applied to most of continuous data gathering protocols; it is therefore significant to future development of sensor network protocols.

This paper addresses the challenge of providing scalable end-to-end differentiated service for group communications. While bandwidth broker (BB) has been proposed for guaranteed services in unicasting, no work has been done to extend it for multicasting. We propose a single-layer BB architecture for intra-domain multicast, and a multi-layer BB for end-to-end services across multiple network domains. Databases in the BB, the join and leave procedures, and their associated control messages are carefully designed. Complexities of the two proposed architectures are formally analyzed. It is shown that, comparing with the single-layer BB architecture, the multi-layer architecture results in much lower join costs and control-message complexity, and is thus highly scalable. Performance is evaluated via simulation of a multi-domain network with heterogeneous receivers. The proposed multi-layer BB architecture successfully provides end-to-end service differentiation in terms of throughput, end-to-end delay, and packet losses. We believe that this work is significant towards offering end-to-end differentiated-service group communications in large-scaled networks.

There has been a growing demand for streaming media distribution over the Internet, which consists of heterogeneous end hosts with different capabilities and using various last-mile network connections. To provide end-to-end differentiated services of streaming media among heterogeneous users, an application-layer multicasting of layered encoded streams is presented. The proposed basic scheme incorporates the existing NICE protocol proposed by Banerjee el al. (2002) with layered multicasting similar to receiver-driven layered multicast (RLM) proposed by McCanne et al. (1996). The resulting protocol is further enhanced with an improved cluster-leader selection process and prioritized control packet treatments. By applying cumulative video encoding scheme, the enhanced protocol provides service differentiation among end hosts of different capabilities, allowing them to receive different video qualities corresponding to one or more layers of encoded streams. When compared with the basic scheme via simulation, the enhanced protocol provides better video quality, higher bandwidth usage, and lower loss rate.

The differentiated services (DiffServ) routers provide per hop behavior (PHBs) to aggregate traffic for different levels of services, the scheduling algorithm used by the DiffServ routers is critical in implementing PHBs. This paper presents a new scheduling scheme, PQWRR that combines existing scheduling schemes, PQ (priority queuing) and WRR (weighted round robin). Our simulation results show that PQWRR provides best delay and jitter performance for EF (expedited forwarding) traffic and at the same time delivers guaranteed bandwidth to AF traffic. PQWRR not only solves the problems of existing PQ and WRR algorithms, but also provides best support for both EF and AF PHBs in the DiffServ domain.

Differentiated services (DiffServ) and MPLS are two major building blocks for providing multi-class services over IP networks. The performance and efficiency of DiffServ architecture can be enhanced with per-class traffic engineering. We propose a new multi-protocol label switching (MPLS) traffic engineering scheme. Based on a constraint-based routing modified from our previously proposed QoS routing algorithm, it enhances the original E-LSP with per-class traffic engineering and load balancing. Major components of the scheme including labeling, load balancing, and routing are described; its features are carefully analyzed against desired traffic engineering requirements. With detailed simulation, we evaluate and compare the proposed scheme with two other schemes: the original E-LSP and E-LSP with load balancing, for their support of expedited forwarding (EF), assured forwarding (AF), and best effort (BE) service classes. We found that, through better utilization of network resources, the proposed scheme is able to accommodate more QoS flows with desired requirements; at the same time it offers better delay and delay jitter for existing EF and AF classes, and improves the overall performance of BE class. We believe that the proposed scheme is an important step for providing scalable, multi-service solution in future IP networks.

There has been active research on quality of service (QoS) multicasting. Very few, however, addresses QoS inter-domain multicasting. We present a QoS extension to BGMP (border gateway multicast protocol) which, proposed by Kumar, et al., is currently one of the most promising inter-domain multicasting proposals. With limited modification to the current BGMP, we extend it to support the so called two-bit differentiated service (including the classes of premium, assured, and best-effort services) proposed by Nichols, Jacobson, and Zhang. Detailed mechanisms of the QoS extension are described. Performance is evaluated through extensive simulation of real-network scenarios, supporting CBR, VBR, and best-effort traffic flows with the three classes of services. We believe that the work presented here is significant to the advancement of the next-generation Internet supporting QoS communications.

There has recently been active research on quality of service (QoS) multicasting. Very few, however, address QoS inter-domain multicasting. In this paper, we present a differentiated service (DS) extension to MBGP (Multicasting Border Gateway Protocol), which is an extension of BGP-4 (Border Gateway Protocol 4) to support multicasting. With limited modification to the MBGP, we extend it to support two prospective differentiated service protocols: expedited forwarding (formally premium service) and assured forwarding (formerly assured service). Detailed mechanisms of the DS extension are described. Performance is evaluated through extensive simulation of real-network scenarios (including nodes joining/leaving), supporting CBR, VBR, and best-effort traffic flows requesting different classes of service. We believe that the work presented here is significant to the advancement of the next-generation Internet supporting DS communications.

QoS multicast routing with more than one metric has always been technically challenging, since many of them are NP-hard. Most existing QoS multicast routing algorithms are heuristic. Furthermore, many of them considered only the unicast shortest paths, either based on propagation delay or the number of hops. In this paper, we observed that the end-to-end delay experienced by network packets is determined not only by the propagation distance, but also (and in many cases largely) by the hop-by-hop transmission and queueing delay, which in turn depends on the available bandwidth. Based on this observation we propose an optimal multicast routing algorithm, which maximizes the reserved bandwidth while satisfying both delay and bandwidth constraints. Its correctness and time-complexity of O(n/sup 2/log n) are formally verified, where n is the number of nodes in the network. We also extend the algorithm to be dynamic, such that nodes are allowed to join or leave the network, with the multicast route updated dynamically. We carefully evaluate both the static and the dynamic algorithms via simulation. We found that the new algorithm significantly reduces the end-to-end delay experienced by network packets; its dynamic version is able to rapidly update the multicast route using little computation time. We believe that both versions of the new algorithm are important to the area of QoS-based multicast routing; their design and analysis may give new insights to network researchers in their future design of routing protocols.

The Internet has recently become the most important, most popular way of communication. A significant new feature of the Internet is the support of telephony. Two main competing signaling protocol standards have been developed for this purpose: H.323, proposed by the ITU, and SIP, proposed by the IETF. Another new feature for the Internet is the support of terminal mobility. The IETF efforts in this field have resulted in the Mobile IP standard. Liao (1999) has shown that Mobile IP does not provide fast enough handoffs to support voice communications. Liao has also demonstrated how H.323 may be extended to offer a solution. In this paper we address several major issues for supporting mobility on SIP. We detail the mechanisms for extending SIP to support location management, registration of roaming mobiles, and handoffs. The mechanisms are either extensions of existing SIP, or based on Mobile IP, and thus may be readily employed over the Internet. We believe that the work presented here is an important step towards supporting mobile telephony over the Internet.

Fast transmission among many sources and destinations usually has the goal of minimizing some objective (such as cost) while satisfying some constraints such as delay or bandwidth. Due to these constraints, the solution sometimes is achieved only through the use of multiple-shared multicast trees (MSMT), which use more than one multicast tree to serve multiple sources and destinations. Minimizing the number of multiple-shared multicast trees, however, has been proven to be NP-complete. In this paper we propose three algorithms for this problem and compare them with an existing one. We can prove, theoretically and through simulations, that our algorithms gives the same good results with a significantly shorter run-time. On the other hand, fast transmission and high reliability are also achieved by selecting the path with large bandwidth. We apply the MBDC (maximum bottleneck bandwidth with delay constraint) algorithm, which we previously proposed, to the MSMT algorithms to optimize them in terms of bandwidth. Proof of correctness, estimation of run-time, and simulation results of these extensions are also provided.

Wireless local area networks (WLAN) are expected to be a major growth factor for communication networks in the up-coming years. They are expected to provide a transparent connection for mobile hosts to communicate with other mobile hosts, and wired hosts on the wired LAN and broadband networks. Two WLAN projects have undergone the standardization process: the IEEE 802.11 and the ETSI HIPERLAN. Most of the existing study of the two MAC protocols focused on simulation results, and none of them has formally analyzed the hidden-terminal effect, which is both crucial and unavoidable in wireless/mobile environment. We formally analyze the hidden-terminal effect on HIPERLAN. Through mathematical analysis, we formulate network throughput under hidden-terminal influence in terms of the original (clear-channel) throughput, hidden-terminal probability, and other protocol parameters. We show that when hidden probability is greater than zero, the achievable throughput is reduced by more than the percentage of hidden probability. We evaluate and compare the two WLAN MAC protocols by simulation of the effect of hidden terminals on (1) network throughput, (2) real-time voice delay, and (3) number of voice and data stations supported while guaranteeing delay for voice. We also evaluate how well the two MAC protocols support real-time traffic while considering the effects of frame size and other network parameters, and measure (1) the distribution of voice delay and (2) number of voice and data stations supported while guaranteeing their quality of service. We found that, compared with IEEE 802.11, HIPERLAN provides real-time packet voice traffic with shorter delay, and at the same time provides the non-real-time packet data with a higher bandwidth.

ATM networks are networks with guaranteed quality of service. The main cause of congestion in ATM networks is over utilization of the physical bandwidth. Unlike constant bit rate traffic, the bandwidth reserved by variable bit rate (VBR) traffic is not fully utilized at all instances. Hence, this unused bandwidth is allocated to available bit rate traffic (ABR). As the bandwidth used by VBR traffic changes, the available bandwidth for ABR traffic varies, i.e., the available bandwidth for ABR traffic is inversely proportional to the bandwidth used by the VBR traffic. Based on this fact, a rate based congestion control algorithm, explicit allowed rate algorithm (EARA), is presented. The EARA is compared with the proportional rate control algorithm (PRCA) and explicit rate indication congestion avoidance algorithm (ERICA), under congestion and fairness configurations. The results show that, with a minimal overhead on the switch, the EARA significantly decreases the required buffer space and improves the network throughput.

The major challenges of designing multicast rate-control protocols for a combined wired/wireless network are the varying transmission characteristics (bandwidth, error, and propagation delay) of the wireless and wired media, and the different, possibly conflicting flow control requests from multiple receivers. To address these issues, in this paper we study multicasting rate-control ABR algorithms for a combined wired/wireless network with unreliable links and irregular network feedback. We propose a new ABR multicast extension algorithm that can readily extend any unicast ABR rate control scheme for multicast services. By formal analysis, we show that it is max-min fair, and that its maximum cell loss is less than or equal to that of an existing algorithm proposed by K.Y. Siu and H.Y. Tseng (1997). We also extend the definition of global feasibility to multicast flows, and propose a delayed-increase policy to ensure global feasibility. Both the waiting time of the delayed-increase policy, and the maximum cell loss in the absence of the policy are formally analyzed. The new algorithm requires a waiting time no longer than, and has a maximum cell loss less than or equal to, that of the existing algorithm. The significance of our approach is illustrated by the formal analysis, which allows us to design a new multicast extension algorithm that is max-min fair, results in a small cell-loss bound, and achieves global feasibility. The analytical method may also assist the design and analysis of other multicasting flow algorithms over ATM and wireless ATM, and other network protocols such as multicast IP and mobile IP.

We study both the correctness and performance of the source/destination protocol of the available bit rate (ABR) service in asynchronous transfer mode (ATM) networks. Although the basic source/destination protocol for congestion management is relatively simple, the protocol specification has to cope with several "real-world" cases such as failures and delayed/lost feedback which may introduce complexity. Rigorous proofs of the correct functioning of the protocol based on a formal specification is necessary. We use a formal extended finite state machine (EFSM) model to show that the ABR source/destination protocol is free of live-locks, so that under all conditions both resource management (RM) and data cells will be transmitted. We also show that the network options of explicit forward congestion indication (EFCI) and explicit rate (ER) interoperate correctly. We use the understanding of the informal English description of the source/destination behavior and of our EFSM model to derive conditions that ensure that the source transmission rate is stable in the presence of delayed or lost feedback RM cells, especially under the operation of a source rule that requires the reduction of the source rate under these conditions. We arrive at bounds on the number of consecutive RM cell losses tolerated while the rate remains stable. We also provide a worst-case analysis of the delay in turning around RM cells at the destination station and the worst-case inter-departure time of forward RM cells from the source.

The complexity of multimedia applications, which integrate a variety of information sources, such as audio, voice, graphics, images, animation, and full-motion video, into a wide range of applications, stresses all the components of computer and communication systems. Many new ideas have been proposed and implemented for advanced LAN (Local Area Network) protocols in order to support multimedia networking. In this paper we study three recently proposed high speed LAN protocols including the 100 Base-T Fast Ethernet, the Ethernet++, and the 100 VG-AnyLAN. We also propose a new enhancement for the 100 VG-AnyLAN. For these four protocol, we describe their design methodology, strengths, weaknesses, and compare their performance in supporting multimedia communications including both real-time and non-real-time traffic. We believe that the results and discussions presented here provide important information for network designers and engineers when designing or deciding network protocols for multimedia systems.

Formal specifications are indispensible for computer-aided verification and testing of communication protocols. However, a large number of the practical protocols, including ATM, have only informal specifications mostly in English. There an no general procedures to derive formal specifications from such informal specifications. As a case study, we consider an important protocol specification-ATM's available bit rate (ABR) service specification. The ABR source/destination policies have been specified using an English description in the main body of the ATM Forum's draft traffic management specification from which it is hard to conduct a formal analysis. Furthermore, while considerable energy has been spent in providing a reasonably precise specification, while allowing for appropriate implementation latitude, an English description still has the potential for different interpretations. We model the protocol by parametrized communicating extended finite state machines with timers, which is often called a transitions system, and present a formal specification by transitions of the system. We also provide insights gained in the derivation of the formal specification. Furthermore, we introduce a scheduler involved in transmitting queued cells at the allowed cell rate to meet the minimal requirements from the source and destination protocols. We present the transitions for the source/destination/scheduler machines, primarily for transmitting cells in-rate.

This paper evaluates and compares six rate-based congestion control protocols for the ABR (available bit rate) traffic over the ATM (asynchronous transfer mode) networks. They include: the EFCI-bit setting, the EFCI-bit setting with separate RM queues, the CI-bit setting in the backward direction, the CI-bit setting in the backward direction with separate RM queues, the CAPC2 ER (explicit rate) scheme, and the EFCI-bit setting with utilization-based congestion indication. Each scheme is simulated and compared in the LAN, WAN, and GFC (general fairness configuration) environments specified by the ATM Forum. The effects of varying the VC (virtual circuits) number and changing the endsystem-switch distance has been investigated. Their fairness is also compared using the GFC configuration. For each simulation run, we measure the average queueing delay, maximum queue length, and network utilization. Traces of the ACR (allowed cell rate) and buffer queue length are also examined. We have found that the ER control scheme performs significantly better than the other five binary control schemes by its faster response to congestion, smoother regulation of bit rates, lower queueing delay, shorter buffer queue length, and fairness. Among the other five schemes, the CI-bit setting scheme performs better than the EFCI (explicit forward congestion indication) bit setting scheme. Providing separate RM queues has significantly improved the EFCI scheme in the WAN environment, but has little effect on the CI scheme. Link utilization-based congestion detection has suffered from either low utilization or an excess cell loss which is unacceptable in most data applications.

In a multiple access broadcast network, all network nodes share a single shared communication channel, and there is the possibility of a collision when two or more nodes transmit at overlapping times. We propose a dynamic prioritized conflict resolution algorithm in which, when a collision occurs, all colliding messages are retransmitted according to their priority. When a new message arrives, it is allowed to participate in the algorithm as soon as it finds its priority is higher than that of some broadcast message. Using a time-slotted model, for any arrival pattern and priority distribution, we show that our algorithm runs in expected linear time, i.e., O(r), where r is the total number of message transmitted. We also show that the expected waiting time for any message x is O(rank(x)+log s), where rank(x) is the expected number of messages with higher priority which transmit while x is waiting, and s is the minimum of the total number of messages participating in the algorithm and the total number of nodes in the network. The analysis presented in this work also includes an improved analysis of our original (static) prioritized conflict resolution algorithm. This work is applicable to multimedia communications where different priority values could be assigned to different kinds of traffic, and our algorithm ensures that the high-priority real-time traffic has the minimum delay.

Multimedia applications integrate a variety of media namely, audio, video, images, graphics, text, and data, each of which has different quality of service (QoS) requirements. To study the support of multimedia traffic on high speed protocols, we examine two significant metrics: delay fairness and worst-case delay performance. Four members of reservation-based high speed protocols are studied DQDB (distributed queue dual bus), CRMA (cyclic reservation multiple access), DQMA (distributed queue multiple access), and FDQ (fair distributed queue). The first part of this work presents delay fairness study of the protocols under various input traffic. Both access delay and message delay experienced by individual network nodes are measured to illustrate their fairness performance. The second part conducts worst-case delay experiments. Too worst-case scenarios of a test message are studied. The delay experienced by this message under varied inter-node distance is measured. The simulation results show that DQDB has the worst fairness performance. CRMA and DQMA present mixed results. FDQ proves itself to be a very fair protocol. The results also suggest that to there is no singe metric to justify a new protocol. Comparing our results with previous results on throughput evaluation of multimedia traffic support, we found that the two results are strongly correlated the fairer a protocol is, the better it is for supporting heterogeneous traffic under heavy network load.

A multiple access broadcast network (MABN) is a computer network in which there is a single shared communication channel and every network node can receive all messages transmitted over the channel. A major difficulty in using broadcast communication is the issue of access to the channel. The authors deal with the following prioritized conflict resolution problem: a collection of c messages is initially distributed among nodes on an MABN; each message is associated with a value representing its priority or deadline; and it is required that the messages be broadcast in ascending order of this value. This problem is solved by a distributed prioritized conflict resolution algorithm. It is proved that the expected waiting time of the ith network node to transmit is Theta (i+log c), which is optimal. It is also shown that the algorithm runs in the expected linear time, which is optimal in both the number of slots used and the total elapsed time including local processing.>

In the above-titled paper by Takine et al. (see ibid., vol.COM-36, no.10, p.1119-27, Oct. 1988) an exact analysis of a nonsymmetric polling system with single message buffers was reported. The commenters provide an alternate method for analyzing the exact mean waiting times of the individual stations in the same system by extending an exact analysis for the two-station case. Some corrections to the numerical results provided in the paper are made.>

The p/sub i/-persistent protocol is an excellent candidate for multiaccess communication over very long and very high-speed (unidirectional) fiber-optic bus networks because it does not suffer from the distance and bandwidth limitations of round-robin-type access mechanisms. The authors develop this protocol further to make it easily implementable, by allowing it to be sensitive to changing load conditions. In particular, they provide and study various properties of a simple algorithm which stations execute independently by using channel feedback information. This results in a fully distributed control mechanism that continuously adjusts the station probabilities p/sub i/ at their proper levels as governed by the ordered traffic, where the p/sub i/ are parameters of the p/sub i/-persistent protocol.>