A hands-on session for creating department-level plans to coordinate Broadening Participation in Computing (BPC) work. Departmental BPC Plans can help provide continuity and greater impact in BPC work and provide opportunities for more faculty to engage. The workshop is organized around a series of guided hands-on activities selecting and refining specific goals and activities with the final result being an outline of a full BPC plan. We also include some discussion of next steps and invite participants to join our active Slack workspace. Individuals interested in this workshop may also be interested in the follow-on workshop, Departmental BPC Plans 2 - Finalizing your Plan.

A hands-on session for finalizing a Departmental BPC Plans for verification on BPCnet.org. Verification of a Departmental BPC Plan enables faculty from an institution to use it as part of a grant submission to the National Science Foundation (NSF). This workshop is intended for people from the United States who have an existing Departmental BPC Plan or who will have created one in the workshop "Departmental BPC Plans 1." The workshop is organized around a series of guided hands-on activities to help participants complete a draft of a Departmental BPC Plan that can be Verified by BPCnet.org.

Network traffic classification is used to identify the nature of traffic on a network. Entities capable of monitoring net-work traffic use classification for all manner of reasons, including identification of mobile applications being used on the network. It is possible that the usage of encrypted messaging applications by users on these networks can be detected, betraying elements of their privacy.In this paper, we describe a system that leverages campus network resources to generate real-world data alongside a more curated dataset captured from Android application traffic. We also explore the ability of machine learning (ML) models to accurately classify traffic from these encrypted messaging applications. Understanding what is revealed from network data is important given that the use of these applications is meant to maximize privacy in the first place.

The primary purpose of PATHS (Path Ambassadors to High Success), an NSF-funded S-STEM scholarship program, is to create new pathways and strengthen existing pathways for academically talented, low-income Colorado high school and community college students to study computer science (CS) at Colorado School of Mines (Mines). PATHS has achieved the following major project goals: 1) Increase number of academically talented, low-income students studying CS in Colorado; 4) Increase retention of these students; 3) Establish an active on-campus community to support PATHS scholars and similar students; 4) Engage scholars to perform CS recruitment and outreach at area high schools and community colleges; 5) Evaluate PATHS activities through comparative analysis to provide new insights on best practices for attracting and retaining academically talented, low-income CS students; 6) Broaden participation of historically underrepresented groups in CS; and 7) Develop a new flexible degree program combining other STEM fields and CS. PATHS students are diverse (e.g., 40.8% from underrepresented groups in computing) and academically successful (e.g., mean GPA is 3.4). Thus far, the program has awarded scholarships to 49 students and retained 93.9% of the students. Of the 49 students, six have graduated and three of the six have also pursued a CS Master's degree.

Colorado Strategic Approach to Rally Teachers (C-START) has provided computer science (CS) professional development to 1,425 K-12 teachers from 2016 to the present impacting over 7,000 students (who are ~33% female and 21% from other underrepresented groups). Pre and post training surveys from the 2019 summer cohort revealed teachers made statistically significant gains in CS content knowledge, confidence in teaching CS, confidence in learning CS, motivation to learn CS, and knowledge of engaging students in CS. C-START professional development trainings are offered each year based on educator requests, such as Beauty and Joy of Computing, Mobile CSP, Java Programming, Web Programming, Snap!, CS Unplugged, and Introduction to Cryptography. Synergystically, the C-START program has hosted the National CSPdWeek three times featuring Bootstrap and Exploring Computer Science. C-START has had a significant positive impact in Colorado CS high school education. For example, in 2015, a year before C-START began, only 170 high school students in Colorado took the AP CS A exam and only 15.4% of those students were female. In 2018, 791 high school students took the AP CS A exam in Colorado and 20.6% of the exam takers were female. Similar positive outcomes exist for students from other underrepresented groups (e.g., from 7.3% to 21.3% for Hispanic/Latino). While C-START is not the only PD opportunity for Colorado teachers, we are confident that C-START is helping more Colorado students access CS education.

This panel provides examples of how faculty refine, expand, and evaluate initiatives to broaden participation in computing using the Data Buddies Survey (https://cra.org/cerp/data-buddies). The Computing Research Association administers the survey annually and prepares summary reports for over 140 member institutions. Participation is free, and the survey gathers information about students' educational experiences, confidence, attitudes, and career goals. Faculty and staff from prospective and participating institutions can benefit from the panelists' discussion of aligning department initiatives for broadening participation with data about students' experiences.

This Birds-of-a-Feather session is for anyone interested in the NSF Scholarships in STEM (S-STEM) program, including current and former Principal Investigators (PIs) and those planning to apply. The S-STEM program funds scholarships and activities to support low-income, academically talented students in STEM. Any institution of higher education may apply, and the program supports a variety of projects. Designing and implementing a successful S-STEM project is challenging. The goal of this session is to catalyze a community of practice for S-STEM PIs. It will provide an opportunity to discuss lessons learned and best practices for proposal writing, project implementation, and providing student support. Specific topics to be discussed include the following: (1) Understanding the solicitation requirements and common proposal mistakes; (2) Scholar recruitment and data-driven approaches for selection; (3) Cohort building including activities for students from different majors or class years and integration of new students into existing cohorts; and (4) Remediation strategies including proactive interventions and peer support. Session leaders will introduce each topic; participants will then join a breakout group discussion of one topic. Lastly, participants will be invited to join a Slack workspace dedicated to S-STEM best practices and lessons.

The Department of Computer Science ([email protected]) at Colorado School of Mines (Mines) was founded in 2016 when the Department of Electrical Engineering and Computer Science (CS) split into separate departments. As a result, CS faculty, who had worked for years to broaden participation in computing without departmental lead- ership support, were able to become more strategic in their efforts. [email protected] faculty, staff, and students now engage in well-defined recruitment, retention, and evaluation strategies, which includes K-12 outreach programs, flexible CS major and minor tracks, a near-peer mentoring program, scholarship programs, and contin- ual evaluation. Ten years ago, the CS degree program at Mines had 157 majors, 17 women (10.8%), and 12 students from underrepre- sented groups in computing (7.6%). As of Fall 2019, [email protected] has 679 majors, 146 women (21.5%), and 132 students from underrep- resented groups (20.2%). Although the concentrated effort focused on increasing the number of women majors, the data clearly shows an increase in students from underrepresented groups as well. The changes achieved by [email protected] are noteworthy considering (1) only 30% of the students at Mines are women, (2) only 17% of the students at Mines are from underrepresented groups in computing, and (3) women and underrepresented groups enrolled in undergraduate CS programs are predominantly not at parity with their respective populations in the United States. [email protected] achieved positive results by applying the Undergraduate Systemic Change Model developed by the National Center for Women & Information Technology. In this paper, we present [email protected] as a case study for positive change and discuss the strategies [email protected] has enacted (74 of the recommended 81).

Given the growing popularity of voice assistants such as Amazon's Alexa/Echo product, this work investigates a potential privacy concern with this type of product. We collect the encrypted TCP traffic moving from the Echo Dot to the Alexa Voice Service (AVS), and then use machine learning techniques to determine who, of a finite set of speakers, is speaking to the Amazon Alexa/Echo product. We achieve close to 80% speaker identification accuracy with two speakers and approximately 30% speaker identification accuracy with 12 speakers. Both results are statistically significant when compared to random guessing. In this work we discuss the privacy implications of encrypted speaker identification using the Amazon Alexa/Echo product. We also present our speaker identification techniques, as well as an analysis of our results. Since this work investigating privacy concerns is in its initial stages, we also outline potential future work.

The CISE directorate of the NSF is rolling out a requirement that all NSF grants include a Broadening Participation in Computing (BPC) plan (www.nsf.gov/cise/bpc/). This has the potential to drive important institutional change across CS departments in the U.S. This panel of BPC experts will offer their perspectives on meaningful BPC activities, talk about existing BPC programs, and share BPC-related resources that can help PIs and departments craft high-quality BPC plans. The panelists will offer contrasting perspectives on topics such as K-12 outreach, the allocation of department funds for BPC, faculty engagement, and first steps departments should take. Ultimately, NSF review panels made up of CISE community members will evaluate BPC plans, but we hope to spark productive conversations in the interest of fostering institutional change to achieve the social imperative of BPC.

As smart speakers like the Amazon Echo become more popular, they have given rise to rampant concerns regarding user privacy. This work investigates machine learning techniques to extract ostensibly private information from the TCP traffic moving between an Echo device and Amazon's servers, despite the fact that all such traffic is encrypted. Specifically, we investigate a supervised classification problem using six machine learning algorithms and three feature vectors. Our "request type classification" problem seeks to determine what type of user request is being answered by the Echo (again, even though the requests are encrypted). With six classes, we achieve 97% accuracy in this task using random forests.

No abstract available.

As wireless sensor networks become more ubiquitous in the world, the need for lightweight, resilient time synchronization protocols is apparent. Wireless nodes' internal clocks are subject to drift over time due to manufacturing imperfections and environmental changes. While various protocols have been introduced that attempt to correct for this drift, they each have their own peculiarities and issues. This paper presents a new protocol, the Sticking Heartbeat Aperture Resynchronization Protocol (SHARP), that reduces synchronization error and resolves shortcomings of existing protocols. We have implemented and compared SHARP to two existing (and noteworthy) time synchronization protocols, Reference Broadcast Synchronization (RBS) and Simple Synchronization Protocol (SISP), on Atmel ATMega328p based microcontroller platforms with IEEE 802.15.4 Xbee radio modules. We show that SHARP exhibits a higher level of synchronization than SISP (which in turn exhibited much better performance than RBS), while requiring significantly fewer messages.

As earth dams and levees (EDLs) across the United States reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. This paper investigates automatic detection of anomalous events in passive seismic data as a step towards continuous real-time monitoring of EDL health. We use a multivariate Gaussian machine-learning model to identify anomalies in experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising; removing different signal components. The best performance is achieved with the Haar wavelets (removing the Level 3 component). We achieve up to 97.3% overall accuracy and less than 1.4% false negatives in anomaly detection. These promising approaches could eventually provide a means for identifying internal erosion events in aging EDLs earlier than is currently possible, thereby allowing more time to prevent or mitigate catastrophic failures.

Professional development (PD) has long been recognized as one of the key ingredients in K-12 CS Education, particularly when addressing the problem of underserved communities. Over the last decade, significant work has been done to create professional development and curricular offerings that are research based, with a proven track record. Bootstrap, Exploring Computer Science and AP CS Principles represent these types of programs. Each of these programs has developed high-quality PD for educators and have been recognized by the White House as exemplar courses. However, economies of scale make it difficult to expand to the vast number of small school districts around the country, including some of the most isolated and underserved areas such as rural communities and Native American reservations. This panel will discuss an alternative model - "CSPdWeek" -- a national event aimed at providing best-in-class PD to teachers across the country. The inaugural CSPdWeek took place in July, 2016 at Colorado School of Mines, and provided a week-long residential experience for teachers attending one of three teacher-focused professional development programs. Over 240 classroom teachers attended CSPdWeek, making this professional development the single largest cross-curricular effort in preparing U.S. teachers to teach computing as part of the "CS for All" movement. This panel will focus on why this year's CSPdWeek was such a success and what was behind social media testimonies from teachers who talked about being part of an educational movement.

Computer Science (CS) Unplugged activities have been deployed in many informal settings to present computing concepts in an engaging manner. To justify use in the classroom, however, it is critical for activities to have a strong educational component. For the past three years, we have been developing and refining a CS Unplugged curriculum for use in middle school classrooms. In this paper, we describe an assessment that maps questions from a comprehensive project to computational thinking (CT) skills and Bloom's Taxonomy. We present results from two different deployments and discuss limitations and implications of our approach.

An undergraduate data structures course is challenging to teach due to the vast number of complex topics that need to be covered. The use of instructional tools, such as an online programming tutor, can reinforce topics students typically find difficult. Prior research has shown using programming tutors can have a positive impact on student learning in an introductory Computer Science course. We hypothesized that we would see similar results using an online tutor in a second year programming course. Our study used an existing programming tutor, Problets, to supplement course instruction on two identified topics (functions and pointers) over one semester in two sections of a data structures course. In the first part of the study, we compared the optional use of an online tutor to no supplemental instruction. In the second part of the study, we compared the use of the online tutor to paper study guides. We conducted a two-tailed unpaired t-test on the students' midterm examination scores. The results from the first module showed that simply providing an optional tool had limited value. Surprisingly, the results from the second module showed no significant difference based on the type of practice. We discuss possible explanations for these results.

Computer Science Unplugged activities have been shown to be successful in increasing student interest in computer science when used in outreach and after school events. There is less research available on adapting these extra-curricular activities for use in a classroom setting, where there are more students and the activities must support educational goals, not just changes in attitude. We describe our work in updating several existing CS Unplugged activities as well as introducing some new activities for use in an American middle school classroom. One challenge when using CS Unplugged activities is to determine what, if anything, students are learning. In this paper we detail one approach that links the updated activities to computational thinking skills, then incorporates worksheets where students illustrate their understanding.

The main goal of this panel is to share survey data being collected on booming enrollments in computer science. Survey data are being collected from two source types: institutions and students. The institution data are being collected through both the CRA Taulbee Survey, which represents computer science departments in North America with doctoral degrees, and the ACM NDC Survey, which represents computer science departments at U.S. 4-year and master's universities and colleges. The student data are being collected via the Data Buddies Project, run by the CRA's Center for Evaluating the Research Pipeline. The institution data will allow us to answer questions such as "what 'boom' are institutions seeing and how are different types of institutions responding to this boom?" The student data will allow us to answer questions such as "why are students suddenly eager for our classes?". The survey data will help us understand (1) the intensity of the "boom" at different types of institutions, (2) how institutions are responding to the increasing demand for computing education, and (3) whether the current enrollment "boom" represents the "third bubble" or whether it is here to stay. The data will also allow us to ask questions that attempt to understand the impact of the boom on underrepresented students. Data collection is ongoing; we will present results "hot off the presses".

Geophysical sensor technologies can be used to understand the structural integrity of Earth Dams and Levees (EDLs). We are part of an interdisciplinary team researching techniques for the advancement of EDL health monitoring and the automatic detection of internal erosion events. We present results from our performance study that uses signal processing, feature extraction, and unsupervised learning on passive seismic data from an experimental laboratory earth embankment. We used popular unsupervised clustering algorithms to gain insights to this real-world problem, and evaluated our results using internal and external validation techniques. In four of the clustering algorithms applied, results consistently show a clear separation of events from non-events. We provide proof of concept and an initial pattern recognition process that could be used as a tool for nonintrusive and long-term EDL monitoring.

Mobile phone and social media communication are sometimes explored as viable sources of location information for human mobility research. To the best of our knowledge, however, no one has studied how location information obtained via these two event-based techniques compare to each other. In this paper, we present a comparative analysis of obtaining location information using mobile phone records and social media communication. The comparison was made under similar conditions. Specifically, the two datasets are from the same geographical area (a developing country in Africa), have the same number of users, and were collected over a similar period of time (months and days). Our analysis shows that the source of the location information used has a significant impact on what can be perceived in terms of individual mobility within a population. Using the results from these two datasets, we conclude that mobile phone communication is a better source of location information for human mobility research when compared to social media communication. We argue that our conclusion exists due to the relation of social media communication to economic and demographic factors.

This panel will discuss the enrollment boom, as well as potential effective practice strategies to respond to the increasing demand for computing education. Does the current enrollment "boom" represent the "third bubble", or is it here to stay? Is there a difference in what's happening at universities that offer Ph.D. degrees or what's happening at 4-year universities and colleges? What 'best practice' strategies exist (if any) to handle the demand? And, importantly, will this "boom" have a positive or negative effect on underrepresented students' Join us for a lively discussion on the recent student interest in computing education at our universities and colleges.

In this article, we rigorously compare compressive sampling (CS) to four state of the art, on-mote, lossy compression algorithms (K-run-length encoding (KRLE), lightweight temporal compression (LTC), wavelet quantization thresholding and run-length encoding (WQTR), and a low-pass filtered fast Fourier transform (FFT)). Specifically, we first simulate lossy compression on two real-world seismic data sets, and we then evaluate algorithm performance using implementations on real hardware. In terms of compression rates, recovered signal error, power consumption, and classification accuracy of a seismic event detection task (on decompressed signals), results show that CS performs comparable to (and in many cases better than) the other algorithms evaluated. The main benefit to users is that CS, a lightweight and non-adaptive compression technique, can guarantee a desired level of compression performance (and thus, radio usage and power consumption) without subjugating recovered signal quality. Our contribution is a novel and rigorous comparison of five state of the art, on-mote, lossy compression algorithms in simulation on real-world data sets and implemented on hardware.

In cognitive radio networks, secondary users need to accurately identify primary user spectrum occupancy in order to use it. Accurate spectrum sensing is hindered by signal fading, hidden terminal problems, byzantine failures, etc. Centralized cooperative spectrum sensing works well if the secondary user network is infrastructure based and there is a centralized basestation making network wide decisions. When the secondary users network is a cognitive radio mobile ad-hoc network (CR-MANET), then decisions need to be made in a distributed manner and cooperative spectrum sensing introduces additional problems due to the presence of malicious users. These malicious secondary users encourage other secondary users to make a wrong spectrum occupancy decision by feeding inaccurate measurements. We study this problem and present a solution to improve primary user spectrum occupancy identification accuracy in the presence of malicious users. A virtual neighbor cluster is created in which the mobile device forms an evolving cluster of past neighbor devices that aids in validating the input gathered from the current neighboring devices. Next, a recursive partitioning around medoids based clustering is performed to identify a tightly bound set of valid inputs. The validated inputs from both the methods form a decision cluster and the data is fused to get the decision on primary user occupancy. Two data fusion strategies are presented and their use depends on the amount of dynamism in the CR-MANET. The analysis and results show the accuracy of primary user occupancy detection even in the presence of large number of malicious users and signal measurement errors.

In this article, we introduce a novel on-mote compressive sampling method called the Randomized Timing Vector algorithm (RTV). In addition to describing this new lightweight algorithm, we provide experimental results that compare RTV to the two existing on-mote compressive sampling algorithms that we are aware: Additive Random Sampling (ARS) and Sparse Binary Sampling (SBS). Experimentation involved three different steps. First, we tested and validated the three on-mote compressive sampling algorithms using a simplistic sinusoid produced by a signal generator. Second, we analyzed the power consumption of the three algorithms and compared them to full sampling. Lastly, we simulated the three algorithms on a real-world passive seismic dataset containing avalanche events collected in the mountains of Switzerland. Results from our experiments indicate that our novel and lightweight RTV algorithm outperforms ARS and SBS in at least two ways. First, unlike ARS and SBS, RTV does not falter at moderate to high sampling rates (e.g., 500 Hz or above). Second, RTV showed the greatest power savings since it eliminates costly floating point calculations and reduces ADC conversions.

The demand for computing professionals continues to grow, while women and minorities remain severely underrepresented at all levels. This opening SIGCSE 2013 keynote asks the crucial question "What can WE do to change the face of computing?" Several answers to this question will be provided in an unusual format with the following all-star cast. Each speaker will take five minutes to share 20 slides (which automatically advance every 15 seconds) to provide a stimulating presentation that ends with "What can YOU do to change the face of computing?"

During the 2010-2011 winter season, we deployed seven geophones on a mountain outside of Davos, Switzerland and collected over 100 days of seismic data containing 385 possible avalanche events (33 confirmed slab avalanches). In this article, we describe our efforts to develop a pattern recognition workflow to automatically detect snow avalanche events from passive seismic data. Our initial workflow consisted of frequency domain feature extraction, cluster-based stratified subsampling, and 100 runs of training and testing of 12 different classification algorithms. When tested on the entire season of data from a single sensor, all twelve machine learning algorithms resulted in mean classification accuracies above 84%, with seven classifiers reaching over 90%. We then experimented with a voting based paradigm that combined information from all seven sensors. This method increased overall accuracy and precision, but performed quite poorly in terms of classifier recall. We, therefore, decided to pursue other signal preprocessing methodologies. We focused our efforts on improving the overall performance of single sensor avalanche detection, and employed spectral flux based event selection to identify events with significant instantaneous increases in spectral energy. With a threshold of 90% relative spectral flux increase, we correctly selected 32 of 33 slab avalanches and reduced our problem space by nearly 98%. When trained and tested on this reduced data set of only significant events, a decision stump classifier achieved 93% overall accuracy, 89.5% recall, and improved the precision of our initial workflow from 2.8% to 13.2%.

Localization is a fundamental problem in wireless sensor networks. In many applications, sensor location information is critical for data processing and understanding. While the global positioning system (GPS) can be used to determine mote locations, the high cost often prohibits the ubiquitous use of GPS for location estimates. The cost of GPS has motivated researchers to develop localization protocols that determine mote locations using existing hardware and wireless measurements. Many of these protocols, however, have trade-offs and only perform well on a subset of networks and network configurations. We have developed a WSN localization algorithm that can be configured to perform well in a wide array of WSN configurations and deployments. We compare our algorithm to two well known and accurate algorithms: MDS-MAP and TSL. Through an extensive and credible analysis, we show that our algorithm, CAIRN, produces low localization error in a variety of network configurations. Additionally, we show how to manipulate algorithm parameters to tailor our algorithm for specific network configurations. Our work contributes an in-network WSN localization algorithm that produces accurate mote location estimates in various network configurations and we show that our algorithm outperforms the best existing protocols available.

Distributed transmit beamforming is a wireless communication technique in which multiple independent antennas transmit a common message while controlling their transmitted carrier phases to constructively interfere the message at a destination. Depending on the transmission goals and choice of network parameters, certain noteworthy benefits arise from beamforming. These benefits may include improved received signal to noise ratios, increased transmission ranges, power consumption reductions, or physical layer security against eavesdropping. We present a distributed cross-layer cooperative beamforming protocol implemented into the IEEE 802.11 wireless communication stack: Distributed Decode and Forward Beamforming. In this paper we conduct a brief literature review of current beamforming research, and present a detailed discussion on the mathematical foundation of the single- and multi-destination decode and forward beamforming technique. We then describe our distributed multi-destination decode and forward beamforming protocol, and present results from our in-depth evaluation of our protocol in a realistic simulation environment. In our evaluation, we investigate five aspects of our distributed beamforming protocol: beamforming event frequency, queue length, latency, participation level, and throughput.

Covariance Matrix Adaptation - Evolutionary Strategy (CMA-ES) is a black-box optimization method useful for applications where no direct inversion is possible. We present the development of a parallel CMA-ES algorithm that reduces the runtime for a specific geophysical data analysis, dipole localization. We compare our parallel algorithm against several other parallel CMA-ES variants on a sample dataset for dipole localization. We improve the performance of CMA-ES for the problem of finding dipoles in a subsurface environment as part of a closed-loop near-real-time wireless bioremediation system, REACTS (near-REal-time Autonomous bioremediation of ConTamination in the Subsurface). The goal of the performance improvement is to enable near-real-time analysis of geophysical data. For this application, our algorithm shows significant performance improvement over the other variants.

Although professional networking is vital to career building, college-age students may not have acquired sufficient skills to network effectively. Several conferences, such as the Grace Hopper Celebration (GHC) of Women in Computing and the Richard Tapia Celebration of Diversity in Computing, now exist to counteract feelings of isolation and encourage participation of women and minorities in computer science. To enable conference attendees to take full advantage of the potential for mentoring at these events, we have developed CONNECT (Creating Open Networks aNd Expanding Connections with Technology), a technology-based system that helps conference attendees meet and exchange information. This paper reports the results of deploying the CONNECT system at the GHC conference on two different years. Although the results were generally positive, with about 75% of the survey respondents indicating they were either satisfied or very satisfied with the support provided by CONNECT, there were also a number of suggestions for improvement. We review these results and provide a list of guidelines for creating an effective tool to encourage conference attendees, especially college students, to network and form communities.

Earthen dams are critical components in our world's water resource infrastructure, but many are at or near their intended design life. To improve safety margins and ensure water availability, advanced monitoring techniques are required. The contribution of our work is to enable cost effective geophysical monitoring via the development of a wireless hardware platform for geophysical measurements. Our custom platform, called gsMote, has the following features: high resolution and low noise analog to digital conversion, high data rate persistent on-mote storage, dynamically configurable gain and signal filters, gigabyte scale persistent data storage, powerful inter-mote radio communications, and flexible mote to gateway high speed serial communications. In this paper, we provide the details of (1) our custom wireless geophysical hardware platform and (2) our gsMote testbed site, which is a dam in Golden, CO. Finally, we include how others can obtain our gsMote platform for their geophysical monitoring application.

In addition to being realistic, a mobility model should be easy to understand and use. Unfortunately, most of the simple mobility models proposed thus far are not realistic and most of the realistic mobility models proposed thus far are not simple to use. The main contribution of this work is to present SMOOTH, a new mobility model that is realistic (e.g., SMOOTH is based on several known features of human movement) and is simple to use (e.g., SMOOTH does not have any complex input parameters). We first present SMOOTH. We then validate that SMOOTH imitates human movement patterns present in real mobility traces collected from a range of diverse scenarios. In addition, we compare SMOOTH with the other mobility models developed based on these mobility traces. Thus, with SMOOTH, we provide researchers with a tool that allows them to leverage the statistical features present in real human movement in a simple and easy to understand manner.

We present TestbedProfiler, a wireless sensor network application suite developed to help guide the installation of WSN testbeds. TestbedProfiler can be used to assist the deployment of a WSN testbed by evaluating proposed mote locations in terms of connectivity and signal strength. We developed TestbedProfiler and used it to evaluate the Casino Lab and the Edgar Mine testbeds owned by the Colorado School of Mines. We found TestbedProfiler to be instrumental in understanding how the motes communicate and how the testbeds could be improved. We believe that TestbedProfiler can improve testbed deployments, leading to improved real-life wireless sensor network applications.

Participation in elementary school classroom settings is dependent on students' abilities to process questions quickly. Pedagogical studies theorize that some students are able to answer a question before other students are able to process the question, creating a division in classroom participation. This paper presents LEVEL (Listening to Everyone's Voice Enhances Learning), a wireless sensor network application to balance, diversify, and encourage in-class participation. LEVEL offers two operating modes: a centralized interface and a distributed adaptive personal interface. The centralized interface features a traffic light to compel students to wait before responding to a question; we LEVEL the participation in the class by visually informing students when a question can be answered. The distributed interface adaptively sets a participation time delay on a per student basis predicated upon previous participation. LEVEL brings a wireless sensor network application to primary school classrooms. LEVEL connects student learning with technology while providing an enhanced in-class experience for all students.

In wireless sensor networks, many applications are based on a two-tier architecture. In a typical two-tier architecture, nodes are grouped into clusters and communicate with a cluster head (CH); the CH then transmits compressed data to the base station (BS). In this paper, we mainly focus on the communication between CHs and the BS. The communication between the CH-BS could be accomplished with traditional medium access control (MAC) protocols such as time division multiple access (TDMA) or carrier sensing multiple access (CSMA). While protocols such as TDMA work well in some situations, they do not work well when the number of clusters is large and the traffic load between the CH-BS is low. In this paper, we propose a new MAC protocol (called A-MAC) for the CH-BS communication that is more efficient than traditional MAC protocols. A-MAC takes only 1/N (N is the number of clusters in the network) of the time spent by TDMA to transmit the same number of packets. We save time by having packets transmitted in parallel and then resolving the collisions (instead of avoiding them). Specifically, we deploy N relay nodes around the BS and have all CHs send their packets to the BS at the same time. When the BS detects a collision, it asks the relay nodes to transmit what was heard. The BS then resolves the collision after it receives enough information from the relay nodes using a multiple-input and multiple-output (MIMO) algorithm. In this paper, we also investigate methods to allocate time for the intra-clustering communication and the CH-BS communication, as well as where to place the relay nodes to maximize throughput. Our simulation results illustrate that A-MAC outperforms TDMA in terms of throughput and delay.

Mobile Ad Hoc Networks (MANETs) are usually studied through simulation. Network simulators allow users, in principle, to set values for several hundred variables. A few of these variables, including node speed, node pause time, and packet size, receive much of the attention in MANET simulation studies, because their values are generally believed to have an important effect on simulation results (e.g., delivery ratio). There are, however, a number of variables that are less discussed, but which have a substantially greater effect on simulation results than those more commonly considered. Given a typical simulation scenario, we demonstrate that variables such as the number of sources and random versus fixed destinations, which have received comparatively little attention in the literature, have a substantially greater impact on delivery ratio than do variables such as node speed and pause time, which have received significant attention in the literature. Failure to consider the values of these variables in simulation studies can result in misleading conclusions regarding the performance of routing protocols. We conclude that before investigators draw firm conclusions about the performance of a MANET routing protocol, they should examine the effects of a wider variety of variables than has generally been the practice.

Choosing an appropriate simulation scenario to study the performance of a MANET routing protocol is an important process. For example, routing will not be properly evaluated when a simulation scenario with a low average hop count or a large degree of network partitioning is used. To ensure that a simulation scenario provides an effective platform for testing a MANET routing protocol, we recommend that researchers use two metrics to characterize their simulation scenarios: the average shortest-path hop count and the average amount of network partitioning. In this paper, we provide researchers with several models that take the desired values for these two metrics as inputs, and output the simulation area and number of nodes required to create a simulation scenario that meets the researcher's target values for these two metrics to a close approximation. In this way, we provide several models that researchers can use to construct simulation scenarios that meet their standards in the evaluation of a MANET routing protocol.

In this paper we look at issues with the simulation of generic mobile ad hoc network (MANET) routing protocols. MANET simulation-based research is an involved process driven by the scenarios used in the simulations. Scenarios must be properly constructed in order to be effective in evaluating the performance of generic MANET routing protocols. For example, in scenarios with a low average hop count, little routing is needed, and poor protocols might appear successful. On the other hand, in scenarios with a high degree of partitioning, many pairs of nodes have no usable route between them, and good protocols might appear unsuccessful. Many standards are needed to establish rigorous evaluations for MANET simulation research, from simulation scenario standards to random number generator standards to results analysis standards. We do not attempt to standardize all of these areas. We do, however, propose two standards that should be employed to ensure long routes are available and used in the evaluation of generic MANET routing protocols. That is, we qualify a simulation scenario to be used for rigorous generic MANET routing protocol evaluation, based on the scenario's average shortest-path hop count and its amount of network partitioning. We construct several simulation scenarios that meet our two standards, using example metric values for our two standards. We note that our method can be modified to generate scenarios with different metric values that a researcher finds appropriate

The network simulator 2 (NS-2) is a popular and powerful simulation environment, and the number of NS-2 users has increased greatly in recent years. Although it was originally designed for wired networks, NS-2 has been extended to work with wireless networks, including wireless LANs, mobile ad hoc networks (MANETs), and sensor networks; however, the network animator (NAM) for NS-2 has not been extended for wireless visualization. In this paper, we discuss a new visualization and analysis tool for use with NS-2 wireless simulations. Visual analysis of a wireless environment is important for three areas of NS-2 based simulation research: (1) validating the accuracy of a mobility model's output and/or the node topology files used to drive the simulation; (2) validation of new versions of the NS-2 simulator itself; and-(3) analysis of the results of NS-2 simulations. Our iNSpect program handles all three of these areas quickly and accurately. We've made our iNSpect program available for other researchers in order to improve the accuracy of their simulations.

In order to ease the challenging task of information dissemination in a MANET, we employ a legend: a data structure passed around a network to share information with all the mobile nodes. Our motivating application of the legend is sharing location information. Previous research shows that a simplistic legend performs better than other location services. To realize the full potential of legend-based location services, we propose three methods for the legend to traverse a network and compare their performance in simulation. We also evaluate several improvements to the traversal methods, and describe our way of making the legend transmission reliable. The result is a simple, lightweight location service that makes efficient use of network resources.

This work classifies the current geocast routing protocols of a mobile ad hoc network (MANET) into three categories. We then simulate a typical geocast routing protocol in each category. We performed four studies designed to highlight the various strengths and weaknesses of the protocols: node density, traffic/congestion, mobility, and a combination study examining all three parameters together.

We combine location information and mobility feedback to create an innovative mobile ad hoc network (MANET) routing protocol which we demonstrate is effective over a wide range of mobility conditions typical in a MANET. We use link duration as our mobility feedback metric, and we demonstrate that mobility feedback using link duration effectively enables adaptive MANET protocols. Using our mobility feedback agent, we develop a hybrid MANET routing protocol which adapts between two MANET routing protocols in order to combine the strengths of both component protocols while avoiding their weaknesses. Our hybrid, adaptive protocol achieves data packet delivery ratios above 80% in VERY demanding network mobility conditions (i.e. link durations less than 4 seconds). In more stable networks (i.e., link durations more than 15 seconds), our protocol achieves data packet delivery ratios above 90%. While other existing MANET routing protocols can achieve similar data packet delivery ratios in stable networks, no other existing MANET routing protocol can achieve such high performance in unstable networks.

Previous research has illustrated that location-based routing protocols improve the effectiveness of mobile ad hoc network (MANET) routing. The goal of a location server, which may be used in conjunction with a location-based routing protocol, is to provide accurate location information on the mobile nodes in the network. In this paper, we propose an information dissemination protocol called LEAP (legend exchange and augmentation protocol), which we use to implement a location server for a MANET. We compare our legend-based location server to three other location service alternatives via extensive simulations, and illustrate that our LEAP implementation offers both effective accuracy and low overhead.

Predicting the location of a mobile via a previously known location can reduce costs over non-predictive methods that always begin paging in the last known location. If the mobility pattern of a mobile can be described by a mathematical model, such as the Gauss-Markov model, a predictive management scheme based on that model is ideal. Typically, however, the mobility pattern of a mobile is too complex to be modeled accurately. In this paper, we propose an adaptive mobility management method which is not based upon a mathematical model. In our method, the location of a mobile is predicted solely on the basis of previously reported locations. We compare our method, through simulations, with a non-predictive method and a method based upon a mathematical model. The results show that our adaptive mobility management method performs well under a wide variety of mobility patterns.

In simulations of mobile ad hoc networks, the probability distribution governing the movement of the nodes typically varies over time and converges to a "steady-state" distribution, known in the probability literature as the stationary distribution. Some published simulation results ignore this initialization discrepancy. For those results that attempt to account for this discrepancy, the practice is to discard an initial sequence of observations from a simulation in the hope that the remaining values will closely represent the stationary distribution. This approach is inefficient and not always reliable. However, if the initial locations and speeds of the nodes are chosen from the stationary distribution, convergence is immediate and no data need be discarded. We derive the stationary distributions for location, speed, and pause time for the random waypoint mobility model. We then show how to implement the random waypoint mobility model in order to construct more efficient and reliable simulations for mobile ad hoc networks. Simulation results, which verify the correctness of our method, are included. In addition, implementation of our method for the NS-2 simulator is available.

Network wide broadcast is a fundamental operation in mobile ad hoc networks (MANETs). Several broadcast protocols have been proposed in the literature that improves on simple flooding by reducing the probability that a receiving node retransmits a packet. We propose analytical models to estimate these probabilities for three broadcast protocols. Our simulations show that these analytical models, which were derived under some simplifying assumptions, predict retransmission probabilities for static and mobile networks quite accurately when only the network layer is considered.

In previous years, many location based routing protocols have been developed for ad hoc networks. Some of these protocols assume a location service exists which provides location information on all the mobile nodes in the network. We evaluate three location service alternatives. One is a reactive protocol; the other two are proactive protocols. Of the proactive protocols, one sends location tables to neighbors and the other sends location information to all nodes. In our evaluation, one proactive protocol proved to have the best performance overall. Thus, we also evaluate the main input parameter associated with this protocol for optimal performance.

Many location based routing protocols have been developed for ad hoc networks. This paper presents the results of a detailed performance evaluation on two of these protocols: location-aided routing (LAR) and distance routing effect algorithm for mobility (DREAM). We compare the performance of these two protocols with the dynamic source routing (DSR) protocol and a minimum standard (i.e., a protocol that floods all data packets). We used NS-2 to simulate 50 nodes moving according to the random waypoint model. Our main goal for the performance investigation was to stress the evaluated protocols with high data loads during both low and high speeds. Our performance investigation produced the following conclusions. First, the added protocol complexity of DREAM does not appear to provide benefits over a flooding protocol. Second, promiscuous mode operation improves the performance of DSR significantly. Third, adding location information to DSR (i.e., similar to LAR) increases both the network load and the data packet delivery ratio; our results conclude that the increase in performance is worth the increase in cost. Lastly, our implementation of DREAM provides a simple location service that could be used with other ad hoc network routing protocols.

Network wide broadcasting in Mobile Ad Hoc Networks provides important control and route establishment functionality for a number of unicast and multicast protocols. Considering its wide use as a building block for other network layer protocols, the MANET community needs to standardize a single methodology that efficiently delivers a packet from one node to all other network nodes. Despite a considerable number of proposed broadcasting schemes, no comprehensive comparative analysis has been previously done. This paper provides such analysis by classifying existing broadcasting schemes into categories and simulating a subset of each category, thus supplying a condensed but comprehensive side by side comparison. The simulations are designed to pinpoint, in each category, specific failures to network conditions that are relevant to MANETs, e.g., bandwidth congestion and dynamic topologies. In addition, protocol extensions using adaptive responses to network conditions are proposed, implemented and analyzed for one broadcasting scheme that performs well in the comparative study.

Abstract In the performance evaluation of a protocol for an ad hoc network, the protocol should be tested under realistic conditions including, but not limited to, a sensible transmission range, limited buffer space for the storage of messages, representative data traffic models and realistic movements of the mobile users (i.e. a mobility model). This paper is a survey of mobility models that are used in the simulations of ad hoc networks. We describe several mobility models that represent mobile nodes whose movements are independent of each other (i.e. entity mobility models) and several mobility models that represent mobile nodes whose movements are dependent on each other (i.e. group mobility models). The goal of this paper is to present a number of mobility models in order to offer researchers more informed choices when they are deciding on a mobility model to use in their performance evaluations. Lastly, we present simulation results that illustrate the importance of choosing a mobility model in the simulation of an ad hoc network protocol. Specifically, we illustrate how the performance results of an ad hoc network protocol drastically change as a result of changing the mobility model simulated. Copyright © 2002 John Wiley & Sons, Ltd.

This paper concerns the development and performance evaluation of protocols that provide geocast communication in an ad hoc network. The goal of a geocasting protocol is to deliver packets to a group of nodes that are within a specified geographical area, i.e., the geocast region. The geocast group consists of the nodes within this region at a given time. We present three different approaches for delivering packets to the geocast group and then evaluate these approaches via simulation. In addition, we consider the effect the random waypoint mobility model has on the performance evaluation of an ad hoc network protocol in general.

With the huge growth and the market for laptop and palmtop computer purchases, a rapid increase of mobile usage in the Internet is expected. As mobile nodes move in a wireless computer network, a mobile node must determine when to switch its link-level point of attachment to the wired network. In this paper, we present six cell switching techniques and discuss their attributes. Specifically, we present the Late, Early, and Strong cell switching techniques and three variations of them. We then investigate the performance of these six techniques to discover the best method a mobile node should use to determine when to perform its re-attachment to the wired network.

We have built a distributed Ben-Ari Concurrent Interpreter (distributed BACI), which provides easy-to-use commands to implement distributed algorithms. In distributed BACI, a send process, receive process, and interpreter process combine to provide the distributed system functionality necessary to implement distributed algorithms. In this paper, we introduce the distributed BACI toolkit, illustrate the toolkit with a distributed mutual exclusion algorithm, and discuss how the toolkit is implemented. Copyright © 2000 John Wiley & Sons, Ltd.

We discuss the historical and current perspectives on women in computer science and the recent downward trend of women's participation in this field. We then present results from a survey that asked respondents their views on why women's participation in the field of computing is decreasing, what we can do to reverse this trend, and what they predict the future holds for women in computing. We conclude with a description of ACM-W (Association for Computing Machinery's Committee on Women in Computing) and its on-going projects.

No abstract available.

In computer science education one should employ creative teaching methods, especially for non-majors or beginning students. Students who are new to computer science typically find the field full of theoretical, technical, or even tedious concepts. Lack of interest, motivation, or understanding creates a difficult mix of students. This paper describes a technique which combines analogy and simple memory techniques to aid in the teaching of technical concepts.

article Free Access Share on After you, Alfonse: a mutual exclusion toolkit Authors: Bill Bynum Department of Computer Science, The College of William & Mary Williamsburg, VA Department of Computer Science, The College of William & Mary Williamsburg, VAView Profile , Tracy Camp Department of Computer Science, The University of Alabama, Tuscaloosa, AL Department of Computer Science, The University of Alabama, Tuscaloosa, ALView Profile Authors Info & Claims ACM SIGCSE BulletinVolume 28Issue 1March 1996 pp 170–174https://doi.org/10.1145/236462.236533Online:01 March 1996Publication History 25citation361DownloadsMetricsTotal Citations25Total Downloads361Last 12 Months10Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

article Free Access Share on Microkernels: a submodule for a traditional operating systems course Authors: Tracy Camp The University of Alabama The University of AlabamaView Profile , Glen Oberhauser The University of Alabama The University of AlabamaView Profile Authors Info & Claims ACM SIGCSE BulletinVolume 27Issue 1March 1995 pp 154–158https://doi.org/10.1145/199691.199764Online:15 March 1995Publication History 0citation652DownloadsMetricsTotal Citations0Total Downloads652Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Flush channels generalize conventional asynchronous communication constructs such as virtual circuits and datagrams. They permit the programmer to specify receipt-order restrictions on a message-by-message basis, providing an opportunity for more concurrency in a distributed program. A Hoare-style partial correctness verification methodology for distributed systems which use flush channel communication is developed, and it is shown that it it possible to reason about such systems in a relatively natural way.>

Flush channels generalize more conventional asynchronous message passing protocols. A distributed system that uses flush channels allows a programmer the flexibility of specifying the delivery order of each message in relation to other messages transmitted on the channel. An implementation technique that follows directly from a verification methodology for flush channels is presented. A relatively formal argument in support of the technique is included.>