Fast, robust, high-accuracy localization is a key enabler for future location-aware applications in streetscape communication networks and next-generation networked autonomous agents. Specifically, massive multiple-input and multiple-output (MIMO) antenna systems have received increasing attention due to high angular resolution. However, in dense multipath environments, such as urban areas, pure direction-of-arrival (DoA)-based techniques have not been very popular due to large localization errors.In this paper, we present and evaluate, on real measurements from the POWDER-RENEW platform, a novel method to carry out DoA estimation from just one antenna array snapshot. The measurements are taken from an indoor testbed that is based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system. Experimental results – in the presence of spatial aliasing – show that for certain emitter locations our proposed universal one-shot DoA estimator outperforms in azimuth/elevation accuracy state-of-the-art subspace-based methods that involve collection of a sufficiently large data record of antenna array snapshots.

Understanding the thought processes of students as they progress from initial (incorrect) answers toward correct answers is a challenge for instructors, both in this pandemic and beyond. This paper presents a general network visualization learning analytics system that helps instructors to view a sequence of answers input by students in a way that makes student learning progressions apparent. The system allows instructors to study individual and group learning at various levels of granularity. The paper illustrates how the visualization system is employed to analyze student responses collected through an intervention. The intervention is BeginToReason, an online tool that helps students learn and use symbolic reasoning-reasoning about code behavior through abstract values instead of concrete inputs. The specific focus is analysis of tool-collected student responses as they perform reasoning activities on code involving conditional statements. Student learning is analyzed using the visualization system and a post-test. Visual analytics highlights include instances where students producing one set of incorrect answers initially perform better than a different set and instances where student thought processes do not cluster well. Post-test data analysis provides a measure of student ability to apply what they have learned and their holistic understanding.

This research involves analytics to address two important questions: (1) Can machine learning help to predict the success of faculty seeking external awards? (2) What are the important factors related to such predictive models? By using data from 3,608 STEM and medical faculty across nine universities/institutions in Florida, our study demonstrates over 0.82 AUC scores in predicting faculty success, and also identifies important factors associated with winning external awards.

We present results in automating the analysis of student verbalizations in online learning environments, using an existing online tool designed to teach students to reason analytically about code as an example. The new extension captures "think-aloud'' data as students work through code reasoning activities. The data is recorded and transcribed automatically and used as input to a natural language processing / machine learning system designed to identify specific student attitudes (e.g., uncertain), behaviors (e.g., guessing), and difficulties (e.g., concept misunderstandings). We present the design and implementation of the tool, an analysis of its transcription accuracy, and an evaluation of its utility in identifying characteristics of student learning.

Effective planning requires understanding the movement patterns of pedestrians and vehicles. This can be achieved by passive localization of WiFi-enabled devices using RSSI measurements from WiFi probe requests received at nearby sensors. In this paper, we continue our work on a mobility intelligence system (MobIntel) [1] and study the performance of two broad approaches to RSSI-based passive outdoor localization. The first is adapted from traditional active localization methods, including multilateration and fingerprinting. The second relies on machine learning methods, including machine learning-boosted multilateration and classification. We compare the localization performance of the two approaches and find that the machine learning methods consistently outperform the adapted traditional methods. The results demonstrate machine learning methods as promising tools for RSSI-based passive outdoor localization.

Aquaculture, especially fish farming, plays a vital role in ensuring food security in the United States and worldwide. However, for fish farming to be sustainable and economically viable, drastic improvements to the current labor-intensive and resource-inefficient operations are required. The hybrid aerial/underwater robotic system (HAUCS) aims to bring fundamental innovations to how pond-based farms operate. HAUCS is an end-to-end framework that consists of three principal subsystems: 1) a team of collaborative aero-amphibious robotic sensing platforms integrated with water quality sensors; 2) a land-based home station that can provide automated charging and sensor cleaning; and 3) a backend processing center that includes a machine-learning-based water quality prediction model and farm control center. HAUCS will be capable of collaborative monitoring and decision-making on farms of varying scales. The HAUCS platform, payload, and prediction model are discussed. The initial deployment of the HAUCS framework at a land-based aquaculture fish farm is presented.

Mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movement. Understanding the causes and effects of changing mobility patterns can help city officials and businesses optimize operations and support economic development. In this paper, we present MobIntel, an alternative to visual surveillance technologies for mobility monitoring. We deployed multiple radio frequency sensors in downtown West Palm Beach and enabled a system for providing valuable metrics concerning pedestrian activity patterns. We discuss several obstacles to accurate trajectory monitoring, such as MAC address randomization and sensor range issues.

In the Internet of Things (loT) era, technology offers the ability to collect and understand information collected from the nature and environment around us. As an essential component of reliable sensing, sensors play a vital role in any IoT system. The point of departure for our work is the anticipated potential of an efficient, low-cost prototyping and testing platform for analog sensors. In this paper, we explore the fundamental building blocks of such a system: an accurate, configurable analog front end, a light-weight communication component, and efficient data-centric middleware, and a user-friendly web portal.

Several research studies have shown the benefits of code tracing to promote student understanding of program behavior. While code tracing on specific input values is a useful starting point, students ultimately need to be able to reason rigorously and logically about the correctness of their code on all (i.e., arbitrary) inputs. Otherwise, they may make false generalizations and may achieve only a shallow understanding. Results of a multi-semester experiment to answer the following research questions: (1) With or without steps, can students learn the basics of tracing code on symbolic input values using an online tool? And how important is classroom instruction? (2) What is the impact of motivation on student attitudes in learning to reason with such a tool? Data was obtained from 297 subjects who used the online reasoning tool in a second-year software development course for CS majors. Analysis indicates that students can do symbolic reasoning to trace code and that instruction and motivation have significant impact.

Internet of Things (IoT) devices have been limited in application by constraints posed by batteries. Batteries add size, weight, and upkeep costs, and limit the lifetime of devices preferred to be small, lightweight, and long-lasting. We present Apis, a software and hardware toolkit for federated power management in energy harvesting applications. By replacing batteries with rapid charging storage capacitors, circuitry to control federated energy storage, and software support to make this architecture accessible to developers, embedded devices can potentially run indefinitely with limited maintenance. We present the Apis hardware for controlling federated energy storage, supporting software, and experiments performed to validate the Apis model.

We are witnessing a revolution in body area sensing, with applications ranging from biometric-based security to personalized healthcare to sports performance training, among numerous others. The key application driver has been the emergence of wireless and/or contact-free technology for sensing human physiology, motion, and posture. We posit an analogous revolution enabled by advancements in sensing of human emotion. The applications are similarly diverse, spanning social skills education, business intelligence, monitoring of doctor-patient dynamics, and numerous others. This paper explores the sensing foundations necessary to achieve reliable detection and classification of human emotions. The approach combines sensing of speech characteristics, natural language processing, facial landmark monitoring, and machine learning.

CS students can typically reason about what a piece of code does on specific inputs. While this is a useful starting point, graduates must also be able to logically analyze, comprehend, and predict the behavior of their code in more general terms, no matter what the inputs are. Results of data collection and analysis from an online educational system show it can help to pinpoint the difficulties in doing this for individual students and groups, and to partition the groups in terms of their difficulties so that instructional interventions may be better targeted. Unlike traditional debugging, this online system helps reveal difficulties in reasoning in more general terms because it is equipped with a verification engine.

Food availability and food waste are significant global problems. Current monitoring methods require manual data collection and are implemented infrequently, providing imprecise information. Smart service systems offer real-time, continuous monitoring, plus feedback to influence human behaviors. This paper presents a smart services approach to automating food waste measurement, and to influencing associated human behaviors. The work is presented in the context of a case study conducted in a university cafeteria.

As a popular routing protocol in wireless sensor networks (WSNs), greedy routing has received great attention. The previous works characterize its data deliverability in WSNs by the probability of all nodes successfully sending their data to the base station. Their analysis, however, neither provides the information of the quantitative relation between successful data delivery ratio and transmission power of sensor nodes nor considers the impact of the network congestion or link collision on the data deliverability. To address these problems, in this paper, we characterize the data deliverability of greedy routing by the ratio of successful data transmissions from sensors to the base station. We introduce n-guaranteed delivery which means that the ratio of successful data deliveries is not less than n, and study the relationship between the transmission power of sensors and the probability of achieving n-guaranteed delivery. Furthermore, with considering the effect of network congestion, link collision, and holes (e.g., those caused by physical obstacles such as a lake), we provide a more precise and full characterization for the deliverability of greedy routing. Extensive simulation and real-world experimental results show the correctness and tightness of the upper bound of the smallest transmission power for achieving n-guaranteed delivery.

Sensor nodes are increasingly used in critical applications. A file storage system that is fast, lightweight, and reliable across device failures is important to safeguard the data that these devices record. A fast and lightweight file system should enable sensed data to be sampled and stored quickly for later transmission, while imposing a small resource footprint. A reliable file system should provide storage integrity in the face of hardware, software, and other failures.

As a popular routing protocol in wireless sensor networks (WSNs), greedy routing has received great attention. The previous works characterize its data deliverability in WSNs by the probability of all nodes successfully sending their data to the base station. Their analysis, however, neither provides the information of the quantitative relation between successful data delivery ratio and transmission power of sensor nodes nor considers the impact of the network congestion or link collision on the data deliverability. To address these problems, in this paper, we characterize the data deliverability of greedy routing by the ratio of successful data transmissions from sensors to the base station. We introduce η-guaranteed delivery which means that the ratio of successful data deliveries is not less than η, and study the relationship between the transmission power of sensors and the probability of achieving η-guaranteed delivery. Furthermore, with considering the effect of network congestion and link collision, we provide a more precise and full characterization for the deliverability of greedy routing. Extensive simulation and real-world experimental results show the correctness and tightness of the upper bound of the smallest transmission power for achieving η-guaranteed delivery.

Sensor networks must be monitored to identify and correct problems as they occur.We present a comparison of two approaches to monitoring deployed sensors.The first relies on configuration parameters to define expected reporting behavior.The second automatically identifies normal reporting patterns based on a combination of configuration parameters and an analysis of reporting times.Using these patterns, the system notifies personnel of possible malfunctions.We present empirical evaluations in the context of the Intelligent River R system [11].

Undergraduate computer science students need to learn analytical reasoning skills to develop high-quality software and to understand why the software they develop works as specified. To accomplish this central educational objective, this article describes a systematic process of introducing reasoning skills into the curriculum and assessing how well students have learned those skills. To facilitate assessment, a comprehensive inventory of principles for reasoning about correctness that captures the finer details of basic skills that students need to learn has been defined and used. The principles can be taught at various levels of depth across the curriculum in a variety of courses. The use of a particular instructional process is illustrated to inculcate reasoning principles across several iterations of a sophomore-level development foundations course and a junior-level software engineering course. The article summarizes how learning outcomes motivated by the inventory of reasoning principles lead to questions that in turn form the basis for a careful analysis of student understanding and for fine-tuning teaching interventions that together facilitate continuous improvements to instruction.

Underlying link-layer protocols of well-established wireless networks that use the conventional “store-and-forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and balancing en/decoding load among nodes. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in a packet's route. To identify the intermediate nodes for the decoding, we mathematically calculate the average packet delay and formalize the problem as a nonlinear integer programming problem. By minimizing the delays, we derive three propositions that: 1) can identify the intermediate nodes that minimize the propagation and transmission delay of a packet; and 2) and 3) can identify the intermediate nodes that simultaneously minimize the queuing delay and maximize the fairness of en/decoding load of all the nodes. Guided by the propositions, we then propose a scalable and distributed scheme in CEDAR to choose the intermediate en/decoding nodes in a route to achieve its objective. The results from real-world testbed “NESTbed” and simulation with MATLAB prove that CEDAR is superior to schemes using hop-by-hop decoding and destination decoding not only in packet delay and throughput but also in energy-consumption and load distribution balance.

Wireless mesh-based backhaul infrastructure is intended to provide reliable data transmission, with high throughput across large-scale networks. Load balancing is essential over a long period of operation to provide high throughput and uninterrupted service to end users. Load across mesh nodes is highly variable as the traffic depends on the number of clients connected to the nodes, as well as the services they use. Existing load balancing solutions are based on theoretical analysis and simulations. Most require distributed routing algorithms executed over compute-intensive routing nodes. It is also challenging to provide practical support for real-time traffic redirection using traditional mesh nodes. In this paper, we develop a prototype mesh infrastructure where flows from a source node can take multiple paths through the network. OpenFlow, an emerging technology that makes network switches programmable via a standard interface, allows flexible control of data flow paths. The Better Approach To Mobile Ad-hoc Networking (B.A.T.M.A.N) mesh protocol is used to provide mesh topology and link quality information. The OpenFlow controller decides the best data path based on this information to ensure high throughput data transfer. To demonstrate the usefulness of our approach, we have implemented three test cases to enable data path setup and redirection with low complexity and overhead. Our test case measurements confirm that OpenFlow is a promising complementary technology to traditional mesh routing protocols for wireless networks.

We revisit the classic object tracking problem with a novel and effective, yet straightforward distributed solution for resource-lean devices. The difficulty of object tracking lies in the mismatch between the limited computational capacity of typical sensor nodes and the processing requirements of typical tracking algorithms. In this paper, we introduce an in-network system for tracking mobile objects using resource-lean sensors. The system is based on a distributed, dynamically-scoped tracking algorithm which alters the event detection region and reporting rate based on object speed. A leader node records the detected samples across the event region and estimates the object's location in situ. We study the performance of our tracking implementation on an 80-node test bed. The results show that it achieves high performance, even for very fast objects, and is readily implemented on resource-lean sensors. While the area is well-studied, the unique combination of algorithmic features represents a significant addition to the literature.

Computational thinking represents a collection of structured problem solving skills that cross-cut educational disciplines. There is significant future value in introducing these skills as early as practical in students' academic careers. Over the past three years, we have developed, piloted, and evaluated a series of K-12 outreach modules designed to introduce fundamental computing concepts. We piloted two modules with more than 340 students, and evaluation results show that the modules are having a positive impact. We combined the two previously piloted modules with a newly developed module and piloted the combined program with over 170 students. Evaluation results again show that the combination is having a positive impact. In this paper, we summarize the program, discuss our experiences piloting it, and summarize key evaluation results. Our hope is to engender discussion and adoption of the materials at other institutions.

This paper summarizes our experiences integrating topics in the software development fundamentals (SDF), programming languages (PL), and software engineering (SE) knowledge areas of the ACM 2013 curriculum within a single course. It is novel in combining object-oriented programming and software development practices with fundamental analytical reasoning about software correctness. The aim is to integrate and cover the topics in an effective fashion. The course description in this paper represents an approach we have applied successfully for over 5 years. Students tend to consider this course to be one of the more challenging encountered in the first two years of study. Interestingly, the challenge appears to stem equally from mastering object-oriented programming and design pattern components of the course, as it does from learning to use specifications for analytical reasoning of component correctness.

Network simulation is an important tool for testing and evaluating wireless sensor network applications. Parallel simulation strategies improve the scalability of these tools. However, achieving high performance depends on reducing the synchronization overhead among simulation processes. In this paper, we present an optimistic simulation algorithm with support for backtracking and re-execution. The algorithm reduces the number of synchronization cycles to the number of transmissions in the network under test. We implement SnapSim, an extension to the popular Avrora simulator, based on this algorithm. The experimental results show that our prototype system improves the performance of Avrora by 2 to 10 times for typical network-centric sensor network applications, and up to three orders of magnitude for applications that use the radio infrequently. We also implement a distributed version of SnapSim, D-SnapSim, which runs on a cluster. The experimental results show that D-SnapSim further improves the performance of SnapSim by up to 10 times for applications that use the radio frequently.

We present an incremental code update strategy used to efficiently reprogram wireless sensor nodes. We adapt a linear space and quadratic time algorithm (Hirschberg's algorithm) for computing maximal common subsequences to build an edit map specifying an edit sequence, required to transform the code running in a sensor network to a new code image. We then present a heuristic-based optimization strategy for efficient edit script encoding to reduce th.e edit map size. Finally, we present experimental results to demonstrate the reduction in data size to reprogram a network using this mechanism. The approach achieves reductions of 99.987% for simple changes, and between 86.95% and 94.58% for more complex changes, compared to full image transmissions - leading to significantly lower energy costs for wireless sensor network reprogramming. We compare the results with reductions achieved by other incremental update strategies described in prior work.

Underlying link-layer protocols of wireless networks use the conventional “store and forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and high throughput. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically-sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in packet's route. To identify the intermediate nodes for the en/decoding for minimizing average packet latency, we mathematically analyze the average packet delay, using Finite State Markovian Channel model and priority queuing model, and then formalize the problem as a non-linear integer programming problem. Also, we propose a scalable and distributed scheme to solve this problem. The results from real-world testbed “NESTbed” and simulation with Matlab prove that CEDAR is superior to the schemes using hop-by-hop decoding and destination-decoding not only in packet delay but also in throughput. In addition, the simulation results show that CEDAR can achieve the optimal performance in most cases.

Networking concepts have been in use for centuries. The human body is a network of organs that must coordinate to survive. The postal service is an example of a network that connects individuals world-wide. It is only natural that networks play an important role in computing --- from networks of sensors collecting and recording data, to social networks, to the most complex network of all, the Internet. Observing the importance of networking concepts in computing, we have developed the second in a series of "serious toys" to use in the K-12 curriculum. In this case, the toy is an embedded hardware device designed to enhance a lecture titled "Learning Networks, Protocols, and Algorithms", by engaging visual and kinesthetic learners. In this paper, we describe our curriculum module and its use in an outreach program involving six middle school classes. We conclude with a summary of evaluation results that show the program produced positive results in terms of content understanding and attitudes toward Computer Science.

Is it possible to excite students about learning the mathematical principles that underlie high-quality software? Can they use a development environment for "hands-on" experimentation with reasoning? Is this possible without displacing existing content? The answer is a resounding yes "from the experiences of professors at several institutions" but it takes the right set of pedagogical principles, reasoning tools, and hands-on exercises. This laboratory will help educators transfer the excitement of learning how to apply mathematical reasoning in building high quality software, by adopting one reasoning concept at a time.

No abstract available.

Due to the limited computational and energy resources available on existing wireless sensor platforms, achieving high-precision classification of high-level events in-network is a challenge. In this article, we present in-network implementations of a Bayesian classifier and a condensed kd-tree classifier for identifying events of interest on resource-lean embedded sensors. The first approach uses preprocessed sensor readings to derive a multidimensional Bayesian classifier used to classify sensor data in real time. The second introduces an innovative condensed kd-tree to represent preprocessed sensor data and uses a fast nearest-neighbor search to determine the likelihood of class membership for incoming samples. Both classifiers consume limited resources and provide high-precision classification. To evaluate each approach, two case studies are considered, in the contexts of human movement and vehicle navigation, respectively. The classification accuracy is above 85% for both classifiers across the two case studies.

This paper presents an ontology-based approach for data quality inference on streaming observation data originating from large-scale sensor networks. We evaluate this approach in the context of an existing river basin monitoring program called the Intelligent River®. Our current methods for data quality evaluation are compared with the ontology-based inference methods described in this paper. We present an architecture that incorporates semantic inference into a publish/subscribe messaging middleware, allowing data quality inference to occur on real-time data streams. Our preliminary benchmark results indicate delays of 100ms for basic data quality checks based on an existing semantic web software framework. We demonstrate how these results can be maintained under increasing sensor data traffic rates by allowing inference software agents to work in parallel. These results indicate that data quality inference using the semantic sensor network paradigm is viable solution for data intensive, large-scale sensor networks.

Network longevity and connectivity are key design goals in any wireless sensor network deployment. In this context, we consider the placement of relay nodes and individual transmission power assignments. Specifically, given a planar deployment of sensors and a base station, we seek the placement of a limited number of relays and optimal sensor power assignments such that the network is connected. We present a polynomial-time bicriteria approximation algorithm for this problem. We also provide an optimal O(n2log n)-time algorithm for a restricted version where nodes lie on a simplified urban grid (that we call a comb-grid). We also study a related variant that assumes fixed transmission power values, with the goal of minimizing the number of relays. We provide extensive simulation results for the comb-grid case.

The need for undergraduate CS students to create and understand mathematical abstractions is clear, yet these skills are rarely taught in a systematic manner, if they are taught at all. This paper presents a systematic approach to teaching abstraction using rigorous mathematical models and a web-based reasoning environment. It contains a series of representative examples with varying levels of sophistication to make it possible to teach the ideas in a variety of courses, such as introductory programming, data structures, and software engineering. We also present results from our experimentation with these ideas over a 3-year period at our institution in a required course that introduces object-based software development, following CS2.

The binary number system is the lingua franca of computing, requisite to myriad areas, from hardware architecture and data storage to wireless communication and algorithm design. Given its significance to such a broad range of computing topics, it is not surprising that the binary number system plays a prominent role in K-12 outreach efforts. It is even less surprising that the topic is often viewed as a dreary introduction to the discipline. Motivated by these observations and the potential of binary arithmetic to connect future students to a wide spectrum of computing topics, we have developed a new approach to teaching binary arithmetic in the K-12 curriculum. The approach relies on the use of a "serious toy", an embedded hardware platform designed to teach the binary number system while engaging visual and kinesthetic learners. We describe the design of the curriculum module and the supporting toy and detail our experiences using the approach in three independent outreach efforts. The results are largely positive, supporting our supposition that teaching the binary number system can achieve strong content understanding and improved attitudes toward the discipline.

Is it possible to excite students about learning the mathematical principles that underlie high-quality software? Can we teach them to apply these principles using modern software tools? Can this be accomplished without displacing existing content? In each case, the answer is a resounding yes - but it takes the right set of pedagogical principles, teaching tools, and classroom exercises. This hands-on laboratory will introduce a set of principles, tools, and exercises that have proven to work. By adopting one content module at a time, educators will better prepare students to reason rigorously about the software they develop and maintain.

CS Unplugged is a set of active learning activities designed to introduce fundamental computer science principles without the use of computers. The program has gained significant momentum in recent years, with proponents citing deep engagement and enjoyment benefits. With these benefits in mind, we initiated a one-year outreach program involving a local high school, using the CS Unplugged program as the foundation. To our disappointment, the results were at odds with our enthusiasm --- significantly. In this paper, we describe our approach to adapting the CS Unplugged materials for use at the high school level, present our experiences teaching it, and summarize the results of our evaluation.

Sensor networks serve as a powerful recruiting vehicle to excite and engage students in socially-relevant applications of computing. In this paper, we describe a technology-assisted scavenger hunt for introducing young learners --- from grade school through high school--- to sensor networks and computer science. The desired outcome is to expand students' content knowledge and positively impact their impressions of the discipline. We describe the outreach program and present promising evaluation results across three pilots involving 5th graders, 7th graders, and 11th graders.

Developing and maintaining reliable object-oriented software requires a precise understanding of how individual classes must be used. Unfortunately, for many systems, especially those that are large, the available documentation is inadequate. Developers are left with incomplete information concerning the allowable set of call sequences that each class can accommodate. Techniques for reverse engineering this information and presenting it to developers in an intellectually scalable manner are critical. In this paper, we present four contributions to address this challenge. First, we describe a runtime trace collection system for large C++ applications. Second, we present a methodology for reverse engineering interface protocols from collected trace data. Third, we present a scalable, tunable algorithm for generating compact specifications of these protocols. Finally, we present a detailed case study involving the Mozilla Necko library. We consider popular applications in common use constructed using this library. The results are promising both in terms of the performance of the approach and the utility of the identified protocols.

In the domain of desktop software, design patterns have had a profound impact; they are applied ubiquitously across a broad range of applications. Patterns serve both to promulgate expert knowledge and as a vocabulary for documenting software design. The result is higher-quality software, reduced development effort, and improved documentation.

We introduce a "hands-on" experimentation approach for teaching mathematical specification and reasoning principles in a software engineering course. The approach is made possible by computer-aided analysis and reasoning tools that help achieve three central software engineering learning outcomes: (i) Learning to read specifications by creating test points using only specifications; (ii) Learning to use formal specifications in team software development while developing participating components independently; and (iii) Learning the connections between software and mathematical analysis by proving verification conditions that establish correctness for software components. Experimentation and evaluation results from two institutions show that our approach has had a positive impact.

We describe an approach to teaching formal interface specifications using aspects of the Collaborative Reasoning Paradigm. The module requires students to construct test cases independently and cooperatively based on their understanding of a given set of method specifications. Students are supported by software-based reasoning assistants that guide them through their exercises and provide realtime feedback as they work --- both for the students and the instructor. We describe the design of the course module, the supporting reasoning assistant, and representative reasoning exercises. We conclude with a discussion of evaluation results from a recent pilot study conducted at Clemson University.

We present a testbed for visualizing the behavior of sensor network applications constructed using TinyOS 2.0. The testbed enables network designers to capture node-level runtime behaviors and to link the captured behaviors to form a network-level snapshot. A multi-resolution approach based on static selectors and dynamic filters helps to achieve manageable visualizations by including only those network nodes and program actions relevant to a global behavior of interest. We demonstrate the system's utility using a standard network example and summarize its resource usage and performance characteristics.

We present DESALalpha, a realization of the dynamic embedded sensor-actuator language for Telos-based devices. The platform provides native support for: (i) rule-based programming; (ii) synchronized action scheduling; (iii) neighborhood management; and (iv) distributed state sharing. We describe the design and implementation of DESALalpha, present examples that illustrate its use, and summarize the resource requirements of compiled applications. Finally, we present lessons learned based on our use of DESALalphaduring the past year.

We present the NePTune approach and supporting system architecture for sensor network optimization. NePTune relies on a control loop strategy with performance monitoring, dynamic source code generation, and network reprogramming. We present an application of the system architecture and explore its efficacy in the context of a resource utilization problem - specifically, to minimize the memory consumption of a neighborhood management service. All experiments are conducted using a physical network testbed consisting of 80 Tmote Sky nodes.

TinyOS has proven to be an effective platform for developing reactive embedded network applications. However, the platform's lean programming model and power-efficient operation come at a price: TinyOS applications are notoriously difficult to construct and debug. The development difficulties stem, in large part, from a programming model founded on events and deferred execution. In short, the model introduces non-determinism in the execution ordering of primitive actions (i.e., commands, events, and tasks). The resulting set of possible execution sequences is typically large, and can swamp developers' unaided intellectual ability to reason about program behavior. In this paper, we present a platform-neutral visualization toolkit for TinyOS 2.0 to aid in program comprehension. The goal is to assist developers in reasoning about the computation forest underlying a system under test, and the particular branches chosen during each run. The toolkit design includes (i) a full-featured static analysis and instrumentation library, (ii) a selection-based probe insertion system, (iii) a lightweight event recording service, (iv) a trace extraction and reconstruction tool, and (v) two visualization front-ends. We demonstrate the utility of the toolkit using standard system examples, and present an analysis of the toolkit's resource usage and performance characteristics.

After over a decade of use, design patterns continue to find new areas of application. In previous work, we presented a contract formalism for specifying patterns precisely, and showed how the use of the formalism can amplify the benefits of patterns. In this paper, our goal is to enable practitioners to test whether their systems, as implemented, meet the requirements, as specified in the pattern contracts, corresponding to the correct usage of the patterns underlying the systems' designs. In our testing approach, corresponding to each design pattern, there is a set of what we call pattern test case templates (PTCTs). A PTCT codifies a reusable test case structure designed to identify defects associated with applications of the particular pattern. The test assertions in the PTCT are based on the requirements specified in the appropriate pattern contract. Next we present a process using which, given any system designed using the pattern, the system tester can generate a test suite from the PTCTs for that pattern that can be used to test the system for bugs in the implementation of the particular pattern. The process allows the system tester to tailor the test suite the needs of the individual system by specifying a set of specialization rules that are designed to reflect the structure and the scenarios in which the defects codified in the PTCTs are likely to manifest themselves in the particular system.

The benefits of design patterns in the design phase are well-established. We claim that patterns can - and should - play equally important roles in later stages of the lifecycle. But to make this feasible, we need to develop suitable ways to precisely specify the requirements associated with the use of specific patterns, and runtime monitoring tools to identify any violations of these requirements. We summarize a specification and monitoring approach focused on pattern-centric behavior that we developed previously, evaluate alternative ways to monitor systems based on the formalism, and discuss the overall utility of the specification and monitoring approach in the context of a case study

Wireless sensor networks (WSNs) will profoundly influence the ubiquitous computing landscape. Their utility derives not from the computational capabilities of any single sensor node, but from the emergent capabilities of many communicating sensor nodes. Consequently, the details of communication within and across single hop neighborhoods is a fundamental component of most WSN applications. But these details are often complex, and popular embedded languages for WSNs do not provide suitable communication abstractions. We propose that the absence of such abstractions contributes to the difficulty of developing large-scale WSN applications. To address this issue, we present the design and implementation of a remote procedure call (RPC) abstraction for nesC and TinyOS, the defacto standard for developing WSN applications. We present the key language extensions, operating system services, and automation tools that enable the proposed abstraction. We illustrate these contributions in the context of a small case study, and draw preliminary conclusions regarding the suitably of our approach to resource-constrained sensor nodes.

Design patterns provide guidance to system designers on how to structure individual classes or groups of classes, as well as constraints on the interactions among these classes, to enable them to implement flexible and reliable systems. Patterns are usually described informally. While such informal descriptions are useful and even essential, if we want to be sure that designers precisely and unambiguously understand the requirements that must be met when applying a given pattern, and be able to reliably predict the behaviors the resulting system exhibits, we also need formal characterizations of the patterns. In this paper, we develop an approach to formalizing design patterns. The requirements that a designer must meet with respect to the structures of the classes, as well as with respect to the behaviors exhibited by the relevant methods, are captured in the responsibilities component of the pattern's specification; the benefits that results by applying the pattern, in terms of specific behaviors that the resulting system is guaranteed to exhibit, are captured in the rewards component. One important aspect of many design patterns is their flexibility; our approach is designed to ensure that this flexibility is retained in the formalization of the pattern. We illustrate the approach by applying it to a standard design pattern.

Strong mobility, which allows multithreaded agents to be migrated transparently at any time, is a powerful mechanism for implementing a peer-to-peer computing environment, in which agents carrying a computational payload find available computing resources. Existing approaches to strong mobility either modify the Java virtual machine or do not correctly preserve the Java semantics when migrating multithreaded agents. We give an overview of our implementation strategy for strong mobility in which each agent thread maintains its own serializable execution state at all times, while thread states are captured just before a move. We explain how to solve the synchronization problems involved in migrating a multithreaded agent and how to cleanly terminate the Java threads in the originating virtual machine. We present experimental results that indicate that our implementation approach is feasible in practice.