Problem-solving is one of the most important skills to develop in today's globally competitive workforce and remains an essential aspect to a diversity of professions and disciplines. Building on the rich literature found on problem-solving, this paper reports on the implementation of a curriculum that applies known strategies and heuristics to develop broadly applicable problem-solving skills in high school students. The curriculum covers existing problem identification and solution development tools including Izakawa fishbone diagrams, Duncker diagrams, statement restatement, vertical thinking, cross fertilization, Kepner-Tregoe analysis, and more. Our pedagogical approach blends active learning frameworks (hands-on projects, case studies, exercises) with self-regulated learning environments (via scaffolding, metacognition). We have successfully deployed the full curriculum in eleven classrooms across five high schools. Students apply their problem-solving abilities through hands-on projects embedded throughout the curriculum. Results from 254 student surveys showed interest and engagement with the course content. Overall, this paper provides a skills-focused approach to outreach, preparation, and participation of students towards engineering and technical fields with the goal of meeting the skill requirements and labor demands of the global workforce.

This work-in-progress paper presents an innovative practice of using oral exams to maintain academic integrity and promote student engagement in large-enrollment engineering courses during remote instruction. With the abrupt and widespread transition to distance learning and assessment brought on by the COVID-19 pandemic, there has been a registered upsurge in academic integrity violations globally. To address the challenge of compromised integrity, in the winter quarter of 2021 we have implemented oral exams across six mostly high-enrollment mechanical and electrical engineering undergraduate courses. We present our oral exam design parameters in each of the courses and discuss how oral exams relate to academic integrity, student engagement, stress, and implicit bias. We also address the challenge of scalability, as most of our oral exams were implemented in large classes, where academic integrity and student-instructor disconnection have generally gotten disproportionately worse during remote learning. Our survey results indicate that oral exams have positively contributed to academic integrity in our courses. Based on our preliminary study and experiences, we expect oral exams can be effectively leveraged to hinder cheating and foster academic honesty in students, even when in-person instruction and assessment resumes.

Passive acoustic monitoring (PAM) involves deploying audio recorders across a natural environment over a long period of time to collect large quantities of audio data. To parse through this data, researchers have worked with automated annotation techniques stemming from Digital Signal Processing and Machine Learning to identify key species calls and judge a region’s biodiversity. To apply and evaluate those techniques, one must acquire strongly labeled data that marks the exact temporal location of audio events in the data, as opposed to weakly labeled data which only labels the presence of an audio event across a clip.Pyrenote was designed to fit the demand for strong manual labels in PAM data. Based on Audino, an open-source, web-based, and easy-to-deploy audio annotation tool, Pyrenote displays a spectrogram for audio annotation, stores labels in a database, and optimizes the labeling process through simplifying the user interface to produce high-quality annotations in a short time frame. This paper documents Pyrenote’s functionality, how the challenge informed the design of the system, and how it compares to other labeling systems.

This paper describes a low cost system for tracking wildlife that is equipped with radio collars. Currently, researchers have to physically go into the field with a directional antenna to try to pinpoint the VHF (very high frequency) signal originating from a wildlife tracking collar. Depending on the terrain, it could take an entire day to locate a single animal. To vastly improve upon this traditional approach, the system proposed here utilizes a small fixed-wing aircraft drone with a simple radio on-board, flying an automated mission. Received signal strength is recorded, and used to create a heat map that shows the collar's position. A prototype of this system was built using off-the-shelf hardware and custom signal processing algorithms. Initial field tests confirm the systems capabilities and its promise for wildlife tracking.

In applications involving networks of sensor-equipped autonomous vehicles, it is crucial to have an energy-efficient communication protocol due to the limited on-board batteries. Unlike traditional sensor networks, vehicle sensor networks typically consist of only a small number of nodes. We exploit this fact in our protocol design by optimizing specifically for these mobile small-scale networks. Our proposed solution, ConverSS, is a hybrid MAC/routing protocol that is energy-efficient for vehicle sensor networks. We have evaluated the protocol through simulation in ns-2 and verified its operation in a testbed network deployment. The results show that ConverSS is robust to real-world link impairments and that it outperforms common layered solutions by as much as a factor of 10.

An important class of sensor network applications aims at estimating the spatiotemporal behavior of a physical phenomenon, such as temperature variations over an area of interest. In such a scenario, the network essentially acts as a distributed sampling system. However, unlike in the event detection case, the notion of sensing range is largely meaningless for sampling-interpolation applications. As a result, existing techniques to exploit sensing redundancy in event detection settings, which rely on the existence of such sensing range, become unusable. Instead, this article presents a new method to exploit redundancy for the sampling class of applications by selecting a suitable set of sensors to act as sampling points. Through online estimation of process characteristics, sufficiently accurate interpolation can be achieved. We illustrate an algorithm to obtain multiple disjoint sets and demonstrate significant reductions in the number of active sensors for a wide range of synthetic sensor data.

Vehicle-based networks need energy-efficient communication to extend the battery lifetime, which subsequently extends the mission time of the node. We observe that these kinds of networks operate under different assumptions than traditional static sensor networks. The main difference is that these a

We focus on dense networks of wireless sensors used for distributed sampling and interpolation. Sensors periodically sample a physical quantity of interest, e.g. temperature, and report their measurements to a data center. A continuous spatial estimate of the quantity can then be constructed through interpolation. In these cases, density in deployment can be exploited to achieve longer operational time for the network. The challenge is how to select multiple disjoint subsets of sensors such that each of them is individually capable of meeting the quality demands of the monitoring application. In this paper we present an efficient sensor selection scheme for the min-max monitoring application, i.e., minimizing the maximum distortion incurred over space through interpolation. Evaluation with synthetic sensor network data shows that significant reductions in the number of active sensors and energy consumption are possible compared to simpler selection methods.

Wireless sensor networks are a fast-growing class of systems. They offer many new design challenges, due to stringent requirements like tight energy budgets, low-cost components, limited processing resources, and small footprint devices. Such strict design goals call for technologies like nanometer-scale semiconductor design and low-power wireless communication to be used. But using them would also make the sensor data more vulnerable to errors, within both the sensor nodes' hardware and the wireless communication links. Assuring the reliability of the data is going to be one of the major design challenges of future sensor networks. Traditional methods for reliability cannot always be used, because they introduce overheads at different levels, from hardware complexity to amount of data transmitted. This paper presents a new method that makes use of the properties of sensor data to enable reliable data collection. The approach consists of creating predictive models based on the temporal correlation in the data and using them for real-time error correction. This method handles multiple sources of errors together without imposing additional complexity or resource overhead at the sensor nodes. We demonstrate the ability to correct transient errors arising in sensor node hardware and wireless communication channels through simulation results on real sensor data.

Wireless sensor nodes are increasingly being tasked with computation and communication intensive functions while still subject to constraints related to energy availability. On these embedded platforms, once all low power design techniques have been explored, duty-cycling the various subsystems remains the primary option to meet the energy and power constraints. This requires the ability to provide spurts of high MIPS and high bandwidth connections. However, due to the large overheads associated with duty-cycling the computation and communication subsystems, existing high performance sensor platforms are not efficient in supporting such an option. In this article, we present the design and optimizations taken in a wireless gateway node (WGN) that bridges data from wireless sensor networks to Wi-Fi networks in an on-demand basis. We discuss our strategies to reduce duty-cycling related costs by partitioning the system and by reducing the amount of time required to activate or deactivate the high-powered components. We compare the design choices and performance parameters with those made in the Intel Stargate platform to show the effectiveness of duty-cycling on our platform. We have built a working prototype, and the experimental results with two different power management schemes show significant reductions in latency and average power consumption compared to the Stargate . The WGN running our power-gating scheme performs about six times better in terms of average system power consumption than the Stargate running the suspend-system scheme for large working-periods where the active power dominates. For short working-periods where the transition (enable/disable) power becomes dominant, we perform up to seven times better. The comparative performance of our system is even greater when the sleep power dominates.

Many ocean processes, both biological and physical, greatly depend on and interact with the intrinsic current dynamics of the underwater environment. A promising approach to understand small and large scale spatio-temporal correlations of these processes is to deploy a networked swarm of drifters that float freely with ocean currents. They form a coordinated distributed sampling system that observes ocean processes within their own moving frame of reference. As data interpretation is impossible without knowledge of sampling positions, a method is required to localize the drifters. Furthermore, the localization has to be repeated periodically as the network topology changes due to the inherent motion of the vehicles. This paper proposes a novel energy-aware, distributed solution based on inter-drifter range measurements. It leverages the realization that actual position estimation can be performed after the mission is over. The proposed broadcast-based solution achieves sufficient localization accuracy with an extremely low overhead: around 0.5 transmissions per node per localization.

Increasing interest in sensor networking and ubiquitous computing has created a trend towards embedding more and more intelligence into our surroundings. This enables thin wireless clients to support powerful applications by using processing resources that are available around them. One of the major challenges is how to schedule both the embedded computing and wireless communication resources to support a broad set of client nodes. This scheduling has to take into account the constraints on the wireless capacity, as well as the resource limitations on the processing elements. We have developed a set of fast algorithms to perform this scheduling, based on an LP-approachand a greedy solution. These algorithms are able to perform the scheduling with a performance close to the optimal exhaustive solution, but with an execution time that is reduced by three or more orders of magnitude (from hours to seconds).

The power consumption of the network interface plays a major role in determining the total operating lifetime of wireless networked embedded systems. In case of on-demand paging, a low power secondary radio is used to wake up the higher power radio, allowing the latter to sleep for longer periods of time. In this paper we present use of Bluetooth radios to serve as a paging channel for the 802.11b wireless LAN. We have implemented an on-demand paging scheme on an infrastructure based WLAN consisting of iPAQ PDAs equipped with Bluetooth radios and Cisco Aironet wireless networking cards. Our results show power saving ranging from 23% to 48% over the present 802.11b standard operating modes with negligible impact on performance.

Multicarrier modulation has established itself as an appealing option for high-performance wireless communication systems. When the channel varies slowly in time, such as in applications with low terminal mobility, adaptive loading further enhances the system performance considerably. However, even with adaptive loading, significant performance fluctuations still occur, which impact the higher layers and the overall system behavior. First, a good understanding of the nature of these fluctuations is crucial to design better higher layers that are able to tolerate or combat this variability. For this reason, we develop a full statistical description of a loaded wireless multicarrier system that accurately characterizes its behavior. Our model abstracts the loaded system as an equivalent single carrier system with flat log-normal fading and has the channel tap as a single parameter. Second, with our model, the physical layer can now be incorporated in an abstracted fashion during simulation, resulting in significant speed-ups. Indeed, the alternative would be to include the entire loaded multicarrier system in these simulations, which is highly time consuming. Third, our statistical model is also important for the mathematical analysis of protocols, which requires knowledge of the physical layer statistics. We derive such statistics for both correlated and uncorrelated Rayleigh fading.

Soft-input soft-output algorithms are the principal component of the iterative decoding used in turbo codes and other 'turbo' feedback schemes. To enable efficient implementation, especially on energy constrained platforms such as portable devices, it is crucial to reduce the computational complexity to a minimum. We propose an enhancement to the MAX-LOG-MAP algorithm by adding a traceback operation similar to that used in the Viterbi algorithm, and devise a new efficient way to initialize the start state of the traceback. This enhancement is effective for each decoding iteration, and provides saving on top of existing techniques such as early termination and memory optimizations. It reduces the computational complexity by an additional 15%, without incurring any performance penalty.

As embedded systems are being networked, often wirelessly, an increasingly larger share of their total energy budget is due to the communication. This necessitates the development of power management techniques that address communication subsystems, such as radios, as opposed to computation subsystems, such as embedded processors, to which most of the research effort thus far has been devoted. In this paper, we present techniques for energy efficient packet scheduling and fair queuing in wireless communication systems. Our techniques are based on an extensive slack management approach that dynamically adapts the output rate of the system in accordance with the input packet arrival rate. We use a recently proposed radio power management technique, dynamic modulation scaling (DMS), as a control knob to enable energy-latency trade-offs during wireless packet transmission. We first analyze a single input stream scenario, and describe a rate adaptation technique that results in significantly lower energy consumption (reductions of up to 10 ×), while still bounding the resulting packet delays. By appropriately setting the various parameters of our algorithm, the system can be made to traverse the energy-latency-fidelity trade-off space. We extend our techniques to a multiple input stream scenario, and present E 2 WFQ , an energy efficient version of the weighted fair queuing (WFQ) algorithm for fair packet scheduling. Simulation results show that large energy savings can be obtained through the use of E 2 WFQ , with only a small, bounded increase in worst case packet latency. Further, our results demonstrate that E 2 WFQ does not adversely affect the throughput allocation (and hence, fairness) of WFQ.

By adapting radio setting, such as the modulation or error code, in response to changes in the wireless channel, the energy consumption of a communication system can be greatly reduced. In paper, we investigate the problem of minimizing the energy for a desired average throughput. When the option of shutting down the radio completely is also considered, deciding on the optimal setting becomes a complex scheduling problem. In this case, we have to take power of both the radio electronics and the power amplifier into account, the relative importance of which has a critical impact on the nature of the adaptation strategy, as we show in this paper. We present a new algorithm that does not require any knowledge of the channel statistics, but instead quickly learns the desired adaptation strategy. It is easy to implement, yet is optimal in terms of energy savings.

With the growing interest in wireless sensor networks, techniques for their systematic analysis design and optimization are essential. Despite numerous research efforts in optimizing hardware, algorithms and protocols for these networks, it remains largely unexplored how these innovations can be all tied together to design a sensor network for a specific practical application. We propose a methodology that starts from four independent quality of service (QoS) parameters and allows the user to completely and unambiguously describe the desired performance, without having to deal with the details of individual devices or protocols. By making appropriate choices in terms of device capabilities and run-time techniques, a design can be positioned in this four-dimensional QoS space. Furthermore, we describe a technique to explore the associated tradeoffs at design time, using both analytical expressions and simulations. To illustrate the benefits of our approach, a design example is worked out, which shows a five fold improvement in network operational lifetime by adapting the event reporting delay.

System-level power management has become a key technique to render modern wireless communication devices economically viable. Despite their relatively large impact on the system energy consumption, power management for radios has been limited to shutdown-based schemes, while processors have benefited from superior techniques based on dynamic voltage scaling (DVS). However, similar scaling approaches that trade-off energy versus performance are also available for radios. To utilize these in radio power management, existing packet scheduling policies have to be thoroughly rethought to make them energy-aware, essentially opening a whole new set of challenges the same way the introduction of DVS did to CPU task scheduling. We use one specific scaling technique, dynamic modulation scaling (DMS), as a vehicle to outline these challenges, and to introduce the intricacies caused by the nonpreemptive nature of packet scheduling and the time-varying wireless channel.

As embedded systems are being networked, often wirelessly, an increasingly larger share of their total energy budget is due to the communication. This necessitates the development of power management techniques that address communication subsystems, such as radios, as opposed to computation subsystems, such as embedded processors, to which most of the research effort thus far has been devoted. In this paper, we present E2WFQ, an energy efficient version of the Weighted Fair Queuing (WFQ) algorithm for packet scheduling in communication systems. We employ a recently proposed radio power management technique, Dynamic Modulation Scaling (DMS), as a control knob to enable energy-latency tradeoffs during wireless packet scheduling. The use of E2WFQ results in an energy aware packet scheduler, which exploits the statistics of the input arrival pattern as well as the variability in packet lengths. Simulation results show that large savings in energy consumption can be obtained through the use of our scheduling scheme, compared to conventional WFQ, with only a small, bounded increase in worst case packet latency.

In wireless sensor networks, energy efficiency is crucial to achieve satisfactory network lifetime. In order to reduce the energy consumption of a node significantly, its radio needs to be turned off. Yet, some nodes have to participate in multi-hop packet forwarding. We tackle this issue by exploiting two degrees of freedom in topology management: the path setup latency and the network density. First, we propose a new technique called Sparse Topology and Energy Management (STEM), which aggressively puts nodes to sleep. It provides a method to wake up nodes only when they need to forward data, where latency is traded off for energy savings. Second, STEM integrates efficiently with existing approaches that leverage the fact that nearby nodes can be equivalent for traffic forwarding. In this case, an increased network density results in more energy savings. We analyze a hybrid scheme, which takes advantage of both setup latency and network density to increase the nodes' lifetime. Our results show improvements of nearly two orders of magnitude compared to sensor networks without topology management.

When designing wireless systems, one of the major challenges is tackling the time depending fading behavior of the channel. To achieve maximum throughput under a power constraint, techniques have been proposed which adapt the modulation on the fly, based on the instantaneous channel condition. However, when the design goal is minimizing the overall energy consumption under a throughput constraint, we have to tackle a more complex scheduling problem. The reason is that energy optimality might require deliberately decreasing the transmission rate at times, if we know that the rate loss can be compensated for in the future when channel conditions are more favorable. We present a solution to the problem of minimum energy scheduling on wireless links by exploiting an analogy with the adaptive bit loading problem in multicarrier systems. Instead of allocating bits across multiple channels with different quality, we formulate the problem as one of allocating bits at the different time instants. The analogy is however not straightforward because one does not know the future, and therefore cannot simply apply conventional bit loading to the time dimension. We devise a new technique that approximates adaptive loading in time, but only depends on the instantaneous channel condition. Our algorithm is simple to implement, and shows up to 5/spl times/ reduction in energy over existing approaches.

In wireless sensor networks, energy efficiency is crucial to achieving satisfactory network lifetime. To reduce the energy consumption significantly, a node should turn off its radio most of the time, except when it has to participate in data forwarding. We propose a new technique, called sparse topology and energy management (STEM), which efficiently wakes up nodes from a deep sleep state without the need for an ultra low-power radio. The designer can trade the energy efficiency of this sleep state for the latency associated with waking up the node. In addition, we integrate STEM with approaches that also leverage excess network density. We show that our hybrid wakeup scheme results in energy savings of over two orders of magnitude compared to sensor networks without topology management. Furthermore, the network designer is offered full flexibility in exploiting the energy-latency-density design space by selecting the appropriate parameter settings of our protocol.

Sensor networks consist of autonomous wireless sensor nodes that are networked together in an ad hoc fashion. The tiny nodes are equipped with substantial processing capabilities, enabling them to combine and compress their sensor data. The aim is to limit the amount of network traffic, and as such conserve the nodes' limited battery energy. However, due to the small packet payload, the MAC header is a significant, and energy-costly, overhead. To remedy this, we propose a novel scheme for a MAC address assignment. The two key features which make our approach unique are the exploitation of spatial address reuse and an encoded representation of the addresses in data packets. To assign the addresses, we develop a purely distributed algorithm that relies solely on local message exchanges. Other salient features of our approach are the ability to handle unidirectional links and the excellent scalability of both the assignment algorithm and address representation. In typical scenarios, the MAC overhead is reduced by a factor of three compared to existing approaches.

In systems that require low energy consumption, voltage scaling is an invaluable circuit technique. It also offers energy awareness, trading off energy and performance. In wireless handheld devices, the communication portion of the system is a major power hog. We introduce a new technique, called modulation scaling, which exhibits benefits similar to those of voltage scaling. It allows us to trade off energy against transmission delay and as such introduces the notion of energy awareness in communications. Throughout our discussion, we emphasize the analogy with voltage scaling. As an example application, we present an energy aware wireless packet scheduling system.

In wireless sensor networks, the vast majority of wide-scale traffic consists of only a few bytes, including all network and application layer IDs. Therefore, MAC addresses, which are vital in a shared medium, present major overhead, particularly because they are traditionally chosen network-wide unique. To tackle this overhead, we propose a dynamic MAC addressing scheme based on a distributed algorithm. The assigned addresses are reused spatially and represented by variable length codewords. Our scheme scales very well with the network size, rendering it well suited for sensor networks with thousands or millions of nodes

Turbo codes are the most recent breakthrough in coding theory. However, the decoder's implementation cost limits their incorporation in commercial systems. Although the decoding algorithm is highly data dominated, no true memory optimization study has been performed yet. We have extensively and systematically investigated different memory optimizations for the maximum a posteriori (MAP) class of decoding algorithms. It turns out that it is not possible to present one decoder structure as being optimal. In fact, there are several tradeoffs, which depend on the specific turbo code, the implementation target (hardware or software), and the selected cost function. We therefore end up with a parametric family of new optimized algorithms out of which the designer can choose. The impact of our optimizations is illustrated by a representative example, which shows a significant decrease in both decoding energy (factor 2.5) and delay (factor 1.7).

Turbo codes are the most recent breakthrough in coding theory.Although their decoding algorithm is highly data dominated, no systematic memory optimization study has been performed yet.We have applied the IMEC Data Transfer and Storage Exploration (DTSE) methodology to the MAP (Maximum A Posteriori) class of turbo decoding algorithms.We present an extensive overview of our optimizations and tradeoffs, which result in a parametric family of new optimized algorithms.The optimal choice of parameters depends on the specific turbo code and on the implementation target, which can be either hardware or software.

The quality of wireless links suffers from temporally and spatially varying channel degradations such as noise, interference, and multipath fading. A proper addressing of these impairments is crucial to sustaining quality of service (QoS) in wireless integrated service packet networks such as wireless ATM. Newer radios that allow parameters such as processing gain, symbol rate, and transmit power to be varied on a per packet basis offer interesting opportunities. This paper explores a highly adaptive approach to wireless link control where the various physical and link layer parameters are continually adapted in response to channel and traffic variations. Furthermore, we also explore adaptivity at the upper layer of the protocol stack, since applications typically can not cope with residual distortions in the packet flow. It is illustrated that lower and upper layer adaptation strategies are tightly intertwined, such that they should be managed jointly. Node and basestation architectures to efficiently support such adaptivity are also described.