Virtual reality (VR) systems are increasingly using physiology to improve human training. However, these systems do not account for the complex intra-individual variability in physiology and human performance across multiple timescales and psychophysiological demands. To fill this gap, we propose a theory of multilevel variability where tractable neurobiological mechanisms generate complex variability in performance over time and in response to heterogeneous sources. Based on this theory, we also present a study that examines changes in cardiovascular activity and performance during a stressful shooting task in VR. We examined physiology and performance at three important levels of analysis: task-to-task, block-to-block, session-to-session. Findings indicated joint patterns of physiology and performance that notably varied by the level of analysis. At the task level, higher task difficulty worsened performance but did not change cardiovascular activation. At the block level, there were nonlinear changes in performance and heart rate variability. At the session level, performance improved while blood pressure decreased and heart rate variability increased across days. Of all the physiological metrics, only heart rate variability was correlated with marksmanship performance. Findings are consistent with our multilevel theory and highlight the need for VR and other affective computing systems to assess physiology across multiple timescales.

Embedded systems are prone to leak information via side-channels associated with their physical internal activity, such as power consumption, timing, and faults. Leaked information can be analyzed to extract sensitive data and devices should be assessed for such vulnerabilities. Side-channel power-supply leakage from embedded devices can also provide information regarding instruction-level activity for control code executed on these devices. Methods proposed to disassemble instruction-level activity via side-channel leakage have not addressed issues related to pipelined multi-clock-cycle architectures, nor have proven robustness or reliability. The problem of detecting malicious code modifications while not obstructing the sequence of instructions being executed needs to be addressed. In this article, instruction sequences being executed on a general-purpose pipelined computing platform are identified and instructions that make up these sequences are classified based on hardware utilization. Individual instruction classification results using a fine-grained classifier is also presented. A dynamic programming algorithm was applied to detect the boundaries of instructions in a sequence with a 100 percent accuracy. A unique aspect of this technique is the use of multiple power supply pin measurements to increase precision and accuracy. To demonstrate the robustness of this technique, power leakage data from ten target FPGAs programmed with a prototype of the pipelined architecture was analyzed and classification accuracies averaging 99 percent were achieved with instructions labeled based on hardware utilization. Individual instruction classification accuracies above 90 percent were achieved using a fine-grained classifier. Classification accuracies were also verified when a target FPGA was subjected to different controlled temperatures. The classification accuracies on discrete (ASIC) pipelined-architecture microcontrollers was 97 percent.

Real-time electroencephalogram (EEG)-based state detection and prediction has many applications in Human-Computer Interactions, for example, adaptive training. Network analysis of brain activity derived from EEG data has been correlated to various cognitive states in the neuroscience literature making these network features an attractive measure to use for state detection. However, deriving these network measures in real-time is non-trivial and relatively unexplored in the literature. In this paper, we examine hardware designs and algorithmic performance of an existing non-linear measure of network activity, structural synchrony, as compared to a correlation-based linear measure of network activity, towards real-time human state classification. Our results indicate that the non-linear structural synchrony metric achieves a higher classification accuracy with similar computation speed. We demonstrate that the structure synchrony method can be accelerated in hardware using less resources while achieving similar latency and throughput. Collectively, our results suggest that a real-time implementation of non-linear, network connectivity measures on EEG data is viable and may facilitate on-line human state estimation.

Accessibility problems such as obstacles on sidewalks can make navigation dangerous for the visually impaired. Detecting these accessibility problems using embedded cameras is a plausible remedy. However, current computer vision algorithms for object detection rely on exhaustive search with high-dimensional features that present a heavy computational burden and incur a long latency, making them non-ideal for real-time object detection on embedded platforms. To address this problem, inspired by prior-based searching schemes from human vision, we accelerate the machine vision process by using scene-specific features to select candidate regions in the view for further processing. Our system, {\hav} achieves speedup by trading off the workload from on-line detection to off-line prior data collection and extraction. We demonstrate a complete, scalable PreSight prototype to accelerate general computer vision object detection algorithms with focus on detecting of sidewalk accessibility problems. Our prototype system automates the process of creating a geo- tagged database of object-specific priors using crowdsourcing and utilizes this prior knowledge to speedup object detection on embedded platforms. Evaluating under two benchmark object detection algorithms, we demonstrate that the detection latency can be reduced by around 8 times with the aid of PreSight.

Side-channel analysis techniques have been demonstrated for secret information extraction, vulnerability assessment and intrusion analysis. However, these techniques have not been applied to identify instructions running on a general purpose pipelined computing platform. In this work we identify instruction execution sequences on these platforms using side-channel power measurements. The technique uses a Principal Component Analysis (PCA) based model to generate multiple power-supply templates and a novel post-processing dynamic programming algorithm for optimal template matching. One unique aspect of this technique is that we take measurements on multiple power supply pins on the device, to increase the precision and accuracy. We apply our dynamic programming algorithm, to detect the sequence of execution clock cycles, on templates for single instructions that provides accuracy in the range of 69.2% to 87.5% for ten thousand observations, from individual power supply pins. We further augment this technique to generate multiple dictionaries for various length instructions and use data from multiple power supply pins to increases the accuracy in the range of 87% to 100%. Further, classification rates for instruction templates based on operand addressing mode and specific hardware used, range from 87% to 100% using as few as 10 principal components. Using our methodology, we can determine malicious insertions in a pre-determined code sequence with a 100% accuracy, as demonstrated in the results.

In recent times, automated business processes and web services have become ubiquitous in diverse application spaces. Efficient composition of web services in real time while providing necessary Quality of Service (QoS) guarantees is a computationally complex problem and several heuristic based approaches have been proposed to compose the services optimally. In this article, we present the design of a scalable QoS-aware service composition mechanism that balances the computational complexity of service composition with the QoS guarantees of the composed service and achieves scalability. Our design guarantees a single QoS parameter using an intelligent search and pruning mechanism in the composed service space. We also show that our methodology yields near optimal solutions on real benchmarks. We then enhance our proposed mechanism to guarantee multiple QoS parameters using aggregation techniques. Finally, we explore search time versus solution quality tradeoff using parameterized search algorithms that produce better-quality solutions at the cost of delay. We present experimental results to show the efficiency of our proposed mechanism.

Activity recognition has applications in a variety of human-in-the-loop settings such as smart home health monitoring, green building energy and occupancy management, intelligent transportation, and participatory sensing. While fine-grained activity recognition systems and approaches help enable a multitude of novel applications, discovering them with non-intrusive ambient sensor systems pose challenging design, as well as data processing, mining, and activity recognition issues. In this paper, we develop a low-cost heterogeneous Radar based Activity Monitoring (RAM) system for recognizing fine-grained activities. We exploit the feasibility of using an array of heterogeneous micro-doppler radars to recognize low-level activities. We prototype a short-range and a long-range radar system and evaluate the feasibility of using the system for fine-grained activity recognition. In our evaluation, using real data traces, we show that our system can detect fine-grained user activities with 92.84% accuracy.

In recent times, automated business processes and web services have become ubiquitous in diverse application spaces. Efficient composition of web services in real time while providing necessary QoS guarantees is a computationally complex problem and several heuristic based approaches have been proposed to compose services optimally. In this paper, we present the design of a scalable but approximate QoS-aware service composition mechanism which balances the computational complexity of service composition with the QoS guarantees of the composed service and achieves scalability for dynamic service composition. We present experimental results to show the efficiency of our proposed mechanism.

Evaluating and debugging solar panel-driven systems is a cumbersome process. The system must be deployed in the wild, software and hardware bugs identified remotely, and the development cycle must be repeated to build a robust system. Emulation platforms for solar panels offer a plausible remedy, however, existing systems have narrow operating range and are not portable. To address these shortcomings, we design, implement, and evaluate the SunaPlayer, a solar panel emulation platform that supports a wide operating current range (430 micro-amps to 1.89 amps) and voltage range (0.02 V to 9.8 V), and can be powered using batteries. SunaPlayer uses a high gain analog device, a PNP darlington transistor, a multi-scale driving and measurement circuit, and a novel state machine based proportional-integral-differential controller to build an accurate non-linear model of the solar panel. We have implemented a fully functional SunaPlayer prototype, and demonstrate that it can emulate a wide range of solar panels with high accuracy, low latency, high sensitivity, and low power consumption. The design methodology for the SunaPlayer can be used to build systems that model hard to emulate non-linear devices.

Tongue gestures are a key modality for augmentative and alternative communication in patients suffering from speech impairments and full-body paralysis. Systems for recognizing tongue gestures, however, are highly intrusive. They either rely on magnetic sensors built into dentures or artificial teeth deployed inside a patient's mouth or require contact with the skin using electromyography (EMG) sensors. Deploying sensors inside a patient's mouth can be uncomfortable for long-term use and contact-based sensors like EMG electrodes can cause skin abrasion. To address this problem, we present a novel contact-less sensor, called Tongue-n-Cheek, that captures tongue gestures using an array of micro-radars. The array of micro-radars act as proximity sensors and capture muscle movements when the patient performs the tongue gesture. Tongue-n-Cheek converts these movements into gestures using a novel signal processing algorithm. We demonstrate the efficacy of Tongue-n-Cheek and show that our system can reliably infer gestures with 95% accuracy and low latency.

Home automation and environmental control is a key ingredient of smart homes. While systems for home automation and control exist, there are few systems that interact with individuals suffering from paralysis, paresis, weakness and limited range of motion that are common sequels resulting from severe injuries such as stroke, brain injury, spinal cord injury and many chronic (guillian barre syndrome) and degenerative (amyotrophic lateral sclerosis) conditions. To address this problem, we present the design, implementation, and evaluation of Inviz, a low-cost gesture recognition system for paralysis patients that uses flexible textile-based capacitive sensor arrays for movement detection. The design of Inviz presents two novel research contributions. First, the system uses flexible textile-based capacitive arrays as proximity sensors that are minimally obtrusive and can be built into clothing for gesture and movement detection in patients with limited body motion. The proximity sensing obviates the need for touch-based gesture recognition that can cause skin abrasion in paralysis patients, and the array of capacitive sensors help provide better spatial resolution and noise cancellation. Second, Inviz uses a low-power hierarchical signal processing algorithm that breaks down computation into multiple low and high power tiers. The tiered approach provides maximal vigilance at minimal energy consumption. We have designed and implemented a fully functional prototype of Inviz and we evaluate it in the context of an end-to-end home automation system and show that it achieves high accuracy while maintaining low latency and low energy consumption.

Variations in the power-distribution network are exacerbated because of scaled supply voltages and smaller noise margins in sub-nanometer designs, which adversely affect performance and yield. Power-Supply noise incurred by excessive simultaneous switching of multiple paths negatively impacts the timing of a circuit. Supply noise is a major issue especially during transition and delay test where test vectors cause increased switching as compared to functional operation resulting in increase in path delays. Test rejects due to excessive noise-induced failures during delay and transition testing negatively impacts yield. Hence there is a need to accurately characterize the resistive and inductive voltage drop caused by excessive switching. To our knowledge, inductive drop has been excluded to simplify noise analysis. In our previous work, we have presented a convolution-based dynamic method (herein referred to as PSANDE) to estimate both IR and Ldi/dt drop on small combinational and sequential circuits. In this paper we show that the effectiveness of the design partitioning technique makes the framework feasible for a larger design. Our dynamic approach involves selectively simulating only extracted switching logic which makes the run-time tractable as compared to prohibitive full-chip SPICE simulations. We also present data to show that PSANDE can accurately predict the power-supply noise due to clock tree switching. Data presented in this paper for power supply noise is based on a large ITC'99 sequential benchmark b17 circuit. with a maximum error of 8.2% in comparison to full-chip SPICE results.

The aftermath of a natural disaster is characterized by lack of a reliable medium for dissemination of information to survivors. The state-of-the-art emergency response systems rely on satellite radio-enabled devices, but survivors, unlike first responders, do not have access to such devices. To mitigate this problem, we present perpetuu, a solar-powered portable GIS microserver. The microserver node can be deployed in a disaster scene and can serve maps to survivors viewable on browsers of off-the-shelf mobile systems. The perpetuu nodes can form a wireless mesh to cover a large geographic region. A key innovation in the design of the perpetuu node is a tiered software and hardware architecture—the system combines a low-power micro-controller with a high-power micro-processor to provide a large spectrum of power states. perpetuu stays in its lowest power state most of the time, and it can in-vitro detect survivors using Wi-Fi sensing, and consequently wake up the higher-power tier to disseminate high-resolution maps on standard web browsers that provide directions to safe locations. The tiered design leverages hardware-assisted energy measurements and a wakeup controller to balance energy harvested from solar panels with energy consumed by the system. We evaluate perpetuu using measurements from our prototype and trace-based simulations, and show that it can function near-perpetually while serving maps to a large number of survivors.

Managing energy in the home is key to creating a sustainable future for our society. More tools are increasingly available to measure home energy usage, however these tools provide little insight into questions such as why an appliance consumes more energy than normal or what kinds of behavioral changes might be most likely to reduce energy usage in the home. To answer these questions, a deeper understanding of the causal factors that influence energy usage is necessary. In this work, we conduct a broad study of factors that influence energy consumption of individual devices in the home. Our first contribution is collection of a context-rich data set from six homes across the United States. The second contribution of this work is a set of insights into key factors influencing energy usage derived by the novel application of a rule mining algorithm to identify significant associations between energy usage and four key features: hour of the day, day of the week, use of other appliances in the home, and user-supplied annotations of activities such as working or cooking. Our analysis confirms our hypothesis that, though most devices show a regular pattern of daily or weekly use, this is not true for all devices. Associations that relate use of two different devices in the same home are often stronger, and are observed for nearly 25% of device uses. Overall, we observe that the associations derived from the first five weeks of data in our data set are sufficient to explain nearly 70% of the device uses in the subsequent five weeks of data, and over 90% of the associations identified during the first five weeks recur in the latter portion of the data set. The associations identified by our approach may be used to to aid in end-user applications that heighten awareness and encourage energy savings, improve energy disaggregation algorithms, or even detect anomalous uses that may signal problems in aging-in-place homes.

WiFi-based indoor localization solutions are actively in commercial use today. WiFi radio maps are typically created in an offline process, and location estimation is performed in realtime (online) using the maps. Both the radio map and the signal strengths provided as input during the online phase are affected by several dynamic factors resident in the environment. We call these “causality factors”. Hence, the online location accuracy is far from the quality achieved in laboratory tests with training data, since the causality factors have varied in between. This impacts the quality expected by heterogeneous applications in real deployments. To address this issue, we investigate a novel online dynamic calibration methodology called KARMA. KARMA utilizes a one-time fingerprint of the space and systematically applies a set of causality calibration functions in real-time to compensate for the change in the factors, at test time. As a result, location providers can now significantly reduce the costly re-learning of the models for different factors, and improve real-time location prediction accuracy. Experimental studies demonstrate that KARMA's strategy, while keeping the fingerprinting task contained, can improve localization quality by a factor of 2x, compared to a typical one-state fingerprinting approach employed in many commercial deployments today. It also compares very favorably with an exhaustive all-state fingerprinting approach.

There is tremendous interest in exploiting smartphones as a “community of sensors”. It is envisioned that this community-driven smartphone sensor network has unprecedented potential to sense heterogeneous phenomena ranging from sound pollution to urban social dynamics. However, designing smartphone-resident middleware for opportunistic and objectiveoriented sensing of these phenomena is an open challenge. In this paper, we propose USense, a novel utility-driven smartphone middleware for executing community-driven sensing tasks. USense is different from other mobile phone sensing frameworks in the following ways: it is (i) Application aware, i.e., it adapts its operation based on demands of the application (ii) User aware, i.e., it incorporates preferences, policies as well as behavioral history of the user carrying the phone, and (iii) Situation aware, i.e. it considers resource dynamics on the phone at any given point. We argue that these three aspects are essentially decoupled in nature and combining them effectively is the key towards designing a re-usable and scalable middleware. Based on an extensible model for `Sensing Moments', USense first allows application developers to easily create sensing tasks. Secondly, we propose a unified device middleware to simultaneously execute the sensing tasks at the right moments across multiple applications. We have implemented USense on the Android platform, and demonstrate its effectiveness through real-life data traces.

Micro-harvesting from sources such as indoor light can enable a plethora of self-sustainable sensing systems for mobile healthcare applications. However, given the minuscule and variable amount of energy harvested from these renewable sources, practical sensing systems powered by micro-harvesting is today limited to light driven motion sensing. In this paper, we design, implement, and evaluate an indoor light driven wearable glove device that uses flex sensors and accelerometers for hand gesture recognition. Through the design, we make a two-fold contribution to micro-harvester driven mobile sensing systems. First, motivated by extensive profiling of panels for indoor light scavenging, we design a harvester that multiplexes panels of different compositions to maximally scavenge energy as a function of lighting conditions. Second, we present a tiered architecture composed of application specific hardware logic, wakeup controllers, a general purpose micro-controller, and a bluetooth device that can adapt to variable and ultra-low energy constraints, and at the same time provide high responsiveness and compute capability for gesture recognition. We evaluate the glove device in the context of a hand gesture driven home automation system for the elderly.

We present the first in-depth analysis of the performance of attempting concurrent AP connections from highly mobile clients. Previous solutions for concurrent Wi-Fi are limited to stationary wireless clients and do not take into account a myriad of mobile factors. Through an analytical model, optimization framework, and numerous outdoor experiments, we show that connection duration, AP response times, channel scheduling, available and offered bandwidth, node speed, and dhcp joins all affect performance. Building on these results, we design, implement, and evaluate a system, Spider, that establishes and maintains concurrent connections to 802.11 APs in a mobile environment. The system uses multi-AP selection, channel-based scheduling, and opportunistic scanning to maximize throughput while mitigating the overhead of association and dhcp. While Spider can manage multiple channels, we empirically demonstrate that it achieves maximum throughput when using multiple APs on a single channel. Our evaluation shows that Spider provides a 400% improvement in throughput and 54% improvement in connectivity over stock Wi-Fi implementations.

Tracing topology of services in real-time is a challenging problem in VoIP networks. The primary reason is that the VoIP networks operate with diverse interconnecting protocols, technologies, and multitude of access technologies. In addition, alliances, changing rules and regulations, mergers, new technologies and services impact architecture of VoIP networks consistently. All of these together results in heterogeneous, complex and dynamic VoIP networks where the network topology changes very frequently. Secondly, in case of voice, each call can follow a different path over the core IP infrastructure in the network. Even the signaling and media path of the same voice call can follow different paths to destination (callee's device). Stitching and providing integrated end to end cross layer view of the path in real-time of an ongoing service flow e.g. voice call under such unpredictable network conditions is difficult.

We investigate attempting concurrent connections to multiple Wi-Fi access points (APs) from highly mobile clients. Previous multi-AP solutions are limited to stationary wireless clients and do not take into account a myriad of mobile factors. We show that connection duration, AP response times, channel scheduling, available and offered bandwidth, node speed, and dhcp joins all affect performance. Building on these results, we present a system, Spider, that establishes and maintains concurrent connections to 802.11 APs in a mobile environment. While Spider can manage multiple channels, we demonstrate that it achieves maximum throughput when using multiple APs on a single channel.

In this paper, we present a novel discrete cosine transform (DCT) architecture that allows aggressive voltage scaling for low-power dissipation, even under process parameter variations with minimal overhead as opposed to existing techniques. Under a scaled supply voltage and/or variations in process parameters, any possible delay errors appear only from the long paths that are designed to be less contributive to output quality. The proposed architecture allows a graceful degradation in the peak SNR (PSNR) under aggressive voltage scaling as well as extreme process variations. Results show that even under large process variations (±3σ around mean threshold voltage) and aggressive supply voltage scaling (at 0.88 V, while the nominal voltage is 1.2 V for a 90-nm technology), there is a gradual degradation of image quality with considerable power savings (71% at PSNR of 23.4 dB) for the proposed architecture, when compared to existing implementations in a 90-nm process technology.

Recent technological advances in mobile-based access to social networking platforms and facilities to update information in real{time (e.g. in Facebook) have allowed an individual's online presence to be as ephemeral and dynamic in nature, as her very thoughts and interests. In this context, micro-blogging has been widely adopted by users as an effective means to capture and disseminate their thoughts and actions to a larger audience on a daily basis. Interestingly, daily chatters of a user obtained from her micro-blogs offer a unique information source to analyze and interpret her context in real-time - i.e. interests, intentions,and activities. In this paper, we gather data from the public timeline of Twitter spanning across ten worldwide cities over a period of four weeks. We use this dataset to (a) explore how users express interests in real-time through micro-blogs, and (b) understand how text mining techniques can be applied to interpret real-time context of a user based on her tweets. Initial findings reported herein suggest that social media sites like Twitter constitute a promising source for extracting user context that can be exploited by novel social networking applications.

With the growing popularity of social networking, traditional Internet Service Providers (ISPs) and telecom operators have both started exploring new opportunities to boost their revenue streams. The efforts have facilitated consumers to stay connected to their favorite social networks,be it from an ISP portal or a mobile device. The use of Web 2.0 technologies and converged communication tools has further led to a rise in both user-generated content as well as contextual information (i.e. rich presence) about users - including their current location, availability, interests and moods. In this evolving landscape, social networking players need to innovate for value-centric usage models that increase customer stickiness,along with business models to monetize the social media. To this end, we present R-U-In? - an activity-oriented social networking system for users to collaborate and participate in activities of mutual interest. Activities can be initiated and scheduled on-demand and be as ephemeral as the user interests themselves. R-U-In? leverages contextual modeling and reasoning techniques to enable ldquosocial searchrdquo based on real-time user interests and finds potential matches for the proposed activity. Further, it exploits next-generation presence and communication technologies to manage the entire activity lifecycle in real-time. Initial survey results, based on a prototype implementation of R-U-In?, attest to the promise of realtime activity-oriented social networking - both in terms of an effective collaboration tool for value-oriented social networking users and an enhanced end-user experience.

Presence, broadly defined as an event publish-notification infrastructure for converged applications, has emerged as a key mechanism for collecting and disseminating context attributes for next-generation services in both enterprise and provider domains. Current presence-based solutions and products lack in the ability to a) support flexible user-defined queries over dynamic presence data and b) derive composite presence from multiple provider domains. Accordingly, current uses of context are limited to individual domains/organizations and do not provide a programmable mechanism for rapid creation of context-aware services. This paper describes a presence virtualization architecture, where a Virtualized Presence Server receives customizable queries from multiple presence clients, retrieves the necessary data from the base presence servers, applies the required virtualization logic and notifies the presence clients. To support both query expressiveness and computational efficiency, virtualization queries are structured to separately identify both the XSLT-based transformation primitives and the presence sources over which the transformation occurs. For improved scalability, the proposed architecture offloads the XSLT-related processing to a high-performance XML processing engine. We describe our current implementation and present performance results that attest to the promise of this virtualization approach.

Power dissipation and robustness to process variation have conflicting design requirements. Scaling of voltage is associated with larger variations, while Vdd upscaling or transistor up-sizing for parametric-delay variation tolerance can be detrimental for power dissipation. However, for a class of signal-processing systems, effective tradeoff can be achieved between Vdd scaling, variation tolerance, and ldquooutput quality.rdquo In this paper, we develop a novel low-power variation-tolerant algorithm/architecture for color interpolation that allows a graceful degradation in the peak-signal-to-noise ratio (PSNR) under aggressive voltage scaling as well as extreme process variations. This feature is achieved by exploiting the fact that all computations used in interpolating the pixel values do not equally contribute to PSNR improvement. In the presence of Vdd scaling and process variations, the architecture ensures that only the ldquoless important computationsrdquo are affected by delay failures. We also propose a different sliding-window size than the conventional one to improve interpolation performance by a factor of two with negligible overhead. Simulation results show that, even at a scaled voltage of 77% of nominal value, our design provides reasonable image PSNR with 40% power savings.

In this paper, we present a novel finite-impulse response (FIR) filter synthesis technique that allows for aggressive voltage scaling by exploiting the fact that all filter coefficients are not equally important to obtain a ldquoreasonably accuraterdquo filter response. Our technique implements a level-constrained common-subexpression-elimination algorithm, where we can constrain the number of adder levels (ALs) required to compute each of the coefficient outputs. By specifying a tighter constraint (in terms of the number of adders in the critical path) on the important coefficients, we ensure that the later computational steps compute only the less important coefficient outputs. In case of delay variations due to voltage scaling and/or process variations, only the less important outputs are affected, resulting in graceful degradation of filter quality. The proposed architecture, therefore, lends itself to aggressive voltage scaling for low-power dissipation even under process parameter variations. Under extreme process variation and supply voltage scaling (0.8 V), filters implemented in the predictive technology model (PTM) 70 nm technology show an average power savings of 25%-30% with minor degradation in filter response in terms of normalized passband/stopband ripple (0.02 at a scaled voltage of 0.8 V compared with 0.005 at a nominal supply).

Networks composed of mobile nodes inherently suffer from intermittent connections and high delays. Performance can be improved by adding supporting infrastructure, including base stations, meshes, and relays, but the cost-performance trade-offs of different designs is poorly understood. To examine these trade-offs, we have deployed a large-scale vehicular network and three infrastructure enhancement alternatives. The results of these deployments demonstrate some of the advantages of each kind of infrastructure; however, these conclusions can be applied only to other networks of similar characteristics, including size, wireless technologies, and mobility patterns. Thus we complement our deployment with a demonstrably accurate analytical model of large-scale networks in the presence of infrastructure. Based on our deployment and analysis, we make several fundamental observations about infrastructure-enhanced mobile networks. First, if the average packet delivery delay in a vehicular deployment can be reduced by a factor of two by adding x base stations, the same reduction requires 2x mesh nodes or 5x relays. Given the high cost of deploying base stations, relays or mesh nodes can be a more cost-effective enhancement. Second, we observe that adding small amount of infrastructure is vastly superior to even a large number of mobile nodes capable of routing to one another, obviating the need for mobile-to-mobile disruption tolerant routing schemes.

We consider the problem of online monitoring of transaction instances in enterprise environments, based on footprints left by the instances in system logs and using a state-based reference model of the transaction. Unlike existing approaches, we do not rely on any platform-specific knowledge, neither do we assume footprints to carry correlating identifiers, as injected through instrumentation. We outline a solution for tracking transaction instances at individual and aggregate levels, present preliminary results on theoretical analysis of monitoring precision and conclude with directions of ongoing and future research.

This paper presents R-U-In? - a social networking application that leverages Web 2.0 and IMS-based Converged Networks technologies to create a rich next-generation service. R-U-In? allows a user to search (in real-time) and solicit participation of like-minded partners for an activity of mutual interest (e.g. a rock concert, a soccer game, or a movie). It is an example of a situational mashup application that exploits content and capabilities of a Telecom operator, blended with Web 2.0 technologies, to provide an enhanced, value-added service experience.

2D discrete cosine transform (DCT) is widely used as the core of digital image and video compression. In this paper, the authors present a novel DCT architecture that allows aggressive voltage scaling by exploiting the fact that not all intermediate computations are equally important in a DCT system to obtain "good" image quality with peak signal to noise ratio (PSNR) > 30 dB. This observation has led us to propose a DCT architecture where the signal paths that are less contributive to PSNR improvement are designed to be longer than the paths that are more contributive to PSNR improvement It should also be noted that robustness with respect to parameter variations and low power operation typically impose contradictory requirements in terms of architecture design. However, the proposed architecture lends itself to aggressive voltage scaling for low-power dissipation even under process parameter variations. Under a scaled supply voltage and/or variations in process parameters, any possible delay errors would only appear from the long paths that are less contributive towards PSNR improvement, providing large improvement in power dissipation with small PSNR degradation. Results show that even under large process variation and supply voltage scaling (0.8V), there is a gradual degradation of image quality with considerable power savings (62.8%) for the proposed architecture when compared to existing implementations in 70 nm process technology

Network topology design problems find application in several real life scenarios. However, most designs in the past either optimize for a single criterion like delay or assume simplistic traffic models like Poisson. Such assumptions make the solutions inapplicable in the practical world. In this paper, we formulate and solve a multiobjective network topology design problem for a realistic Internet traffic model which is assumed to be self similar. We optimize for the average packet delivery delay and network layout cost to construct realistic network topologies. We present a multiobjective evolutionary algorithm (MOEA) to obtain the diverse near-optimal network topologies. For fair comparison, we design a multiobjective deterministic heuristic based on branch exchange – we call the heuristic Pareto Branch Exchange (PBE). We empirically show that the MOEA used performs well for real networks of various sizes, and generated topologies are quite different with significantly larger delays for the self similar traffic model.

Power dissipation and tolerance to process variations pose conflicting design requirements. Scaling of voltage is associated with larger variations, while Vdd upscaling or transistor up-sizing for process tolerance can be detrimental for power dissipation. However, for certain signal processing systems such as those used in color image processing, we noted that effective trade-offs can be achieved between Vdd scaling, process tolerance and “output quality”. In this paper we demonstrate how these tradeoffs can be effectively utilized in the development of novel low-power variation tolerant architectures for color interpolation. The proposed architecture supports a graceful degradation in the PSNR (Peak Signal to Noise Ratio) under aggressive voltage scaling as well as extreme process variations in sub-70nm technologies. This is achieved by exploiting the fact that some computations are more important and contribute more to the PSNR improvement compared to the others. The computations are mapped to the hardware in such a way that only the less important computations are affected by Vdd-scaling and process variations. Simulation results show that even at a scaled voltage of 60% of nominal Vdd value, our design provides reasonable image PSNR with 69% power savings

Disruption Tolerant Networks rely on intermittent contacts between mobile nodes to deliver packets using store-carry-and-forward paradigm. The key to improving performance in DTNs is to engineer a greater number of transfer opportunities. We earlier proposed the use of throwbox nodes, which are stationary, battery powered nodes with storage and processing, to enhance the capacity of DTNs. However, the use of throwboxes without efficient power management is minimally effective. If the nodes are too liberal with their energy consumption, they will fail prematurely. However if they are too conservative, they may miss important transfer opportunities, hence increasing lifetime without improving performance. In this paper, we present a hardware and software architecture for energy efficient throwboxes in DTNs. We propose a hardware platform that uses a multi-tiered, multi-radio, scalable, solar powered platform. The throwbox employs an approximate heuristic for solving the NP-Hard problem of meeting an average power constraint while maximizing the number of bytes forwarded by it. We built and deployed prototype throwboxes in UMassDieselNet -a bus DTN testbed. Through extensive trace-driven simulations and prototype deployment we show that a single throwbox with a 270 cm2solar panel can run perpetually while improving packet delivery by 37% and reducing message delivery latency by at least 10% in the network.

In this paper, we present a novel FIR filter synthesis technique that allows aggressive voltage scaling by exploiting the fact that all filter coefficients are not equally important to obtain a "reasonably accurate" filter response. Our technique implements a Level Constrained Common Subexpression Elimination (LCCSE) algorithm, where we can constrain the number of adder levels required to compute each of the coefficient outputs. By specifying a tighter constraint (in terms of number of adders in the critical path) on the important coefficients, we ensure that the later computational steps compute only the less important coefficient outputs. In case of delay variations due to voltage scaling and/or process variations, only the less important outputs are affected, resulting in graceful degradation of filter quality. The proposed architecture, therefore, lends itself to aggressive voltage scaling for low-power dissipation even under process parameter variations. Under extreme process variation and supply voltage scaling (0.8V), filters implemented in BPTM 70 nm technology show an average power savings of 25-30% with minor degradation in filter response.

The ease of deployment of battery-powered and mobile systems is pushing the network edge far from powered infrastructures. A primary challenge in building untethered systems is offering powerful aggregation points and gateways between heterogeneous end-points---a role traditionally played by powered servers. Microservers are battery-powered in-network nodes that play a number of roles: processing data fromclients, aggregating data, providing responses to queries, and actingas a network gateway. Providing QoS guarantees for theseservices can be extremely energy intensive. Since increasedenergy consumption translates to a shorter lifetime, there is a need for a new way to provide these QoS guarantees at minimal energy consumption.

Cellular operators are facing stiff competitions from low cost technology providers such as VoIP service providers. Under this circumstance, a viable option for the operators is to integrate the low cost technologies into their network to provide converged services with richer features at a competitive price. In this paper, we infer from a game-theoretic study that an apparent price reduction in the service offerings from the operators ultimately proves to be beneficial in terms of profit earned by the operators due to greater demand of the converged service among the customers.

Clock power consumes a significant fraction of total power dissipation in high speed precharge/evaluate logic styles. In this paper, we present a novel low-cost design methodology for reducing clock power in the active mode for dynamic circuits with fine-grained clock gating. The proposed technique also improves switching power by preventing redundant computations. A logic synthesis approach for domino/skewed logic styles based on Shannon expansion is proposed, that dynamically identifies idle parts of logic and applies clock gating to them to reduce power in the active mode of operation. Results on a set of MCNC benchmark circuits in predictive 70nm process exhibit improvements of 15% to 64% in total power with minimal overhead in terms of delay and area compared to conventionally synthesized domino/skewed logic

With the increasing popularity of Voice over IP (VoIP) the threat of vamming or VoIP spam calls is looming large over the telecom industry. This threat arises out of the openness of the IP-based network such as the Internet, which enables anyone to join the network without proving reasonable trustworthiness. Although this problem could be solved satisfactorily in managed networks, it proves to be a nightmare in distributed, unmanaged peer-to-peer (p2p) networks. A possible approach to solve this problem, at least to a certain extent, is to associate some level of trust with each p2p entity. In this paper, we propose a trust enforcement framework consisting of computation and memory bound functions that associate trust implicitly to the p2p VoIP entities. Based on this trust, one can judiciously decide whether a call from a p2p VoIP entity could be accepted or not, thus implementing a preliminary screening mechanism for malicious callers.

The emergence of peer-to-peer (p2p) Internet telephony is now enabling telecom service architectures to adopt a p2p model. However, there are certain services that require explicit network support. In this paper, we propose and describe the design of a middleware based service architecture to enable the telecom services, including those requiring explicit network support, in p2p networks

Power consumption in datapath modules due to redundant switching is an important design concern for high-performance applications. Operand isolation schemes that reduce this redundant switching incur considerable overhead in terms of delay, power, and area. This paper presents novel operand isolation techniques based on supply gating that reduce overheads associated with isolating circuitry. The proposed schemes also target leakage minimization and additional operand isolation at the internal logic of datapath to further reduce power consumption. We integrate the proposed techniques and power/delay models to develop a synthesis flow for low-power datapath synthesis. Simulation results show that the proposed operand isolation techniques achieve at least 40% reduction in power consumption compared to original circuit with minimal area overhead (5%) and delay penalty (0.15%)

Due to exponential increase in subthreshold leakage with technology scaling and temperature increase, leakage power is becoming a major fraction of total power in the active mode. We present a novel low-cost design methodology with associated synthesis flow for reducing both switching and active leakage power using dynamic supply gating. A logic synthesis approach based on Shannon expansion is proposed that dynamically applies supply gating to idle parts of general logic circuits even when they are performing useful computation. Experimental results on a set of MCNC benchmark circuits in a predictive 70nm process exhibits improvements of 15% to 88% in total active power compared to the results obtained by a conventional optimization flow.

Operating frequency of a pipelined circuit is determined by the of the slowest pipeline stage. However, under statistical delay variation in sub-100 nm technology regime, the slowest stage is not readily identifiable and the estimation of the pipeline yield with respect to a target delay is a challenging problem. We have proposed analytical models to estimate yield for a pipelined design based on delay distributions of individual pipe stages. Using the proposed models, we have shown that change in logic depth and imbalance between the stage delays can improve the yield of a pipeline. A statistical methodology has been developed to optimally design a pipeline circuit for enhancing yield. Optimization results show that, proper imbalance among the stage delays in a pipeline improves design yield by 9% for the same area and performance (and area reduction by about 8.4% under a yield constraint) over a balanced design.

Power consumption in datapath modules due to redundant switching is an important design concern for high-performance applications. Operand isolation schemes are adopted to reduce redundant switching in datapaths. However, they incur considerable overhead in terms of delay, power, and area. This paper presents novel operand isolation techniques based on supply gating that reduce the overheads associated with isolating circuitry. The proposed schemes also target leakage minimization and application of operand isolation at the internal logic of datapath to further reduce power consumption. We integrate the proposed techniques and power/delay models to develop a complete flow for low-power datapath synthesis. Simulation results show that the proposed operand isolation techniques can achieve at least 40% reduction in power consumption compared to the original circuit with minimal area overhead (5%) and small delay penalty (0.15%).

Maintaining optimal consistency in a distributed system requires that nodes be always-on to synchronize information. Unfortunately, mobile devices such as laptops do not have adequate battery capacity for constant processing and communication. Even by powering off unnecessary components, such as the screen and disk, current laptops only have a lifetime of a few hours. Although PDAs and sensors are similarly limited in lifetime, a PDA's power requirement is an order-of-magnitude smaller than a laptop's, and a sensor's is an order-of-magnitude smaller than a PDA's. By combining these diverse platforms into a single integrated laptop, we can reduce the power cost of always-on operation. This paper presents the design, implementation, and evaluation of Turducken, a Hierarchical Power Management architecture for mobile systems. We focus on a particular instantiation of HPM, which provides high levels of consistency in a laptop by integrating two additional low power processors. We demonstrate that a Turducken system can provide battery lifetimes of up to ten times that of a standard laptop for always-on operation and three times for a system that periodically sleeps.

Application-level protocol abstraction is required to support seamless mobility in next generation heterogeneous wireless networks. Session initiation protocol (SIP) provides such an abstraction in providing mobility support in such networks. However, the handoff procedure with SIP suffers from undesirable delay and hence packet loss for some cases, which is detrimental to applications such as voice over IP (VoIP) or streaming video with stringent quality of service (QoS) requirements. In this paper, we propose a SIP based architecture that supports soft handoff for IP centric wireless networks alleviating the problem of packet loss. The proposed architecture ensures that there is no packet loss and the end-to-end delay jitter is kept under control, thus maintaining two important parameters dictating the QoS for streaming multimedia applications

Flow and admission control algorithms have been proposed for efficient resource utilization in wireless networks with scarce and randomly varying resource availability. Transport and lower layer control protocols administer generic control behavior, ignoring particular application requirements or characteristics. Network-aware applications, on the other hand, enable us to develop application specific control mechanisms. We propose a novel control theory based adaptive resource management framework for network-aware scalable multimedia applications in wireless networks. In particular, we design an application-aware pro-active middleware based on level crossing analysis of stochastic models assisting a set of control algorithms founded on automatic control theory, thus avoiding network congestion and ensuring optimal resource utilization and application level QoS. Simulation experiments show that the resource management framework is capable of achieving bandwidth utilization within 1.5% of the optimum (theoretical) value and a delay jitter of 1.3 msec/sup 2/ only, for real-time video streaming. Also, it ensures a low session blocking rate (less than 10% for high session arrival rate) and high percentage of sessions with good quality video.

Reducing the energy consumption of a real-time system has emerged as an important design concern. In this paper, we propose GAARP, an adaptive scalable architecture targeted toward algorithm-specific tasks for just-in-time performance using the right amount of power. The architecture consists of Globally Asynchronous and Locally Synchronous (GALS) building blocks, where the processing hardware is realized by a set of smaller slices of similar structure, each running synchronously with independent clocks. We demonstrate that, for different real-time commercial applications with algorithm-specific jobs like online transaction processing, digital filtering, Fourier transform, etc., the proposed architecture allows dynamic load-balancing and adaptive intertask voltage scaling based on the load in each of the processing units. Compared to a synchronous implementation of the same functionality, we show that the proposed hardware can achieve higher efficiency in terms of power and performance by exploiting the flexibility to balance the load and change the supply voltage. The architecture also lends itself to process tolerance since it can detect process-shifts for the individual processing units and determine the appropriate operating voltage/frequency for each unit. Simulation results for two representative applications show that, for a modest system configuration and random job distribution, we obtain up to 67 percent improvement in MOPS/W (millions of operations per second per watt) over a fully synchronous implementation.

This work deals with multisensor data fusion to obtain landcover classification. The role of feature-level fusion using the Dempster-Shafer rule and that of data-level fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and classification accuracy for the test data is evaluated. Two examples of data fusion of optical images and a synthetic aperture radar image are presented, each set having been acquired on different dates. Classification accuracies of the technique proposed are compared with those of some recent techniques in literature for the same image data.

Networks-on-chip (NoC) has been proposed as a solution for addressing the design challenges of future high-performance nanoscale architectures. Innovative system-level performance models are required for designing NoC based architectures. This paper presents a VHDL based cycle accurate register transfer level model for evaluating the latency, throughput, dynamic, and leakage power consumption of NoC based interconnection architectures. We implemented a parameterized register transfer level design of the NoC architecture elements. The design is parameterized on (i) size of packets, (ii) length and width of physical links, (iii) number, and depth of virtual channels, and (iv) switching technique. The paper discusses in detail the architecture and characterization of the various NoC components. The paper presents results obtained by application of the model towards design space exploration, and power versus performance trade-off analysis of 4/spl times/4 mesh based NoC architecture.

Networks-on-a-Chip (NoC) has been proposed as a solution for addressing the design challenges of future high-performance nanoscale architectures. Real-time applications require multiple service levels to account for traffic with low delay jitter. As technology scales toward deep submicron, on-chip interconnects are becoming more and more sensitive to noise sources such as power supply noise, crosstalk, radiation induced effects, that are likely to reduce the reliability of data. This paper addresses two important aspects of NoC architecture, QoS (Quality of Service) and Error Control and makes the following contributions: (i) It presents techniques for supporting guaranteed throughput and best-effort traffic quality levels in NoC router, (ii) It provides models for integrating error control schemes in the NoC router architecture, and (iii) It presents cycle accurate power and performance models of the two architecture enhancements for a 4x4 mesh based NoC architecture.

In recent years, wide bandwidth and low cost wireless LAN (WLAN) technology has emerged as a competitive choice for high speed wireless Internet access. To support the Internet mobility for the frequently moving mobile nodes in the local WLAN environment, Mobile IP has been found to be inefficient due to its high location update and packet delivery cost. This paper proposes an alternative architecture for network-layer mobility support using dynamic client configuration and transparent proxy mechanism. The attractive feature of our solution is that it requires no change in the legacy network infrastructure. We have implemented a prototype of the proposed architecture, and have taken experimental measurements to evaluate its performance. Experimental results show that the proposed architecture is capable of supporting IP mobility with the handoff delay less than a second. In addition, we have done an analytical study to test the scalability of the proposed architecture. Results show that performance of the architecture is not severely affected by the user mobility and a commonplace router is capable of supporting a considerable number of such users.

As the wireless Internet is burgeoning with a variety of multicast based applications (e.g. teleconferencing, live video and stock quote broadcasts), a cost analysis of multicasting in wireless networks is needed to help service providers in their cost-benefit tradeoff. This paper presents such an analysis in IP-based cellular networks, considering user mobility which directly affects the multicast group size and indirectly affects the cost of the multicast delivery tree. Our analytical results show that the expected multicast group size increases linearly with the number of network nodes. Moreover, the expected number of nodes added/deleted from the multicast tree increases with the number of network nodes, but saturates with time. For an increased number of receivers, the estimated cost of multicast delivery tree in a cellular network is found to be less than that in a static MBone network.

The increasing demand of real-time wireless communication has led to the development of quality-of-service (QoS) based routing. Most of the wireless multimedia applications require strict QoS guarantee (e.g. delay, bandwidth) during the communication between a single source and multiple destinations. This gives rise to the need for an efficient multicast QoS-routing strategy. Determination of such QoS-based optimal multicast routes boils down to a multi-objective optimization problem, which is computationally intractable in polynomial time. We propose a new multicast tree selection algorithm based on non-dominated sorting technique of the genetic algorithm to simultaneously optimize multiple QoS parameters. Simulation results demonstrate that the proposed algorithm is capable of discovering a set of QoS-based near optimal, non-dominated multicast routes within a few iterations. From this set the user can choose desirably favorite solution depending on specific QoS requirements. The scalability and the performance of the algorithm with increasing number of network nodes is also presented.

In this paper, we propose a novel multicast route discovery scheme for wireless networks, satisfying quality of service (QoS) constraints such as bandwidth requirement and end-to-end delay guarantees. The underlying approach is based on a genetic algorithm (GA). The problem of (optimal) multicast route discovery is NP-hard when the network state information is inaccurate, which is so common in the wireless domain. In general, this makes it difficult to determine multicast routes on demand, and hence the network resources are never used to their full potential. The proposed GA based method, however, is shown to discover multicast routes for large networks within few iterations, even with imprecise network information, and thus it ran be used for an on-demand basis. We observed an approximate linear relationship between the computation time and the number of nodes in a network. In addition we have studied the effects of choosing the fitness function of the GA.