A broad range of embedded networked sensing (ENS) applications have appeared for large-scale systems, introducing new requirements leading to new embedded architectures, associated algorithms, and supporting software systems. These new requirements include the need for diverse and complex sensor systems that present demands for energy and computational resources, as well as for broadband communication. To satisfy application demands while maintaining critical support for low-energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. In this article, we described the LEAP design approach, in which the system is able to adaptively select the most energy-efficient hardware components matching an application’s needs. The LEAP platform supports highly dynamic requirements in sensing fidelity, computational load, storage media, and network bandwidth. It focuses on episodic operation of each component and considers the energy dissipation for each platform task by integrating fine-grained energy-dissipation monitoring and sophisticated power-control scheduling for all subsystems, including sensors. In addition to the LEAP platform’s unique hardware capabilities, its software architecture has been designed to provide an easy way to use power management interface and a robust, fault-tolerant operating environment and to enable remote upgrade of all software components. LEAP platform capabilities are demonstrated by example implementations, such as a network protocol design and a light source event detection algorithm. Through the use of a distributed node testbed, we demonstrate that by exploiting high energy-efficiency components and enabling proper on-demand scheduling, the LEAP architecture may meet both sensing performance and energy dissipation objectives for a broad class of applications.

Bicycling is an affordable, environmentally friendly alternative transportation mode to motorized travel. A common task performed by bikers is to find good routes in an area, where the quality of a route is based on safety, efficiency, and enjoyment. Finding routes involves trial and error as well as exchanging information between members of a bike community. Biketastic is a platform that enriches this experimentation and route sharing process making it both easier and more effective. Using a mobile phone application and online map visualization, bikers are able to document and share routes, ride statistics, sensed information to infer route roughness and noisiness, and media that documents ride experience. Biketastic was designed to ensure the link between information gathering, visualization, and bicycling practices. In this paper, we present architecture and algorithms for route data inferences and visualization. We evaluate the system based on feedback from bicyclists provided during a two-week pilot.

The rapid adoption of mobile devices that are able to capture and transmit a wide variety of sensing modalities (media and location) has enabled a new data collection paradigm - participatory sensing. Participatory sensing initiatives organize individuals to gather sensed information using mobile devices through cooperative data collection. A major factor in the success of these data collection projects is sustained, high quality participation. However, since data capture requires a time and energy commitment from individuals, incentives are often introduced to motivate participants. In this work, we investigate the use of micro-payments as an incentive model. We define a set of metrics that can be used to evaluate the effectiveness of incentives and report on findings from a pilot study using various micro-payment schemes in a university campus sustainability initiative.

As mobile phones advance in functionality and capability, they are being used for more than just communication. Increasingly, these devices are being employed as instruments for introspection into habits and situations of individuals and communities. Many of the applications enabled by this new use of mobile phones rely on contextual information. The focus of this work is on one dimension of context, the transportation mode of an individual when outside. We create a convenient (no specific position and orientation setting) classification system that uses a mobile phone with a built-in GPS receiver and an accelerometer. The transportation modes identified include whether an individual is stationary, walking, running, biking, or in motorized transport. The overall classification system consists of a decision tree followed by a first-order discrete Hidden Markov Model and achieves an accuracy level of 93.6% when tested on a dataset obtained from sixteen individuals.

Cellular and Wi-Fi networks now form a global substrate that provides billions of mobile phone users with consistent, location-aware communication and multimedia data access. On this substrate is emerging a new class of mobile phone applications that use the phones location, image and acoustic sensors, and enable people to choose what to sense and when to share data about themselves and their surroundings. Peoples' natural movement through and among living, work, and ldquothirdrdquo spaces, provides spatial and temporal coverage for these modalities, the character of which is impossible to achieve through embedded instrumentation alone. This paper proposes a network service architecture for participatory sensing, describing challenges in (1) network coordination services enabling applications to efficiently select, incentivize and task mobile users based on measures of coverage, capabilities and interests; (2) attestation mechanisms to enable data consumers to assign trust to the data they access; and (3) participatory privacy regulation mechanisms used by data contributors to control what data they share.

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.

The rapid adoption of mobile phones by society over the last decade and the increasing ability to capture, classifying, and transmit a wide variety of data (image, audio, and location) have enabled a new sensing paradigm - where humans carrying mobile phones can act as sensor systems. Human-in-the-loop sensor systems raise many new challenges in areas of sensor data quality assessment, mobility and sampling coordination, and user interaction procedures.

This paper proposes a power-efficient PWM DC/DC converter design with a novel zero voltage switching (ZVS) control technique. The ZVS control is realized by an inner feedback loop which is implemented by simple digital circuitry between the input and output of the power transistors and achieves real-time zero voltage switching (ZVS) for various loading and device parameters with power efficiencies over 90.0%. In addition, an outer feedback loop is used to ensure that the output precisely tracks a reference voltage level. We have also built the relationship between the output voltage ripple and the speed of the voltage comparators which has shown to introduce new low-frequency signals to the loops and cause significant output voltage ripples. Experiment results show that the output ripple could be reduced by 4x by carefully handling the generation and propagation of these low frequency signals.

Modern advances in reconfigurable analog technologies are allowing field-programmable analog arrays (FPAAs) to dramatically grow in size, flexibility, and usefulness. Our goal in this paper is to develop the first placement algorithm for large-scale floating-gate-based FPAAs with a focus on the minimization of the parasitic effects on interconnects under various device-related constraints. Our FPAA clustering algorithm first groups analog components into a set of clusters so that the total number of routing switches used is minimized and all IO paths are balanced in terms of routing switches used. Our FPAA placement algorithm then maps each cluster to a computational analog block (CAB) of the target FPAA while focusing on routing switch usage and balance again. Experimental results demonstrate the effectiveness of our approach

Modern advances in reconfigurable analog technologies are allowing field-programmable analog arrays (FPAAs) to dramatically grow in size, flexibility, and usefulness. Our goal in this paper is to develop the first hierarchical placement algorithm for large-scale floating-gate based FPAAs with a focus on the minimization of the parasitic effects on interconnects under various device-related constraints. In our hierarchical approach, our FPAA clustering algorithm first groups analog components into a set of clusters so that the total number of routing switches used is minimized and all IO paths are balanced in terms of routing switches used. Our FPAA placement algorithm then maps each cluster to a computational analog block (CAB) of the target FPAA while focusing on routing switch usage again. Experimental results demonstrate the effectiveness of our approach.

Modern advances in reconfigurable analog technologies are allowing field-programmable analog arrays (FPAAs) to dramatically grow in size, flexibility, and usefulness. With these advances, analog circuits and systems can be programmable, reconfigurable, adaptive, implemented on standard CMOS to take advantage of scaled CMOS technology, and at a density comparable to digital memories. Our goal in this paper is to develop the first physical design automation toolset for floating-gate based FPAA with focus on minimization of parasitic effects on FPAA interconnect. We provide graph-based analog circuit and FPAA device modeling suitable for efficient mapping. Our FPAA clustering algorithm constructs Computational Analog Blocks (CAB) from analog circuit elements while improving the utilization of the device and reducing its impact on the total number of routing switches used. Experimental results demonstrate the effectiveness of our approach.