SARLink is a passive satellite backscatter communication system that uses existing spaceborne synthetic aperture radar (SAR) imaging satellites to provide connectivity in remote regions. It achieves orders of magnitude more range than traditional backscatter systems, enabling communication between a passive ground node and a satellite in low earth orbit. The system is composed of a cooperative ground target, a SAR satellite, and a data processing algorithm. A mechanically modulating reflector was designed to apply amplitude modulation to ambient SAR backscatter signals by changing its radar cross section. These communication bits are extracted from the raw SAR data using an algorithm that leverages subaperture processing to detect multiple bits from a target in a single image dataset. A theoretical analysis of this communication system using on-off keying is presented, including the expected signal model, throughput, and bit error rate. The results suggest a 5.5 ft by 5.5 ft modulating corner reflector could send 60 bits every satellite pass, enough to support low bandwidth sensor data and messages. Using Sentinel-1A, a SAR satellite at an altitude of 693 km, we deployed static and modulating reflectors to evaluate the system. The results, successfully detecting the changing state of a modulating ground target, demonstrate our algorithm's effectiveness for extracting bits, paving the way for ultra-long-range, low-power satellite backscatter communication.

This paper asks: “Can we detect whether a fragile product, made of porcelain or glass is damaged as it travels along the supply chain, without opening its packaging?” We ask this question in the context of the multi-billion dollar global supply chain industry of fragile products that experience large overheads due to product returns. This paper presents MiLTOn, a novel acoustic and mm-wave based solution for through-box non-invasive product integrity sensing that is sensitive to even minute sub-mm cracks in the object. MiLTOn is inspired by acoustic vibrometry used for instance to monitor cracks in railroads. Unlike traditional vibrometry, MiL-TOn is unique in its ability to sense products non-invasively using an external transducer and microphone, neither of which are in direct physical contact of the object within the box. MiLTOn pro-cesses measurements from the microphone to design a robust and environment-independent product signature that can be used to sense presence of product defects. Our extensive evaluation on a large number of fragile products of diverse materials demonstrates 97% accuracy in identifying product damage.

Longer range, in rural/urban IoT networks, allow a large geographical coverage with only a few base-stations, making their deployment and operation economical. In this paper we explore the 150-174 MHz spectrum for long range IoT networks comprising unlicensed MURS and licensed VHF narrowbands. Range in these bands is boosted by the lower RF frequencies as well as higher transmit powers allowed by the FCC. Through a 400 sq km wide area deployment study, we show that, these spectrum bands can provide > 20× the geographical coverage than that in the 900 MHz ISM band LoRa. Increased range translates to greater uplink IoT device traffic. The key contribution of this paper is a novel technique - Blind Distributed MU-MIMO, that allows capacity to scale with the number of antennas (base- stations) while not requiring any coordinated channel measurements between the devices and IoT base- stations. This requirement is crucial since in IoT networks power constrained IoT devices typically sleep and wake up to transmit short messages in response to unpredictable events without any coordination with the base-stations. We demonstrate the efficacy of Blind Distributed MU-MIMO through a real wide area deployment.

As the scale and density of data centers continue to grow, cost-effective data center management (DCM) is becoming a significant challenge for enterprises hosting large-scale online and cloud services. Machines need to be monitored, and the scale of operations mandates an automated management with high reliability and real-time performance. The limitations of today’s typical DCM network are many-fold. Primarily, it is a fixed wired network, and hence scaling it for a large number of servers increases its cost. In addition, with server densities increasing over recent years, this network also has to be cabled correctly and the management of this network parallels the complexity of managing a data network, since it needs to be networked with multiple switches and routers. In this article, we propose a wireless sensor network as a cost-effective networking solution for DCM while satisfying the reliability and latency performance requirements of DCM. We have developed CapNet, a real-time wireless sensor network for power capping, a time-critical DCM function for power management in a cluster of servers. CapNet employs an efficient event-driven protocol that triggers data collection only on the detection of a potential power capping event. We deploy and evaluate CapNet in a data center. Using server power traces, our experimental results on a cluster of 480 servers inside the data center show that CapNet can meet the real-time requirements of power capping. CapNet demonstrates the feasibility and efficacy of wireless sensor networks for time-critical DCM applications.

We consider the problem of continuous computer-vision based analysis of video streams from mobile cameras over extended periods. Given high computational demands, general visual processing must currently be offloaded to the cloud. To reduce mobile battery and bandwidth consumption, recent proposals offload only "interesting" video frames, discarding the rest. However, determining what to discard is itself typically a power-hungry computer vision calculation, very often well beyond what most mobile devices can afford on a continuous basis. We present the Glimpse system, a re-design of the conventional mobile video processing pipeline to support such "early discard" flexibly, efficiently and accurately. Glimpse is a novel architecture that gates wearable vision using low-power vision modalities. Our proposed architecture adds novel sensing, processing, algorithmic and programming-system components to the camera pipeline to this end. We present a complete implementation and evaluation of our design. In common settings, Glimpse reduces mobile power and data usage by more than one order of magnitude relative to earlier designs, and moves continuous vision on lightweight wearables to the realm of the practical.

Location is a fundamental service for mobile computing. Typical GPS receivers, although widely available for navigation purposes, may consume too much energy to be useful for many applications. Observing that in many sensing scenarios, the location information can be post-processed when the data is uploaded to a server, we design a cloud-offloaded GPS (CO-GPS) solution that allows a sensing device to aggressively duty-cycle its GPS receiver and log just enough raw GPS signal for post-processing. Leveraging publicly available information such as GNSS satellite ephemeris and an Earth elevation database, a cloud service can derive good quality GPS locations from a few milliseconds of raw data. Using our design of a portable sensing device platform called CLEON, we evaluate the accuracy and efficiency of the solution. Compared to more than 30 seconds of heavy signal processing on standalone GPS receivers, we can achieve three orders of magnitude lower energy consumption per location tagging.

Different battery chemistries perform better on different axes, such as energy density, cost, peak power, recharge time, longevity, and efficiency. Mobile system designers are constrained by existing technology, and are forced to select a single chemistry that best meets their diverse needs, thereby compromising other desirable features. In this paper, we present a new hardware-software system, called Software Defined Battery (SDB), which allows system designers to integrate batteries of different chemistries. SDB exposes APIs to the operating system which control the amount of charge flowing in and out of each battery, enabling it to dynamically trade one battery property for another depending on Application And/Or User Needs. Using microbenchmarks from our prototype SDB implementation, and through detailed simulations, we demonstrate that it is possible to combine batteries which individually excel along different axes to deliver an enhanced collective performance when compared to traditional battery packs.

This paper presents a remote gesture input solution for interacting indirectly with user interfaces on mobile and wearable devices. The proposed solution uses a wearable ring platform worn on users index finger. The ring detects and interprets various gestures performed on any available surface, and wirelessly transmits the gestures to the remote device. The ring opportunistically harvests energy from an NFC-enabled phone for perpetual operation without explicit charging. We use a finger-tendon pressure-based solution to detect touch, and a light-weight audio based solution for detecting finger motion on a surface. The two level energy efficient classification algorithms identify 23 unique gestures that include tapping, swipes, scrolling, and strokes for hand written text entry. The classification algorithms have an average accuracy of 73% with no explicit user training. Our implementation supports 10 hours of interactions on a surface at 2 Hz gesture frequency. The prototype was built with off-the-shelf components has a size similar to a large ring.

Due to poor signal strength, multipath effects, and limited on-device computation power, common GPS receivers do not work indoors. This work addresses these challenges by using a steerable, high-gain directional antenna as the front-end of a GPS receiver along with a robust signal processing step and a novel location estimation technique to achieve direct GPS-based indoor localization. By leveraging the computing power of the cloud, we accommodate longer signals for acquisition, and remove the requirement of decoding timestamps or ephemeris data from GPS signals. We have tested our system in 31 randomly chosen spots inside five single-story, indoor environments such as stores, warehouses and shopping centers. Our experiments show that the system is capable of obtaining location fixes from 20 of these spots with a median error of less than 10 m, where all normal GPS receivers fail.

Mobile wallets promise a future where users do not need to carry physical payment cards. However, the slow adoption of contactless point of sales (POS) terminals by merchants limits the potential of Near-Field Communication (NFC) based payment devices. In this paper, we present Wild Card, a secure and backward compatible solution for making mobile payments at conventional magnetic stripe based POS terminals. Our solution resembles a traditional credit card in its physical dimensions and stays in the phone case. It can be programmatically set by an NFC-enabled mobile phone to any card number that the user owns. The key technologies that enable Wild Card are a fully programmable magnetic stripe, an energy harvesting system that allows the card to be charged and programmed by the phone through NFC, and a security mechanism that makes card information resilient to attacks on mobile devices. With a prototype, we evaluate the feasibility of Wild Card in terms of functionality and energy budget.

Data center management (DCM) is increasingly becoming a significant challenge for enterprises hosting large scale online and cloud services. Machines need to be monitored, and the scale of operations mandates an automated management with high reliability and real-time performance. Existing wired networking solutions for DCM come with high cost. In this paper, we propose a wireless sensor network as a cost-effective networking solution for DCM while satisfying the reliability and latency performance requirements of DCM. We have developed Cap Net, a real-time wireless sensor network for power capping, a time-critical DCM function for power management in a cluster of servers. Cap Net employs an efficient event-driven protocol that triggers data collection only upon the detection of a potential power capping event. We deploy and evaluate Cap Net in a data center. Using server power traces, our experimental results on a cluster of 480 servers inside the data center show that Cap Net can meet the real-time requirements of power capping. Cap Net demonstrates the feasibility and efficacy of wireless sensor networks for time-critical DCM applications.

Network traffic prioritization is gaining attention in the WSN community, as more and more features are being integrated into sensor networks. Real-world deployment experience suggests that WSN brings new challenges to existing problems, such as resource constraints, low data-rate radios, and diverse application scenarios. We present the RushNet framework that prioritizes two common traffic patterns in multi-hop sensor networks: low-priority (LP) traffic that is large-volume but delay-tolerant, and high-priority (HP) traffic that is sporadic but latency-sensitive. RushNet achieves schedule-free and coordination-free delivery differentiations with the following features. First, RushNet works with most data collection protocols to deliver LP traffic. Second, RushNet leverages transmission power difference and radio capture effect to implement on-demand HP packet delivery with low overhead. Third, RushNet proposes a retrodiction technique to help nodes minimize the overhead of recovering LP packet loss due to concurrent HP traffic. We evaluate RushNet performance with micro-benchmarks and a crowdsourced office comfort monitoring deployment. The deployment results suggest RushNet can achieve a throughput close to network capacity, and deliver 98% of the HP packets with a latency of less than four seconds.

Near Field Communication (NFC) on mobile phones presents new opportunities and threats. While NFC is radically changing how we pay for merchandise, it opens a pandora's box of ways in which it may be misused by unscrupulous individuals. This could include malicious NFC tags that seek to compromise a mobile phone, malicious readers that try to generate fake mobile payment transactions or steal valuable financial information, and others. In this work, we look at how to protect mobile phones from these threats while not being vulnerable to them. We design a small form-factor "patch", EnGarde, that can be stuck on the back of a phone to provide the capability to jam malicious interactions. EnGarde is entirely passive and harvests power through the same NFC source that it guards, which makes our hardware design minimalist, and facilitates eventual integration with a phone. We tackle key technical challenges in this design including operating across a range of NFC protocols, jamming at extremely low power, harvesting sufficient power for perpetual operation while having minimal impact on the phone's battery, designing an intelligent jammer that blocks only when specific blacklisted behavior is detected, and importantly, the ability to do all this without compromising user experience when the phone interacts with a legitimate external NFC device.

Near Field Communication (NFC) on mobile phones presents new opportunities and threats. While NFC is radically changing how we pay for merchandise, it opens a pandora's box of ways in which it may be misused by unscrupulous individuals. This could include malicious NFC tags that seek to compromise a mobile phone, malicious readers that try to generate fake mobile payment transactions or steal valuable financial information, and others. In this demo, we show the capabilities of our hardware solution, EnGarde, that protects a mobile phone from these vulnerabilities.

A major hurdle to frequently performing mobile computer vision tasks is the high power consumption of image sensing. In this work, we report the first publicly known experimental and analytical characterization of CMOS image sensors. We find that modern image sensors are not energy-proportional: energy per pixel is in fact inversely proportional to frame rate and resolution of image capture, and thus image sensor systems fail to provide an important principle of energy-aware system design: trading quality for energy efficiency. We reveal two energy-proportional mechanisms, supported by current image sensors but unused by mobile systems: (i) using an optimal clock frequency reduces the power up to 50% or 30% for low-quality single frame (photo) and sequential frame (video) capturing, respectively; (ii) by entering low-power standby mode between frames, an image sensor achieves almost constant energy per pixel for video capture at low frame rates, resulting in an additional 40% power reduction. We also propose architectural modifications to the image sensor that would further improve operational efficiency. Finally, we use computer vision benchmarks to show the performance and efficiency tradeoffs that can be achieved with existing image sensors. For image registration, a key primitive for image mosaicking and depth estimation, we can achieve a 96% success rate at 3 FPS and 0.1 MP resolution. At these quality metrics, an optimal clock frequency reduces image sensor power consumption by 36% and aggressive standby mode reduces power consumption by 95%.

A hurdle to frequently performing mobile computer vision tasks is the high energy cost of image sensing. In particular, modern image sensors are not energy proportional; for low resolution and low frame rate capture, the image sensor consumes almost the same amount of energy as it does at high resolutions and high frame rates. We reveal two system-level energy proportional mechanisms: (i) using an optimal pixel clock frequency; (ii) entering low power standby mode between frames. These techniques can be implemented by the image sensor driver with minimal hardware adjustment. Further improvements can be made by designing sensors with heterogeneous hardware architectures. With energy proportionality, computer vision frameworks can be optimized for power consumption, continuously requesting low resolution frames with low energy while only occasionally using high energy to request high resolution frames. This will in turn enable low power continuous mobile vision applications.

Due to poor signal-strength, multi-path effects, and a lack of adequate visible satellite vehicles (SV), GPS receivers do not work indoors. This work addresses these challenges by using a mechanically steerable, high-gain directional antenna as the front-end of the GPS receiver along with a robust signal processing technique to acquire satellites in indoor environments. Our experiment on a local warehouse shows that, the system is capable of acquiring 5 or more SVs (which is a requirement for instant GPS technique [4]), whereas a Garmin device barely sees 3, and the system is capable of estimating indoor locations with 3--18 m errors when compared to the ground truth.

The major challenge for accurate fingerprint-based indoor localization is the design of robust and discriminative wireless signatures. Even though WiFi received signal strength indicator (RSSI) signatures are widely available indoors, they vary significantly over time and are susceptible to human presence, multipath, and fading due to the high operating frequency. To overcome these limitations, we propose to use FM broadcast radio signals for robust indoor fingerprinting. Because of the lower frequency, FM signals are less susceptible to human presence, multipath, and fading, they exhibit exceptional indoor penetration, and according to our experimental study they vary less over time when compared to WiFi signals. In this paper, we demonstrate through a detailed experimental study in three different buildings across the US, that FM radio signal RSSI values can be used to achieve room-level indoor localization with similar or better accuracy to the one achieved by WiFi signals. Furthermore, we propose to use additional signal quality indicators at the physical layer (i.e., SNR, multipath, etc.) to augment the wireless signature, and show that localization accuracy can be further improved by more than 5 percent. More importantly, we experimentally demonstrate that the localization errors of FM and WiFi signals are independent. When FM and WiFi signals are combined to generate wireless fingerprints, the localization accuracy increases as much as 83 percent (when accounting for wireless signal temporal variations) compared to when WiFi RSSI only is used as a signature.

The availability of multiple sensors on mobile devices offers a significant new capability to enable rich user and context aware applications. Many of these applications run in the background to continuously sense user context. However, running these applications on mobile devices can impose a significant stress on the battery life, and the use of supplementary low-power processors has been proposed on mobile devices for continuous background activities. In this paper, we experimentally and analytically investigate the design considerations that arise in the efficient use of the low power processor and provide a thorough understanding of the problem space. We answer fundamental questions such as which segments of the application are most efficient to be hosted on the low power processor, and how to select an appropriate low power processor. We discuss our measurements, analysis, and results using multiple low power processors and existing phone platforms.

The maturity and availability of network protocols have enabled wireless sensor networks (WSN) designers to build heterogeneous applications by composing different protocols. A common heterogeneous application combines data collection and dissemination for environmental monitoring with node retasking. While these co-located protocols on the same node have different goals, many of them share requirements and characteristics. Examples of commonalities include the use of bi-directional traffic for reliable transmissions and tree for packet routing. This work explores how the MAC layer can reduce the network transmission overhead of heterogeneous applications by taking advantage of protocol commonalities to aggregate outgoing packets. In other words, this aggregation creates a train of packets destined to the same receiver. Finally, we discuss a strawman implementation of packet train and how our data center monitoring deployment leverages it.

The major challenge for accurate fingerprint-based indoor localization is the design of robust and discriminative wireless signatures. Even though WiFi RSSI signatures are widely available indoors, they vary significantly over time and are susceptible to human presence, multipath, and fading due to the high operating frequency. To overcome these limitations, we propose to use FM broadcast radio signals for robust indoor fingerprinting. Because of the lower frequency, FM signals are less susceptible to human presence, multipath and fading, they exhibit exceptional indoor penetration, and according to our experimental study they vary less over time when compared to WiFi signals. In this work, we demonstrate through a detailed experimental study in 3 different buildings across the US, that FM radio signal RSSI values can be used to achieve room-level indoor localization with similar or better accuracy to the one achieved by WiFi signals. Furthermore, we propose to use additional signal quality indicators at the physical layer (i.e., SNR, multipath etc.) to augment the wireless signature, and show that localization accuracy can be further improved by more than 5%. More importantly, we experimentally demonstrate that the localization errors of FM andWiFi signals are independent. When FM and WiFi signals are combined to generate wireless fingerprints, the localization accuracy increases as much as 83% (when accounting for wireless signal temporal variations) compared to when WiFi RSSI only is used as a signature.

Near Field Communications (NFC) is an emerging technology that conveniently establishes radio communication by bringing two entities in close proximity of one another. Many use cases for devices equipped with this technology have been proposed ranging from payment systems to convenient data exchange.

We demonstrate the CLEO mobile sensing platform, including ultra-portable, low-power sensors, phone-based data upload software, and a location resolution and data management web service in the cloud. The sensing platform leverages cloud-offloaded GPS (CO-GPS) for location sensing. A sensor communicates with mobile devices using an audio-port-based connector. The back-end cloud service supports OData protocol to make data accessible to other systems, such as World-Wide Telescope (WWT).

Location is a fundamental service for mobile computing. Typical GPS receivers, although widely available, consume too much energy to be useful for many applications. Observing that in many sensing scenarios, the location information can be post-processed when the data is uploaded to a server, we design a Cloud-Offloaded GPS (CO-GPS) solution that allows a sensing device to aggressively duty-cycle its GPS receiver and log just enough raw GPS signal for post-processing. Leveraging publicly available information such as GNSS satellite ephemeris and an Earth elevation database, a cloud service can derive good quality GPS locations from a few milliseconds of raw data. Using our design of a portable sensing device platform called CLEO, we evaluate the accuracy and efficiency of the solution. Compared to more than 30 seconds of heavy signal processing on standalone GPS receivers, we can achieve three orders of magnitude lower energy consumption per location tagging.

Recent works have shown the usefulness of network and application layer protocols that connect low-power sensor nodes directly to multiple applications and users on the Internet. We propose a security solution for this scenario. While previous works have provided security support for various communication patterns in sensor networks, such as among nodes, from nodes to a base station, and from users to nodes, the security of communication from sensor nodes to multiple users has not been sufficiently addressed. Specifically, we explore this design space and develop a security solution, named Secure Tiny Web Service, for efficient authentication of data sent by a resource-constrained sensor node to multiple users, using digital signatures. We investigate the resource overheads in communication and computation of four suitable signature schemes—the Elliptic Curve Digital Signature Algorithm, the (elliptic curve) Schnorr signature, and the Boneh–Lynn–Shacham and Zhang–Safavi-Naini–Susilo short signature schemes. We implement these schemes on two popular sensor node architectures (based on AVR ATmega128L and MSP430 processors with 802.15.4 radios) and experimentally characterize relevant trade-offs.

Trajectory-based services require continuous user location sensing. GPS is the most common outdoor location sensor on mobile devices. However, the high energy consumption of GPS sensing prohibits it to be used continuously in many applications. In this paper, we propose a Low Energy Assisted Positioning (LEAP) solution that carefully partitions the GPS signal processing pipeline and shifts delay tolerant position calculations to the cloud. The GPS receiver only needs to be on for less than a second to collect the sub-millisecond level propagation delay for each satellites signal. With a reference to a nearby object, such as a cell tower, the LEAP server can infer the rest of the information necessary to perform GPS position calculation. We analyze the accuracy and energy benefit of LEAP and use real user traces to show that LEAP can save up to 80% GPS energy consumption in typical trajectory-based service scenarios.

Although mobile phones are ideal platforms for continuous human centric sensing, the state of the art phone architectures today have not been designed to support continuous sensing applications. Currently, sampling and processing sensor data on the phone requires the main processor and associated components to be continuously on, creating a large energy overhead that can severely impact the battery lifetime of the phone. We will demonstrate Little Rock, a novel sensing architecture for mobile phones, where sampling and, when possible, processing of sensor data is offloaded to a dedicated low-power processor. This approach enables the phone to perform continuous sensing three orders of magnitude more energy efficiently compared to the normal approaches.

This paper derives energy-optimal batching periodsfor asynchronous multistage data processing on sensor nodes in the sense of minimizing energy consumption while meeting end-to-end deadlines. Batching the processing of (sensor) data maximizes processor sleep periods, hence minimizing the wakeup frequency and the corresponding overhead. The algorithm is evaluated on mPlatform, a next-generation heterogeneous sensor node platform equipped with both a low-end microcontroller(MSP430) and a higher-end embedded systems processor (ARM). Experimental results show that the total energy consumption of mPlatform, when processing data flowsat their optimal batching periods, is up to 35% lower than that for uniform period assignment. Moreover, processing data at the appropriate processor can use as much as 80% less energy than running the same task set on the ARM alone and 25% less energy than running the taskset on the MSP430 alone.

Frequency overlap across wireless networks with different radio technologies can cause severe interference and reduce communication reliability. The circumstances are particularly unfavorable for ZigBee networks that share the 2.4 GHz ISM band with WiFi senders capable of 10 to 100 times higher transmission power. Our work first examines the interference patterns between ZigBee and WiFi networks at the bit-level granularity. Under certain conditions, ZigBee activities can trigger a nearby WiFi transmitter to back off, in which case the header is often the only part of the Zig-Bee packet being corrupted. We call this the symmetric interference regions, in comparison to the asymmetric regions where the ZigBee signal is too weak to be detected by WiFi senders, but WiFi activity can uniformly corrupt any bit in a ZigBee packet. With these observations, we design BuzzBuzz to mitigate WiFi interference through header and payload redundancy. Multi-Headers provides header redundancy giving ZigBee nodes multiple opportunities to detect incoming packets. Then, TinyRS, a full-featured Reed Solomon library for resource-constrained devices, helps decoding polluted packet payload. On a medium-sized testbed, BuzzBuzz improves the ZigBee network delivery rate by 70%. Furthermore, BuzzBuzz reduces ZigBee retransmissions by a factor of three, which increases the WiFi throughput by 10%.

Embedded systems with heterogeneous processors extend the energy/timing trade-off flexibility and provide the opportunity to fine tune resource utilization for particular applications. In this paper, we present a resource model that considers the time and energy costs of run-time mode switching, which considerably improves the accuracy of existing models. Given an application, the software partitioning problem then becomes an optimization over energy cost given deadline constraints, which can be formulate as an integer linear programming (ILP) problem. We apply the resource modeling and software partitioning techniques to a multimodule embedded sensing device, the mPlatform, and present a case study of configuring the platform for a real-time sound source localization application on a stack of MSP430 and ARM7 processor based sensing and processing boards.

We study the problem of concurrently supporting multiple radios with different capabilities and interfaces on a single sensor node platform. Through a detailed experimental study on hardware multi-radio platforms, using the two representative radio technologies 802.15.4 and 802.11, we identify bottlenecks and design tradeoffs that are usually overlooked and that, as we show, have a significant impact on the sensor network's performance and energy efficiency. Our findings are threefold. We show that a proper pairing of processor and radio is crucial for taking the full advantage of the energy efficiency of higher bandwidth radios. The processor/radio pairing affects the energy balance of a sensor node, thus making the design of dynamic switching among multiple radios more challenging. Second, we demonstrate and quantify the impact of network traffic on energy consumption of a sensor node while varying network parameters, and illustrate the deficiency of existing energy-optimizing protocols. Our results indicate that by properly adjusting network parameters, such as packet size and transmission period, energy savings of up to 50% can be achieved under heavy network traffic conditions when a CSMA-based MAC is used. We conclude by presenting a set of guidelines for designing and implementing energy efficient multi-radio platforms.

There are many scenarios where interoperability is required for sensor devices. We demonstrate one approach to achieve interoperability: using web services. Hosting a Web service challenges the battery- life, bandwidth, and processing power constraints of low power sensor nodes. We demonstrate a lightweight implementation on MSP430 based sensor nodes with 802.15.4 radios. The implementation allows standards compliant web service clients to use the sensors but minimizes code size and energy at the sensor nodes. It allows sensor nodes to enter sleep modes. We prototype an example application for a home sensor network along with two types of sensor nodes required for it. We also show how our system enables sensor nodes to be used easily from applications written in high level languages using existing development tools.

We present a web service based approach to enable an evolutionary sensornet system where additional sensor nodes may be added after the initial deployment. The functionality and data provided by the new nodes is exposed in a structured manner, so that multiple applications may access them. The result is a highly inter-operable system where multiple applications can share a common evolving sensor substrate. A key challenge in using web services on resource constrained sensor nodes is the energy and bandwidth overhead of the structured data formats used in web services. Our work provides a detailed evaluation of the overheads and presents an implementation on a representative sensor platform with 48k of ROM, 10k of RAM and a 802.15.4 radio. We identify design choices that optimize the web service operation on resource constrained sensor nodes, including support for low latency messaging and sleep modes, quantifying trade-offs between the design generality and resource efficiency. We also prototyped an example application, for home energy management, demonstrating how evolutionary sensor networks can be supported with our approach.

We present mPlatform, a new reconfigurable modular sensornet platform that enables real-time processing on multiple heterogeneous processors. At the heart of the mPlatform is a scalable high-performance communication bus connecting the different modules of a node, allowing time-critical data to be shared without delay and supporting reconfigurability at the hardware level. Furthermore, the bus allows components of an application to span across different processors/modules without incurring much overhead, thus easing the program development and supporting software reconfigurability. We describe the communication architecture, protocol, and hardware configuration, and the implementation in a low power, high speed complex programmable logic device (CPLD). An asynchronous interface decouples the local processor of each module from the bus, allowing the bus to operate at the maximum desired speed while letting the processors focus on their real time tasks such as data collection and processing. Extensive experiments on the mPlatform prototype have validated the scalability of the communication architecture, and the high speed, reconfigurable inter-module communication that is achieved at the expense of a small increase in the power consumption. Finally, we demonstrate a real-time sound source localization application on the mPlatform, with four channels of acoustic data acquisition, FFT, and sound classification, that otherwise would be infeasible using traditional buses such as I2C.

The localization problem is to determine an assignment of coordinates to nodes in a wireless ad-hoc or sensor network that is consistent with measured pairwise node distances. Most previously proposed solutions to this problem assume that the nodes can obtain pairwise distances to other nearby nodes using some ranging technology. However, for a variety of reasons that include obstructions and lack of reliable omnidirectional ranging, this distance information is hard to obtain in practice. Even when pairwise distances between nearby nodes are known, there may not be enough information to solve the problem uniquely. This paper describes MAL, a mobile-assisted localization method which employs a mobile user to assist in measuring distances between node pairs until these distance constraints form a "globally rigid'* structure that guarantees a unique localization. We derive the required constraints on the mobile's movement and the minimum number of measurements it must collect; these constraints depend on the number of nodes visible to the mobile in a given region. We show how to guide the mobile's movement to gather a sufficient number of distance samples for node localization. We use simulations and measurements from an indoor deployment using the Cricket location system to investigate the performance of MAL, finding in real-world experiments that MAL's median pairwise distance error is less than 1.5% of the true node distance.

We study the problem of tracking a moving device under two indoor location architectures: an active mobile architecture and a passive mobile architecture. In the former, the infrastructure has receivers at known locations, which estimate distances to a mobile device based on an active transmission from the device. In the latter, the infrastructure has active beacons that periodically transmit signals to a passively listening mobile device, which in turn estimates distances to the beacons. Because the active mobile architecture receives simultaneous distance estimates at multiple receivers from the mobile device, it is likely to perform better tracking than the passive mobile system in which the device obtains only one distance estimate at a time and may have moved between successive estimates. However, an passive mobile system scales better with the number of mobile devices and puts users in control of whether their whereabouts are tracked.We answer the following question: How do the two architectures compare in tracking performance? We find that the active mobile architecture performs better at tracking, but that the passive mobile architecture has acceptable performance; moreover, we devise a hybrid approach that preserves the benefits of the passive mobile architecture while simultaneously providing the same performance as an active mobile system, suggesting a viable practical solution to the three goals of scalability, privacy, and tracking agility.

Physical location is an important attribute of a sensor’s data stream in a large number of sensor network applications. In addition, geographic information, for instance in the form of node coordinates in some common coordinate system, is a useful primitive in routing protocols such as geographic routing, information dissemination protocols such as directed diffusion using location attributes, and sensor query processing systems. We present a method to facilitate large-scale deployment of location-aware sensor networks. We show that large networks of location-aware sensors can be made cooperatively self-configuring, that is, that each sensor can run an algorithm locally, interacting only with neighboring nodes, such that after a number of iterations all sensors will have reached a consensus about their coordinates in some coordinate system. By doing this in an automated manner, large-scale sensor networks can eliminate the cumbersome and unscalable process of manually configuring sensor nodes with their location. In non-urban outdoor settings, nodes may obtain location information using an existing infrastructure such as GPS. However, GPS receivers may be too expensive, too large, too power-intensive for the desired application, or simply unavailable. One solution to this problem is an alternative location infrastructure such as Cricket that works in places that GPS does not. Another solution to these problems is to equip sensors with hardware capable of estimating distances to nearby nodes, and to have the sensors themselves selfconfigure into a consistent coordinate system.

This paper describes methods for service selection and service access for mobile, sensor-enhanced web clients such as wireless cameras or wireless PDAs with sensor devices attached. The clients announce their data-creating capabilities in "Produce" headers sent to servers; servers respond with forms that match these capabilities. Clients fill in these forms with sensor data as well as text or file data. The resultant system enables clients to access dynamically discovered services spontaneously, as their users engage in everyday nomadic activities.

The ability to determine the orientation of a device is of fundamental importance in context aware and location-dependent mobile computing. By analogy to a traditional compass, knowledge of orientation through the Cricket compass attached to a mobile device enhances various applications, including efficient way-finding and navigation, directional service discovery, and “augmented-reality” displays. Our compass infrastructure enhances the spatial inference capability of the Cric ketindoor location system [20], and enables new pervasive computing applications.

This paper presents the design, implementation, and evaluation of Cricket, a location-support system for in-building, mobile, location-dependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration.