Energy efficiency in serverless computing has remained under-explored despite its growing adoption. To fill this gap, we propose LoAPE, a load-aware and power-elastic serverless platform. Unlike existing methods limited to CPU core frequency scaling or density-based consolidation, LoAPE leverages c-states and uncore frequency scaling, which offer greater energy benefits but pose deployment challenges. Uncore changes affect the performance of all co-located functions, and cstates require sustained idle periods for effective energy savings. LoAPE’s insight is to strategically exploit function elasticity. By creating idle intervals across cores and servers, it enables aggressive c-state activation and uncore frequency reduction. To achieve this goal, LoAPE adopts topology-aware instance scheduling and eviction, which scale the number of active servers along with serverless functions. Within servers, LoAPE proactively manages active cores based on incoming loads. Our evaluations demonstrate that LoAPE delivers a 3.02× improvement in cluster energy reduction compared to frequency-scaling frameworks and extends savings by 1.17× over state-of-the-art density-aware schedulers, while meeting performance requirements.

The recent advances in information technology and artificial intelligence have fueled a rapid expansion of the data center (DC) industry worldwide, accompanied by an immense appetite for electricity to power the DCs. In a typical DC, around 30-40% of the energy is spent on the cooling system rather than on computer servers, posing a pressing need for developing new energy-saving optimization technologies for DC cooling systems. However, optimizing such real-world industrial systems faces numerous challenges, including but not limited to a lack of reliable simulation environments, limited historical data, and stringent safety and control robustness requirements. In this work, we present a novel physics-informed offline reinforcement learning (RL) framework for energy efficiency optimization of DC cooling systems. The proposed framework models the complex dynamical patterns and physical dependencies inside a server room using a purposely designed graph neural network architecture that is compliant with the fundamental time-reversal symmetry. Because of its well-behaved and generalizable state-action representations, the model enables sample-efficient and robust latent space offline policy learning using limited real-world operational data. Our framework has been successfully deployed and verified in a large-scale production DC for closed-loop control of its air-cooling units (ACUs). We conducted a total of 2000 hours of short and long-term experiments in the production DC environment. The results show that our method achieves 14-21% energy savings in the DC cooling system, without any violation of the safety or operational constraints. We have also conducted a comprehensive evaluation of our approach in a real-world DC testbed environment. Our results have demonstrated the significant potential of offline RL in solving a broad range of data-limited, safety-critical real-world industrial control problems.

The increasing demand for artificial intelligence and cloud computing has led to skyrocketing energy consumption of data centers (DCs). This paper focuses on tackling this energy challenge through cooling control system optimization, which aims to ensure thermal safety with minimal cooling energy consumption. Current industry practice involves human operators, while many data-driven methods have also been proposed. However, human intervention often results in unnecessary energy consumption, particularly in the face of fluctuating server loads, whereas existing data-driven methods struggle to maintain thermal safety in practice. To overcome these issues, we propose TESLA, a thermally safe, load-aware, and energy-efficient cooling control system for data centers. TESLA employs a novel data-driven framework that integrates domain knowledge to predict DC temperature and cooling energy under dynamic server load. Based on these predictions, a Bayesian optimizer (BO) finds the energy-optimal settings for the cooling system at every control step. Besides cooling energy, BO’s optimization objective also includes minimizing cooling interruption that causes rapid temperature rise within the data center and leads to thermal safety violations. We deploy TESLA on a real data-center testbed and show that it achieves on average <?TeX $10.1\%$?> Math 1 cooling energy saving relative to a fixed cooling system parameter setting and no thermal safety violation relative to previous data-driven methods.

The connection between solar-induced fluorescence (SIF) and vegetation gross primary productivity is being widely investigated across spatial, temporal, and biological scales, including: a) studies at the leaf [1], [2], plant canopy [2]–[4] or satellite pixel scale [5], [6], b) temporally with studies spanning from diurnal [7] to seasonal scales [1], [3], [5], and b) biologically with studies covering various plant functional types (PFTs), e.g., crops [4], [7], deciduous [8] or evergreen forests [1], [3], in response to different sources of stress.

Given the emerging concerns over app privacy-related risks, major app distribution providers (e.g., Microsoft) have been exploring approaches to help end users to make informed decision before installation. This is different from existing approaches of simply trusting users to make the right decision. We build on the direction of risk rating as the way to communicate app-specific privacy risks to end users. To this end, we propose to use sensitivity analysis to infer whether an app requests sensitive on-device resources/ data that are not required for its expected functionality. Our system, Privet, addresses challenges in efficiently achieving test coverage and automated privacy risk assessment. Finally, we evaluate Privet with 1,000 Android apps released in the wild.

We present Travi-Navi-a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high-quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds shortcuts whenever possible. In this paper, we describe the key techniques to solve several practical challenges, including robust tracking, shortcut identification, and high-quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a four-step offset, and detect deviation events within nine steps. We also characterize the power consumption of Travi-Navi on various mobile phones.

As the number of connected devices explodes, the use scenarios of these devices and data have multiplied. Many of these scenarios, e.g., home automation, require tools beyond data visualizations, to express user intents and to ensure interactions do not cause undesired effects in the physical world. We present SIFT, a safety-centric programming platform for connected devices in IoT environments. First, to simplify programming, users express high-level intents in declarative IoT apps. The system then decides which sensor data and operations should be combined to satisfy the user requirements. Second, to ensure safety and compliance, the system verifies whether conflicts or policy violations can occur within or between apps. Through an office deployment, user studies, and trace analysis using a large-scale dataset from a commercial IoT app authoring platform, we demonstrate the power of SIFT and highlight how it leads to more robust and reliable IoT apps.

Web browsing is a key application on mobile devices. However, mobile browsers are largely optimized for performance, imposing a significant burden on power-hungry mobile devices. In this work, we aim to reduce the energy consumed to load web pages on smartphones, preferably without increasing page load time and compromising user experience. To this end, we first study the internals of web page loading on smartphones and identify its energy-inefficient behaviors. Based on our findings, we then derive general design principles for energy-efficient web page loading, and apply these principles to the open-source Chromium browser and implement our techniques on commercial smartphones. Experimental results show that our techniques are able to achieve a 24.4% average system energy saving for Chromium on a latest-generation big.LITTLE smartphone using WiFi (a 22.5% saving when using 3G), while not increasing average page load time. We also show that our proposed techniques can bring a 10.5% system energy saving on average with a small 1.69\% increase in page load time for mobile Firefox web browser. User study results indicate that such a small increase in page load time is hardly perceivable.

Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5 m, 1 m, and 8 m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5 m for localization and 0.9 m for tracking in the office environment. When using only the magnetism, Magicol consumes 9 × less energy in tracking compared to WiFi-based tracking.

Advances in mobile and wearable devices are making it feasible to deploy sensing systems at a large-scale. However, slower progress is being made in activity recognition which remains often unreliable in everyday environments. In this paper, we investigate how to leverage the increasing capacity to gather data at a population-scale towards improving existing models of human behavior. Specifically, we consider the various social phenomena and environmental factors that cause people to develop correlated behavioral patterns, especially within communities connected by strong social ties. Reasons underpinning correlated behavior include shared externalities (e.g., work schedules, weather, traffic conditions), that shape options and decisions; and cases of adopted behavior, as people learn from each other or assume group norms due to social pressure. Most existing approaches to modeling human behavior ignore all of these phenomena and recognize activities solely on the basis of sensor data captured from a single individual. We propose the Networked Community Behavior (NCB) framework for activity recognition, specifically designed to exploit community-scale behavioral patterns. Under NCB, patterns of community behavior are mined to identify social ties that can signal correlated behavior, this information is used to augment sensor-based inferences available from the actions of individuals. Our evaluation of NCB shows it is able to outperform existing approaches to behavior modeling across four mobile sensing datasets that collectively require a diverse set of activities to be recognized.

Diversity in training data density and environment locality is intrinsic in the real-world deployment of indoor localization systems and has a major impact on the performance of existing localization approaches. In this paper, through micro-benchmarks, we find that fingerprint-based approaches are preferable in scenarios where a dense database is available; while model-based approaches are the method of choice in the case of sparse data. It should be noted, however, that practical situations are complex. A single deployment often features both sparse and dense sampled areas. Furthermore, the internal layout affects the propagation of radio signals and exhibits environmental impacts. A certain number of measurement samples may be sufficient for one part of the building, but entirely insufficient for another. Thus, finding the right indoor localization algorithm for a given large-scale deployment is challenging, if not impossible; there is no one-size-fits-all indoor localization approach.

Scalable and comprehensive testing of mobile apps is extremely challenging. Every test input needs to be run with a variety of contexts, such as: device heterogeneity, wireless network speeds, locations, and unpredictable sensor inputs. The range of values for each context, e.g. location, can be very large. In this paper we present Caiipa, a cloud service for testing apps over an expanded mobile context space in a scalable way. It incorporates key techniques to make app testing more tractable, including a context test space prioritizer to quickly discover failure scenarios for each app. We have implemented Caiipa on a cluster of VMs and real devices that can each emulate various combinations of contexts for tablet and phone apps. We evaluate Caiipa by testing 265 commercially available mobile apps based on a comprehensive library of real-world conditions. Our results show that Caiipa leads to improvements of 11.1x and 8.4x in the number of crashes and performance bugs discovered compared to conventional UI-based automation (i.e., monkey-testing).

We present Travi-Navi - a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds the most efficient shortcuts whenever possible. We encounter and solve several challenges, including robust tracking, shortcut identification, and high quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a 4-step offset, and detect deviation events within 9 steps.

The ever increasing popularity of social networks and the ever easier photo taking and sharing experience have led to unprecedented concerns on privacy infringement. Inspired by the fact that the Robot Exclusion Protocol, which regulates web crawlers' behavior according a per-site deployed robots.txt, and cooperative practices of major search service providers, have contributed to a healthy web search industry, in this paper, we propose Privacy Expressing and Respecting Protocol (PERP) that consists of a Privacy.tag -- a physical tag that enables a user to explicitly and flexibly express their privacy deal, and Privacy Respecting Sharing Protocol (PRSP) -- a protocol that empowers the photo service provider to exert privacy protection following users' policy expressions, to mitigate the public's privacy concern, and ultimately create a healthy photo-sharing ecosystem in the long run. We further design an exemplar Privacy.Tag using customized yet compatible QR-code, and implement the Protocol and study the technical feasibility of our proposal. Our evaluation results confirm that PERP and PRSP are indeed feasible and incur negligible computation overhead.

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.

Indoor physical analytics calls for high-accuracy localization that existing indoor (e.g., WiFi-based) localization systems may not offer. By exploiting the ever increasingly wider adoption of LED lighting, in this paper, we study the problem of using visible LED lights for accurate localization. We identify the key challenges and tackle them through the design of Pharos. In particular, we establish and experimentally verify an optical channel model suitable for localization. We adopt BFSK and channel hopping to achieve reliable location beaconing from multiple, uncoordinated light sources over shared light medium. Preliminary evaluation shows that Pharos achieves the 90th percentile localization accuracy of 0.4m and 0.7m for two typical indoor environments. We believe visible light based localization holds the potential to significantly improve the position accuracy, despite few potential issues to be conquered in real deployment.

Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.

The proliferation of location-based services and applications calls for provisioning of location service as a first class system component that can return accurate location fix in short response time and is energy efficient. In this paper, we present the design, implementation and evaluation of WheelLoc - a continuous system location service for outdoor scenarios. Unlike previous localization efforts that try to directly obtain a point location fix, WheelLoc adopts an indirect approach: it seeks to capture a user mobility trace first and to obtain any point location by time- and speed-aware interpolation or extrapolation. WheelLoc avoids energy-expensive sensors completely and relies solely on commonly available cheap sensors such as accelerometer and magnetometer. With a set of novel techniques and the leverage of publicly available road maps and cell tower information, WheelLoc is able to meet those requirements of a first class component. Experimental results confirmed the effectiveness of WheelLoc. It can return a location estimate within 40ms with an accuracy about 40 meters, consumes only 240mW energy, and effectively strikes a better energy-accuracy tradeoff than GPS duty-cycling.

Energy is an important resource in today's computing systems, from mega data centers to tiny embedded devices. But the energy efficiency of these systems can be limited by our inability to accurately model and predict the energy usage. In this talk, I will describe our research in designing and deploying resource-constrained wireless sensor and mobile systems, and our work on monitoring and optimizing data centers with Internet-scale workloads. I will focus on the principles and tradeoffs learned from working on these systems. The design and optimization for energy efficiency requires a rethinking of the entire stack, from hardware to systems, networking, programming, and all the way to applications.

Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations.

Kineograph is a distributed system that takes a stream of incoming data to construct a continuously changing graph, which captures the relationships that exist in the data feed. As a computing platform, Kineograph further supports graph-mining algorithms to extract timely insights from the fast-changing graph structure. To accommodate graph-mining algorithms that assume a static underlying graph, Kineograph creates a series of consistent snapshots, using a novel and efficient epoch commit protocol. To keep up with continuous updates on the graph, Kineograph includes an incremental graph-computation engine. We have developed three applications on top of Kineograph to analyze Twitter data: user ranking, approximate shortest paths, and controversial topic detection. For these applications, Kineograph takes a live Twitter data feed and maintains a graph of edges between all users and hashtags. Our evaluation shows that with 40 machines processing 100K tweets per second, Kineograph is able to continuously compute global properties, such as user ranks, with less than 2.5-minute timeliness guarantees. This rate of traffic is more than 10 times the reported peak rate of Twitter as of October 2011.

Automated and scalable approaches for understanding the semantics of places are critical to improving both existing and emerging mobile services. In this paper, we present [email protected] (CSP), a framework that exploits a previously untapped resource -- opportunistically captured images and audio clips from smartphones -- to link place visits with place categories (e.g., store, restaurant). CSP combines signals based on location and user trajectories (using WiFi/GPS) along with various visual and audio place "hints" mined from opportunistic sensor data. Place hints include words spoken by people, text written on signs or objects recognized in the environment. We evaluate CSP with a seven-week, 36-user experiment involving 1,241 places in five locations around the world. Our results show that CSP can classify places into a variety of categories with an overall accuracy of 69%, outperforming currently available alternative solutions.

This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meter-level indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area.

Many indoor sensing applications leverage knowledge of relative proximity among physical objects and humans, such as the notion of "within arm's reach". In this paper, we quantify this notion using "proximity zone", and propose a methodology that empirically and systematically compare the proximity zones created by various wireless technologies. We find that existing technologies such as 802.15.4, Bluetooth Low Energy (BLE), and RFID fall short on metrics such as boundary sharpness, robustness against interference, and obstacle penetration. We then present the design and evaluation of a wireless proximity detection platform based on magnetic induction - LiveSynergy. LiveSynergy provides sweet spot for indoor applications that require reliable and precise proximity detection. Finally, we present the design and evaluation of an end-to-end system, deployed inside a large food court to offer context-aware and personalized advertisements and diet suggestions at a per-counter granularity.

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.

Mobile phones have become an ideal platform for physiological and environmental sensing. A number of research and commercial smartphone "accessories" have emerged in recent years that try to extend the sensing capabilities of a mobile phone. However, the major drawback of these devices is that they either require the user to act in some specific way or change their lifestyle and habit to some extent. In this demo, we present Septimu V2 (Septimu2) -- a novel non-intrusive physiological and environmental sensing platform which is fully embedded in a conventional earphone, works with existing smartphones, and does not require the user to change habits in any way. Septimu2 is a continuation of [1], and integrates a suite of new sensors. In addition to 3-axis accelerometer and gyroscope, Septimu2 incorporates remote IR temperature sensor, IR LED, IR photodiode and two additional microphones. The baseboard performs signal condition and sends the data to cellphone via Bluetooth. Septimu2 enables a number of applications, including heart-rate monitoring, fine grained posture detection, and external sound source localization and classification.

MusicalHeart is a biofeedback-based, context-aware, automated music recommendation system for smartphones. We introduce a new wearable sensing platform, Septimu, which consists of a pair of sensor-equipped earphones that communicate to the smartphone via the audio jack. The Septimu platform enables the MusicalHeart application to continuously monitor the heart rate and activity level of the user while listening to music. The physiological information and contextual information are then sent to a remote server, which provides dynamic music suggestions to help the user maintain a target heart rate. We provide empirical evidence that the measured heart rate is 75% -- 85% correlated to the ground truth with an average error of 7.5 BPM. The accuracy of the person-specific, 3-class activity level detector is on average 96.8%, where these activity levels are separated based on their differing impacts on heart rate. We demonstrate the practicality of MusicalHeart by deploying it in two real world scenarios and show that MusicalHeart helps the user achieve a desired heart rate intensity with an average error of less than 12.2%, and its quality of recommendation improves over time.

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.

The emerging field of mobile sensing has engaged computer scientists from a variety of existing communities, such as, mobile systems, machine learning and human computer interaction. Each community approaches the challenges of mobile sensing research with its own unique perspective. The purpose of this workshop is to provide a forum to discuss the state of the art in mobile sensing and promote increased cooperation and interaction among the participating research communities.

Sensor-enabled smartphones are opening a new frontier in the development of mobile sensing applications. The recognition of human activities and context from sensor-data using classification models underpins these emerging applications. However, conventional approaches to training classifiers struggle to cope with the diverse user populations routinely found in large-scale popular mobile applications. Differences between users (e.g., age, sex, behavioral patterns, lifestyle) confuse classifiers, which assume everyone is the same. To address this, we propose Community Similarity Networks (CSN), which incorporates inter-person similarity measurements into the classifier training process. Under CSN every user has a unique classifier that is tuned to their own characteristics. CSN exploits crowd-sourced sensor-data to personalize classifiers with data contributed from other similar users. This process is guided by similarity networks that measure different dimensions of inter-person similarity. Our experiments show CSN outperforms existing approaches to classifier training under the presence of population diversity.

The state of the art rigid object recognition algorithms are based on the bag of words model, which represents each image in the database as a sparse vector of visual words. We propose a new algorithm to select informative features from images in the database. which can save the memory cost when the database is large and reduce the length of the inverted index so it can improve the recognition speed. Experiments show that only using the informative features selected by our algorithm has better recognition performance than the previous methods.

Sensor convergence on the mobile phone is spawning a broad base of new and interesting mobile applications. As applications grow in sophistication, raw sensor readings often require classification into more useful application-specific high-level data. For example, GPS readings can be classified as running, walking or biking. Unfortunately, traditional classifiers are not built for the challenges of mobile systems: energy, latency, and the dynamics of mobile.

In this demonstration, we present the architecture, implementation, and applications of LiveSynergy --- a system that provides reliable proximity sensing and open interactive abstractions for physical spaces and objects, to enable rich interactions between humans and their environment.

Virtualization is often used in cloud computing platforms for its several advantages in efficiently managing resources. However, virtualization raises certain additional challenges, and one of them is lack of power metering for virtual machines (VMs). Power management requirements in modern data centers have led to most new servers providing power usage measurement in hardware and alternate solutions exist for older servers using circuit and outlet level measurements. However, VM power cannot be measured purely in hardware. We present a solution for VM power metering, named Joulemeter. We build power models to infer power consumption from resource usage at runtime and identify the challenges that arise when applying such models for VM power metering. We show how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error. Our approach is designed to operate with extremely low runtime overhead while providing practically useful accuracy. We illustrate the use of the proposed metering capability for VM power capping, a technique to reduce power provisioning costs in data centers. Experiments are performed on server traces from several thousand production servers, hosting Microsoft's real-world applications such as Windows Live Messenger. The results show that not only does VM power metering allows virtualized data centers to achieve the same savings that non-virtualized data centers achieved through physical server power capping, but also that it enables further savings in provisioning costs with virtualization.

Local search engines are very popular but limited. We present Hapori, a next-generation local search technology for mobile phones that not only takes into account location in the search query but richer context such as the time, weather and the activity of the user. Hapori also builds behavioral models of users and exploits the similarity between users to tailor search results to personal tastes rather than provide static geo-driven points of interest. We discuss the design, implementation and evaluation of the Hapori framework which combines data mining, information preserving embedding and distance metric learning to address the challenge of creating efficient multidimensional models from context-rich local search logs. Our experimental results using 80,000 queries extracted from search logs show that contextual and behavioral similarity information can improve the relevance of local search results by up to ten times when compared to the results currently provided by commercially available search engine technology.

Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smartphones do include a GPS receiver, it's frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. Our design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used. Our method automatically determines the dynamic accuracy requirement for mobile search-based applications. As the user moves, both the accuracy requirements and the location sensor errors change. A-Loc continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments are performed with Android G1 and AT&T Tilt phones, on paths that include outdoor and indoor locations, using war-driving data from Google and Microsoft. The experiments show that a-Loc not only provides significant energy savings, but also improves the accuracy achieved, because it uses multiple sensors.

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.

A sensor network data gathering and visualization infrastructure is demonstrated, comprising of global sensor networks (GSN) middleware and Microsoft SensorMap. Users are invited to actively participate in the process of monitoring real-world deployments and can inspect measured data in the form of contour plots overlayed onto a high resolution map and a digital topographic model. Users can go back in time virtually to search for interesting events or simply to visualize the temporal dependencies of the data. The system presented is not only interesting and visually enticing for non-expert users but brings substantial benefits to environmental scientists. The easily installed data acquisition component as well as the powerful data sharing and visualization platform opens up new ground in collaborative data gathering and interpretation in the spirit of Web 2.0 applications.

RACNet is a sensor network for monitoring a data center's environmental conditions. The high spatial and temporal fidelity measurements that RACNet provides can be used to improve the data center's safety and energy efficiency. RACNet overcomes the network's large scale and density and the data center's harsh RF environment to achieve data yields of 99% or higher over a wide range of network sizes and sampling frequencies. It does so through a novel Wireless Reliable Acquisition Protocol (WRAP). WRAP decouples topology control from data collection and implements a token passing mechanism to provide network-wide arbitration. This congestion avoidance philosophy is conceptually different from existing congestion control algorithms that retroactively respond to congestion. Furthermore, WRAP adaptively distributes nodes among multiple frequency channels to balance load and lower data latency. Results from two testbeds and an ongoing production data center deployment indicate that RACNet outperforms previous data collection systems, especially as network load increases.

We present Cypress, a novel framework to archive and query massive time series streams such as those generated by sensor networks, data centers, and scientific computing. Cypress applies multi-scale analysis to decompose time series and to obtain sparse representations in various domains (e.g. frequency domain and time domain). Relying on the sparsity, the time series streams can be archived with reduced storage space. We then show that many statistical queries such as trend, histogram and correlations can be answered directly from compressed data rather than from reconstructed raw data. Our evaluation with server utilization data collected from real data centers shows significant benefit of our framework.

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 present SenseWeb, an open and scalable infrastructure for sharing and geocentric exploration of sensor data streams. SenseWeb allows sensor owners to share data streams across multiple applications and users, thus amortizing sensor deployment costs effectively. It also provides mechanisms to transparently index and cache data, to process spatio-temporal queries on real-time and historic data, and to aggregate and present results on a geocentric web interface. In this paper, we present the architecture of SenseWeb, its techniques to enable global sharing of heterogeneous sensors, and its mapbased front-end for spatio-temporal data exploration. We enable interactive geocentric data exploration in the mapbased front-end using techniques for rapidly changing map overlaid visualizations of numerous data streams. We also demonstrate flexibility and scalability of the architecture by evaluating a deployed prototype of SenseWeb, which has been publicly available since March 2008.

A great opportunity exists to fuse information from populations of privately-held sensors to create useful sensing applications. For example, GPS devices, embedded in cellphones and automobiles, might one day be employed as distributed networks of velocity sensors for traffic monitoring and routing. Unfortunately, privacy and resource considerations limit access to such data streams. We describe principles of community sensing that offer mechanisms for sharing data from privately held sensors. The methods take into account the likely availability of sensors, the context-sensitive value of sensor information, based on models of phenomena and demand, and sensor owners' preferences about privacy and resource usage. We present efficient and well-characterized approximations of optimal sensing policies. We provide details on key principles of community sensing and highlight their use within a case study for road traffic monitoring.

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.

Mobile phones have two sensors: a camera and a microphone. The widespread and ubiquitous nature of mobile phones around the world makes it attractive to build a large-scale sensor network using the phones as its sensor nodes. There are several interesting challenges in realizing such a system, such as providing efficient methods for the sensor nodes to make their data available to the network, allowing the sensor network applications to access the data from potentially disconnected and highly mobile devices, ensuring that privacy constraints are met, and allowing application developers to program the sensor network as required to build new applications. We demonstrate an initial system prototype that addresses some of these concerns.

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.

This paper studies task assignment in a network of resource constrained computing platforms (called microservers). A task is an abstraction of a computational agent or data that is hosted by the microservers. For example, in an object tracking scenario, a task represents a mobile tracking agent, such as a vehicle location update computation, that runs on microservers, which can receive sensor data pertaining to the object of interest. Due to object motion, the microservers that can observe a particular object change over time and there is overhead involved in migrating tasks among microservers. Furthermore, communication, processing, or memory constraints, allow a microserver to only serve a limited number of objects at the same time. Our overall goal is to assign tasks to microservers so as to minimize the number of migrations, and thus be kinetically stable, while guaranteeing that as many tasks as possible are monitored at all times. When the task trajectories are known in advance, we show that this problem is NP-complete (even over just two time steps), has an integrality gap of at least 2, and can be solved optimally in polynomial time if we allow tasks to be assigned fractionally. When only probabilistic information about future movement of the tasks is known, we propose two algorithms: a multi-commodity flow based algorithm and a maximum matching algorithm. We use simulations to compare the performance of these algorithms against the optimum task allocation strategy.

We present a spreadsheet approach to simplifying the process of managing, programming, and interacting with sensor networks and visualizing, archiving and retrieving sensor data. An Excel spreadsheet prototype has been built to demonstrate the idea. This environment provides Excel users, who are already familiar with spreadsheet applications, a convenient and powerful tool for programming and data analysis. We discuss the architecture of this prototype and our experience in implementing the tool. We show two different classes of sensor-net applications built using this platform. We also present performance data on the scalability of the tool with respect to data rate and number of data streams.

Location knowledge of nodes in a network is essential for many tasks such as routing, cooperative sensing, or service delivery in ad hoc, mobile, or sensor networks. This paper introduces a novel iterative method ILS for node localization starting with a relatively small number of anchor nodes in a large network. At each iteration, nodes are localized using a least-squares based algorithm. The computation is lightweight, fast, and any-time. To prevent error from propagating and accumulating during the iteration, the error control mechanism of the algorithm uses an error registry to select nodes that participate in the localization, based on their relative contribution to the localization accuracy. Simulation results have shown that the active selection strategy significantly mitigates the effect of error propagation. The algorithm has been tested on a network of Berkeley Mica2 motes with ultrasound TOA ranging devices. We have compared the algorithm with more global methods such as MDS-MAP and SDP-based algorithm both in simulation and on real hardware. The iterative localization achieves comparable location accuracy in both cases, compared to the more global methods, and has the advantage of being fully decentralized.

Feng Zhao (http://research.microsoft.com/~zhao) is a Principal Researcher at Microsoft Research, where he manages the Networked Embedded Computing Group. He received his PhD in Electrical Engineering and Computer Science from MIT and has taught at Stanford University and Ohio State University. Dr. Zhao was a Principal Scientist at Xerox PARC and directed PARC's sensor network research effort. He serves as the founding Editor-In-Chief of ACM Transactions on Sensor Networks, and has authored or co-authored more than 100 technical papers and books, including a recent book published by Morgan Kaufmann, "Wireless Sensor Networks: An Information Processing Approach." He has received a number of awards, and his work has been featured in news media such as BBC World News, BusinessWeek, and Technology Review.

No abstract available.

Microservers are networked embedded devices that accept user tasks on demand and execute them on real world information collected by sensors. Sharing intermediate sensing and computing results among these tasks is critical for optimal resource utilization. This paper presents a service-oriented microserver runtime --- Share and its semantics-based task management design. Event semantics checking and conversion are based on a signal type system (STS) that captures both data values and service triggering. Based on the compatibility of event semantics, redundant computations in uncoordinated tasks are removed from the runtime. A prototype of Share has been experimented with a parking garage sensor network executing three uncoordinated user queries.

No abstract available.

No abstract available.

In a sensor network, data routing is tightly coupled to the needs of a sensing task, and hence the application semantics. This paper introduces the novel idea of information-directed routing, in which routing is formulated as a joint optimization of data transport and information aggregation. The routing objective is to minimize communication cost, while maximizing information gain, differing from routing considerations for more general ad hoc networks. The paper uses the concrete problem of locating and tracking possibly moving signal sources as an example of information generation process, and considers two common information extraction patterns in a sensor network: routing a user query from an arbitrary entry node to the vicinity of signal sources and back, or to a prespecified exit node, maximizing information accumulated along the path. We derive information constraints from realistic signal models, and present several routing algorithms that find near-optimal solutions for the joint optimization problem. Simulation results have demonstrated that information-directed routing is a significant improvement over a previously reported greedy algorithm, as measured by sensing quality such as localization and tracking accuracy and communication quality such as success rate in routing around sensor holes.

No abstract available.

Many networked embedded sensing and control systems can be modeled as hybrid systems with interacting continuous and discrete dynamics. These systems present significant challenges for monitoring and diagnosis. Many existing model-based approaches focus on diagnostic reasoning assuming appropriate fault signatures have been generated. However, an important missing piece is the integration of model-based techniques with the acquisition and processing of sensor signals and the modeling of faults to support diagnostic reasoning. This paper addresses key modeling and computational problems at the interface between model-based diagnosis techniques and signature analysis to enable the efficient detection and isolation of incipient and abrupt faults in hybrid systems. A hybrid automata model that parameterizes abrupt and incipient faults is introduced. Based on this model, an approach for diagnoser design is presented. The paper also develops a novel mode estimation algorithm that uses model-based prediction to focus distributed processing signal algorithms. Finally, the paper describes a diagnostic system architecture that integrates the modeling, prediction, and diagnosis components. The implemented architecture is applied to fault diagnosis of a complex electro-mechanical machine, the Xerox DC265 printer, and the experimental results presented validate the approach. A number of design trade-offs that were made to support implementation of the algorithms for online applications are also described.

This paper presents a new group communication scheme, roamingcast, for collaborative information processing in wireless sensor networks. Roamingcast enables efficient communication among a subset of mobile terminals in a collaboration group. Unicast and multicast communication can be considered as special cases of roamingcast in which the subset contains one and all group members, respectively. We propose a Roaming Hub Based Architecture (RoamHBA, pronounced as 'rumba') as one solution to support roaming-cast. We present the distributed construction and dynamic update of a multicast tree, referred as the roaming hub. This roaming hub has the property that an average pair of terminals communicate using the hub with only constant degradation in path length compared to the best possible path. We have developed network layer protocols implementing this mechanism and evaluated their performance in comparison with roaming restricted flooding. We simulated our design using NS-2.

This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. State-space models of physical phenomena such as those arising from tracking multiple interacting targets, while commonly used in signal processing and control, suffer from the curse of dimensionality as the number of phenomena of interest increases. Furthermore, mapping an inference algorithm onto a distributed sensor network must appropriately allocate scarce sensing and communication resources. We address the state-space explosion problem by developing a distributed state-space model that switches between factored and joint state spaces as appropriate. We develop a collaborative group abstraction as a mechanism to effectively support the information ow within and across subspaces of the state-space model, which can be efficiently supported in a communication-constrained network. The approach has been implemented and demonstrated in a simulation of tracking multiple interacting targets.

This paper proposes a vehicle-to-vehicle communication protocol for cooperative collision warning. Emerging wireless technologies for vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communications such as DSRC are promising to dramatically reduce the number of fatal roadway accidents by providing early warnings. One major technical challenge addressed in this paper is to achieve low-latency in delivering emergency warnings in various road situations. Based on a careful analysis of application requirements, we design an effective protocol, comprising congestion control policies, service differentiation mechanisms and methods for emergency warning dissemination. Simulation results demonstrate that the proposed protocol achieves low latency in delivering emergency warnings and efficient bandwidth usage in stressful road scenarios.

The ability to characterize sensing quality is central to the design and deployment of practical distributed sensor networks. This paper introduces the concept of a sensing field defining, for each point in the physical space of a phenomenon of interest, a measure of how well a sensor network can sense the phenomenon at that point. Using target localization and tracking as examples, the paper derives an upper bound for this measure of goodness measure, using the Cramer-Rao bound and models of sensor observation and network layout. It then evaluates the validity of statistical observation models used by a family of estimators. Simulation results of applying the analytical analysis to a randomly spaced network are presented.

Sensor tasking is essential to many sensing applications in resource-constrained wireless ad hoc sensor networks. In this paper, we present a multi-step lookahead algorithm for sensor selection and information routing. The algorithm is based on the information-driven sensor querying (IDSQ) that uses mutual information as a utility measure for potential information contribution of individual sensors, and extend it to prediction of information gain over a finite horizon while balancing cost such as the number of communication hops. Simulation results on target tracking problems have shown that the multi-step lookahead algorithm significantly improves the tracking performance compared to the original greedy algorithm, when "sensor holes" are present in a sensor network.

The development of lightweight sensing andcommunication protocols is a key requirement for designing resource constrained sensor networks. This paper introduces a set of efficient protocols and algorithms, DAM, EBAM, and EMLAM, for constructing and maintaining sensor aggregates that collectively monitor target activity in the environment. A sensor aggregate comprises those nodes in a network that satisfy a grouping predicate for a collaborative processing task. The parameters of the predicate depend on the task and its resource requirements. Since the foremost purpose of a sensor network is to selectively gather information about the environment, the formation of appropriate sensor aggregates is crucial for optimally allocating resources to sensing and communication tasks.This paper makes minimal assumptions about node onboard processing and communication capabilities so as to allow possible implementations on resource-constrained hardware. Factors affecting protocol performance are discussed. The paper presents simulation results showing how the protocol performance varies as key network and task parameters are varied. It also provides probabilistic analyses of network behavior consistent with the simulation results. The protocols have been experimentally validated on a sensor network testbed comprising 25 Berkeley MICA sensor motes.

Spatial Aggregation abstracts data arising from distributed embedded sensing and control applications as a set of so-called spatio-temporal objects. Locality and continuity in the underlying physics of a problem domain give rise to spatially coherent and temporally contiguous objects in an appropriate metric space. Once parameterized by physical properties such as location, intensity (e.g., light, temperature, pressure), and motion (e.g., velocity), these objects can be aggregated and abstracted into more abstract descriptions. Applications are written as the creation and transformation of these abstract objects. We illustrate how these objects naturally arise from applications such as distributed sensing and actuation, and use an air-jet table system to demonstrate how such a physics-based encapsulation modularizes the design of sensing and control software. Unlike in traditional software design, where objects and operations are defined mathematically and possess a semantics independent of possible implementations, the objects in distributed embedded software are defined by the physics of the application, algorithmic considerations, and task requirements, as well as optimization criteria. The air-jet table example demonstrates that the grouping and abstraction of actuation devices are determined by laws of motion, the type of force allocation algorithms used, and the desired performance of the controller; this encapsulation greatly simplifies the design and implementation of a force allocation algorithm for the system and improves software modularity. Based on our practical experiences in designing several massively distributed sensing and actuation systems, we present a set of recommendations for distributed embedded software modeling and design.

This paper describes information-based approaches to processing and organizing spatially distributed, multimodal sensor data in a sensor network. Energy-constrained networked sensing systems must rely on collaborative signal and information processing (CSIP) to dynamically allocate resources, maintain multiple sensing foci, and attend to new stimuli of interest, all based on task requirements and resource constraints. Target tracking is an essential capability for sensor networks and is used as a canonical problem for studying information organization problems in CSIP. After formulating a CSIP tracking problem in a distributed constrained optimization framework, the paper describes information-driven sensor query and other techniques for tracking individual targets as well as combinatorial tracking problems such as counting targets. Results from simulations and experimental implementations have demonstrated that these information-based approaches are scalable and make efficient use of scarce sensing and communication resources.

Model based approaches to diagnosis for dynamic systems have been based on continuous and discrete event models. Systems that combine continuous and discrete behaviors, i.e., hybrid systems have been typically abstracted into discrete event models or approximated by continuous models with steep slopes so that existing algorithms can be applied for fault isolation tasks. This approach runs into problems when both discrete events and continuous behaviors provide vital diagnostic information. We propose a diagnostic methodology that uses hybrid models of the system to perform diagnosis.

Visual thinking plays an important role in scientific reasoning. Based on the research in automating diverse reasoning tasks about dynamical systems, nonlinear controllers, kinematic mechanisms, and fluid motion, we have identified a style of visual thinking, imagistic reasoning. Imagistic reasoning organizes computations around image-like, analogue representations so that perceptual and symbolic operations can be brought to bear to infer structure and behavior. Programs incorporating imagistic reasoning have been shown to perform at an expert level in domains that defy current analytic or numerical methods. We have developed a computational paradigm, spatial aggregation, to unify the description of a class of imagistic problem solvers. A program written in this paradigm has the following properties. It takes a continuous field and optional objective functions as input, and produces high-level descriptions of structure, behavior, or control actions. It computes a multi-layer of intermediate representations, called spatial aggregates, by forming equivalence classes and adjacency relations. It employs a small set of generic operators such as aggregation, classification, and localization to perform bidirectional mapping between the information-rich field and successively more abstract spatial aggregates. It uses a data structure, the neighborhood graph, as a common interface to modularize computations. To illustrate our theory, we describe the computational structure of three implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the spatial aggregation generic operators by mixing and matching a library of commonly used routines.

Generality, representation, and control have been the central issues in machine recognition. Model-based recognition is the search for consistent matches of model and image features. We present a comparative framework for the evaluation of different approaches, particularly those of ACRONYM, RAF, and Ikeuchi et al. The strengths and weaknesses of these approaches are discussed and compared, and remedies are suggested. Various trade-offs made in the implementations are analyzed with respect to the systems' intended task domains. The requirements for a versatile recognition system are motivated. Several directions for future research are pointed out.