Open RAN is an emerging paradigm in mobile networks where the Radio Access Network (RAN) functions are disaggregated and virtualized on commodity servers. Despite the importance of Open RAN research, existing platforms often lack the fidelity and stability required to address a wide range of research problems. In response to this limitation, we have developed an enterprise-scale Open RAN testbed aimed at conducting state-of-the-art research in key areas that have received limited attention due to the lack of suitable platforms. In this poster, we provide an overview of the testbed we have created and examples of the research it has enabled, with the hope of catalyzing future open RAN research and innovation.

We organized an online fingerprint-based indoor localization competition in 2021. It attracted 1,170 teams worldwide. The teams were provided with a 60 GB dataset including WiFi, BLE, IMU, and geomagnetic field strength data collected from 204 buildings to build their localization algorithms, which were then evaluated against a separate test dataset. The competition received 28,009 submissions. The top team achieved an average accuracy of 1.50m. This paper reports the lessons we learned from analyzing the submissions, as well as our experiences in organizing the competition, through both qualitatively studying the teams' algorithms and quantitatively characterizing the competition results.

With advancement of recent network and chip technologies, IoT devices are becoming smarter with increasing compute power, bandwidth, and storage available on the device. This enables intelligent decision making and information transferring on the devices and unleashes the power of AIoT (Artificial Intelligence of Things) that supports applications such as smart city/agriculture/manufacturing/health care and self-driving scenarios.

With advancement of recent network and chip technologies, IoT devices are becoming smarter with increasing compute power, bandwidth, and storage available on the device. This enables intelligent decision making and information transferring on the devices and unleashes the power of AIoT (Artificial Intelligence of Things) that supports scenarios such as smart city/agriculture/manufacturing/health care and self-driving scenarios. The AIoT Workshop is a forum for researchers, scientists, engineers, and practitioners to share and learn AI powered IoT solutions. The AIoT is a multi-disciplinary area, which include but not limited to IoT, AI/ML, embedded systems, and networking. The 4th AIoT workshop will be hosted virtually in conjunction with the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021). The workshop program consists of keynote(s), invited talks, accepted technical paper presentations, as well as an indoor location competition panel.

Cameras are deployed at scale with the purpose of searching and tracking objects of interest (e.g., a suspected person) through the camera network on live videos. Such cross-camera analytics is data and compute intensive, whose costs grow with the number of cameras and time. We present Spatula, a cost-efficient system that enables scaling cross-camera analytics on edge compute boxes to large camera networks by leveraging the spatial and temporal cross-camera correlations. While such correlations have been used in computer vision community, Spatula uses them to drastically reduce the communication and computation costs by pruning search space of a query identity (e.g., ignoring frames not correlated with the query identity’s current position). Spatula provides the first system substrate on which cross-camera analytics applications can be built to efficiently harness the cross-camera correlations that are abundant in large camera deployments. Spatula reduces compute load by $8.3\times$ on an 8-camera dataset, and by $23\times-86\times$ on two datasets with hundreds of cameras (simulated from real vehicle/pedestrian traces). We have also implemented Spatula on a testbed of 5 AWS DeepLens cameras.

The world is witnessing an unprecedented increase in camera deployment. The USA and UK, for instance, have one camera for every 8 people. Video analytics from these cameras are becoming more and more pervasive, exerting important functions on a wide range of verticals including manufacturing, transportation, and retails. While vision techniques have seen considerable advancement, they have come at the expense of compute and network cost.

I am very happy to report that TSC has gained an Impact Factor (IF) of 3.520 and the 5-year IF of 4.245, both of which represent significant increases from the previous years. This further speaks to the global reputation of the journal and the amazing work done by the past EICs, all the current and past EB members, and reviewers - all of whom have volunteered their precious time despite their very busy schedule to support and contribute to the growth of this journal. I hope to count on your continued engagement for the future growth of this journal. Over this past year, several esteemed EB members have completed their terms of service to TSC after serving for several years. On behalf of the Services Computing community and the TSC EAB, I would like to thank the following Associate Editors who retired from TSC EB in 2017 for their invaluable service and contributions to the journal. Overall, I am very proud of the success that TSC has achieved in 2017. This would not have been possible without the continued support of the authors, readers, reviewers, TSC EAB, TSC EB, and the staff of IEEE and IEEE Computer Society. I look forward to exploring ways to further enhance the reputation and impact of our journal. I would love to hear your suggestions and comments, and I hope to have your continued support.

Steerable surveillance cameras offer a unique opportunity to support multiple vision applications simultaneously. However, state-of-art camera systems do not support this as they are often limited to one application per camera. We believe that we should break the one-to-one binding between the steerable camera and the application. By doing this we can quickly move the camera to a new view needed to support a different vision application. When done well, the scheduling algorithm can support a larger number of applications over an existing network of surveillance cameras. With this in mind we developed Panoptes, a technique that virtualizes a camera view and presents a different fixed view to different applications. A scheduler uses camera controls to move the camera appropriately providing the expected view for each application in a timely manner, minimizing the impact on application performance. Experiments with a live camera setup demonstrate that Panoptes can support multiple applications, capturing up to 80% more events of interest in a wide scene, compared to a fixed view camera.

Live streaming is an increasingly popular way to broadcast videos ranging from formal news channels to kitten cams to home security camera feeds. Live streaming marries the rich detail of video with the timeliness of live transmission and the ease of use of consumer cameras, thus promising to vastly increase the amount of detailed, up-to-the minute information available about the real world. The volume of potentially interesting footage brings up the question of how end-users can avoid being glued to one (or worse, many) streams of videos waiting for events of interest. In this demo, we present Lookout, a system that allows users to register standing queries, called triggers over live video streams. Lookout then notifies the user when events of interest to them occur in their streams of interest. For example, a user could point to a cat cam and write a trigger that sends a notification when the cat wakes up and starts moving. Users can also write triggers to look for certain news being covered in a live new channel, a gamer moving to a certain level in a Twitch stream, a stranger showing up in a outdoor surveillance camera, etc.

In this work we propose ParkMaster, a low-cost crowdsourcing architecture which exploits machine learning techniques and vision algorithms to evaluate parking availability in cities. While the user is normally driving ParkMaster enables off the shelf smartphones to collect information about the presence of parked vehicles by running image recognition techniques on the phones camera video streaming. The paper describes the design of ParkMaster's architecture and shows the feasibility of deploying such mobile sensor system in nowadays smartphones, in particular focusing on the practicability of running vision algorithms on phones.

The need for Internet access from moving vehicles has been steadily increasing in the past few years. Solutions that rely on cellular connectivity are becoming impractical to deploy due to technical and economic reasons. Car-Fi proposes an approach that leverages existing home Wi-Fi access points configured in dual-access mode, in order to offload all data traffic from the congested and expensive cellular infrastructure to whatever Wi-Fi network is available. Thanks to an improved scanning algorithm and numerous optimizations to the connection setup, Car-Fi makes downloading large amounts of data from a moving car feasible.

Internet-enabled cameras pervade daily life, generating a huge amount of data, but most of the video they generate is transmitted over wires and analyzed offline with a human in the loop. The ubiquity of cameras limits the amount of video that can be sent to the cloud, especially on wireless networks where capacity is at a premium. In this paper, we present Vigil, a real-time distributed wireless surveillance system that leverages edge computing to support real-time tracking and surveillance in enterprise campuses, retail stores, and across smart cities. Vigil intelligently partitions video processing between edge computing nodes co-located with cameras and the cloud to save wireless capacity, which can then be dedicated to Wi-Fi hotspots, offsetting their cost. Novel video frame prioritization and traffic scheduling algorithms further optimize Vigil's bandwidth utilization. We have deployed Vigil across three sites in both whitespace and Wi-Fi networks. Depending on the level of activity in the scene, experimental results show that Vigil allows a video surveillance system to support a geographical area of coverage between five and 200 times greater than an approach that simply streams video over the wireless network. For a fixed region of coverage and bandwidth, Vigil outperforms the default equal throughput allocation strategy of Wi-Fi by delivering up to 25% more objects relevant to a user's query.

Glimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the algorithms for object recognition entail significant computation, Glimpse runs them on server machines. When the latency between the server and mobile device is higher than a frame-time, this approach lowers object recognition accuracy. To regain accuracy, Glimpse uses an active cache of video frames on the mobile device. A subset of the frames in the active cache are used to track objects on the mobile, using (stale) hints about objects that arrive from the server from time to time. To reduce network bandwidth usage, Glimpse computes trigger frames to send to the server for recognizing and labeling. Experiments with Android smartphones and Google Glass over Verizon, AT&T, and a campus Wi-Fi network show that with hardware face detection support (available on many mobile devices), Glimpse achieves precision between 96.4% to 99.8% for continuous face recognition, which improves over a scheme performing hardware face detection and server-side recognition without Glimpse's techniques by between 1.8-2.5×. The improvement in precision for face recognition without hardware detection is between 1.6-5.5×. For road sign recognition, which does not have a hardware detector, Glimpse achieves precision between 75% and 80%; without Glimpse, continuous detection is non-functional (0.2%-1.9% precision).

Glimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the algorithms for object recognition entail significant computation, Glimpse runs them on server machines. To achieve high accuracy, Glimpse uses an active cache of video frames on the mobile device. A subset of the frames in the active cache are used to track objects on the mobile, using (stale) hints about objects that arrive from the server from time to time. To reduce network bandwidth usage, Glimpse computes trigger frames to send to the server for recognizing and labeling.

Low latency analytics on geographically distributed datasets (across datacenters, edge clusters) is an upcoming and increasingly important challenge. The dominant approach of aggregating all the data to a single datacenter significantly inflates the timeliness of analytics. At the same time, running queries over geo-distributed inputs using the current intra-DC analytics frameworks also leads to high query response times because these frameworks cannot cope with the relatively low and variable capacity of WAN links. We present Iridium, a system for low latency geo-distributed analytics. Iridium achieves low query response times by optimizing placement of both data and tasks of the queries. The joint data and task placement optimization, however, is intractable. Therefore, Iridium uses an online heuristic to redistribute datasets among the sites prior to queries' arrivals, and places the tasks to reduce network bottlenecks during the query's execution. Finally, it also contains a knob to budget WAN usage. Evaluation across eight worldwide EC2 regions using production queries show that Iridium speeds up queries by 3× -- 19× and lowers WAN usage by 15% -- 64% compared to existing baselines.

The great French writer, historian and philosopher Voltaire once asked, "Is there anyone so wise as to learn by the experience of others?" In celebration of the 20th MobiCom conference, join us for a thought provoking discussion between pioneers of our field on the lessons they have learned over their illustrious career paths that accelerated the pace of technology adoption and enabled world-wide societal impact. Learn from our heroes as they share with us gems of wisdom on how to choose a great problem, what to avoid and how to be successful as you build your own remarkable careers.

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.

A growing number of mobile apps are exploiting smartphone sensors to infer user behavior, activity, or context. Inference requires training using labeled ground truth data. Obtaining labeled data for new apps is a "chicken-egg" problem. Without a reasonable amount of labeled data, apps cannot provide any service. But until an app provides useful service it is not worth installing and has no opportunity to collect user data. This paper aims to address this problem. Our intuition is that even though users are different, they exhibit similar patterns on certain sensing dimensions. For instance, different users may walk and drive at different speeds, but certain speeds will indicate driving for all users. These common patterns could be used as "seeds" to model new users through semi-supervised learning. We prototype a technique to automatically extract the commonalities to seed personalized inference models for new users. We evaluate the proposed technique through example apps and real world data.

Many context aware applications can benefit from using high-level sensing results with semantic meanings (e.g, busy/idle). This paper proposes a platform design that provides high-level "virtual sensor" abstractions and enables new virtual sensors to be bootstrapped from existing ones.

The 2010 FCC ruling on white spaces proposes relying on a database of incumbents as the primary means of determining white space availability at any white space device (WSD). While the ruling provides broad guidelines for the database, the specifics of its design, features, implementation, and use are yet to be determined. Furthermore, architecting a network where all WSDs rely on the database raises several systems and networking challenges that have remained unexplored. Also, the ruling treats the database only as a storehouse for incumbents. We believe that the mandated use of the database has an additional opportunity: a means to dynamically manage the RF spectrum. Motivated by this opportunity, in this paper, we present SenseLess, a database-driven white spaces network. As suggested by its very name, in SenseLess, WSDs rely on a database service to determine white spaces availability as opposed to spectrum sensing. The service, using a combination of an up-to-date database of incumbents, sophisticated signal propagation modeling, and an efficient content dissemination mechanism to ensure efficient, scalable, and safe white space network operation. We build, deploy, and evaluate SenseLess and compare our results to ground truth spectrum measurements. We present the unique system design considerations that arise due to operating over the white spaces. We also evaluate its efficiency and scalability. To the best of our knowledge, this is the first paper that identifies and examines the systems and networking challenges that arise from operating a white space network, which is solely dependent on a channel occupancy database.

Accurate, fine-grained online energy estimation and accounting of mobile devices such as smartphones is of critical importance to understanding and debugging the energy consumption of mobile applications. We observe that state-of-the-art, utilization-based power modeling correlates the (actual) utilization of a hardware component with its power state, and hence is insufficient in capturing several power behavior not directly related to the component utilization in modern smartphones. Such behavior arise due to various low level power optimizations programmed in the device drivers. We propose a new, system-call-based power modeling approach which gracefully encompasses both utilization-based and non-utilization-based power behavior. We present the detailed design of such a power modeling scheme and its implementation on Android and Windows Mobile. Our experimental results using a diverse set of applications confirm that the new model significantly improves the fine-grained as well as whole-application energy consumption accuracy. We further demonstrate fine-grained energy accounting enabled by such a fined-grained power model, via amanually implemented eprof, the energy counterpart of the classic gprof tool, for profiling application energy drain.

No abstract available.

Supporting interactive, multiplayer games on mobile phones over cellular networks is a difficult problem. It is particularly relevant now with the explosion of mostly single-player or turn-based games on mobile phones. The challenges stem from the highly variable performance of cellular networks and the need for scalability (not burdening the cellular infrastructure, nor any server resources that a game developer deploys). We have built a service for matchmaking in mobile games -- assigning players to games such that game settings are satisfied as well as latency requirements for an enjoyable game. This requires solving two problems. First, the service needs to know the cellular network latency between game players. Second, the service needs to quickly group players into viable game sessions. In this paper, we present the design of our service, results from our experiments on predicting cellular latency, and results from efficiently grouping players into games.

The 60 GHz wireless technology that is now emerging has the potential to provide dense and extremely fast connectivity at low cost. In this paper, we explore its use to relieve hotspots in oversubscribed data center (DC) networks. By experimenting with prototype equipment, we show that the DC environment is well suited to a deployment of 60GHz links contrary to concerns about interference and link reliability. Using directional antennas, many wireless links can run concurrently at multi-Gbps rates on top-of-rack (ToR) switches. The wired DC network can be used to sidestep several common wireless problems. By analyzing production traces of DC traffic for four real applications, we show that adding a small amount of network capacity in the form of wireless flyways to the wired DC network can improve performance. However, to be of significant value, we find that one hop indirect routing is needed. Informed by our 60GHz experiments and DC traffic analysis, we present a design that uses DC traffic levels to select and adds flyways to the wired DC network. Trace-driven evaluations show that network-limited DC applications with predictable traffic workloads running on a 1:2 oversubscribed network can be sped up by 45% in 95% of the cases, with just one wireless device per ToR switch. With two devices, in 40% of the cases, the performance is identical to that of a non-oversubscribed network.

There are a lot of applications that run on modern mobile operating systems. Inevitably, some of these applications fail in the hands of users. Diagnosing a failure to identify the culprit, or merely reproducing that failure in the lab is difficult. To get insight into this problem, we interviewed developers of five mobile applications and analyzed hundreds of trouble tickets. We find that support for diagnosing unexpected application behavior is lacking across major mobile platforms. Even when developers implement heavy-weight logging during controlled trials, they do not discover many dependencies that are then stressed in the wild. They are also not well-equipped to understand how to monitor the large number of dependencies without impacting the phone's limited resources such as CPU and battery. Based on these findings, we argue for three fundamental changes to failure reporting on mobile phones. The first is spatial spreading, which exploits the large number of phones in the field by spreading the monitoring work across them. The second is statistical inference, which builds a conditional distribution model between application behavior and its dependencies in the presence of partial information. The third is adaptive sampling, which dynamically varies what each phone monitors, to adapt to both the varying population of phones and what is being learned about each failure. We propose a system called MobiBug that combines these three techniques to simplify the task of diagnosing mobile applications.

We argue that heterogeneity is hindering technological innovation in the home---homes differ in terms of their devices and how those devices are connected and used. To abstract these differences, we propose to develop a home-wide operating system. A HomeOS can simplify application development and let users easily add functionality by installing new devices or applications. The development of such an OS is an inherently inter-disciplinary exercise. Not only must the abstractions meet the usual goals of being efficient and easy to program, but the underlying primitives must also match how users want to manage and secure their home. We describe the preliminary design of HomeOS and our experience with developing applications for it.

This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.

The use of cellular data networks is increasingly popular due to the widespread deployment of 3G technologies and the rapid adoption of smartphones, such as iPhone and GPhone. Besides email and web browsing, a variety of network applications are now available, rendering smartphones potentially useful substitutes for their desktop counterparts. Nevertheless, the performance of smartphone applications in the wild is still poorly understood due to a lack of systematic measurement methodology.

The proposed demonstration is based on commodity 802.11 wireless cards and a low cost 2.4GHz sniffing device and shows how current WLAN based networks can benefit from spectrum awareness and dynamic access to the assigned band. The demonstrator presents a solution to problems in current wireless networks, like inefficient radio spectrum usage and limited ability to withstand interferences. The presented spectrum-aware radio management framework flexibly negotiates transmission parameters based on spectrum usage and application requirements and opportunistically utilizes available bandwidth. A key feature of this demonstrator is the fact that it breaks the traditional fixed channel bandwidth limitation and enables dynamic bandwidth allocation according to the needs of applications. It continuously monitors the assigned spectrum, tracks application behaviour, and dynamically adapts to the radio environment, such as interference and competition. Furthermore, a cross-media roaming mechanism is provided in the framework to support seamless handovers within and between radio technologies. Our enhancements are transparent to upper layer applications and will automatically benefit any software using network resources. The presented demonstrator uses wireless video streaming in a typical household scenario to illustrate the benefits without changes to the multimedia applications.

Networking over UHF white spaces is fundamentally different from conventional Wi-Fi along three axes: spatial variation, temporal variation, and fragmentation of the UHF spectrum. Each of these differences gives rise to new challenges for implementing a wireless network in this band. We present the design and implementation of Net7, the first Wi-Fi like system constructed on top of UHF white spaces. Net7 incorporates a new adaptive spectrum assignment algorithm to handle spectrum variation and fragmentation, and proposes a low overhead protocol to handle temporal variation. builds on a simple technique, called SIFT, that reduces the time to detect transmissions in variable channel width systems by analyzing raw signals in the time domain. We provide an extensive evaluation of the system in terms of a prototype implementation and detailed experimental and simulation results.

By studying trouble tickets from small enterprise networks, we conclude that their operators need detailed fault diagnosis. That is, the diagnostic system should be able to diagnose not only generic faults (e.g., performance-related) but also application specific faults (e.g., error codes). It should also identify culprits at a fine granularity such as a process or firewall configuration. We build a system, called NetMedic, that enables detailed diagnosis by harnessing the rich information exposed by modern operating systems and applications. It formulates detailed diagnosis as an inference problem that more faithfully captures the behaviors and interactions of fine-grained network components such as processes. The primary challenge in solving this problem is inferring when a component might be impacting another. Our solution is based on an intuitive technique that uses the joint behavior of two components in the past to estimate the likelihood of them impacting one another in the present. We find that our deployed prototype is effective at diagnosing faults that we inject in a live environment. The faulty component is correctly identified as the most likely culprit in 80% of the cases and is almost always in the list of top five culprits.

Currently deployed IEEE 802.11 WLANs (Wi-Fi networks) share access point (AP) bandwidth on a per-packet basis. However, the various stations communicating with the AP often have different signal qualities, resulting in different transmission rates. This induces a phenomenon known as the rate anomaly problem, in which stations with lower signal quality transmit at lower rates and consume a significant majority of airtime, thereby dramatically reducing the throughput of stations transmitting at high rates.We propose a practical, deployable system, called Soft-Repeater, in which stations cooperatively address the rate anomaly problem. Specifically, higher-rate Wi-Fi stations opportunistically transform themselves into repeaters for stations with low data-rates when transmitting to/from the AP. The key challenge is to determine when it is beneficial to enable the repeater functionality. In this paper, we propose an initiation protocol that ensures that repeater functionality is enabled only when appropriate. Also, our system can run directly on top of today's 802.11 infrastructure networks.We evaluate our system using simulation and testbed implementation, and find that SoftRepeater can improve cumulative throughput by up to 200%.

Traditionally, the channelization structure in IEEE 802.11-based wireless LANs has been fixed: Each access point (AP) is assigned one channel and all channels are equally wide. In contrast, it has recently been shown that even on commodity hardware, the channel-width can be adapted dynamically purely in software. Leveraging this capability, we study the use of dynamic-width channels, where every AP adaptively adjusts not only its center-frequency, but also its channel-width to match its traffic load. This gives raise to a novel optimization problem that differs from previously studied channel assignment problems. We propose efficient spectrum-distribution algorithms and evaluate their effectiveness through analysis and simulations using real-world traces. Our results indicate that by allocating more spectrum to highly-loaded APs, the overall spectrum-utilization can be substantially improved and the notorious load-balancing problem in WLANs can be solved naturally.

We study a fundamental yet under-explored facet in wireless communication -- the width of the spectrum over which transmitters spread their signals, or the channel width. Through detailed measurements in controlled and live environments, and using only commodity 802.11 hardware, we first quantify the impact of channel width on throughput, range, and power consumption. Taken together, our findings make a strong case for wireless systems that adapt channel width. Such adaptation brings unique benefits. For instance, when the throughput required is low, moving to a narrower channel increases range and reduces power consumption; in fixed-width systems, these two quantities are always in conflict. We then present a channel width adaptation algorithm, called SampleWidth, for the base case of two communicating nodes. This algorithm is based on a simple search process that builds on top of existing techniques for adapting modulation. Per specified policy, it can maximize throughput or minimize power consumption. Evaluation using a prototype implementation shows that SampleWidth correctly identities the optimal width under a range of scenarios. In our experiments with mobility, it increases throughput by more than 60% compared to the best fixed-width configuration.

The Federal Communications Commission (FCC) is currently exploring the use of TV bands for unlicensed communication. This step has sparked significant interest in the research and corporate community as it opens up new possibilities for high speed and long range wireless communication. In this paper, we present the design and implementation of a complete system that can detect the presence of TV signals, and perform high-speed data communication in an available TV band without interfering with neighboring TV bands. To the best of our knowledge, this is the first known system with all the above capabilities.

A number of studies have shown the abundance of unused spectrum in the TV bands. This is in stark contrast to the overcrowding of wireless devices in the ISM bands. A recent trend to alleviate this disparity is the design of Cognitive Radios, which constantly sense the spectrum and opportunistically utilize unused frequencies in the TV bands. A key challenge in the design of such networks is that of Spectrum Allocation, which enables nodes to reserve chunks of the spectrum for certain periods of time. In this paper, we introduce the concept of a time-spectrum block to model spectrum reservation, and use it to present a theoretical formalization of the spectrum allocation problem. We also present a centralized and a distributed protocol for spectrum allocation and show that these protocols are close to optimal in most scenarios. We have implemented the distributed protocol in QualNet and show that our analysis closely matches the simulation results.

IP based telephony is rapidly gaining acceptance over traditional means of voice communication. Wireless LANs are also becoming ubiquitous due to their inherent ease of deployment and decreasing costs. In enterpriseWi-Fi environments, VoIP is a compelling application for devices such as smart phones with multiple wireless interfaces. However, the high energy consumption of Wi-Fi interfaces, especially when a device is idle,presents a significant barrier to the widespread adoption of VoIP over Wi-Fi.To address this issue, we present Cell2Notify, a practical and deployable energy management architecture that leverages the cellular radio on a smart phone to implement wakeup for the high-energy consumption Wi-Fi radio. We present detailed measurements of energy consumption on smart phone devices, and we show that Cell2Notify, can extend the battery lifetime of VoIPover Wi-Fi enabled smart phones by a factor of 1.7 to 6.4.

Localizing the sources of performance problems in large enterprise networks is extremely challenging. Dependencies are numerous, complex and inherently multi-level, spanning hardware and software components across the network and the computing infrastructure. To exploit these dependencies for fast, accurate problem localization, we introduce an Inference Graph model, which is well-adapted to user-perceptible problems rooted in conditions giving rise to both partial service degradation and hard faults. Further, we introduce the Sherlock system to discover Inference Graphs in the operational enterprise, infer critical attributes, and then leverage the result to automatically detect and localize problems. To illuminate strengths and limitations of the approach, we provide results from a prototype deployment in a large enterprise network, as well as from testbed emulations and simulations. In particular, we find that taking into account multi-level structure leads to a 30% improvement in fault localization, as compared to two-level approaches.

Wireless LAN administrators often have to deal with the problem of sporadic client congestion in popular locations within the network. Existing approaches that relieve congestion by balancing the traffic load are encumbered by the modifications that are required to both access points and clients. We propose cell breathing, a well-known concept in cellular telephony, as a load balancing mechanism to handle client congestion in a wireless LAN. We develop power management algorithms for controlling the coverage of access points to handle dynamic changes in client workloads. We further incorporate hand-off costs and manufacturer specified power level constraints into our algorithms. Our approach does not require modification to clients or to the standard. It only changes the transmission power of beacon packets and does not change the transmission power of data packets to avoid the interactions with auto-rating. We analyze the worst-case bounds of the algorithms and show that they are either optimal or close to optimal. In addition, we evaluate our algorithms empirically using synthetic and real wireless LAN traces. Our results show that cell breathing significantly outperforms the commonly used fixed power scheme and performs at par with sophisticated load balancing schemes that require changes to both the client and access points

We present a framework for monitoring enterprise wireless networks using desktop infrastructure. The framework is called DAIR, which is short for Dense Array of Inexpensive Radios. We demonstrate that the DAIR framework is useful for detecting rogue wireless devices (e.g., access points) attached to corporate networks, as well as for detecting Denial of Service attacks on Wi-Fi networks.Prior proposals in this area include monitoring the network via a combination of access points (APs), mobile clients, and dedicated sensor nodes. We show that a dense deployment of sensors is necessary to effectively monitor Wi-Fi networks for certain types of threats, and one can not accomplish this using access points alone. An ordinary, single-radio AP can not monitor multiple channels effectively, without adversely impacting the associated clients. Moreover, we show that a typical deployment of access points is not sufficiently dense to detect the presence of rogue wireless devices. Due to power constraints, mobile devices can provide only limited assistance in monitoring wireless networks. Deploying a dense array of dedicated sensor nodes is an expensive proposition.Our solution is based on two simple observations. First, in most enterprise environments, one finds plenty of desktop machines with good wired connectivity, and spare CPU and disk resources. Second, inexpensive USB-based wireless adapters are commonly available. By attaching these adapters to desktop machines, and dedicating the adapters to the task of monitoring the wireless network, we create a low cost management infrastructure.

There is a fair amount of evidence that mesh (static multihop wireless) networks are gaining popularity, both in the academic literature and in the commercial space. Nonetheless, none of the prior work has evaluated the feasibility of applications on mesh through the use of deployed networks and real user traffic. The state of the art is the use of deployed testbeds with synthetic traces consisting of random traffic patterns.In this paper, we evaluate the feasibility of a mesh network for an all-wireless office using traces of office users and an actual 21-node multi-radio mesh testbed in an office area. Unlike previous mesh studies that have examined routing design in detail, we examine how different office mesh design choices impact the performance of user traffic. From our traces of 11 users spanning over a month, we identify 3 one hour trace periods with different characteristics and evaluate network performance for them. In addition, we consider different user-server placement, different wireless hardware, different wireless settings and different routing metrics.We find that our captured traffic is significantly different from the synthetic workloads typically used in the prior work. Our trace capture and replay methodology allows us to directly quantify the feasibility of office meshes by measuring the additional delay experienced by individual transactions made by user applications. Performance on our mesh network depends on the routing metric chosen, the user-server placement and the traffic load period. The choice of wireless hardware and wireless settings has a significant impact on performance under heavy load and challenging placement. Ultimately we conclude that for our traces and deployed system, under most conditions, all-wireless office meshes are feasible. In most cases, individual transactions incur under 20ms of additional delay over the mesh network. We believe this is an acceptable delay for most applications where a wired network to every machine is not readily available. We argue that our results are scalable to a network of over 100 users.

Radio spectrum allocated for use in unlicensed wireless networks is distributed across non-contiguous frequency bands. Existing MAC protocols, like IEEE 802.11, operate only in contiguous bands. Several small slices of frequency are available in lower frequency bands that are not utilized. We propose utilizing a sliver of unused spectrum in the lower frequency band as a low rate control channel to improve the capacity of infrastructure and multi-hop wireless networks. The proposed control channel-based MAC Protocol (C/sup 2/M) increases the throughput by moving the contention resolution overheads to the separate low rate channel. We allow simultaneous channel contention and data transmission by incorporating advance reservation on the control channel, and data aggregation on the data channel. Simulation results show that compared to IEEE 802.11, C/sup 2/M significantly improves network performance.

A discussion of "openness" and wireless networking focusing on open source, open spectrum, and open architecture issues and their interrelations. This panel is composed of experts in open source development, legal issues, economic issues, and industry concerns; and is moderated by a policy and regulatory expert from the Federal Communications Commission. Panelists were chosen for their expertise in specific areas as well as a demonstrated breadth of knowledge in other facets in wireless communications. The plenary provides multiple perspectives on the importance of reforming access to the public airwaves -- including different foci and priorities -- while allowing ample time for audience participation and panelist debate. The moderator and four panelists will each hand out a "topic briefing" for plenary audience members that summarizes the take-home message and most important points from each of their perspectives.

Effective network troubleshooting is critical for maintaining efficient and reliable network operation. Troubleshooting is especially challenging in multihop wireless networks because the behavior of such networks depends on complicated interactions between many unpredictable factors such as RF noise, signal propagation, node interference, and traffic flows. In this paper we propose a new direction for research on fault diagnosis in wireless networks. Specifically, we present a diagnostic system that employs trace-driven simulations to detect faults and perform root cause analysis. We apply this approach to diagnose performance problems caused by packet dropping, link congestion, external noise, and MAC misbehavior. In a 25 node multihop wireless network, we are able to diagnose over 10 simultaneous faults of multiple types with more than 80% coverage. Our framework is general enough for a wide variety of wireless and wired networks.

Fairness is an important issue when accessing a shared wireless channel. With fair scheduling, it is possible to allocate bandwidth in proportion to weights of the packet flows sharing the channel. This paper presents a fully distributed algorithm for fair scheduling in a wireless LAN. The algorithm can be implemented without using a centralized coordinator to arbitrate medium access. The proposed protocol is derived from the Distributed Coordination Function in the IEEE 802.11 standard. Simulation results show that the proposed algorithm is able to schedule transmissions such that the bandwidth allocated to different flows is proportional to their weights. An attractive feature of the proposed approach is that it can be implemented with simple modifications to the IEEE 802.11 standard.

The topology of a wireless multi-hop network can be controlled by varying the transmission power at each node. In this paper, we give a detailed analysis of a cone-based distributed topology-control (CBTC) algorithm. This algorithm does not assume that nodes have GPS information available; rather it depends only on directional information. Roughly speaking, the basic idea of the algorithm is that a node u transmits with the minimum power p/sub u,/spl alpha// required to ensure that in every cone of degree /spl alpha/ around u, there is some node that u can reach with power p/sub u,/spl alpha//. We show that taking /spl alpha/=5/spl pi//6 is a necessary and sufficient condition to guarantee that network connectivity is preserved. More precisely, if there is a path from s to t when every node communicates at maximum power then, if /spl alpha//spl les/5/spl pi//6, there is still a path in the smallest symmetric graph G/sub /spl alpha// containing all edges (u,v) such that u can communicate with v using power p/sub u,/spl alpha//. On the other hand, if /spl alpha/>5/spl pi//6, connectivity is not necessarily preserved. We also propose a set of optimizations that further reduce power consumption and prove that they retain network connectivity. Dynamic reconfiguration in the presence of failures and mobility is also discussed. Simulation results are presented to demonstrate the effectiveness of the algorithm and the optimizations.

We present a link layer protocol called the multi-radio unification protocol or MUP. On a single node, MUP coordinates the operation of multiple wireless network cards tuned to non-overlapping frequency channels. The goal of MUP is to optimize local spectrum usage via intelligent channel selection in a multihop wireless network. MUP works with standard-compliant IEEE 802.11 hardware, does not require changes to applications or higher-level protocols, and can be deployed incrementally. The primary usage scenario for MUP is a multihop community wireless mesh network, where cost of the radios and battery consumption are not limiting factors. We describe the design and implementation of MUP, and analyze its performance using both simulations and measurements based on our implementation. Our results show that under dynamic traffic patterns with realistic topologies, MUP significantly improves both TCP throughput and user perceived latency for realistic workloads.

There are a number of scenarios where it is desirable to have a wireless device connect to multiple networks simultaneously. Currently, this is possible only by using multiple wireless network cards in the device. Unfortunately, using multiple wireless cards causes excessive energy drain and consequent reduction of lifetime in battery operated devices. We propose a software based approach, called MultiNet, that facilitates simultaneous connections to multiple networks by virtualizing a single wireless card. The wireless card is virtualized by introducing an intermediate layer below IP, which continuously switches the card across multiple networks. The goal of the switching algorithm is to he transparent to the user who sees her machine as being connected to multiple networks. We present the design, implementation, and performance of the MultiNet system. We analyze and evaluate buffering and switching algorithms in terms of delay and energy consumption. Our system is agnostic of the upper layer protocols, and works well over popular IEEE 802.11 wireless LAN cards.

The wide-scale deployment of IEEE 802.11 wireless networks has generated significant challenges for Information Technology (IT) departments in corporations. Users frequently complain about connectivity and performance problems, and network administrators are expected to diagnose these problems while managing corporate security and coverage. Their task is particularly difficult due to the unreliable nature of the wireless medium and a lack of intelligent diagnostic tools for determining the cause of these problems.This paper presents an architecture for detecting and diagnosing faults in IEEE 802.11 infrastructure wireless networks. To the best of our knowledge, ours is the first paper to address fault diagnostic issues for these networks. As part of our architecture, we propose and evaluate a novel technique called Client Conduit, which enables boot-strapping and fault diagnosis of disconnected clients. We describe techniques for analyzing performance problems faced in a wireless LAN deployment. We also present an approach for detecting unauthorized access points. We have built a prototype of our fault diagnostic architecture on the Windows operating system using off-the-shelf IEEE 802.11 cards. The initial results show that our mechanisms are effective; furthermore, they impose low overheads when clients are not experiencing problems.

Capacity improvement is one of the principal challenges in wireless networking. We present a link-layer protocol called Slotted Seeded Channel Hopping, or SSCH, that increases the capacity of an IEEE 802.11 network by utilizing frequency diversity. SSCH can be implemented in software over an IEEE 802.11-compliant wireless card. Each node using SSCH switches across channels in such a manner that nodes desiring to communicate overlap, while disjoint communications mostly do not overlap, and hence do not interfere with each other. To achieve this, SSCH uses a novel scheme for distributed rendezvous and synchronization. Simulation results show that SSCH significantly increases network capacity in several multi-hop and single-hop wireless networking scenarios.

The demand for an all-in-one phone with integrated personal information management and data access capabilities is beginning to accelerate. While personal digital assistants (PDAs) with built-in cellular, WiFi, and Voice-Over-IP technologies have the ability to serve these needs in a single package, the rate at which energy is consumed by PDA-based phones is very high. Thus, these devices can quickly drain their own batteries and become useless to their owner.In this paper, we introduce a technique to increase the battery lifetime of a PDA-based phone by reducing its idle power, the power a device consumes in a "standby" state. To reduce the idle power, we essentially shut down the device and its wireless network card when the device is not being used---the device is powered only when an incoming call is received. Using this technique, we can increase the battery lifetime by up to 115%.In this paper, we describe the design of our "wake-on-wireless" energy-saving strategy and the prototype device we implemented. To evaluate our technique, we compare it with alternative approaches. Our results show that our technique can provide a significant lifetime improvement over other technologies.

With the increasing popularity of handheld devices and wireless local area networks (LANs), real-time applications such as Internet telephony are poised to become ubiquitous. While there has been a substantial amount of research on quality of service problems in the Internet, most end-to-end bandwidth allocation approaches, such as RSVP, have had limited success due to scalability and deployment issues. Starting with the observation that reserving bandwidth in the Internet backbone requires substantial infrastructure support, but reserving bandwidth in the first hop does not, we only focus on the first-hop reservation. We evaluate several first hop allocation schemes and determine their effectiveness in improving end-to-end performance. Since utilization of the reserved first-hop bandwidth depends on the remaining Internet path throughput, we characterize this throughput using traces collected from a popular Web site. Our analysis shows that different clients experience widely different throughputs, and that a significant portion of the clients receive very low throughput (e.g. less than 20 Kbps). We then evaluate several bandwidth allocation schemes for various congestion scenarios. Our results show that the scheme which takes into account of both the application data rate and available Internet path bandwidth yields the best performance. Moreover, the scheme performs even better if it adapts to the changing path properties. We discuss how path bandwidth can be measured without active probing, how frequently it needs to be measured, and how this measurement is incorporated into the first-hop bandwidth allocation algorithm.

This paper presents and analyzes user behavior and network performance in a public-area wireless network using a workload captured at a well-attended ACM conference. The goals of our study are: (1) to extend our understanding of wireless user behavior and wireless network performance; (2) to characterize wireless users in terms of a parameterized model for use with analytic and simulation studies involving wireless LAN traffic; and (3) to apply our workload analysis results to issues in wireless network deployment, such as capacity planning, and potential network optimizations, such as algorithms for load balancing across multiple access points (APs) in a wireless network.

Alerts refer to the delivery of user-subscribed information to the user. As the number of alert services and the types of information delivery devices increase, a new model that allows users to manage alert delivery and avoid alert overflow is needed. The unique dependability challenge in the management of alerts is in the proper use of redundancy to achieve timeliness and reliability without being unduly intrusive or cumbersome. We describe the design, implementation, and user experience of an alert service architecture, called SIMBA. SIMBA utilizes Instant Messaging with acknowledgements as the universal, reliable alert delivery channel, with emails being the fallback channel. All alerts that a user subscribes to are first directed to the user's MyAlertBuddy, which allows centralized delivery preference customization and acts as a personal alert router to protect the privacy of user addresses. Delivery modes, each of which involves multiple user addresses to accommodate communication failures, are supported as an abstraction for specifying personalized dependability levels. A working implementation of the SIMBA system, which integrates five different types of alert services, is described. Challenges and techniques in maintaining a highly available MyAlertBuddy to avoid single-point of failure are discussed. The concept of exception handling automation is introduced for enhancing the robustness of applications that drive third-party communication client software through automation interfaces.

We have built a network, called the CHOICE network, which globally authenticates users and then securely connects them to the Internet via a high-speed local area wireless network. Our network provides easy-to-use, individual-centric, service-oriented wireless Internet access in places other than the traditional corporate offices and homes. Our architecture is hardware and protocol agnostic and is built on an easily deployable software module called the protocol for authorization and negotiation of services or PANS. PANS provides authorization, access, privacy, security, policy enforcement, quality of service (QoS) and accounting. In this paper, we describe PANS in detail. We discuss our system design and operation, implementation and performance. We evaluate PANS and show that it is scalable and secure. Our network has been deployed and is operational at a local mall in Bellevue, Washington.

The topology of wireless multihop ad hoc networks can be controlled by varying the transmission power of each node. We propose a simple distributed algorithm where each node makes local decisions about its transmission power and these local decisions collectively guarantee global connectivity. Specifically, based on the directional information, a node grows it transmission power until it finds a neighbor node in every direction. The resulting network topology increases the network lifetime by reducing the transmission power and reduces traffic interference by having low node degrees. Moreover, we show that the routes in the multihop network are efficient in power consumption. We give an approximation scheme in which the power consumption of each route can be made arbitrarily close to the optimal by carefully choosing the parameters. Simulation results demonstrate significant performance improvements.

Wireless local area networks (W-LANs) have become increasingly popular due to the recent availability of affordable devices that are capable of communicating at high data rates. These high rates are possible, in part, through new modulation schemes that are optimized for the channel conditions bringing about a dramatic increase in bandwidth efficiency. Since the choice of which modulation scheme to use depends on the current state of the transmission channel, newer wireless devices often support multiple modulation schemes, and hence multiple datarates, with mechanisms to switch between them Users are given the option to either select an operational datarate manually or to let the device automatically choose the appropriate modulation scheme (data rate) to match the prevailing conditions. Automatic rate selection protocols have been studied for cellular networks but there have been relatively few proposals for W-LANs. In this paper we present a rate adaptive MAC protocol called the Receiver-Based AutoRate (RBAR) protocol. The novelty of RBAR is that its rate adaptation mechanism is in the receiver instead of in the sender. This is in contrast to existing schemes in devices like the WaveLAN II [15]. We show that RBAR is better because it results in a more efficient channel quality estimation which is then reflected in a higher overall throughput Our protocol is based on the RTS/CTS mechanism and consequently it can be incorporated into many medium access control protocols including the widely popular IEEE 802.11 protocol. Simulation results of an implementation of RBAR inside IEEE 802.11 show that RBAR performs consistently well.

The topology of a wireless multi-hop network can be controlled by varying the transmission power at each node. In this paper, we give a detailed analysis of a cone-based distributed topology control algorithm. This algorithm, introduced in [16], does not assume that nodes have GPS information available; rather it depends only on directional information. Roughly speaking, the basic idea of the algorithm is that a node u transmits with the minimum power pu, α required to ensure that in every cone of degree α around u, there is some node that u can reach with power pu, α. We show that taking α = 5π/6 is a necessary and sufficient condition to guarantee that network connectivity is preserved. More precisely, if there is a path from s to t when every node communicates at maximum power then, if α ⪇ 5π/6, there is still a path in the smallest symmetric graph Gα containing all edges (u, v) such that u can communicate with v using power pu, α. On the other hand, if α > 5π/6, connectivity is not necessarily preserved. We also propose a set of optimizations that further reduce power consumption and prove that they retain network connectivity. Dynamic reconfiguration in the presence of failures and mobility is also discussed. Simulation results are presented to demonstrate the effectiveness of the algorithm and the optimizations.

The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.

Fairness is an important issue when accessing a shared wireless channel. With fair scheduling, it is possible to allocate bandwidth in proportion to weightsof the packet flows sharing the channel. This paper presents a fully distributed algorithm for fair scheduling in a wireless LAN. The algorithm can be implemented without using a centralized coordinator to arbitrate medium access. The proposed protocol is derived from the Distributed Coordination Function in the IEEE 802.11 standard. Simulation results show that the proposed algorithm is able to schedule transmission such that the bandwidth allocated to different flows is proportional to their weights. An attractive feature of the proposed approach is that it can be implemented with simple modifications to the IEEE 802.11 standard.

The HomeRF Working Group (WG) is a consortium of more than 100 companies from the computer, telecommunications, and consumer electronics industries. This group has developed an open specification called the Shared Wireless Access Protocol-Cordless Access (SWAP-CA) that enables radio frequency (RF) wireless connectivity between a diverse set of devices and computing resources in and around a typical home. Built around an RF spectrum with worldwide availability, SWAP-CA includes operational support for both managed and ad hoc networks of devices. It combines and extends wireless networking and cordless telephony into a single unified protocol allowing mobile devices to communicate via both voice and data traffic simultaneously over the Internet and/or over the public switched telephone network (PSTN). For batter-operated devices, it includes a power management mechanism than ensures connection longevity. The technology has been specifically optimized for consumer applications and price points, and, consequently, the HomeRF WG has the broad backing of the major corporate stakeholders interested in enabling tetherless networking within the home.

Adaptive reservation multiple access protocol (ARMAP) has been designed to provide explicit support for integrated services over wireless radio networks. It allows terminals to communicate with multiple traffic types, including data, voice, and digital video, providing quality of service (QoS) guarantees to video connections and a high priority to voice connections. The regularity in the video packet generation process is exploited in the protocol to provide timely and contention free channel access for dynamic reservations. An adaptive reservation-slot scheduling algorithm ensures near-optimum bandwidth usage and near-optimum power consumption by the radio terminal. Simulation with realistic parameters reveals that ARMAP achieves a promising combination of bandwidth efficiency, and quality of service for time bounded isochronous traffic.

Wireless ATM networks require efficient mobility management to cope with frequent mobile handoff and rerouting of connections. Although much attention has been given in the literature to network architecture design to support wide-area mobility in public ATM networks, little has been done to the important issue of user mobility estimation and prediction to improve the connection reliability and bandwidth efficiency of the underlying system architecture. This paper treats the problem by developing a hierarchical user mobility model that closely represents the movement behavior of a mobile user, and that, when used with appropriate pattern matching and Kalman filtering techniques, yields an accurate location prediction algorithm, HLP, or hierarchical location prediction, which provides necessary information for advance resource reservation and advance optimal route establishment in wireless ATM networks.

The task of supporting integrated multi-rate multimedia traffic in a bandwidth poor wireless environment poses a unique and challenging problem for network managers. In this paper we propose a novel bandwidth allocation strategy which partitions the available bandwidth amongst the different traffic classes in a manner that ensures quality of service (QoS) guarantees for digital video while minimizing the maximum blocking probability for voice and data connections. At the connection level, optimum utilization of the reserved bandwidth is achieved through intra-frame statistical multiplexing, while at the system-level, the delicate task of partitioning the bandwidth is accomplished by developing an efficient algorithm which uses traffic parameters consisting only of aggregate traffic load and the total available bandwidth. The algorithm built on non-trivial mathematical results, is simple, robust, and well suited for practical implementations.

The handwritten word recognition problem is modeled in the framework of the hidden Markov model (HMM). The states of HMM are identified with the letters of the alphabet. The optimum symbols are then generated experimentally using 15 different features. Both the first- and second-order HMMs are proposed for the recognition tasks. Using the existing statistical knowledge of English, the calculation scheme of the model parameters are immensely simplified. Once the model is established, the Viterbi algorithm is used to recognize the sequence of letters consisting the word. Some experimental results are also provided indicating the success of the scheme.>

An algorithm for fitting a conic shape to a set of given data is described. By a modified medial axis transform, described in the paper, the algorithm first determines the orientation of the shape such that the shape could be rotated to align its axis with the X-axis. The standard nonlinear equations of the conic, both closed and open, are then fitted using a linearized least square approach. Some experimental results are provided indicating the convergence and good performance of the algorithm.>