Lenin Ravindranath

Enhancing the security of corporate Wi-Fi networks using DAIR

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.

COMBINE: leveraging the power of wireless peers through collaborative downloading

Mobile devices are increasingly equipped with multiple network interfaces: Wireless Local Area Network (WLAN) interfaces for local connectivity and Wireless Wide Area Network (WWAN) interfaces for wide-area connectivity. The WWAN typically provides much wider coverage but much lower speeds than the WLAN. To address this dichotomy, we present COMBINE, a system for collaborative downloading wherein devices that are within WLAN range pool together their WWAN links, significantly increasing the effective speed available to them. COMBINE makes a number of novel contributions overprior work in this area, including: (a) a framework of incentives for collaboration that addresses several practical issues including the unification of monetary and energy costs, and on-the-fly estimation of the energy cost of communication in a system in operation; (b) a protocol for collaborative group formation and workload distribution that is energy efficient and adaptive to fluctuations in network conditions; and (c) an application-level striping procedure that eases deployment by avoiding the need for special-purpose proxies in the infrastructure. We present experimental results based on the prototype we have implemented that showen couraging speeds-ups with COMBINE.

DirCast: A practical and efficient Wi-Fi multicast system

IP multicast applications such as live lecture broadcasts are being increasingly used in enterprise and campus networks. In many cases, end hosts access these multicast streams using Wi-Fi networks. However, multicast over Wi-Fi suffers from several well-known problems such as low data rate, high losses and unfairness vis-a-vis other contending unicast transmissions. In this paper we present DirCast, a system to solve many of these problems. DirCast requires no changes to the 802.11 MAC protocol or the wireless access points. Software changes are required on clients only if they wish to participate in multicast sessions. The aim of DirCast system is to minimize the airtime consumed by the multicast traffic, while simultaneously improving client experience. To meet these goals, the DirCast converts multicast packets to unicast packets targeted to certain selected clients; other clients receive these packets by listening in promiscuous mode. The target clients are carefully selected to minimize loss rate experienced by the non-targeted clients. If necessary, clients are forced to change the AP they are associated with. In addition, DirCast uses proactive adaptive FEC to further reduce the loss rate and implements a novel virtual multicast interface in order to be compatible with the security needs of the enterprise. We demonstrate the effectiveness of DirCast using extensive experiments in a Wi-Fi prototype implementation and through large-scale simulations.

Code in the air : simplifying sensing and coordination tasks on smartphones

A growing class of smartphone applications are tasking applications that run continuously, process data from sensors to determine the users context (such as location) and activity, and optionally trigger certain actions when the right conditions occur. Many such tasking applications also involve coordination between multiple users or devices. Example tasking applications include location-based reminders, changing the ring-mode of a phone automatically depending on location, notifying when friends are nearby, disabling WiFi in favor of cellular data when moving at more than a certain speed outdoors, automatically tracking and storing movement tracks when driving, and inferring the number of steps walked each day. Today, these applications are non-trivial to develop, although they are often trivial for end users to state. Additionally, simple implementations can consume excessive amounts of energy. This paper proposes Code in the Air (CITA), a system which simplifies the rapid development of tasking applications. It enables non-expert end users to easily express simple tasks on their phone, and more sophisticated developers to write code for complex tasks by writing purely server-side scripts. CITA provides a task execution framework to automatically distribute and coordinate tasks, energy-efficient modules to infer user activities and compose them, and a push communication service for mobile devices that overcomes some shortcomings in existing push services.

Demo: Glimpse -- Continuous, Real-Time Object Recognition on Mobile Devices

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 Glimpses 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).

Collaborative Downloading for Multi-homed Wireless Devices

Mobile devices are increasingly equipped with multiple network interfaces: Wireless Local Area Network (WLAN) interfaces for local connectivity and Wireless Wide Area Network (WWAN) interfaces for wide-area connectivity. The WWAN typically provides much wider coverage but much lower speeds than the WLAN. To address this dichotomy, we consider collaborative downloading among mobile devices in close proximity. We demonstrate the potential benefits of such an approach and discuss the many challenges to realizing it in practice: incentivizing cooperation by adequately compensating nodes, effecting such cooperation via an efficient protocol, and facilitating it with a suitable user interface. We present our current thinking on these as we design a collaborative downloading system called COMBINE.

Timecard: controlling user-perceived delays in server-based mobile applications

Providing consistent response times to users of mobile applications is challenging because there are several variable delays between the start of a users request and the completion of the response. These delays include location lookup, sensor data acquisition, radio wake-up, network transmissions, and processing on both the client and server. To allow applications to achieve consistent response times in the face of these variable delays, this paper presents the design, implementation, and evaluation of the Timecard system. Timecard provides two abstractions: the first returns the time elapsed since the user started the request, and the second returns an estimate of the time it would take to transmit the response from the server to the client and process the response at the client. With these abstractions, the server can adapt its processing time to control the end-to-end delay for the request. Implementing these abstractions requires Timecard to track delays across multiple asynchronous activities, handle time skew between client and server, and estimate network transfer times. Experiments with Timecard incorporated into two mobile applications show that the end-to-end delay is within 50 ms of the target delay of 1200 ms over 90% of the time.

SmartAds: bringing contextual ads to mobile apps

A recent study showed that while US consumers spent 30% more time on mobile apps than on traditional web, advertisers spent 1600% less money on mobile ads. One key reason is that unlike most web ad providers, todays mobile ads are not contextual---they do not take into account the content of the page they are displayed on. Thus, most mobile ads are irrelevant to what the user is interested in. For example, it is not uncommon to see gambling ads being displayed in a Bible app. This irrelevance results in low clickthrough rates, and hence advertisers shy away from the mobile platform. Using data from top 1200 apps in Windows Phone marketplace, and a one-week trace of ad keywords from Microsofts ad network, we show that content displayed by mobile apps is a potential goldmine of keywords that advertisers are interested in. However, unlike web pages, which can be crawled and indexed offline for contextual advertising, content shown on mobile apps is often either generated dynamically, or is embedded in the apps themselves; and hence cannot be crawled. The only solution is to scrape the content at runtime, extract keywords and fetch contextually relevant ads. The challenge is to do this without excessive overhead and without violating user privacy. In this paper, we describe a system called SmartAds to address this challenge. We have built a prototype of SmartAds for Windows Phone apps. In a large user study with over 5000 ad impressions, we found that SmartAds nearly doubles the relevance score, while consuming minimal additional resources and preserving user privacy.

SixthSense: RFID-based Enterprise Intelligence

RFID is widely used to track the movement of goods through a supply chain. In this paper, we extend the domain of RFID by presenting SixthSense, a platform for RFID-based enterprise intelligence systems. We consider an enterprise setting where people (or rather their employee badges) and their personal objects such as books and mobiles are tagged with cheap, passive RFID tags, and there is good coverage of RFID readers in the workplace. SixthSense combines mobility information obtained from RFID-based sensing with information from enterprise systems such as calendar and presence, to automatically draw inferences about the association and interaction amongst people, objects, and workspaces. For instance, SixthSense is able to automatically distinguish between people and objects, learn the identities of people, and infer the ownership of objects by people. We characterize the performance of a state-of-the-art RFID system used in our testbed, present our inference algorithms, and evaluate these both in a small testbed and via simulations. We also present the SixthSense programming model that exposes a rich API to applications. To demonstrate the capabilities of the SixthSense platform, we present a few applications built using these APIs, including a mis placed object alert service, an enhanced calendar service, and rich annotation of video with physical events. We also discuss the issue of safeguarding user privacy in the context of SixthSense.

Video: Procrastinator: pacing mobile apps' usage of the network

Generations of computer programmers are taught to prefetch network objects in computer science classes. In practice, prefetching can be harmful to the users wallet when she is on a limited or pay-per-byte cellular data plan. Many popular, professionally-written smartphone apps today prefetch large amounts of network data that the typical user may never use. We present Procrastinator, which automatically decides when to fetch each network object that an app requests. This decision is made based on whether the user is on Wi-Fi or cellular, how many bytes are remaining on the users data plan, and whether the object is needed at the present time. Procrastinator does not require app developer effort, nor app source code, nor OS changes -- it modifies the app binary to trap specific system calls and inject custom code. Our system can achieve as little as no savings to 4X reduction in total bytes transferred by an app, depending on the user and the app. These savings for the data-poor user come with a 300ms median latency penalty on LTE.