Network Function Virtualization has gained traction as a network function deployment alternative due to its flexibility and cost benefits. The telecommunication (telecom) operators and infrastructure providers are looking for high throughput, low latency NFV deployment model to avail the benefits of NFV. Moreover, NFV is one of the core technology for the next-generation communication network such as 5G. Furthermore, telecom operators employ groups of network functions(NFs) that process packets in linear order so that the output of one NF becomes an input for another, thus forming the network function chain (NFC). However, these NFCs should be flexible, as all telecom packets do not necessarily need to be processed by the same set of NFs. It has been earlier shown that GPU increases the throughput of NFV chains. To the best of our knowledge, none of the GPU-based frameworks supports dynamic NFV chains. Furthermore, discrete GPUs are expensive and consume a fair amount of energy. This paper presents the design and evaluation of Hummingbird, a framework to support high throughput, dynamically routed NFV chain on Heterogeneous System Architecture (HSA). Though HSAs are affordable and power-efficient, they lack high throughput GPU-CPU synchronization. Furthermore, current technology does not provide a zero-copy mechanism for network IO between GPU and NIC for HSAs. Hummingbird addressed those challenges. As per our knowledge, this is the first such framework that provides high throughput dynamic NFV chaining, with NFs chained across GPU and CPU and designed in conformance to OpenCL 2.0 standard. Hummingbird achieves 6x throughput per-core and 3.5x throughput per unit of energy consumption compared to state-of-the-art NFV deployment framework G-net, which uses powerful and costly discrete GPU.

Network Function Virtualization (NFV) paradigm offers flexibility, cost benefits, and ease of deployment by decoupling network function from hardware middleboxes. The service function chains (SFC) deployed using the NFV platform require efficient sharing of resources among various network functions in the chain. Graphics Processing Units (GPUs) have been used to improve various network functions’ performance. However, sharing a single GPU among multiple virtualized network functions (virtual machines) in a service function chain has been challenging due to their proprietary hardware and software stack. Earlier GPU architectures had a limitation: a single physical GPU can only be allocated to one virtual machine (VM) and cannot be shared among multiple VMs. The newer GPUs are virtualization-aware (hardware-assisted virtualization) and allow multiple virtual machines to share a single physical GPU. Although virtualization-aware, these GPUs still lack support for custom scheduling policies and do not expose the preemption control to users. When network functions (hosted within virtual machines) with different processing requirements share the same GPU, virtualization-aware GPUs’ default round-robin scheduling mechanism proves to be inefficient, resulting in packet drops and lower throughput. This paper presents Simmer, an efficient mechanism for scheduling a network function service chain on virtualization-aware GPUs. Our scheduling solution considers the processing requirement of NFs in a GPU-based SFC, thus improving overall throughput by up to 29% and reducing the packet drop to zero compared to vanilla setup.

Roads form a critical part of any region's infrastructure. Their constant monitoring and maintenance is thus essential. Traditional monitoring mechanisms are heavy-weight, and hence have insufficient coverage. In this paper, we explore the use of crowd-sourced intelligent measurements from commuters' smart-phone sensors. Specifically, we propose a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings. Through extensive data collection of over 36 hours on different kinds of roads, and subsequent evaluation based on this, we show that the approach can achieve high accuracy (98.5%) in a three-way classification of road surface quality. We also show how the classification can be extended to a finer grained 11-point scale of road quality. The model is also efficient: it can be implemented on today's smart-phones, thus making it practical. Our approach, called RoadCare, enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the "smoothest" road route to a destination.

Introduction of OFDMA to 802.11 WLANs has opened up many opportunities. Especially it makes the WLAN a closed loop system, where AP assigns OFDMA resource units (that is, delivers an OFDMA schedule) to the STAs for uplink transmissions, optimizing the throughput. For time sensitive traffic, making most of the packets meet their deadlines is more important than optimizing the throughput. In order to be able to deliver a schedule that honors packet deadlines, the AP needs to know the deadlines of packets in each STA's queue. Since these deadlines are not explicitly known to the AP, we present a novel approach that estimates the deadlines on the AP side and makes a scheduling decision such that the number of packets dropped due to deadlines are minimized. Our approach shows up to 6 fold reduction in the number of drops of delay bound packets at the STAs, compared to the approach that minimizes overall upload time.

Road safety is a critical issue worldwide. We believe that mobile devices can play a positive role in this context by detecting dangerous conditions and providing feedback. This paper focuses on a specific problem in developing countries: the stopping behaviour of buses in the vicinity of bus stops. For instance, buses could arrive at a bus stop but continue rolling forward instead of coming to a complete halt, or could stop some distance away from the bus stop, possibly even in the middle of a busy road. Such behaviors put at risk the passengers boarding or alighting the bus, and also the people waiting at a bus stop. We present FullStop, a smartphone-based system that detects safety risks emanating from stopping behavior like the ones listed above. We show that the GPS and inertial sensors are unable to perform the fine-grained detection needed. Therefore, our approach in FullStop is based on the view obtained from looking out to the front of the vehicle using the camera of a smartphone that is mounted on the front windshield. Using optical flow vectors, with several refinements, FullStop running on a smartphone is able to effectively detect various unsafe bus stopping behaviours.

Current smartphone-based navigation applications fail to provide lane-level information due to poor GPS accuracy. Detecting and tracking a vehicle’s lane position on the road assists in lane-level navigation. For instance, it would be important to know whether a vehicle is in the correct lane for safely making a turn, or whether the vehicle’s speed is compliant with a lane-specific speed limit. Recent efforts have used road network information and inertial sensors to estimate lane position. While inertial sensors can detect lane shifts over short windows, it would suffer from error accumulation over time. In this article, we present DeepLane, a system that leverages the back camera of a windshield-mounted smartphone to provide an accurate estimate of the vehicle’s current lane. We employ a deep learning--based technique to classify the vehicle’s lane position. DeepLane does not depend on any infrastructure support such as lane markings and works even when there are no lane markings, a characteristic of many roads in developing regions. We perform extensive evaluation of DeepLane on real-world datasets collected in developed and developing regions. DeepLane can detect a vehicle’s lane position with an accuracy of over 90%, and we have implemented DeepLane as an Android app.

Traffic congestion on urban roadways is a serious problem requiring novel ways to detect and mitigate it. Determining the routes that lead to the traffic congestion segment is also vital in devising mitigation strategies. Further, crowdsourcing this information allows for use of these strategies quickly and in places where infrastructure is not available. In this work, we present an unconventional method, using the barometer sensor of mobile phones to (a) detect road traffic congestion and (b) estimate the paths that lead to the congested road segment. We make the observation that roads are not completely flat and very often, altitude varies along the road. The barometer sensor chips are sensitive enough to measure these variations and consume very little energy of the mobile phone, compared to other sensors such as the GPS or accelerometer. We devise a feature set to map the rate of change of this altitude as the user moves into activities characterized as “still” and “motion,” which are further used by the traffic congestion detection algorithm (RoadSphygmo) to classify the group of users as being in “moving,” “congestion,” or “stuck” states. To estimate the paths that lead to the congested road segment, we compare the user’s barometer sensor readings with a pre-stored road signature of barometer values using Dynamic Time Warping (DTW). We show that by using correlation of barometer sensor values, we can determine if users are in the same vehicle. We crowdsource this information from multiple mobile phones and use majority voting technique to improve the accuracy of traffic congestion detection and path estimation. We find a significant increase in the accuracies using crowdsourced information as compared to individual mobile phones. Further, we show that we can use barometer sensor for other applications such as bus occupancy, boarding/deboarding of a vehicle, and so on. The validation of the state determined by RoadSphygmo is done by comparing it with average GPS speed calculated during the same time period. The path estimation is validated over different intersections and considering various cases of commuter travel. The results obtained are promising and show that the traffic state determination and the estimation of the path taken by the commuter can achieve high accuracy.

Frame aggregation improves performance of 802.11 networks by eliminating many overheads the single-frame transmissions incur. An originator builds and sends an aggregate (A-MPDU) of packets (MPDUs or subframes) from a window of 64 sequence numbers, and a block ACK then acknowledges the same window. The mechanism works fine till there are no packet losses. With the advent of packet losses (and their subsequent reporting by the block ACK), holes appear in the originator's aggregation window. An A-MPDU constructed immediately after by the originator then becomes smaller in size due to these holes. We discuss the effect of such inadvertent shortening of transmitted A-MPDUs on the performance of WLAN. We propose modifications to the block ACK and aggregation mechanisms, and show through our ns-3 simulations that the change improves the network throughput by up to 20% in hidden node scenario in simple basic service sets (BSSs) consisting of 32 to 48 STAs. Our analysis shows that improvement in the worst case throughput can be as high as 400% for the highest VHT MCS indexes with our proposed modifications.

Frame aggregation in 802.11n and ac gives higher throughputs because larger aggregates reduce inter-frame timing overheads. When aggregate (A-MPDU) sizes are forced to be smaller the throughput benefit dwindles. During normal operation the A-MPDU sizes fall from the maximum possible due to the loss of individual constituent subframes. In the worst case, the size can be as low as a single subframe. We show that, compared to the worst case, full-sized A-MPDUs can give up to 400% higher throughput for the highest MCS indexes of 802.11ac. In this paper, we explain how fixed block ACK size coupled with subframe retransmission are the main causes of reduction in A-MPDU size. We present the modification in the block ACK mechanism needed to attain larger sizes of aggregates even in the presence of subframe losses.

Road safety is a major public health issue the world over. Many studies have found that the primary factors responsible for road accidents center on the driver and her/his driving. Hence, there is the need to monitor driver's state and her/his driving, with a view to providing effective feedback. Our proposed demo is of HAMS, a windshield-mounted, smartphone-based system that uses the front camera to monitor the driver and back camera to monitor her/his driving behaviour. The objective of HAMS is to provide ADAS-like functionality with low-cost devices that can be retrofitted onto the large installed base of vehicles that lack specialized and expensive sensors such as LIDAR and RADAR. Our demo would show HAMS in action on an Android smartphone to monitor the state of the driver, specifically such as drowsiness, distraction and gaze, and vehicle ranging, lane detection running on pre-recorded videos from drives.

In recent years, WiFi has grown in capacity as well as deployment demand. WiFi system administrators (sysads) want a simple answer to the question “Is my WiFi network healthy?”, and a possible follow-up “What is wrong with it?”, if it is reported as “unhealthy”. But we are far from having such an interface today. It is this gap that this work attempts to fill. We present Witals, a system for WiFi performance diagnosis. We first design a causal diagnosis graph, that extensively identifies the underlying cause(s) of WiFi performance problems. Next, we identify a set of metrics corresponding to nodes in this causal graph. These metrics are measured in real-time by an operational AP, and help in quantifying the effect of each cause. We design a diagnosis algorithm based on the causal graph and the metrics, which ultimately presents a sanitized view of WiFi network health to the sysad. We have implemented a prototype of Witals on an enterprise grade 802.11n AP platform. Using a variety of controlled as well as real-life measurements, we show that our diagnosis framework follows ground truth accurately. Witals has also helped the sysads uncover some unexpected diagnoses.

Determining the location of a mobile user is central to several crowd-sensing applications. Using a Global Positioning System is not only power-hungry, but also unavailable in many locations. While there has been work on cellular-based localization, we consider an unexplored opportunity to improve location accuracy by combining cellular information across multiple mobile devices located near each other. For instance, this opportunity may arise in the context of public transport units having multiple travelers. Based on theoretical analysis and an extensive experimental study on several public transportation routes in two cities, we show that combining cellular information across nearby phones considerably improves location accuracy. Combining information across phones is especially useful when a phone has to use another phone’s fingerprint database, in a fingerprinting-based localization scheme. Both the median and 90 percentile errors reduce significantly. The location accuracy also improves irrespective of whether we combine information across phones connected to the same or different cellular operators. Sharing information across phones can raise privacy concerns. To address this, we have developed an id-free broadcast mechanism, using audio as a medium, to share information among mobile phones. We show that such communication can work effectively on smartphones, even in real-life, noisy-road conditions.

Considerable experimentation is happening in today's classrooms to handle large classes. In this paper, we present SAFE (Smart Authenticated Fast Exams), a tool that enables continuous assessment in the form of regular quizzes in classes. SAFE is based on a BYOD (bring your own device) model that leverages student smart-phones to conduct auto-graded, cheating-free exams in a proctored class room setting. SAFE has 3 components: a smart-phone app, a web server and WiFi infrastructure to enable app-server communication. SAFE support a rich set of features to handle various types of questions as well as instructor preferences. In the design of SAFE, we set to achieve 4 goals: easy setup, cheating-free operation, robustness and scale. Easy setup is achieved predominantly due to the BYOD model and online mode of exams. Cheating is prevented via locking the app for the intended purpose along with reporting to the server any user attempts at cheating. Robustness is handled via periodic syncing of messages and careful consideration of corner cases. Scale is handled by carefully tuning the WiFi as well as via application level scheduling.

How does a dense WiFi network perform, specifically for the common case of TCP download? While the empirical answer to this question is `poor', analysis and experimentation in prior work has indicated that TCP clocks itself quite well, avoiding contention-driven WiFi overload in dense settings. This paper focuses on measurements from a real-life use of WiFi in a dense scenario: a classroom where several students use the network to download quizzes and instruction material. We find that the TCP download performance is poor, contrary to that suggested by prior work. Through careful analysis, we explain the complex interaction of various phenomena which leads to this poor performance. Specifically, we observe that a small amount of upload traffic generated when downloading data upsets the TCP clocking, and increases contention on the channel. Further, contention losses lead to a vicious cycle of poor interaction with autorate adaptation and TCP's timeout mechanism. To reduce channel contention and improve performance, we propose a modification to the AP scheduling policy to improve the performance of large TCP downloads. Our solution, WiFiRR, picks only a subset of clients to be served by the AP during any instant, and varies this set of “active” clients periodically in a round-robin fashion over all clients to ensure that no client starves. We have done extensive evaluation of WiFiRR in simulation and in real settings. By reducing the number of contending nodes at any point of time, WiFiRR improves the download time of large TCP flows upto 3.5x of our classroom scenario. We also compare WiFiRR with state-of-the-art prior work WiFox, WiFiRR improves download time by 2.25× over WiFox.

TDMA based wireless mesh networks have gained prominence as some of the recent standards such as WiMAX and 802.11s have proposed the use of TDMA based MAC protocol for mesh networks. Even though HTTP web browsing traffic constitutes a significant percentage of Internet traffic, but as of yet there have been no attempts to study the performance of HTTP based web browsing traffic in TDMA mesh networks. HTTP web browsing traffic has different characteristics compared with other types of traffic. In particular, as HTTP traffic consists of large number of small sized file transfers (median file size is 10 KB), it can impose high scheduling overhead. As we highlight, HTTP traffic requires that RTT (round trip time) be small and also it requires that large sized flows be allocated higher share of bandwidth. Given these characteristics of HTTP traffic, it is not clear what protocol design for TDMA mesh networks performs best. In this work, we have studied the performance comparison of four different TDMA MAC protocols for HTTP web browsing traffic. Two of these protocols follow distributed scheduling, one centralized and the other naive static fixed schedule approach. We compare the TDMA MAC protocols for various aspects such as optimum values of different TDMA MAC parameters, design of TDMA scheduling algorithm, performance under varying load, in presence of wireless loss, etc. Our study highlights a crucial aspect that the two distributed protocols specified by the recent WiMAX and 802.11s standards, are incapable of handling wireless packet loss and perform poorly in presence of large HTTP file downloads. As against this, the centralized protocol performs well for the different experiment cases that we have considered. However, our analysis reveals that the performance of the centralized protocol can be further improved if we eliminate the flow set-up phase and minimize the delay in propagating the TDMA schedule. Likewise, we carry out required modifications to the centralized protocol and observe that the performance of the modified protocol, which we term as the sf-centralized (set-up free centralized) protocol, improves by as much as 19 percent under moderate load and 47 percent under high load.

We present Witals, a system for WiFi performance diagnosis. In Witals, a live access point diagnoses the health of the WiFi network by examining the health of the air around it. Such diagnosis is critical functionality for sysads, and is an important addition to the state of the art. In the demo we will show WiFi performance diagnosis by Witals live at MobiCom venue and also from past collected traces in other settings & for controlled experiments.

Wireless mesh networks in general, and WiFi mesh networks in particular, offer a cost-effective option to provide broadband connectivity in sparse regions. Effective support for real-time as well as high throughput applications requires a TDMAbased approach. However, multihop TDMA implementations in wireless have been few and far-between, and for good reasons. These present significant issues in terms of time synchronization, TDMA schedule dissemination, multichannel support, routing integration, spatial reuse and so on. And achieving these efficiently, in the face of wireless channel losses presents a formidable challenge. In this work, we present an implementation of LiT MAC, a full-fledged multihop TDMA MAC, on commodity WiFi platforms. We undertake extensive evaluations using microbenchmarks as well as application level performance, using outdoor as well as indoor testbeds. We also present an integration of LiT MAC with various routing metrics, and a routing stability study of recently proposed routing metrics (ROMA, SLIQ). Our results show that we can achieve μs granularity time synchronization across several hops, and TDMA slot size as small as 2 ms. These imply low control overheads. Experiments over several days, on our nine-node outdoor testbed shows that LiT MAC's soft-state-based approach is effective in robust operation even in the presence of significant external interference.

An unprecedented rate of growth in the number of vehicles has resulted in acute road congestion problems worldwide, especially in many developing countries. In this article, we present Road-RFSense, a practical RF sensing--based road traffic estimation system for developing regions. Our first contribution is a new mechanism to sense road occupancy, based on variation in RF link characteristics, when line of sight between a transmitter-receiver pair is obstructed. We design algorithms to classify traffic states into two classes, free-flow versus congested, at timescales of 20 seconds with greater than 90% accuracy. We also present a traffic queue length measurement system, where a network of RF sensors can correlate the traffic state classification decisions of individual sensors and detect traffic queue length in real time. Deployment of our system on a Mumbai road gives correct estimates, validated against 9 hours of image-based ground truth. Our third contribution is a large-scale data-driven study, in collaboration with city traffic authorities, to answer questions regarding road-specific classification model training. Finally, we explore multilevel classification into seven different traffic states using a larger set of RF-based features and careful choice of classification algorithms.

Unprecedented rate of growth in the number of vehicles has resulted in acute road congestion problems worldwide. Better traffic flow management, based on enhanced traffic monitoring, is being tried by city authorities. In many developing countries, the situation is worse because of greater skew in growth of traffic vs the road infrastructure. Further, the existing traffic monitoring techniques perform poorly in the chaotic non-lane based traffic here.

In this article, we consider the goal of achieving high throughput in a wireless sensor network. Our work is set in the context of those wireless sensor network applications which collect and transfer bulk data. We present PIP (Packets in Pipe), a MAC primitive for use by the transport module to achieve high throughput. PIP has a unique set of features: (a) it is a multihop connection-oriented primitive, (b) it is TDMA based, (c) it uses multiple radio channels, and (d) it is centrally controlled. This represents a significant shift from prior MAC protocols for bulk data transfer. PIP has several desirable properties: (a) its throughput degrades only slightly with increasing number of hops, (b) it is robust to variable wireless error rates, (c) it performs well even without any flow control, and (d) requires only small queue sizes to operate well. We substantiate these properties with a prototype implementation of PIP on the Tmote-Sky CC2420-based platform. PIP achieves about eleven times better throughput than the state-of-the-art prior work, over a network depth of 24 hops. We also show that PIP can be interagted with duty cycling, and that PIP can support streaming data from/to flash at little overhead.

Road congestion is a common problem all over the world. In many developed countries, automated congestion detection techniques have been deployed, that are used in road travel assisting applications. But these techniques are mostly inapplicable in many developing regions due to high cost and their assumptions of orderly traffic. Efforts in developing regions have been few. In this paper, we present RoadSoundSense, an acoustic sensing based technique, for near real time congestion monitoring on chaotic roads, at a moderate cost. We present the detailed design of an acoustic sensing hardware prototype, which has to be deployed by the side of the road to be monitored. This unit samples and processes road noise to compute various metrics like amount of vehicular honks and vehicle speed distribution, with speeds calculated from honks using differential Doppler shift. The metrics are sent to a remote server over GPRS every alternate minute. Based on the metric values, the server can decide the traffic condition on the road. Data from deployment of this prototype in six different Mumbai roads, validated against manually observed ground truth, shows feasibility of per minute congestion monitoring from a remote server. K-means clustering gives on average 90% accuracy to group unlabeled data on a new road into two clusters of congested and free-flow. Deployment data from one road for six days shows the temporal variation in traffic state for that road. Though we test our technique in Mumbai, we believe that most of our claims and experimental results can be extended to city roads of other developing regions as well.

A TDMA-based MAC protocol is essential to achieve high throughput and low delay/jitter values, in WiFi mesh networks. An important component of such a network is the interference map, which quantifies the level of interference between pairs of links in the network. Past work in this domain has been relatively sparse, although many protocols and algorithms have been proposed which assume an interference map. In a TDMA network where carrier-sense is disabled, all interference shows up as hidden-terminal cases. Prior work on building interference maps has not focused on the hidden terminal case. Further, most prior evaluation has been in indoor settings. In this work, we present an SIR based interference measurement technique. We start by measuring the average RSS for each link in the network. This is subsequently used to get an estimated average SIR. The average SIR is then used in conjunction with an SIR-versus-PER plot, to estimate the link error rate under interference. We show through measurements on an outdoor 802.11b/g testbed, that the above estimate is accurate for (1) non-interfering links, as well as (2) fully interfering links, but is less accurate for (3) intermediate link pairs. The resultant 3-way classification of inter-link interference will be useful input for subsequent TDMA scheduling algorithms.

Road congestion is a common problem worldwide. Existing Intelligent Transport Systems (ITS) are mostly inapplicable in developing regions due to high cost and assumptions of orderly traffic. In this work, we develop a low-cost technique to estimate vehicular speed, based on vehicular honks. Honks are a characteristic feature of the chaotic road conditions common in many developing regions like India and South-East Asia.

In this paper, we consider the goal of achieving high throughput in a wireless sensor network. Our work is set in the context of those wireless sensor network applications which collect and transfer bulk data. We present PIP (Packets in Pipe), a MAC primitive for use by the transport module to achieve high throughput. PIP has a unique set of features: (a) it is a multi-hop connection oriented primitive, (b) it is TDMA-based, (c) it uses multiple radio channels, and (d) it is centrally controlled. This represents a significant shift from prior MAC protocols for bulk data transfer.

In this work, we consider the goal of enabling effective voice communication in a TDMA, multi-hop mesh network, using low cost and low power platforms. We consider two primary usage scenarios: (1) enabling a local voice communication within a village-like setting, in developing regions (2) supporting an on-site local communication among a team of users e.g. during emergency response systems.

In the last few years, long-distance WiFi networks have been used to provide Internet connectivity in rural areas. The strong requirement to support real-time applications in these settings leads us to consider TDMA link scheduling. In this paper, we consider the FRACTEL architecture for long-distance mesh networks. We propose a novel angular interference model, which is not only practical, but also makes the problem of TDMA scheduling tractable. We then consider delay-bounded scheduling and present an algorithm which uses at most 1/3rd more time-slots than the optimal number of slots required without the delay bound. Our evaluation on various network topologies shows that the algorithm is practical, and more efficient in practice than its worst-case bound.

Outdoor community mesh networks based on 802.11 have seen tremendous growth in the recent past. The current understanding is that wireless link performance in these settings in inherently unpredictable, due to multipath delay spread. Consequently, researchers have focused on developing intelligent routing techniques to achieve the best possible performance. In this paper, we are specifically interested in mesh networks in rural locations. We first present detailed measurements to show that the PHY layer in these settings is indeed stable and predictable. There is a strong correlation between the error rate and the received signal strength. We show that interference, and not multipath fading, is the primary cause of unpredictable performance. This is in sharp contrast with current widespread knowledge from prior studies. Furthermore, we corroborate our view with a fresh analysis of data presented in these prior studies. Based on our results, we argue that outdoor rural mesh networks can indeed be built with the link abstraction being valid. This has several design implications, and opens up a fresh perspective on a wide range of technical issues in this domain.

Railway systems are critical in many regions, and can consist of several tens of thousands of bridges, being used over several decades. It is critical to have a system to monitor the health of these bridges and report when and where maintenance operations are needed. This paper presents BriMon, a wireless sensor network based system for such monitoring. The design of BriMon is driven by two important factors: application requirements, and detailed measurement studies of several pieces of the architecture. In comparison with prior bridge monitoring systems and sensor network prototypes, our contributions are three-fold. First, we have designed a novel event detection mechanism that triggers data collection in response to an oncoming train. Next, BriMon employs a simple yet effective multi-channel data transfer mechanism to transfer the collected data onto a sink located on the moving train. Third, the BriMon architecture is designed with careful consideration of the interaction between the multiple requisite functionalities such as time synchronization, event detection, routing, and data transfer. Based on a prototype implementation, this paper also presents several measurement studies to show that our design choices are indeed quite effective.

Several research efforts as well as deployments have chosen IEEE802.11 as a low-cost, long-distance access technology to bridge the digital divide. In this paper, we consider the important issue of planning such networks to the minimize system cost. This is a non-trivial task since it involves several sets of variables: the network topology, tower heights, antenna types to be used and the irorientations, and radio transmit powers. The task is further complicated due to the presence of network performance constraints, and the inter-dependence among the variables. Our first contribution in this paper is the formulation of this problem in terms of the variables, constraints and the optimization criterion. Our second contribution is in identifying the dependencies among the variables and breaking-down the problem into four tractable sub-parts. In this process, we extensively use domain knowledge to strike a balance between tractability and practicality.

Information and Communication Technology (ICT) has the potential to bring in development to rural areas in the third world. Any deployment of technology however should be backed by a positive economic activity to be sustainable. This paper reports on experience with the economics, as well as the social aspects of Voice-over-IP (VoIP) as a service. In Jan 2003, we established a WiFi link from a site with wired Internet connection to a remote village site. On learning the demand for a telephone facility at the village, we setup a VoIP service. The setup has been running with reasonable success since then. We have collected a wide variety of statistics on the usage of the service. Although we used the relatively low-cost WiFi technology, the expenses have been high. The capital expense is dominated by antenna tower (40m: U.S.$6,600). The WiFi equipment and the solar power system added significantly to the cost too (about U.S.$3,100). The revenue generated from the phone calls has averaged at about $52/month. Hence this service alone may not be sufficient, and other targeted services are necessary to sustain ICT in rural villages. However, on the positive side we think that telephony facility can attract potential ICT users to the setup since people can relate to the service immediately (unlike say, web browsing).

Abstract — IEEE 802.11 (WiFi) has been used beyond its original intended purpose of a tether-free LAN. In this paper, we are interested in the use of 802.11 in mesh networks. Specifically, we consider those which involve directional antennas and longdistance point-to-point links. In recent work, the 2P MAC protocol has been designed to suit such a network architecture. In this paper, we assume the use of the 2P MAC protocol in the links of the network, and consider the problem of link channel allocation. We first formulate the problem of minimizing the mismatch between link capacities desired by the network operator and that achieved under a channel allocation. We show that this problem is NP-hard. We then explore several heuristics for channel allocation and find a set of heuristics that achieve the optimal allocation in most scenarios. I.

The use of 802.11 long-distance links is a cost-effective means of providing wireless connectivity to rural areas. Although deployments in this setting are increasing, a systematic study of the performance of 802.11 in these settings is lacking. The contributions of this paper are two-fold: (a)we present a detailed performance study of a set of long-distance 802.11b links at various layers of the network stack, and (b)we document the various non-obvious experiences during our study.Our study includes eight long-distance links, ranging from 1km to 37km in length. Unlike prior studies of outdoor 802.11 links, we find that the error rate as a function of the received signal strength behaves close to theory. Time correlation of any packet errors is negligible across a range of time-scales. We have observed at least one of the link to be robust to rain and fog. But any interference on the long-distance links can be detrimental to performance. Apart from this however, such long-distance links can be planned to work well with predictable performance. During our measurements, we have observed a few hardware/driver quirks as well as system bottlenecks apart from the wireless link itself. We believe that our measurements and the documentation of our experience will help future network planning as well as protocol design for these networks.

In bridging the digital divide, two important criteria are cost-effectiveness, and power optimization. While 802.11 is cost-effective and is being used in several installations in the developing world, typical system configurations are not really power efficient. In this paper, we propose a novel "Wake-on-WLAN" mechanism for coarse-grained, on-demand power on/off of the networking equipment at a remote site. The novelty also lies in our implementation of a prototype system using low-power 802.15.4-based sensor motes. We describe the prototype, as well as its evaluation on field in a WiFi testbed. Preliminary estimates indicate that the proposed mechanism can save significant power in typical rural networking settings.

802.11 has been used well beyond its original intended use of WLANs. Of particular interest to us in this paper is its use in long-distance mesh networks being designed/used for low-cost rural connectivity. We describe in detail a new MAC protocol, called 2P, that is suited for such networks in terms of efficiency. A significant challenge here is the implementation of this protocol on top of off-the-shelf 802.11 hardware, to preserve the cost benefits. We show how this can be achieved, by exploiting the flexibilities available within Prism2-based chipsets. We then present the dependence of 2P on the network topology, and show that it is indeed possible to design in practice, network topologies compatible with 2P. We describe experimental as well as simulation-based evaluations of 2P, and show that 2P achieves significant performance improvement (as much as 20 times more throughput) over 802.11 CSMA/CA in long-distance mesh networks.

Service composition enables flexible creation of new services by assembling independent service components. We are focused on the scenario where such composition takes place across the wide-area Internet. We envision independent providers deploying and managing service instances and portal providers composing them to quickly enable new applications in next-generation networks. One of the important goals in such service composition is load balancing across service instances. While load balancing has been studied extensively for web-server selection, the presence of composition presents new challenges. First, each client session involving composition requires a set of service instances and not just one server. Second, unlike web-mirror selection, we also concern ourselves with load balancing in the presence of failure recovery during a client session. We introduce (a) a metric to choose the set of service instances for composed client sessions: the least-inverse-available-capacity (LIAC) metric, as well as (b) a piggybacking mechanism to give quick feedback about server load. We then introduce an additional factor in the load balancing metric to avoid choosing far away service instances. Our experiments, based on an emulation testbed, show that our load balancing mechanism works well under a variety of scenarios including network path failures.

The CONTROL project at U.C. Berkeley has developed technologies to provide online behavior for data-intensive applications. Using new query processing algorithms, these technologies continuously improve estimates and confidence statistics. In addition, they react to user feedback, thereby giving the user control over the behavior of long-running operations. This demonstration displays the modifications to a database system and the resulting impact on aggregation queries, data visualization, and GUI widgets. We then compare this interactive behavior to batch-processing alternatives.