Kuldeep Yadav

SMSAssassin: crowdsourcing driven mobile-based system for SMS spam filtering

Due to increase in use of Short Message Service (SMS) over mobile phones in developing countries, there has been a burst of spam SMSes. Content-based machine learning approaches were effective in filtering email spams. Researchers have used topical and stylistic features of the SMS to classify spam and ham. SMS spam filtering can be largely influenced by the presence of regional words, abbreviations and idioms. We have tested the feasibility of applying Bayesian learning and Support Vector Machine(SVM) based machine learning techniques which were reported to be most effective in email spam filtering on a India centric dataset. In our ongoing research, as an exploratory step, we have developed a mobile-based system SMSAssassin that can filter SMS spam messages based on bayesian learning and sender blacklisting mechanism. Since the spam SMS keywords and patterns keep on changing, SMSAssassin uses crowd sourcing to keep itself updated. Using a dataset that we are collecting from users in the real-world, we evaluated our approaches and found some interesting results.

iTrust : an integrated trust framework for wireless sensor networks

Designing security solutions for Wireless Sensor Networks is a challenging task due to the potential hostile and unattended environment in which they operate as well as their resource constrained nature. A trust management framework can be useful for detecting untrustworthy nodes under such operational conditions. In an unattended autonomous network, the attacker can capture a sensor node and modify its regular functioning. Consequently, the compromised node will thereafter behave erratically, which, in most cases, is observable by nodes in the corresponding neighborhood. In this paper, we propose iTrust- an integrated trust framework in which monitor nodes, a set of specialty nodes, will evaluate neighborhood nodes based on their behavior in a session wise manner. Monitor nodes, in promiscuous mode, will garner information about nodes in their neighborhood. After each session, they will share trust indices of each node with their neighbors, which is used for future decision-making. We have simulated iTrust framework with a tolerance of 5%--25% network error rate and evaluated its performance. We have further evaluated the attack detection effectiveness of iTrust framework by simulating different attack scenarios and confirmed its robustness to several known attacks.

Low Energy and Sufficiently Accurate Localization for Non-smartphones

Location-aware mobile applications are steadily gaining popularity across the world. However, lack of Global Positioning System (GPS) and absence of Wi-Fi infrastructure prevent users with non-Smart phones (majority of population in developing countries) from using location-aware applications as their phones do not have access to their current location. Existing GSM based approaches such as Cell ID-based works on non-Smart phones but they require access to a comprehensive database of Cell IDs. Such a database either does not exist or is very limited in developing countries. In this paper, we propose a novel GSM-based approach of using Cell Broadcast Service (CBS) messages for getting current location on the phone. Proposed approach does not depend on a comprehensive database and can run on programmable low end phones. We demonstrate the effectiveness of our approach on data collected in New Delhi, India across two different operators and propose two space-time history based algorithms to improve upon the localization accuracy of our baseline CBS approach. The proposed algorithms provide up to 35% improvement in accuracy over the baseline method. Further, we compare accuracy of our CBS-based approach with that of Cell ID-based approach and also, present a multimodal approach that uses combination of both CBS and Cell ID (wherever available) to improve the localization accuracy.

Challenges and novelties while using mobile phones as ICT devices for Indian masses: short paper

Mobile phones have emerged as truly pervasive and affordable Information and Communication Technology (ICT) platform in the last decade. Large penetration of cellular networks and availability of advanced hardware platforms have inspired multiple innovative research opportunities in mobile computing domain. However, most of the research challenges have focused on typical scenarios existing in the developed economies. In this paper, we present research challenges and novelties in mobile computing domain that take account for differences between developing in particular India and developed economies. Our research is based on commonly available mobile platforms, communication cost, differences in user behavior and acceptable societal norms, among others.

Human sensors: Case-study of open-ended community sensing in developing regions

With the growing number of cities and population, continuous monitoring of citys infrastructure and automated collection of day-to-day events (such as traffic jam) is essential and can help in improving life style of citizens. It is extremely costly and ineffective to install hardware sensors to sense these events in developing regions. Due to advent of smartphones, citizens can play role of sensors and actively participate in collection of the events which can be shared with others for information or can be used in decisions which affects city development. In this paper, we describe an architecture of crowdsensing testbed for capturing and processing events affecting citizens in cities in India. One of the design principle of our testbed is that it encourages users to do an open-ended sensing under five broad categories: Civic complaints, traffic, neighbourhood issues, emergency and others. As part of testbed, we allow events submissions from different submission modes i.e. mobile application, SMSes and web. Our mobile application exploits different sensing interfaces provided by todays smartphones to add contextual data with event reports such as images, audio, fine-grained location etc. Proposed testbed is used by university students across India to report event happening around them. Finally, we describe the data collected and uncover some of challenges and opportunities which may help future designs of crowdsensing based systems.

Unity: Collaborative downloading content using co-located socially connected peers

Large proliferation of mobile phone applications result in extensive use of data intensive services such as multimedia download and social network communication. With limited penetration of 3G/4G networks in developing countries, it is common to use low bandwidth 2G services for data communication, resulting in larger download time and correspondingly high power consumption. In this paper, we present a system architecture, Unity, that enables collaborative downloading across co-located peers. Unity uses short range radio interfaces such as Bluetooth/WiFi for local coordination, while the actual content is downloaded using a cellular connection. Unity is designed to support mobile phones with diverse capabilities. End-to-end implementation and evaluation of Unity on Android based phones, with varying workload sizes and number of peers, show that Unity can result in multifold increase in download rate for the co-located peers. We also describe architecture of cloud-based Unity which uses principles of mobility prediction, social interactions, and opportunistic networking to make collaboration more pervasive and useful.

EcoBroker: an economic incentive-based brokerage model for efficiently handling multiple-item queries to improve data availability via replication in mobile-p2p networks

This work proposes EcoBroker, a novel economic incentive-based brokerage model for improving data availability via replication for multiple-item queries in Mobile-P2P networks. In Ecobroker, data requestors need to pay the price (in virtual currency) of their requested data items to data-providers. The main contributions of EcoBroker are two-fold. First, its economic incentive model effectively combats free-riding by incentivizing MPs to become brokers and to host replicated data, thereby improving data availability. Second, its brokerage model facilitates efficient processing of queries involving multiple data items. Our performance evaluation indicates that EcoBroker indeed improves data availability and querying-related communication overhead in Mobile-P2P networks.

Alternative localization approach for mobile phones without GPS

Location is a primary indicator of context and forms the core basis of several context-aware applications. Most common way of getting location information is to use specialized hardware like GPS. However, GPS is expensive and is available only on high-end phones restricting its use to a smaller population in developing countries. Further, GPS also consumes a lot of battery power during its operation, thereby making it infeasible to run for longer durations with limited mobile phone battery. An alternative to GPS-based localization is GSM-based localization that is more suitable for developing countries due to much lower power consumption and ability to run even on low-end phones. Currently available, network-operator independent, GSM-based solutions require building perceptual map of cell towers in a city using war-driving.n In this paper, we present a novel low cost GSM-based solution based on Cell Broadcast (CBS) Messages. Location accuracy in our approach does not depend on building extensive cell ID database, typically built using war-driving. We present empirical studies (performed in the sub-city of Dwarka, New Delhi, India) comparing location accuracy of our approach with other GSM-based localization scheme that uses one of the most extensive open source database of cell IDs. We also compare power consumption of our proposed solution with GPS-based localization leading to energy-accuracy tradeoff that can be further exploited for a hybrid solution.

Take Control of Your SMSes: Designing an Usable Spam SMS Filtering System

Short Message Service (SMS) is one of the most frequently used services in the mobile phones, next to calls. In developing countries like India, SMS is the cheapest mode of communication. The advantage of this fact is exploited by the advertising companies to reach masses. The unsolicited SMS messages (a.k.a. spam SMS) generates notifications, thus consuming precious user attention. To formulate spam SMS problem and understand users needs and preceptions, we conducted an online survey with 458 participants in different cities of India. Most of the survey participants admitted that they are quite annoyed with burst of SMS spams and in-effectiveness of regulatory solutions. However, some participants reported that, they do get useful information from spam SMSes sometime(e.g. discounts at a popular food joint). In this paper, we present design and implementation of a user-centric spam SMS filtering application i.e. SMSAssassin that uses content based machine learning techniques with user generated features to filter unwanted SMSes and reduces the burden of notifications for a mobile user.

VideoKen: Automatic Video Summarization and Course Curation to Support Learning.

The number of high quality online videos is increasing rapidly. Online courses as well as universities do not fully leverage the content due to several open challenges in video search, indexing, summarization and customization requirements for specific courses, instructors or learners. We present a new web-based social learning platform called Videoken. Using novel video summarization algorithms, Videoken automatically creates Table of Contents for videos. This allows a textbook-like facility for non-linear search and navigation through the video, enables extraction of semantically coherent clips from within a video and improves video search through better semantic indexing. The platform also allows new ways of course creation and sharing of learning modules; and can be both integrated with existing Learning Management Systems and used independently.