Anindya Iqbal

A Novel Anonymization Technique to Trade Off Location Privacy and Data Integrity in Participatory Sensing Systems

In participatory sensing system community people contribute information to be shared by everybody. However, none would be tolerant enough to contribute voluntarily if her privacy is not protected. This has evoked the idea of research in the area of preserving privacy in participatory sensing system. On the other hand, data integrity is desired imperatively to make the service trustworthy and user-friendly. In this paper, we have investigated the performance of a greedy algorithm and its randomized variant to achieve an acceptable tradeoff between these two orthogonal key parameters. We have also analyzed the ability of a third party adversary to decode privacy-sensitive data by eavesdropping. Our experimental results show that the proposed method is performing satisfactorily as an approach of balancing user privacy and data integrity.

SentiCR: A customized sentiment analysis tool for code review interactions

Sentiment Analysis tools, developed for analyzing social media text or product reviews, work poorly on a Software Engineering (SE) dataset. Since prior studies have found developers expressing sentiments during various SE activities, there is a need for a customized sentiment analysis tool for the SE domain. On this goal, we manually labeled 2000 review comments to build a training dataset and used our dataset to evaluate seven popular sentiment analysis tools. The poor performances of the existing sentiment analysis tools motivated us to build SentiCR, a sentiment analysis tool especially designed for code review comments. We evaluated SentiCR using one hundred 10-fold cross-validations of eight supervised learning algorithms. We found a model, trained using the Gradient Boosting Tree (GBT) algorithm, providing the highest mean accuracy (83%), the highest mean precision (67.8%), and the highest mean recall (58.4%) in identifying negative review comments.

Attack-Resistant Sensor Localization under Realistic Wireless Signal Fading

In a decentralized sensor network, localization process relies on the integrity of participating sensors. Existence of malicious beacon nodes in the vicinity of non-beacon nodes affects this process. This paper presents a trilateration-based secure localization technique, which is capable of estimating the location of a sensor with high accuracy so long four neighbouring beacon nodes are benign, irrespective of the number of neighbouring liars and without assuming any trust model. In realistic scenarios of wireless environment where transmitted signals attenuate randomly due to fading, the liar-tolerance level of this attack-resistant technique has to be relaxed accordingly. Superiority of this technique against the state-of-the-art has been established with extensive simulation results in terms of location estimation accuracy and liar-filtering probability.

Verifiable and Privacy Preserving Electronic Voting with Untrusted Machines

Designing a trustworthy voting system that uses electronic voting machines (EVMs) for efficiency and accuracy is a challenging task. It is difficult, if not impossible, to ensure the trustworthiness of EVMs that possess computation, storage, and communication capabilities. Thus an electronic voting system that does not assume trusted EVMs is clearly desirable. In this paper, we have proposed a k-anonymized electronic voting scheme that achieves this goal by assuming a hardware-controlled trusted random number generator external to the EVM. The proposed scheme relies on a k-anonymization technique to protect privacy and resort to joint de-anonymization of the votes for counting. Since the joint de-anonymization takes into account all the votes, it is difficult to manipulate an individual vote, even by the EVM, without being detected. Besides the anonymization technique, the proposed scheme relies on standard cryptographic hashing and the concept of floating receipt to provide end-to-end verifiability that prevents coercion or vote trading.

A hybrid wireless sensor network framework for range-free event localization

In event localization, wireless sensors try to locate the source of an event from its emitted power. This is more challenging than sensor localization as the power level at the source of an event is neither predictable with precision nor can be controlled. Considering the emerging trend of long sensing range for cost-effective sensor deployment, locating events within a region much smaller than the sensing area of a single sensor has gained research interest. This paper proposes the first range-free event localization framework, which avoids expensive hardware needed by the range-based counterparts. Our approach first develops a sensing range model from the statistical information on the emitted power of a type of events so that user-defined event-detection quality can be provisioned using a minimal network of static sensors. Then an accurate event location boundary estimation technique is developed from the sensing feedbacks, which also facilitates guided expansion of the area of possible event location (APEL) to deal with sensing errors. Finally, user-defined event-localization quality guarantee is provisioned cost-effectively by inviting mobile sensors on-demand to target positions. Analytical solutions are provided whenever appropriate and comprehensive simulations are carried out to evaluate localization performance. The proposed event localization technique outperforms the state-of-the-art range-based counterpart (Xu et al., 2011) in realistic environment with path loss, shadow fading, and sensor positioning errors.

On demand-driven movement strategy for moving beacons in sensor localization

In wireless sensor networks, estimating sensor location demands a large number of neighbor location references due to the unavoidable wireless signal attenuation problem. However, the cost of deployment increases with the increase in beacon location references. This limitation can be overcome using moving beacons exploiting the control over the number, position, and strength of beacon transmissions. In this scenario, the trade-off between localization cost and accuracy, which are directly linked up with anchor movement and transmission pattern, introduces many challenges that have recently attracted research interest. This paper aims to propose a noise-tolerant and cost-effective range-free localization technique using moving beacons that localize randomly deployed sensor nodes within a maximum localization error bound while minimizing the cost of beacon traversal and transmissions. We found that the mean localization error can be kept within 20-35% of the maximum transmission radius by selecting the movement and beacon transmission parameters according to user demand. The proposed schemes are compared with other works and also shown to be robust against positional errors of the moving beacon.

Range-free passive localization using static and mobile sensors

In passive localization, sensors try to locate an event without any knowledge of events emitted power. So, this is a more challenging problem compared to active localization. Existing passive localization schemes use expensive and noise-vulnerable range-based techniques. In this paper, we propose, to the best of our knowledge for the first time, a cost-effective range-free passive localization scheme exploiting hybrid sensor network model where mobile sensors are deployed on demand once an event is sensed by a static sensor. Efficient use of mobile sensors leads to two concomitant optimization problems: (1) positioning the mobile sensors so that the expected possible event location area is minimized; and (2) minimizing their overall traversed distance. To solve the first problem, we have developed a novel arc-coding based range-free localization technique that can accurately define the area of possible event location from the feedback of arbitrarily placed sensors without relying on expensive hardware to estimate range of signals. We have achieved significantly high localization accuracy with a low number of mobile sensors even after considering significant environmental noise. To solve the second problem, three alternative deployment strategies for the mobile sensors were simulated to recommend the best.

Demand-driven Movement Strategy for Moving Beacons in Distributed Sensor Localization

Abstract In a wireless sensor network, range-free localization with a moving beacon can reduce susceptibility to communication noises while concomitantly eliminate need for large number of expensive anchor nodes that are vulnerable to malicious attacks. This paper presents a moving beacon aided range-free localization technique, which is capable of estimating the location of a sensor with high accuracy. A novel distributed localization scheme is designed to optimally determine beacon movement strategy according to user demand. Superiority of this scheme to the state-of-the-art has been established in terms of location estimation quality, measured by the theoretical expected maximum error and simulated mean error while optimizing the beacon location density or traversal path length.

Towards Automated Traffic System Using Vehicular Network with Directional Antenna

Vehicular network became one of the most active and emerging fields of research during last decade. Its use in diversified applications (for example safe driving, congestion avoidance and even entertainment) will soon bring revolutionary changes in transportation system. However, one of the most important features in intelligent transportation system (ITS) is automated traffic signaling and monitoring. In this paper, we investigate these problems towards developing an automated traffic system and design a novel vehicular system architecture using directional antenna. The system follows an addressing scheme for identification of vehicles and their location and stores related information on hierarchically distributed servers. Furthermore, we claim that other applications by vehicular network can also be integrated into the system architecture. Finally, we have concluded our paper focusing on some future directions.

Incentive model design for participatory sensing: Technologies and challenges

Participatory sensing is a crowd-sourcing technique for exploring interesting aspects of the world ranging from personal to community. Recently participatory sensing has attracted considerable attention of research community with a number of open issues and challenges. Motivating people to participate in sensing the phenomena around and report those to dedicated servers is the key factor for the success of this system. Different incentive mechanisms have been proposed to increase the participation rate of users in this context. This paper presents a brief survey of these techniques identifying their respective limitations and finally reports the issues yet to be addressed to develop an widely acceptable incentive model.