International Conference on Distributed Computing and Internet Technology 

About: International Conference on Distributed Computing and Internet Technology is an academic conference. The conference publishes majorly in the area(s): Computer science & Wireless sensor network. Over the lifetime, 663 publications have been published by the conference receiving 3956 citations.

Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces

Abstract: The recent wave of sensor-rich, Internet-enabled, smart mobile devices such as the Apple iPhone has opened the door for a novel paradigm for monitoring the urban landscape known as participatory sensing. Using this paradigm, ordinary citizens can collect multi-modal data streams from the surrounding environment using their mobile devices and share the same using existing communication infrastructure (e.g., 3G service or WiFi access points). The data contributed from multiple participants can be combined to build a spatiotemporal view of the phenomenon of interest and also to extract important community statistics. Given the ubiquity of mobile phones and the high density of people in metropolitan areas, participatory sensing can achieve an unprecedented level of coverage in both space and time for observing events of interest in urban spaces. Several exciting participatory sensing applications have emerged in recent years. For example, GPS traces uploaded by drivers and passengers can be used to generate realtime traffic statistics. Similarly, street-level audio samples collected by pedestrians can be aggregated to create a citywide noise map. In this talk, we will provide a comprehensive overview of this new and exciting paradigm and outline the major research challenges.

Scribbling interactions with a formal foundation

Abstract: In this paper we discuss our ongoing endeavour to apply notations and algorithms based on the π-calculus and its theories for the development of large-scale distributed systems. The execution of a largescale distributed system consists of many structured conversations (or sessions) whose protocols can be clearly and accurately specified using a theory of types for the π-calculus, called session types. The proposed methodology promotes a formally founded, and highly structured, development framework for modelling and building distributed applications, from high-level models to design and implementation to static checking to runtime validation. At the centre of this methodology is a formal description language for representing protocols for interactions, called Scribble. We illustrate the usage and theoretical basis of this language through use cases from different application domains.

Using KNN and SVM Based One-Class Classifier for Detecting Online Radicalization on Twitter

Abstract: Twitter is the largest and most popular micro-blogging website on Internet. Due to low publication barrier, anonymity and wide penetration, Twitter has become an easy target or platform for extremists to disseminate their ideologies and opinions by posting hate and extremism promoting tweets. Millions of tweets are posted on Twitter everyday and it is practically impossible for Twitter moderators or an intelligence and security analyst to manually identify such tweets, users and communities. However, automatic classification of tweets into pre-defined categories is a non-trivial problem problem due to short text of the tweet the maximum length of a tweet can be 140 characters and noisy content incorrect grammar, spelling mistakes, presence of standard and non-standard abbreviations and slang. We frame the problem of hate and extremism promoting tweet detection as a one-class or unary-class categorization problem by learning a statistical model from a training set containing only the objects of one class . We propose several linguistic features such as presence of war, religious, negative emotions and offensive terms to discriminate hate and extremism promoting tweets from other tweets. We employ a single-class SVM and KNN algorithm for one-class classification task. We conduct a case-study on Jihad, perform a characterization study of the tweets and measure the precision and recall of the machine-learning based classifier. Experimental results on large and real-world dataset demonstrate that the proposed approach is effective with F-score of 0.60 and 0.83 for the KNN and SVM classifier respectively.

GAR: An Energy Efficient GA-Based Routing for Wireless Sensor Networks

Abstract: Routing with energy consideration has paid enormous attention in the field of Wireless Sensor Networks (WSNs). In Some WSNs, some high energy sensors called relay nodes are responsible to route the data towards a base station. Reducing energy consumption of these relay nodes allow us to prolong the lifetime and coverage of the WSN. In this paper, we present a Genetic algorithm based routing scheme called GAR (Genetic Algorithm-based Routing) that considers the energy consumption issues by minimizing the total distance travelled by the data in every round. Our GA based approach can quickly compute a new routing schedule based on the current network state. The scheme uses the advantage of computational efficiency of GA to quickly find out a solution to the problem. The experimental results demonstrate that the proposed algorithm is better than the existing techniques in terms of network life time, energy consumption and the total distance covered in each round.

An approach to find embedded clusters using density based techniques

Abstract: This paper presents an efficient clustering technique which can identify any embedded and nested cluster over any variable density space. The proposed algorithm is basically an enhanced version of DBSCAN [4] and OPTICS [7]. Experimental results are reported to establish that the proposed clustering technique outperforms both DBSCAN and OPTICS in terms of complex cluster detection.