Poonam Saini

Presence of an Ecosystem: a Catalyst in the Knowledge Building Process in Crowdsourced Annotation Environments

The phenomenal success of certain crowdsourced online platforms, such as Wikipedia, is accredited to their ability to tap the crowds potential to collaboratively build knowledge. While it is well known that the crowds collective wisdom surpasses the cumulative individual expertise, little is understood on the dynamics of knowledge building in a crowdsourced environment. A proper understanding of the dynamics of knowledge building in a crowdsourced environment would enable one in the better designing of such environments to solicit knowledge from the crowd. Our experiment on crowdsourced systems based on annotations shows that an important reason for the rapid knowledge building in such environments is due to variance in expertise. We use, as our test bed, a customized Crowdsourced Annotation System (CAS) which provides a group of users the facility to annotate a given document while trying to understand it. Our results show the presence of different genres of proficiency amongst the users of an annotation system. We observe that the crowdsourced knowledge ecosystem comprises of mainly four categories of contributors, namely: Probers, Solvers, Articulators and Explorers. We infer from our experiment that the knowledge garnering mainly happens due to the synergetic interaction across these categories.

Authentication and privacy preserving message transfer scheme for vehicular ad hoc networks (VANETs)

Vehicular Ad hoc Networks (VANETs) are likely to be deployed for real-time applications in the coming years, thus, forming the most relevant form of mobile ad hoc networks. In such hostile environment, security is a major concern. The paper presents a novel architecture for VANETs to achieve authentication and privacy preserving message transfer among the vehicles. We have designed a four-phase protocol which employs Elliptic Curve Cryptography (ECC). Also, the paper discusses the performance of ECC over RSA in terms of key size and computation from the existing data set. Furthermore, the paper presents a static analysis to prove robustness and efficiency of the proposed approach.

A hybrid routing algorithm against routing attacks in Mobile Adhoc Networks (MANETs)

Dynamically changing network conditions pose a challenge for routing in Mobile Adhoc Networks (MANETs). The network conditions like topology, node density, energy constraint makes difficult to decide which protocol suits best for the scenario. The routing in MANETs exploits Proactive and Reactive Routing. Proactive routing provides better packet delivery while Reactive routing has low overheads. Thus, Hybrid routing approach is needed to carry out the routing using both proactive and reactive approaches. The paper proposes a Hybrid Routing algorithm which utilizes the benefits of both reactive and proactive routing in MANETs. The protocol mainly focuses on trust factor of the node which uses frequency of the node and number of retransmissions from the node. The protocol changes its behavior according to different applications in MANETs.

Arbitration based distributed group mutual exclusion algorithm for mobile ad hoc networks

The group mutual exclusion problem is a simplification of mutual exclusion problem such that a set of processes in the similar group can enter a critical section all together. To handle group mutual exclusion, the interrelated solutions are chiefly classified into quorum-based, token-based as well as permission-based protocols. The proposed algorithm is based on tokens; a process that grasps a token can invoke a critical section. To our knowledge, only some algorithms have been proposed in the literature which follows permission-based or token-based approach. In this paper, we put the entire nodes into dissimilar regions. Arbitrator nodes have been introduced in this paper that forms the relationship among two neighboring regions. “Look-ahead” procedure is used which helps in dropping message complexity. We present the distributed group mutual exclusion algorithm developed for MANETs. The outcomes have been substantiated by simulations and experiments. We observed that the proposed algorithm outperforms the TQGmx algorithm in performance parameters such as synchronization delay, response time in addition to message complexity.

An efficient permission-cum-cluster based distributed mutual exclusion algorithm for mobile adhoc networks

Mutual exclusion (Mutex) is a major coordination problem in distributed systems. To handle Mutex, the corresponding solutions are mainly classified into permission-based, token-based and quorum-based protocols. Mobile Ad-hoc Networks referred as MANETs introduce new obstacles for designing algorithms that help in solving distributed mutual exclusion problem. To our knowledge, few algorithms have been proposed in the literature which follows token-based or permission-based approach. In this paper, we propose an efficient permission-cum-cluster based distributed mutual exclusion algorithm for mobile adhoc networks. The significant motivation for our algorithm is to ensure that every node should transmit the lowest number of effective messages in the underlying network. Moreover, it will lead to reduced communication delay in order to retrieve the required permissions. We observed that the proposed algorithm outperforms the Mobile_RA algorithm in performance parameters such as synchronization delay, response time as well as message complexity. The results have been substantiated by simulation experiments.

A Time-Efficient Active Learning-Based Instance Matching System for Data Linking

Learning of link configuration can be classified into two subcategories, namely, supervised and unsupervised. The supervised learning of link configuration has an advantage over unsupervised learning in terms of high accuracy and low complexity. However, a common shortfall of supervised systems is the availability of training data. Here, Active Learning is a favorable semi-supervised learning technique in which a learning algorithm will actively query the user to label the most informative link candidate. This method is desirable in situation where data is abundant and manual labeling of links is expensive. Moreover, while dealing with large datasets, runtime efficiency of the instance matching system is also desirable. The paper proposes an extended framework to our previous work on a supervised learning-based instance matching system with runtime efficiency.

Human Fall Detection System over IMU Sensors Using Triaxial Accelerometer

A sudden increase in the number of deaths over the past few years by slipping and falling, especially in case of patients in hospitals and aged people at homes, is a serious concern and calls for the need of an autonomous system for detection of fall and alerting caretaker in case of emergency. We propose an algorithm which, first, derives features from an input stream of data sensed and uses it in learning of our system and further, provides it with the capability of classifying a sequence into either fall or activity of daily living sequence implemented using support vector machine. We propose a space and time efficient system, minimizing its cost by using only 3-axial accelerometer as sensor. Choice of type and number of features along with their operational complexity is a crucial factor for our system. Performance analysis is done by first training our system and then testing its accuracy in classifying test sequences using machine learning algorithm.

How Heterogeneity Affects the Design of Hadoop MapReduce Schedulers: A State-of-the-Art Survey and Challenges

MapReduce (MR) computing paradigm and its open source implementation Hadoop have become a de facto standard to process big data in a distributed environment. Initially, the Hadoop system was homogeneous in three significant aspects, namely, user, workload, and cluster (hardware). However, with growing variety of MR jobs and inclusion of different configurations of nodes in the existing cluster, heterogeneity has become an essential part of Hadoop systems. The heterogeneity factors adversely affect the performance of a Hadoop scheduler and limit the overall throughput of the system. To overcome this problem, various heterogeneous Hadoop schedulers have been proposed in the literature. Existing survey works in this area mostly cover homogeneous schedulers and classify them on the basis of quality of service parameters they optimize. Hence, there is a need to study the heterogeneous Hadoop schedulers on the basis of various heterogeneity factors considered by them. In this survey article, we first discuss different heterogeneity factors that typically exist in a Hadoop system and then explore various challenges that arise while designing the schedulers in the presence of such heterogeneity. Afterward, we present the comparative study of heterogeneous scheduling algorithms available in the literature and classify them by the previously said heterogeneity factors. Lastly, we investigate different methods and environment used for evaluation of discussed Hadoop schedulers.

An Energy Efficient Algorithm for Time Synchronization in Sensor Networks

The time synchronization is a well-known challenge in wireless ad hoc networks. The paper introduces an algorithm for time synchronization in wireless sensor networks. It aims to conserve the battery power of sensors to be utilized for synchronization among the nodes in the network. The proposed synchronization approach is topology driven. In the existing literature, a wide range of protocols use flooding as the basic mechanism to create a topology. However, our method is based on the clustering of nodes with minimum hop distance from the cluster head and the chaining of cluster heads in order to create an energy-efficient topology. Also, it results in substantial reduction in the total transmission overhead. Further, it restricts the energy consumption in synchronizing the nodes of the network. Moreover, the approach avoids the loss of network coverage by electing a new cluster head in the event of cluster head failure. Finally, the simulation results exhibits that the proposed approach has significant energy savings as compared to many popular algorithms.

Agreement-Based Interference-Aware Dynamic Channel Allocation in Cognitive Radio Network (CRN)

Cognitive Radio Networks (CRNs) is an intelligent wireless communication network that senses its environment to adjust the transmitter parameters in order to exploit the unused portions of available spectrum. The objective here is to ensure reliable communication with minimum intereference to Primary Users (PUs) and efficient spectrum utilization. The spectrum assigned to licensed users is underutilized and the growing demand causes starvation to the unlicensed users. Thus, CRN senses the available spectrum to find the most appropriate spectrum for allocation. Further, to maximize the efficient use of available spectrum, agreement (consensus) may be used wherein all users agree on a common decision value. In the paper, we discuss various techniques of spectrum allocation in CRN. Lastly, we propose an interference-aware protocol that achieves load balancing, high throughput and less number of reallocations to maximize spectrum utilization. Also, the paper validates the proposed algorithm using the simulation results.