Narula Institute of Technology

About: Narula Institute of Technology is a based out in . It is known for research contribution in the topics: Quantum dot cellular automaton & Cognitive radio. The organization has 288 authors who have published 490 publications receiving 2258 citations. The organization is also known as: NiT.

Intention aware misbehavior detection for post-disaster opportunistic communication over peer-to-peer DTN

Abstract: Delay tolerant network (DTN) has been successfully proposed for setting up emergency post disaster communication networks when normal communication infrastructure is typically incapacitated. DTN being a network where participating nodes transmit messages to the final destination in multiple hops, its success depends on the cooperation of these participating nodes. Performance of such cooperation based networks get severely affected by misbehaving nodes that do not participate in message forwarding either due to reasons that are beyond its control (non-availability of appropriate forwarders, decreasing battery life, etc.) or out of certain malicious intentions. The misbehavior detection schemes, proposed so far, rarely investigate the actual intention behind misbehavior of participating nodes and do not attempt to restore the reputation of falsely alleged nodes. In this paper, we propose iDetect, a reputation based intention aware misbehavior detection scheme that uses contextual evidences to analyze the intention behind a node’s misbehavior. The scheme boosts the low reputation of incorrectly ostracized nodes and includes them in future communication. Re-inclusion of these nodes increases the number of genuine forwarders in the network which, in turn, assists in efficient delivery of crucial post disaster situational messages. Results of extensive simulation, using ONE simulator, substantiate the effectiveness of the proposed iDetect scheme over state-of-the-art competing schemes, in terms of detection ratio, availability ratio, etc. while not compromising on standard network performance in a post disaster communication scenario.

Dehazing technique based on dark channel prior model with sky masking and its quantitative analysis

Abstract: Outdoor image and video corrupt easily owing to fog, mist etc. Several techniques have been proposed to overcome these problems. But there is no optimum method as these scenarios are unpredictable and all situations are unique in their own way. Dark channel prior — a new prior-gives a significant prediction about the quantity of airlight that causes haze. Dark channel with contrast controller and sky masking improves the visibility with significantly reduced time. In this paper a innovative technique has been studied and it's qualitative and quantitative analysis carried out.

An Efficient Single-Fault Detection Technique for Micro-fluidic Based Biochips

Abstract: This paper presents a new fault detection technique for digital micro-fluidic based biochips. Due to recent advances in microfluidics, digital micro-fluidic biochips are revolutionizing laboratory procedures, e.g., biosensing, clinical diagnostics etc. Because of the underlying mixed-technology and mixed energy domains, biochips exhibit unique failure mechanisms and defects. Off-line and on-line test techniques are required to ensure system dependability. In this paper, a new technique is presented to traverse all the cells and cell boundary, for the purpose of detecting a single fault within the bio-chip. Experimental results show that the proposed technique improves the fault detection time to a great extent.

An IoT-Based Congestion Control Framework for Intelligent Traffic Management System

Abstract: The concept of smart city helps to improve the quality of urban life of the citizens while keeping in mind the environmental impacts. Smart and sustainable transportation system is one of the major contributors in order to make the city smart. Major cities around the world face enormous vehicular growth due to socioeconomic growth and rural to urban migration of the people. These results in high traffic congestion on road, road accidents, delay and have an adverse environmental impact, thus effecting smooth mobility of the citizens. Hence, traffic management authorities face difficulties to manage and reduce traffic congestion, road accidents and air pollution. In order to overcome the above-mentioned challenges, this paper proposes a framework for managing road traffic congestion in intelligent traffic management system which utilizes the available infrastructures and resources in an optimum way. The proposed framework comprises of four different modules, namely data collection module, data storage module, data processing module and business application module.

Sample shifting technique (SST) for estimation of harmonic power in polluted environment

Abstract: In this paper, an attempt has been made to estimate real and reactive power components for each harmonic in a polluted sinusoidal environment based on a newly proposed sample shifting technique (SST) A special feature of this technique is that each harmonic power component is estimated from the samples of voltage and current signals only, without measuring the power factor angle, or adopting FFT or DFT etc as used by traditional methods The present method also determines the DC power component along with the sinusoids The method of SST is supported by simulation results along with experimental validation