TCP is most widely used transport layer protocol. Most of the applications such as e-mails, file transfers use TCP due to its reliable communication. There are various mechanisms to control the congestion in the network. The variants of TCP implement slow start, congestion avoidance, fast retransmit and fast recovery algorithms in different ways for congestion control. In this paper, we have simulated four TCP variants namely Tahoe, Reno, New-Reno and Vegas in mobile ad hoc network over AODV and DSR routing protocols. Simulation is done in NS2. Comparison of throughput, end-to-end delay and packet delivery fraction is made against pause time and node speed variation to determine the performance of these four TCP congestion control algorithms.

A novel impulse noise filter that preserves the image details and effectively suppresses high-density noise has been proposed in this work. The proposed filter works in two phases: (i) noise pixel detection phase and (ii) noise pixel restoration phase. In the detection phase, the impulse noise corrupted pixels are detected using a neighbourhood decision approach. In the second phase, the true values of corrupted pixels are restored using a first-order neighbourhood decision approach. Experiments are carried out with both grey scale and colour images of various resolutions, texture and structures. The proposed scheme has high peak-signal-to-noise ratio and better visual quality in comparison to the standard median filter, modified decision based unsymmetrical trimmed median filter and improved fast peer-group filter with a varying noise density from 10 to 90%.

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Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

Abstract. In the routing strategic approach, mostly in wireless scenario, primary emphasis is given on path routing and routing protocol selection. Again in Mobile Ad hoc Network (MANET) a routing protocol is to be selected in such a way that the network can be suitably designed to give best data delivery as well data integrity. So performance analysis of the protocols is the major step to select these protocols. In this paper comparative performance analysis like delay, throughput, control overhead, and PDR is done over protocols like Ad hoc On demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequenced Distance Vector (DSDV) in NS2 Simulator. Based on these parameters a proper protocol can be designed for an efficient MANET.