Faisal Riaz

IMPROVED QUALITY OF SERVICE PROTOCOL FOR REAL TIME TRAFFIC IN MANET

The technologies like Wi-Fi, Blue tooth, WiMax etc. have made Mobile Ad hoc Networks common in our Real life. Multi-media applications need to be supported on MANET. A certain level of QoS (Quality of Service) support is essential for Real time data. Our proposed protocol provides the requi red QoS without having negative impact on Best Effort data traffic. An efficient rout discovery mechanism for AODV routing protocol as well as transmission technique for real time data are proposed. This technique gives more transmission opportunities to real time data traffic results in decreasing transmission delay and increasing throughput. A modified version of the popular AODV routing protocol to provide QoS guarantee for real time traffic in MANETs is proposed. The simulation shows better performanceresults for proposed protocol over the basic AODV.

Enhanced Image Encryption Techniques Using Modified Advanced Encryption Standard

Protecting multimedia data has become an important and sensitive issue since vast exchange of multimedia data on the internet. For this purpose usually naive approach is used which provides high level of security but it also need special, costly hardware for real time encryption. Such approach is not practical in situations where we have limited processing power, low bandwidth, and small storage of data. So there is an apparent tradeoff between security and the speed of encryption algorithms. In such situations selective encryption is suggested. We have proposed three new selective encryption techniques for image encryption. We modify AES and make it light and faster. We test our proposed techniques with Modified Advance Encryption Standard on text and images using c# .NET .Achieved results help us to conclude that selective image encryption techniques with MAES can be a better approach to decrease image encryption time.

QoS routing for real time traffic in mobile ad hoc network

Real time applications are getting increasing popularity in almost all kinds of networks, including MANETs. The support for these applications on best effort network, like IPN, requires the development of protocols that can guarantee a certain level of Quality of Service (QoS). This paper is concerned with a routing protocol for MANETs that provides QoS guarantees using the Time Slot Bandwidth Reservation (TSBR) method. This technique reserves larger bandwidth for real time data thus reducing the queuing delay. In this paper, we propose enhancement in the popular AODV routing protocol to provide QoS support for real time traffic in MANETs. The simulation results show the performance advantages achieved using the proposed protocol over the basic AODV.

White space optimization using memory enabled genetic algorithm in vehicular cognitive radio

Recently cognitive radio based approaches have been introduced in inter-vehicle communication (IVC) system due to inherent advantages. Spectrum mobility is very frequent in IVC due to the mobility of vehicles and unpredictable radio frequency (RF) channel. This requires an efficient white space optimization technique to ensure spectrum management function. In this paper, a modified genetic algorithm, by introducing memory concept, is employed to obtain the most suitable white space against the quality of service (QoS) requested by vehicles. Simulation results demonstrate that the proposed approach is more efficient in finding white spaces.

A Novel White Space Optimization Scheme Using Memory Enabled Genetic Algorithm in Cognitive Vehicular Communication

AbstractA dedicated single short range communication link is not efficient for an inter-vehicular communication system and results into degraded performance. To address the problem, a cognitive radio site is proposed as an intelligent vehicular device to implement an inter-vehicular communication network using multiple radio access technologies. Further, the whitespace optimization at vehicular speed is achieved by the memory enabled genetic algorithm. The algorithm makes use of four cognitive radio decision variables as genes including frequency, power, data rate and modulation scheme in the chromosome structure. The performance of the proposed approach is validated against the classical genetic algorithm and particle swarm optimization algorithm. In this research, a statistical evaluation is also presented to confirm the potential of cognitive radio paradigm employing multiple radio access technologies as an option to fulfill the increasing bandwidth demand of an inter-vehicular communication system. Experimental results demonstrate the effectiveness of the approach by ensuring efficient bandwidth utilization and fulfilling varying nature of users’ quality of service requirements in real time.

Vehicle-to-vehicle communication enhanced by cognitve approach and multi-radio technologies

According to the report of world health organization (WHO), about 1.27 million people lost their lives in 2009 due to the road accidents and it was the ninth foremost reason of deaths. The vehicle-to-vehicle communication system (V2V) is one of the solutions to dwindle the accident ratios. The existing V2Vs are fixed based on single radio and are inept and erratic especially in the hilly areas due to low SNR and partial coverage. Their performance is degraded in the metropolitans due to crowded population and over-burdened traffic routes. Keeping above disadvantages in mind, multi-radio access technologies (GSM/GPRS, CDMA, Wi-Fi) based V2V using cognitive radio framework has been proposed in this paper. The scheme introduces an in-vehicle cognitive radio site with the propensities of spectrum sensing, spectrum decision and spectrum mobility. Investigational results reveal that the proposed solution does not overtax the existing networks in affected area and vehicles remain aware of other vehicles even in low SNR by single radio technology and partial coverage areas.

A collision avoidance scheme for autonomous vehicles inspired by human social norms

Abstract This paper introduces the novel idea of using human social norms and human emotions to improve the collision avoidance of Autonomous Vehicles (AVs). Until now, the literature has been concerned with theoretical debates regarding ethical issues connected to AVs, while no practical steps have yet been undertaken. This paper introduces the concept of an artificial society of AVs with different personalities and with social norms coded into their autopilot so that they act like well-behaved drivers. For proof of concept, the standard agent modelling tool Netlogo is utilized to simulate the artificial society of AVs. Furthermore, comparisons are made with random walk-based non-social-based collision avoidance techniques. Extensive testing has been carried out using the behaviour space tool to determine the performance of the proposed approach regarding the number of collisions. A comparative study undertaken with a random walk method indicates that the proposed approach provides a better option for tailoring the autopilots of future AVs, while also promising to be more socially acceptable and trustworthy regarding safe road travel.

Efficient spectrum optimization and mobility in cognitive radio based inter-vehicle communcation system

An efficient spectrum optimization and spectrum mobility schemes have been proposed to enhance the potentials of cognitive radio based inter vehicle communication (IVC) system. At vehicular speed performing spectrum optimization and then spectrum mobility is a challenging chore. Spectrum mobility is very frequent in IVC due to extreme mobile nature of vehicles and unpredictable RF channel. A proficient white space optimization technique is a basic requirement to perform in time spectrum decision and so in time spectrum mobility. Genetic Algorithm (GA) is considered as one of the best optimization techniques for white space optimization. But at vehicular speed simple genetic algorithm (GA) has been botched to perform white space optimization in real time. To anticipate this problem we have already proposed Memory enabled genetic algorithm (MEGA) in our previous work. In this research work we have further improved the convergence time of MEGA by manipulating the generation gap and mutation operator. Simulation results have proven that enhanced memory enabled genetic algorithm (EMEGA) is 0.522 ms faster than MEGA. In the next phase of research an efficient spectrum mobility scheme using human emotion (fear) has been proposed. Simulation results reveal that using our proposed spectrum decision and spectrum mobility schemes a more efficient cognitive radio based vehicular networks can be tailored.

Enhanced emotion enabled cognitive agent-based rear-end collision avoidance controller for autonomous vehicles

Amongst collisions, rear-end collisions are the deadliest. Several rear-end collision avoidance solutions have been proposed recently in the literature. A key problem with existing solutions is their dependence on precise mathematical models. However, real world driving is influenced by a number of nonlinear factors. These include road surface conditions, driver reaction time, pedestrian flow, and vehicle dynamics. These factors involve so many different variations that precise mathematical solutions are hard to obtain, if not impossible. This problem with precise control-based rear-end collision avoidance schemes has also previously been addressed using fuzzy logic, but the excessive number of fuzzy rules straightforwardly prejudices their efficiency. Furthermore, such fuzzy logic-based controllers have been proposed without the use of an appropriate modeling technique. One such modeling technique is agent-based modeling. This technique is suitable because it allows for mimicking the functions of an artificial human driver executing fuzzy rules. Keeping in view these limitations, we propose an enhanced emotion enabled cognitive agent (EEEC_Agent)-based controller. The proposed EEEC_Agent helps autonomous vehicles (AVs) avoid rear-end collisions with fewer rules. One key innovation in its design is to use the human emotion of fear. The resultant agent is very efficient and also uses the Ortony–Clore–Collins (OCC) model. The fear generation mechanism of EEEC_Agent is verified through NetLogo simulation. Furthermore, practical validation of EEEC_Agent functions is performed by using a specially built prototype AV platform. Finally, a qualitative comparison with existing state-of-the-art research works reflects that the proposed model outperforms recent research proposals.

Multi-Factors Based Road Accident Prevention System

About 1.24 million deaths occur due to road accidents yearly. According to different research studies the main cause of these accidents is human drivers due to the different causes. We have classified these accident causes into three factors, namely environmental, physical and mental. These three accidents leading factor levels have been identified from existing literature using extensive survey. Multi-Factors Based Road Accident Prevention System (MFBRAPS) works on the rules defined in the Fuzzy system and take actions according to the severity of the condition to avoid the road collisions. Further the fuzzy logic based MFBRAPS has been validated using a VOMAS agent in the agent based simulator i.e, NetLogo 5.12. The proposed research is a useful guide for the prevention of accidents for all levels of accident factors in a single lane.