Asmita Roy

Optimized secondary user selection for quality of service enhancement of Two-Tier multi-user Cognitive Radio Network: A game theoretic approach

Abstract Cognitive Radio Networks (CRN) is the solution for the spectrum scarcity problem, that has surfaced with the exponential increase in number of mobile users. In CRN, the cognitive devices sense the spectrum, and transmits, on detecting an available channel. Thus CRN resourcefully utilize the idle spectrum bands. In CRN, the users are classified into two types - Primary users and Secondary users. Secondary users are further classified into two tiers- Real Time users (RT) and Non-Real Time users (NRT). Users from both the tiers share a single channel for transmission, resulting in efficient spectrum usage. However, with ever growing number of secondary mobile users having diverse QoS requirements, the cognitive interactions among them need to be optimized. This paper uses game theory to study the conflict and cooperation among the two tiers of secondary users. An auction game model is proposed that analyzes the complex decision making process and efficiently allocates an idle channel to a pair of RT and NRT secondary users from a pool of users. The game model is further extensively analyzed mathematically, using Markov chain, showing a reduction in user Blocking probability, user Dropping probability, Channel Saturation probability and increase in user Acceptance probability, compared to the Basic One-Tier CRN and Non-Optimized Two-Tier CRN. The optimized network model is then simulated using Qualnet 7.1. The simulation result shows improved Bandwidth Utilization Efficiency, Spectral efficiency and increased number of secondary users served, compared with respect to Basic One-Tier CRN and Non-Optimized Two-Tier CRN.

Multi-objective optimization technique for resource allocation and task scheduling in vehicular cloud architecture

With onset of Intelligent Transport Systems, vehicles are equipped with internet enabled powerful computation units that provide smart driving assistance, along with various infotainment applications. These applications require web assistance and high computation power, which cannot be executed by standalone onboard units of the smart vehicles. Third party infrastructures like centralized cloud and cloudlets are introduced, to meet the requirement for such vehicular, web based, resource hungry applications. Offloading jobs to centralized cloud, exhausts network bandwidth and causes network delay, whereas frequent offloading to cloudlet results in resource starvation due to limited cloudlet resources. These problems lead to the introduction of Vehicular Cloud Computing (VCC) where the onboard units of several local smart vehicles collectively form a cloud. The concept of multi layered cloud brings centralized cloud, cloudlet and vehicular cloud together to coexist and provide on demand services to mobile and vehicular users. In this work, a three tier architecture is proposed consisting of vehicular cloud, roadside cloudlet and centralized cloud. We have developed an optimized resource allocation and task scheduling algorithm to efficiently serve huge number of task requests arriving from on road users, while maintaining improved Quality of Service. These task requests are optimally mapped to cloud resources among the three cloud layers. The optimization process is carried out using the proposed Hybrid Adaptive Particle Swarm Optimization (HAPSO) algorithm which is a combination of Genetic Algorithm and Adaptive Particle Swarm Optimization. Further the proposed model is simulated using SUMO 0.30.0, NS 3.26 and MATLAB R2014a. The results show that for this problem domain, HAPSO converges faster than Standard Particle Swarm Optimization and Self Adaptive Particle Swarm Optimization with ~98.92% reduction in mean square error. The results also show ~34% improvement in task response time and reduced energy consumption upto ~32.5%.

Vertical Handoff Mechanisms in VANET: A Survey

Vehicular ad-hoc network is a modern paradigm for vehicular communication on the move. Vehicular-ad-hoc network is a technology that facilitates three types of communication (vehicle-to-infrastructure, vehicle-to-vehicle and vehicle-to-person) and improves the quality of service of these communication categories. Now since the vehicles move and the Infrastructures do not, handoff is an important domain in the field of vehicular-ad-hoc networks. Handoff takes place when a vehicle moves from one network area covered by an access point or base station to another network area covered by a different access point. Handoff algorithms can be classified into two categories:-handoff execution algorithms and handoff decision algorithm. In this paper, a literature survey is done on different types of handoff algorithms and a detailed comparison is made on the basis of parameters like addressing, handoff delay, handoff decision, technology used etc. Also, some improvements are proposed over the present handoff mechanisms which lead to a wide area of research in this field.

A novel Vertical Handoff scheme for vehicular ad-hoc networks

With the growth in population in recent times, numbers of vehicles on road also have increased drastically. And with this increase in vehicle numbers, accident rates, traffic congestion also have increased proportionately. A better way of establishing communication between vehicles reduces accident rates and traffic congestion significantly. Establishing a connection between two moving nodes is considered as a highly challenging task. Although several Vertical Handoff strategies have come up in recent times but most of them suffer from high latency, greater processing overhead and single point of failure. In this paper we have designed a new scheme that efficiently overcomes these issues by sharing the load among the infrastructure and vehicles through the deployment of distributed network architecture. This ensures lower network overhead and reduced handoff latency.

Distributed Task Scheduling in Cloud Platform: A Survey

Cloud computing is a booming area in distributed computing and parallel processing. Cloud provides services to its customer on pay-per-use basis. It has gained a lot of attention due to its unique features—elasticity, scalability, and on-demand services. Cloud facilitates both computational and storage service to its customers. This reduces the cost of deployment and maintenance for any organization. As a result, demand for cloud computing has increased considerably. To provide the services, cloud service provider needs to utilize all resources in an optimal way. To utilize all resources efficiently, task schedule plays a significant role. It is responsible for scheduling users’ tasks in the cloud environment. The task scheduler arranges tasks in a queue for the available connected resources. This arrangement benefits the cloud service provider to achieve maximum performance in a cost efficient manner. In this paper, an extensive study of some well-known task-scheduling algorithms in cloud environment is done while identifying the advantages and weaknesses of these existing algorithms. Future research areas and further improvement on the existing methodologies are also suggested.

A Hybrid Task Scheduling Algorithm for Efficient Task Management in Multi-cloud Environment

Cloud computing is an emerging area in the field of computation where various IT infrastructures are leveraged to users based on their requirement. With ever-growing number of cloud users, the number of task requests that needs to be handled in one-time instance is huge. At the same time, to deliver a good QoS, CSPs need to achieve best performance in a cost-efficient manner with minimal completion time along with reduced delay and latency. Thus, an efficient task scheduling algorithm in a multi-cloud environment needs to deploy a hybrid approach considering multiple factors for allocating tasks among several available clouds. In this work, a well-defined efficient hybrid task scheduling algorithm is developed where tasks are scheduled in a multi-cloud environment. The task scheduler employs a priority-based algorithm that determines the priority level of every task based on the computation cost, time required and power consumed to execute the task. The simulation result shows that our approach attains better efficiency in comparison with other existing approaches.

A Comparative Analysis of Cloud Forensic Techniques in IaaS

With the growth of cloud users, various criminal activities, attacks, suspicious, or malicious activities in cloud environment are growing day by day. It is the responsibility of cloud service providers (CSP) to secure the user data residing on cloud. Cloud forensic is the field of study that helps in investigating the criminal activities taking place in the cloud environment. Proper identification of the threat and presenting it in the court of law is desired. Traditional digital forensics method is not applicable in the multi-tenant cloud environment which gave rise to cloud forensics. There are certain proposed cloud forensics methods which help in identifying the malicious users. These approaches have certain advantages and limitations. This paper focuses on the various cloud forensics methods that are proposed for Infrastructure as a Service (IaaS) model. IaaS model is more vulnerable to attacks because virtual machine (VM) works in that layer. Generally most of the crimes take place using VM. A detailed comparative analysis is done in this paper highlighting each of the existing cloud forensics methods, their advantages, and drawbacks. Further, the future research areas are pointed out which will help in developing a better cloud forensic model by overcoming the drawbacks of the various existing methods.

Energy Aware Task Scheduling Algorithms in Cloud Environment: A Survey

Cloud computing is a developing area in distributed computing and parallel processing domain. Popularity of cloud computing is increasing exponentially due to its unique features like on-demand service, elasticity, scalability, and security. Cloud service providers provide software, platform, high-end infrastructure, storage, and network services to its customers. To provide such services to its customers, all cloud resources need to be utilized in the best possible way. This utilization is efficiently handled by task scheduling algorithms. Task schedulers aim to map customer service requests with various connected resources in a cost-efficient manner. In this paper, an extensive study of some scheduling algorithm that aims to reduce the energy consumption, while allocating various tasks in cloud environment is done. The advantages and disadvantages of these existing algorithms are further identified. Future research areas and further improvements on the existing methodologies are also suggested.

An Efficient Handoff Using RFID Tags

With the introduction of VANET, inter-vehicular communication and communication between vehicle and network infrastructure has become very convenient. This advancement has brought in challenges with itself. The vehicles are mobile; moving around with very high speed whereas infrastructure is stationary. Thereby handoff plays a critical role in VANET. In our approach, we have used radio frequency identification (RFID) tags for efficient hand off mechanism with reduced delay. RFID tags are deployed in the chassis of every vehicle containing the unique MAC address of that vehicle. RFID scanners are deployed on road which scans the RFID tags and sends the MAC address to the handoff gateway which communicates the information to the nearest access point. This makes the access point aware of the incoming vehicle thereby reducing the delay. Using this novel approach not only the delay in handoff is reduced but also hand off is performed efficiently with minimal information.

Femtocell-Based Load Balancing Using a Noncooperative Game

The increase in smart phone devices has lead to an exponential growth in the number of MUs (mobile user). With increasing number of MUs, the demand for data rate also increases. So, femtocells are deployed in indoor regions to give efficient coverage. Users on entering the system gets randomly connected to one of the femtocell under its coverage. This leads to some femtocell being overloaded and some femtocells remaining underutilized. In this paper, a non-cooperative game is played between the users for deciding among the femtocells under its coverage which gives an optimum performance; without hampering the performance of other users. The critical selection is based on reaching Nash Equilibrium of the game with the help of the utility function. This function is based on two important network parameters path loss and data rate of the femtocell. Simulations shows this approach helps in balancing the overall load of the system.