DOTNET 2013 Abstracts(Networking, Network-Security, Mobile Computing, Cloud Computing, Wireless Sensor Network, Datamining, Webmining, Artificial Intelligence, Vanet, Ad-Hoc Network)

1. An Ant Colony Optimization Approach for Maximizing the Lifetime of Heterogeneous Wireless Sensor Networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs

2. Maximizing Lifetime Vector in Wireless Sensor Networks

Maximizing the lifetime of a sensor network has been a subject of intensive study. However, much prior work defines the network lifetime as the time before the first data-generating sensor in the network runs out of energy or is not reachable to the sink due to network partition. The problem is that even though one sensor is out of operation, the rest of the network may well remain operational, with other sensors generating useful data and delivering those data to the sink. Hence, instead of just maximizing the time before the first sensor is out of operation, we should maximize the lifetime vector of the network, consisting of the lifetimes of all sensors, sorted in ascending order. For this problem, there exists only a centralized algorithm that solves a series of linear programming problems with high-order complexities. This paper proposes a fully distributed algorithm that runs iteratively. Each iteration produces a lifetime vector that is better than the vector produced by the previous iteration. Instead of giving the optimal result in one shot after lengthy computation, the proposed distributed algorithm has a result at any time, and the more time spent gives the better result. We show that when the algorithm stabilizes, its result produces the maximum lifetime vector. Furthermore, simulations demonstrate that the algorithm is able to converge rapidly toward the maximum lifetime vector with low overhead.

3. Collaboration-Based Cloud Computing Security Management Framework

Although the cloud computing model is considered to be a very promising internet-based computing platform, it results in a loss of security control over the cloud-hosted assets. This is due to the outsourcing of enterprise IT assets hosted on third-party cloud computing platforms. Moreover, the lack of security constraints in the Service Level Agreements between the cloud providers and consumers results in a loss of trust as well. Obtaining a security certificate such as ISO 27000 or NIST-FISMA would help cloud providers improve consumers trust in their cloud platforms' security. However, such standards are still far from covering the full complexity of the cloud computing model. We introduce a new cloud security management framework based on aligning the FISMA standard to fit with the cloud computing model, enabling cloud providers and consumers to be security certified. Our framework is based on improving collaboration between cloud providers, service providers and service consumers in managing the security of the cloud platform and the hosted services. It is built on top of a number of security standards that assist in automating the security management process. We have developed a proof of concept of our framework using. NET and deployed it on a test bed cloud platform. We evaluated the framework by managing the security of a multi-tenant SaaS application exemplar.

DOTNET 2013 Abstracts(Networking, Network-Security, Mobile Computing, Cloud Computing, Wireless Sensor Network, Datamining, Webmining, Artificial Intelligence, Vanet, Ad-Hoc Network)

4. CDA: A Cloud Dependability Analysis Framework for Characterizing System Dependability in Cloud Computing Infrastructures

Cloud computing has become increasingly popular by obviating the need for users to own and maintain complex computing infrastructure. However, due to their inherent complexity and large scale, production cloud computing systems are prone to various runtime problems caused by hardware and software failures. Dependability assurance is crucial for building sustainable cloud computing services. Although many techniques have been proposed to analyze and enhance reliability of distributed systems, there is little work on understanding the dependability of cloud computing environments. As virtualization has been an enabling technology for the cloud, it is imperative to investigate the impact of virtualization on the cloud dependability, which is the focus of this work. In this paper, we present a cloud dependability analysis (CDA) framework with mechanisms to characterize failure behavior in cloud computing infrastructures. We design the failure-metric DAGs (directed a cyclic graph) to analyze the correlation of various performance metrics with failure events in virtualized and non-virtualized systems. We study multiple types of failures. By comparing the generated DAGs in the two environments, we gain insight into the impact of virtualization on the cloud dependability. This paper is the first attempt to study this crucial issue. In addition, we exploit the identified metrics for failure detection. Experimental results from an on-campus cloud computing test bed show that our approach can achieve high detection accuracy while using a small number of performance metrics.

5. STAR: A proposed architecture for cloud computing applications

With rapid development of cloud computing, the need for an architecture to follow in developing cloud computing applications is necessary. Existing architectures lack the way cloud applications are developed. They focus on clouds' structure and how to use clouds as a tool in developing cloud computing applications rather than focusing on how applications themselves are developed using clouds. This paper presents a survey on key cloud computing concepts, definitions, characteristics, development phases, and architectures. Also, it proposes and describes a novel architecture, which aid developers to develop cloud computing applications in a systematic way. It discusses how cloud computing transforms the way applications are developed/delivered and describes the architectural considerations that developers must take when adopting and using cloud computing technology.

6. A Self-tuning Failure Detection Scheme for Cloud Computing Service

Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good

DOTNET 2013 Abstracts(Networking, Network-Security, Mobile Computing, Cloud Computing, Wireless Sensor Network, Datamining, Webmining, Artificial Intelligence, Vanet, Ad-Hoc Network)

performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.

7. Framework of a national level electronic health record system

Electronic health is vital for enabling improved access to health records, and boosting the quality of the health services provided. In this paper, a framework for an electronic health record system is to be developed for connecting a nation's health care facilities together in a network using cloud computing technology. Cloud computing ensures easy access to health records from anywhere and at any time with easy scalability and prompt on demand availability of resources. A hybrid cloud is to adopted in modeling the system and solutions are proposed for the main challenges faced in any typical electronic health record system.

8. A rainfall prediction model using artificial neural network

The multilayered artificial neural network with learning by back-propagation algorithm configuration is the most common in use, due to of its ease in training. It is estimated that over 80% of all the neural network projects in development use back-propagation. In back-propagation algorithm, there are two phases in its learning cycle, one to propagate the input patterns through the network and other to adapt the output by changing the weights in the network. The back-propagation-feed forward neural network can be used in many applications such as character recognition, weather and financial prediction, face detection etc. The paper implements one of these applications by building training and testing data sets and finding the number of hidden neurons in these layers for the best performance. In the present research, possibility of predicting average rainfall over Udupi district of Karnataka has been analyzed through artificial neural network models. In formulating artificial neural network based predictive models three layered network has been constructed. The models under study are different in the number of hidden neurons.

9. Rethinking Vehicular Communications: Merging VANET with cloud computing

Despite the surge in Vehicular Ad Hoc NETwork (VANET) research, future high-end vehicles are expected to under-utilize the on-board computation, communication, and storage resources. Olariu et al. envisioned the next paradigm shift from conventional VANET to Vehicular Cloud Computing (VCC) by merging VANET with cloud computing. But to date, in the literature, there is no solid architecture for cloud computing from VANET standpoint. In this paper, we put forth the taxonomy of VANET based cloud computing. It is, to the best of our knowledge, the first effort to define VANET Cloud architecture. Additionally we divide VANET clouds into three architectural frameworks named Vehicular Clouds (VC), Vehicles using Clouds (VuC), and Hybrid Vehicular Clouds (HVC). We also outline the unique security and privacy issues and research challenges in VANET clouds.

10. A VANET based Intelligent Road Traffic Signalling System

Road Traffic Information System is a key component of the modern intelligent transportation system. Road signaling systems can be made more efficient if real time information from different road sensors and vehicles can be fed in to a wide area controller to optimize the traffic flow, journey time, as well as safety of road users. The VANET architecture provides an excellent framework to develop an advanced road traffic signaling system. In this paper we present a unique VANET based road traffic signaling system that could significantly improve traffic flow, energy efficiency and safety of road users. The VANET based system has been developed using a distributed architecture by incorporating the distributed networking feature. In this paper we first introduce a new Intelligent Road Traffic Signaling System (IRTSS) system based on the VANET architecture. The paper presents some initial simulation results which are obtained by using an OPNET based simulation model. Simulation results show that the proposed architecture can efficiently serve road traffic using the 802.11p based VANET network.