Sunita Barve

A Performance based Routing Classification in Cognitive Radio Networks

Cognitive Radio Networks (CRN) is offering tremendous performance and operational benefit by providing high bandwidth to mobile users via dynamic spectrum access techniques. In this paper, we address the problem of routing in CRN which concerns about identification and maintenance of the optimal path from source to destination through intermediate relay nodes and spectrum on each link using available common channels. In this survey, the characteristics features and limiting factors of existing routing protocols are thoroughly investigated with its performance evaluation criterias. First, the overview of the routing with its unique challenges is given under the restriction of interference and fairness to increase overall network throughput. Next, a detailed classification of the routing strategies is given according to performance evaluation matrices which are considered according to specific demand and requirements of network users. A representative selection of these strategies is discussed in detail in this paper together with services given to unique challenges of CRN. Important issues and future directions are also discussed, while highlighting the need of close coupling between interaction of network users and dynamic decision theories.

Multi-Agent Reinforcement Learning Based Opportunistic Routing and Channel Assignment for Mobile Cognitive Radio Ad Hoc Network

Opportunistic spectrum access using cognitive radio technology enables exploring vacant licensed spectrum bands and thereby improving the spectrum utilization. However, it will have a significant impact on upper layer performance like routing as the reliable knowledge of topology and channel statistics are not available, especially in Mobile Cognitive Radio Ad hoc Network (MCRAN). To address specific requirements of MCRAN, this paper is proposing online opportunistic routing algorithm using multi-agent reinforcement learning. The proposed routing scheme jointly addresses, link and relay selection based on transmission success probabilities. This sophisticated learning mechanism successfully explores opportunities in partially observable and non-stationary environment of MCRAN. Simulation results show the effectiveness of this algorithm.

Dynamic channel selection and routing through reinforcement learning in Cognitive Radio Networks

Recent exploration in Cognitive Radio Network proved itself as emerging paradigm to attempt the underutilization of wireless spectrum. Routing is challenging problem due to intermittent spectrum availability and incomplete knowledge of environment. This paper proposes reinforcement learning based combined framework of channel selection and routing for multi-hop cognitive radio network. Reinforcement learning is generic method for resource utilization in a partially observable and non-stationary environment. In this paper, channel selection and routing is modeled as Markao Decision Process to design the methodology of learning the best resource allocation policies adopted in the process state, based on the feedback received from the environment. First the design of the reward, transition and value function is described which helps in evolving the policy for selecting channel which results in increased spectrum utilization. The routing strategy is described which is exploring different state-action pair to come up with various routing solution which are ranked according to their reinforcement signal. Overhead of rerouting is also minimized by providing backup routes. Agent experiences in the form of reinforcement signal can be used by each cognitive node to further refine the routing strategies.

Real-Time Feature based Face Detection and Tracking I- Cursor

project aims to present an application that is able of replacing the traditional mouse with the human face as a new way to interact with the computer. Facial features (nose tip and eyes) are detected and tracked in real-time to use their actions as mouse events. In our work we were trying to compensate people who have hands disabilities that prevent them from using the mouse by designing an application that uses facial features (nose tip and eyes) to interact with the computer. It can be applied to a wide range of face scales. Our basic strategy for detection is fast extraction of face candidates with a Six-Segmented Rectangular (SSR) filter and face verification by a support vector machine. A motion cue is used in a simple way to avoid picking up false candidates in the background. In face tracking, the patterns of between-the eyes are tracked with updating template matching.

Routing Protocols used for CRN : A Survey

Radio (CR) is an emerging technology in the wireless communication. CR nodes have the capability to change its transmission or reception efficiently without interfering with licensed users. The network formed with CR nodes communicating with each other is called Cognitive Radio Network (CRN). CRN utilizes the unutilized frequency spectrum. Routing in CRN is a main challenge due the rapid changes in the data rates and available channels. In this paper we present the routing protocols used for CRN .We first discuss the routing differences and challenges in CRN. Furthermore we classify the routing protocols depending on the protocol operation. General Terms Cognitive Radio, wireless communication, frequency spectrum, challenges for routing in Cognitive Radio Networks, classification based on routing protocols.

Routing Approaches for Cognitive Radio Ad-hoc Networks and Challenges

Cognitive radio networks (CRNs) are composed of cognitive devices capable of changing their configurations on Real time, based on the spectrum environment. This capability provides chance to opportunistically reuse the portion of the spectrum as required resigned by the licensed users (PU’s). From another point of view, the tractability in the spectrum access level attaches to an increased complexity for designing of communication protocols at each layer. This brings focuses on the problem of designing efficient routing results for multihop CRNs that become a central issue in cognitive networking paradigm. We furnish a broad overview of the research in the area of routing for CRNs, distinguishing two main categories: full spectrum knowledge base, and local spectrum knowledge base procedures and protocols. For each category we depict on proposed design methodologies and routing metrics. Finally, potential future research directions are also suggested. General Terms Algorithms, Methods, Techniques, Approaches