SUMMARYBandwidth management and traffic control are critical issues to guarantee the quality of service in cognitive radio networks. This paper exploits a network load refinement approach to achieve the efficient resource utilization and provide the required quality of service. A connection admission control approach is introduced in cognitive radio multimedia sensor networks to provide the data transmission reliability and decrease jitter and packet end-to-end delay. In this approach, the admission of multimedia flows is controlled based on multimedia sensors' correlation information and traffic characteristics. We propose a problem, connection admission control optimization problem, to optimize the connection admission control operation. Furthermore, using a proposed weighting scheme according to the correlation of flows issued by multimedia sensors enables us to convert the connection admission control optimization problem to a binary integerprogramming problem. This problem is a kind of a Knapsack problem that is solved by a branch and bound method. Simulation results verify the proposed admission control method's effectiveness and demonstrate the benefits of admission control and traffic management in cognitive radio multimedia sensor networks.
a b s t r a c tPerformance evaluation of transport layer protocols in cognitive radio sensor networks (CRSNs) is useful to provide quality-of-service for real-time reliable applications. This paper develops an analytical framework to model the steady-state sending rate of collecting cognitive radio (CR) sensors in rate-based generic additive-increase multiplicative-decrease (AIMD) and additive-increase additive-decrease (AIAD) congestion control schemes. Evolution process of sending rate is modeled by a discrete time Markov chain (DTMC) in the terms of queue length. We model the queue length distribution of a CR node by a semi-Markov chain (SMC) with assuming general probability density functions (PDFs) of input rate and attainable sending rate of the node. These PDFs are derived based on the parameters of MAC and physical layers and CRSN configuration. The proposed models are verified through various simulations.
Investigating the optimality and the modeling of congestion control schemes is crucial in order to provide quality of service (QoS) for various applications in cognitive radio sensor networks (CRSNs). This paper develops an analytical framework to study the optimality of rate-based generic AIMD and AIAD congestion control schemes. In this way, a congestion model is introduced to describe the congestion behavior of CRSNs. A semi-Markov chain (SMC) is proposed to model the steady-state sending rate distribution of source nodes based on the congestion model. The optimality of generic AIMD and AIAD, based on the proposed models, is analyzed in order to maximize the defined rate-congestion ratio (RCR). The analytical results are verified through various NS2-based simulations in CRSNs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.