A correlation‐based and spectrum‐aware admission control mechanism for multimedia streaming in cognitive radio sensor networks

Abstract

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.

Optimal SMDP-Based Connection Admission Control Mechanism in Cognitive Radio Sensor Networks

“…In addition, when the PU arrival Complete sharing SMDP-based Threshold-based PU activity (r d , r a ) Packet end to end delay (s) rate is equal to the PU departure rate, this state is considered to be a "medium PU activity" state [10]. PU activity (r d , r a ) is determined by the length of ON and OFF periods of PU transmissions.…”

“…The simulation results indicate the superiority of the proposed CAC mechanism over the proposed admission control in [10] in terms of packet loss probability, end-to-end delay, and jitter. The network state is composed of the number of active PUs, the ID, and the number of CR sensors that are sending data toward the sink node during the decision instances.…”

“…Therefore, different weights are calculated for the data flows requesting transmission in the proposed weighting scheme in [10]. The number of CR sensor users, CR relay nodes, and PUs are denoted by N S , N R , and N PU , respectively.…”

Modeling of rate-based congestion control schemes in cognitive radio sensor networks

“…CR users 9 can use the licensed bands in the absence of PUs. If a pri- 10 mary user is appeared in the licensed band, CR user leaves 11 the spectrum immediately [1]. Cognitive radio technology 12 can be used in wireless sensor networks (WSNs) to over- 13 come spectrum shortage problem and reserve the limited 14 resources of sensors in WSNs [3].…”

“…However, 35 there is a limited amount of work on the performance eval- 36 uation of transport layer protocols in CRSNs. Most of pre-37 vious studies [4][5][6][7][8][9][10][11][12][13][14] concentrated on the simulation-based 38 performance evaluation of transport layer protocols. How-39 ever, analytical investigation is required to model the per- 40 formance of congestion control schemes in CRSNs for the 41 following motivations: (1) transport-level delay (delay over- 42 head of transport layer protocols) depends on the sending 43 rate of source nodes in this layer.…”