Performance Comparison of Routing Protocols for Cognitive Radio Networks

Abstract

Cognitive radio networks (CRNs) have emerged as a promising solution to the ever-growing demand for additional spectrum resources and more efficient spectrum utilization. A large number of routing protocols for CRNs have been proposed recently, each based on different design goals, and evaluated in different scenarios, under different assumptions. However, little is known about the relative performance of all these protocols, let alone the tradeoffs among their different design goals. In this paper, we conduct the first detailed, empirical performance comparison of three representative routing protocols for CRNs, under the same realistic set of assumptions. Our extensive simulation study shows that the performance of routing protocols in CRNs is affected by a number of factors, in addition to PU activity, some of which have been largely ignored by the majority of previous works. We find that different protocols perform well under different scenarios, and investigate the causes of the observed performance. Furthermore, we present a generic software architecture for the experimental evaluation of CRN routing protocols on a testbed based on the USRP2 platform, and compare the performance of two protocols on a 6 node testbed. The testbed results confirm the findings of our simulation study.

Cross-Layer Optimization for Enhanced IoT Connectivity: A Novel Routing Protocol for Opportunistic Networks

“…• Dynamic Topology: Opportunistic networks exhibit dynamic topology due to node mobility and intermittent contacts. Advanced routing protocols incorporate mobility prediction, contextaware routing, and network coding techniques to improve routing efficiency and adaptability [8]. • Resource Constraints: IoT devices operating in opportunistic networks have limited resources, including battery power and bandwidth.…”

“…To benchmark the efficacy of our proposed algorithm, we have conducted comparative analyses against several prominent DTN routing algorithms, all implemented within the ONE simulator. These algorithms include Spray and Wait, Prophet, Epidemic, and Fresh [2,8,27]. In this simulation setup, the Ping Pong application plays a pivotal role.…”

Assessment of cognitive radio networks through military capability development viewpoint

“…On the other hand, [32] lists dozens of studies of CRNs with different design goals, e.g., maximizing throughput or route stability, minimizing delay or route recovery/maintenance cost, most of which are based upon relatively static scenario or configuration. Many such studies will not yield militarily useful results, especially in case hostile enemy actions (kinetic + non-kinetic) need to be considered.…”

Artificial intelligence based cognitive routing for cognitive radio networks

“…Sun et al [137] have conducted a detailed performance evaluation of three representative CRN routing protocols: SAMER [118], Coolest Path [123], and CRP [122] using both simulations (on the NS2 simulator) and an empirical evaluation (on a testbed of 6 node testbed based on USRP2 platform). The three protocols evaluated (SAMER [118], Coolest Path [123], and CRP [122] all have different design objectives.…”