تجزیه و تحلیل پویایی تحويل اطلاعات در شبكه هاي حسگر
Abstract
I- Introduction
II- Background and Related Works
III- System Model
IV- Analysis of Interference at SU
V- Analysis of Interference-Aware Information Dynamics
VI- Performance Evaluation
VII- Conclusion
References
Abstract
To fully empower sensor networks with cognitive Internet of Things (IoT) technology, efficient medium access control protocols that enable the coexistence of cognitive sensor networks with current wireless infrastructure are as essential as the cognitive power in data fusion and processing due to shared wireless spectrum. Cognitive radio (CR) is introduced to increase spectrum efficiency and support such an endeavor, which thereby becomes a promising building block toward facilitating cognitive IoT. In this paper, primary users (PUs) refer to devices in existing wireless infrastructure, and secondary users (SUs) refer to cognitive sensors. For interference control between PUs and SUs, SUs adopt dynamic spectrum access and power adjustment to ensure sufficient operation of PUs, which inevitably leads to increasing latency and poses new challenges on the reliability of IoT communications. To guarantee operations of primary systems while simultaneously optimizing system performance in cognitive radio ad hoc networks (CRAHNs), this paper proposes interference-aware flooding schemes exploiting global timeout and vaccine recovery schemes to control the heavy buffer occupancy induced by packet replications. The information delivery dynamics of SUs under the proposed interference-aware recovery-assisted flooding schemes is analyzed via epidemic models and stochastic geometry from a macroscopic view of the entire system. The simulation results show that our model can efficiently capture the complicated data delivery dynamics in CRAHNs in terms of end-to-end transmission reliability and buffer occupancy. This paper sheds new light on analysis of recovery-assisted flooding schemes in CRAHNs and provides performance evaluation of cognitive IoT services built upon CRAHNs.
Introduction
Cognitive Internet of Things (IoT) technology features advanced machine intelligence toward improved data sensing and analysis [1], [2], which is particularly appealing to applications involving sensor networks. To fully incorporate cognitive IoT technology in sensor networks, intelligent and efficient medium access control protocols enabling the coexistence of sensor networks with current wireless infrastructure and simultaneously optimizing the performance of IoT applications are emergent challenges. Consequently, in addition to cognitive power in data sensing and computation, another crucial factor toward cognitive sensor networks is the cognition in spectrum access in order to fully deploy cognitive IoT. Over the past decade, cognitive radio (CR) has received dramatic attention as it holds tremendous promise for increasing the utilization of scarce radio spectrum shared by primary (licensed) systems (PSs) and secondary (unlicensed or conditionally licensed) systems (SSs), e.g., cognitive sensor networks. In particular, recent works on cognitive IoT technology have identified CR as a critical building block that supports cognitive sensor networks [3]–[7], where PSs refer to existing wireless infrastructure and SSs refer to cognitive radio networks. Using CR terminology, secondary users (SUs) sense surrounding environment and adapt their operations around those of the primary users (PUs) to opportunistically exploit available resources while limiting their interference to PUs. In interweave paradigm [8], SUs seek and exploit the temporary spectrum opportunities without causing any interference to PUs. To further improve the spectrum usage, SUs in underlay paradigm are allowed to concurrently transmit with PUs as long as sufficient operation of PUs is ensured. When IoT applications are built upon CR-enabled cognitive sensor networks, the information delivery dynamics are jointly affected by the activities of PUs and CR medium access protocols, which inevitably leads to increasing latency for communications among IoT devices. This paper aims to study the data delivery dynamics of flooding schemes in cognitive sensor networks, which specifies the effect of packet delivery control and dynamic spectrum access on buffer occupancy and endto-end reliability in data transmission, and therefore provides a novel analysis framework for evaluating the performance of information delivery in cognitive sensor networks.