Energy Harvesting-Aided Spectrum Sensing and Data Transmission in Heterogeneous Cognitive Radio Sensor Network (1604.01519v1)

Abstract: The incorporation of Cognitive Radio (CR) and Energy Harvesting (EH) capabilities in wireless sensor networks enables spectrum and energy efficient heterogeneous cognitive radio sensor networks (HCRSNs). The new networking paradigm of HCRSNs consists of EH-enabled spectrum sensors and battery powered data sensors. Spectrum sensors can cooperatively scan the licensed spectrum for available channels, while data sensors monitor an area of interest and transmit sensed data to the sink over those channels. In this work, we propose a resource allocation solution for the HCRSN to achieve the sustainability of spectrum sensors and conserve energy of data sensors. The proposed solution is achieved by two algorithms that operate in tandem, a spectrum sensor scheduling algorithm and a data sensor resource allocation algorithm. The spectrum sensor scheduling algorithm allocates channels to spectrum sensors such that the average detected available time for the channels is maximized, while the EH dynamics are considered and PU transmissions are protected. The data sensor resource allocation algorithm allocates the transmission time, power and channels such that the energy consumption of the data sensors is minimized. Extensive simulation results demonstrate that the energy consumption of the data sensors can be significantly reduced while maintaining the sustainability of the spectrum sensors.

• The paper proposes a bifurcated resource allocation framework for Heterogeneous Cognitive Radio Sensor Networks (HCRSNs) combining energy harvesting for spectrum sensing and energy efficiency for data transmission.
• It employs a Cross-Entropy algorithm for scheduling energy harvesting sensors to maximize detected spectrum availability and a Joint Time and Power Allocation algorithm to minimize energy consumption for data sensors.
• Extensive simulations validate the framework, showing significant energy consumption reduction for data sensors and robustness against varying energy harvesting and spectrum conditions.

Energy Harvesting-Aided Spectrum Sensing and Data Transmission in Heterogeneous Cognitive Radio Sensor Networks

The integration of Energy Harvesting (EH) mechanisms into Cognitive Radio Sensor Networks (CRSNs) has led to the development of a novel framework known as Heterogeneous Cognitive Radio Sensor Networks (HCRSNs). This paper addresses the challenges and proposes a solution for efficient spectrum sensing and data transmission in HCRSNs, characterized by EH-enabled spectrum sensors working alongside battery-powered data sensors.

HCRSNs leverage the cooperative capabilities of CR technology with the sustainability provided by EH, aiming to improve spectrum utilization while ensuring energy efficiency. The principal challenge addressed in this paper lies in the strategic allocation of resources to achieve effective spectrum sensing and sustainable data transmission. The authors propose a bifurcated resource allocation framework that includes a spectrum sensor scheduling algorithm and a data sensor resource allocation strategy.

The spectrum sensor scheduling algorithm is designed to maximize the average detected available time for channels while incorporating EH dynamics. It protects Primary User (PU) transmissions and optimizes spectrum sensing by scheduling EH-enabled spectrum sensors. This is structured as a nonlinear integer programming problem, addressed through a Cross-Entropy (C-E) based algorithm. The C-E method provides a probabilistic framework to evaluate and optimize channel assignments among sensors, ensuring convergence to solutions that respect the energy constraints and maximally utilize available spectrum time.

The subsequent phase employs a data sensor resource allocation algorithm, emphasizing the minimization of energy consumption among data sensors. It formulates the allocation of transmission time, power, and channels as a biconvex optimization problem. The proposed Joint Time and Power Allocation (JTPA) algorithm efficiently navigates this optimization, dynamically adjusting power and time allocations across data sensors, thus optimizing their energy expenditure while maintaining satisfactory data throughput.

This framework's applicability extends to environments heavily reliant on energy efficiency due to EH's sporadic nature, aligning with the broader objectives of CRSNs to opportunistically harness underutilized spectrum. Extensive simulation results corroborate the efficacy of this proposed bifurcated solution, showcasing significant reductions in energy consumption for data sensors, and illustrating the robustness of the scheduling mechanism despite varying EH dynamics and spectrum availability scenarios.

Theoretical implications suggest an essential pathway for future research in integrating EH mechanisms into cognitive systems, guiding the development of even more sustainable and adaptive wireless networks. Practical implications are predominantly seen in the realms of smart city infrastructure, environmental monitoring systems, and healthcare applications where prolonged operational lifetimes and efficient spectrum use are critical.

Future directions may explore the extension of this model into multi-hop networking environments or the inclusion of real-time adaptive mechanisms that respond to fluctuating EH rates more dynamically. Overall, this paper contributes substantively to our understanding of resource optimization in HCRSNs, marrying concepts from cognitive radio technologies and energy harvesting to forge a path toward more efficient wireless sensor networks.