Maximizing Energy Efficiency of Cognitive Wireless Sensor Networks With Constrained Age of Information

Valehi, Ali; Razi, Abolfazl

doi:10.1109/tccn.2017.2749232

Cited by 60 publications

(27 citation statements)

References 32 publications

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“…Last term stands for the maximum action-value among all values for the next state S t+1 . While (7) defines the Q table and values for one UAV, (8) introduces the updating matrix for all agents: (2) . .…”

Section: B Location Optimization At Individual Uavs Using Reinforcemmentioning

confidence: 99%

An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations

et al. 2020

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This paper studies the problem of spectrum shortage in an unmanned aerial vehicle (UAV) network during critical missions such as wildfire monitoring, search and rescue, and disaster monitoring. Such applications involve a high demand for high-throughput data transmissions such as real-time video-, image-, and voice-streaming where the assigned spectrum to the UAV network may not be adequate to provide the desired Quality of Service (QoS). In these scenarios, the aerial network can borrow additional spectrum from the available terrestrial networks in trade of a relaying service for them. We propose a spectrum sharing model in which the UAVs are grouped into two classes of relaying UAVs that service the spectrum owner and the sensing UAVs that perform the disaster relief mission using the obtained spectrum. The operation of the UAV network is managed by a hierarchical mechanism in which a central controller assign the tasks of the UAVs based on their resources and determine their operation region based on the level of priority of impacted areas and then the UAVs autonomously fine-tune their position using a modelfree reinforcement learning algorithm to maximize the individual throughput and prolong their lifetime. We analyze the performance and the convergence for the proposed method analytically and with extensive simulations in different scenarios.INDEX TERMS Autonomous UAV networks, multi-agent systems, Q-learning, reinforcement learning, spectrum sharing.

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Section: B Location Optimization At Individual Uavs Using Reinforcemmentioning

confidence: 99%

An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations

et al. 2020

Self Cite

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“…From the recent research work, it has been concluded that the AoI is higher in NOMA than OMA [164]. Hence, many research works have investigated optimizing AoI using different techniques [165] while there are fewer researches on enhancing energy efficiency of NOMA under AoI constraints [166]. AoI is higher in NOMA mainly due to the higher computational complexity which should be addressed properly to minimize AoI.…”

Section: Age Of Information (Aoi)mentioning

confidence: 99%

A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G

et al. 2020

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Energy efficiency is a major concern in the emerging mobile cellular wireless networks since massive connectivity is to be expected with high energy requirements from the network operators. Non-orthogonal multiple access (NOMA) being the frontier multiple access scheme for 5G, there exists numerous research attempts on enhancing the energy efficiency of NOMA enabled wireless networks while maintaining its outstanding performance metrics such as high throughput, data rates and capacity maximized optimally.The concept of green NOMA is introduced in a generalized manner to identify the energy efficient NOMA schemes. These schemes will result in an optimal scenario in which the energy generated for communication is managed sustainably. Hence, the effect on the environment, economy, living beings, etc is minimized. The recent research developments are classified for a better understanding of areas which are lacking attention and needs further improvement. Also, the performance comparison of energy efficient, NOMA schemes against conventional NOMA is presented. Finally, challenges and emerging research trends, for energy efficient NOMA are discussed.

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“…First of all, different from the FCFS model considered in [32] and the ideal generate-at-will model considered in [33], we adopt the packet management policy such that the IoT devices can discard the stale status updates and serve new generated status updates, which is important for reducing the AoI. Furthermore, [32] and [33] only studied the case that the secondary system is age-sensitive. We consider a more challenging scenario that both primary and secondary systems are age-sensitive, where the tangled AoI evolution of both systems makes the theoretical analysis non-trivial.…”

Section: A Related Workmentioning

confidence: 99%

Minimizing Age of Information in Cognitive Radio-Based IoT Systems: Underlay or Overlay?

Chen

Zhai

et al. 2019

IEEE Internet Things J.

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We consider a cognitive radio-based Internet-of-Things (CR-IoT) network consisting of one primary IoT (PIoT) system and one secondary IoT (SIoT) system. The IoT devices of both the PIoT and the SIoT respectively monitor one physical process and send randomly generated status updates to their associated access points (APs). The timeliness of the status updates is important as the systems are interested in the latest condition (e.g., temperature, speed and position) of the IoT device. In this context, two natural questions arise: (1) How to characterize the timeliness of the status updates in CR-IoT systems? (2) Which scheme, overlay or underlay, is better in terms of the timeliness of the status updates. To answer these two questions, we adopt a new performance metric, named the age of information (AoI). We analyze the average peak AoI of the PIoT and the SIoT for overlay and underlay schemes, respectively. Simple asymptotic expressions of the average peak AoI are also derived when the PIoT operates at high signalto-noise ratio (SNR). Based on the asymptotic expressions, we characterize a critical generation rate of the PIoT system, which can determine the superiority of overlay and underlay schemes in terms of the average peak AoI of the SIoT. Numerical results validate the theoretical analysis and uncover that the overlay and underlay schemes can outperform each other in terms of the average peak AoI of the SIoT for different system setups.

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Maximizing Energy Efficiency of Cognitive Wireless Sensor Networks With Constrained Age of Information

Cited by 60 publications

References 32 publications

An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations

An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations

A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G

Minimizing Age of Information in Cognitive Radio-Based IoT Systems: Underlay or Overlay?

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