Distributed Group Location Update Algorithm for Massive Machine Type Communication

Frequent location updates of individual Internet of Things (IoT) devices can cause several problems (e.g., signaling overhead in networks and energy depletion of IoT devices) in massive machine type communication (mMTC) systems. To alleviate these problems, we design a distributed group location update algorithm (DGLU) in which geographically proximate IoT devices determine whether to conduct the location update in a distributed manner. To maximize the accuracy of the locations of IoT devices while maintaining a sufficiently small energy outage probability, we formulate a constrained stochastic game model. We then introduce a best response dynamics-based algorithm to obtain a multi-policy constrained Nash equilibrium. From the evaluation results, it is demonstrated that DGLU can achieve an accuracy of location information that is comparable with that of the individual location update scheme, with a sufficiently small energy outage probability.

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Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things

Sensors ◽

10.3390/s22020448 ◽

2022 ◽

Vol 22 (2) ◽

pp. 448

Author(s):

Yumi Kim ◽

Mincheol Paik ◽

Bokyeong Kim ◽

Haneul Ko ◽

Seung-Yeon Kim

Keyword(s):

Internet Of Things ◽

Outage Probability ◽

Multiple Access ◽

Stochastic Game ◽

Average Energy ◽

Base Station ◽

Data Rate ◽

Transmission Power ◽

Response Dynamics ◽

Iot Devices

In a non-orthogonal multiple access (NOMA) environment, an Internet of Things (IoT) device achieves a high data rate by increasing its transmission power. However, excessively high transmission power can cause an energy outage of an IoT device and have a detrimental effect on the signal-to-interference-plus-noise ratio of neighbor IoT devices. In this paper, we propose a neighbor-aware NOMA scheme (NA-NOMA) where each IoT device determines whether to transmit data to the base station and the transmission power at each time epoch in a distributed manner with the consideration of its energy level and other devices’ transmission powers. To maximize the aggregated data rate of IoT devices while keeping an acceptable average energy outage probability, a constrained stochastic game model is formulated, and the solution of the model is obtained using a best response dynamics-based algorithm. Evaluation results show that NA-NOMA can increase the average data rate up to 22% compared with a probability-based scheme while providing a sufficiently low energy outage probability (e.g., 0.05).

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An Energy Efficient UAV-Based Edge Computing System with Reliability Guarantee for Mobile Ground Nodes

Sensors ◽

10.3390/s21248264 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8264

Author(s):

Seung-Yeon Kim ◽

Yi-Kang Kim

Keyword(s):

Energy Efficient ◽

Stochastic Game ◽

Computing System ◽

Edge Computing ◽

Task Completion ◽

Computing Systems ◽

Best Response ◽

Aerial Vehicle ◽

Complete Computation ◽

The Individual

An edge computing system is a distributed computing framework that provides execution resources such as computation and storage for applications involving networking close to the end nodes. An unmanned aerial vehicle (UAV)-aided edge computing system can provide a flexible configuration for mobile ground nodes (MGN). However, edge computing systems still require higher guaranteed reliability for computational task completion and more efficient energy management before their widespread usage. To solve these problems, we propose an energy efficient UAV-based edge computing system with energy harvesting capability. In this system, the MGN makes requests for computing service from multiple UAVs, and geographically proximate UAVs determine whether or not to conduct the data processing in a distributed manner. To minimize the energy consumption of UAVs while maintaining a guaranteed level of reliability for task completion, we propose a stochastic game model with constraints for our proposed system. We apply a best response algorithm to obtain a multi-policy constrained Nash equilibrium. The results show that our system can achieve an improved life cycle compared to the individual computing scheme while maintaining a sufficient successful complete computation probability.

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