Computational complexity reduction algorithms for Markov decision process based vertical handoff in mobile networks

2021 ◽  
Author(s):  
Rida Gillani ◽  
Ali Nasir
Keyword(s):  
Computational Complexity ◽  
Markov Decision Process ◽  
Mobile Networks ◽  
Decision Process ◽  
Complexity Reduction ◽  
Vertical Handoff ◽  
Markov Decision

A Markov-Based Multi-Attribute Vertical Handoff Decision Algorithm

2013 ◽  
Vol 785-786 ◽  
pp. 1403-1407
Author(s):  
Qing Yang Song ◽  
Xun Li ◽  
Shu Yu Ding ◽  
Zhao Long Ning
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Decision Problem ◽  
Vertical Handoff ◽  
Decision Algorithm ◽  
Reward Function ◽  
Total Reward ◽  
Call Dropping ◽  
Markov Decision ◽  
The Impact

Many vertical handoff decision algorithms have not considered the impact of call dropping during the vertical handoff decision process. Besides, most of current multi-attribute vertical handoff algorithms cannot predict users’ specific circumstances dynamically. In this paper, we formulate the vertical handoff decision problem as a Markov decision process, with the objective of maximizing the expected total reward during the handoff procedure. A reward function is formulated to assess the service quality during each connection. The G1 and entropy methods are applied in an iterative way, by which we can work out a stationary deterministic policy. Numerical results demonstrate the superiority of our proposed algorithm compared with the existing methods.

scholarly journals Development of a Shipment Policy for Collection Centers

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1385
Author(s):  
Irais Mora-Ochomogo ◽  
Marco Serrato ◽  
Jaime Mora-Vargas ◽  
Raha Akhavan-Tabatabaei
Keyword(s):  
Climate Change ◽  
Natural Disasters ◽  
Markov Decision Process ◽  
Decision Process ◽  
Necessary Conditions ◽  
Decision Makers ◽  
Humanitarian Organizations ◽  
The World ◽  
Markov Decision ◽  
Unsatisfied Demand

Natural disasters represent a latent threat for every country in the world. Due to climate change and other factors, statistics show that they continue to be on the rise. This situation presents a challenge for the communities and the humanitarian organizations to be better prepared and react faster to natural disasters. In some countries, in-kind donations represent a high percentage of the supply for the operations, which presents additional challenges. This research proposes a Markov Decision Process (MDP) model to resemble operations in collection centers, where in-kind donations are received, sorted, packed, and sent to the affected areas. The decision addressed is when to send a shipment considering the uncertainty of the donations’ supply and the demand, as well as the logistics costs and the penalty of unsatisfied demand. As a result of the MDP a Monotone Optimal Non-Decreasing Policy (MONDP) is proposed, which provides valuable insights for decision-makers within this field. Moreover, the necessary conditions to prove the existence of such MONDP are presented.

An Optimal Life Cycle Reprofiling Strategy of Train Wheels Based on Markov Decision Process of Wheel Degradation

2021 ◽  
pp. 1-11
Author(s):  
Yuanchen Zeng ◽  
Dongli Song ◽  
Weihua Zhang ◽  
Bin Zhou ◽  
Mingyuan Xie ◽  
...  
Keyword(s):  
Life Cycle ◽  
Markov Decision Process ◽  
Decision Process ◽  
Markov Decision

scholarly journals Dynamic Task Migration Combining Energy Efficiency and Load Balancing Optimization in Three-Tier UAV-Enabled Mobile Edge Computing System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 190
Author(s):  
Wu Ouyang ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Genghua Yu ◽  
Heng Zhang
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Markov Decision Process ◽  
Decision Process ◽  
Base Station ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Migration ◽  
Migration Strategy ◽  
Markov Decision

As transportation becomes more convenient and efficient, users move faster and faster. When a user leaves the service range of the original edge server, the original edge server needs to migrate the tasks offloaded by the user to other edge servers. An effective task migration strategy needs to fully consider the location of users, the load status of edge servers, and energy consumption, which make designing an effective task migration strategy a challenge. In this paper, we innovatively proposed a mobile edge computing (MEC) system architecture consisting of multiple smart mobile devices (SMDs), multiple unmanned aerial vehicle (UAV), and a base station (BS). Moreover, we establish the model of the Markov decision process with unknown rewards (MDPUR) based on the traditional Markov decision process (MDP), which comprehensively considers the three aspects of the migration distance, the residual energy status of the UAVs, and the load status of the UAVs. Based on the MDPUR model, we propose a advantage-based value iteration (ABVI) algorithm to obtain the effective task migration strategy, which can help the UAV group to achieve load balancing and reduce the total energy consumption of the UAV group under the premise of ensuring user service quality. Finally, the results of simulation experiments show that the ABVI algorithm is effective. In particular, the ABVI algorithm has better performance than the traditional value iterative algorithm. And in a dynamic environment, the ABVI algorithm is also very robust.

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