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Title: Satisfaction-aware Data Offloading in Surveillance Systems
In this paper, we exploit the capabilities of Fully Autonomous Aerial Systems' (FAAS) and the Mobile Edge Computing (MEC) to introduce a novel data offloading framework and support the energy and time efficient video processing in surveillance systems based on game theory in satisfaction form. A surveillance system is introduced consisting of Areas of Interest (AoIs), where a MEC server is associated with each AoI, and a FAAS is flying above the AoIs to collectively support the IP cameras' computing demands. Each IP camera adopts a utility function capturing its Quality of Service (QoS) considering the experienced time and energy overhead to offload and process its data either remotely or locally. A non-cooperative game among the cameras is formulated to determine the amount of offloading data to the MEC server and/or the FAAS. The novel concept of Satisfaction Equilibrium (SE) is introduced where the IP cameras satisfy their minimum QoS prerequisites instead of maximizing their performance by wasting additional system resources. A distributed learning algorithm determines the IP cameras' stable data offloading, while a reinforcement learning algorithm determines the FAAS's movement among the AoIs exploiting the accuracy, timeliness, and certainty of the collected data by the IP cameras per AoI. more » The performance evaluation of the proposed framework is achieved via modeling and simulation. « less
Authors:
; ;
Award ID(s):
1849739
Publication Date:
NSF-PAR ID:
10228430
Journal Name:
CHANTS'19: Proceedings of the 14th Workshop on Challenged Networks
Page Range or eLocation-ID:
21 to 26
Sponsoring Org:
National Science Foundation
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