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With the rapid growth of wireless compute-intensive services (such as image recognition, real-time language translation, or other artificial intelligence applications), efficient wireless algorithm design should not only address when and which users should transmit at each time instance (referred to as wireless scheduling) but also determine where the computation should be executed (referred to as offloading decision) with the goal of minimizing both computing latency and energy consumption. Despite the presence of a variety of earlier works on the efficient offloading design in wireless networks, to the best of our knowledge, there does not exist a work on the realistic user- level dynamic model, where each incoming user demands a heavy computation and leaves the system once its computing request is completed. To this end, we formulate a problem of an optimal offloading design in the presence of dynamic compute-intensive applications in wireless networks. Then, we show that there exists a fundamental logarithmic energy- workload tradeoff for any feasible offloading algorithm, and develop an optimal threshold-based offloading algorithm that achieves this fundamental logarithmic bound.
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Optimized Compression Policy for Flying Ad hoc Networks
Managing energy consumption for computation and communication is a key requirement for flying ad hoc networks (FANET) to prolong the network lifetime. In many applications, the main role of drones is to collect imagery information and relay them to a ground station for further processing and decision making. In this paper, we present a predictive compression policy to maximize the end-to-end image quality penalized by communication and computation costs. The idea is to predict the number of remaining links to the destination for a given routing algorithm and use it to re-compress image frames at intermediate nodes such that the overall energy consumption is minimized. Numerical results confirm that the performance of this method is within 4% of the global optima and higher than the current fixed-rate policies with a significant margin.
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- Award ID(s):
- 1755984
- PAR ID:
- 10133282
- Date Published:
- Journal Name:
- 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)
- Page Range / eLocation ID:
- 1 to 2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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With the rapid growth of wireless compute-intensive services (such as image recognition, real-time language translation, or other artificial intelligence applications), efficient wireless algorithm design should not only address when and which users should transmit at each time instance (referred to as wireless scheduling) but also determine where the computation should be executed (referred to as offloading decision) with the goal of minimizing both computing latency and energy consumption. Despite the presence of a variety of earlier works on the efficient offloading design in wireless networks, to the best of our knowledge, there does not exist a work on the realistic user-level dynamic model, where each incoming user demands a heavy computation and leaves the system once its computing request is completed. To this end, we formulate a problem of an optimal offloading design in the presence of dynamic compute-intensive applications in wireless networks. Then, we show that there exists a fundamental logarithmic energy-workload tradeoff for any feasible offloading algorithm, and develop an optimal threshold-based offloading algorithm that achieves this fundamental logarithmic bound.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CCNC.2019.8651761
