Abstract
The low energy requirements of devices comprising the Internet of Things have made them ubiquitous. However, it is also a huge bottleneck in real world deployments. Computational offloading can improve network lifetimes wherein these devices move heavy computation away to the edge and the cloud. However, this also increases power consumption and network latencies. Optimum performance can be achieved by intelligently deciding if the computation should be offloaded or performed locally—a task that is inherently difficult given the complexity of the systems. In this work, we formulate a distributed network optimization problem to achieve a balance between performance and energy consumption. Our simulation results show substantial improvements in network latency and power consumption.
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Ghosh, S., Agrawal, D.P. (2021). Optimizing IoT Networks Through Combined Estimations of Resource Allocation and Computation Offloading. In: Kim, H., Kim, K.J., Park, S. (eds) Information Science and Applications. Lecture Notes in Electrical Engineering, vol 739. Springer, Singapore. https://doi.org/10.1007/978-981-33-6385-4_31
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DOI: https://doi.org/10.1007/978-981-33-6385-4_31
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