TY - GEN
T1 - Unlocking Efficiency
T2 - 2023 IEEE Military Communications Conference, MILCOM 2023
AU - Souza, Abel
AU - Ng, Nathan
AU - Abdelzaher, Tarek
AU - Towsley, Don
AU - Shenoy, Prashant
N1 - Research supported by NSF grants 2211302, 2211888, 2213636, 2105494, US Army contract W911NF-17-2-0196, Adobe, and ACE (an SRC JUMP 2.0 Center).
PY - 2023
Y1 - 2023
N2 - Internet of Things (IoT) devices have proliferated across a wide range of smart environments that generate vast amounts of data, necessitating the emergence of distributed edge-cloud infrastructures. However, these IoT-edge-cloud infrastructures encounter several challenges in providing efficient and effective services to users, such as real-time service delivery, robustness and resilience, and efficient deployment. Moreover, recent advancements in machine learning, particularly within the realm of deep learning, have ushered in a new era for IoT applications. These applications increasingly lean on data-intensive models to perform a multitude of functions, including classification, detection, data analytics, and decision-making. A key aspect is that many of these tasks exhibit sensitivity to latency and hinge on the deployment of models at the network's edge and on how to efficiently handle data-intensive workloads, especially when network conditions are constrained. In response to these challenges, we present an analytical discussion that delves into the intricacies of distributed IoT pipelines and workloads deployed across both edge and cloud computing environments.
AB - Internet of Things (IoT) devices have proliferated across a wide range of smart environments that generate vast amounts of data, necessitating the emergence of distributed edge-cloud infrastructures. However, these IoT-edge-cloud infrastructures encounter several challenges in providing efficient and effective services to users, such as real-time service delivery, robustness and resilience, and efficient deployment. Moreover, recent advancements in machine learning, particularly within the realm of deep learning, have ushered in a new era for IoT applications. These applications increasingly lean on data-intensive models to perform a multitude of functions, including classification, detection, data analytics, and decision-making. A key aspect is that many of these tasks exhibit sensitivity to latency and hinge on the deployment of models at the network's edge and on how to efficiently handle data-intensive workloads, especially when network conditions are constrained. In response to these challenges, we present an analytical discussion that delves into the intricacies of distributed IoT pipelines and workloads deployed across both edge and cloud computing environments.
KW - Analytical Pipelines
KW - Edge computing
KW - IoT
KW - ML inference
UR - http://www.scopus.com/inward/record.url?scp=85182390306&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182390306&partnerID=8YFLogxK
U2 - 10.1109/MILCOM58377.2023.10356378
DO - 10.1109/MILCOM58377.2023.10356378
M3 - Conference contribution
AN - SCOPUS:85182390306
T3 - MILCOM 2023 - 2023 IEEE Military Communications Conference: Communications Supporting Military Operations in a Contested Environment
SP - 150
EP - 155
BT - MILCOM 2023 - 2023 IEEE Military Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 30 October 2023 through 3 November 2023
ER -