The rapid expansion of Internet of Things (IoT) devices and edge computing has introduced new challenges in data throughput, latency reduction, and energy efficiency. This paper introduces Baymac , a novel middleware architecture designed to bridge the gap between centralized cloud analytics and resource-constrained edge nodes. Baymac leverages adaptive Bayesian inference and media access control (MAC)-layer optimization to dynamically allocate bandwidth and computational tasks. Our simulations show that Baymac improves data processing efficiency by up to 34% compared to traditional round-robin edge schedulers, while reducing average packet latency by 18%.
Baymac (typically stylized as ) is a membership-based organization that provides legal protection, advocacy, and support services specifically tailored for law enforcement officers and public safety professionals. baymac
Baymac’s performance gain stems from its ability to balance local computation and communication in real time. However, the Bayesian model adds computational overhead (≈5% CPU on an ARM Cortex-A53), which may be nontrivial for ultra-low-power devices. Future work includes hardware acceleration for the inference engine and exploring multi-hop Baymac coordination. The rapid expansion of Internet of Things (IoT)
The rapid expansion of Internet of Things (IoT) devices and edge computing has introduced new challenges in data throughput, latency reduction, and energy efficiency. This paper introduces Baymac , a novel middleware architecture designed to bridge the gap between centralized cloud analytics and resource-constrained edge nodes. Baymac leverages adaptive Bayesian inference and media access control (MAC)-layer optimization to dynamically allocate bandwidth and computational tasks. Our simulations show that Baymac improves data processing efficiency by up to 34% compared to traditional round-robin edge schedulers, while reducing average packet latency by 18%.
Baymac (typically stylized as ) is a membership-based organization that provides legal protection, advocacy, and support services specifically tailored for law enforcement officers and public safety professionals.
Baymac’s performance gain stems from its ability to balance local computation and communication in real time. However, the Bayesian model adds computational overhead (≈5% CPU on an ARM Cortex-A53), which may be nontrivial for ultra-low-power devices. Future work includes hardware acceleration for the inference engine and exploring multi-hop Baymac coordination.