Mobility -Aware Resource Allocation in Edge Networks for Smart Neighborhoods

Wireless Sensor Networks (WSNs) comprise sensor and actuator nodes that either generate or consume data. These end devices typically have limited memory, energy, and computational capacity, making them ill-suited for intensive processing tasks. Edge computing addresses this limitation by offloading data collection, processing, and forwarding to edge nodes with greater resources. This architecture aligns well with WSNs, but efficient allocation of edge resources remains a significant challenge, particularly under node mobility and fluctuating traffic. The resource allocation problem at the edge is NP-hard, and while static solutions exist, they often fail under dynamic network conditions. Memory management for packet queues is especially critical. The scheduling and dropping policies at edge nodes directly influence Quality of Service (QoS). Weighted Fair Queuing (WFQ), a popular scheduling method, assigns different weights to traffic classes, affecting uplink bandwidth distribution. However, bursty data, varying packet sizes, and node mobility complicate fair and efficient weight assignment. This study proposes a federated learning-based framework for dynamic queue and bandwidth allocation in mobile WSNs. The model adapts to real-time changes in traffic and topology while reducing communication overhead. Simulation outcomes demonstrate improved resource utilization and network performance, validating the effectiveness of the proposed approach in dynamic WSN environments.