Energy-Efficient Hybrid Learning for Secure Wireless Sensor Networks
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Abstract
Wireless Sensor Networks (WSNs) power critical applications from environmental monitoring to Internet-of-Medical-Things healthcare yet their tiny batteries and low-end microcontrollers leave them exposed to network-layer Denial-of-Service (DoS) attacks such as Blackhole, Grayhole, Flooding and TDMA scheduling. Signature IDSs miss zero-day variants and shallow machine-learning detectors produce many false alarms, while running monolithic deep-learning models on every node exhaust energy reserve. We introduce a two-stage hybrid IDS in which each sensor executes an integer-only rule filter that costs ≤0.05 mJ per packet and discards ≈95% of benign traffic, forwarding only flagged flows over BLE/LoRa to an edge gateway. There, a 50 %-pruned, 8-bit CNN-LSTM processes 32-window batches in 28 mJ and ≈42 ms. Experiments on the public WSN-DS corpus, augmented by ns-3 simulations of a 50-node LoRa network, show that the scheme achieves 98 % accuracy, 0.93 macro-F1 and minority-class recalls of 0.840.95 while extending network lifetime (T50) to 69 days an 82 % gain over on-node GRU and 35 % over a signature IDS. Removing the rule filter erases most of the lifetime benefit without affecting accuracy, confirming that local triage, not downsized deep models, is the key to energy efficiency. The evaluation answers four research questions covering optimal hybrid architecture, rule-filter tuning, node-level energy overhead, and performance trade-offs against traditional ML and standalone DL baselines. These findings demonstrate that intelligent workload partitioning can deliver deep-learning-level security without shortening the lifetime of resource-constrained WSN deployments.
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