# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
## 2 Related Works  
A study introduced an improved DEEC protocol called IoT-DEEC [5] designed to enhance the energy efficiency and lifetime of Wireless Sensor Networks (WSNs), especially in IoT scenarios. The protocol incorporates a threshold-based cluster head selection and dynamic power level switching for nodes. Simulation results showed that IoT-DEEC significantly increases the number of packets delivered to the base station and extends the network's operational rounds compared with traditional DEEC and its variants, making it highly effective for large-scale, energy-constrained WSN deployments. A new variant of the DEEC protocol for heterogeneous WSNs was introduced (M-DEEC) [26]. M-DEEC employs a unique clustering strategy that further reduces energy consumption and extends network lifespans. Simulation results confirmed that M-DEEC outperforms the original DEEC and other variants in terms of energy efficiency, stability period, and overall network longevity, making it suitable for modern heterogeneous sensor network applications. A study related to IoT presented a modified version of the DEEC protocol [27]. This protocol optimises cluster head selection and energy management, resulting in an 843% increase in the number of packets sent to the base station and a substantial extension in the number of alive nodes compared with the original DEEC. The study demonstrated that the new protocol is highly effective in prolonging WSN lifetimes and improving data delivery in heterogeneous IoT environments. Another study investigated how various parametric constraints affect the latency and lifetime of DEEC-based protocols in IoT-oriented WSNs [28]. The authors proposed a constraint optimisation approach for distributed clustering, aiming to enhance both latency and network longevity. The study provides insights into how tuning protocol parameters can lead to significant improvements in the performance of DEEC-based WSNs, especially for time-sensitive IoT applications. An enhanced version of the DEEC protocol that incorporates a priority queue mechanism to better balance energy consumption among sensor nodes was proposed in [29]. The enhanced protocol achieves a longer network lifetime and improved energy efficiency compared with the standard DEEC. Simulation results demonstrated that the proposed enhancements lead to more balanced energy usage and greater network performance in WSNs. Another investigation focused on DEEC implementations for 1-level, 2-level, and 3-level heterogeneous WSNs, proposing a new 3-level heterogeneous model [23]. The study showed that increasing the total network energy and heterogeneity can significantly extend a network's lifetime. The proposed protocol was compared with existing DEEC versions, demonstrating improved energy efficiency and network longevity performance. A further study compared the DEEC and EDEEC protocols, focusing on their application in heterogeneous WSNs [24]. DEEC uses two energy levels (normal and advanced nodes), while EDEEC introduces a third level (super nodes). The study discussed how these protocols manage energy consumption and cluster head selection, concluding that EDEEC performs better in highly heterogeneous environments. The integration of ML algorithms with the DEEC protocol to optimise packet transmission in WSNs was proposed in [25]. The study highlighted how ML can be used to enhance network longevity and efficiency, and it explored hybrid meta-heuristic optimisation methods for energy-efficient protocol design. That research proposed a hybrid routing protocol that combines fuzzy logic (an AI technique) with DEEC for cluster head selection and cluster formation [30]. The fuzzy system considers factors like distance to base station, node energy, and compactness to improve load balancing and node longevity. The results showed significant improvements in energy consumption, network lifetime, and throughput compared with conventional protocols. A study compared DEEC and EDEEC protocols in the context of heterogeneous WSNs.