Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1110 https://internationalpubls.com An Energy-Delay Optimized Model for Efficient WBAN Communication in Iot-Enabled Autism Monitoring Sagar Sudhakar Birade1, Dr. Mallikarjun C. Sarsamba2 1Research Scholar, Department of Electronics and Communication Engineering, HSIT Nidasoshi, Affiliated to Visveswaraya Technological University, Belagavi-590018, India, ORCID iD: 0009-0008-4029-1953, sagarsb.phd@gmail.com 2Professor and Head, Department of Electronics and Communication Engineering, HSIT Nidasoshi, Affiliated to Visveswaraya Technological University, Belagavi-590018, India, hod.ece@hsit.ac.in Article History: Received: 12-01-2025 Revised: 15-02-2025 Accepted: 01-03-2025 Abstract: Wireless Body Area Networks (WBANs) have revolutionized healthcare by enabling continuous monitoring of physiological parameters, making them crucial for managing conditions like autism, where real-time data collection and analysis are vital. However, WBANs face challenges such as energy inefficiency, high delay, and communication overhead, particularly in dynamic IoT environments with mobility and emergency scenarios. This study addresses these challenges by proposing an Energy-Delay Optimized Data Communication Model (EDODCM) for WBANs. The primary objective is to enhance energy efficiency, increase network lifetime, reduce end-to-end delay, and minimize communication overhead while ensuring reliable data transmission from wearable sensors to gateways. The EDODCM employs an unequal clustering approach, an optimized duty-cycling mechanism, multi-hop routing for normal and emergency scenarios, and a TDMA-based Optimized Medium Access Control (TDMA-OMAC) for efficient data aggregation and transmission. Simulation results demonstrate that EDODCM improves average energy efficiency by 24.52%, extends average network lifetime by 20.1%, reduces average end-to-end delay by 9.65%, and decreases average communication overhead by 22.18% compared to existing approaches. The novelty of this work lies in its adaptive routing strategies for dynamic scenarios and its focus on integrating WBANs within IoT for real-time autism monitoring. These findings highlight EDODCM’s potential for scalable and efficient WBAN communication, paving the way for improved healthcare solutions. Keywords-WBAN, IoT, autism, energy efficiency, delay optimization, clustering, TDMA-OMAC, real-time monitoring, healthcare technology. 1. Introduction Autism Spectrum Disorder (ASD) is a complex developmental condition that manifests through challenges in social interaction, communication, and a propensity for repetitive behaviours. In recent years, it is seen that some ASD individuals experience profound difficulties in verbal communication and require extensive support, while others might demonstrate exceptional skills in specific areas yet struggle with subtler aspects of social interaction [1], [2]. This diversity in presentation makes ASD a highly individualized condition that defies a one-size-fits-all approach to understanding or managing it. Moreover, symptoms of ASD typically appear within the first three years of life, with early signs javascript:popup_orcidDetail('https://orcid.org'%20,'0009-0008-4029-1953'); mailto:sagarsb.phd@gmail.com mailto:hod.ece@ Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1111 https://internationalpubls.com sometimes evident even in infancy, such as a lack of response to their name or limited eye contact [3].Although there is no definitive cure for ASD, early diagnosis and intervention have been shown to make a substantial difference in the quality of life and developmental progress for individuals with this condition [4]. Specific designed therapeutic strategies, such as behavioural interventions, speech and language therapy, and sensory integration techniques, can enhance their ability to communicate, interact, and navigate daily life [5]. Recent statistics from the Centres for Disease Control and Prevention (CDC) highlight that approximately 1 in 36 children in the United States is diagnosed with ASD [6], a figure that reflects its growing prevalence and underscores the urgent need for improved diagnostic, monitoring, and therapeutic tools. This increased prevalence necessitates an evolving framework of support systems that can accommodate the diverse and dynamic needs of the ASD population. Furthermore, individuals with ASD often exhibit heightened or diminished responses to sensory stimuli, such as lights, sounds, textures, or smells, which can significantly affect their behaviour and emotional regulation [6], [7]. Monitoring these sensory responses provides essential insights into behavioural patterns, allowing caregivers and healthcare professionals to create interventions designed for every individual’s specific needs. For instance, understanding how a child reacts to different sensory inputs can inform strategies to reduce anxiety, improve focus, or enhance participation in social and learning environments [8].Recent advancements in technology, particularly in the realms of the Internet of Things (IoT) and Wireless Body Area Networks (WBAN), have opened new avenues for healthcare applications, including ASD management and monitoring [9], [10]. WBANs, which utilize sensors placed on or near the body, are designed to collect real-time data on physiological and behavioural parameters [11]. For individuals with ASD, these sensors can track movements, physiological responses, and other sensory indicators, providing caregivers with a detailed, objective understanding of sensory processing over time. By doing so, they enable personalized intervention strategies based on accurate, data-driven insights. Despite their potential, these systems face significant challenges, particularly in energy consumption and data communication efficiency [12], [13]. Sensors, which often rely on battery power, must operate continuously to provide uninterrupted monitoring. However, limited battery life can significantly restrict the duration and reliability of monitoring [14]. Furthermore, inefficient data transmission methods can result in communication overhead and delays, increased power consumption, and data loss, which compromise the effectiveness of the monitoring system [15]. These limitations pose a barrier to the seamless and real-time delivery of actionable insights that caregivers and healthcare providers rely upon. To overcome these challenges, there is an urgent need for a robust data communication model designed specifically for WBAN systems in ASD monitoring. Such a model must optimize energy consumption, extend sensor lifespan, and ensure efficient, low-delay data transmission. Addressing these issues will not only enhance the functionality of monitoring systems but also significantly improve the ability of caregivers and healthcare professionals to respond effectively to the needs of individuals with ASD. Hence, this work presents an Energy-Delay Optimized Data Communication Model (EDODCM) for efficient data collection from WBAN sensors in IoT environment with better energy efficiency, reduced communication overhead and latency. the contributions of the work are as follows: Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1112 https://internationalpubls.com • The proposed EDODCM minimizes energy consumption in WBAN sensors by optimizing the data transmission process and by optimized Cluster Head (CH) selection. • EDODCM incorporates delay optimization approach on basis of priority to ensure timely delivery of sensory data. By prioritizing critical data packets and reducing transmission delays, the model supports real-time monitoring of physiological and behavioural parameters, which is vital for effective ASD management. • The EDODCM introduces a technique to streamline data communication between WBAN sensors and processing units, reducing unnecessary data exchanges. This enhances the system’s scalability and reliability, making it suitable for prolonged use in ASD monitoring applications. • The work provides a detailed evaluation of the EDODCM in terms of network lifetime, energy consumption, communication overhead, and average End-to-End Delay (EED). Comparative analysis with existing WBAN data communication models demonstrate the superior performance of EDODCM, particularly in scenarios demanding continuous and accurate monitoring. The manuscript is structured to provide a comprehensive overview of the proposed work. Section II discusses the existing WBAN approaches developed in recent years for various scenarios. Section III introduces the proposed EDODCM in detail, explaining the inter- and intra-cluster data communication strategies for efficient transmission of sensor data to the gateway. Section IV presents the results of ELDCM, focusing on its performance in terms of energy consumption, delay, and communication overhead, compared with existing methods. Finally, Section V concludes the study and outlines potential directions for future research. 2. Objective Development of an effective sensory information collection mechanism using optimized classification techniques. 3. Methods The Figure 1 illustrates the data communication process using WBAN sensors (sensing nodes) in an IoT environment towards IoT gateway. In this environment, WBAN sensing nodes are attached to the human body to monitor sensory and physiological parameters and transmit the collected data. Further, the Figure 2 shows the complete process of how EDODCM collects data from WBAN sensors integrated within the IoT environment. In this work, the IoT environment employs an unequal clustering approach with three distinct cluster sizes: small (Cluster 𝔸), medium (Cluster 𝔹), and large (Cluster ℂ). The WBAN sensor within each cluster communicates/transmits their data to the CH through intra-cluster data communication. Once data is gathered at the CH, inter-cluster data communication/transmission occurs, where CHs share information with other CHs, which is critical in facilitating data transmission across the network. After the data has been aggregated at the CH level, it is transmitted to an IoT gateway. This gateway acts as the intermediary, ensuring the collected information is forwarded to the processing servers. As the communication process from the medium and large clusters CHs to the IoT gateway requires higher energy consumption, this results in increased delay and communication overhead, especially for larger clusters. To address these challenges, the EDODCM optimizes the data transmission process by reducing energy consumption Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1113 https://internationalpubls.com during communication, minimizing EED and decreasing communication overhead by employing efficient routing technique designed for WBANs for normal and emergency scenarios. Figure 1. Architecture of WBAN Data Collection Method using IoT Environment. Figure 2.EDODCM Data Collection process from WBAN sensing nodes integrated within the IoT network. As EDODCM is built on unequal-clustering approach, it helps in balancing energy consumption and handles load distribution between WBAN sensor nodes. In this work, if more WBAN sensor nodes exist in a cluster, then the size of cluster will be big, else will be small. In EDODCM, the WBAN sensor nodes first collect information and then transmit it towards CHs, and then CHs transmit it to IoT gateway for which this work presents a novel intra-cluster and inter-cluster data communication approach. Intra-Cluster Communication Approach In intra-cluster communication approach, one WBAN sensor node within the cluster is elected to serve as a CH, where the elected CH transmits data to other CHs. The process of electing CH is done on basis of cost-function denoted as 𝐾𝑦. The 𝐾𝑦 is dependent on WBAN sensing node initial energy denoted as ℰ, total nodes present in a cluster denoted as 𝑋𝑣, average distance among nodes denoted Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1114 https://internationalpubls.com as 𝑀→. The 𝐾𝑦 also relies on the weights of ℰ, 𝑋𝑣, and 𝑀→ denoted as 𝐷ℯ, 𝐷𝑥 and 𝐷𝑚 respectively. From these parameters, the 𝐾𝑦 and 𝑀→ are evaluated using Eq. (1) and Eq. (2) respectively. 𝐾𝑦 = 𝑀→∗𝐷𝑚 (𝑋𝑣∗𝐷𝑥)∗(ℰ∗𝐷ℯ) (1) 𝑀→ = 𝐾ℎ 𝒜⁄ (2) In Eq. (2), 𝐾ℎ denotes overall distance among every WBAN sensing nodes and 𝒜 denotes distance among every WBAN sensing nodes neighbors. From the Eq. (1) and Eq. (2), it is seen that, when a WBAN sensing node has least energy and less neighboring WBAN sensing nodes within a cluster, that WBAN sensing node is elected as CH.Further, this work presents an optimized duty-cycling approach which works on basis of distance of WBAN sensors nodes from CH. Consider 𝑁𝐶 total WBAN sensing nodes present in a cluster, then the optimized duty-cycling approach denoted as 𝑂𝐷𝐶 is evaluated using Eq. (3). 𝑂𝐷𝐶 = 𝑒 − 𝑑𝑆𝑁𝐶𝐻 𝑑 ∑ 𝑒 − 𝑑𝑆𝑁𝐶𝐻 𝑑 𝑁𝐶−1 𝑛=1 (3) In Eq. (3), 𝑑𝑆𝑁𝐶𝐻 is distance among the WBAN sensing node and CH and 𝑑 is distance threshold. The Eq. (3) helps in conflict-free data-aggregation by allowing the WBAN sensing node to transmit data efficiently in a sequential way.Also, this approach helps to periodically put WBAN sensing nodes into sleep mode when not sensing and for transmitting data towards CH. Inter-Cluster Communication Approach Further, in inter-cluster data communication, i.e., data transmission from CH to CH, this work presents a novel multi-hoprouting approach for normal and emergency scenarios. Consider the established connection from WBAN sensing node to CH and from CH to CH as 𝑡 which has path of transmission denoted as 𝑙. Also consider𝒮𝒟(𝑡) as connection established from transmitter to receiver and𝒟𝒮(𝑡) as connection established from receiver to transmitter. From this the hop-routing can be evaluated using Eq. (4). 𝒢𝑙 = ∑ 𝒮𝒟(𝑡) ∗ 𝒟𝒮(𝑡)𝑡∈𝑙 (4) In Eq. (4), 𝒢𝑙 denotes hop-count for transmission of data and 𝒮𝒟(𝑡) ∗ 𝒟𝒮(𝑡) denotes the data transmission delivery-ratio. For evaluating the average number of hops per unit of distance, the inverse of 𝒢𝑙 is considered denoted as 𝒢�̅�, which is evaluated using Eq. (5). 𝒢�̅� = 1 ∑ 𝒮𝒟(𝑡)∗𝒟𝒮(𝑡)𝑡∈𝑙 (5) Further, consider a normal scenario, where the sensory data has to be sent periodically to monitor the autism patients. Also, consider an emergency scenario where an autism patient requires immediate attention, then in this scenario, it is important to send sensory information to the IoT gateway, having highest priority. Hence, for normal scenarios, a 𝕌 path is established and for emergency scenarios, a ℕ path is established. Both the 𝕌 path and ℕ path is evaluated using Eq. (6) and Eq. (7) respectively. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1115 https://internationalpubls.com 𝕌 = 𝕃 𝕃+𝕁 𝔾 (6) ℕ = 𝕁 𝕁+𝕃 𝔾 (7) In Eq. (6) and Eq. (7), 𝕃 and 𝕁 denotes the WBAN sensing node flag variable, i.e., 𝕃 gives 𝑓𝑙𝑎𝑔 = 0 for normal scenarios and 𝕁 gives 𝑓𝑙𝑎𝑔 = 1 for emergency scenarios. Also, the 𝔾 denotes the multi- hop routing approach which is obtained using Eq. (8). 𝔾 = 𝕌 + ℕ (8) Using Eq. (8), the multi-hop is established for normal and emergency scenarios. Further, when there are more clusters and CHs, there are chances of increased delay and packet-failure rate during emergency scenario data transmission. TDMA-based Optimized MAC approach For reducing packet-failure rate and delay during data transmission from WBAN sensing node to CH and CH to CH, this work presents a TDMA-based Optimized Medium Access Control (TDMA- OMAC) approach.As, TDMA-OMAC helps in evaluating packet-failure rates by providing a structured and deterministic communication framework that eliminates collisions, reduces retransmissions, and offers consistent intervals for real-time analysis. Its ability to isolate and monitor failures in individual time slots makes it a powerful method for evaluating and improving packet-delivery in IoT environment. Hence, in this work theTDMA-OMACevaluatespacket-failure rate denoted as 𝐿′ 𝑝 using Eq. (9). 𝐿′ 𝑝 = 1 − (1 − 𝐿′ 𝒷) ℬ𝒽 (9) In Eq. (9), 𝐿′ 𝒷denotes mean Bit-Error-Rate (BER) for the communication channel which is evaluated by considering Signal-to-Noise Ration (SNR) denoted as 𝛾 for distance 𝑠, i.e., from WBAN sensing node to CH and CH to CH. Also, ℬ𝒽 represents the data collected from WBAN sensing nodes and transmitted towards CH and further transmitted to other CH, so that it can be transmitted to IoT gateway. The ℬ𝒽 is represented using Eq. (10). ℬ𝒽 = ∑ 𝒷𝑜 𝑁𝐶 𝑜=1 (10) In Eq. (10), 𝒷𝑜 denotes the information in bits collected by WBAN sensing nodes, which is transmitted to CH and further to IoT gateway and 𝑁𝐶 denotes total WBAN sensing nodes present in a cluster.Moreover, finding the best path for transmitting data from a WBAN sensing node to CH and from the CH to the IoT gateway is essential for optimizing energy use, minimizing delay, reducing packet loss, and ensuring reliable communication. Hence, the best route for data transmission is evaluated using Eq. (11). 𝐿ℳ = ℰ𝑣 + 𝒢𝑙 + 𝒢�̅� + 𝐿′ 𝑝 (11) In Eq. (11), 𝐿ℳ denotes best route for data transmission, ℰ𝑣 denotes the energy-level of WBAN sensing nodes which also includes CHs, 𝒢𝑙 denotes hop-count for transmission of data, 𝒢�̅� denotes inverse of 𝒢𝑙, and 𝐿′ 𝑝 denotes packet-failure rate.Moreover, as most of the MAC approaches are used Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1116 https://internationalpubls.com for enhancing IoT networks environment by reducing energy and communication overhead, providing better delay[24], the presented TDMA-OMAC optimizes the process of transmission of packets among CHs for transmitting it to IoT gateway for emergency scenarios, having better energy efficiency, delay and less communication overhead. The complete process of TDMA-OMAC is presented in Algorithm 1. Algorithm 1: TDMA-OMAC for efficient CH-to-CHcommunication. Step 1 Start Step 2 CH Selection: Evaluate 𝐾𝑦 using Eq. (1) to identify CHs for all clusters. The CH set is denoted as 𝒟.Sort CHs by cluster size, prioritizing nodes with lower energy and fewer neighbors, resulting in a CH set [𝛽1, 𝛽2, 𝛽3, … , 𝛽𝐷]. Step 3 For Each Round Step 4 Compute 𝒢𝑙 and 𝒢�̅� using Eq. (4) and Eq. (5) to find the optimal hop. Step 5 Determine the scenario (normal or emergency) using Eq. (8) Step 6 If emergency scenario Step 7 Transmit data to IoT gateway using best path 𝐿ℳ using Eq. (11). Step 8 Evaluate packet-failure rate 𝐿′ 𝑝 using Eq. (9). Step 9 Else Step 10 Transmit data IoT gateway using the best route 𝐿ℳ using Eq. (11). Step 11 End if Step 12 End For Step 13 Stop Energy Consumption, Delay and Communication Overhead Evaluation Further, the energy consumption for transmission of data from WBAN sensors to IoT gateway is primarily influenced by three factors, i.e., intra-cluster communication (WBAN sensing nodes to CH), inter-cluster communication (CH-to-CH or CH-to-IoT gateway), and WBAN sensing node operations, which includes sensing, processing, and transmitting data. Hence, from this, the total energy consumption 𝐸𝑡𝑜𝑡𝑎𝑙 in the network is evaluated using Eq. (12) 𝐸𝑡𝑜𝑡𝑎𝑙 = ∑ (𝐸𝑆𝑒𝑛𝑠𝑒 + 𝐸𝑝𝑟𝑜𝑐𝑒𝑠𝑠 + 𝐸𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑡) + ∑ 𝐸𝐶𝐻 𝐶 𝑐=1 𝑁𝐶 𝑛=1 (12) In Eq. (12), 𝐸𝑆𝑒𝑛𝑠𝑒 denotes energy consumed by WBAN sensing nodes for sensing data, 𝐸𝑝𝑟𝑜𝑐𝑒𝑠𝑠 denotes energy consumed by WBAN sensing nodes for processing data, 𝐸𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑡 denotes energy consumed by WBAN sensing nodes for transmitting data to CHs, 𝐶 denotes total number of clusters and 𝐸𝐶𝐻 denotes energy consumed by CHs for aggregation, inter-cluster communication, and Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1117 https://internationalpubls.com transmission to the IoT gateway. The energy consumed by a WBAN sensing node to transmit data to the CH, i.e., energy for intra-cluster data communication is evaluated using Eq. (13). 𝐸𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑡 = 𝑁𝐶 ∙ 𝑃𝑡 ∙ 𝑇𝑆𝑁𝐶𝐻 (13) In Eq. (13), 𝑃𝑡 denotes power required for transmission, 𝑇𝑆𝑁𝐶𝐻denotes time required for WBAN sensing nodes to transmit data to CH. Further, the energy consumed by the CH to transmit aggregated data to other CHs or the IoT gateway, i.e., energy for inter-cluster data communication is evaluated using Eq. (14). 𝐸𝐶𝐻 = 𝑃𝐶𝐻 ∙ 𝑇𝐶𝐻 + 𝐸𝑎𝑔𝑔 (14) In Eq. (14), 𝑃𝐶𝐻 is power required for CH to transmit aggregated data, 𝑇𝐶𝐻 is time required for CH to transmit data and 𝐸𝑎𝑔𝑔 is the energy consumed by CH for data aggregation which is evaluated as 𝐸𝑎𝑔𝑔 = 𝜆 ∙ 𝑆𝑎𝑔𝑔, where 𝜆 denotes energy coefficient for aggregation and 𝑆𝑎𝑔𝑔 denotes size of aggregated data. The energy consumed by WBAN sensing nodes for sensing data is evaluated using Eq. (15) 𝐸𝑆𝑒𝑛𝑠𝑒 = 𝑁𝐶 ∙ 𝑃𝑆 ∙ 𝑇𝑆𝑒𝑛𝑠𝑒 (15) In Eq. (15), 𝑃𝑆 denotes power required for sensing data and 𝑇𝑠𝑒𝑛𝑠𝑒 is time required for sensing. Using Eq. (13), Eq. (14) and Eq. (15), when substituted in Eq. (12), the total energy consumption 𝐸𝑡𝑜𝑡𝑎𝑙 is obtained as presented in Eq. (16). 𝐸𝑡𝑜𝑡𝑎𝑙 = ∑ (𝑃𝑠 ∙ 𝑇𝑆𝑒𝑛𝑠𝑒 + 𝑃𝑡 ∙ 𝑇𝑆𝑁𝐶𝐻 + 𝐸𝑝𝑟𝑜𝑐𝑒𝑠𝑠) + ∑ (𝑃𝐶𝐻 ∙ +𝑇𝐶𝐻 + 𝜆 ∙ 𝑆𝑎𝑔𝑔)𝐶 𝑐=1 𝑁𝐶 𝑛=1 (16) The EDODCM main objective is to minimize 𝐸𝑡𝑜𝑡𝑎𝑙while ensuring reliable communication by adjusting the parameters 𝑃𝑡, 𝑃𝐶𝐻, 𝑇𝑆𝑁𝐶𝐻, 𝑇𝐶𝐻 and 𝑆𝑎𝑔𝑔. Additionally, the 𝑂𝐷𝐶 in (Eq. (3)) helps reduce unnecessary energy consumption by putting nodes into sleep mode when idle. The total network delayℒ in this work is evaluated using Eq. (17). ℒ = ∑ ( 𝑃ℎ 𝑅ℎ + 𝐷𝑝)𝐻 ℎ=1 (17) In Eq. (17), 𝐻 is total number of hops in the path, 𝑃ℎ is the size of the packet transmitted at hop ℎ (in bits), 𝑅ℎ is data-transmission rate at ℎ (in bits/second) and 𝐷𝑝 is processing delay at each node (in seconds). The total communication overhead 𝒞 is evaluated using Eq. (18). 𝒞 = ∑ (𝜂𝑎𝑔𝑔 + 𝜂𝑐𝑡𝑟𝑙 + 𝜂𝑓𝑎𝑖𝑙) 𝑁𝐶 𝑛=1 (18) In Eq. (18), 𝜂𝑎𝑔𝑔 is overhead due to data aggregation at CHs, 𝜂𝑐𝑡𝑟𝑙 is the overhead because of data and 𝜂𝑓𝑎𝑖𝑙 is overhead due to retransmissions caused by packet-failure rate. The 𝜂𝑎𝑔𝑔, 𝜂𝑐𝑡𝑟𝑙, and 𝜂𝑓𝑎𝑖𝑙 are evaluated using Eq. (19), Eq. (20) and Eq. (21) respectively. 𝜂𝑎𝑔𝑔 = 𝛼 ∙ ( 𝐷𝑆𝑁𝐶𝐻 𝑁𝐶 ) (19) 𝜂𝑐𝑡𝑟𝑙 = 𝛽 ∙ 𝐶𝐶𝑡𝑟𝑙 (20) 𝜂𝑓𝑎𝑖𝑙 = 𝛾 ∙ 𝐿′ 𝑝 ∙ ℬ𝒽 (21) Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1118 https://internationalpubls.com In Eq. (19), Eq. (20) and Eq. (21),𝛼is aggregation factor, 𝐷𝑆𝑁𝐶𝐻 is the total data transmitted by WBAN nodes to CH, 𝛽 is control data frequency factor,𝐶𝑐𝑡𝑟𝑙 is the total size of control packets transmitted between nodes, CHs, and the gateway, 𝛾 denotes retransmission factor, 𝐿′ 𝑝 is packet- failure rate (Eq. (9)) and ℬ𝒽 is total data transmitted in bits (Eq. (10)).This presented EDODCM combines clustering, duty-cycling, multi-hop routing, and a TDMA-based MAC protocol to optimize data transmission for WBAN sensing nodes to IoT gateway. The approach ensures energy efficiency, low delay, and communication overhead under both normal and emergency scenarios. By dynamically selecting CHs and leveraging optimized routes, the system enhances performance in IoT-integrated healthcare environments. The results of the EDODCM in terms of energy consumption, delay and communication overhead using Eq. (16), Eq. (17) and Eq. (18) respectively, is discussed in next section. 4. Results To evaluate the performance of the proposed EDODCM and the existing DC-ACO approach [22], simulations were conducted using the SENSORIA simulator [25]. The simulation parameters are summarized in Table 1. The network was simulated for a total of 100 rounds, with the number of WBAN nodes varying between 50 and 300. Each data packet had a size of 80 bytes, and the transmission range was set between 10 to 25 meters, ensuring a realistic communication environment. The data rate was fixed at 50 kbps, while the mobility rate ranged from 0 to 50 m/s to mimic dynamic scenarios. The simulation area covered a 50×50 m² space, representing a typical Autism WBAN deployment. The IEEE 802.11 MAC protocol was utilized to manage medium access control. Each WBAN node was initialized with an energy level of 100 Joules, providing a uniform starting point to assess energy efficiency and network performance. These simulation settings were chosen to provide a comprehensive evaluation of the models in terms of energy consumption, network lifetime, communication overhead, and delay under diverse network conditions. Table 1. Simulation Parameters Simulation Parameters Value Total Rounds 100 WBAN nodes 50~300 Data Packet Size 80 bytes Transmission Range 10m-25m Data Rate 50 kbps Mobility Rate 0-50 m/s Area Considered 50×50 MAC Protocol IEEE 802.11 Initial Energy 100 Joules Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1119 https://internationalpubls.com 5. Discussion 1.1 Network Lifetime This section presents the network lifetime performance of EDODCM compared with DC-ACOP. The results are presented in Figure 3, where the results demonstrate better network lifetime performance for proposed EDODCM compared to the existing DC-ACO model across various scenarios. For a network with 50 nodes, EDODCM extended the network lifetime to 4425 seconds, surpassing DC- ACO’s network lifetime performance, i.e., 3898 seconds. As the network size increased to 300 nodes, EDODCM maintained its superior performance, achieving a network lifetime of 3864 seconds compared to DC-ACO’s network lifetime performance, i.e., 2698 seconds. The average improvement in network lifetime across all scenarios was 20.1%, with the most significant gains observed in larger networks. This improvement is attributed to EDODCM’s unequal clustering approach, which balances energy consumption and load distribution, particularly in clusters with higher node density. Additionally, the optimized duty cycling and presented routing approach reduces unnecessary energy consumption, further contributing to extended network longevity.These findings emphasize EDODCM’s potential to address the critical challenge of limited energy resources in WBANs, ensuring sustainable and efficient data transmission. The results validate EDODCM as a robust solution for applications requiring prolonged network operation, particularly in healthcare scenarios where uninterrupted monitoring is essential. Figure 3. Network Lifetime Performance by varying WBAN sensor nodes. 1.2 Energy Efficiency This section presents the energy efficiency performance of EDODCM compared with DC-ACOP. The results are presented in Figure 4, where the results demonstrate better energy efficiency for proposed EDODCM compared to the existing DC-ACO model across various scenarios. For a network size of 50 nodes, EDODCM achieved an energy efficiency of 50.74%, significantly outperforming DC-ACO's energy efficiency, i.e., 35.87%. As the network size increased, EDODCM consistently maintained higher energy efficiency, reaching 60.78% for a network of 300 nodes, compared to DC-ACO's 49.87%. The average improvement across all scenarios for EDODCM was Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1120 https://internationalpubls.com notable, with an average energy efficiency of 24.52% compared to DC-ACO. The consistent performance gains indicate that EDODCM can handle increasing network sizes without substantial degradation in energy efficiency, a critical factor for WBAN applications where sensor nodes are resource-constrained. The results also emphasize that EDODCM is particularly effective in minimizing energy consumption in larger networks, where challenges like increased communication overhead and latency are more pronounced. The findings validate the robustness of EDODCM in optimizing energy utilization while maintaining reliable and efficient data communication, making it a promising solution for energy-sensitive IoT environments integrating WBANs. Figure 4. Energy Efficiency Performance by varying WBAN sensor nodes. 1.3 End-to-End Delay This section presents the EED performance of EDODCM compared with DC-ACOP. The results are presented in Figure 5, where the results demonstrate the significant reduction in communication delay achieved by the proposed EDODCM model compared to the DC-ACO approach across various network sizes. For network with 50 nodes, EDODCM reduced the EED from 133.51 seconds in comparison with DC-ACOEED of 124.51 seconds, reflecting an improvement of 6.74%. As the network size increased, the EDODCM achieved EED of 161.41 seconds for 300 nodes, compared to 183.41 seconds for DC-ACO, resulting in an improvement of approximately 11.99%.The average EED reduction across all network configurations was 9.65%, highlighting the model’s consistent effectiveness. This improvement was attributed to EDODCM’s optimized intra-cluster and inter- cluster communication approaches, which minimized latency by streamlining data transmission paths and employing efficient routing approach. Furthermore, the TDMA-OMAC approach ensured orderly and collision-free communication, reducing retransmissions and delays, particularly in larger and denser networks.The reduction in EED achieved by EDODCM is critical for real-time applications such as healthcare monitoring, where timely data transmission can be a matter of urgency. These results validate EDODCM's ability to deliver faster and more reliable communication, enhancing the overall performance and responsiveness of WBAN-based IoT systems. 0 20 40 60 80 50 100 150 200 250 300 E n e r y E ff ic ie n c y ( % ) WBAN Sensor Nodes Energy Efficiency DC-ACOP EDODCM Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1121 https://internationalpubls.com Figure 5. End-to-End Delay Performance by varying WBAN sensor nodes. 1.4 Communication Overhead This section presents the communication overhead performance of EDODCM compared with DC- ACOP. The results are presented in Figure 6, where the results reveal a notable reduction in communication overhead achieved by the EDODCM model compared to the DC-ACO approach across various network configurations. For a network size of 50 nodes, EDODCM achieved communication overhead of 0.078 compared to 0.089 in DC-ACO, marking an improvement of 12.36%. Further, for 300 nodes, EDODCM achieved communication overhead of 0.12, compared to 0.1647 in DC-ACO, representing a significant reduction of 27.14%.On average, EDODCM consistently reduced communication overhead by 22.18% across all tested configurations. These improvements are primarily attributed to the model’s optimized data aggregation approach, unequal clustering, and energy-efficient routing approach. The multi-hop routing mechanism used by EDODCM ensures efficient utilization of network resources, minimizing redundant transmissions and thus reducing communication overhead.The substantial reduction in communication overhead underscores EDODCM’s ability to streamline data communication processes, making it more efficient for applications where minimizing overhead is crucial. This improvement enhances the scalability and performance of WBANs, particularly in scenarios requiring high reliability and low operational cost. Figure 6. Communication Overhead Performance by varying WBAN sensor nodes. 0 100 200 50 100 150 200 250 300 E n d -t o -E n d D e la y ( se c ) WBAN Sensor Nodes End-to-End Delay DC-ACOP EDODCM 0 0.05 0.1 0.15 0.2 50 100 150 200 250 300C o m m u n ic a ti o n O v e r h e a d ( % ) WBAN Sensor Nodes Communication Overhead DC-ACOP EDODCM Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 1122 https://internationalpubls.com 6 Conclusion In this work, an EDODCM for WBANs integrated into the IoT environment is proposed. WBANs, a critical technology for health monitoring systems, face challenges of energy inefficiency, high communication overhead, and increased delay, especially in dynamic scenarios involving mobility and emergency data transmission. Existing approaches like DC-ACO, while effective and provided better performance, have limitations in optimizing these parameters simultaneously. This study addresses these gaps with a novel unequal clustering-based communication model that enhances energy efficiency, reduces delay, and minimizes communication overhead. The proposed EDODCM employs an optimized duty-cycling mechanism, multi-hop routing for normal and emergency scenarios, and a TDMA-based Optimized Medium Access Control (TDMA-OMAC) approach to streamline data aggregation and transmission. Simulation results demonstrate that EDODCM outperforms DC-ACO across key metrics. The model improves average energy efficiency by up to 24.52%, extends average network lifetime by 20.1%, reduces average EED by 9.65%, and lowered average communication overhead by 22.18%. These improvements highlight the efficacy of EDODCM in addressing the complex requirements of WBANs in IoT environments. Future work will focus on incorporating advanced machine learning algorithms for predicting the collected Autism data. References [1] S. K. 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