Today’s critical goals in sensor network research are extending the lifetime of wireless sensor networks (WSNs) and lowering power consumption. A WSN necessitates the use of energy-efficient communication networks. Clustering, storage, communication capacity, high configuration complexity, low communication speed, and limited computation are also some of the energy limitations of WSNs. Moreover, cluster head selection remains problematic for WSN energy minimization. Sensor nodes (SNs) are clustered in this work using the Adaptive Sailfish Optimization (ASFO) algorithm with K-medoids. The primary purpose of research is to optimize the selection of cluster heads through energy stabilization, distance reduction, and latency minimization between nodes. Because of these constraints, achieving optimal energy resource utilization is an essential problem in WSNs. An energy-efficient cross-layer-based expedient routing protocol (E-CERP) is used to determine the shortest route, dynamically minimizing network overhead. The proposed method is used to evaluate the packet delivery ratio (PDR), packet delay, throughput, power consumption, network lifetime, packet loss rate, and error estimation, and the results were superior to existing methods. PDR (100%), packet delay (0.05 s), throughput (0.99 Mbps), power consumption (1.97 mJ), network lifespan (5908 rounds), and PLR (0.5%) for 100 nodes are the performance results for quality-of-service parameters.

Internet of things is an emerging trend and Wireless sensor networks are embedded with IoT to measure various parameters of a vehicle. Measurements and standards of measurements are the most important stages for measuring an object’s size, length, weight etc., Measuring the weight of small objects does not need much time and efforts, but on the other hand weighing large objects needs lots of time and efforts. A new approach is proposed to monitor the location of the vehicle, engine temperature, tyre pressure, oil level, speed control and load measurement of a vehicle. Even if the device is offline, the logs will be stored in the memory card and it can be used for future analysis. In addition to this theft control is introduced for the security of the vehicle. CC3200 microcontroller is connected with load cell to monitor the total weight of the vehicle and engine temperature is monitored using the temperature sensor. By GPS the location and speed of the vehicle is monitored periodically and the location is uploaded in the cloud environment. Vehicle monitoring with load calculation, anti-theft control and the data acquisition from various sensors incorporated with the Electronic Control Unit (ECU) is collected and processed in the cloud. The proposed framework is effectively adaptable at low cost.

Wireless sensor network (WSN) sensor nodes should have adequate energy. Reduced energy usage is essential to maximize the endurance of WSNs. Combining WSN with a more significant energy source, a cluster head (CH), is another effective strategy for extending WSN durability. A CH is dependent on the communication inside and between clusters. A CH’s energy level extends the cluster’s life for the complete WSN. Determining the energy required in WSNs while developing clustering algorithms is challenging. For maintaining energy efficiency in WSNs, this research offers K-medoids with sunflower-based clustering and a cross-layer-based optimal routing approach. An efficient fitness function generated from diverse objectives is used to choose the CH. After CH selection, sunflower optimization (SFO) indicates the best data transmission line to the sink node. The proposed protocol, SFO-CORP, increased the network lifetime by 19.6%, 13.63%, 11.13%, and 4.163% compared to the LEACH, EECRP, FEEC-IIR, and CL-IoT protocols, respectively. The experimental results showed that it performed better for packet delivery ratio, energy consumption, end-to-end delay, network lifetime, and computation efficiency.

The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these signals remains a challenge due to their inherent complexity and non-linearity. This study introduces a novel method, PCA-ANFIS, which integrates Principal Component Analysis (PCA) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to enhance cognitive pattern recognition in multimodal brain signal analysis. PCA reduces the dimensionality of EEG data while retaining salient features, enabling computational efficiency. ANFIS combines the adaptability of neural networks with the interpretability of fuzzy logic, making it well-suited to model the non-linear relationships within brain signals. Performance metrics of our proposed method, such as accuracy, sensitivity, and computational efficiency. These additions highlight the effectiveness of the method and provide a concise summary of the findings. The proposed method achieves superior classification performance, with an unprecedented accuracy of 99.5%, significantly outperforming existing approaches. Comprehensive experiments were conducted using a diverse multimodal EEG dataset, demonstrating the method’s robustness and sensitivity. The integration of PCA and ANFIS addresses key challenges in multimodal brain signal analysis, such as EEG artifact contamination and non-stationarity, ensuring reliable feature extraction and classification. This research has significant implications for both cognitive neuroscience and clinical practice. By advancing the understanding of cognitive processes, the PCA-ANFIS method facilitates accurate diagnosis and treatment of cognitive disorders and neurological conditions. Future work will focus on testing the approach with larger and more diverse datasets and exploring its applicability in domains such as neurofeedback, neuromarketing, and brain-computer interfaces. This study establishes PCA-ANFIS as a capable tool for the precise and efficient classification of cognitive patterns in brain signal processing.