International Journal of Sensors Wireless Communications and Control - Volume 15, Issue 3, 2025

Numerous sensor nodes spread out across the surveillance region form the Wireless-Sensor Network (WSN), a smart, self-organizing network. Since the lumps can typically only be motorized by batteries, creating a WSN while maintaining an optimal energy balance and extending the network's lifetime is the biggest issue.

A novel network architecture that integrates nanotechnology with sensor networks is known as a Wireless-NanoSensor-Network(WNSN). A new area of focus in research is intra-body-iWNSNs, which are WNSNs with promising potential applications in biomedicine, damage detection, and intra-body health monitoring. We suggest an energy-balance-clustering-routing protocol (EBCR) for iSN nodes that have limited energy storage, short communication range, and low computation and processing capabilities. The protocol uses a novel hierarchical clustering approach to lessen the communication burden on nano-nodes.

Cluster nano-nodes can use one-hop routing to send data directly to the Cluster-Head(CH) nodes, and the CH-nodes can utilize multi-hop routing to send data to the nano control node. In addition, selecting the next hop node to minimize energy usage while guaranteeing successful data packet delivery involves balancing distance and channel capacity. The protocol's strengths in energy efficiency, network-lifetime extension, and data-packet transmission success rate were highlighted by the simulation results.

It is clear that the EBCR protocol is a viable option for iWNSNs' routing system.

Cold Chain Logistics is defined as the secure storage, transportation, and handling of temperature-sensitive items from the origin of production to the consumer end. Transport and warehouses with refrigeration systems have been used in this process. Cold chain logistics plays a key role in safely transporting food items to consumers.

In this research article, we have proposed an improved Artificial Bee Colony (ABC) algorithm for the path optimization method of cold chain logistics vehicles. It is difficult to transfer resources across borders, there is no information sharing, and there are geographical restrictions with traditional regional distribution systems. Reaching the consumer end in less time, distance, and expense is made possible in large part by the cold chain logistics vehicle. Data about the flow of traffic on the roads is taken into account when choosing routes.

Path optimization methods are employed in this study. One of the artificial swarm intelligence algorithms that is based on the functionality of bee colonies is the ABC algorithm. The Cold Chain Logistics (CCL) path is optimized with the aid of Internet technology and Global Positioning System technology. For the delivery of temperature-sensitive items, the most efficient short route is identified.

The main aim of this research is to reduce the time, distance, and cost involved in transportation. The algorithm has provided an accuracy of 98.74% in attaining effective transportation of cold logistics and optimal path selection.

The integration of communication tools has allowed for effective decision-making in smart cities and Internet-of-Things (IoT). One major issue that people who commute to cities everyday encounter is traffic congestion. Thanks to the progress and backing of ICTs, transportation solutions have been designed and implemented, leading to the development of ITSs and the provision of numerous innovative services.

These services include ones that guarantee safety, provide drivers with useful information, enable greater street movement, and avoid congestion, among many others. In industrialized nations, traffic data is collected by specialized sensors that can anticipate future patterns. Commuters are kept informed of any traffic updates through the Internet. When there is little or no physical infrastructure and Internet connection, these methods become unworkable. Internet access is still a problem in rural regions, and there are no roadside units in underdeveloped nations. This article presents an architecture for smart cities' intelligent vehicular networks that can impromptu accept data from nearby vehicles in real time and use it to choose routes. As embedded devices in vehicles, we utilized Android-based smartphones with Wi-Fi Direct capabilities. To set up our smart transportation system, we utilized a vehicular ad hoc network.

Data was collected and processed using separate methods between two major cities in a developing nation. Resource utilization, transmission delay, packet loss, and total trip time were measured against several fixed- and dynamic-route-selection algorithms to assess the framework's performance.

When equated to a conventional fixed-route-selection procedure, our results reveal a 33.3% reduction in trip times.

Network security detection has become increasingly complex due to the proliferation of Internet nodes and the ever-changing nature of network architecture. To address this, a multi-layer feedforward neural-network has been employed to construct a model for security threat detection, which has enhanced network security protection.

Improving prediction accuracy and real-time performance, this research suggests an optimal strategy based on Clockwork Recurrent-Neural-Networks (CW-RNNs) to handle nonlinearity and temporal dynamics in network security circumstances. We get the model to pick up on both the short-term and long-term temporal aspects of network-security situations by using the clock-cycle RNN. To further improve the network security scenario prediction model, we tune the network hyperparameters using the Grey-Wolf-Optimization (GWO) technique. By incorporating a clock-cycle for hidden units, the model can improve its pattern recognition capabilities by learning short-term knowledge from high-frequency update modules and preserving long-term memory from low-frequency update modules.

The optimized clock-cycle RNN achieves better prediction accuracy than competing network models when it comes to extracting nonlinear and temporal characteristics of network security scenarios, according to the experimental data.

In addition, our method is perfect for tracking massive amounts of data transmitted by sensor networks because of its minimal time complexity and outstanding real-time performance.

Cybersecurity issues have grown as technology rapidly extends the digital landscape. Intrusion-Detection Systems (IDSs) are essential for detecting malicious network traffic. Hardware or software can power these systems. Traditional IDS approaches generally fail to protect data privacy and detect complicated, unique intrusions, principally in WSNs.

A hybrid model for WSN intrusion detection utilizing the Convolutional Neural Network and Bidirectional Long-Short-Term-Memory (CNN-BiLSTM) model to overcome these constraints is proposed. Federated-Learning(FL) improves intrusion recognition and privacy in this approach. The FL-based CNN-BiLSTM model is unusual in that numerous sensor nodes can train a central model without revealing private data, addressing privacy issues. By carefully studying local and temporal network links, the CNN-BiLSTM model detects sophisticated and undiscovered cyber threats using deep learning. The WSN-DS and CIC-IDS2017 datasets were used to create the model to detect and classify DoS attacks.

Experimental results showed that the FL-CNN-BiLSTM model outperformed existing IDS models in detecting complex and unknown assaults. On both datasets, the model had 99.9% precision and recall, decreasing false positives and negatives. FL and deep-learning (DL) can improve WSN security and privacy, according to our research.

The FL-CNN-BiLSTM architecture helps identify complex cyber threats and shows how deep learning may improve intrusion detection systems while protecting user data.

A regional WSN of independent SNs collects environmental and physical data. Sensors for specific applications measure light, pressure, temperature, humidity, etc. WSNs can transmit real-time data without infrastructure via radio frequencies. Smart cities, agriculture, industrial automation, environmental monitoring, and healthcare use WSN data. Many SNs monitor the network architecture's physical environment.

This design lets SNs talk to neighbors and base stations. Due to power limits, WSNs must prioritise energy efficiency. ML enhances data processing, energy efficiency, and WSN performance. WSN sensors assist ML in predicting events. Machine learning improves data compression, anomaly detection, adaptive network management, WSN predictive modelling, industrial WSN performance, energy efficiency, data analysis, etc. In this work, the dataset, which has parameters such as SN’s positions, energy, distance from cluster head, and SN lifetime is provided as an input to the following ML models Linear Regression, Random Forest, SVM, Naive Bayes, PCA, K Nearest Neighbour, XG Boost, and NN.

The result showed that the Linear Regression, Random Forest, SVM, Naive Bayes Classifier, PCA, K Nearest Neighbour, and XG Boost were all examined on the training and testing data.

The training data accuracies are as follows: Linear Regression (46.64%), Random Forest (99.67%), XG Boost (99.99%), SVM (32.10%), K Nearest Neighbor (98.56%), Naïve Bayes Classifier (96.45%), Principle Component Analysis (97.88%) and NN (34.86%). The testing data accuracies are as follows: Linear Regression (22.00%), Random Forest (97.18%), XG Boost (96.94%), SVM (21.75%), K Nearest Neighbor (97.66%), Naïve Bayes Classifier(64.49%), PCA(99.38%) and NN(28.94%). Linear Regression, Random Forest, SVM, Naive Bayes Classifier, PCA, K Nearest Neighbour, and XG Boost were all examined. Results revealed that

The integration of Cooperative Communications (CC), Cognitive Radio (CR) technology, and Non-Orthogonal Multiple Access (NOMA) techniques, termed Cooperative Cognitive Radio used NOMA (CCR-NOMA) systems, has emerged as a promising solution to address spectrum scarcity and connectivity challenges anticipated in sixth generation (6G) networks.

This study aims to investigate the Bit Error Rate (BER) performance of underlay CCR-NOMA systems.

We derive precise closed-form expressions for BER at distant users under perfect and imperfect Channel State Information (CSI) conditions. These mathematical formulations are validated through Monte Carlo simulations.

Our results indicate that the near user 1 exhibits superior performance compared to the far user 2. Additionally, distant users utilizing the CCR-OMA protocol demonstrate better BER performance than those employing the CCR-NOMA protocol.

The presence of imperfect CSI adversely affects BER performance. Moreover, the derived closed-form expressions for BER in the investigated system align well with Monte Carlo simulations. These findings provide valuable insights for optimizing the performance of CCR-NOMA systems in real-world scenarios.

Wireless Medical Sensor Networks (WMSNs) are crucial for monitoring patients' health in Wireless Body Area Networks (WBANs). However, the energy consumption of medical sensors (MSs) presents a significant challenge, impacting the efficiency and longevity of these networks. Optimizing energy consumption while maintaining spectral efficiency is essential for enhancing the performance of WMSNs.

This study aims to optimize energy consumption in WMSNs by exploring the balance between energy conservation and spectral efficiency. The focus is on selecting optimal modulation schemes that minimize total energy consumption while considering various distances and the Additive White Gaussian Noise (AWGN) channel.

The research investigates energy and constellation optimization using different modulation types, particularly in the context of the Internet of Medical Things (IoMT). A coefficient is introduced to vary circuit consumption independently of power emitted. Additionally, the study derives a performance-optimized energy efficiency measure for circuits and provides a closed-form transmission efficiency measure for M-ray quadrature amplitude modulation (M-QAM), validated numerically.

The findings reveal the optimal modulation schemes for various conditions, demonstrating significant energy savings while maintaining adequate spectral efficiency. The introduced coefficient effectively decouples circuit consumption from emitted power, optimizing energy use in WMSNs.

This research offers a comprehensive approach to energy consumption optimization in WMSNs, contributing to more efficient WBANs. The proposed methods and findings support the development of energy-efficient, remote medical care systems, enhancing the reliability and longevity of IoMT-based healthcare solutions.