The Internet of Things (IoT) has witnessed exponential growth, connected billions of devices, and enabled various applications of distributed and heterogeneous nature. The extensive embrace of IoT devices has sparked noteworthy apprehensions regarding security. The majority of these devices prioritize user-friendliness during manufacturing rather than robust security. Consequently, conventional security measures frequently prove inadequate in safeguarding the integrity, confidentiality, and accessibility of IoT networks. Together, these issues give rise to substantial threats to the entire system. Each IoT device generates a cryptographic hash of its data and records it on the blockchain. Smart contracts and consensus mechanisms can be utilized to facilitate node status management and consensus on the state of the IoT network. Integration of blockchain technology for detecting malicious activity in IoT networks brings several benefits, including increased transparency, data integrity assurance, and decentralized trust. Blockchain technology offers a trustless environment in which multiple parties can collaboratively verify and confirm the legitimacy of data and transactions. This characteristic is achieved through consensus algorithms like proof-of-work and proof-of-stake, effectively thwarting attempts by malicious entities to manipulate or jeopardize the integrity of the system. Blockchain's resilience to attacks and tampering provides an additional layer of security for IoT systems. Nodes can refer to the blockchain for verifying the integrity and authenticity of the data generated by other nodes, enabling the identification and isolation of malicious actors in the network. <!----> <!----> <!----> <!---->

Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT.

Wireless Sensor Networks (WSNs) have a decisive share in various monitoring and control systems. However, their distributed and resource-constrained nature makes them vulnerable to anomalies caused by factors such as environmental noise, sensor faults, and cyber intrusions. In this paper, HEXADWSN, a hybrid ensemble learning-based explainable anomaly detection framework for anomaly detection to improve reliability and interpretability in WSNs, has been proposed. The proposed framework integrates an ensemble learning approach using Autoencoders, Isolation Forests, and One-Class SVMs to achieve robust detection of time-series-based irregularities in the Intel Lab dataset. The framework uses stack and vote ensemble learning. The stack ensemble achieved the highest overall performance, indicating strong effectiveness in detecting varied anomaly patterns. The voting ensemble demonstrated moderate results and offered a balance between detection rate and computation, whereas LSTM, which is efficient at capturing temporal dependencies, exhibited a relatively low performance in the processed dataset. SHAP, LIME, and Permutation Feature Importance techniques are employed for model explainability. These techniques offer insights into feature relevance and anomalies at global and local levels. The framework also measures the mean energy consumption for anomalous and normal data. The interpretability results identified that temperature, humidity, and voltage are the most influential features. HEXADWSN establishes a scalable and explainable foundation for anomaly detection in WSNs, striking a balance between accuracy, interpretability, and energy management insights.

In the ever-evolving healthcare sector, the widespread adoption of Internet of Things and wearable technologies facilitates remote patient monitoring. However, the existing client/server infrastructure poses significant security and privacy challenges, necessitating strict adherence to healthcare data regulations. To combat these issues, a decentralized approach is imperative, and blockchain technology emerges as a compelling solution for strengthening Internet of Things and medical systems security. This paper introduces HNMblock, a model that elevates the realms of epidemiological monitoring, medical system security, and wellness enhancement. By harnessing the transparency and immutability inherent in blockchain, HNMblock empowers real-time, tamper-proof tracking of epidemiological data, enabling swift responses to disease outbreaks. Furthermore, it fortifies the security of medical systems through advanced cryptographic techniques and smart contracts, with a paramount focus on safeguarding patient privacy. HNMblock also fosters personalized healthcare, encouraging patient involvement and data-informed decision-making. The integration of blockchain within the healthcare domain, as exemplified by HNMblock, holds the potential to revolutionize data management, epidemiological surveillance, and wellness, as meticulously explored in this research article.