Recently, blockchain technology has gained considerable attention from researchers and practitioners. This is mainly due to its unique features including decentralization, security, reliability, and data integrity. Despite this growing interest, little is known about the current state of knowledge and practice regarding the use of blockchain technology in education. This article is a systematic review of research investigating blockchain-based educational applications. It focuses on three main themes: (1) educational applications that have been developed with blockchain technology, (2) benefits that blockchain technology could bring to education, and (3) challenges of adopting blockchain technology in education. A detailed results analysis of each theme was conducted as well as an intensive discussion based on the findings. This review also offers insight into other educational areas that could benefit from blockchain technology.

Wireless sensor networks (WSNs) play a huge part in arising innovations like smart applications, the Internet of Things, and numerous self-designed, independent applications. Energy exhaustion and efficient energy consumption are principal issues in wireless sensor networks. Energy is a significant and valuable asset of sensor nodes; early energy depletion ultimately leads to a shorter network lifetime and the replacement of sensor nodes. This research proposes a novel Power-Efficient Cluster-based Routing (PECR) algorithm. It takes in clustering using K-Means, the arrangement of Cluster Heads (CHs) and a Main Cluster Head (MCH), the optimal route choice, communication in light of the energy utilization model, cluster heads, and main cluster head alternation based on residual energy and relative location. PECR decreases traffic overburden, restricts energy usage, and at last, expands the network lifetime. Sensor nodes sense the information and transmit traffic to a Base Station (BS) through a legitimate channel. The results confirm it decreases the traffic overhead and effectively utilizes the energy assets. The simulation results show that PECR’s performance is 44% more improved than LEACH, EC, EECRP, and EECA algorithms. It is suitable for networks that require a stretched life.

Cognitive Radio Network (CRN) is an emerging technology used to solve spectrum shortage problems in wireless communications. In CRN, unlicensed secondary users (SUs) and licensed primary users (PUs) use spectrum resources at the same time by avoiding any interference from SUs. However, the spectrum sensing process in CRN is often disturbed by a security issue known as the Primary User Emulation Attack (PUEA). PUEA is one of the main security issues that disrupt the whole activity of CRN. The attacker transmits false information to interrupt the spectrum sensing process of CRN, which leads to poor usage of the spectrum. The proposed study uses a proficient Time Difference of Arrival (TDOA) based localization method using the Differential Evolution (DE) algorithm to identify the PUEA in CRNs. The DE algorithm is used to solve the objective function of TDOA values. The proposed methodology constructs a CRN and identifies PUEA. The proposed method aims to sense and localize PUEA efficiently. Mean Square Error (MSE) is the performance evaluation parameter that is used to measure the accuracy of the proposed technique. The results are compared with the previously proposed Firefly optimization algorithm (FA). It is clear from the results that DE converges faster than FA.

In wireless sensor networks (WSNs), sensor nodes are randomly distributed to transmit sensed data packets to the base station periodically. These sensor nodes, because of constrained battery power and storage space, cannot utilize conventional security measures. The widely held challenging issues for the network layer of WSNs are the packet-dropping attacks, mainly sinkhole and wormhole attacks, which focus on the routing pattern of the protocol. This thesis presents an improved version of the second level of the guard to the system, intrusion detection systems (IDSs), to limit the hostile impact of these attacks in a Low Energy Adaptive Clustering Hierarchy (LEACH) environment. The proposed system named multipath intrusion detection system (MIDS) integrates an IDs with ad hoc on-demand Multipath Distance Vector (AOMDV) protocol. The IDS agent uses the number of packets transmitted and received to calculate intrusion ratio (IR), which helps to mitigate sinkhole attacks and from AOMDV protocol round trip time (RTT) is computed by taking the difference between route request and route reply time to mitigate wormhole attack. MATLAB simulation results show that this cooperative model is an effective technique due to the higher packet delivery ratio (PDR), throughput, and detection accuracy. The proposed MIDS algorithm is proven to be more efficient when compared with an existing LEACH-based IDS system and MS-LEACH in terms of overall energy consumption, lifetime, and throughput of the network.

Ensuring data security in IoT systems requires effective anomaly detection, particularly in multivariate time series data generated by sensor networks. This study introduces a transformer-based method to detect anomalies by capturing complex temporal patterns and long-range dependencies. The model adapts to diverse anomaly types across datasets, leveraging adversarial perturbations to enhance robustness and accuracy. Integration of the Streaming Peaks Over Threshold (SPOT) mechanism further improves thresholding. Experiments on MSL, SMD, NAB, and SWaT datasets validate the model’s effectiveness, demonstrating its competitive performance in strengthening IoT systems and ensuring data security in dynamic environments.