Residential and commercial buildings remain highly dependent on manually operated or fixed window curtains, leading to inefficient daylight utilization, increased artificial lighting consumption, and compromised indoor comfort. In practical smart home deployments, most automated curtain solutions rely on limited sensing or user-driven control, which restricts their adaptability in changing indoor conditions. To address these limitations, the proposed system adaptively regulates curtain position and indoor lighting using fuzzy logic and ZigBee based wireless communication. The system continuously monitors indoor conditions through multiple environmental sensors measuring ambient light, occupancy, temperature, and air quality. A Mamdani-type fuzzy inference engine implemented on embedded microcontrollers processes the sensor data and generates smooth control actions for curtain and lighting adjustment. Experimental evaluation using a hardware prototype demonstrates fast system response and stable operation under varying daylight conditions. The results show that the proposed approach can reduce artificial lighting energy consumption by up to 40% during daytime operation while maintaining occupant comfort and indoor environmental quality. The developed smart win-curtain system offers a practical and scalable solution for intelligent and sustainable smart home automation.

Summary The internet of things (IoT) has recently become extremely important in our lives. With the advancement in IoT, wireless sensor networks (WSNs) have gained much importance. In WSNs, the routing of clusters has gained more interest. However, there is still a problem with the hotspot. One way to address this issue is uneven clustering. In these, nodes closer to the base station have the smallest cluster size. It decreases the relay pressure of the nodes which are close to the base station. In this paper, the unequal clustering routing based on fuzzy logic and bat algorithm is proposed, that is, FUCBR. Relative intra‐cluster costs and relative inter‐cluster costs are used in a very innovative manner. Fuzzy logic is used to create unequal clusters while the bat algorithm is used for multi‐hop routing. Unequal clustering minimizes the energy utilization for the member of the clusters. A novel probability technique is implemented in the cluster‐forming process to allow cluster members to determine which cluster they wish to join. The bat algorithm's objective function is carefully chosen to optimize the cluster heads' energy usage. After simulation, it was discovered that the proposed algorithm utilizes less energy and extends the network lifespan. The simulation results show that FUCBR's performance is 30% more improved than fuzzy‐based unequal clustering algorithm, genetic algorithm based unequal clustering and routing protocol, and grey wolf optimizer‐C algorithms. It is suitable for networks that require a long life.

Summary Vehicular delay tolerant networks (VDTNs) suffer due to insufficient connection opportunities in sparse infrastructure. The sparsity of the network results in poor delivery ratio (DR). Deployment of additional small nodes named throwbox increases the contact opportunities, resulting in improved performance in terms of DR. However, randomly deployed throwboxes do not provide optimum results. Therefore, this article proposes the throwbox deployment as an optimization problem. Customized binary particle swarm optimization‐based optimization method is employed to solve the problem. With the objective of maximizing the DR, the method optimize the placement of‐throwboxes in the network. The performance of the proposed methods is tested and compared by simulating a VDTN model in opportunistic network environment simulator. The optimization framework was implemented in Matlab. Exhaustive experiments were carried out by varying the number of throwboxes (). A statistical comparison of the results with the existing throwbox deployment methods validates the effectiveness of the proposed optimization‐based approach. The article also presents an analysis for choice of value of for a desired network performance.

Security is the utmost importance for the safe operation of the Internet of Things (IoT) devices. Behind IoT, a sensor network operates. Hence, security and lifetime extensions of the sensor networks are the primary concern. Node clustering is a technique to lengthen the lifetime of sensor networks. In a clustering technique, Cluster Heads (CH) are chosen. To provide the security to the sensor network, reliable CHs are elected. In this article, a protocol is proposed which elects reliable CHs based on node features such as residual energy, distance to the sink and node behavior such as the number of packets transmitted and received successfully, data consistency factor. The simulation results obtained prove that the proposed protocol performs better.

Community question-answering (CQA) sites are popular as information-seeking platforms where users communicate to their peers. Security-related posts are gaining popularity with the rapid development of information technology in these sites and. CQA sites contains wide range of posts from classic cryptography to recently popular mobile security. Investigating such posts can be useful for researchers, teachers and developers. In this article, spectral clustering and frequent term-based summarization techniques are proposed for security related posts. The proposed method is developed in three stages. In the first stage, security related folksonomies are created and security post profile matrix is built with the help of tag frequency-inverse security post frequency. In the second stage, security related posts are grouped with help of spectral clustering algorithms. Finally, in the third stage, frequent terms are extracted from each cluster for security related post summarization with the help of frequent words and semantic similarity.