DEICA: A differential evolution-based improved clustering algorithm for IoT-based heterogeneous wireless sensor networks

With the evolution of technology, many modern applications like habitat monitoring, environmental monitoring, disaster prediction and management, and telehealth care have been proposed on wireless sensor networks (WSNs) with Internet of Things (IoT) integration. However, the performance of these networks is restricted because of the various constraints imposed due to the participating sensor nodes, such as nonreplaceable limited power units, constrained computation, and limited storage. Power limitation is the most severe among these restrictions. Hence, the researchers have sought schemes enabling energy-efficient network operations as the most crucial issue. A metaheuristic clustering scheme is proposed here to address this problem, which employs the differential evolution (DE) technique as a tool. The proposed scheme achieves improved network performance via the formulation of load-balanced clusters, resulting in a more scalable and adaptable network. The proposed scheme considers multiple parameters such as nodes' energy level, degree, proximity, and population for suitable network partitioning. Through various simulation results and experimentation, it establishes its efficacy over state-of-the-art schemes in respect of load-balanced cluster formation, improved network lifetime, network resource utilization, and network throughput. The proposed scheme ensures up to 57.69%, 33.16%, and 57.74% gains in network lifetime, energy utilization, and data packet delivery under varying network configurations. Besides providing the quantitative analysis, a detailed statistical analysis has also been performed that describes the acceptability of the proposed scheme under different network configurations.     

Effects of Silicon and Organic Manure on Growth,Fruit Yield,and Quality of Grape Tomato Under Water-Deficit Stress

Silicon - Mitigation of deleterious effects of drought stress on the growth and productivity of agronomic and horticultural crops warrants urgent and sustainable actions. Soil application of...     

Boosting Sensitivity and Reliability in Field-Effect Transistor-Based Biosensors with Nanoporous MoS2 Encapsulated by Non-Planar Al2O3 (Adv. Funct. Mater. 42/2023)

Field-effect transistors-based biosensors (bio-FETs) have been considered an important technology for label-free and ultrasensitive point-of-care diagnostics. However, practical applications using bio-FETs are limited due to the trade-off between sensing reliability and sensitivity. This study suggests a reliable and sensitive bio-FETs based on nanoporous molybdenum disulfide (MoS₂) channels encapsulated by a non-planar high-k aluminum oxide (Al₂O₃) dielectric layer. Nanoporous MoS₂ thin film is fabricated with an abundant edge area and periodically ordered nanopores via block copolymer lithography. The ultra-thin Al₂O₃ dielectric layer deposited along the nanoporous structure of the MoS₂ realizes effective electrostatic control of charged biomolecules over the MoS₂ channel. In addition, it plays important roles in not only enhancing the electrical performance of the nanoporous MoS₂ bio-FETs, that is, mobility, hysteresis, and subthreshold swing, but also achieving effective biomolecular immobilization on the device surface. The nanoporous MoS₂ channel structure surrounded by non-planar Al₂O₃ detects a prostate cancer biomarker with an ultra-low limit of detection of 1 fg mL⁻¹. Moreover, the excellent selectivity, high sensitivity, and clinical reliability of the nanoporous MoS₂ bio-FETs are also confirmed. The proposed device platform provides new insights and technical advances in the field of FETs based sensors for future point-of-care devices.