Sodium nitrate (NaNO 3) sensor based on graphene coated micro�ber

We demonstrate the coating of graphene onto the silica optical microfiber sensor for improving sodium nitrate detection at room temperature. The graphene obtained from graphene- polylactic acid filament was coated onto the microfiber based on drop casting technique. In the proposed sensor, the graphene acts as cladding to interact with analyte as well as functions to trap either sodium cation or nitrate anion and increases the effective refractive index of the cladding. The proposed sensor shows a good sensitivity of 1.29 dBm/% and resolution of 0.049%. The sensitivity, repeatababilty and reversible response of the sensor were improved by the coating of graphene layer. The presence of graphene on the surface microfiber works as passive cladding and influence the light propagation through the microfiber

Tapered plastic optical fiber coated with ZnO nanostructures for the measurement of uric acid concentrations and changes in relative humidity

Simple sensors are proposed and demonstrated using a tapered POF coated with ZnO nanostructures for measurement of different concentrations of uric acid in de-ionized water and changes in relative humidity (RH). The sensor operates based on intensity modulation technique. The tapered POF were fabricated by etching method using acetone, sand paper and de-ionized water to achieve a waist diameter of 0.45 mm and tapering length of 10 mm. The tapered fiber were then coated with ZnO nanostructures using sol–gel immersion method on ZnO seeded and non-seeded fiber. As the concentration of the uric acid was varied from 0 ppm to 500 ppm, the output voltage of the sensor using tapered POF with seeded ZnO nanostructures increased linearly with a higher sensitivity of 0.0025 mV/ppm compared to 0.0009 mV/ppm for unseeded tapered POF coated with ZnO. Both samples showed almost similar linearity of the response cuves of about 98.2%. The tapered POF with ZnO nanostructures interact with uric acid due to strong electrostatic interaction resulting in the increase in response with the increasing concentration. In addition, the seeded ZnO nanostructure could significantly enhance the transmission of the sensor that is immersed in solutions of higher concentration. On the other hand, for both samples, the change in the intensity of the transmitted light of the tapered POF coated with ZnO nanostructures decreases linearly with relative humidity. The tapered POF with grown (seeded) ZnO provides better sensitivity at 0.0258 mV/% with a slope linearity of 95.48%. The ZnO nanostructures that are exposed to an environment of humidity causes rapid surface adsorption of water molecules and changes in optical properties. The tapered POF coated with ZnO nanostructures using the seeding technique causes an increase in both effective RI of surrounding medium and absorption coefficient of the ZnO nanostructures surfaces and leads to larger leakage of light. Results show that tapered POF with seeded ZnO nanostructures enable to increase the sensitivity of fiber for uric acid detection as well as relative humidity. The proposed sensors provide numerous advantages such as simplicity of design, low cost of production, higher mechanical strength and easier to handle compared to silica fiber optic

Trust Management in Vehicular Ad-Hoc Networks and Internet-of-Vehicles: Current Trends and Future Research Directions

Vehicular ad-hoc network (VANET) and internet-of-vehicles (IoV) are complex networks which provide a unique platform for vehicles to communicate and exchange critical information (such as collision avoidance warnings) with each other in an intelligent manner. Thus, the information disseminated in the network should be authentic and originated from legitimate vehicles. Creating a trusted environment in the network can enable the vehicles to identify and revoke malicious ones. Trust is an important concept in VANET and IoV to achieve security in the network, where every vehicle equipped with an appropriate trust model can evaluate the trustworthiness of the received information and its sender. This chapter discusses trust in both VANET and IoV and identifies various trust models developed for VANET and IoV. The contribution of this chapter is threefold. First, the authors present a detailed taxonomy of trust models in VANET and IoV. Second, they provide current trends in the domain of trust management specifically for VANET and IoV, and finally, they provide various open research directions