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Rapid developments in modern wireless communication permit the trade of spectrum scarcity.  Higher data rate and wider bandwidth emerge the development in growing demand of wireless communication system. The innovative solution for the spectrum scarcity is cognitive radio (CR). Cognitive radio is the significant technology used to utilize the spectrum effectively. The important aspect of CR is sensing the spectrum band and detects the presence or absence of the primary user in the licensed band. Moreover, another serious issue in next generation (5G) wireless communication is to decide the less complex 5G waveform candidate for achieving higher data rate, low latency and better spectral efficiency. Universal filtered multi-carrier (UFMC) is one of the noticeable waveform candidates for 5G and its applications. In this article, we investigate the spectrum sensing methods in multi-carrier transmission for cognitive radio network applications. Especially, we integrate the sensing algorithm into UFMC transceiver to analyze the spectral efficiency, higher data rates and system complexity. Through the simulation results, we prove that the UFMC based cognitive radio applications outperform the existing Orthogonal Frequency Division Multiplexing (OFDM) based CR applications.

The food waste management has become a very important part in the modern world. In the olden days, the population was very low, so that the low waste produced was easily disposed by dumping it under the soil. However, nowadays, due to high population, amount of waste is produced high which could not be dumped due to the land pollution. To overcome this problem, the food waste must be managed or utilized properly to give zero waste. Therefore, this work focused on the production of biogas from food waste through the anaerobic digestion process using iron oxide nanoparticles. The iron oxide nanocatalyst from red mud reduced the process time for the anaerobic digestion was by 5.38% compared to the noncatalytic process. The produced biogas analysis done through GCMS analysis and calculated by comparing the both anaerobic degradation setup with and without nanocatalyst. Nanocatalyzed degradation contains 50% high amount of methane and 23.5% of total amount of biogas when compared to nonnanocatalyzed degradation.

In olden days street lights were not operated in an automatic way. Automation of street lights has become apparent these days. But we can notice that we do not require high intensity light during night hours, i.e. when there is no traffic, no people in the streets or on roads and even in the early mornings. As per requirement, the light intensity can be reduced using dimmer circuit. Light dependent resistor (LDR) sensors are used to sense the darkness and Passive Infrared (PIR) sensors are to detect the objects. Raspberry Pi (Master node) and Arduino (Slave node) will communicate each other and they help the proposed system to work more effectively. Current sensor and Voltage sensor are used to measure the current and voltage respectively. By reducing the intensity at these times, energy can be saved to some extent and the data is uploaded to the cloud. We can monitor and control the street lights in a smart way as per our requirement. Fault detection, minimization of cost, reducing the loss of electricity and man power are also possible. Hence, this proposed smart street lighting system will be helpful to the society in cost effective way.