Intelligent energy optimization in park-wide farming considering user’s preferences

With the development of park-level agricultural, agricultural production and household electricity fusion, it is of great significance to promote users to actively respond to power consumption plan based on their own habits. In this paper, a multi-objective household intelligent power consumption optimization model is proposed from two aspects of economy and comfort. Firstly, the operating constraints of interruptible loads and non-interruptible loads were established based on the working characteristics of various household appliances. Then, the expenditure model was constructed to take into account the electricity sales situation of surplus electricity generated by photovoltaic, and a three-layer index system quantifying the influence of user preference on comfort level was constructed. The preference coefficient was determined by analytic hierarchy process, which was used to construct the users’ comfort level model. Finally, the multi-objective particle swarm optimization algorithm was applied to obtain optimization results. Considering the seasonal difference, the simulation showed that this model minimized the expenditure and increased the comfort level during summer and winter by 26.0% and 27.5% respectively.

Rate Fairness and Power Consumption Optimization for NOMA-Assisted Downlink Networks

This paper considers a non-orthogonal multiple access (NOMA) downlink network, where a hybrid of NOMA and beamforming designs is developed to enhance the channel capacity. We aim to improve the system performance in terms of rate fairness and power consumption. Hence, a multi-objective problem with a joint optimization of user equipment pairing, power control, and quality-of-service requirements is addressed. To efficiently solve the problem, we propose two low-complexity algorithms based on the inner-approximation method, with the first algorithm using the relaxation method and the second one using graph theory. Numerical results are provided to demonstrate the effectiveness of the two proposed algorithms in comparison with the exhaustive search and existing methods.

QoS Enabled IoT Based Low Cost Air Quality Monitoring System with Power Consumption Optimization

Air pollution has emerged as a major concern of the current century. In recent times, fellow researchers have conducted numerous researches in the area of air quality monitoring. Still, air quality monitoring remains an open research area due to various challenges such as sophisticated topology design, privacy and security, power backup, large memory requirements and deployment of such systems at resource-constrained sites. The proposed research work is an attempt to address the issues of communication topology design, assessment of the Quality of Service (QoS) levels against accuracy, sensing throughput and power consumption optimization. In the undertaken work, the proposed IoT based Air Quality Monitoring system has been deployed at indoor and outdoor sites to measure air quality parameters such as PM10, PM2.5, carbon monoxide, temperature and humidity. The proposed system is also tested at variety of quality of service levels at the indoor and outdoor sites. The conducted experiments have also recorded accuracy in terms of reliable delivery of the messages under employed protocol.