Efficient energy management for the internet of things in smart cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities

Malaysia Royal Customs and the e-Eksais system: Review and prospect

The e-Eksais system is a pre-Internet solution for faster processes involving the excise duties and sales tax payment between automobile manufacturers and the Malaysia Royal Custom (MRC). Being the second most important revenue raiser agency to the government, MRC had developed the e-Eksais system to provide efficient process in collecting billions of excise duties and sales taxes from local automobile manufacturers.The system utilises the electronic data interchange (EDI) to expedite the vehicle release process of and payment of excise duties from manually to electronics forms.The system claims to benefit the manufacturers’ clients and MRC in staff and cost efficiency.The e-Eksais service is getting matured where the take-up and usage of e-Eksais is almost extended to most local automobile manufacturers and dealers.However, the state of the art of the MRC’s information and technologies clients systems needs to incorporate a Web-based system to allow for wider usage and a low cost means of interacting with MRC

Distributed Relay Selection for Heterogeneous UAV Communication Networks Using A Many-to-Many Matching Game Without Substitutability

This paper proposes a distributed multiple relay selection scheme to maximize the satisfaction experiences of unmanned aerial vehicles (UAV) communication networks. The multi-radio and multi-channel (MRMC) UAV communication system is considered in this paper. One source UAV can select one or more relay radios, and each relay radio can be shared by multiple source UAVs equally. Without the center controller, source UAVs with heterogeneous requirements compete for channels dominated by relay radios. In order to optimize the global satisfaction performance, we model the UAV communication network as a many-to-many matching market without substitutability. We design a potential matching approach to address the optimization problem, in which the optimizing of local matching process will lead to the improvement of global matching results. Simulation results show that the proposed distributed matching approach yields good matching performance of satisfaction, which is close to the global optimum result. Moreover, the many-to-many potential matching approach outperforms existing schemes sufficiently in terms of global satisfaction within a reasonable convergence time.Comment: 6 pages, 4 figures, conferenc

An Expert System Based on Least Mean Square and Neural Network for Classification of Power System Disturbances

This paper proposes a new solution method for power quality (PQ) classification using least mean square (LMS) and neural network (NN). The proposed hybrid LMS-NN method comprises of LMS based effective feature extractor and PQ classifier based on a multi layer perceptron neural network (MLP-NN). First, the LMS method is employed to estimate the efficient features such as amplitude, slope, and harmonic indication from the measured voltage signals where the developed structure is merely simple. Further, the PQ classification is executed with the aid of MLP-NN. The different voltage signals analyzed for this research work are pure sine, sag, swell, outage, harmonics, sag with harmonics, and swell with harmonics. The performance and efficiency of the presented hybrid LMS-NN classifier is assessed by testing total 1400 voltage samples which are simulated based on PQ disturbance model. The rate of average correct classification is about 96.71 for the different PQ disturbance signals under noise conditions