Toward energy‐efficient data management design for sustainable cities and societies

Demanding continues communication and diverse interaction between various devices is witnessed over Internet as we probe into Internet of Things (IoT). The sustainable cities are designed based on the concept of IoT. IoT devices consume enormous energy continuously, which needs to be managed efficiently. The efficiency of energy is based on the optimization of the energy infrastructures. In addition, heterogeneous devices in sustainable cities produce enormous data. This huge data needs to be processed and analyzed along with smart energy management to achieve smart decisions. The planning is starting to be realistic through the quantity of data provided in sustainable cities. In this porch, the objective is to illustrate the data generated in sustainable cities in real time. To deal with the aforementioned requirements, this work demonstrates a novel architecture that spotlights the ecology of sustainable cities comprised of sensors, cameras, and other objects along with energy management (eg, Internet of Energy). The proposed system is a layered architecture composed of data collection and energy management, data computation, and decision‐making layers. Energy‐efficient clustering algorithm and optimized sleeping scheduling methods are utilized in the first layer to collect data from IoT devices with regard to energy management. The second layer is responsible for computation of data resourcefully along with energy efficiency. The third layer is used to provide valuable insights and make intelligent decisions. The architecture is verified with reliable datasets related to smart parking using IoT devices to test and reveal the effectiveness. The assessments disclose that the proposed scheme presents precious insights in the context of energy efficiency in sustainable cities and societies. • Generic architecture is proposed that spotlights the ecology of sustainable cities. • Energy‐efficient algorithm is utilized along with Big Bata processing using Apache Hadoop • Several datasets are tested using multi nodes' cluster to verify the performance and efficiency