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

Design and implementation of low power consumption wireless sensor node

This work proposes an implementation of wireless sensor node characterized by its very low power consumption. The node comprises three main components: Xbee module, low power PIC microcontroller and digital sensor. The node can be set to sense then transmit data via one of two transmission methods: periodic and by interruptions. To evaluate the power consumption; currents in the node is measured during the different transmission stages for both methods. As a result, a significant reduction in the power consumption is shown particularly in sleep mode compared to conventional transmission methods. The characteristic of low power consumption makes the proposed node practically ecologic. It can also be fed with the extrem low power supplied by an energy harvesting system

Low-Cost Energy-Autonomous Sensor Nodes Through RF Energy Harvesting and Printed Technology

The irruption of Internet of Things and 5G in our society comes along with several technological challenges to overcome. From an overall perspective, the low-cost and environmental friendliness of these technologies need to be ensured for their universal deployment in different areas, starting with the sensors and finishing with the power sources. To address these challenges, the production and maintenance of a great number of sensor nodes incur costs, which include manufacturing and integration in mass of elements and sub-blocks, changing or recharging of batteries, as well as management of natural resources and waste. In this article, we demonstrate how Radio Frequency Energy Harvesting (RFEH) and printed flexible technology (a growing technology for sensors) can solve these concerns through costeffective mass-production and utilization of energy harvesting for the development of energy-autonomous nodes, as part of a wireless sensor network. We present as illustration a sprayed flexible relative humidity sensor powered with RFEH under the store-and-use principle.This work was partially supported by the ECSEL Joint Undertaking through the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 737434. This Joint Undertaking receives support from the German Federal Ministry of Education and Research and the European Union’s Horizon 2020 research and innovation program and Slovakia, Netherlands, Spain, Italy. In addition, the Spanish Ministry of Education, Culture and Sport (MECD) and the European Union supported it through the pre-doctoral grant FPU16/01451 and the fellowship H2020-MSCA-IF-2017794885-SELFSENS

DTER: Schedule Optimal RF Energy Request and Harvest for Internet of Things

We propose a new energy harvesting strategy that uses a dedicated energy source (ES) to optimally replenish energy for radio frequency (RF) energy harvesting powered Internet of Things. Specifically, we develop a two-step dual tunnel energy requesting (DTER) strategy that minimizes the energy consumption on both the energy harvesting device and the ES. Besides the causality and capacity constraints that are investigated in the existing approaches, DTER also takes into account the overhead issue and the nonlinear charge characteristics of an energy storage component to make the proposed strategy practical. Both offline and online scenarios are considered in the second step of DTER. To solve the nonlinear optimization problem of the offline scenario, we convert the design of offline optimal energy requesting problem into a classic shortest path problem and thus a global optimal solution can be obtained through dynamic programming (DP) algorithms. The online suboptimal transmission strategy is developed as well. Simulation study verifies that the online strategy can achieve almost the same energy efficiency as the global optimal solution in the long term

Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information

This paper considers utility optimal power control for energy harvesting wireless devices with a finite capacity battery. The distribution information of the underlying wireless environment and harvestable energy is unknown and only outdated system state information is known at the device controller. This scenario shares similarity with Lyapunov opportunistic optimization and online learning but is different from both. By a novel combination of Zinkevich's online gradient learning technique and the drift-plus-penalty technique from Lyapunov opportunistic optimization, this paper proposes a learning-aided algorithm that achieves utility within $O(\epsilon)$ of the optimal, for any desired $\epsilon>0$, by using a battery with an $O(1/\epsilon)$ capacity. The proposed algorithm has low complexity and makes power investment decisions based on system history, without requiring knowledge of the system state or its probability distribution.Comment: This version extends v1 (our INFOCOM 2018 paper): (1) add a new section (Section V) to study the case where utility functions are non-i.i.d. arbitrarily varying (2) add more simulation experiments. The current version is published in IEEE/ACM Transactions on Networkin