Software Toolkits: Practical Aspects of the Internet of Things—A Survey <s> IoT Energy Harvesting <s> Wireless sensor nodes (WSNs) are employed today in many different application areas, ranging from health and lifestyle to automotive, smart building, predictive maintenance (e.g., of machines and infrastructure), and active RFID tags. Currently these devices have limited lifetimes, however, since they require significant operating power. The typical power requirements of some current portable devices, including a body sensor network, are shown in Figure 1. <s> BIB001 </s> Software Toolkits: Practical Aspects of the Internet of Things—A Survey <s> IoT Energy Harvesting <s> Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions. <s> BIB002 </s> Software Toolkits: Practical Aspects of the Internet of Things—A Survey <s> IoT Energy Harvesting <s> Internet of Things applications using RFID sensors are a challenging task due to the limited capacity of batteries. Thus, energy efficient control has become more critical design with RFID sensor integrating complex tag identification processing techniques. Previous works on power efficient control in multiple RFID tags identification systems often design tag anti-collision protocols in identification process, and seldom consider the features of tags are able to detect energy within its radio rang among each other. This paper is dedicated to developing a share energy provisioning (SEP) strategy for energy-limited multiple RFID tag identification system. First, SEP can dynamically adapt the variable energy resources due to the cognitive radio technique. Second, SEP combines the energy control with tags groups in wait time T, through classifying the tag into different groups according to its distances. Third, it introduces the optimization theoretical analysis energy for multiple RFID tags identification system, so as to minimize the time and energy of it takes to send tags data to reader. Finally, it shares the energy resource as different energy harvest under energy-limited RFID systems. Experimental results demonstrate the energy efficiency of the proposed approach. <s> BIB003 </s> Software Toolkits: Practical Aspects of the Internet of Things—A Survey <s> IoT Energy Harvesting <s> An energy harvester is presented to scavenge ambient vibration energy using Terfenol-D/PMN-PT/Terfenol-D laminate magnetoelectric transducers. The harvester uses eight magnets to make up a magnetic circuit to produce a concentrated flux gradient, which makes the harvester generate high power. The nonlinear vibration characteristics and the electrical-output performances of the harvester at resonance are analyzed. A prototype has been fabricated and tested. The experimental results are in agreement with the analytical results. The prototype can produce a sufficient power to supply low consumption wireless sensors applied in the Internet of Things. <s> BIB004 </s> Software Toolkits: Practical Aspects of the Internet of Things—A Survey <s> IoT Energy Harvesting <s> This paper is concerned with the problem of periodic task scheduling in sensor nodes powered by energy harvesters. We address this issue by proposing two energy-aware scheduling algorithms, respectively called Green-RTO and Green-BWP. They aim to guarantee an acceptable Quality of Service (QoS) measured in terms of deadline success ratio. <s> BIB005 </s> Software Toolkits: Practical Aspects of the Internet of Things—A Survey <s> IoT Energy Harvesting <s> With the popularity of photovoltaic based green energy in smart grid systems, accurate information gathering becomes a critical issue in predicting microgrid power input. In this paper, we propose a new quality-optimized multimedia information gathering scheme, in the energy harvesting wireless sensor networks based Internet of things system, to provide the best-effort sky camera information accuracy for further predicting available photovoltaic power. In the proposed approach, the power control and relay node selection strategies are jointly optimized to achieve maximum sky camera image quality subject to the harvestable energy neutrality constraint. Simulation results show that the proposed scheme can improve multimedia data transmission quality by exploring adaptive transmission power and relay selection strategy. <s> BIB006 </s> Software Toolkits: Practical Aspects of the Internet of Things—A Survey <s> IoT Energy Harvesting <s> Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algorithms. Hence, we focus on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. In this paper, we describe methods for estimating harvested energy from acceleration traces. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, cycling), we analyze a motion dataset with over 40 participants. Based on acceleration measurements that we collected for over 200 hours, we study energy generation processes associated with day-long human routines. We also briefly summarize our experiments with moving objects. We develop energy allocation algorithms that take into account practical IoT node design considerations, and evaluate the algorithms using the collected measurements. Our observations provide insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms. <s> BIB007
With the development of the IoT, an increasing number of resource-constrained wireless sensors are being applied in industrial, commercial, medical, consumer, and military fields. Minimizing power consumption is the key to extending the application life of wireless sensors and reducing costs. Energy harvesting is a technique for utilizing energy that is harvested from the environment. Energy collected from the environment is stored after conversion and then assigned to various parts of the wireless sensor, as shown in Figure 4 . The efficient use of energy in WSNs involves energy supply and energy consumption. These issues are core to successfully designing and deploying WSNs. Most research has focused on harvesting and using ambient energy. In BIB007 , Gorlatova et al. proposed a kinetic energy collection method involving experiments with briefly moving objects that provided insights into the suitability of different scenarios for harvesting energy. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, and cycling), Gorlatova et al. analyzed a motion data set with more than 40 participants. Their observations provided insights into the design of networking algorithms and motion energy harvesters, which could then be embedded into mobile devices. If this technology is embedded into wireless sensors, moving wireless sensor nodes would achieve selfsufficiency in terms of energy. In BIB004 , Zhang et al. introduced an effective method for converting ambient energy to electrical energy. Their research enables wireless sensor nodes to power themselves by converting ambient energy into electrical energy. Among the various energy types, ambient vibration energy is particularly attractive owing to its abundance. In BIB002 , Sudevalayam and Kulkarni compared the physical needs and energy requirements for processing and communication tasks conducted by sensor nodes. After demonstrating hard limits, Sudevalayam and Kulkarni compared relevant energy harvesting methods and determined the most likely candidates applicable to the IoT. Table 3 shows various ambient energy harvesting methods, influencing productions, factors, and applications BIB001 BIB003 BIB005 BIB006  . Methods for using natural energy are straightforward and cost is typically low, but there are many unsolved issues, including low energy efficiency and dependence on the environment. Other unnatural energy sources are easily controlled, but the process of generating energy is typically very complex and expensive. The limited battery power carried by wireless sensors is not suitable in the long run. Therefore, we must identify long-standing, low-power, easyto-use energy harvesting methods for wireless sensors.