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A system-on-chip passive UHF RFID tag with embedded temperature sensor is developed in a standard 0.18µm CMOS process for the EPC Gen-2 protocol from 860-960MHz [1]. Flip-chip technology is used to bond the developed tag IC to an antenna to realize a complete tag inlay, which is successfully demonstrated and evaluated in real-time wireless communications with commercial RFID readers. Figure 17.1.1 shows the block diagram of the tag IC. Multiple supply voltages, generated by a power management unit (PMU) using a separate-storage-capacitor technique to save area, are employed to optimize the performance of individual building blocks, while minimizing the total power consumption. A dualpath clock generator is used to generate on-chip clocks for signal processing to support both applications with either very accurate link frequency or very low power consumption. A low-power temperature sensor is also embedded with a gain-compensation technique that makes use of current correlation from the same bandgap reference (BGR) for the clock generator to reduce the temperature sensing error due to process variations.Figure 17.1.2 shows the PMU. The rectifier (RECT1) supplies the 670nA BGR while the core RF-DC conversion is performed by a triple-output rectifier (RECT2). Further supply regulation is done using low-dropout regulators (LDRs). For the duration of PW (=Tari/2) [1], as the RF input is significantly attenuated due to the data modulation, the rectifier becomes inactive, and a storage capacitor C S is employed to supply both the load current I L and the reverse leakage current I leak that flows back to the rectifier, which results in a ripple voltage of VR CS =(I L +I leak )×PW/C S . In existing work [2], all the blocks share one single capacitor C S , which needs to be large enough to meet the ripple voltage requirement of the most noise-sensitive block. To provide a highly stable current for the dual-path clock generator, the BGR is required to have a small supply ripple of 0.1V, and, as a result, the single C S would need to be at least 1.75nF to supply the nominal total I L of 14µA when Tari=25µs. In our work, three separate capacitors, C Sx (x=1,2,3), are employed for different blocks with different optimal ripple voltages (0.4V, 0.25V, 0.1V, respectively), which helps to significantly reduce the total capacitance to 805pF, even with >10% margins. The switches S x (x=1,2,3) are controlled by the demodulator's output to cut off I leak during PW, which helps further reduce both the sizes of C Sx and the required input power for replenishing. M ST1 and M ST2 are used for start-up. High voltages of 3.5V and 7.8V, used for programming the OTP memory, are generated by three charge pumps (QPs). During the WRITE operation, a VCO is activated to regulate the QP_VPP's output at 7.8V with an output current up to 20µA. With sufficient input power, the power detector (PD) sends a Power-Good (PG) signal to turn on S 4 to power up LDR 3 for the injection-locked frequency divider (ILFD) in the dual-path clock generator. Figure 17...

Most existing simulators for WSNs (wireless sensor networks) model battery-powered sensors and provide MAC and routing protocols designed for battery-powered WSNs. Recently, however, increasingly extensive studies of energy harvesting sensor systems require the development of appropriate simulators, but there are few related studies on such simulators. Unlike existing simulators, simulators for energy harvesting WSNs require a new energy model that is integrated with the energy harvesting, rechargeable battery, and energy consuming models. Additionally, the new model must enable applications of the well-known MAC and routing protocols designed for energy harvesting WSNs and have a convenient user-friendly interface. In this work, we design and implement a user-friendly simulator for solar energy harvesting WSNs.

An energy-harvesting wireless sensor network mitigates the energy shortage problems of existing battery-based wireless sensors; however, its hotspot area sensor nodes still experience 3 blackouts, thereby reducing network connectivity. Techniques that transfer energy directly to sensor nodes using wireless power transfer (WPT) have been studied in recent years to address this issue. In this paper, we propose a technique that uses a drone (quadcopter), which is a type of unmanned aerial vehicle (UAV), as a mobile sink. The drone selects and manages anchor nodes that aggregate data temporarily, collects data by visiting the anchor nodes to mitigate the hotspot issue, and then prevents blackouts by supplying energy to low-energy nodes, thereby improving network connectivity. The anchor nodes are carefully selected after considering the energy capacity of the drone, the size of the network, the amount of collected data, and the energy consumed by the nodes to increase the network’s energy efficiency. Furthermore, energy is transferred from the drone to the anchor nodes to support their energy consumption. In our study, this method reduced the blackouts of sensor nodes, including anchor nodes, in hotspot regions, and increased network connectivity, thereby improving the amount of data gathered by the mobile sink.