Self‐powered wireless sensor network using event‐triggered energy harvesters for monitoring and identifying intrusion activities

Abstract

Monitoring concerned activities and identifying activity types usually require sensors and corresponding data processing circuits, which are often restricted by the limited power supply in wireless sensor networks (WSNs). This study presents a self-powered smart WSN for passively monitoring and distinguishing different vibration events. In the proposed WSN, the sensing function is performed by vibration-threshold-triggered energy harvesters (VTT-EHs). The output power of the VTT-EH dramatically increases when an input vibration exceeds the pre-set vibration-threshold of the harvester, indicating the occurrence of specific concerned events. On the basis of this principle, two VTT-EHs with different thresholds were designed to detect and distinguish vibration events with different vibration characteristics. Meanwhile, electromagnetic EHs were applied to generate sufficient power for wirelessly transmitting the alarm signals within several seconds. The prototype of the proposed WSN was developed and evaluated. The sensor node was able to identify two types of intrusive activities: weak shake and strong knock. The alarming signals were first sent to a router node and then transmitted to a mobile phone through the global system of mobile communication network. The mobile phone received the alarming text messages with correct event type within 2 s after the excitation occurred.

A high‐efficiency power management unit with wide dynamic range for RF energy harvesting system

“…As the current flowing through M1, M2, and M3 is assumed to be equal. By setting the size of M2 and M3 to be equal, the expression for V REFB can be obtained in (8).…”

“…In (8), the term related to process corner is V TH2 -(m 2 /m 1 ) V TH1 . At different process corners, the length dependence of V TH will decrease with the increase of the length or width of transistor, that is, the process sensitivity of the threshold voltage of the small-sized MOS transistor is greater [23,24].…”

“…With the development of low-power consumption Internet of Things (IoT) smart nodes, self-sustaining power supply for IoT nodes by using energy harvesting technology is a future development trend. Benefiting from its convenient control and contactless power transmission of a far-field radiation pattern, RF energy sources feature huge potential to extend the life of batteries, and the research on RF energy harvesting technology has gradually become a research hotspot in recent years [1][2][3][4][5][6][7][8][9].…”

Dynamic event‐triggered model predictive control of Vienna rectifiers in DC charging system

“…However, the same control actions would still be continually generated in the adjacent control cycles. Eventtriggered control is an optimization strategy that can effectively reduce redundant operations [21,22]. Static event-triggered model predictive control (SETMPC) is a control algorithm combining event-triggered control with MPC only when the DC voltage difference is detected in real-time to determine whether the system meets the event-triggered condition [23].…”

Data-Driven Optimization of Piezoelectric Energy Harvesters via Pattern Search Algorithm

“…With the urgent demand of sustainable power supplies for low-power electronic applications such as wireless sensor network systems [ 1 , 2 , 3 , 4 ] in Internet of Things, implantable medical devices [ 5 , 6 ] and other devices in some extreme environments [ 7 ], energy harvesting from the ambient environments has attracted broad attention and provided potential solutions to the periodical replacement of batteries during the last few decades. Vibration energy is ubiquitous and robust mechanical energy that exists widely, including bridges [ 8 ], roads [ 9 ], human body [ 10 , 11 , 12 ], cars [ 7 ], etc.…”