Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to predict faults in the sensor and isolate their cause.A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults.This technique identifies the faulty sensor and determines the correct working of the sensor.Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described.There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique.So,some solutions are provided to overcome the limitations of the fall curve technique.In this paper,a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years.Its novelty is to predict a fault before its occurrence by looking at the fall curve.The sensing of current flow in devices is important to prevent a major loss.So,the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices.The analysis result proved that if any of the current sensors gets faulty,then the fall curve will differ and the value will immediately drop to zero.Various evaluation metrics for fault prediction are also described in this paper.At last,this paper also addresses some possible open research issues which are important to deal with false IoT sensor data.

Boosting the energy harvesting performance of cantilever structured magneto-mechano-electric generator by controlling magnetic flux intensity on magnet proof mass

A cantilever-structured magneto-mechano-electric(MME)generator comprising a magnetoelectric composite with a magnet proof mass is a potential candidate for powering autonomous wireless sensor networks.Recently,the concept of a magnetic flux concentrator(MFC)to enhance the output performance of the MME generator by focusing the ultralow-intensity magnetic field into the MME generator was introduced.However,the MFC-concentrated magnetic flux mostly focused on the end tip of the MME cantilever rather than at the magnet proof mass located on the cantilever beam.Considering that the torque generated by the magnet proof mass contributes more than half of the output power of an MME generator,optimizing the volume and position of the proof-mass with MFC is crucial for better performance.Furthermore,a smaller proof-mass is desirable for the long-term reliability of cantilevertype harvesters.Hence,we investigated the effect of the position and weight(volume)of the magnet proof mass with respect to the MFC on the output performance of the MME generator through finite element analysis and experiments.The MME generator with the lighter magnet proof mass at the optimized position generated a maximum power of 5.35 mW under a 10 Oe magnetic field,which was 210%of that of the MME configuration used in our previous study.Furthermore,the MME generator showed broadband characteristics around the practical frequency of 60 Hz,which could provide more freedom to design the harvester with high performance.