Machine Learning Approaches for Convergence of IoT and Blockchain. Группа авторов

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been in use for quite some time now, but its functioning has largely been impacted by these technologies [20]. With the help of IoT, smart cameras have come up. What makes these unique and useful is that along with cameras that deliver live footage and some also save recent recording, we have installed other sensors to it [21]. A microphone accompanying the camera allows the user to communicate via means of voice to the person or event that they witness on the camera. Cameras come with vision both during day and night time. Motion sensitive sensors are attached to the camera to the camera, now what happens if we can program the device for if it is in the night mode or the user does not expect any motion at any particular time; these motions sensors may be activated. In case any movement is present in the range of the camera, it will capture multiple images for those instances along with the video footage so that the cause of the happening can be recorded. This is a great feature from the security point of view as if it happens to be a trespasser or maybe even a thief there is high probability that the suspect would be captured. Along with this alarm can also be incorporated within this device so that in case motion sensors are evoked or someone tries to damage the equipment, the alarm is set off [22]. Additionally, fire or smoke detectors can also be attached with this device making the equipment multipurpose. With the help of IoT, the camera along with all the sensors can be connected to the user’s mobile phone and all information gather by the camera and the sensors can be directly received by the user. To enhance this further, all the data that is generated by the device and all its attached sensors can directly be linked to a server or cloud storage and should be stored using a blockchain.

Schematic illustration of specialized surveillance using IoT and blockchain.

      This will ensure that once the camera captures any particular event it cannot be erased or tampered with [23]. Another feature that can be combined with the surveillance technology mentioned so far is that once the images captured by the camera are sent out to the cloud, it can be put under two step processing. First step is face detecting and extraction wherein if there happens to be face in the pictures or videos captured by the camera, it would be recognized and an image of the face would be extracted as clear as possible. The second step is face matching, which will give details about the anonymous defaulter to be traced. However, only authorized personal will be able to access the required database resource for face matching in order to ensure that it cannot by misused. The surveillance cameras mentioned thus far can be used for residential areas as well as public spaces. The cameras with the face detection involved particularly can be used to track down drivers who do not abide by the traffic rules through the data delivered by the camera. Thus, IoT and blockchain together have set new standards for ensuring specialized safety [24].

Schematic illustration of smart street lighting enabled with IoT and blockchain.

      In Indian scenario, cases like robbery, assault, and kidnapping can be tracked down and prevented. The problems that this technique solves are conserving energy by preventing wastage and turning the street light on or off as and when required; it also contributes to making streets safer by helping police trace defaulters and prevent many crimes. This also eliminates the need of man power required to regulate the power of street lights. Since this technique involves sensors and storage over cloud using blockchain, we refer to this as a “smart” street lighting system.

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