Sericulture (silk production) is a major occupation of rural community. Producing about 15% share of the world silk produce, India is the 2nd largest silk producer after China whose total produce amounts to a staggering 80%. Analysis of sericulture practices in India shows a clear need of automation especially during pre-cocoon stages. The silkworms undergo crucial bodily changes that determine the quality as well as quantity of the silk produce, during this phase. Maintenance of optimum values of abiotic factors, like temperature, humidity etc. thus yields a dramatic change in quantity and quality of silk produce. An Intelligent Sericulture plant automation system, using zone-based cascade control of physical parameters can be one of the solutions. Currently, such systems for pre-cocoon stages are purely manual, crude, and lack intelligence. The system comprises of a data acquisition sub-system corresponding to the predetermined zones for the rearing unit, an intelligent master controller facility, data repository of past corrective actions, and cheap actuators like fans, bulbs in the zones. The master control facilitates the optimum corrective action and directs the decisions to the identified actuator sub-system based on abiotic data obtained from the respective data acquisition subsystem. The actuator sub-system achieves the corrective measures using the actuators placed in that zone of the unit. A continuous real-time feedback facilitates accurate and quick implementation of corrective steps. The system aims for increased quantity and quality of silk which is determined by reeling factor, holding capacity, roughness of silk. Also, the zonebased implementation decreases production and maintenance cost making it suitable for rural usage.

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms.