AU2021101149A4

AU2021101149A4 - Iot based secure educational system

Info

Publication number: AU2021101149A4
Application number: AU2021101149A
Authority: AU
Inventors: Kiran Deep; Tejinder Deep; Bharat Singh Deora; Ankita Gupta; Amanpreet Kaur; Rajbir KAUR; Sapinderjit Kaur; Prabh Deep Singh; Priyanka Soni
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-03-04
Filing date: 2021-03-04
Publication date: 2021-05-06
Anticipated expiration: 2029-03-04

Abstract

A system for secure data transmission in education, the system comprising an IOT layer associated with the system to collect data from a plurality of smart modules from different locations, wherein the IOT layer comprises of a plurality of sensors to collect the data, an FOG layer to analyze, process and classify the data received from the IOT layer via a communication module, wherein the FOG layer classifies the real time data on the basis of data sensitivity and sets priority for highly sensitive data, a cloud layer for storing the information processed by the FOG layer and simultaneously process low sensitive data during requirement, wherein the cloud layer stores the processed information in a repository and a plurality of security layers is linked with the layers to encrypt the data during transmission. 32 I WI 0 z _ j> 3 B: WLI IY-4 LI IL 0 - - - - - - - - - - - --0- - - - -

Description

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IOT BASED SECURE EDUCATIONAL SYSTEM FIELDOFINVENTION

The present invention generally relates to a field of Internet of Things. More specifically, the present invention relates to a secure educational system using Internet of Things.

BACKGROUND OF THE INVENTION

The Internet of Things (IoT) is a novel paradigm that is rapidly gaining ground in the scenario of modern wireless telecommunications. The basic idea of the concept is the pervasive presence around us of a variety of things or objects such as Radiofrequency Identification (RFID) tags, sensors, actuators and mobile phones. the emergence of IoT has influenced various application domain such smart cities, e learning, health-care applications, agriculture, and so on.

In to- day era, the connected devices combined with cloud computing has become the pioneer to provide education accessible across the globe. Education plays an important role in the growth of a country. The higher education is provided to students by opening new universities and colleges across the country to be transformed by IoT. By 2020, more than 5 billion devices will be connected worldwide. In the education system, IoT delivers basic connectivity among students, faculty members, and staff. It enables the integration of smart laboratories with new smart types of equipment and systems.

The latest survey shows that nearly 2/3 of total workloads in traditional IT space will be shifted to the IoT enabled cloud. In cloud based systems, the increasing workload on cloud data-centers leads to introduce network congestion, high latency and more energy consumption of data-centers. On the other hand, the storage of educational data needs a secure transmission protocol and security of data-centers.

Fog Computing, proposed by Cisco in 2014, plays a bridge between end-user and cloud that provide services with low latency and less traffic congestion. The fog layer consists geo-distributed fog servers those process computations at the edge of systems. Each fog server has equivalent computing capabilities to process a huge amount of workload at the edge. Thus, a very less amount of workload is transferred to the cloud for storage, analytic or further processing purpose. Therefore, fog computing becomes a major driver of the IoT in education systems. With an ever-increasing number of connected devices generating an unprecedented amount of data, connecting everything to a central cloud is become possible with fog computing.

It is observed that all the above ongoing educational issues were focused to resolve by using the integration of several technologies and communications solutions. Almost all of the research focused on storage, cost reduction, and power consumption of cloud data-centers and IoT devices. There is no research work available in the Scopus database to address low latency and a low delay between IoT devices and fog/cloud data-centers. Even, none of the researchers is working to improve the security of the educational system.

A researcher proposed a hierarchical based distributed architecture that performs processing at the edge of the network using fog Computing. They presented a novel dimension of IoT which adds to Big Data and Analytics.

Another researcher combined Internet of Things with analytic in the constructionist 21st-century learning model. Authors use sample data set of some students and applied above-said technologies to collect the result towards the benefits of students for their performance.

Another researcher used fog Computing from the perspective of the healthcare. They discussed major characteristics and services provided by fog Layer.

Another researcher proposed a four-tier framework which was concentrated on the factors in the smart learning environment for the smart education of 21 century. They also proposed a radical 3 tier architecture along with its key functions for emphasizes the role of smart computing in smart education.

Another researcher provided a pervasive penetration of cloud computing with the analysis of the rapidly growing cloud market.

Another researcher proposed integrated framework using CoT and fog Computing for smart city application. They also implemented and demonstrated the same using various examples related to the smart city.

Another researcher presented an architecture and algorithm for digital teaching platforms that make a balance between the local cloud resources and federated respectively. They also implemented the architecture of the system and get the result using various practical scenarios.

Another researcher designed a platform that performed computations by placing the physical entities near the computation resources using adaptive and decentralized algorithms.

In order to overcome the above-mentioned limitations, there exists a need to develop a secure system for storing and processing education data.

The technical advancements disclosed by the present invention overcomes the limitations and disadvantages of existing and convention systems and methods.

SUMMARY OF THE INVENTION

The present invention generally relates to a system and a method for secure data transmission using Internet of Things.

An object of the present invention is to provide a hassle-free education data monitoring system.

Another object of the present invention is to provide a highly secure data transmission method in the education system.

Another object of the present invention is to provide a cost-effective system.

According to an embodiment of the present invention, the system comprises of an IOT layer, a FOG layer, a Cloud layer, a repository, a plurality of security layers and a communication module.

The IOT layer is associated with the system to collect data from a plurality of smart modules from different locations, wherein the IOT layer comprises of a plurality of sensors to collect the data. The IoT Devices Layer composed of various sensors and devices used in the educational system. The IoT enabled educational devices are placed in this layer are responsible to collect the data from the various distributed geographical location.

The IoT devices distributed across the campus of university/college to collect the data and submit to the fog layer. For the same, various wireless communication mediums can be used to transfer the educational data such as 3G/4G/CDMA/GPRS. Sensor nodes at this layer such as mobile devices, tablets, IoT enabled board, smart pens, attendance tracking systems, security cameras, doorbells and locks are connected with IoT gateways. The educational data is collected and forwarded to the secure fog manager.

The FOG layer associated to the IOT layer to analyze, process and classify the data received from the IOT layer via a communication module, wherein the FOG layer classifies the real time data on the basis of data sensitivity and sets priority for highly sensitive data.

This layer is monitored by the secure fog manager that analyzes and classifies the data on the basis of time sensitivity. The data is categorized by the secure fog manager as real-time sensitive and less time sensitive data. The fog manager is the core component that manages resources of the fog layer in such a way that Quality of Service (QoS) and resource utilization are optimized.

The real-time data is processed by the fog nodes that are closest to the devices those are generating the data. The second type of data that can tolerate minutes of processing is called less-time sensitive data, sent to the cloud data-centers for processing and further analysis. The resources of fog nodes can be provisioned on-demand from geographically distributed routers and switches etc.

The real time-sensitive applications that need immediate processing are processed at the fog layer. This layer is responsible for the preliminary processing to be completed and appropriate decision making. The processing and storage requirements of the real-time applications are fulfilled by aggregating the computing capabilities of network elements residing at the fog layer. The processed data or results are stored in the repository of the cloud data-centres.On the other hand, the less-time sensitive data categorized by the layer is directly sent to the cloud servers.

The cloud layer associated to the FOG layer for storing the information processed by the FOG layer and simultaneously process low sensitive data during requirement, wherein the cloud layer stores the processed information in a repository. This layer consists of cloud servers and data- centres those provide processing and storage to the applications computing at cloud layer.

The less-time sensitive data and processed data coming from the fog layer is processed and stored respectively. The processed data put less overhead on the cloud servers and data-centers whereas the less-time sensitive data is fully handled by the cloud servers and data-centres.

The plurality of security layers is linked with the layers to encrypt the data during transmission; wherein a first security layer provides data security between the layers; wherein a second security layer provides security to the processed data at the FOG layer and the cloud layer.

The security of the proposed framework is provided in two folds. (1) the data transfer between layers must be secure and faster along with minimum energy consumption of devices located at the corresponding layer and (2) security of processed data at both the fog layer and cloud layer.

A demand signal repository (DSR) or the first security layer for secure and faster data transfer between the IOT layer, the FOG layer and the cloud layer, wherein the DSR is intended for unidirectional and asymmetric data transfer with time delay.

The proposed modified DSR improves the efficiency of the network. It has two components that cooperate to permit the revelation and maintenance of source nodes. The root discovery and route maintenance operations are monitored by modified DSR. It is intended to permit uni-directional connections and asymmetric routes with minimum efforts. The proposed modified DSR allows secure data transfer with minimum time delay and provides optimization in energy consumption. It also improves the efficiency of each layer. The modified DSR is implemented at both source and destination node. Following are the steps performed at the source node.

An offset codebook mode (OCB) or the second layer provides data security by encryption methodology to the processed data in the FOG layer and the cloud layer, wherein the OCB layer uses parallel mode for block-cipher that provides privacy and authenticity to the data. The OCB has a variety of desirable security proper- ties as compared to other existing standards. It uses a parallel mode for block-cipher which simultaneously provides privacy and authenticity.

According to an embodiment, the IoT Devices Layer composed of various sensors and devices used in the educational system. The IoT enabled educational devices are placed in this layer are responsible to collect the data from the various distributed geographical location. All IoT devices distributed across the campus of university/college to collect the data and submit to the fog layer. For the same, various wireless communication mediums can be used to transfer the educational data such as 3G/4G/CDMA/GPRS. Sensor nodes at this layer such as mobile devices, tablets, IoT enabled board, smart pens, attendance tracking systems, security cameras, doorbells and locks are connected with IoT gateways. The educational data is collected and forwarded to the secure fog manager.

The FOG layer is monitored by the secure fog manager that analyzes and classifies the data on the basis of time sensitivity. The data is categorized by the secure fog manager as real-time sensitive and less time sensitive data. The fog manager is the core component that manages resources of the fog layer in such a way that Quality of Service (QoS) and resource utilization are optimized. The real-time data is pro- cessed by the fog nodes that are closest to the devices those are generating the data. The second type of data that can tolerate minutes of processing is called less-time sensitive data, sent to the cloud data-centres for processing and further analysis. The resources of fog nodes can be provisioned on-demand from geographically distributed routers and switches etc. The real time sensitive applications that need immediate processing are processed at the fog layer. This layer is responsible for the preliminary processing to be completed and appropriate decision making. The processing and storage requirements of the real-time applications are fulfilled by aggregating the computing capabilities of network elements residing at the fog layer. The processed data or results are stored in the repository of the cloud data-centres. On the other hand, the less-time sensitive data categorized by the layer is directly sent to the cloud servers.

The Cloud layer consists of cloud servers and data- centres those provide processing and storage to the applications computing at cloud layer. The less-time sensitive data and processed data coming from the fog layer is processed and stored respectively. The processed data put less overhead on the cloud servers and data-centres whereas the less-time sensitive data is fully handled by the cloud servers and data centres.

The layers are secured with the encryption layer. The proposed modified DSR improves the efficiency of the network. The layer has two components that cooperate to permit the revelation and maintenance of source nodes. The root discovery and route maintenance operations are monitored by modified DSR.

The layer is intended to permit uni-directional connections and asymmetric routes with minimum efforts. The proposed modified DSR allows secure data transfer with minimum time delay and provides optimization in energy consumption. The layer also improves the efficiency of each layer. The modified DSR is implemented at both source and destination node. The steps followed at the source node are:

Step 1: Destination nodes are sent route reply packets to the source node which are accepted by the source node.

Step 2: After getting the first route reply, the source node sends data packets.

Step 3: Other route replies are received by the source node. And hop count are compared. If [hop count < original path] new path is selected else keep it in its route cache.

The steps followed at the destination node are:

Step 1: Based on the node address, all Route Request packets are accepted.

Step 2: Request are valued by checking the sequence number.

Step 3: New request is stored in a table and initiate route reply.

Step 4: Old request, the path is evaluated.

Step 5: If the path is unique, check whether a number of hops are not exactly or equivalent to the hops in the table.

Step 6: If path hop count is more noteworthy than the access path, the packet is dropped else start route reply and insert it into route cache.

According to an embodiment, the simulation tool comprises of three level structure. At level 0 cloud servers/ data-centres are placed and in the next level 1, fog devices are places as the gateway, and at the last level, two educational devices are placed.

To obtain correctness of the result during the experiments, various topologies are used with different placement strategies. The configuration of each physical entity such as cloud data- center's, gateways etc.

The Energy consumption of cloud data-centres is calculated for various strategies. Further, the delay corresponds to each layer is computed to find the total delay in our framework. The latency of all individual devices is also calculated in both situations i.e., when all devices directly interact with cloud and with the presence of fog layer.

In order to compute the latency for all the packets following metrics are being used: N Total latency = [Received time(K) - Sent time(k)] (1) K=1 N is the total number of successfully received packets. Average latency is computed as:

Average Latency= Total latency/ Total packets received.

The energy consumption of the proposed framework in the heterogeneous computing environment, the energy consumed by each layer is computed. At the IoT device layer and Fog layer, all edge devices are taken into account respectively. The energy consumption is given by :

E diff = E (IOT device layer) + E (Fog Layer)

E (IOT device layer)= I (E of IOT layer) energy consumed by all the IOT device.

E (FOG layer)= Y(E of FOG layer) energy consumed by the FOG layer.

According to an embodiment, the simulation of the DSR is shown in the form of graph. the standard DSR and modified DSR were studied for compression in NS-2 simulation environment. The NS-2 was chosen because it is a descriptive simulator, which has been widely utilized and tested, and it is a simulator that inserts in deep services, also supporting the measurement of energy consumption and application delays.

The total 25 number of nodes were fixed those moves inside the simulation part of 1000 m-2000 m. The nodes move with a maximal speed of 25 m/s and in line with the random way-point flexibility model. In this model, a node randomly selects an area in the ruse area and a velocity for the next move, which is uniformly allocated between and the maximal velocity.

According to an embodiment, the graph shows the average delay in milli-seconds on the Y-axis and pause time in X-axis. The source node for the modified DSR is indicated at 13 milli-seconds at 0 seconds and reaches 22 milli-seconds at 1000 seconds. For DSR the source node starts at 18 milli-seconds at 0 seconds and the destination node at 30 milli-seconds at 1000 seconds.

According to an embodiment, the different components are destined to send their data from the source node in a predefined interval of time which is as follows:

The gateway at the source reaches the cloud at destination in 100 milli-seconds. The mobile devices and tablets at source reach the cloud destination at 80 milli-seconds. The IOT enabled board at source reaches the destination at gateway in 6 milli-seconds. The attendance tracking module at the source reaches the gateway at 4 milli-seconds. The security cameras at the source reaches the gateway at 8 milli seconds. The doorbell and locks at the source node reach the destination at gateway in 3 milli-seconds.

According to an embodiment, the delay occurring in the IOT, FOG and cloud layer for different configurations. The delay is found as the lowest among both fog layer delay and cloud delay. The other evaluation is performed by all educational IoT devices. The first block shows the delay in FOG layer, followed by cloud layer delay and last by fog-cloud delay. The graph is taken for 6 different configurations.

According to an embodiment, the processing capability, RAM, uplink and downlink bandwidth and their levels in the layer. The cloud entity has a processing capability of 44000 MIPS with 4000 MB of RAM, 100 uplink bandwidth, 10000 downlink bandwidths at level 0. The gateway of the physical entity has a processing capability of 2000 MIPS with 4000 MB of RAM, 10000 uplink bandwidth and 10000 downlink bandwidths with level 1. The Mobile device and tablet has a processing capability of 500 MIPS with 2000 MB RAM, 10000 uplink bandwidth and 2500 downlink bandwidth at level 1. The IOT enabled board has a processing capability of 100 MIPS with 1000 MB RAM, 10000 uplink bandwidth and 10000 downlink bandwidths at level 2. The attendance tracking module has a processing capability of 100 MIPS with 1000 MB RAM, 10000 uplink bandwidth and 10000 downlink bandwidths at level 2. The security camera has a processing capability of 100 MIPS with 2000 MB RAM, 10000 uplink bandwidth and 10000 downlink bandwidths at level 2. The doorbell and locks have a processing capability of 100 MIPS with 500 MB RAM, 10000 uplink bandwidth and 10000 downlink bandwidths at level 2.

The proposed system architecture facilitates a secure IoT based educational cloud fog architecture. This makes a higher education system, more efficient to achieve better future results. The design of the proposed system is presented with three layers where the real time monitoring of student's data is collected through educational IoT devices. The fog layer efficiently categorizes the data into real-time sensitive to be process at fog layer and less-time sensitive data to be process at cloud layer. In addition, OCB method is used to provide security to the processed educational data and modified DSR is proposed that keep secure interaction between fog and cloud layers.

To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings

BRIEF DESCRIPTION OF FIGURES

These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

Figure 1 illustrates a block diagram of all the components involved in the system of IOT enabled smart educational system.

Figure 2 illustrates a flow diagram of the method used in the system.

Figure 3 discloses about the architecture of the different layers of the system.

Figure 4 illustrates the exemplary embodiment of the open-source simulation module used in the evaluation of the system

Figure 5 illustrates the graphical representation of the simulation tool

Figure 6 shows the comparison graph between Modified DSR and DSR

Figure 7 illustrates the tabular representation between various components from source to destination in milli-seconds

Figure 8 illustrates the graphical representation of the various time delay occurring in the IOT, FOG and cloud layer for different configurations

Figure 9 illustrates the graphical representation of the various educational IOT devices in different configurations

Figure 10 illustrates the tabular representation of the different configurations of the physical entity.

Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.

Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.

Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.

Figure 1 illustrates a block diagram of all the components involved in the system of IOT enabled smart educational system. The system comprises of an IOT layer 102, a FOG layer 104, a Cloud layer 106, a repository 108, a plurality of security layers 112 and a communication module 110.

The IOT layer 102 is associated with the system to collect data from a plurality of smart modules from different locations, wherein the IOT layer 102 comprises of a plurality of sensors to collect the data. The IoT Devices Layer composed of various sensors and devices used in the educational system. The IoT enabled educational devices are placed in this layer are responsible to collect the data from the various distributed geographical location.

The FOG layer associated to the IOT layer 102 to analyze, process and classify the data received from the IOT layer 102 via a communication module 110, wherein the FOG layer classifies the real time data on the basis of data sensitivity and sets priority for highly sensitive data.

The real-time data is processed by the fog nodes that are closest to the devices those are generating the data. The second type of data that can tolerate minutes of processing is called less-time sensitive data, sent to the cloud data-centres for processing and further analysis. The resources of fog nodes can be provisioned on-demand from geographically distributed routers and switches etc.

The real time-sensitive applications that need immediate processing are processed at the fog layer. This layer is responsible for the preliminary processing to be completed and appropriate decision making. The processing and storage requirements of the real-time applications are fulfilled by aggregating the computing capabilities of network elements residing at the fog layer. The processed data or results are stored in the repository 108 of the cloud data-centres. On the other hand, the less-time sensitive data categorized by the layer is directly sent to the cloud servers.

The cloud layer 106 associated to the FOG layer for storing the information processed by the FOG layer and simultaneously process low sensitive data during requirement, wherein the cloud layer 106 stores the processed information in a repository 108. This layer consists of cloud servers and data- centres those provide processing and storage to the applications computing at cloud layer 106.

The less-time sensitive data and processed data coming from the fog layer is processed and stored respectively. The processed data put less overhead on the cloud servers and data-centres whereas the less-time sensitive data is fully handled by the cloud servers and data-centres.

The plurality of security layers 112 is linked with the layers to encrypt the data during transmission; wherein a first security layer 112 provides data security between the layers; wherein a second security layer 112 provides security to the processed data at the FOG layer and the cloud layer 106.

The security of the proposed framework is provided in two folds. (1) the data transfer between layers must be secure and faster along with minimum energy consumption of devices located at the corresponding layer and (2) security of processed data at both the fog layer and cloud layer 106.

A demand signal repository 108 (DSR) or the first security layer 112 for secure and faster data transfer between the IOT layer 102, the FOG layer and the cloud layer 106, wherein the DSR is intended for unidirectional and asymmetric data transfer with time delay.

The proposed modified DSR improves the efficiency of the network. It has two compo- nents that cooperate to permit the revelation and maintenance of source nodes. The root discovery and route maintenance operations are monitored by modified DSR. It is intended to permit uni-directional connections and asymmetric routes with minimum efforts. The proposed modified DSR allows secure data transfer with minimum time delay and provides optimization in energy consumption. It also improves the efficiency of each layer. The modified DSR is implemented at both source and destination node. Following are the steps performed at the source node.

An offset codebook mode (OCB) or the second layer provides data security by encryption methodology to the processed data in the FOG layer and the cloud layer 106, wherein the OCB layer uses parallel mode for block-cipher that provides privacy and authenticity to the data. The OCB has a variety of desirable security proper- ties as compared to other existing standards. It uses a parallel mode for block-cipher which simultaneously provides privacy and authenticity.

Figure 2 illustrates a flow diagram of the method used in the system.

Step 202 depicts about collecting data from a plurality of smart modules using an IOT layer 102 associated with the system from different locations, wherein the IOT layer 102 comprises of a plurality of sensors to collect the data.

Step 204 depicts about analyzing, processing and classifying the data received from the IOT layer 102 using an FOG layer, wherein the FOG layer classifies the real time data on the basis of data sensitivity and sets priority for highly sensitive data, wherein the data is received via a communication module 110;

Step 206 depicts about storing the information processed by the FOG layer on a cloud layer 106 and simultaneously process low sensitive data during requirement, wherein the cloud layer 106 stores the processed information in a repository 108; and

Step 208 depicts about encrypting the data during transmission using a plurality of security layers 112 linked with the layers; wherein a first security layer 112 provides data security between the layers; wherein a second security layer 112 provides security to the processed data at the FOG layer 104 and the cloud layer 106.

Figure 3 discloses about the architecture of the different layers of the system. The layers comprise of IOT device layer at the base, FOG layer 104 above the IOT layer 102 and cloud layer 106 above the FOG layer 104.

IoT Devices Layer composed of various sensors and devices used in the educational system. The IoT enabled educational devices are placed in this layer are responsible to collect the data from the various distributed geographical location. All IoT devices distributed across the campus of university/college to collect the data and submit to the fog layer 104. For the same, various wireless communication mediums can be used to transfer the educational data such as 3G/4G/CDMA/GPRS. Sensor nodes at this layer such as mobile devices, tablets, IoT enabled board, smart pens, attendance tracking systems, security cameras, doorbells and locks are connected with IoT gateways. The educational data is collected and forwarded to the secure fog manager.

The FOG layer 104 is monitored by the secure fog manager that analyzes and classifies the data on the basis of time sensitivity. The data is categorized by the secure fog manager as real-time sensitive and less-time sensitive data. The fog manager is the core component that manages resources of the fog layer 104 in such a way that Quality of Service (QoS) and resource utilization are optimized. The real-time data is pro- cessed by the fog nodes that are closest to the devices those are generating the data. The second type of data that can tolerate minutes of processing is called less-time sensitive data, sent to the cloud data-centres for processing and further analysis. The resources of fog nodes can be provisioned on-demand from geographically distributed routers and switches etc. The real time-sensitive applications that need immediate processing are processed at the fog layer 104. This layer is responsible for the preliminary processing to be completed and appropriate decision making. The processing and storage requirements of the real-time applications are fulfilled by aggregating the computing capabilities of network elements residing at the fog layer 104. The processed data or results are stored in the repository 108 of the cloud data-centres. On the other hand, the less-time sensitive data categorized by the layer is directly sent to the cloud servers.

The Cloud layer 106 consists of cloud servers and data- centres those provide processing and storage to the applications computing at cloud layer 106. The less-time sensitive data and processed data coming from the fog layer 104 is processed and stored respectively. The processed data put less overhead on the cloud servers and data centres whereas the less-time sensitive data is fully handled by the cloud servers and data-centres.

The steps followed at the destination node are:

Step 1: Based on the node address, all Route Request packets are accepted.

Step 2: Request are valued by checking the sequence number.

Step 3: New request is stored in a table and initiate route reply.

Step 4: Old request, the path is evaluated.

Figure 4 illustrates the exemplary embodiment of the open-source simulation module used in the evaluation of the system. The simulation tool comprises of three level structure. At level 0 cloud servers/ data-centres are placed and in the next level 1, fog devices are places as the gateway, and at the last level, two educational devices are placed.

The Energy consumption of cloud data-centres is calculated for various strategies. Further, the delay corresponds to each layer is computed to find the total delay in our framework. The latency of all individual devices is also calculated in both situations i.e., when all devices directly interact with cloud and with the presence of fog layer 104.

In order to compute the latency for all the packets following metrics are being used: N Total latency = E[Received time(K) - Sent time(k)] (1) K=1 N is the total number of successfully received packets. Average latency is computed as:

Average Latency= Total latency/ Total packets received.

The energy consumption of the proposed framework in the heterogeneous computing environment, the energy consumed by each layer is computed. At the IoT device layer and Fog layer 104, all edge devices are taken into account respectively. The energy consumption is given by :

E diff = E (IOT device layer) + E (Fog Layer)

E (IOT device layer)= I (E of IOT layer) energy consumed by all the IOT device.

E (FOG layer)= Y(E of FOG layer) energy consumed by the FOG layer 104.

Figure 5 illustrates the graphical representation of the simulation tool.

The simulation of the DSR is shown in the form of graph. the standard DSR and modified DSR were studied for compression in NS-2 simulation environment. The NS-2 was chosen because it is a descriptive simulator, which has been widely utilized and tested, and it is a simulator that inserts in deep services, also supporting the measurement of energy consumption and application delays.

Figure 6 shows the comparison graph between Modified DSR and DSR. The graph shows the average delay in milli-seconds on the Y-axis and pause time in X-axis. The source node for the modified DSR is indicated at 13 milli-seconds at 0 seconds and reaches 22 milli seconds at 1000 seconds. For DSR the source node starts at 18 milli seconds at 0 seconds and the destination node at 30 milli-seconds at 1000 seconds.

Figure 7 illustrates the tabular representation between various components from source to destination in milli-seconds.

The different components are destinated to send their data from the source node in a predefined interval of time which is as follows:

Figure 8 illustrates the graphical representation of the various time delay occurring in the IOT, FOG and cloud layer for different configurations.

The delay is found as the lowest among both fog layer 104 delay and cloud delay. The other evaluation is performed by all educational IoT devices. The first block shows the delay in FOG layer 104, followed by cloud layer 106 delay and last by fog-cloud delay. The graph is taken for 6 different configurations.

Figure 9 illustrates the graphical representation of the various educational IOT devices in different configurations. The latency of each device which clearly shows the latency is reduced with the presence of fog layer 104.

The table represents the processing capability, RAM, uplink and downlink bandwidth and their levels in the layer. The cloud entity has a processing capability of 44000 MIPS with 4000 MB of RAM, 100 uplink bandwidth, 10000 downlink bandwidths at level 0. The gateway of the physical entity has a processing capability of 2000 MIPS with 4000 MB of RAM, 10000 uplink bandwidth and 10000 downlink bandwidths with level 1. The Mobile device and tablet has a processing capability of 500 MIPS with 2000 MB RAM, 10000 uplink bandwidth and 2500 downlink bandwidth at level 1. The IOT enabled board has a processing capability of 100 MIPS with 1000 MB RAM, 10000 uplink bandwidth and 10000 downlink bandwidths at level 2. The attendance tracking module has a processing capability of 100 MIPS with 1000 MB RAM, 10000 uplink bandwidth and 10000 downlink bandwidths at level 2. The security camera has a processing capability of 100 MIPS with 2000 MB RAM, 10000 uplink bandwidth and 10000 downlink bandwidths at level 2. The doorbell and locks have a processing capability of 100 MIPS with 500 MB RAM, 10000 uplink bandwidth and 10000 downlink bandwidths at level 2.

The proposed system architecture facilitates a secure IoT based educational cloud fog architecture. This makes a higher education system, more efficient to achieve better future results. The design of the proposed system is presented with three layers where the real time monitoring of student's data is collected through educational IoT devices. The fog layer 104 efficiently categorizes the data into real time sensitive to be process at fog layer 104 and less-time sensitive data to be process at cloud layer 106. In addition, OCB method is used to provide security to the processed educational data and modified DSR is proposed that keep secure interaction between fog and cloud layers 106.

The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.

Claims

WE CLAIM

1. A system for secure data transmission in education, the system comprising:

an IOT layer associated with the system to collect data from a plurality of smart modules from different locations, wherein the IOT layer comprises of a plurality of sensors to collect the data;

an FOG layer associated to the IOT layer to analyze, process and classify the data received from the IOT layer via a communication module, wherein the FOG layer classifies the real time data on the basis of data sensitivity and sets priority for highly sensitive data;

a cloud layer associated to the FOG layer for storing the information processed by the FOG layer and simultaneously process low sensitive data during requirement, wherein the cloud layer stores the processed information in a repository; and

a plurality of security layers is linked with the layers to encrypt the data during transmission; wherein a first security layer provides data security between the layers; wherein a second security layer provides security to the processed data at the FOG layer and the cloud layer.

2. The system as claimed in claim 1, wherein the repository stores the processed data using an encryption methodology to provide data security and providing access to the processed data upon successful decryption.

3. The system as claimed in claim 1, wherein the plurality of security layer comprises of:

a demand signal repository (DSR) or the first security layer for secure and faster data transfer between the IOT layer, the FOG layer and the cloud layer, wherein the DSR is intended for unidirectional and asymmetric data transfer with time delay; and an offset codebook mode (OCB) or the second layer provides data security by encryption methodology to the processed data in the FOG layer and the cloud layer, wherein the OCB layer uses parallel mode for block-cipher that provides privacy and authenticity to the data.

4. The system as claimed in claim 3, wherein the DSR layer implemented both source and destination nodes, wherein the steps for source node comprises steps of:

sending route reply packets from destination node to source node;

receiving route reply from source node and sending data packets from source code; and

receiving route replies from source node for other nodes and calculating a hop count, wherein if hop count is less than original path new path is selected.

5. The system as claimed in claim 4, wherein the steps for destination node comprises steps of:

accepting route request of all route packets from the node address; checking sequence of request created for all the incoming request and value the sequence as per priority; storing all the incoming new request and initiating a route reply; evaluating the path for all the existing request on the basis of the priority; and checking the number of hops in a path and finding the best route for performing the task.

6. The system as claimed in claim 1, wherein the communication module is preferably a wireless communication medium for transferring education data between the layers.

7. The system as claimed in claim 1, wherein the plurality of smart modules includes mobile devices, tablets, smart board, smart pen, attendance, security cameras and other such essential devices connected to the IOT layer for sending input data.

8. The system as claimed in claim 1, wherein an open source simulation technique is used for analyzing the performance of the layers and energy consumed by the cloud repository by allotting different levels to different layers.

9. The system as claimed in claim 8, wherein the cloud layer is placed at level 0, the FOG layer is placed at level 1, the IOT layer is placed at top layer.

10. A method for secure data transmission in education, the method comprising: collecting data from a plurality of smart modules using an IOT layer associated with the system from different locations, wherein the IOT layer comprises of a plurality of sensors to collect the data; analyzing, processing and classifying the data received from the IOT layer using an FOG layer, wherein the FOG layer classifies the real time data on the basis of data sensitivity and sets priority for highly sensitive data, wherein the data is received via a communication module; storing the information processed by the FOG layer on a cloud layer and simultaneously process low sensitive data during requirement, wherein the cloud layer stores the processed information in a repository; and encrypting the data during transmission using a plurality of security layers linked with the layers; wherein a first security layer provides data security between the layers; wherein a second security layer provides security to the processed data at the FOG layer and the cloud layer.

102 108 IOT LAYER REPOSITORY

104 110 COMMUNIC FOG LAYER ATION MODULE 106 112 CLOUD LAYER SECURITY LAYER

FIGURE. 1

FIGURE. 3

FIGURE.

4 FIGURE. 5

FIGURE. 6 FIGURE. 7

FIGURE. 9 FIGURE. 8

FIGURE. 10