Mobile Grid Computing Based On Unique ID For Patient Healthcare In IoT Security Model
Mobile gird computing is one of the enabling technology for accessing resources from the multiple location to reach a common goal. It acts as the central server has all the information related to the patient health care condition based on unique ID. However, there is a security issue, while accessing the information from the mobile grid computing to overcome this a new model is proposed. The proposed mobile grid computing provides the IoT security model has two cooperative schemes in which the source and destination can communicate via trusted relays.
The two cooperative schemes are i) decode and forward, and ii) amplify and forward, it is a transmission protocol in wireless network for secure communication. Furthermore, the proposed methods achieve improvement in accuracy, throughput, power consumption than existing methods.
Introduction
Mobile devices pose several obstacles to grid computing. Existing cellular phones vary significantly, both in terms of hardware and software, as well as capability. Like the personal computers of the early 1980s, they lack a standard platform, instruction set, and operating system. Precious resources such as power and network bandwidth are, at least for the current generation of mobile technology, still significant factors to be considered. Security is another consideration. Any framework for execution of grid applications on millions of mobile devices is also an attractive target for exploitation. We believe that bandwidth and power consumption need not deter phone owners from donating processing time to grid computing. Participation of mobile devices in voluntary grid computing would most likely take advantage of nightly device recharge cycles. During this time devices still have network access, remain powered on, and have idle CPU cycles. Likewise, many cellular phone owners have unlimited data plans, and the bandwidth needs of many BOINC applications are relatively. Grid computing and mobile devices to date has focused on the interaction of mobile devices with grid services.
The IT industry is ever buzzing with revolutionary inventions since the very first computer came into the picture. The purpose of the computer to perform different tasks and applications hasn’t changed over the last six decades. The only difference is that now these tasks are performed in a cheaper, faster and portable manner. A group of computers or servers are tied together to form a system called as Cloud Computing. Cloud Computing is the new form of application mode in the era of the Internet and it has become the hot topic of research in industrial and scientific communities. It provides the consumers the resources and computing infrastructure as per their requirements. The consumers can use the services and applications available on the cloud through their Internet connection. Cloud computing is not just limited to personal computers; it has a major impact even on the mobile technology. Mobility and ubiquity are the key features of the next generation network.
Review of literature
Different technologies are emerged in the computing process for resource management and security level.Skoutaset.al used the new paradigm to meet certain condition such as low delay and jitter, etc. The main objective to provide the security attack challenges and task completion times. But it will solve only issues not concerning the interoperability mainly with respect to the efficiency of mobile Grid solutions so as to enable the deployment in commercial and everyday life applications.
Min et.al used the new job scheduling algorithm, where the mobile devices are effectively incorporated into this grid either as the service recipients or as more valuable service providers., it will mainly focus on a disconnected operation problem, because the mobile devices are mostly disconnected by communication issue and device mobility. Relatively poor local resources (speed, battery power) is the main disadvantage this technique.
Another important solution in the field of data concealment is steganography. Steganography allows hiding data inside files of a different format. This approach involves deception of the user. Thus, it is unacceptable within volunteer computing. This approach involves the modification of the Virtual-Box engine. Thus, volunteers would have to install a modified Virtual-Box software which seems to be hardly feasible.
Florez et.al used web based Mobile Grid Computing solutions control smart devices to reduce the processing operation to be performed by the service infrastructure. The main aim SaW (Social at Work) is to provide the Web-based solution as a mobile grid to balance a grid media service for image analysis on videos. Besides, improvements in the bindings of Web browsers to support WebGL and WebCL is needed, which will result in a more efficient use of the hardware resources. To improve the security level new model is proposed.
The main aim of the paper is to develop an IoT based technology data centric patient healthcare. However, there is a security issue in mobile grid computing to overcome this a new model is proposed. The proposed mobile grid management framework acts as the vital enabling technology proposed for Internet-of-Things- (IoT-), which is based on next-generation ubiquitous healthcare solutions. In this new model requires real-time in the-field processing of wirelessly obtained data about the health condition of patients in security model. Furthermore, the proposed methods achieve security and accuracy than existing methods.
The manuscript is arranged as follows: - In the second section, the proposed work provides key enabling technology in Internet-of-Things (IoT) based next-generation ubiquitous healthcare solutions. Then the experimental analysis and the performance evaluation in security level is discussed in the third section. The overall conclusion for the mobile grid computing is given in the last section.
Proposed work
In this proposed work, the information about the patient health care condition will be stored in MGC based on unique ID card number.so all information about the patient health care will accessed by the requester for further analysis and clarification. The proposed work is manly divided into three logical roles i) service requester, which request the resources to access from service provider ii) service provider, which can provide the resources to process the information and iii) broker, will acts as the mediator in between the service providers and requesters to provide the information about the corresponding patient to the requesters.
Service request will request to access the resources from the internet. Data providers provide the key sign and also related information about the requested resources. The resources provider gives the storage information, communication and time period to access the data. The broker – an additional role it acts as a mediator in between the service provider and service requester is provided by a novel energy aware resource allocation engine it will allocate the tasks among the service providers. This way, it ensures that the energy will not reduce by performing this task and maximize the life time because the sensor data is important for ubiquitous healthcare applications.
Patient Data’s are successfully send in IoT security level. To send the security related data will be in the central data base with the help of Wi-Fi module. All the information is stored in a file. once the data reaches the client with the help of username and password in IoT level. To send the data in secure level the IoT security level is proposed.
Internet of Things in security level. To improve security, an IoT device can access the data from service provider without any traffic and time lagging. A wireless network model with a sender-receiver -pair, M1 are considered. The eavesdropper channel, transmitter encoding schemes, decoding schemes and cooperative protocol is considered as public, only the sender message is considered as private. In this two major cooperative schemes: Decode-and-forward and the amplify-and-forward. A source S sends confidential information to a destination D via K relays. In the first stage, source will send the encoded message to the trusted relay by transmission slot. In the second stage it will decode it successfully then re encode the message and send to the destination.
Amplify-and-forward
Amplify-and-forward is also a two-stage scheme it will amplify and forward the signal to the destination. All the process is same as DF excluding the transmit power is dissimilar. The message will be transmitted between the sender and receiver has a noise N. The transmitted signal of all the relays can be denoted as the product of, where V is weight vector and is given by (1).
Experimental analysis and result
The resources that selected to meet the task performance depends on the parameter such as throughput, power consumption, latency, response time, etc. Some of the required parameters are resource accessibility, system workload, network performance, etc. In many applications such as real time applications, it is important for a given system to response to an event within a short period of time. Several factors in mobile gird computing can influence the response time. These include, the processing time of the application at the grid resource, the processing time on the mobile device, network latency, and data transport time.
Latency: in MCC, latency is defined as “the time in offloading the calculation and receiving back the result from the infrastructure”. Many variables that can affect latency such as program size, location of the required data, and network bandwidth.
Average latency=min +max/2Min=0Max=time for send the data
Throughput: In MCC, the delivery time is a key factor to improve the throughput and it is defined as the ratio of message delivered over the time.
Throughput=msg deliver /time Power consumption: mobile devices are equipped with batteries that currently have a relatively short life time. Many mobile applications are power-hungry. Therefore, running such applications on mobile devices can reduce battery life drastically. E=t*P/1000T=timeP=power
Computing offloading: computing offloading is the bottleneck in mobile grid computing. It refers to the partitions of a mobile application that have to be executed on the grid or nearby infrastructure.
Conclusion
From all the data it is clear that IoT security model is very crucial concern in the mobile grid computing. The management of resources and security is important to convey the secured information in computing. Security related data is captured and sent to the client for further analysis and clarification. The proposed mobile grid management framework serves as a key enabling technology for Internet-of-Things- (IoT-) based next-generation ubiquitous healthcare solutions. Moreover, it can improve the security, task completion time resources management in mobile grid computing.
