An experimental study of fog and cloud computing in CEP-based Real-Time IoT applications Full Text

IoT can help both the teachers and the students with such applications as it not only clears the doubts of the students well making them understand better but also helps the teachers in teaching properly. IoT provides teachers with every tool at their disposal for the teaching purpose. It can suggest books for them clear any doubt which a teacher may not know, stating facts and quotes or even perform visual experiments for the class . Not only that but it can help out the teachers in the correction of the examination papers and assignment as well. All these could make small differences in the day to day learning and teaching experience and as a result, a better learning platform is provided . The data engineering on the fog layer deal with data collection of different units from several sensors to perform calculations of different types of measurements.

This approach reduces the amount of data that needs to be sent to the cloud.

Influence of network technology on the latency

Some of the conditions that were worked on were variants in the type of the access network, the idle-active time of the nodes, number of downloads per user, etc. Moreover, the authors determine that under most conditions the fog computing platform shows favourable indicators in energy reduction. Hence, the authors conclude that in order to take advantage of the benefits of fog computing, the applications whose execution on this platform have an efficient consumption of energy throughout the system must be identified.

• Carrión have directed the Conceptualisation, Formal Analysis, Writing – Review & Editing.
• Fog computing allows us to locate data on each node on local resources, thus making data analysis more accessible.
• F fog computing works similarly to cloud computing to meet the growing demand for IoT solutions.
• The framework is considered as the leading parameter for those were target of searches use shared fog nodes, smart gateways in terms of fog nodes.
• Not only lighting but provision of audio-visual learning is another major benefit of IoT.

The OpenFog Consortium is an association of major tech companies aimed at standardizing and promoting fog computing. Therefore, Fig.10 shows the average latency data, broken down by each sector indicated above. In it, it can be seen that in both architectures, the element that contributes most to latency is the MQTT Broker in the two phases of communication. It is important to note that the number of alarms can be increased by sending more topics in less timeframes, so we can set the maximum number of alarms per minute. Therefore, for all the tests, 10-minute simulations were made simulating a controlled number of alerts every minute in an equidistant manner, that is, 10 tests were carried out generating the same number of alerts every minute. Whenever a complex event is detected, a new publication to its corresponding topic is made into the MQTT broker, notifying the alarm.

CEP pattern

Internet of Things and Cloud Computing have enabled real time decision making in systems like irrigation system, weather monitoring systems, smart home, driver-less vehicles etc. The cloud infrastructure has great computation power but involves high network latency. Thus, is unsuitable for critical health care applications like ECG monitoring, where the delay, in decision making, communicating emergency and giving timely treatment may become fatal.

• It is also challenging to acquire and batch process data with various dimension from diverse sources, which may need different levels of intelligence .
• Fog computing is commonly used where low latency is needed or real-time decisions are made, for instance in IoT deployments, industrial automation, autonomous vehicles, predictive maintenance and video surveillance.
• On November 19, 2015, Cisco Systems, ARM Holdings, Dell, Intel, Microsoft, and Princeton University, founded the OpenFog Consortium to promote interests and development in fog computing.

Fog computing is commonly used where low latency is needed or real-time decisions are made, for instance in IoT deployments, industrial automation, autonomous vehicles, predictive maintenance and video surveillance. High Security – because the data is processed by multiple nodes in a complex distributed system.

Fog computing in education IoT systems

While developing fog architecture it is important to focus the latency and reliability of the system. The total time consumed by the system can be reduced by re-planning the development process. Moreover, there is a need to develop a real time response system that will be capable of providing better support towards the education system.

This type of attack is allowing hackers to fake identities which can help them gaining unauthorised access to sensitive information and compromising IoT applications and real-time services. Therefore, a mobile sybil defence scheme has been proposed by Quercia and Hailes in 2010 to match the communities of mobile users and identify the trusted users in untrusted communities as sybil attackers . The main objective of this section is to ensure that the fog computing has been discussed properly for providing a better way of demonstrating the importance in education IoT systems. The IoT systems are used for the purpose of offering a better connected and collaborative future towards the education sector . IoT devices have the potential to provide the students with better access and will also ensure that all the materials are communicated successfully among the student so that it becomes easy to provide real time experience towards them. Governance is considered as the most important method for the IoT education system.

However, fog computing is a more viable option for managing high-level security patches and minimizing bandwidth issues. Fog computing allows us to locate data on each node on local resources, thus making data analysis more accessible. For every new technological concept, standards are created and they exist to provide users with regulations or directions when making use of these concepts.

In order to have a better efficiency over the system it is important to increase the number of both staffs and the students that are accessing the learning management system. The most efficient applications that can be used for such computing purposes include the power school that is an oracle 12 the databases, skyward and a pro care. It is important to ensure that the operations are carried out efficiently after evaluating all the necessary networking requirements so that it does not impact the performance. The main advantage that can be obtained with the use of fog computing is that it offers it rained IT personnel and also helps in remote management of jobs . Secondly with the use of modern technology in class it becomes easy to attract the students and also becomes easy to manage the online courses.

Evaluation of fog computing

The second issue of big data analytics in education focuses on the privacy of student’s data . Much information about student’s behaviour is confidential like personal data that cannot be gathered without special approvals. Furthermore, tracing student’s performance is required to be stretched by their personal information, for example, personality type. Though, several students are not willing to provide this type of information about them to the university.

Likewise, we can observe that the enhancement of this metric entails improvements in different ones, such as, for example, the reduction of energy consumption , improving the QoS , maximising the Quality of Experience , among others. In this sense, for the analysis of the distribution of computational resources it is necessary to be able to evaluate this type of architectures. However, fog devices are usually constrained resources and this may be one of the main drawbacks of the system. Thus, the model known as cloud computing, executor of interconnectivity and execution in IoT, faces new challenges and limits in its expansion process.