With artificial intelligence and the internet of things playing a stellar role in the market of technology, means that humans will be able to interact intelligently with every device through the internet. Howsoever, these devices will store data for the users and there comes a huge need for storage space. A local server will store the data and provide you with the data in your local region, but cloud computing allows you to store the data and access it from anywhere around the globe. 

Cloud computing is an Information Technology shift, enabling you to store your data virtually. Virtualization is the major technology that works with cloud computing. The term ‘cloud’ is used because the saved information has no physical presence and it is in a shape of a cloud for the users. The word is used as a metaphor for the internet to denote a network on telephony schematics. For users to use the data at remote locations, Dynamic virtual machines are created to provide access to actual infrastructure for them.  

Cloud computing, has a broad scope for the PhD candidates in the field of electronics, in various sub-areas, including resource allocation, cloud data security, job scheduling, etc. The question of the protection of the data on the cloud has been a prominent question, ever since people has started to store data on it. Recently, the researchers have come up with the encrypted code as a solution to the breach of data, yet there is still scope for the scholars to research on the issue of security. Also, there are dynamic resource allocation schemes for the PhD candidates to study on the topic. 

To research the field of cloud computing, one has to narrow down the research area from the areas mentioned above initially. You can not take the topic of security and conduct research on it; you have to narrow down the matter of security to ‘secure cloud architecture’, ‘cloud cryptography’, ‘access control and key management’, and various other security management problems of this virtual computing. Narrowing down the topic will help the researcher set an aim and objective to conduct the research.

Once the area of the research has been narrowed down from a broader field to a more taper area, the researcher shall review the recent researches on the topic to find the issue in the area. Reviewing the literature review will give the researcher with a list of drawbacks in the area as he considers all the researches in the field. Analysing the drawbacks and issues in the existing literature will not solve the problem of the topic, the literature will also provide you with a scope of researching the topic you have chosen and are going forward with. Define the algorithm or technique by which you aim to resolve the issue and build a better cloud computing structure. 

For a real-time application, the researcher can employ AWS (Amazon Web Services), where various objectives can be catered like resource allocation, data security can be modelled. A real-time application is an application programme that functions within the current time frame, and with AWS the users can buy space on the cloud to develop applications. However, for prototype modelling, the same can be used in cloudsim simulator and dedicating a certain number of virtual machines over which the data can be carried out. Using AWS or cloudsim is based on the application the person is using.

Beginning in the 1970s, Very Large Scale Integration (VLSI), was used when communication technologies were being developed. VLSI is a process of creating an integrated circuit to help deal with the complex semiconductors and generate a microchip performing advanced functions of a primary transistor. With the small transistors, the developers saw the difficulties and complexities, thus bringing up the possibility of constructing advanced circuits. Howsoever, low power VLSI came to rescue for the complex circuits consuming large supply of power. 

With overall supply voltage lowered, the power loss of the circuits can be reduced to a large extent. Another reason to scale down the power is the voltage restriction. At a low power supply, a low voltage is preferable to maintain the balance in performance without compromising the frequency or the speed. Low power VLSI has emerged as an important research area because of the research scope it offers to the researchers. Researchers have a wide array of limitations and advancements to work on and improve the efficiency. 

However, how to develop a PhD project in low power VLSI? For finalising a research topic in low power VLSI, formulate an objective to consider. A researcher can choose the objectives like leakage power reduction, reducing dynamic power dissipation, glitch reduction, path delay optimisation. While power dissipation can be in divided into three categories : 

• Dynamic power consumption
• Short circuit current
• Leakage current

A researcher has to study the literature on the basis of the objective formulated by him. Going through the literature of the formulated objective will help the researcher build research problem and know how to fill the existing gap of the literature. The scholar should analyse various recent researches to develop a framework of what has been performed recently in the field of low power VLSI. Once the objective and research problem is formulated, it gives a clear mindset to the PhD scholar to define a scope of the research and continue with it. The extent of the gap will help the researcher to overcome the issue.

Draw upon an algorithm to reduce the existing problem in current researches and to enhance the result. The conventional algorithms to minimise the issues are the genetic algorithm, local minimisation algorithm. An algorithm or technique is used to reduce the complexity, and maintain regularity in the functioning of the process. The low power design technique provides partitioning and power down system with logical style circuit and energy recovery, and threshold reduction technology.  The researcher should incorporate different objectives together and identify techniques for finalising the research scope. Once the proposed scheme of research is implemented, then compare the same with related recent researches and validate the proposed approach. There are various evaluation parameters to evaluate the studies and compare them; however, the comparison will lead the scholar to a better thesis. The research should be concluded by stating the effectiveness of proposed theme. 

The proposed theme and the research will undoubtedly be effective for the academic strata of the field to observe the results and findings of the research and use it to carry forward their research. 

Broadly speaking, the term ‘Internet of Things’ is about connecting everything to the internet, i.e., your regular day-to-day things being connected to the internet and providing access for sharing the information. The devices are connected to the internet through the hardware like sensors and actuators, and making them from simple regular things to be internet accessible, gather information, analyse it, and take action on the basis of that information. 

With the Internet of Things being a hot topic for the scholars to study and research, most of the researchers are conducting the research on this topic for their doctoral degree. While IoT is all about network, device, and data, to make the devices able to communicate to each other. 

Well aware of the fact that, before researching for your PhD, every scholar has to submit the research proposal; depicting the research question, statement, literature review, data collection methodology, analysis & interpretations, results & findings, and conclusion of the whole research. However, to begin with, your proposal in the domain of Internet of Things, start with narrowing down the topic to a specific area of Internet of Things.

Narrowing down to a specific area related to the Internet of Things means to get to a niche of the topic. The researcher will either research on the module of a smart city, smart homes, smart hospital, smart schools, or any other area pertaining to the need of data transmission. When you narrow down the specific field of the research, you are dividing your work and lessening your pressure of working in the domain of IoT. 

After you narrow down the specific area, perform a background study on the concerned topic. The background study will help you find the research gap in the subject and know its literature. When you go through a background study on your topic, you review variously related writings to identify the scope of your research. Examining a number of existing researches will give you the drawbacks of the topic to know where your topic lacks, and where you have to work on. 

Formulate the objectives based on your review. The objective will make your mind clear on the reason behind your research. Thence, you will be more confident with your research and to why is it needed. The need for the study will help you examine how it will benefit on the larger scale. 

Once you are clear about your topic and the objective of your research, decide on a methodology to conduct the research. The methodology will bring advancement in the concerned area with an enhancement to the existing results of the field. The security algorithm and techniques should be identified to conclude the research. While you provide a conclusion to your research with the techniques and algorithms in your mind, jot down the possible drawbacks of your research as well. Since everything has some pros and cons, your research will too, have a few issues and drawbacks. 

Note down the drawbacks of your research as to make sure that it will not hinder your research and hypothesis. Therefore, after identifying all the aspects of your research, you should write it down and present it to your supervisor for further consideration. 

In-depth interviews are labour-intensive. They cannot finish in one attempt. The process of data analysis starts when you have collected material. However, you still cannot separate the process of gathering data from analysis. In fact, you will start analysing data as you commence an interview. Analysing the data sets off as you hear something from your participants. However, you have to be considerate during this stage as it might contaminate the next part of the interview. 

Tape-recording interviews

For better analysis, you should have words of participants in written form. The primary method to transform spoken words to written text is to transcribe the recorded material. Your consciousness will play a paramount role in the interpretation of data as it must interact with the words of participants. 

Another benefit of tape-recording interviews is you have access to the original data. If something is not clear in a transcript, you can return to the source and check accuracy. Further, you can also avoid the accusation of mishandling data by demonstrating your accountability to data. The tape-recording method also enables you to improve your interview techniques.

Transcribe interview tapes

Transcribing interview tapes is an arduous task and potentially costly too. As a substitute, you should listen to tapes a number of times and then pick up the most important section and transcribe that part only. However, this is not an advisable approach because it may lead to premature judgment about what is essential and what is not. 

How to study and analyse the text

In-depth interviews generate a colossal amount of information. Organised in several stages, these interviews emanate records full of long sentences, words, paragraphs and pages. It is requisite that you pick up information that is most relevant to answer your research questions and meet research objectives. The most crucial thing is that you reduce data by inductive approach rather than deductive approach. 

The first step to reduce the text is to read it and mark with brackets the passages that seem important and interesting. While winnowing the text, you may feel unable to decide on the significant paragraphs. You will feel like falling into the trap of self-delusion. Therefore, you can later check with the participants to see if what they have marked as important seem interesting to participants or not.  

Sharing interview data

Your ultimate goal of marking text as important from a transcript is to shape it in a presentable form. You can use two basic ways to share interview data. First, you can develop profiles of individual participants and group them into categories. Second, you can mark individual passages, group and study them in categories.

There is no right way to craft profiles for sharing interview data. Some researchers present the text in charts and graphs, and some give priority to words more than a graphical representation. Once you have read the transcript, marked passages of interest, and labelled those passages, you will put all those passages in a single transcript. This version may result in one-third of the original interview script. 

The next step is to read the new version and underline paragraphs that are compelling. Be faithful to the words of participants. You may be tempted to introduce some words to make transitions between passages. You can let readers know when words of participants have not been used by using ellipses or brackets around your own text. 

Making and analysing thematic connections

While reading interview transcripts, you can label the text that you find interesting and important in the context of your research questions. Further, you will decide on a word or phrase in which labelled passages can fit. Sometimes, you may find a term from a paragraph itself. Assigning a particular term to each selected paragraph is known as categorising. This entire process is also known as coding. This is essential so that a computer programme can quickly sort and classify interview data. 

Interpreting the material

Interpretation is not a stage that begins at the end of an interview. The interpretation process starts as you ask questions from your participants. Marking interesting passages, labelling them and categorising them is an analytic work that calls for an interpretation. Visit here for detailed information on Data Analysis.

“Big Data”, well the phrase is there almost everywhere. The term was coined quite a while back but got officially incorporated in the Oxford English Dictionary in the year 2013. It has surpassed all levels of inflated expectations and has become nothing less than a rage. But do all of us know what it actually means?

In a very fundamental definition of Big Data, there are three V’s: Volume, Velocity and Variety. However, there are some counter arguments associated with it, which say that thesis of the data is not determinant for categorising it.  Rather, it is based on the tools being used in the data and the kind of insights that are being drawn from it.

If this definition excites you and generates interest in your mind, I have some good news for you. The demand for Big Data scientists is far ahead than the supply at present. With the way the data scientists are getting in demand and the highly competitive industries are looking for them, only those candidates which have qualification of the top most level are the ones who have the brightest chances. If you want to acquire a PhD as a data scientist, here are some quick tips for you:

Have a strong focus on academics: Successful data scientists can be from diverse backgrounds. A good data scientist is the one who has the multi disciplinary experience of applying scientific tools, as this proves quite useful in the application aspect of the data scientist. Stay abreast with latest research trends and read as many journals as possible.

Have the knowledge of business background: A strong background in business and strategy can make a good foundation for the career of a data scientist and can help to jettison the career of a data scientist in the preliminary stage itself. As data scientist you must know:

• How the business works?
• How is the data collected?
• How is the data intended to be used?
• What is expected to be achieved from the data analysis?

The curriculum of PhD in Big Data in your university: Not all universities at the moment are abreast and equipped enough to offer a PhD in Big Data. If you are sure off doing one and have shortlisted a few options, do look into choosing the one that gives you lot of practical exposure and understanding of application of concepts with a business acumen as eventually, if you do a PhD in Big data, you wouldn’t be getting into academics. Corporate would be your calling and you must enter into it, equipped enough to justify your education.

Nowadays Big Data is seen everywhere and there is suddenly a crucial requirement for collecting and preserving whatever data gets generated, due to the fear of missing out something vital. Do you know that there is significant amount of data floating around? What we actually do is what will lead to our business success. This is the reason why Big Data has suddenly become in rage in the IT sector.

Bid Data has become vital as it helps in providing much needed leverage over competitors, aids in decision making and improving business. This is true for professionals as well as organizations in the analytics domain. For professionals who are well versed with Big Data Analytics, there are significant opportunities to explore. If you still require more convincing, read on to know the 5 reasons why Big Data is in trend.

Top priority of large numbers of organizations
Various surveys have concluded that Big Data Analytics is one of the key priorities for the organizations. They are of the opinion that it enhances the performances of their organization significantly.

Big Data Analytics Adoption is Enhancing
The advent of new technologies is making it extremely easy to perform highly sophisticated data analytics on a large and diverse datasets. Various reports have clearly indicated that large numbers of organizations are using some form of Big Data analytics for data mining tasks, predictive analytics and business intelligence.

Big Data is Utilized Everywhere
Due to its extraordinary features, big data analytics is nowadays in huge demand. The significant growth is also due to the various domains across which Big Data is used.

A Key Factor in Decision Making
Do you know that today analytics is a crucial competitive resource for various companies. It plays a crucial role in driving business strategy and making vital business decisions.

Significant job opportunities
According to various researches, the demand for analytics skills is going up significantly, but there is still a tremendous deficit on the supply side. Do you know that the gap between demand and supply is happening globally and is not confined to any particular country? Even though Big Data Analytics is a hot job opportunity, but still there are significant numbers of unfilled jobs across the world. The demand for talents Big Data experts are expected to be significant as large numbers of global organizations is nowadays outsourcing their work to the companies and people who understand and viably use Big Data!

Often scientists need to know how things will work in the real world. When the experiment that is being conducted is on a large scale and the risks are too high and the cost is too great, it is not possible to implement it in real life. This is where simulations come in useful, wherein the scientists are able to replicate the model that they expect to see in real life and obtain the results that will help them in their process.

There are many applications that are available that can allow researchers to simulate what they expect to see in real life. Those working in the field of networks would be familiar with NS2 or Network Simulator 2. It is what is called as a discrete event simulator. The simulator creates a model where the operations of a system act as a sequence of events and as the time changes the state of the system changes as well. There is another application called NS3 or Network Simulator 3 and this is open source software. That means it is publicly available and free for anyone to use and develop.

One of the benefits offered by the simulator is that it can also be an emulator. That means that it can be connected to a live network. Once it has been connected to a live network, it will receive data from the incoming network traffic allowing the researcher to work on it and analyse it.

There are two languages which are used to build this application. The first is C++. This is the language that is used to create the internal system of the simulator. The second is OTCL. This language controls the discrete events and simulations. The simulator gives results in both a text format as well as an animation format.