Tag Archives: home

Home- still a dream of many

Modernization, the want to step ahead of time, and fight between the human race, has become one of the reasons why people try and judge each other for what kind of house they live in.

Whenever a person go to someone’s home, the first thing they tend to judge, is there home well furnished? Is the arrangement of their house proper? And etc. etc. But forget that, income and prices are the two factors that do not go hand in hand. Also, the country which we reside in, is one of the developing nations in the world, where “A perfect home” is still a dream of millions. Thus, how can we expect that when a person can’t even afford to live peacefully under a roof, how can the other render to make everything look pretty.

Urban and rural differences –

In India, most of the population hail from village areas and move towards cities in search of better opportunities, such as, jobs in secondary and tertiary sectors, better resources, and better standard of living. Instead, they fail to survive most of the times and hang in between the air with less access to resources and low standard of living, since affording such standard of living remains out of their approaches.

According to the report of World Bank’s collection of development indicators, compiled from officially recognized sources, rural population in India was reported at 65.97%

While people hailing from rural areas face tough times with city lifestyle. People residing in cities too confront with problems of proper houses to live in, also they are detained from basic resources that are searched by village people in the limelight of cities. They often end up living in congestion, without proper sanitation facilities, water and electricity, and lag behind in most of the basic values that are necessary to live a prosperous life. Also population remains a constant reminder that the land is unavailable to fit such a large amount of people who seek to live in the middle of the cities instead of country sides. The pressure upon the land has become a huge chunk of tension for the policymakers, which is why most people remain neglected from a proper facility of house and housing.

Dharavi, Asia’s biggest slum is full of such people who either migrate to cities in search for resources, or the people who are unable to afford a lifestyle that is basic necessity and right of every human being. It is a place with 2.1 sq. Km. and a population as estimated in 2016, 900,000 – 1,000,000 people.

Problem of resources –

While most of us are happy with the lives and are living peacefully in our own homes. There are still millions of people who do not even have access to basic resources such as –

  • Sanitation facilities
  • Housing
  • Water supply
  • Electricity
  • Food resources
  • Proper job facilities

We can find people sleeping on the pavements of the roads, they could be seen eating on the roadside food joints, and earn only on per day basis, which are not sufficient to provide them with a proper place to live in.

According to NSSO, landlessness in India is a major problem and the total population works out to be 200 million that is counted to be landless.

Thus, a major proportion of the country’s population still have a dream to have their own place to live in. Also, slums remain an inevitable problem in India that cannot be replaced due to their presence from a very long past. It is likely to achieve a figure where most of the Indians could have their own home sweet home.


What is Internet of Things (IoT)?

The Internet of things (IoT) is a system of interrelated computing devices, mechanical and digital machines provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

The definition of the Internet of things has evolved due to the convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems. Traditional fields of embedded systems, wireless sensor networks, control systems, automation (including home and building automation), and others all contribute to enabling the Internet of things. In the consumer market, IoT technology is most synonymous with products pertaining to the concept of the “smart home”, covering devices and appliances (such as lighting fixtures, thermostats, home security systems and cameras, and other home appliances) that support one or more common ecosystems, and can be controlled via devices associated with that ecosystem, such as smartphones and smart speakers.

History of IoT

The idea of adding sensors and intelligence to basic objects was discussed throughout the 1980s and 1990s (and there are arguably some much earlier ancestors), but apart from some early projects – including an internet-connected vending machine – progress was slow simply because the technology wasn’t ready. Chips were too big and bulky and there was no way for objects to communicate effectively.

Processors that were cheap and power-frugal enough to be all but disposable were needed before it finally became cost-effective to connect up billions of devices. The adoption of RFID tags – low-power chips that can communicate wirelessly – solved some of this issue, along with the increasing availability of broadband internet and cellular and wireless networking. The adoption of IPv6 – which, among other things, should provide enough IP addresses for every device the world is ever likely to need – was also a necessary step for the IoT to scale.

Kevin Ashton coined the phrase ‘Internet of Things’ in 1999, although it took at least another decade for the technology to catch up with the vision.

Adding RFID tags to expensive pieces of equipment to help track their location was one of the first IoT applications. But since then, the cost of adding sensors and an internet connection to objects has continued to fall, and experts predict that this basic functionality could one day cost as little as 10 cents, making it possible to connect nearly everything to the internet.

The IoT was initially most interesting to business and manufacturing, where its application is sometimes known as machine-to-machine (M2M), but the emphasis is now on filling our homes and offices with smart devices, transforming it into something that’s relevant to almost everyone. Early suggestions for internet-connected devices included ‘blogjects’ (objects that blog and record data about themselves to the internet), ubiquitous computing (or ‘ubicomp’), invisible computing, and pervasive computing. However, it was Internet of Things and IoT that stuck.


Ambient intelligence and autonomous control are not part of the original concept of the Internet of things. Ambient intelligence and autonomous control do not necessarily require Internet structures, either. However, there is a shift in research (by companies such as Intel) to integrate the concepts of the IoT and autonomous control, with initial outcomes towards this direction considering objects as the driving force for autonomous IoT. A promising approach in this context is deep reinforcement learning where most of IoT systems provide a dynamic and interactive environment. Training an agent (i.e., IoT device) to behave smartly in such an environment cannot be addressed by conventional machine learning algorithms such as supervised learning. By reinforcement learning approach, a learning agent can sense the environment’s state (e.g., sensing home temperature), perform actions (e.g., turn HVAC on or off) and learn through the maximizing accumulated rewards it receives in long term.

IoT intelligence can be offered at three levels: IoT devices, Edge/Fog nodes, and Cloud computing. The need for intelligent control and decision at each level depends on the time sensitiveness of the IoT application. For example, an autonomous vehicle’s camera needs to make real-time obstacle detection to avoid an accident. This fast decision making would not be possible through transferring data from the vehicle to cloud instances and return the predictions back to the vehicle. Instead, all the operation should be performed locally in the vehicle. Integrating advanced machine learning algorithms including deep learning into IoT devices is an active research area to make smart objects closer to reality. Moreover, it is possible to get the most value out of IoT deployments through analyzing IoT data, extracting hidden information, and predicting control decisions. A wide variety of machine learning techniques have been used in IoT domain ranging from traditional methods such as regression, support vector machine, and random forest to advanced ones such as convolutional neural networks, LSTM, and variational autoencoder.

In the future, the Internet of Things may be a non-deterministic and open network in which auto-organized or intelligent entities (web services, SOA components) and virtual objects (avatars) will be interoperable and able to act independently (pursuing their own objectives or shared ones) depending on the context, circumstances or environments. Autonomous behavior through the collection and reasoning of context information as well as the object’s ability to detect changes in the environment (faults affecting sensors) and introduce suitable mitigation measures constitutes a major research trend, clearly needed to provide credibility to the IoT technology. Modern IoT products and solutions in the marketplace use a variety of different technologies to support such context-aware automation, but more sophisticated forms of intelligence are requested to permit sensor units and intelligent cyber-physical systems to be deployed in real environments