The Internet of Things (IoT) is the network of physical objectsâ€”devices, vehicles, buildings and other items which are embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. Implementing this concept is not an easy task by any measure for many reasons including the complex nature of the different components of the ecosystem of IoT.Â To understand the gravity of this task, we will explain all the five components of IoT Implementation.
According to (IEEE) sensors can be defined as: An electronic device that produces electrical, optical, or digital data derived from a physical condition or event. Data produced from sensors is then electronically transformed, by another device, into information (output) that is useful in decision making done by â€œintelligentâ€ devices or individuals (people).
Types of Sensors:Â Active Sensors & Passive Sensors.
The selection of sensors greatly impacted by many factors, including:
- Purpose (Temperature, Motion, Bioâ€¦etc.).
- Level of Intelligence (dealing with noise and interference).
The driving forces for using sensors in IoT today are new trends in technology that made sensors cheaper, smarter and smaller.
Challenges facing IoT sensors:
- Power consumption.
The second step of this implantation is to transmit the signals collected by sensors over networks with all the different components of a typical network including routers, bridges in different topologies, including LAN, MAN and WAN.Â Connecting the different parts of networks to the sensors can be done by different technologies including Wi-Fi, Bluetooth, Low Power Wi-Fi , Wi-Max, regular Ethernet , Long Term Evolution (LTE) and the recent promising technology of Li-Fi (using light as a medium of communication between the different parts of a typical network including senores).
The driving forces for wide spread network adoption in IoT can be summarized as follows:
- High Data rate.
- Low Prices of data usage.
- Virtualization (X â€“ Define Network trends).
- XaaS concept (SaaS, PaaS, and IaaS).
- IPv6 deployment.
Challenges facing network implementation in IoT
- The enormous growth in number of connected devices.
- Availability of networks coverage.
- Power consumption.
The third stage in the implementation process includes the sum of all activities of handling, processing and storing the data collected from the sensors. This aggregation increases the value of data by increasing, the scale, scope, and frequency of data available for analysis but aggregation only achieved through the use of various standards depending on the IoT application in used.
Types of Standards
Two types of standards relevant for the aggregation process; technology standards (including network protocols, communication protocols, and data-aggregation standards) and regulatory standards (related to security and privacy of data, among other issues).
- Network Protocols (e.g.: Wi-Fi).
- Communications Protocols (e.g.: HTTP).
- Data aggregation standards (e.g.: Extraction, Transformation, Loading (ETL).
Set and administrated by government agencies like FTC, for example Fair Information Practice Principles (FIPP) and US Health Insurance Portability and Accountability Act (HIPAA) just to mention few.
Challenges facing the adoptions of standards within IoT
- Standard for handling unstructured data: Structured data are stored in relational databases and queried through SQL. Unstructured data are stored in different types of noSQL databases without a standard querying approach.
- Security and privacy issues: There is a need for clear guidelines on the retention, use, and security of the data as well as metadata (the data that describe other data).
- Regulatory standards for data markets: Data brokers are companies that sell data collected from various sources. Even though data appear to be the currency of the IoT, there is lack of transparency about, who gets access to data and how those data are used to develop products or services and sold to advertisers and third parties.
- Technical skills to leverage newer aggregation tools: Companies that are keen on leveraging big-data tools often face a shortage of talent to plan, execute, and maintain systems.
The fourth stage in IoT implementation is extracting insight from data for analysis, Analysis is driven by cognitive technologies and the accompanying models that facilitate the use of cognitive technologies.
With advances in cognitive technologiesâ€™ ability to process varied forms of information, vision and voice have also become usable. Below is a list of selected cognitive technologies that are experiencing increasing adoption and can be deployed for predictive and prescriptive analytics:
- Computer vision refers to computersâ€™ ability to identify objects, scenes, and activities in images.
- Natural-language processing refers to computersâ€™ ability to work with text the way humans do, extracting meaning from text or even generating text that is.
- Speech recognition focuses on accurately transcribing human speech.
Factors driving adoption intelligent analytics within the IoT
- Artificial intelligence models can be improved with large data sets that are more readily avail- able than ever before, thanks to the lower storage.
- Growth in crowdsourcing and open- source analytics software: Cloud-based crowdsourcing services are leading to new algorithms and improvements in existing ones at an unprecedented.
- Real-time data processing and analysis: Analytics tools such as complex event processing (CEP) enable processing and analysis of data on a real-time or a near-real-time basis, driving timely decision making and..
Challenges facing the adoptions of intelligent analytics within IoT
- Inaccurate analysis due to flaws in the data and/or model: A lack of data or presence of outliers may lead to false positives or false negatives, thus exposing various algorithmic limitations.
- Legacy systemsâ€™ ability to analyze unstructured data: Legacy systems are well suited to handle structured data; unfortunately, most IoT/business interactions generate unstructured data.
- Legacy systemsâ€™ ability to manage real- time data: Traditional analytics software generally works on batch-oriented processing, wherein all the data are loaded in a batch and then analyzed.
Intelligent actions can be expressed as M2M and M2H interfaces for example with all the advancement in UI and UX technologies.
Factors driving adoption of intelligent actions within the IoT
- Lower machine prices.
- Improved machine functionality.
- Machines â€œinfluencingâ€ human actions through behavioral-science rationale.
- Deep Learning tools.
Challenges facing the adoption of intelligent actions within IoT
- Machinesâ€™ actions in unpredictable situations.
- Information security and privacy.
- Machine interoperability.
- Mean-reverting human behaviors.
- Slow adoption of new technologies.
The Internet of Things (IoT) is an ecosystem of ever-increasing complexity, itâ€™s the next weave of innovation that will humanize every object in our life, which is the next level to automating every object in our life. Convergence of technologies will make IoT implementation much easier and faster, which in turn will improve many aspects of our life at home and at work and in between.
IoT Expert | Faculty | Author |Â Conferenciante
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