Internet of things is an umbrella term of an omnipresent network where customized information are collected and transferred at required locations by utilizing a broad range of technologies, applications and use cases. IoT could be meant for Industrial purposes, for the Internet itself and many more with wide variety of acronyms peculiar to their uses. IoT has its own IoT technology stack that starts from connecting things, capturing the data and communicating it and ending with applications. Data is inherently dumb. We must decide what we do with it and also prioritize which data, turned into information. It’s here among others that edge computing with IoT and fog computing comes in. IoT is the next stage in the internet whereby things and objects with sensors and actuators are connected to the Internet so they can gather, send and get data, leading to smarter solutions and in some cases also act upon data.

Here it is important that we know what is and what is not Internet of Things. Not all connected devices are IoT-connected devices but all IoT-connected devices are connected devices. Moreover, in the Internet of Things we use Internet Protocol (IP), more specifically IPv6. So, we only speak about the Internet of Things when ‘things’ are uniquely addressable, using an IP address or Uniform Resource Identifier. It is essentially an ecosystem of inevitably related processes and other technologies from the perspective of a goal within a specific use case.

A.I. is also a component of the Internet of Things (IoT), which is consists of the collection of data by sensors, A.I. analytics that identifies patterns in the movement, usage, or other changes in people or objectives enables various data-driven decision-making and the automation of some tasks.

For all the advancement in lab techniques and kits, lab management remains rudimentary. Researchers depend on colleagues with the longest tenure in the lab to find equipment that is stored, misplaced or secretly stashed in various locations.

With IoT, each piece of equipment can be tagged, tracked and easily located. This way, the equipment will become much more visible and much less likely to be misplaced or hidden. As a result, the overall usage of equipment will go up reducing the waste that occurs due to equipment under-utilization. Tracking equipment alse helps researchers understand which machines are under used and which are hard-to-access even in full availability, so that they will omit expenditure on the former and, instead, spend more money on the latter.

IoT can automate data recording which can minimize errors in manual recording where much information and know-how are lost as outgoing researchers transfer their reagents and lab notebooks to their colleagues. In the process, if researchers do not get everything sorted before the other person leaves the lab, they may lose a lot of institutional knowledge on how an experiment is done, where the reagents are, and what the quality and characteristics of each batch of cells or proteins are, and so on. Furthermore, deciphering the other person’s handwriting on the notebook can be excruciating. With the automation of data recording, the person taking over a project may have clearer idea of what has been done, how it was done and where things are at the present.

IoT can help in restocking supplies. The lack of supply may disrupt an experiment and force the researcher to repeat the experiment later. Right now, technicians and lab managers shoulder much of the responsibility in tracking inventory. Checking inventory is a tedious, inefficient and sometimes inaccurate process. By tracking the inventory supplies with IoT, researchers can be alerted when things are running low, Also, stock replenishment can be automated. With view-only capability, a researcher can see if neighboring labs have some extra supply from which they can borrow. In the same vein, digital records can help labs keep track of who has borrowed what from them.

IoT can assist in predictive maintenance. In a lab, some machines are more heavily used and may require more frequent maintenance while less-used ones require attention. With usage analysis by IoT, researchers can identify the most frequently-used equipment and schedule more frequent services for them proactively, so that they are less likely to break down unexpectedly.

IoT can also help identify equipment that is on the way out. Conventional monitoring relies largely on luck; somebody noticing something is wrong sometimes. If nobody has noticed, then researchers will not be alerted until a breakdown has occurred.

With IoT, data will be collected continuously, and analytics can illustrate the overall data trend. This way, researchers will know which machine is nearing a breakdown and schedule to repair preemptively. Other applicantions of IoT in the lab may include using it to analyze the traffic flow in the lab so that researchers can move equipment around to increase usage or reduce congestion. Overall, many successful applications of IoT in the hospital setting can be repurposed to the lab to increase its productivity and efficiency.



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