Contact us anytime to know more — Kuldeep K., Founder & CEO CISIN
The Internet of Things, or connected devices and infrastructure that can be remotely managed, has transformed many aspects of life for modern humans.
As it develops quickly and introduces innovative new products to market expected to exceed $250B by 2027 its industry is continuously growing and offering more products for sale than ever.
Many companies rely on IoT apps for remote control of their IoT devices. Not only have things advanced over time but so has software used for controlling them remotely meaning apps and devices tailored specifically for specific requirements like smart homes or medical equipment in hospitals and larger scale solutions, like smart sprinklers, can now exist and other iot manufacturing industry solution industries.
IoT Software Engineering involves systematically creating and deploying IoT infrastructure using software and hardware components.
Engineers in IoT use sensor data collected to transform it into real-world applications using intuitive user interfaces for visual presentation and intuitive UIs in different industrial sectors for business success
IoT applications span an array of sectors. Common IoT usage scenarios include applications in aviation and avionics for monitoring status and sustainability, automobile industries for vehicle parameters and safety purposes, and broadcast apps include these examples:
- Smart Cities
- Construction
- Smart power grids
- Medical services
- Smart market-based analysis
IoT Software Development Technologies
Organizations need three components to develop IoT solutions: OS, development platform, and programming language.
IoT Development Platforms
Platforms serve as the cornerstone for developing IoT products. Therefore, they must be carefully considered when choosing their development platforms, considering each platform's features here are some examples:
- IBM Watson: IBM provides its IBM Watson development platform as an IoT development solution, promising quick and secure implementations, online data analysis capabilities, and visualization of critical risks.
- Microsoft Azure IoT Platform: Microsoft's Azure IoT platform features capabilities like data collection, analysis, and visualization that enable an IoT app to grow by collecting more data or interoperability with more devices - without needing major alterations or code modifications to reach full scale.
- Amazon Web Services: IoT platform features AI integration, multilayered safety protections, and scalability, plus analytics, device software updates, connectivity features, and control mechanisms to keep IoT projects operating smoothly and safely.
IoT OSes
Internet-of-Thing (IoT) systems typically consist of processing units with limited power consumption, RAMs with small memory capacities, and limited storage.
As a result, their OS must not eat up too many resources - here are a few IoT operating systems to choose from:
- Raspbian: Raspbian, one of the world's leading IoT operating systems, was specifically tailored for Raspberry Pi hardware, making it user-friendly with over 35,000 packages available through compile time alone.
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Arm Mbed OS: Arm Mbed OS is an open-source OS designed to meet all the specifications necessary for the Internet of Things systems.
Mbed OS features multilayer security and drivers for Bluetooth connectivity, thread processing, 6LoWPAN networks, and Wi-Fi access points.
IoT Programming Languages
Finding an effective programming language to build IoT applications can be crucial. When dealing with limited resources and compile time limitations, code should be concise and straightforward - here is a selection of programming languages used in developing these applications:
- C And C++: C and C++ are widely-used languages written with hardware in mind, while Java's mobile programming capabilities make it well-suited for IoT devices.
- Java: Known as the mobile programming language, Java is appropriate for Internet of Things (IoT) devices and is interoperable with many peripheral devices.
- Python: Python is an ideal choice for IoT applications because it can handle data-rich applications.
Understanding IoT System Architecture
Contrasting it with traditional custom software development processes, understanding IoT architecture differs significantly.
An IoT-based system comprises four stages of architectural development.
Stage 1: Sensors And Actuators
At this stage of architecture design, sensors, and actuators collect environmental or object data and turn it into useful data points for processing by an AI application.
Stage 2: Data Preprocessing
Data Preprocessing Sensor data collected in stage one are typically analog, raw data that must be aggregated and converted to digital format for IoT applications to use.
Data acquisition systems connected with sensor networks conduct this conversion from analog-to-digital format.
Stage 3: IT systems
IT Systems The converted data can only flow directly into a data center after being preprocessed - engineers must address limited space and security issues before only sending significant results to cloud providers.
Stage 4: The Cloud
Once data has been successfully extracted and organized for processing, it is stored at traditional data centers or the cloud so IT specialists can reshape it and make it more readable by end users.
Top Functionality Of An IoT Software
This section will look at the primary elements of an Internet of Things software mobile app, website, or dashboard and its internet of things services that meet users' requirements.
Draw your attention not just to the main feature itself but to other, less obvious aspects as well.
# 1: Data Collection
Your software's backend should be responsible for data collection. You can take multiple approaches when organizing it to collect this information efficiently, which could save energy, resources, and money overall if selected wisely.
As part of your IoT app development project, it's vital that a robust data collection system be established.
Your data collection strategy depends upon a range of considerations:
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Frequency & Accuracy: This way, you correlate the data's desired accuracy with the collection frequency needed to reach it.
A certain level of accuracy may be sufficient; thus, a higher level will not provide any additional value.
You will be able to reduce the measurement frequency, saving you resources used for data collection while still allowing option accuracy level.
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Time: If the data must remain current and relevant for a certain amount of time, you can specify a "maximum time gap" between the most recent and the next measurement.
This way, your data collection will be performed with an interval fully tailored to your use case.
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Energy: Actually, this one is comparable to the case of frequency.
However, in this case, energy consumption plays a crucial role, so a given level of data accuracy and quantity is sufficient, and the energy consumption can be kept to a minimum as it's enough to reach the goal.
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Privacy: You can add so-called "noise" to the measurement process if you simply need to measure a certain amount of data and don't want to risk accidentally digging deeper.
It'll interfere with the process once the needed level of data accuracy is reached, allowing you to still comply with all the necessary regulations & respect privacy.
# 2: Data Processing
Data processing requires substantial resources; as a result, care must be taken when choosing what information needs processing and which can wait.
Dashboard and mobile applications for IoT should provide real-time user data. Before beginning analysis on data for system functioning purposes, first, determine how much is necessary and limit yourself to this amount to be more cost, energy, and time efficient in processing this amount.
Remember that you'll require additional tools for processing this amount, which may incur extra expenses.
If you use third-party processing solutions, make sure of their statistics and your service agreement (often neglected).
Some areas to investigate include how often breaches occur within their system and terms regarding changing vendors, etc. As well as real-time data processing capabilities, real-time processing is essential for industries like agriculture, healthcare, weather forecasting, and smart door locks.
# 3: Data Storage
External Storage On IoT devices, typically, only a little storage capacity exists, necessitating using external storage solutions for most use cases.
An IoT application may store its data on cloud platforms or any other form of storage media. So when selecting storage solutions, pay special attention to how quickly and efficiently the data can be retrieved for any need.
Furthermore, selecting one which allows searching by time-stamp or other filters to locate what you require quickly is also key. Furthermore, making data exportable and transferrable into various formats is helpful for analysis and comparison.
# 4: Data Engineering
Data analytics is an indispensable component of iot solutions company to function and provide value to end-users.
The core function is extracting pertinent information from daily "thing" activity to be distributed as useful knowledge to users.
Before visualizing it for customers, this process must first occur so they won't only see raw data but also something useful that allows them to track dynamics, draw clear conclusions, and make data-driven decisions.
You could create IoT technology that enables analytical services.
Real-time data analytics is vital, enabling users to take immediate actions if required (depending on each specific use case).
Data analytics also play a valuable role in the following:
- Be carried out over an extended period of time so that consumers can spot trends and general tendencies.
- Offer conclusions and steps needed to be taken based on the analysis.
- Assist in improving the personalization and targeting of marketing initiatives, etc.
# 5: Data Visualization
To present data to end-users, certain tools for visualization will be necessary. Most frequently, this function implies:
- The visualization itself (Real-time table, sensor overview, charts & tables, etc.)
- History so users can access previous data visuals.
- Notifications to make sure that users know when something new comes up.
- Alerts something that requires immediate attention and/or action.
Attention is focused on alerts. Establish a habit of immediately paying attention and reacting when urgent situations arise - this will benefit both users and providers alike.
Consider including special sound effects when alerts come through or marking them with flashy icons. Hence, they stand out among all of the rest.
Be sure to categorize visualized data. More specifically, avoid providing too many graphs that represent similar variables simultaneously; instead, make it manageable so users can choose what variable will appear for them.
Take smart sprinkles as an example: instead of showing humidity across each field with its chart, include an option where users can choose which field(s) to show at once.
# 6: Device Management
Device Administration You will likely require users to add features for controlling various parts of the "thing." These could include:
- Provisioning (adding a device to the system) and Authentication.
- Configuration and Control (turn on/off, change modes, etc.) It's important to take even more control of the device's components (like frequency of data measurements, data transferring to the cloud, etc.)
- Monitoring and Diagnostics.
This feature is intended to display all processes, warn if something is off, and regularly diagnose to see if there are any breakdowns.
- Software Maintenance and Updates.
This can help you & your users fix everything remotely.
Control of IoT mobile devices can be managed using IoT apps or solutions provided. Your device's sensors should also be organized into groups; you could create a hierarchy of these groupings so, for instance, sensors might reside on one device while all fleets of them form one grouping.
Think about adding an enhanced searching capability to your device management feature, for instance, searching by:
- Device ID.
- Device state (on/off, working/broken, etc.)
- Device type (if you have several) & others.
This searching feature is more suitable for broader "things," e.g., for hospitals; however, it applies to devices of any type & scale.
It all depends on your use case.
Read More: What are the 9 Examples of Internet of Things or IoT?
Top Considerations When Creating Software For An Internet of Things Device
Software development for IoT applications can be complex and technically intricate, yet to make the development process of the Internet of Things smoother; we suggest paying special attention to certain details during development.
Firmware
Firmware is the software responsible for communicating between different hardware devices.
- Hardware Boards.
- Edge Computing.
- Sensors.
Their development can use various languages from C to Python, Arduino being one of the simplest & easiest platforms available for creating firmware development features.
At CISIN, we use various technology stacks, such as React Native/Java/Kotlin, amongst many others, to develop software management features for successfully functioning firmware features.
Here, we would like to bring up one important topic: firmware updating. Nowadays, one of the easiest methods is through Over-The-Air (OTA) updates; these download newer versions remotely for installation.
OTA updates can be especially important since sometimes users can only keep older versions installed which blocks access once updated.
Imagine an individual arriving late for their flight and not being able to open the door due to an outdated version of firmware preventing them from unlocking it due to being late; such an incident would tarnish both brand reputation and trust with customers.
Therefore, make it optional or alert users about it multiple times before any update; additionally, it might also help if users could schedule it independently.
Third-Party Integrations
If your use case includes integration of third-party devices, make sure that these aspects of IoT software development are taken into consideration:
- Data Acquisition Module.
- Data Processing Module.
- Communications Module.
Let's review them one by one:
# 1: Data Acquisition Module
A Data Acquisition Module is part of hardware used to convert physical signals received from things into digital forms that a computer can recognize as data for processing.
The physical signals here imply:
- Temperature.
- Vibration.
- Motion.
- Light & others.
An important part of Internet of Things (IoT) app development is building a Data Acquisition Module.
There are a number of factors that we advise considering when working with this part:
- Signals that will be measured.
Deciding what sensors and devices for measurement you'll need is essential.
- The number of sensors.
- The speed is needed to measure physical signal indicators such as sample rate.
- The needed measurement accuracy indicators, such as sensor resolution.
Once you have answers to these questions, you can determine the requirements for this module (in particular, your use case).
# 2: Data Processing Module
This part of the device serves several goals:
- Data processing.
- Analytics.
- Data storing & other computing operations.
Data Storage and Processing. A Data Processing Module is essential in building IoT apps for any connected device, ranging from smart homes to medical IoT systems.
Your role here should be to understand and articulate its end goal to guide both development and engineering teams in its creation.
Here, the key points might be:
- The amount of data that'll be processed.
The amount of sensors, whether you'll require real-time control and advanced analytics, how much room you'll want to leave for future updates, any size restrictions, etc.
will all affect this indicator in the manufacturing process in machine learning.
- The amount of data you'll need to store locally.
Therefore, the information you acquire will either be uploaded to the Cloud (or any other external storage) or kept on-site.
Thus, you'll need to decide how much data to store temporarily to do all the necessary calculations or for buffering purposes in case your cloud connection crashes.
Besides, don't forget to consider whether or not you'll need your "thing" to work offline, and if yes, for how long that will define the capacity of the local storage.
At first, it may not seem that significant, but eventually, these two indicators will greatly impact your development costs, size of the "thing," number & complexity of features, etc.
# 3: Communications Module
This piece of hardware enables communication between your external storage device and any third-party integrations/devices you wish to integrate.
Communication can occur using USBs and wireless protocols - more on which in a later section.
Internet of Things applications for plant watering and smart homes provide examples of Internet of Things applications.
Your device may integrate seamlessly into other parts of the hardware or be an independent device; one option, gateway architecture, may prove more cost-efficient by collecting sensor data through one location where all sensors will send their readings directly into storage.
Considerations Of IoT Development
Internet of Things development entails exchanging vast quantities of data, so developers and IT teams must take measures to secure it as it passes between devices connected via networks, potentially leaving themselves open to security risks posed by vulnerabilities that arise through multiple device connections with communication channels at its heart forming the backbone of IoT software engineering requiring strict protocols and established schemes to protect from problems that might arise during development.
How To Choose The Right IoT Connectivity Option?
When creating an IoT platform, data must flow between many layers and parts of its structure, such as "things," gateways, sensors, servers, and end-user solutions.
Communication among them takes place via so-called IoT protocols - which act like language for machines communicating data transmission.
IoT software solutions connect with cloud platforms and devices via various protocols. When selecting one for your IoT solution, multiple factors should be considered, including bandwidth usage, connectivity range, power consumption requirements, price, industry requirements, etc.
To better help our readers understand each protocol, we created a table listing their main characteristics:
Wi-Fi: Wi-Fi connectivity range is 10 to 100 meters; its usage and affordability make it popular and widely accepted.
But its effectiveness depends on how close a signal source the device is located.
Bluetooth Technology: Options available range between 1-100 meters in terms of connectivity range, data exchange speed, security and cost-effectiveness.
LPWAN: LPWANs boast long ranges (up to 40 km or even further), data transfer speeds are fast, and energy consumption remains minimal - making these networks suitable for agriculture, urban planning, and transport applications as well.
Ethernet: Controls the transfer of data via Local Area Network (LAN), physically connects devices using cables, and allows high data downloading or uploading speeds.
RFID: Radio Frequency Identification technology involves readers, tags, and applications, which allow objects to be equipped with tags to enable reading signals remotely from various places without direct line-of-sight contact between readers and objects.
Cellular: While offering the widest bandwidth of all protocols, high energy usage may make cellularly unsuitable for remote usage with battery-powered networks.
ZigBee and other Mesh Protocols: With low current consumption and intermediate power needs over 100 meters connectivity range.
Simple in use, resistant to unauthorized readings/errors, and highly scalable (large numbers of nodes can be placed.)
MQTT: Suited to battery-powered devices that feature low energy usage and are user-friendly with wireless networks while using minimum bandwidth resources.
DDS: DDS was specifically created for real-time M2M communications that provide fast data exchange rates between devices connected by Internet of Things solutions; though not widely adopted among IoT solutions, it still finds some use among Industrial IoT applications such as transports and robotics systems.
XMPP: consumer IoT applications that scale poorly; however, their security can be severely reduced due to no quality-of-service (QoS) or end-to-end encryption (E2EE).
AMQP: Excellent choice for queuing, message orientation, and routing (publish-and-subscribe and point-to-point.) Provides adequate security with robust communication models.
Not recommended for sensor devices with limited memory, power, network bandwidth capabilities.
LwM2M: Specially created for resource-constrained devices and used mainly for device management and telemetry applications.
Potential Pitfalls & How To Avoid Them
Before developing an IoT application, it would be useful to be aware of potential pitfalls which could compromise costs, user experience, or development speed if approached incorrectly.
Let's briefly address some of the potential challenges in development:
# 1: Firmware
As discussed previously, the firmware allows your hardware components to communicate efficiently. Still, its development speed depends heavily on whether it's ready for use.
An Internet of Things app requires this type of communication between its various connected parts; once ready to go, any delays would only come from bugs which should usually be addressed quickly by you as the developer.
However, early-stage firmware development can considerably stymie development time; controlling both application development and firmware testing processes, as well as fixing bugs, can become complicated at the same time and become time-consuming.
Therefore, we advise looking for devices with fully developed and tested firmware before beginning Internet of Things software development.
# 2: Security
One of the major hurdles of the IoT industry is security, as there need to be more standards that developers or engineers must abide by to make systems fully reliable - forcing them to come up with their methods to achieve that end goal.
Internet of Things (IoT) apps or software must also be secure due to not being standard across industries - something made even harder by having more sensors connected and increasing vulnerability that makes breaches more easily detectable by hackers.
Consider these strategies for increasing security:
- Security by design.
It's a system that allows us to prevent rather than fix any breaches in a security system with the help of regular testing, authentication safeguards, and the best cybersecurity practices.
- Regular updates.
Through trial and error, you can determine the best ways to make the device as secure as possible.
We recommend updating your security system as soon as you have something to improve.
- Use reliable certificates such as SSL/TLS for encryption and authentication.
- Multi-factor authentication.
It may involve biometric identification techniques including voice, face, and fingerprint scanning, end-to-end encryption (full encryption, which implies that only the sender and the recipient can read whatever it is), personal questions, email/phone number confirmation, complex password that's changed every once in a while, etc.
# 3: Inconsistency
Your app designed to allow users to control multiple devices may encounter difficulties being compatible with devices from different brands; for instance, if someone purchases both your smart toaster and another fridge from different companies neither may be controllable in your app, which could compromise both quality and reputation of service provided.
In IoT app development, consider that your mobile app must be consistent with IoT devices. Unfortunately, standardization isn't unified between companies using different APIs, protocols, and security features; you cannot change this situation directly; one potential solution might include integrating third-party services via their APIs, but that will depend on your use case.
# 4: Recovery Mode
It might be wise to include a so-called "recovery mode" feature in your IoT application should users need to restore lost data from the cloud or another storage service, for instance.
App developers for the Internet of Things could build recovery modes into apps containing them - however, this feature should ideally be integrated before app creation; its implementation requires accessing device bootloader files which would likely be easier if done before producing the "thing" itself.
# 5: Quality Assurance
Every update of an IoT firmware should be rigorously assessed as it affects applications to detect new bugs, test its behavior within applications and see whether its cloud storage remains compatible.
However, one key thing to remember here is that these quality tests cannot be reliably run as simulations; therefore, your development team might have to perform them manually or use actual devices, if possible, to create services with as smooth an experience as possible for their customers for IoT technologies.
Conclusion
The IoT market is expanding at an astronomical rate. It is projected by Research's global forecast to reach approx $561 billion in 2023.
As IoT-centric environments expand further, so will their demand for IoT software engineering increase exponentially.
