Building upon the advancements in the recent years, a new paradigm in technology has emerged in Internet of Things (IoT). IoT has allowed for communication with the surrounding environment through a multitude of sensors and actuators, yet operating on limited energy. Several researchers have presented IoT architectures for respective applications, often challenged by requiring major updates for adoption to a different application. Further, this comes with several uncertainties such as type of computational device required at the edge, mode of wireless connectivity required, methods to obtain power efficiency, and not ensuring rapid deployment. This paper starts with providing a horizontal overview of each layer in IoT architecture and options for different applications. Then it presents a broad application-driven modular architecture, which can be easily customized for rapid deployment. This paper presents the diverse hardware used in several IoT layers such as sensors, embedded processors, wireless transceivers, internet gateway, and application management cloud server. Later, this paper presents implementation results for diverse applications including healthcare, structural health monitoring, agriculture, and indoor tour guide systems. It is hoped that this research will assist the potential user to easily choose IoT hardware and software as it pertains to their respective needs.
The “Internet of Things (IoT)” refers to a global network of objects, or “things,” seamlessly connected to the internet which can fundamentally shift the way we interact with our surroundings. This IoT enables physical objects to see, listen, think, and perform tasks by sharing information and coordinating decisions. IoT transforms objects from being traditional to being smart by exploiting its underlying technologies such as sensor networks, embedded devices, communication technologies, and ubiquitous and pervasive computing. A growing number of physical objects are being connected to the internet at an unprecedented rate, leading to diverse range of applications including but not limited to smart cities [
This led to the development of complex and efficient applications and further technological challenges. Applications that have been developed on these new technologies must be tested, verified, and improved prior to real-time deployment. Simulation tools are useful as they offer a quick, flexible, and economical way to verify the behavior of an application. However, they lead to assumptions on several key factors in the test environment, leading to huge uncertainty. IoT applications and wireless sensor network are very much influenced by unpredictable events and physical characteristics that often result in inaccurate results during simulation.
Accordingly, there exists a strong demand to deploy real-time IoT systems, conduct hardware experiments, and benefit from appropriate tools for experimentation. Building upon this, several testbed platforms have been deployed by many researchers with various architectures, hardware, and topologies for a diverse range of applications. As IoT involves interconnecting a massive number of devices from many manufacturers and industries and performance strategy widely varies on different applications and user requirements, heterogeneity of devices and information is of paramount importance. Accordingly, architecture has been the backbone of IoT system, and traditional internet architecture needs to be revised to meet IoT challenges [
Numerous IoT architectures are presented in the literature [
Primary contributions of this paper in comparison to related literature can be listed as follows: In comparison to related literature, this research proposes a modular IoT architecture that can be customized for a diverse range of applications. The proposed architecture allows researchers to get up to speed and quickly deploy their IoT applications without having to dig through the details of standards specifications. An overview of some key IoT implementations from the recent literature is presented. Comparative analysis of different choices at each architectural level is presented.
The rest of this paper is organized as follows: Section
This section presents an overview of existing literature in the IoT area, categorized based on the application and scalability. Of course, we do not pretend to be exhaustive, since the number of IoT applications in the world is huge.
The first category represents the related IoT architectures that were proposed for healthcare and medical applications. The use of IoT in healthcare became of paramount importance in recent years, aiming at enabling preventive medicine and wellbeing preservation. IoT provides technology solutions to build networks of informed and connected e-patients, whereby communication among patients and healthcare and social care providers can take place in real time [
Research in [
Structure health monitoring (SHM) is a vital means to sustain the safety and maintainability of critical structures such as bridges, tunnels, and modern and historical buildings. SHM allows for nondestructive evaluations to detect the location and extent of structural damage, calculate the remaining life, and predict an upcoming accident. IoT-based SHM systems have recently increased due to its ease of deployment, low maintenance cost, and flexibility compared to traditional SHM technologies [
Precision agriculture (PA) is another application area where IoT increases the efficiency, productivity, and profitability of many agricultural production systems. Real-time environmental information is remotely gathered from the agricultural environments and transferred to where it can be processed to discover problems, store data, or take necessary actions. Accordingly, several IoT systems have been developed for precision agriculture monitoring [
One of the fundamental applications that can utilize IoT is indoor target localization and object tracking. Target localization and tracking systems play a crucial role in several context-aware applications through providing crucial information for positioning, tracking, and navigation, where the global positioning system (GPS) is typically infeasible indoors for poor satellite reception. This led to the development of a multitude of practical applications such as indoor localization in smart buildings [
In [
The above-mentioned works concentrated their work on some specific features or specific applications of IoT but did not focus on testing their platform in other applications, limiting the scalability and modularity of the architectures.
It is important to consider that any proposed system must utilize minimum resources and should be easy to implement. Kolios et al. [
Datta et al. [
The purpose of an IoT architecture is to collect and utilize information from the environment to monitor, control, optimize, and automate applications. However, the process of performing these operations is often not straightforward. First, the physical entity needs to be sensed by a transducer, and the change in the electrical property must be converted into a numerical value, done by the physical sensors and actuators layer. This raw data needs to be filtered, processed at an abstract level at first, and done by the low-power embedded processor layer. As the data is obtained from different remote locations in the environment, wireless transceivers are used to send the data from multiple low-power embedded processors to the local internet gateway. This internet gateway further analyzes and relays the information to application management cloud server for storage, extensive data analysis, and user feedback. As such, different tasks are to be performed at each layer in the architecture.
There are several architectures that consist of emulators, simulators, and physical environment with providing security and availability. Large scale projects can be easily deployed using multisite or federation deployment. Testbeds such as NET Eye and IoT-LAB are good scheduling systems and can easily configure the selected nodes [
Layers in proposed modular IoT architecture.
Information flow with inputs and outputs at each layer.
This section presents a modular IoT architecture for diverse applications including but not limited to healthcare, wearable assistive services, infrastructure health monitoring, and precision agriculture. The modular design in the proposed research comprises an architecture that is subdivided into different systems (layers) with several choices for each system and can be replaced or combined without affecting the functionality of the overall system. Broadly, the different layers in the proposed system include sensors, microprocessor, a wireless transceiver, gateway, and application management cloud server, as presented in Figures
As all subsystem choices used in this research are off-the-shelf components with open-source software libraries available, minimal effort is required when the system is implemented for a different application. The input-output relation between each of the layers of the architecture is shown in Figure
The physical layer of the proposed modular architecture consists of the different application-specific sensors and actuators, as used to measure the attributes of different target applications. These sensors include healthcare sensors (electrocardiogram, pulse oximeter, and airflow), building ambience sensors (temperature, humidity, light intensity, presence, and indoor positioning), structural health sensors (piezoelectric and acoustic), and agricultural sensors (soil moisture, soil temperature, soil volumetric water content, wind speed, wind direction, rain meter, solar radiation, and leaf wetness) as presented in Table
Sensors operational with modular IoT architecture.
Sensor | Model | Reference |
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Accelerometer | ADXL345 | [ |
Air humidity | HTU21D | [ |
Air temperature | SHT11 | [ |
Camera | PixyCam, Raspberry Pi | [ |
Capacitive touch | MPR 121 | [ |
Depth | Microsoft Kinect 1 & 2 | [ |
Force | Flexiforce A20-25 | [ |
Indoor localization | iBeacon | [ |
Light | GL5528 | [ |
Proximity | HC-SR501 | [ |
RGB color | TCS34725 | [ |
Soil moisture | SEN0114 | [ |
Sound | SEN-12642 | [ |
Distance | HC-SR04 | [ |
Pulse oximeter | SP02 | [ |
Body temperature | [ | |
Piezoelectric | PZT | [ |
At the fundamental layer, these sensors are interfaced to a low-power embedded processor to capture and parse raw data. As each sensor provides data in a unique format and the context of application might be different, the sensors first convert the sensed phenomena (e.g., temperature) into an equivalent electric voltage or current. Further, an onboard data converter is used to decode the information for faster processing and compatibility to the embedded processor. One such data converter to be used is a 6-bit CA3306 CMOS parallel ADC designed for low-power applications [
The majority of sensors in Table
In addition to capturing data, the fundamental IoT layer should also be able to provide feedback in visual or physical format. Accordingly, the proposed modular architecture is capable of providing this feedback. Per the visual feedback devices, the system can readily provide feedback through seven-segment, LED, and LCD displays while following UART, SPI, or I2C communication protocols. When a physical feedback is necessary, it can be provided through mechanical controllers such as haptic actuators, DC motor, servo motor, and stepper motors, all of which are powered by an external battery. As the actuators and mechanical controllers use analog control signals, the embedded processors have to use an external digital to analog converter (DAC). One such data converter used in the proposed architecture is an MCP4725 DAC [
The second layer in the proposed modular IoT architecture is the low-power embedded processor. The primary role of this processor is to capture sensor information, perform fundamental data analysis, provide feedback to the user as necessary, and relay information to the internet gateway. Depending on the application, this embedded processor can be powered from a battery or an energy scavenging module. Due to the scope of the portable IoT application, this section is limited to battery powered embedded processor. Also, it has to be considered that an exhaustive list of embedded processors is not feasible, and accordingly only the most promising systems in terms of rapid deployment and low-power usage are presented in this study. A comparative list of these embedded processors is presented in Table
Comparison of low-power microcontrollers.
Characteristic | Arduino |
NUCLEO-F401RE | ATmega32U4 | ATtiny10 | CC2640 | MSP430-F2410 | Adafruit Pro Trinket | Adafruit FLORA |
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CPU | ATmega328 | STM32-Cortex M4 | AVR | ATtiny10 | ARM Cortex M3 | MSP430 | ATmega328 | ATtiny85 |
Built-in transceiver |
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BLE or ZigBee |
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ADC: size (bits), samples/s | 10, – | 16, 2400 | 10, 15 k | 8, 15 | 12, 200 k | 12, – | 10, – | 10, – |
Clock speed (MHz) | 20 | 84 | 16 | 12 | 48 | 16 | 12 | 8 |
Bus width (bits) | 8 | 32 | 8 | 8 | 16 | 16 | 8 | |
Memory (kB) | 32 | 512 | 32 | 1 | 128 | 56 | 28 | 8 |
I/O connectivity | UART, I2C, SPI | UART, I2C, SPI | UART, I2C, SPI | ISP | UART, SPI, I2C, GPIO | UART, IrDA, I2C, GPIO, SPI | UART, I2C, SPI | UART |
Real-time clock |
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Coin cell battery operation |
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Arduino based microcontrollers (MCU) are one of the most popular prototyping platforms among researchers and are readily available for quick implementation. While the most popular MCU from Arduino is the Uno [
The NUCLEO-F401RE is one MCU considered here due to its high-performance capability and open-source software [
ATmega32U4 is a low-power Atmel 8-bit AVR RISC-based MCU with 32 KB self-programming flash program memory, 2.5 KB SRAM, 1 KB EEPROM, and 12-channel 10-bit ADC. The device achieves up to 16 MIPS throughput at 16 MHz. This MCU is very close to the Arduino Pro Mini MCU but has a smaller collection of sensors that could be readily interfaced. ATtiny10 [
MSP430F2410 is a member of the Texas Instruments MSP430 family of ultra-low-power MCUs optimized to achieve extended battery life in portable IoT applications [
The Texas Instruments CC2640 is a SimpleLink wireless MCU with a built-in powerful ARM Cortex M3 processor operating at a speed of 48 MHz [
Adafruit Pro Trinket 3 V [
Lastly, Adafruit FLORA is a miniature MCU board that is an ideal choice for wearable IoT applications [
The presented MCUs have been programmed for a simple IoT application, whereby they had to capture data from a light sensor at a frequency of 10 Hz and transmit it to the gateway. Current consumption from these MCUs is obtained and presented in Table
Current consumption of MCUs in different modes.
MCU | Low-power (mA) | Normal (mA) |
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NUCLEO-F401RE | n/a | 120.0 |
Arduino UNO | 18.9 | 25.4 |
Arduino Pro Mini | 23.3 | 37.4 |
Adafruit Pro Trinket | 3.8 | 8.4 |
CC2640 | n/a | 8.2 |
The wireless transceiver provides connectivity to transmit and receive data between the different components of the architecture. However, wireless communication comes at a cost of increased power usage due to the high-energy consumption during transmission [
Comparison of wireless technologies.
Characteristic | BLE | ZigBee | Wi-Fi | IrDA | NFC | ANT+ | nRF24 |
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Peak current (mA) | 12–16 | 30–40 | 116 | 10 | 50 | 17 | 18 |
Max throughput (kbps) | 305 | 100–250 | 6000 | 106 | 424 | 20 | 1000 |
Range (m) | 50 | 100–300 | 50–150 | 0.10 | 0.20 | 10 | 10 |
Latency (ms) | 2.5 | 20 | 1.5 | 25 | 1000 | 0 | 1000 |
Power efficiency ( |
0.153 | 185.9 | 0.00525 | 11.7 | – | 0.71 | 2.48 |
Coin cell battery operation |
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Battery time (approximate days) | 191 |
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52 | 170 |
Bluetooth Low Energy (BLE) is a short-range standard developed by the Bluetooth Special Interest Group (SIG) [
ZigBee was developed by a group of 16 companies and operates based on the IEEE 802.15.4 standard [
Wireless Fidelity (Wi-Fi) follows the IEEE 802.11 standard and operates in the 2.4 GHz frequency spectrum. It has been designed for large data transfer with a high throughput and hence consumes relatively higher electric current. With a peak current of 116 mA, it is not suitable for coin cell battery operation and hence for wearable IoT systems. However, Wi-Fi is a good candidate for communications between the microcontrollers and the Internet due to its high data rates (up to 150 Mbps).
The Infrared Data Association (IrDA) SIG developed the IrDA technology for a high throughput of 1 Gbps for near-field communication [
ANT+ is a proprietary technology operating in 2.4 GHz spectrum [
Another proprietary wireless technology is the nRF24 single-chip ANT™ ultra-low-power wireless solution designed to work with Nike and Apple devices. This technology is developed by Nordic Semiconductors and operates in the unlicensed 2.4 GHz band. With a peak current of 12.3 mA, it can operate on a single coin cell battery. For example, a Nike+ unit using this technology would last approximately 42 days with a constant data rate of 34 bytes/s [
The different front-end nodes collect sensor data and relay it to an internet gateway. This gateway further relays the information (possibly after analyzing it) to the application management cloud server for storage and extensive data analysis. The gateway also forwards requests from the server to the embedded processors for actuators and feedback devices on the front-end nodes.
An internet gateway in the proposed modular IoT architecture is mainly composed of four modules: (1) a low-power wireless interface module, which serves as a means to receive data wirelessly from the several nodes in its vicinity, (2) a data collection module, which offers the memory space needed to collect all the data received from the nodes, (3) an MCU or single-board computer that performs intermediate data analysis, (4) and a high-rate wireless/wired interface that passes output data to the application management server for storage and detailed analysis.
In addition to parsing data between the nodes and cloud, this gateway has the ability to serve as a supernode with significant performance capability. Furthermore, it serves as the cluster head or a network coordinator that is in charge of overlooking a few networking functionalities such as Medium Access Control (MAC) and multihop data routing decision. For instance, it can compute the time division multiple access (TDMA) schedules through which nodes can access the gateway and share it with its connected nodes. Moreover, it can serve as the root node of an IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) tree, which is then used for multihop data forwarding. The gateway node is also in charge of network-wide security services such as data authentication and encryption, since the low-power MCUs do not have enough resource to handle such energy-hungry security services. Table
Comparison of Internet gateway.
Characteristic | Intel Edison | Raspberry Pi 3 | BeagleBone Black | ODROID-XU4 | Arduino Yún |
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CPU | Intel Atom + Intel Quark | ARM Cortex-A53 | ARM Cortex-A8 + Dual PRU | Samsung Exynos 5422 Cortex™-A15 and Cortex-A7 | ATmega32U4 + Atheros AR9331 |
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GPU |
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Broadcom VideoCore-IV | PowerVR SGX530 GPU | ARM® Mali™-T628 MP6 |
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Built-in wireless transceiver | Wi-Fi, BLE | Wi-Fi, BLE |
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Wi-Fi | Wi-Fi |
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ADC: channels, bits |
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8, 12 |
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6, 12 |
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Clock speed (MHz) | 500, 100 | 1200 | 1000, 200 | 2000 | 16, 400 |
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Bus width (bits) | 32 | 32 | 32 | 32 | 8 |
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RAM (MB) | 1024 | 1024 | 512 | 2048 | 0.025, 64 |
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Flash storage (GB) | 4 |
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4 |
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0.000032, 0.016 |
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Low-level peripherals | UART, PWM, SPI, I2C, I2S | UART, SPI, I2C, I2S | UART, PWM, SPI, I2C, CAN | UART, SPI, I2C, I2S | UART, SPI, I2C |
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Power ratings | 100 mA @ 3.3 V | 800 mA @ 5 V | 460 mA @ 5 V | 4 A @ 5 V | 50 mA @ 3.3 V, |
Intel Edison is a single-board computer, used in low-power sensor network applications [
The Raspberry Pi 3 single-board computer is a widely used candidate for implementing gateway nodes. This can be powered by a 5 V Li-Ion battery and features a Linux operating system with a wide set of programming and connectivity options [
BeagleBone Black is another single-board computer with a low-cost platform for IoT applications running Linux operating system [
ODROID-XU4 is another device with more powerful hardware and a smaller form factor [
Finally, Arduino Yún is another MCU based on ATmega32U4 and Atheros AR9331 [
ATmega32U4 has 32 KB (with 4 KB used for the bootloader).
As the input-output connectivity, form factor, and applicability with a diverse range of sensors are almost similar in all these gateway controllers, one simple test that can be performed is evaluating the current consumption. Accordingly, these gateway controllers have been programmed with a simple IoT application, whereby they had to capture data from an MCU at a frequency of 10 Hz and transmit it to the application cloud server. Current consumption from these gateway controllers is obtained and presented in Table
Current consumption of gateway controllers with wireless transceivers.
Gateway MCU | Average current consumption (mA) |
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Intel Edison with BLE | 285.30 |
Raspberry Pi 2 with BLE | 294.83 |
Raspberry Pi 3 with BLE | 328.69 |
Raspberry Pi 3 with nRF24L01 | 347.40 |
ODROID-XU4 with BLE | 606.89 |
BeagleBone Black with BLE | 320.24 |
Arduino Yún with BLE | 283.01 |
The application management cloud server is responsible for facilitating the end-users’ ability to access the sensed data. This is achieved by implementing several services including, but not limited to, data storage, data analytics, and data visualization in addition to providing an appropriate application program interface (API) and software tools through which the end-user can access the data as in Figures
Comparison of application management systems.
Platform | E1 | E2 | E3 | E4 | E5 | E6 | E7 |
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TinyREST [ |
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n/a |
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Evrythng [ |
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ThingWorx [ |
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Sensorpedia [ |
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Paraimpu [ |
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Proposed |
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Architecture of application management cloud server.
Device discovery is one fundamental feature necessary in IoT applications, but the majority of the existing systems with the exception of TinyREST [
Security and privacy to data are two of the most common attributes to be considered in IoT application, and, accordingly, the most common platforms account for the same. Platforms such as Evrythng, ThingWorx [
The proposed system architecture has been implemented in hardware and subjected to several tests for a variety of applications, including but not limited to healthcare, smart home, and structural health monitoring. As low-power consumption is the key factor in portable IoT application, a sampling rate of 1 Hz was set for all sensors, except in case of accelerometer, where a higher rate was necessary. Data from the individual or combination of sensors are collected and packaged into 8 bytes with information of source location and sensor data as in Figure
Configuration of data packet between MCU and gateway controller.
One of the fundamental tests in assessing the efficiency of the proposed system is identifying current consumption and also reliable communication distance. Accordingly, during the first test, the power amplifier level is set at minimum (−18 dBm) and the data rate has been changed to find the current consumption and maximum communication distance. Results obtained as presented in Table
Power consumption of sensor nodes with varied data rate.
Data rate (Mbps) | 0.25 | 1 | 2 |
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Power consumption (mW) | 0.47 | 10.48 | 10.55 |
Reliable communication distance (m) | >100 | 56.41 | 39.75 |
Power consumption of sensor nodes with varied power levels.
Power amplifier level (dBm) | −18 | −12 | −6 | 0 |
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Power consumption (mW) | 10.47 | 10.48 | 10.48 | 10.50 |
Maximum communication distance (m) | >100 | >100 | >100 | >100 |
Healthcare applications are one area of interest where the proposed architecture could be implemented. With posture and physical activity recognition being two of the fundamental and yet complex tasks, the proposed system has been customized to perform these tasks. First, the sensors utilized in this test are an accelerometer for physical activity recognition, body temperature sensor for monitoring user health, and force sensor to detect sedentary activity. Second, with portability and low-power consumption being a major deciding factor in wearables, Adafruit FLORA has been chosen as the MCU and BLE transceiver has been chosen to relay information to the gateway. As this gateway had to collect information from multiple users simultaneously and relay it to the application cloud server, a system with decent computational power and wired connectivity to the network would suffice. Accordingly, Raspberry Pi 2 has been chosen. Data collected from the sensors required the application of complex mathematical algorithms to detect physical activity. Accordingly, the custom-designed cloud server analyzed this data in MATLAB and provided feedback to the user via a simple text message as presented in Figure
Architectural components of the prototype for healthcare applications.
Data obtained from the application management cloud server for activities such as walking, jogging, and running are presented in Figure
Movement patterns information as recorded on the cloud server.
Classification of user activity on the application management server.
Detection of sudden falls by the user.
The deteriorating health of complex and intricate infrastructure has challenged the engineers to find efficient solutions in infrastructure health monitoring. Addressing this challenge, the proposed framework has been customized to design, build, and test an IoT-based SHM system as presented in Figure
Architectural components of the prototype for SHM.
Damage detection results as available on cloud server.
This data stored in the cloud server can be monitored remotely from any mobile device, and an alert could be triggered by the user whenever the values from a location pass a threshold. Overall, the presented framework and model utilized has been demonstrated to obtain a maximum of 1.03% error for the damage location and a maximum of 8.43% error for the damage width.
The advancement in BLE technology revolutionized indoor contextual awareness and allowed for advancement in a diverse range of applications. One such application is location-aware systems to serve as tour guides in the museums, historical sites, and academic environments. The proposed architecture was validated for this location-based service through the design and implementation of a portable tour guide module presented in Figure
Handheld tour module.
Architectural components of the prototype for portable tour module.
The two technologies often utilized in indoor localization are passive RFID and BLE. Additionally, one popular feature in tour module is the ability to take pictures and/or record videos while in the tour. Accordingly, first, the presented system has been equipped with RFID and BLE transceivers and a 5-Megapixel camera to take pictures and videos by the user as appropriate. Second, in addition to recording videos, one method often used to relay information to users while in a tour is showcasing video as related to the object or location of interest. Accordingly, the presented module is enabled with a small LCD screen to display and play videos while in the tour. Further, performing these graphics-intensive applications require an embedded processor with onboard GPU, and, as such, a Raspberry Pi 3 has been used. Information from this module was relayed to the cloud server via Wi-Fi. One particular feature required in this tour module is presenting the location of the user on the campus map in real time. Accordingly, ThingWorx application management cloud server was used to track the user location and presented on Google Maps as in Figure
Localization of user tagged on Google Maps through cloud server.
The passive RFID reader and built-in BLE transceivers collected and sent information on all tags and respective RSSI values through the embedded processor and gateway controller to ThingWorx application management server. This information was processed on the cloud server to identify the location, and an appropriate website address was sent back to the tour module. Accordingly, the tour module would open the website to play a video and provide the user with up to date information on the respective laboratory or classroom. Implemented in an academic environment, the presented system was able to localize with an accuracy of 0.73 m, play videos of respective labs, allow users to take pictures and videos, and helped them learn about the university.
Precision agriculture is one of the paradigms which can use the IoT advantages to optimize the production efficiency and uniformity across the agriculture fields, optimize the quality of the crops, and minimize the negative environmental impact. Accordingly, the proposed modular architecture has been implemented as presented in Figure
Weather sensing IoT system as utilized in a farm.
Architectural components utilized in agriculture system.
The sensors utilized in this application test are a temperature sensor, soil moisture sensor, wind speed and direction sensor, and a humidity sensor to enable for economical usage of natural resources. As rapid deployment of the application system is an important factor, Adafruit Pro Trinket has been chosen as the MCU, and nRF transceiver has been chosen to relay information to the gateway. As the gateway in this application had to collect information from multiple weather modules simultaneously and relay it to the application cloud server, a system with decent computational power and yet consumes low power was required. Accordingly, Intel Edison has been chosen to serve as the internet gateway. Data collected from the sensors required the application of complex custom algorithms to activate agricultural systems and often require user intuition. Accordingly, the cloud server analyzed this data in MATLAB and provided visual feedback on weather patterns to the user as in Figures
Tracking wind direction and speed via the custom application management cloud server.
Tracking temperature and humidity via the custom application management cloud server.
IoT applications are now rapidly evolving. The primary purpose of IoT is to communicate with the surrounding environment through a multitude of sensors and actuators and facilitate the user to make constructive decisions. Much of the published work in IoT architecture is theoretical and is tied to a specific application. Addressing the challenges in the selection of computational devices, wireless connectivity, internet gateway, and application cloud server, this research presented a real-time, low-power, low-cost, and reliable modular IoT architecture to facilitate implementation in diverse applications.
The proposed work addressed the existing limitation by proposing a modular IoT architecture with a wide choice of subsystems that are readily available in the market, along with the advantages and limitations in each. Based on the application and its respective specification in consideration, the designer can review the architectural layer choices presented in Section
The proposed framework has been verified throughout real-time hardware implementation for different applications such as healthcare, wearables, structural health monitoring, object tracking, and connected vehicles. Further, research in this area is anticipated in the near future to address more applications.
The authors declare that they have no conflicts of interest.
This work has been sponsored by the National Science Foundation under Grant no. 1542368.