A Plug and Play IoT Wi-Fi Smart Home System for Human Monitoring

Bassoli, Marco; Bianchi, Valentina; Munari, Ilaria De

doi:10.3390/electronics7090200

Cited by 49 publications

(43 citation statements)

References 41 publications

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“…The board hosts a TI CC3200 system on chip (SoC) device, with a 32-bit architecture ARM Cortex-M4 MCU clocked at 80 MHz. This device, thanks to an embedded Wi-Fi radio module, is widely used to implement devices and systems compatible with the Internet of Things and wireless sensor network paradigms [42][43][44][45][46][47], topics in which error correction techniques were demonstrated to gain an advantage in recent implementations [48,49]. Data about execution speeds were collected by measuring a general-purpose input/output (GPIO) pin voltage, which was toggled inside the C code at the computation's start and end.…”

Section: Hardware Configurationmentioning

confidence: 99%

Comparison of FPGA and Microcontroller Implementations of an Innovative Method for Error Magnitude Evaluation in Reed–Solomon Codes

2020

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Reed-Solomon (RS) codes are one of the most used solutions for error correction logic in data communications. RS decoders are composed of several blocks: among them, many efforts have been made to optimize the error magnitude evaluation module. This paper aims to assess the performance of an innovative algorithm introduced in the literature by Lu et al. under different systems configurations and hardware platforms. Several configurations of the encoded message chosen between those typically used in different applications have been designed to be run on an FPGA (field programmable gate array) device and an MCU (microcontroller unit). The performances have been evaluated in terms of resource usage and output delay for the FPGA and in terms of code execution time for the MCU. As a benchmark in the analysis, the well-established Forney's method is exploited: it has been implemented in the same configurations and on the same hardware platforms for a proper comparison. The results show that the theoretical finding are fully confirmed only in the MCU implementation, while on FPGA, the choice of one method with respect to the other depends on the optimization feature (i.e., time or area) that has been decided as a preference in the specific application.Electronics 2020, 9, 89 2 of 15 ECCs can correct up to one error in the message and are characterized by low parity information, which makes them suitable for applications requiring high data rates, but low error occurrence. To improve Hamming codes, Bose-Chaudhuri-Hocquenghem (BCH) codes were presented in the works of [9][10][11]. They solve the limitation of the maximum correctable errors by introducing Galois Fields [12]. By exploiting the multiplication, the addition, and the inversion defined in the field, the system can be configured to correct the desired amount of errors in the message by increasing the parity information appended to the message itself. A subset of BCH codes are Reed-Solomon (RS) codes [13], which ease the implementation in binary encoded messages. RS codes are among the most implemented solutions in a wide set of areas, such as data storage, bar and QR (quick response) codes, space and TV transmission, and so on [14][15][16][17].As all communication systems, RS ECCs are composed of an encoder at the sender side and a decoder at the receiver side. Most of the works in the literature [18][19][20] focus on the decoder part, as it holds the most complex operations. Several approaches have been proposed to improve the performance of the decoder [21,22]. Among them, many research works focus on the optimization of error magnitude evaluation [23][24][25]. A traditional implementation exploits the Forney method [23]. Komo and Joiner proposed an iterative algorithm in the work of [24] based on a Vandermonde matrix, which results in an improvement of the processing speed of about 2.8 times compared with the Forney method. Then, an innovative method has been proposed by Lu et al. [25], allowing the same result to be obtained with less system complexity and...

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Section: Hardware Configurationmentioning

confidence: 99%

Comparison of FPGA and Microcontroller Implementations of an Innovative Method for Error Magnitude Evaluation in Reed–Solomon Codes

2020

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“…Regarding IoT enabling infrastructure, numerous researches, developments have been envisioned, proposed, designed and developed the prototypes of ubiquitous applications that can transform from static physical into smart environments [5][6][7]. An IDEA (Integrated ADL detection estimation of functional redundancy and alerting for maintenance visit) system [5] wherein a novel sensor management system for IoT-enabled smart environments is proposed.…”

Section: Figure 1 Sample Of Mounted Automated Tissue Dispensermentioning

confidence: 99%

“…contact, temperature and motion sensors are triggered once user undertakes various ADL activity such as sleeping, eating, preparing meals, taking medicines and personal hygiene, and the results show that the IDEA leads to 3 to 40 times fewer maintenance personnel than usual scheme (failed sensors are fixed without considering their impact). A new system architecture suitable for human monitoring based on Wi-Fi connectivity is introduced [6]. The authors propose a plug and play IoT Wi-Fi smart home system to lower the cost and implementation burden using standard home modem routers.…”

Section: Figure 1 Sample Of Mounted Automated Tissue Dispensermentioning

confidence: 99%

ITDS: An Intelligent Tissue Dispenser System

Man¹,

Bakar²,

Mohd³

2019

IJRTE

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The Internet of Things (IoT) technology is the main contributor in numerous smart applications. The reason is because it offers for 24/7 hours of control and maintenance geographically apart, thus reduces labor or manpower cost significantly. The 3 main components for any IoT applications are the source of power (energy), the microcontroller and the sensor (s) involved. Previous issues mainly related to how long the source of power could last for the applications to continue its operation. This paper presents IoT technology for hygiene application to address the utilization of toilet tissue named as Intelligent Tissue Dispenser System (iTDS). The iTDS device relies on the microcontroller and sensor in order to operate the intended task. The microcontroller used is an IoT based device called ESP8266 which is a WiFi-embedded microcontroller that utilized standard everyday WiFi band frequency which is at 2.4 GHz. For the sensor, an ultrasonic distance measurement device is used. The ultrasonic sensor transmit an ultrasonic wave that hit the object to be measured. Upon hitting the surface of the object to be measured, the wave is then reflected to the receiver of the sensor and the time difference between transmitted wave and received wave is calculated to get the actual distance of the object from the sensor. The main contribution of iTDS is to monitor and track for the toilet tissue to be refilled. The implementation shows the iTDS ables to update for the status of each tissue which reducing the cost of manually human checking for tissue refill.

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“…Recent advancements in the fifth-generation (5G) have enabled abundant technologies such as vehicular networks, autonomous control, and Internet of Things (IoT) [1][2][3]. Many sensors are distributed in IoT, and they need to collect many data and execute real-time communication tasks, which are quite latency-sensitive.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Fog Computing Assisted Wireless Powered Networks: Joint Energy and Time Minimization

et al. 2019

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This paper studies the optimal design of the fog computing assisted wireless powered network, where an access point (AP) transmits information and charges an energy-limited sensor device with Radio Frequency (RF) energy transfer. The sensor device then uses the harvested energy to decode information and execute computing. Two candidate computing modes, i.e., local computing and fog computing modes, are considered. Two multi-objective optimization problems are formulated to minimize the required energy and time for the two modes, where the time assignments and the transmit power are jointly optimized. For the local computing mode, we obtain the closed-form expression of the optimal time assignment for energy harvesting by solving a convex optimization problem, and then analyze the effects of scaling factor between the minimal required energy and time on the optimal time assignment. For the fog computing mode, we derive closed-form and semi-closed-form expressions of the optimal transmit power and time assignment for offloading by adopting the Lagrangian dual method, the Karush-Kuhn-Tucker (KKT) conditions and Lambert W Function. Simulation results show that, when the sensor device has poor computing capacity or when it is far away from the AP, the fog computing mode is the better choice; otherwise, the local computing is preferred to achieve a better performance.Electronics 2019, 8, 137 2 of 17 and far away from wireless sensor devices, thus offloading computation tasks to centralized cloud servers may results in heavy access burden and transmission delay. Therefore, a new communication and computing paradigm called fog computing was presented to extend cloud computing from network center to network edge [7,8]. Compared with cloud servers, fog servers are closer to user terminal equipments. Thus, fog computing is able to achieve lower response delay. Moreover, by offloading computation intensive tasks from ultra-low-power sensor devices to fog servers, the energy consumption of sensors may be saved and computation capabilities of wireless sensors is capable of being supplemented.Apart from the limited computation capacity of ultra-low-power sensor devices, another key issue is how to supply sustainable and stable power to sensor devices, because IoT sensor devices are often widely deployed in large-scale networks and powered by small-size batteries with limited energy storage capacities. To release the cost and risk of battery replacement, in large-scale applications and toxic environment, wireless power transfer (WPT) was presented as a promising solution. As for wireless power transfer, there are three different kinds of technologies, i.e., induction coupling, magnetic resonance coupling and RF radiation. Among them, induction coupling and magnetic resonance coupling are near-field power transfer technologies, which basically are able to charge the devices in the range of tenths of watts, over short distances of up to one meter [9]. Particularly, induction coupling needs tight alignment of the coils of chargers ...

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A Plug and Play IoT Wi-Fi Smart Home System for Human Monitoring

Cited by 49 publications

References 41 publications

Comparison of FPGA and Microcontroller Implementations of an Innovative Method for Error Magnitude Evaluation in Reed–Solomon Codes

Comparison of FPGA and Microcontroller Implementations of an Innovative Method for Error Magnitude Evaluation in Reed–Solomon Codes

ITDS: An Intelligent Tissue Dispenser System

Multi-Objective Optimization of Fog Computing Assisted Wireless Powered Networks: Joint Energy and Time Minimization

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