The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

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Federated Learning for Internet of Things: A Comprehensive Survey

Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems.

https://www.mdpi.com/1424-8220/20/4/1042/pdf

IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture

Automated Irrigation System Using a Wireless Sensor Network and GPRS Module

Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities

Federated learning (FL) is a distributed machine learning strategy that generates a global model by learning from multiple decentralized edge clients. FL enables on-device training, keeping the client’s local data private, and further, updating the global model based on the local model updates. While FL methods offer several advantages, including scalability and data privacy, they assume there are available computational resources at each edge-device/client. However, the Internet-of-Things (IoTs) enabled devices, e.g., robots, drone swarms, and low-cost computing devices (e.g., Raspberry Pi), may have limited processing ability, low bandwidth and power, or limited storage capacity. In this survey paper, we propose to answer this question: how to train distributed machine learning models for resource-constrained IoT devices? To this end, we first explore the existing studies on FL, relative assumptions for distributed implementation using IoT devices, and explore their drawbacks. We then discuss the implementation challenges and issues when applying FL to an IoT environment. We highlight an overview of FL and provide a comprehensive survey of the problem statements and emerging challenges, particularly during applying FL within heterogeneous IoT environments. Finally, we point out the future research directions for scientists and researchers who are interested in working at the intersection of FL and resource-constrained IoT environments.

A Survey on Federated Learning for Resource-Constrained IoT Devices

Ensuring minimum rights and safety of the garment workers has become a burning issue nowadays. The workers of garment factories are facing some labyrinths and broken out of fire is surely one of them. The investors are losing their interest and the prominence of this sector is getting toneless. In this paper, we have propounded a system which is capable to detect fire and can provide the location of the affected region. Raspberry Pi 3 has been used to control multiple Arduino which are integrated with a couple of sensors and camera. A 360° relay motor is assembled with the camera so that it can snap the image in whatever angle the fire is detected. We have provided a confirmation of the fire suspecting system to avoid any false alarm. The system will immediately send a message along with the image of the affected spot and Arduino's location. An admin can confirm or deny the impeachment and if the admin confirms the situation as a breaking out of fire, then the system will immediately raise an alarm and an automatic message will be sent to the nearby fire brigade.

An IoT based fire alarming and authentication system for workhouse using Raspberry Pi 3

This paper propounds a design for automatic water supplying system in farmland using raspberry pi 3, Arduino microcontrollers, WiFi module, GSM shield, relay boards and couple of sensors. The components we used in our system ensures overall fecund, scalable and spirited implementation. Depending upon the moisture level of farmland and daylight intensity, the system can detect the appropriate time of water supply in the trees and can also keep track of the water level to prevent water from being accumulated around the roots of the saplings. The analog data received from the sensors are transmitted by Arduino as digital signal via Wifi Module to the Raspberry Pi 3. The system is able to notify the administrator if water shortage arises in the main water supply and an administrator can also communicate with the system by sending SMS (Short message service) of a particular keyword. This system can be applied in farmland as well as small pot plants. Using this system, a very promising outcome is found in sustaining and cherishing the plants in a more scientific way.

IoT based autonomous percipient irrigation system using raspberry Pi

There is a dearth of research in developing low-cost solutions for distributed decision making in IoT networks. Most studies in the literature require the deployment of additional sensors for data collection. In this study, we propose to leverage available sensors built-in smartphones, to properly collect and broadcast data for different decision-making purposes in smart cities infrastructures, for example, intelligent transportation networks, smart health services, security and emergencies, industrial control, smart agriculture, home automation and so on. To this end, we first introduce our new platform (including software and mobile app implementation) to identify available sensors at each end-user device. We have identified a wide range of sensors including gyroscope, ambient light sensor, temperature, magnetic field sensor, orientation sensor, game rotation vector, linear acceleration, relative humidity, gravity, geomagnetic rotation vector, etc. As the sensors are already integrated within the phone, therefore, using these sensors can be beneficial considering the complexity, efficiency, and cost of the overall system. The challenge is to design a system that can trigger distributed devices to be self-activated and agreed to generate all available sensors data. Besides, as devices can send a continuous stream of data, therefore, size of data could be mounted and could be in the haphazard structure, which would give us hurdles to identify a device sensor data from another and to make an intelligent decision. To tackle all of these, we propose a distributed sensing approach that is capable to identify a device using token, can activate distributed end-user devices to send data to the cloud whenever it requires and store data in the cloud server maintaining proper format. This approach enables remote data collection leveraging available end-user devices and reduces the cost of installing new sensors for autonomous IoT applications. We then build on our efficient sensing platform to enable distributed intelligence among a network of smart devices. To this end, we leverage the computational capacity of these devices for local decision making, i.e., instead of broadcasting all sensing information to a centralized agent and solve a large-scale decision-making problem, each smart device communicates with a limited set of neighboring devices. This will also pave the way for implementing federated learning as a promising solution for distributed decision making.

Distributed Sensing Using Smart End-User Devices: Pathway to Federated Learning for Autonomous IoT

Vehicle accident is the paramount thread for the people's life which causes a serious wound or even dead. The automotive companies have made lots of progress in alleviating this thread, but still the probability of detrimental effect due to an accident is not reduced. Infringement of spFieed is one of the elementary reasons for a vehicle accident. Therewithal, external pressure and change of tilt angle with road surface blameworthy for this mishap. As soon as the emergency service could divulge about an accident, the more the effect would be mitigated. For this purpose, we developed an Android based application that detects an accidental situation and sends emergency alert message to the nearest police station and health care center. This application is integrated with an external pressure sensor to extract the outward force of the vehicle body. It measures speed and change of tilt angle with GPS and accelerometer sensors respectively on Android phone. By checking conditions, this application also capable of reducing the rate of false alarm.

Ahmed Imteaj

Papers

A Survey on Federated Learning for Resource-Constrained IoT Devices

An IoT based fire alarming and authentication system for workhouse using Raspberry Pi 3

IoT based autonomous percipient irrigation system using raspberry Pi

Distributed Sensing Using Smart End-User Devices: Pathway to Federated Learning for Autonomous IoT

Smart vehicle accident detection and alarming system using a smartphone