The integration of emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and blockchain is transforming the landscape of modern healthcare. These technologies enable real-time monitoring, data-driven diagnostics, personalized treatment, and remote patient care, leading to more efficient and accessible healthcare delivery. This paper presents a comprehensive review of smart and advanced healthcare solutions driven by these technologies, exploring their architecture, applications, benefits, and limitations. It categorizes healthcare use cases into critical domains, including wearable health devices, intelligent diagnostics, telemedicine, and emergency response systems. Furthermore, the paper critically examines key challenges such as interoperability, energy efficiency, data quality, security, and ethical concerns. To address these issues, it discusses current solutions and highlights future research directions essential for scalable and sustainable healthcare innovation.

Industry 5.0 represents a significant advancement from Industry 4.0, emphasizing improved cooperation between humans and machines, eco-friendly practices, and customized solutions in manufacturing. In contrast to earlier research on Industry 4.0, this survey provides a thorough examination of the function of Machine Learning (ML) in Industry 5.0 by examining the ways in which various learning paradigms facilitate sustainable production, human-machine cooperation, and customized industrial solutions. We first examine cutting-edge learning approaches, encompassing supervised, unsupervised, semi-supervised, and reinforcement learning methodologies, and their role in advancing Industry 5.0 objectives. Next, we emphasizes the incorporation of ML techniques to improve manufacturing productivity and optimize decision-making processes. Even with advancements, Industry 5.0 may suffer several challenges and limitations, such as intelligent decision-making, security, data privacy, etc. To address the challenges of Industry 5.0, it is crucial to comprehend its protocols, which this survey examines alongside relevant technologies. Therefore our survey also explores the primary challenges encountered when incorporating ML into Industry 5.0, such as integrating data, ensuring interoperability, and maintaining sustainability. Furthermore, the study elaborates on how these techniques drives substantial progress in various emerging applications by enhancing operational procedures and enabling innovative capabilities. Finally, we discuss potential research routes to overcome these hurdles and achieve the practical implementation of Industry 5.0.

Long-range Wide-area Network (LoRaWAN) is an innovative and prominent communication protocol in the domain of Low-power Wide-area Networks (LPWAN), known for its ability to provide long-range communication with low energy consumption. However, the practical implementation of the LoRaWAN protocol, operating at the Medium Access Control layer and specially built to work upon the LoRa physical layer, presents numerous research challenges, including network congestion, interference, optimal resource allocation, collisions, scalability, and security. To mitigate these challenges effectively, the adoption of cutting-edge data-driven technologies such as Deep Learning (DL) and Machine Learning (ML) emerges as a promising approach. Interestingly, very few existing surveys or tutorials have addressed the importance of ML- or DL-based techniques for LoRaWAN. This article provides a comprehensive survey of current LoRaWAN challenges and recent solutions, particularly using DL and ML algorithms. The primary objective of this survey is to stimulate further research efforts to enhance the performance of LoRa networks and facilitate their practical deployments. We begin by emphasizing the characteristics of LoRaWAN compared to other LPWAN technologies and then examine the technical specifications of LoRaWAN that have been released so far, as well as the current research trends. Furthermore, we discuss an overview of the most utilized DL and ML algorithms for overcoming LoRaWAN challenges. We also present an interoperable reference architecture for LoRaWAN and validate its effectiveness using a wide range of applications. Additionally, we shed light on several evolving challenges of LoRa and LoRaWAN for the future digital network, along with possible solutions. Finally, we conclude our discussion by briefly summarizing our work.

Long-Range Wide-Area Network (LoRaWAN) is receiving wide acceptance by offering long-distance, low-power, robust, and cost-effective communication of sensory data to the server. LoRaWAN end devices open two receive windows to receive a downlink after the transmission of data to the server. However, optimizing the delay in opening these receive windows (known as RXDelay) is pivotal for efficient uplink and downlink communication. This task becomes even more challenging when application requirements change over time. To address such challenges, this paper proposes a novel approach to estimate and configure the optimal RXDelay of end devices. By leveraging the concept of the age of information at the network server, the proposed approach dynamically adapts RXDelay based on the specific demands of different scenarios, allowing for timely data transmission. Through extensive experimentation and evaluation on a real-world LoRaWAN deployment, the proposed approach demonstrates significant improvements in communication efficiency and overall network performance. Specifically, it outperforms existing methods in packet reception rate and energy efficiency for both confirmed and unconfirmed messages across diverse network conditions, while still ensuring a balanced throughput.

Smart spaces system equipped with sensors to collect data that can be used to generate insights about its environmental conditions. Those collected data is then transmitted to the applications to enhance the comfort, quality of life, and security of the space. Long Range (LoRa) technology provides long distance coverage and consumes low energy which makes it suitable for smart space application. There are six virtual channels to transmit data in LoRa, however network faces the interference problem when nodes transmitted data at the same time. The interference problem makes LoRa less suitable for time-critical applications. To mitigate the interference problem, a spreading factor should be allocated in an optimal way. This paper assigns the spreading factor to the LN using Rate-Monotonic scheduler to ensures data transmission within deadline with minimum energy consumption. To quantify delay in receiving the information, we use the ‘Age of Information’ metric. The proposed approach is validated using Network Simulator-3 and results show that it effectively reduces delay and energy and prolongs the network utility.

This poster presents a novel approach that harnesses large-sized deep neural networks to craft lightweight variants, addressing constraints in storage, processing speed, and task execution time on heterogeneous edge devices. Knowledge distillation is employed to refine the training of lightweight deep neural networks, and a novel early termination technique is introduced to optimize resource utilization and expedite the training process. This approach yields satisfactory accuracy while accommodating diverse heterogeneous edge device constraints.

Low Power Wide Area Networks (LPWAN) offer a promising wireless communications technology for Internet of Things (IoT) applications. Among various existing LPWAN technologies, Long-Range WAN (LoRaWAN) consumes minimal power and provides virtual channels for communication through spreading factors. However, LoRaWAN suffers from the interference problem among nodes connected to a gateway that uses the same spreading factor. Such interference increases data communication time, thus reducing data freshness and suitability of LoRaWAN for delay-sensitive applications. To minimize the interference problem, an optimal allocation of the spreading factor is requisite for determining the time duration of data transmission. This paper proposes a game-theoretic approach to estimate the time duration of using a spreading factor that ensures on-time data delivery with maximum network utilization. We incorporate the Age of Information (AoI) metric to capture the freshness of information as demanded by the applications. Our proposed approach is validated through simulation experiments, and its applicability is demonstrated for a crop protection system that ensures real-time monitoring and intrusion control of animals in an agricultural field. The simulation and prototype results demonstrate the impact of the number of nodes, AoI metric, and game-theoretic parameters on the performance of the IoT network.

With the availability of low-cost and low-power sensors, it becomes easier to assess river water quality. The existing work on water quality assessment require a large amount of correctly annotated data for training. However, in the real-world scenario, obtaining such annotated data is costly and time consuming. In this work, we propose a sensor-based river water quality assessment system using the deep neural network (DNN). The system first presents a technique to estimate the water quality index (WQI) for labeling the given lab samples. WQI is a vital matrix used to transform large quantities of water data into a single unified number. Next, we present an automatic annotation technique that assigns labels to the sensory data instances using lab data. Finally, the labeled sensory data instances are used to build a DNN classifier that predicts water quality. This work also proposes a noise handling loss function to accommodate noisy labels. We evaluate the performance of the system on the river data set of major Indian rivers. We use four performance metrics during the experiment, including precision, recall, accuracy, and$F1$score. Additionally, the system achieves an accuracy of more than 90%, despite 20% noisy labels. The code is athttps://github.com/sourcecodecselab/river_water_monitoring.

Industrial IoT (IIoT) accomplishes digital manufacturing that incorporates various devices, simulators, and tools with multiple sensors. These sensors provide sufficient data to coordinate and monitor industrial systems. IIoT requires dedicated supporting devices for an application to execute a given task in maximum allowable response time. The requirement of dedicated devices increases system cost. Task containerization is a process of exploiting available resources of the host machines to meet out varying demands of different applications. It avoids the additional cost to buy dedicated IIoT devices while adding new applications. This article proposes an approach to securely process a given IIoT task within an allowable response time. We use the game theory approach to estimate the fractions of the task to be containerized on the machines. Next, the estimated fractions for each machine maximize the system utility. Finally, we illustrate the experimental results to validate the performance of the proposed approach.

Various wireless local area network technologies are developed for Internet of Things of which Long-Range Wide Area Network is preferred for low power long range communication. In the LoRa network, multiple LoRa nodes can simultaneously communicate with a LoRa gateway which causes the interference problem. This article presents an approach for estimating the association time duration between each LoRa node and the LoRa gateways for transmitting the data of end users to the LoRa gateways with high packet delivery ratio. The approach uses a Beta distribution based reputation model for estimating the association time duration between each LoRa node and LoRa gateways and Bayesian Game strategy which accommodates unknown private information of the LoRa nodes. The approach is validated by simulating the LoRa network using network simulator-3. We also demonstrate an on-campus traffic monitoring system to detect the reckless driving action and estimate the vehicle speed using sensors embedded nodes deployed along both sides of the road.

A Fog computing-based transportation system envisions to reduce energy consumption and communication delay. This paper presents a Fog computing-based scheme for assisting passengers, which involves task offloading and reallocation. We consider a dynamic environment where the passengers frequently change their locations. Additionally, the scheme mitigates the sudden failure of the Fog devices. We employ a game-theoretic approach to determine optimal fractions of a task associated with the passengers to be offloaded among the Fog devices and Cloud. It also supports the reallocation of the allocated fractions of the task. This offloading and reallocation of tasks ensures the execution within a given time constraint and requires minimal execution cost. We also prove the existence of near Nash equilibrium for the allocated fractions of the task on Fog devices. Further, this work covers different possibilities of dependencies among the fractions of the task and corresponding utilities of Fog devices in the dynamic environment. Finally, we present the empirical and real-world evaluations to verify the effectiveness of the proposed scheme in terms of the number of Fog devices, the deadline of the task, and game parameters.

An important research issue in smart metering is to correctly transfer the smart meter readings from consumers to the operator within the given time period by consuming minimum energy. In this paper, we propose an energy efficient smart metering system using Edge computing in Long Range (LoRa). We assume that all appliances in a house are connected to a smart meter that is affixed with Edge device and LoRa node for processing and transferring the processed smart meter readings, respectively. The energy consumption of the appliances can be represented as an energy multivariate time series. The system first proposes a deep learning based compression-decompression model for reducing the size of the energy time series at the Edge devices. Next, it formulates an optimization problem for finding the suitable compressed energy time series to reduce the energy consumption and delay of the system. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed time series to the operator in the given time. Our simulation and prototype results demonstrate the impact of the parameters of the compression model, network, and the number of smart meters and appliances on delay, energy consumption, and accuracy of the system.

In this paper, we develop techniques that create smart space in an efficient manner, wherein the efficiency is defined in terms of all-together: energy, security, delay, and cost. We design an energy-efficient smart space system using the Long-Range (LoRa) network. The system consists of various sensors that generate sensory data represented as Multi-dimensional Time Series (MTS). The sensors are connected with an Edge device and LoRa node for processing and transferring the MTS, respectively. The system first proposes a deep learning-based compression-decompression model for reducing the size of MTS at the Edge devices. Next, it uses game theory for finding a minimum-cost security mechanism to facilitate the secure transmission of MTS. Then, we formulate an optimization problem to obtain a suitable compression ratio and security mechanism to reduce the system's energy consumption, delay, and security cost. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed and secured MTS to the application server in the given time period with desired accuracy. We evaluate the proposed system over the simulation platform and demonstrate the impact of the parameters of the compression model, network, security mechanism, and the number of sensors on energy consumption, delay, cost, and system accuracy.

With the expeditious evolution in technology, recent years have witnessed significant growth in passenger assistance applications in the transportation system. Such applications have varying demands for resources and quality of services. This paper presents a Fog computing based transportation system. The system uses multiple Fog devices to provide assistance to the passengers. The passengers and vehicles work as end-users and the Edge devices in the system, respectively. A passenger generates a task and using the Edge device forwards it to the Fog devices for further processing. Selected Fog devices parallel process the fraction of the task, so that the complete task processes within the given time constraint. We use the gamma function based reputation model of Fog devices, which provides the confidence to complete a given task successfully. We present a Knapsack based task offloading algorithm, which helps to fully utilize the resources of the Fog devices. We also present a competitive game model and near Nash Equilibrium solution for estimating the optimal value of the fraction of the task process at Fog devices. Finally, we develop a prototype and present results to investigate the performance of the propose system.

Long Range (LoRa) is a wireless communication technology which enables Internet of Things (IoT) devices to efficiently and robustly communicate over long distances with low power consumption. The LoRa supports six Spreading Factors (SFs) and therefore a limited number of virtual channels are possible at a given time instance. Such limited number of channels restate the simultaneous use of a LoRa Gateway (LG) by the large number of LoRa Nodes (LNs) in a dense LoRa network and create the bottleneck problem at the LG. The bottleneck problem reduces the proper use of the allocated SFs and the utility of the LNs. In this paper, we propose a game theoretic approach that allocates the time duration to the LNs in the network for accessing the SFs. Such time duration maximizes the utility of the LNs in the network. The time duration determined on different SFs are then scheduled to minimize the waiting time of LNs. The proposed approach is validated by simulating the LoRa network using Network Simulator-3. Our simulations show that the proposed approach effectively reduces the waiting time and prolongs the network utility.

Wireless Local Area Networks (WLANs) are one of the most popular networks for the Internet-of-Things (IoT) applications. Among various WLAN technologies, the Long-Range WAN (LoRaWAN) has gained a high demand in recent years because of its low power consumption and long-range communication. However, the Long-Range (LoRa) network suffers from interference problem among LoRa Devices (LDs) that are connected to the LoRa gateway by using the same Spreading Factors (SFs). In this article, we propose a game theory-based approach for estimating the time duration of transmission of data on suitable SFs such that interference problem is reduced and network devices maximize their utilities. We next propose a scheduling algorithm that schedules the allocated time duration on the SFs such that the waiting time of the network can be minimized. We finally use the network simulator-3 for validating the propose work. Various experiments are performed which demonstrate the improvement in the network performance.

Internet of Things (IoT) finds their applications in many areas that include environmental monitoring, industrial control, traffic congestion control, smart metering, and smart parking. An important issue of research in IoT is to successfully transmit the data to the cloud and yet minimize the energy consumption of the battery-powered IoT devices. Long Range (LoRa) is a long-range wireless communication protocol that competes against other low-power wide-area networks. In this paper, we propose a Stackelberg Game based model for allocation of the appropriate power levels to the LoRa nodes in energy-efficient LoRa network. The utility of the network server, works as a leader player, is to successfully receive the data from the LoRa nodes. The utility of the LoRa nodes, work as followers player, is to reduce the power consumption during transmission of the data to the LoRa gateway. Simulation results evaluate the performance of the proposed game model to validate its effectiveness.

Water is one of the necessary things required for living. Sensor networks play a major role in detecting the pollution level of the river water. Detection of the sudden changes in the pollution level of the river water is a challenging problem because it requires continuous monitoring of the river water. In this paper, we propose a Stackelberg Game based system to detect the sudden changes of the pollution level of the river water. We use Long Range (LoRa) technology for transferring the sensory data to the server. The LoRa uses different Spreading Factors which helps to reduce the communication energy and enhances the lifetime of the system. We demonstrate the utility of the system and study the impact of the sudden changes of the pollution level of the water on the energy consumption of the system.