Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

With the continuous development of the Internet of Things (IoT) and the needs of the times, the number of IoT edge devices is rapidly increasing, and to be able to provide users with more reliable information services and decision-making, this paper constructs an IoT system based on cloud-edge collaboration. Based on cloud-edge collaboration, data mining technology is used to categorize data, and regression models and branch neural networks are combined to further improve the IoT system based on cloud-edge collaboration. Finally, the particle swarm algorithm is used to optimize reinforcement learning, and the framework of an IoT system built on cloud-edge partnership is established. Through the data processing experiments on IoT edge computing, the data mining performance of the IoT edge computing-based data mining method is improved by 0.0295, 0.1870, and 0.2059 on the large datasets of Dataset4 and Dataset5, respectively. the average turnaround time is lower than 6.5 for different edge computing power levels. The system rewards are gradually improved from 5.3 to 8.2, and the number of packet loss of the algorithm is less than 5. The feasibility and effectiveness of the IoT data mining method and system based on cloud-edge collaboration are confirmed by this study, and a feasible method for data mining can be applied to IoT systems under cloud-edge collaboration.

Commonly, an Internet of Things (IoT) system follows the architecture of the Cloud‐centric Internet of Things (CIoT) in which the physical objects are represented in the form of Web resources that are managed by the servers in the global Internet. In general, the common IoT system involves three major technologies: embedded systems, middleware, and cloud services. Although the CIoT model is a common approach to implement IoT systems, it is facing the growing challenges in IoT. This chapter discusses foundations of computing paradigms for realizing emerging IoT applications, especially fog and edge computing, their background, characteristics, architectures and open challenges. It presents related technologies to fog and edge computing. The chapter describes how fog and edge can improve CIoT and explains the hierarchy of fog and edge computing environments. It illustrates the business models of fog and edge computing, and provides the information regarding the opportunities and challenges in fog and edge computing.

Intelligent Transportation Systems (ITS) are quickly changing urban mobility in smart cities, leveraging cutting edge technologies to address critical challenges in urban transportation. This paper provides a comprehensive review of the integration of Internet of Things (IoT), Artificial Intelligence (AI), and autonomous vehicles in ITS for sustainable urban mobility. Through a systematic literature review, we analyze the latest developments in traffic flow prediction, real-time traffic control, and vehicle-to-infrastructure communication. Our findings show that IoT-enabled sensors and AI-driven models significantly improve traffic flow prediction accuracy, while autonomous and connected vehicles show promise in improving intersection management and overall system efficiency. However, critical challenges in cybersecurity, data privacy, and infrastructure readiness that must be addressed for successful implementation is identified. Analysis suggests that when properly implemented, these technologies can lead to safer, more efficient, and environmentally friendly urban transportation systems, thereby enhancing the overall sustainability of cities.

In recent times, a massive amount of smart devices or objects are connected that enhances the scale of the digital world. These smart objects are referred as “things” or physical devices that have the potential to sense the real-world physical objects, collect the data, and network with others. The objects are connected through the internet, which crafts the terminology of Internet of Things (IoT). IoT has been developed and become the center of consideration due to the novelty of embedded device and a rapid enhancement in its number. This increase is resulting in the creative applications of smart environments. Smart transportation is a central stake for the quality of life of citizens in smart environment. Smart transportation involves the use of devices and sensors in the control system of vehicle; for example navigation system of cars, traffic signal management system, number recognition system and speed monitoring system. In this research article, we propose architecture for smart transportation system using Big Data analytics, in order to achieve real time processing and facilitate a friendly communication in the environment of IoT based smart transportation. The proposed architecture is a 3-phase scheme which is responsible for the organization and management of Big Data, real-time processing of Big Data and service management. The proposed architecture is a generic solution for the smart transportation planning using real time Big Data processing. The proposed scheme is realized using Spark over single node Hadoop setup with various input libraries. A huge amount of data from different authentic and reliable sources is measured to validate the proposed architecture. In addition, the effectiveness of proposed scheme also highlighted with regard to throughput.

In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical since it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages the Internet of Things (IoT) paradigm in combination with deep learning and edge computing to classify the extension–flexion movement of one’s knee via embedded machine learning (ML) classification algorithms. The contribution of the proposed work is multilayered, as this paper proposes a system tackling the challenges at the embedded system level, algorithmic level, and user-friendliness level considering a performance evaluation, including the metrics at the power consumption level, delay level, and throughput requirement level, as well as its accuracy and reliability. Furthermore, as an outcome of this work, a dataset of labeled knee movements is freely available to the research community with no limitations. It also provides real-time movement detection with an accuracy reaching 100%, which is achieved with an ML model trained to fit a low-cost off-the-shelf Bluetooth Low Energy platform. The proposed edge computing approach allows predictions to be performed on device rather than solely relying on a Cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99.9% wireless data transfer reduction, a critical 39% prediction delay reduction, and a valuable 17% increase in the event prediction rate considering a reference period of 60 s. Finally, enhanced privacy comprises another significant benefit from the implemented edge computing ML model, as sensitive data can be processed on site and only events or predictions are shared with medical personnel.

Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

The Internet of Things (IoT), which enables a wide variety of embedded devices, sensors, and actuators (known as smart things) to interconnect and exchange data, is a promising network scenario for bridging physical devices and virtual objects in the cyber world. Considering the limited capacity of smart things, cloud computing has been introduced to store and process the huge amount of data collected by the IoT. The appropriate integration of cloud computing and the IoT can be regarded as the best of two worlds, simultaneously providing omnipresent sensing services and powerful processing capabilities. Undoubtedly, the cloud-assisted IoT will boost the advancement of innovative applications and services including smart cities, industrial IoT, intelligent transportation, and electronic health systems. Despite the benefits of cloud-aided IoT, it is impossible to overlook the significance of security and privacy in this kind of highly heterogeneous and interconnected system. To deal with security threats to smart devices and sensitive data, hundreds of security solutions have recently been put forward for either the cloud or IoT environments. However, a few important characteristics such as heterogeneity and scalability have not been properly considered in these solutions.

Since the last decade, the Internet of Things (IoT) is one of the most talked technology by Academicians & Industries due to its wide variety of applications. IoT has evolved a lot from M2M (Machine to Machine) Communication, WSN (Wireless Sensor Networks) CPS (Cyber-Physical System) to the latest Cloud, Fog & Edge Computing. Enabling the Smart Cities is one of the most promising applications of the Internet of Things as it affects the many human lives directly. Smart city promises Health & Environment monitoring, Smart Transportation, Water Quality monitoring, School & Apartment Building Monitoring Energy Management etc. these all together will facilitate the Municipal, District & State Authorities in Enforcement of Schemes & Laws so, it is termed as Smart City Governance or IoTaaSG (IoT as a Service to Governance). Due to huge data generated from billions of sensors implemented for different applications, it becomes a tedious task to manage such large data & get desired information out of it. In this paper, we try to focus on dedicated scalable architecture for IoTaaSG using Edge Computing.

PurposeThe purpose of this paper is to explore the significance of the internet of things (IoT) system for smart cities and deliberate on the technological aspects involved in developing smart cities along with the framework, impact and benefits of IoT for smart cities.Design/methodology/approachThis research is based on the review and synthesis of the papers on the broader areas of IoT for the application and implication towards the smart cities. The prime focus of this paper is to realize the IoT systems for smart city’s development and implementation of various technologies in the context of the Indian environment.FindingsThe outcome of the paper explores the highlights of the importance of the IoT system, including the technological framework, impact and benefits for smart cities. The outcome also highlights the application of IoT for smart cities. This paper provides direction regarding future degrees, potential conceivable outcomes and issues concerning the technological side of smart cities. IoT can change the lives of the people and support evolving urban areas for developing smart cities in India.Originality/valueThe paper deliberates on the novel techno-managerial approach towards the endeavour of smart cities using the IoT.

Survival of the Internet of Things (IoT) is due to the spread of smart devices throughout the world. An enormous amount of data from IoT-enabled devices are getting generated in real-time. Real-time data originates from IoT-enabled devices, builds a knowledge database, and magnifies the wisdom in society. E-governance, smart transport, smart waste management, smart water management, smart energy management makes the smart city. IoT technologies like RFID, Sigfox, Zigbee, Lora, RPMA, Ingenu, NB-IoT, GSM, etc., enables city IoT enabled and helped in making a city smart. Data collected through sensors are analyzed using IoT analytics and which contributes to making smart decisions. Smart city implementation helps in enriching the quality of human life.

Internet of Things (IoT) systems are characterized by highly automated operating environments, which comprise several IoT end devices (IDs) that generate vast amounts of data with strict real-time communication and high data rate requirements. Edge computing facilities are an alternative to traditional cloud computing and support massive data processing in IoT systems while reducing the burden on data centers. In this paper, we consider an edge-based IoT system that comprises an edge server (ES), edge gateways (EGs), and IDs that communicate wirelessly. The EGs reduce the load on the ES by preprocessing data received from ID. However, it may not be possible for a few EGs to accommodate a sheer number of IDs, given the limited computing power and communication coverage of the EGs. Therefore, it is necessary for a few IDs to directly connect to the ES without the support of EGs. Thus, we propose a resource orchestration scheme between EGs and ES and/or among EGs based on a Lagrangian and the Karush-Kuhn-Tucker condition. The scheme allocates optimal resources by considering the computing capacities of EGs and ES and manages interference among the EGs to maximize the efficiency of IoT systems. The performance evaluation indicates that the proposed scheme outperforms the existing schemes in terms of aggregate throughput, latency, data reception rate, and workload fairness among EGs by 42%, 59%, 37%, and 40%, respectively.

The Internet of Things (IoT) is driving the technology and advances of the Internet and every day it is becoming more popular to talk about IoT systems in multiple domains: Smart Cities, Agriculture, and Industrial Internet, etc. Increasingly, IoT systems will need to interact and be interconnected for offering the always-promoted everything-connected paradigm. Current IoT systems rely on semantic web technologies for integrating data and ensure web services interoperability. However there are yet a gap to ensure semantic interoperability among IoT systems. Most of the existing proposed (open) approaches and solutions lack on formal methodologies for interoperability in technology and standard format of the data. We studied and analyzed most available semantic-based IoT approaches to identify the main requirements hindering IoT semantic interoperability. In this paper, we present SEG 3.0 a methodology to federate, unify and provide semantic interoperability. SEG 3.0 emerges from methodologies for ontology engineering and the idea of unification and federated systems. We propose SEG 3.0 and apply it to Internet of Things (IoT) and particularly on use cases for smart cities as proof of concept. Firstly, we define characteristics required for the methodology. Secondly, we describe the processes and the different formal steps. Thirdly, we provide a proof of concept framework and architecture applying this methodology, thus the benefits of using SEG 3.0 methodology in IoT domains are described. Finally, we demonstrate that the SEG 3.0 methodology is applied to three use cases: (1) the M3 framework to assist developers in designing semantic-based IoT applications, (2) the VITAL EU project for smart cities, and (3) the FIESTA-IoT EU project for IoT semantic interoperability. SEG 3.0 is a formal methodology generic enough to be applied to other domains than IoT and smart cities, since the main benefit of the SEG 3.0 is integrating heterogeneous data and adding value to it to build innovative applications.

The rapid increase in population has increased the traffic density in urban areas. In current scenario, the major challenges faced by transportation systems are congestion, accidents and pollution control, etc. In recent decade’s development of Internet of Things (IoT) and communication technologies and connected devices have paved way for the involvement of information and communication in all aspects of smart city. IoT has transformed the transportation system by enhancing customer experience, safety, and operational performance. The smart or intelligent transportation system is possible that can provide in vehicle transportation and navigation system and assistance. Various new techniques are required to further enhance the intelligence and capabilities of applications. Cyber-physical system is an integral part of intelligent transportation system. CPS can help in gathering information in distributed manner over various diverse networks and analysing useful information to control operations of Intelligent Transportation system. This paper provides the description of the main elements of traffic management system in intelligent transportation systems. The focus is on the algorithms used to increase the efficiency of transport management in Intelligent Transportation System.

This research paper explores "Green IoT for Smart Cities," the integration of green technologies and Internet of Things (IoT) applications to foster environmentally sustainable urban environments. The study reviews the state-of-the-art developments in energy-efficient sensor networks, smart waste management, intelligent transportation systems, and optimized energy consumption in buildings. Real-world implementations of Green IoT initiatives in smart city projects worldwide are analyzed, highlighting successes and challenges in achieving sustainability objectives. The paper outlines potential benefits, including reduced carbon footprint, optimized resource allocation, improved air quality, and enhanced quality of life for citizens, as well as economic implications for cities and businesses. Limitations such as data security, privacy concerns, scalability, and interoperability among IoT systems are identified, along with future research directions. Overall, this paper emphasizes the significance of integrating Green IoT technologies into smart city frameworks to address environmental challenges in urbanization, offering valuable insights for policymakers, researchers, and practitioners.

The Internet of Things (IoT) is impacting the world’s connectivity landscape. More and more IoT devices are connected, bringing many benefits to our daily lives. However, the influx of IoT devices poses non-trivial challenges for the existing cloud-based computing paradigm. In the cloud-based architecture, a large amount of IoT data is transferred to the cloud for data management, analysis, and decision making. It could not only cause a heavy workload on the cloud but also result in unacceptable network latency, ultimately undermining the benefits of cloud-based computing. To address these challenges, researchers are looking for new computing models for the IoT. Edge computing, a new decentralized computing model, is valued by more and more researchers in academia and industry. The main idea of edge computing is placing data processing in near-edge devices instead of remote cloud servers. It is promising to build more scalable, low-latency IoT systems. Many studies have been proposed on edge computing and IoT, but a comprehensive survey of this crossover area is still lacking. In this survey, we first introduce the impact of edge computing on the development of IoT and point out why edge computing is more suitable for IoT than other computing paradigms. Then, we analyze the necessity of systematical investigation on the edge-computing-driven IoT (ECDriven-IoT) and summarize new challenges occurring in ECDriven-IoT. We categorize recent advances from bottom to top, covering six aspects of ECDriven-IoT. Finally, we conclude lessons learned and propose some challenging