IoT Based Intelligent Transportation System (IoT-ITS) for Global Perspective: A Case Study

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.

Background/Objectives: Scalability in the Internet of Things is meant for future extension in Internet connected devices. The scalability can be classified into two broad sense in terms of future flexibility i.e., vertical and horizontal scalability and in terms of infrastructure of the Internet of things i.e., device, communication, gateway, and server levels. Novelty: In this article, a novel taxonomy of scalability is proposed and presented according to infrastructure and it is categorized into four levels i.e., device, gateway, communication,and server levels. Moreover, a performance analysis of vertical scalability is conducted at the server level for a smart campus transportation system on a custom-based emulator developed in Visual Studio. Findings: From the results,it is observed that by increasing the load the response time of the server increases. And by scaling up, response time decreases. By scaling up at a certain limit and increasing the load, response time again increases. It is strongly believed that this research will help in assessing the capacity of present and future smart systems. Keywords: Internet of things; vertical scalability; horizontal scalability; smart campus; smart transport system

Purpose: In today’s world, with the presence of huge volumes of data, although organizations have faced many problems, using big data analysis has been able to significantly improve their efficiency and integrate information in the supply chain through the topic of computing. Cloud and big data achieve coordination between components and improve communication. On the other hand, Internet of Things (IoT) technology tools are one of the most important sources of big data production, and understanding and correct use of this data and their timely analysis using big data analysis techniques and technologies based on artificial intelligence can be effective steps to improve supply chain processes. Also, the use of these technologies can play an important role in process agility and, as a result, supply chain resilience. Methodology: In this study, the dimensions and key components of the use of large data obtained from the Internet of Things (IoT) in an industry's supply chain are investigated as a case study. Finally, a model for implementing an agile and lean supply chain based on IoT data analysis to improve the supply chain performance of these industries during emergency drug distribution during critical conditions is presented. Findings: This study shows that these technologies can be used as a powerful enabler, especially in the distribution of fast-acting pharmaceutical products.                                                                         Originality/Value: In this paper a model for implementing an agile and lean supply chain based on IoT data analysis to improve the supply chain performance of these industries during emergency drug distribution during critical conditions is presented.

Sensors are being extensively deployed and are expected to expand at significant rates in the coming years. They typically generate a large volume of data on the internet of things (IoT) application areas like smart cities, intelligent traffic systems, smart grid, and e-health. Cloud, edge and fog computing are potential and competitive strategies for collecting, processing, and distributing IoT data. However, cloud, edge, and fog-based solutions need to tackle the distribution of a high volume of IoT data efficiently through constrained and limited resource network infrastructures. This paper addresses the issue of conveying a massive volume of IoT data through a network with limited communications resources (bandwidth) using a cognitive communications resource allocation based on Reinforcement Learning (RL) with SARSA algorithm. The proposed network infrastructure (PSIoTRL) uses a Publish/ Subscribe architecture to access massive and highly distributed IoT data. It is demonstrated that the PSIoTRL bandwidth allocation for buffer flushing based on SARSA enhances the IoT aggregator buffer occupation and network link utilization. The PSIoTRL dynamically adapts the IoT aggregator traffic flushing according to the Pub/Sub topic's priority and network constraint requirements.

Recently, extensive research into healthcare services and their technological advancements is being carried out globally. The Internet of Things (IoT) has proven to be a predominantly potential candidate in these advancements. IoT supports infrastructure development in connecting a wide range of medical devices, sensors, and health practitioners in order to ensure the best medical treatment for patients in both cities and remote places. This has enabled the IoT to provide major support in medical application, giving rise to assisted healthcare facilities. The IoT ecosystem has a complicated architecture, with several components interacting and an evolving variety of applications for users. This is a comprehensive and interdependent system that involves real-time data analysis, interconnections of device, data transfers, and analysis. The IoT creates a connected ecosystem developed from several intelligent systems that integrates respondents with machines and allows for statistics decision making. A network of interconnected devices that perceive vital data related to medical conditions of patients in real time is referred to as the Internet of Medical Things (IoMT), a healthcare application of IoT technology. Sensors used for sensing real-time data related to monitoring temperature, blood pressure levels, heartbeat levels, specialized signal converters, and communication interfaces can be added to existing medical facilities to turn them into IoMT devices. IoMT can be used to create wearable devices, home-care and personal-care medical devices, and mobile healthcare apps, along with creating communication with medical specialists in a given area. The IoMT system’s challenges and ecosystem interface requirements are also a matter of concern considering the complexity of the systems. This study offers a comprehensive evaluation of the various domains of use of IoMT, as well as its future scope and challenges. Recent advances in sensor technology, networks, cloud storage and processing, mobility, and big data analytics have made it possible to create affordable smart medical devices and a connected healthcare environment.

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.

With the popularisation and development of the internet of things, big data and cloud computing, the search of data in cloud-based internet of things becomes a hot research topic. However, the sensitive data, such as the medical data collected by wearable devices, is inevitable to be stored in the cloud server. Homomorphic encryption has the ability to calculate the ciphertext without decryption. We separate the calculating and the decryption into different security domains to preserve the privacy of sensitive data, so the original plaintext would not be exposed in the cloud. Hence, we can compare two ciphertexts to get the difference of them in a privacy preserving way. In order to accelerate the search process of range query, we build an encrypted self-balancing binary index tree. Based on oblivious RAM, the searching scheme can hide the access patterns of the node of tree. The actual nodes and logic relation of tree are stored on different servers. A sample implementation of the proposed scheme is given, and the experimental results and analysis are presented to illustrate the scheme's effectiveness and security.

At present, Intelligent Transportation Systems (ITS) are fast developing and have been applied in many fields. The Internet of Things (IOT) provides a new direction for their development. This paper first introduces the concept and structure of the IOT and states the high integration between the IOT and ITS. At the same time, it presents the frame of the ITS based on the IOT and describes the system function. Then, the paper elucidates the application of an intelligent sensor, radio frequency identification (RFID), positioning technology and a geographic information system (GIS) in ITS from three stages: data collection, data transmission and information processing. The paper discusses the differences between the new system frame and the existing system frame. Finally, the paper presents the application prospects of ITS based on IOT.

Privacy preserving Internet of Things: From privacy techniques to a blueprint architecture and efficient implementation

With one-tap check-ins, digital concierge services, voice-activated gadgets, chatbots, smart in-room technology, and advanced analytics, the hotel sector has been quickly embracing new technologies to meet and exceed consumer expectations and digitize the customer experiences. The traditional hotel management with internet-based customer service could not handle dynamic real-time data efficiently due to increased data volume. Thus, this work analyzes hotel management practices with the internet of things (IoT) and big data. The IoT and big data significantly impact the guest experience since businesses can provide consumers with unique services to their needs. Automated check-in and checkout, pre-booking, registration, and user-chosen payment methods are just a few self-services that improve the visitor experience. For enhancing visitor satisfaction and offering tailored services, this paper looks at how IoT and big data analytics can help the hotel sector. It moreover examines how IoT can be used within the business. This extended research finds excellent results in hotel management through IoT and big data. A client occupancy detection model (CODM) simulation scenario finds the best detection accuracy of 97.51%.

This study investigates hybrid energy harvesting(EH) as a sustainable and efficient power solution for Internet of Things (IoT) devices. Traditional battery-powered and single-source energy systems have limitations, such as limited lifetime and unreliable power availability. To address these issues, a hybrid energy harvesting system is proposed that combines photovoltaic energy (solar), radio frequency energy(RF) , and vibration energy sources. These sources capture energy from the surrounding environment, allowing the system to operate under different conditions. By using multiple energy sources together, the proposed model provides more reliable and continuously power in both urban and remote environments. The system performance is evaluated using simulated environmental data for different deployment scenarios. The results shows that the harvested energy is sufficient to support low-power IoT devices. Unlike, conventional systems, the proposed approach reduces the need for frequent battery replacement, which increases device lifespan and lowers maintenance requirements. The study demonstrates that hybrid energy harvesting is a practical and effective solution for autonomous Internet of things (IoT) applications. This work supports the development of self-powered, scalable, and environmentally friendly IoT networks, especially in locations that are difficult to access. Keywords Wireless Energy Harvesting(WSN), Internet of Things(IoT), Hybrid Energy Harvesting, Solar Energy, Vibration, Radio Frequency Energy, Self-Powered Iot, Energy Management.

In this modern era, the number of accidents that occur around us is increasing unprecedently. The reasons for accidents might be the driver’s health issues, drowsiness and alcohol consumption. To prevent accidents and improve the driver’s safety, this research work has proposed a smart and secure transportation system. The proposed paper describes the design and implementation of smart and secure transportation system by using Raspberry Pi and Internet of Things. For alcohol detection, MQ3 sensor is used, and to detect heart attack, pulse sensor is used. Here, a gadget has been proposed with the intention to discover coronary heart assault via tracking the heart rate based on Internet of Things (IoT). Monitoring and alerting an individual, when the comatose gains cognizance by using the movement detection gadget. Two sensors are used to monitor the health of the patient. Pulse sensor is used to monitor the pulse rate functioning of heart. Alcohol sensor is used to detect alcohol percentage in air. By using these sensors, the physical condition of the driver can be detected. The system locks the vehicle if the driver is determined to be alcoholic or with improper pulse rate. Hence, it prevents the road accidents.KeywordsRaspberry PiSecure transportationAlcohol detectionInternet of Things (IoT)PythonArduino boardMicrocontrollerSensors

Recently, two terms, namely Big Data and Internet of Things (IoT) have gained popularity individually. However, their interconnections are not fully explored and understood. It is expected that the fusion of Big Data and IoT would create many complex systems for smart cities. While the data from IoT lies in Big Data, the scale of operations are completely different in terms of providing the required real-time analytics for such smart systems. Even though NoSQL databases and other next generation solutions could be deployed to achieve real-time responses, the major security challenges need to be understood as mission critical and sensitive data intertwines Big Data and IoT. In this paper, we identify the security challenges shared by the closely-knit Big Data and IoT in three main risk areas: i) NoSQL security vulnerabilities, ii) mobile IoT (M-IoT) security and privacy constraints and iii) encryption key security threats. We perform a comparative study of security vulnerabilities of NoSQL databases and identify the security and privacy constraints of M-IoT networks. Encryption key attacks for resource constrained IoT devices are also illustrated mathematically. Overall, this paper explores new research directions in these prime areas of security and privacy that would result in solution opportunities for a meaningful fusion of Big Data and IoT for a smart environment.

Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities' efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people's standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.

Nowadays Internet of Thing (IoT) technology has accessed all the aspects of our life. One of the IoT applications is the smart transportation. Smart transportation gains a lot of attention, as it is part of the smart cities. The data transmission in smart transportation is done through the wireless communication and especially through the Vehicular ad hoc networks (VANETs) which made the data vulnerable to many issues concerning security. Therefore, security techniques should be applied to secure vehicle's user data. In this paper, the RC5 encryption algorithm will be used as a security technique to protect users' privacy in smart transportation. The simulation of RC5 will be implemented with FPGA using Quartus Prime Lite Edition version 18. The purpose is to check hardware resources of RC5 and make sure it is suitable for smart transportation.