Security Requirement Management for Cloud-Assisted and Internet of Things—Enabled Smart City

The world is rapidly changing with the advance of information technology. The expansion of the Internet of Things (IoT) is a huge step in the development of the smart city. The IoT consists of connected devices that transfer information. The IoT architecture permits on-demand services to a public pool of resources. Cloud computing plays a vital role in developing IoT-enabled smart applications. The integration of cloud computing enhances the offering of distributed resources in the smart city. Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability, security, performance, confidentiality, and privacy. The key reason for cloud- and IoT-enabled smart city application failure is improper security practices at the early stages of development. This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications. Its three-layered architecture includes privacy preserved stakeholder analysis (PPSA), security requirement modeling and validation (SRMV), and secure cloud-assistance (SCA). A case study highlights the applicability and effectiveness of the proposed framework. A hybrid survey enables the identification and evaluation of significant challenges.     

Sustainability of smart rural mobility and tourism: A key performance indicators-based approach

The concept of smart mobility and tourism has evolved from a technology-driven approach to one that focuses on sustainable solutions to address economic, social, and environmental issues. The United Nations (UN) sustainable development goals (SDGs) provide a framework for measuring and tracking progress toward sustainability goals. Key performance indicators (KPIs) are a useful tool for measuring and tracking progress towards these goals, allowing for continuous monitoring and evaluation of progress, identification of areas for improvement, and directing targeted interventions. This research aims to develop an indicators-based framework to evaluate the sustainability of smart and sustainable mobility and tourism in rural areas. Rural areas have often been neglected, or at least less prioritized, in the sustainability development of the mobility sector. The study also seeks to identify the overlap of KPIs between rural tourism and mobility, and how improved green mobility services can enhance sustainable rural tourism. Smart mobility and tourism indicators have a strong mutual relationship in rural communities, driving economic development, improving the quality of life for residents and visitors, and creating more sustainable and livable communities. Smart mobility and tourism indicators also play a crucial role in supporting the UN SDGs by providing data and insights that can inform policy and decision-making. The results of this research conclude how the target and performance setting of projects on sustainable mobility and tourism in rural communities support each other, and how they support achieving SDGs.     

M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT

In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture known as a Multi-class Classification based Intrusion Detection Model (M-IDM), which typically relies on data collected by real devices and the use of convolutional neural networks (i.e., it exhibits better performance compared with conventional machine learning algorithms, such as naïve Bayes, support vector machine (SVM)). Unlike existing studies, the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices, such as a patient’s monitors (i.e., electrocardiogram and thermometers). The proposed architecture classifies the data into multiple classes: Critical, informal, major, and minor, for intrusion detection. Further, we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms, including naïve Bayes, SVM, and logistic regression, using neural networks.     

OTS Scheme Based Secure Architecture for Energy-Efficient IoT in Edge Infrastructure

For the past few decades, the Internet of Things (IoT) has been one of the main pillars wielding significant impact on various advanced industrial applications, including smart energy, smart manufacturing, and others. These applications are related to industrial plants, automation, and e-healthcare fields. IoT applications have several issues related to developing, planning, and managing the system. Therefore, IoT is transforming into G-IoT (Green Internet of Things), which realizes energy efficiency. It provides high power efficiency, enhances communication and networking. Nonetheless, this paradigm did not resolve all smart applications’ challenges in edge infrastructure, such as communication bandwidth, centralization, security, and privacy. In this paper, we propose the OTS Scheme based Secure Architecture for Energy-Efficient IoT in Edge Infrastructure to resolve these challenges. An OTS-based Blockchain-enabled distributed network is used at the fog layer for security and privacy. We evaluated our proposed architecture’s performance quantitatively as well as security and privacy. We conducted a comparative analysis with existing studies with different measures, including computing cost time and communication cost. As a result of the evaluation, our proposed architecture showed better performance.     

Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations. Internet of Things (IoT) based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production. This objective requires intensive monitoring, prediction, and control by optimizing leading factors that impact fish growth, including temperature, the potential of hydrogen (pH), water level, and feeding rate. This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming. The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems. Fish farm indoor and outdoor values are applied to predict the water quality parameters, whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters. Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption. To evaluate the efficiency of the proposed system, the overall approach has been deployed to the fish tank as a case study, and a number of experiments have been carried out. The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30% of energy efficiency at the maximum actuator control rate compared with other control levels.     

A contextualized study of the usage of the Internet of things (IoTs) in smart farming in a typical Middle Eastern country within the context of Unified Theory of Acceptance and Use of Technology model (UTAUT)

Smart Farming is the application of modern technologies, tools and gadgets for increasing the agricultural crops quality and quantity. The Internet of Things (IoT) technology has had a prominent role in the establishment of smart farming. However, the application of this technology could be hard and, in some cases, challenging for the Middle Eastern users. Therefore, the research purpose is to identify the influential factors in the adoption and then application of IoT in smart farming by farmers with a contextualized approach in Iran, a typical Middle Eastern country. Thus, the Unified Theory of Acceptance and Use of Technology (UTAUT) has contextually been used as the theoretical model of the research. The results accentuated and proved the positive impacts of performance expectancy (H1), effort expectancy (H2), social influence (H3), individual factors (H4), and facilitating conditions (H5), on the intention to use IoT technology. Ultimately, the results were indicating the significant impact of behavioral intention on the actual usage of IoT technology (H6). One of the implications of the research is for the IT policymakers in the agricultural sector in the Middle East, where water and cultivable land are two valuable but scarce economic resources. Hence, smart farming could not be promoted unless the farmers had fulfilled its prerequisite factors proposed by the research results for using the IoT technology.     

Secure Irrigation System for Olive Orchards Using Internet of Things

Smart irrigation system, also referred as precision irrigation system, is an attractive solution to save the limited water resources as well as to improve crop productivity and quality. In this work, by using Internet of things (IoT), we aim to design a smart irrigation system for olive groves. In such IoT system, a huge number of low-power and low-complexity devices (sensors, actuators) are interconnected. Thus, a great challenge is to satisfy the increasing demands in terms of spectral efficiency. Moreover, securing the IoT system is also a critical challenge, since several types of cybersecurity threats may pose. In this paper, we address these issues through the application of the massive multiple-input multiple-output (M-MIMO) technology. Indeed, M-MIMO is a key technology of the fifth generation (5G) networks and has the potential to improve spectral efficiency as well as the physical layer security. Specifically, by exploiting the available M-MIMO channel degrees of freedom, we propose a physical layer security scheme based on artificial noise (AN) to prevent eavesdropping. Numerical results demonstrate that our proposed scheme outperforms traditional ones in terms of spectral efficiency and secrecy rate.     

A multi-criteria analysis for an internet of things application recommendation system

Various internet of things applications are available that cover every aspect of daily life and users can subscribe to numerous IoT applications. Selecting the most suitable IoT applications for individual users is a critical challenge. This study aims to solve this challenge by proposing recommendation system using a hybrid multicriteria decision-making approach based on the analytical hierarchy process and simple additive weight methods. Based on the opinions and preferences of experts, the model and the hierarchy were designed to assess and compare three crucial criteria, namely smart objects, applications, and providers. The results show that applications criterion is more important for users than the other two criteria. In specific, privacy, reliability, and availability are crucial criteria for IoT applications.     

Optimal Confidential Mechanisms in Smart City Healthcare

Smart City Healthcare (SHC²) system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner. The system also concedes the freedom of a patient. IoT is a part of this system and it helps in providing care to the patients. IoT-based healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate care. Existing methodologies exhibit few shortcomings in terms of computational complexity, cost and data security. Hence, the current research article examines SHC² security through Light Weight Cipher (LWC) with Optimal S-Box model in PRESENT cipher. This procedure aims at changing the sub bytes in which a single function is connected with several bytes’ information to upgrade the security level through Swam optimization. The key contribution of this research article is the development of a secure healthcare model for smart city using SHC² security via LWC and Optimal S-Box models. The study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC² information, constrained by Mutual Authentication (MA). The security challenges, in healthcare information systems, emphasize the need for a methodology that immovably concretes the establishments. The methodology should act practically, be an effective healthcare framework that depends on solidarity and adapts to the developing threats. Healthcare service providers integrated the IoT applications and medical services to offer individuals, a seamless technology-supported healthcare service. The proposed SHC² was implemented to demonstrate its security levels in terms of time and access policies. The model was tested under different parameters such as encryption time, decryption time, access time and response time in minimum range. Then, the level of the model and throughput were analyzed by maximum value i.e., 50 Mbps/sec and 95.56% for PRESENT-Authorization cipher to achieve smart city security. The proposed model achieved better results than the existing methodologies.     

Smart Security Framework for Educational Institutions Using Internet of Things (IoT)

Educational institutions are soft targets for the terrorist with massive and defenseless people. In the recent past, numbers of such attacks have been executed around the world. Conducting research, in order to provide a secure environment to the educational institutions is a challenging task. This effort is motivated by recent assaults, made at Army Public School Peshawar, following another attack at Charsada University, Khyber Pukhtun Khwa, Pakistan and also the Santa Fe High School Texas, USA massacre. This study uses the basic technologies of edge computing, cloud computing and IoT to design a smart emergency alarm system framework. IoT is engaged in developing this world smarter, can contribute significantly to design the Smart Security Framework (SSF) for educational institutions. In the emergency situation, all the command and control centres must be informed within seconds to halt or minimize the loss. In this article, the SSF is proposed. This framework works on three layers. The first layer is the sensors and smart devices layer. All these sensors and smart devices are connected to the Emergency Control Room (ECR), which is the second layer of the proposed framework. The second layer uses edge computing technologies to process massive data and information locally. The third layer uses cloud computing techniques to transmit and process data and information to different command and control centres. The proposed system was tested on Cisco Packet Tracer 7. The result shows that this approach can play an efficient role in security alert, not only in the educational institutions but also in other organizations too.     

Key factors for sustainable operation of smart rural communities in aging societies: Voices of Korean community leaders

The purpose of this study is to identify the success factors for achieving sustainable operation of smart rural communities. Smart rural communities, enabled by smart technology, are regarded as a potential solution to the challenges posed by aging populations in Korea. However, the sustainable operation of smart rural communities is complex, as a variety of factors should be addressed. To address issues of aging problems in rural communities, this study proposes a priority factor for smart rural community service by applying an analytical hierarchy process (AHP) method and in-depth interviews. The AHP model was designed based on a socio-technical system, and an AHP survey was conducted with community leaders who have experience in smart rural community projects. This study proposes a total of 4 criteria and 16 factors along with their corresponding priorities. The results indicate that the local culture is the most important for a sustainable smart rural community compared to other factors. Specifically, factors such as empathy of rural residents, stable financial support, and the presence of smart technology professionals and technical staff are critical for achieving successful smart rural communities for the elderly people. This study found that the cultural and human aspects are more critical for the sustainable operation of smart rural communities than technology infrastructure. The findings of this study provide valuable insights to other countries similar to Korea facing aging problems in rural areas planning to introduce smart rural communities.     

An Efficient and Reliable Multicasting for Smart Cities

The Internet of thing (IoT) is a growing concept for smart cities, and it is compulsory to communicate data between different networks and devices. In the IoT, communication should be rapid with less delay and overhead. For this purpose, flooding is used for reliable data communication in a smart cities concept but at the cost of higher overhead, energy consumption and packet drop etc. This paper aims to increase the efficiency in term of overhead and reliability in term of delay by using multicasting and unicasting instead of flooding during packet forwarding in a smart city using the IoT concept. In this paper, multicasting and unicasting is used for IoT in smart cities within a receiver-initiated mesh-based topology to disseminate the data to the cluster head. Smart cities networks are divided into cluster head, and each cluster head or core node will be responsible for transferring data to the desired receiver. This protocol is a novel approach according to the best of our knowledge, and it proves to be very useful due to its efficiency and reliability in smart cities concept because IoT is a collection of devices and having a similar interest for transmission of data. The results are implemented in Network simulator 2 (NS-2). The result shows that the proposed protocol shows performance in overhead, throughput, packet drop, delay and energy consumption as compared to benchmark schemes.     

IoT patent roadmap for smart logistic service provision in the context of Industry 4.0

Abstract

Industry 4.0 enables the management of factories manufacturing products with complexity and flexibility. The corresponding logistic services must provide greater accuracy and efficiency in logistic operations. The Internet of Things (IoT) is an important aspect for smart logistics in the context of Industry 4.0. For instance, intelligent logistics models use IoT integrated technologies, e.g. radio frequency identification (RFID), wireless sensor network (WSN) and cloud computing, to enhance the traceability and decision supports of logistic processes in real-time speed, high accuracy, and flexibility. This research focuses on analyzing the related technology roadmaps for the adoption of IoT technologies in smart logistic services. A case research is conducted specifically to identify the relationship between IoT-oriented technologies and deployed advanced logistic services. The logistic operations are organized into an ontology schema based on a four level service framework. The research proposes a roadmap approach to visualize the patent allocations and evolutions corresponding to logistic services at each level. Although the patent roadmap methodology is generic, this research focuses on the two industry leaders, which are UPS and IBM. Using the roadmap methodology, the IoT enabled smart logistic patents are analyzed to identify technology-related business strengths and strategies.     

Secure Multifactor Remote Access User Authentication Framework for IoT Networks

The term IoT refers to the interconnection and exchange of data among devices/sensors. IoT devices are often small, low cost, and have limited resources. The IoT issues and challenges are growing increasingly. Security and privacy issues are among the most important concerns in IoT applications, such as smart buildings. Remote cybersecurity attacks are the attacks which do not require physical access to the IoT networks, where the attacker can remotely access and communicate with the IoT devices through a wireless communication channel. Thus, remote cybersecurity attacks are a significant threat. Emerging applications in smart environments such as smart buildings require remote access for both users and resources. Since the user/building communication channel is insecure, a lightweight and secure authentication protocol is required. In this paper, we propose a new secure remote user mutual authentication protocol based on transitory identities and multi-factor authentication for IoT smart building environment. The protocol ensures that only legitimate users can authenticate with smart building controllers in an anonymous, unlinkable, and untraceable manner. The protocol also avoids clock synchronization problem and can resist quantum computing attacks. The security of the protocol is evaluated using two different methods: (1) informal analysis; (2) model check using the automated validation of internet security protocols and applications (AVISPA) toolkit. The communication overhead and computational cost of the proposed are analyzed. The security and performance analysis show that our protocol is secure and efficient.     

Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0

Additive Manufacturing (AM) requires integrated networking, embedded controls and cloud computing technologies to increase their efficiency and resource utilisation. However, currently there is no readily applicable system that can be used for cloud-based AM. The objective of this research is to develop a framework for designing a cyber additive manufacturing system that integrates an expert system with Internet of Things (IoT). An Artificial Neural Network (ANN) based expert system was implemented to classify input part designs based on CAD data and user inputs. Three ANN algorithms were trained on a knowledge base to identify optimal AM processes for different part designs. A two-stage model was used to enhance the prediction accuracy above 90% by increasing the number of input factors and datasets. A cyber interface was developed to query AM machine availability and resource capability using a Node-RED IoT device simulator. The dynamic AM machine identification system developed using an application programme interface (API) that integrates inputs from the smart algorithm and IoT interface for real-time predictions. This research establishes a foundation for the development of a cyber additive design for manufacturing system which can dynamically allocate digital designs to different AM techniques over the cyber network.     

Intelligent System Application to Monitor the Smart City Building Lighting

A smart city incorporates infrastructure methods that are environmentally responsible, such as smart communications, smart grids, smart energy, and smart buildings. The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality, with energy resources taking precedence. To achieve optimal energy management in the multidimensional system of a city tribe, it is necessary not only to identify and study the vast majority of energy elements, but also to define their implicit interdependencies. This is because optimal energy management is required to reach this objective. The lighting index is an essential consideration when evaluating the comfort indicators. In order to realize the concept of a smart city, the primary objective of this research is to create a system for managing and monitoring the lighting index. It is possible to identify two distinct phases within the intelligent system. Once data collection concludes, the monitoring system will be activated. In the second step, the operation of the control system is analyzed and its effect on the performance of the numerical model is determined. This evaluation is based on the proposed methodology. The optimized results were deemed satisfactory because they maintained the brightness index value (79%) while consuming less energy. The intelligent implementation system generated satisfactory outcomes, which were observed 1.75 times on average.     

Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

In recent times, the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features. The IoT has shown wide adoption in various applications including smart cities, healthcare, trade, business, etc. Among these applications, fitness applications have been widely considered for smart fitness systems. The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities. Thus, scheduling such a huge number of requests for fitness exercise is a big challenge. Secondly, the user fitness data is critical thus securing the user fitness data from unauthorized access is also challenging. To overcome these issues, this work proposed a blockchain-based load-balanced task scheduling approach. A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling approaches. The proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’ requests and improve resource utilization. The performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection ratio. The obtained results confirm the efficiency of the proposed scheduling approach. For investigating the performance of the blockchain, various experiments are performed using the Hyperledger Caliper concerning latency, throughput, resource utilization. The Solo approach has shown an improvement of 32% and 26% in throughput as compared to Raft and Solo-Raft approaches respectively. The obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.     

Novel Architecture of OneM2M-Based Convergence Platform for Mixed Reality and IoT

There have been numerous works proposed to merge augmented reality/mixed reality (AR/MR) and Internet of Things (IoT) in various ways. However, they have focused on their specific target applications and have limitations on interoperability or reusability when utilizing them to different domains or adding other devices to the system. This paper proposes a novel architecture of a convergence platform for AR/MR and IoT systems and services. The proposed architecture adopts the oneM2M IoT standard as the basic framework that converges AR/MR and IoT systems and enables the development of application services used in general-purpose environments without being subordinate to specific systems, domains, and device manufacturers. We implement the proposed architecture utilizing the open-source oneM2M-based IoT server and device platforms released by the open alliance for IoT standards (OCEAN) and Microsoft HoloLens as an MR device platform. We also suggest and demonstrate the practical use cases and discuss the advantages of the proposed architecture.     

Cyber–Physical Production Systems for Data-Driven,Decentralized, and Secure Manufacturing—A Perspective

With the concepts of Industry 4.0 and smart manufacturing gaining popularity, there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm, targeting innovation, automation, better response to customer needs, and intelligent systems. Within this context, this review focuses on the concept of cyber–physical production system (CPPS) and presents a holistic perspective on the role of the CPPS in three key and essential drivers of this transformation: data-driven manufacturing, decentralized manufacturing, and integrated blockchains for data security. The paper aims to connect these three aspects of smart manufacturing and proposes that through the application of data-driven modeling, CPPS will aid in transforming manufacturing to become more intuitive and automated. In turn, automated manufacturing will pave the way for the decentralization of manufacturing. Layering blockchain technologies on top of CPPS will ensure the reliability and security of data sharing and integration across decentralized systems. Each of these claims is supported by relevant case studies recently published in the literature and from the industry; a brief on existing challenges and the way forward is also provided.     

Energy-Efficient Transmission Range Optimization Model for WSN-Based Internet of Things

With the explosive advancements in wireless communications and digital electronics, some tiny devices, sensors, became a part of our daily life in numerous fields. Wireless sensor networks (WSNs) is composed of tiny sensor devices. WSNs have emerged as a key technology enabling the realization of the Internet of Things (IoT). In particular, the sensor-based revolution of WSN-based IoT has led to considerable technological growth in nearly all circles of our life such as smart cities, smart homes, smart healthcare, security applications, environmental monitoring, etc. However, the limitations of energy, communication range, and computational resources are bottlenecks to the widespread applications of this technology. In order to tackle these issues, in this paper, we propose an Energy-efficient Transmission Range Optimized Model for IoT (ETROMI), which can optimize the transmission range of the sensor nodes to curb the hot-spot problem occurring in multi-hop communication. In particular, we maximize the transmission range by employing linear programming to alleviate the sensor nodes’ energy consumption and considerably enhance the network longevity compared to that achievable using state-of-the-art algorithms. Through extensive simulation results, we demonstrate the superiority of the proposed model. ETROMI is expected to be extensively used for various smart city, smart home, and smart healthcare applications in which the transmission range of the sensor nodes is a key concern.