Provide Internet Of Things Services Research Articles

Dynamic Coalition Formation among IoT Service Providers: A Systematic Exploration of IoT Dynamics Using an Agent-Based Model.

This paper introduces an Agent-Based Model (ABM) designed to investigate the dynamics of the Internet of Things (IoT) ecosystem, focusing on dynamic coalition formation among IoT Service Providers (SPs). Drawing on insights from our previous research in 5G network modeling, the ABM captures intricate interactions among devices, Mobile Network Operators (MNOs), SPs, and customers, offering a comprehensive framework for analyzing the IoT ecosystem's complexities. In particular, to address the emerging challenge of dynamic coalition formation among SPs, we propose a distributed Multi-Agent Dynamic Coalition Formation (MA-DCF) algorithm aimed at enhancing service provision and fostering collaboration. This algorithm optimizes SP coalitions, dynamically adjusting to changing demands over time. Through extensive experimentation, we evaluate the algorithm's performance, demonstrating its superiority in terms of both payoff and stability compared to three classical coalition formation algorithms: static coalition, non-overlapping coalition, and random coalition. This study significantly contributes to a deeper understanding of the IoT ecosystem's dynamics and highlights the potential benefits of dynamic coalition formation among SPs, providing valuable insights and opening future avenues for exploration.

AutoPKI: public key infrastructure for IoT with automated trust transfer

IoT deployments grow in numbers and size, which makes questions of long-term support and maintainability increasingly important. Without scalable and standard-compliant capabilities to transfer the control of IoT devices between service providers, IoT system owners cannot ensure long-term maintainability, and risk vendor lock-in. The manual overhead must be kept low for large-scale IoT installations to be economically feasible. We propose AutoPKI, a lightweight protocol to update the IoT PKI credentials and shift the trusted domains, enabling the transfer of control between IoT service providers, building upon the latest IoT standards for secure communication and efficient encodings. We show that the overhead for the involved IoT devices is small and that the overall required manual overhead can be minimized. We analyse the fulfilment of the security requirements, and for a subset of them, we demonstrate that the desired security properties hold through formal verification using the Tamarin prover.

Exploring Technology Readiness and Acceptance of Small-scale Farmers in Sabah towards the Adoption of Internet of Things Technology

This study aims to explore and propose a conceptual framework to study the determinants of Internet of Things (IoT) adoption intention of small-scale farmers in Sabah for their farming practices. Report from Twelfth Malaysia Plan presented a tremendous decrease of workforce in agriculture sector over the years. In addition, in this contemporary era marked by technological advancements, all industry players in the agriculture sector are compelled to integrate new technologies relevant to agrobusiness. Hence, there is an urgent need of intervention to solve this issue as it will affect food production in Malaysia in the near future. The objective of this study is to formulate a conceptual framework based on semi-structured literature reviews on agricultural technology, incorporating theoretical frameworks from case studies, government reports and statistics. The paper discusses on which framework and its mode of integration that could be adopted in order to study the intention of IoT adoption among small scale farmers in Sabah. This initial conceptual framework provides insights into the needs and concerns of small-scale farmers regarding IoT adoption, serving as a guide for IoT service providers and policymakers in shaping initiatives for agricultural transformation in Sabah.

A Reference Design Model to Manage Consent in Data Subjects-Centered Internet of Things Devices

Internet of Things (IoT) devices have changed how billions of people in the world connect and interact with each other. But, as more people use IoT devices, many questions arise about how these devices handle private data and whether they properly ask for permission when using it. Due to information privacy regulations such as the EU’s General Data Protection Regulation (GDPR), which requires companies to seek permission from data subjects (DS) before using their data, it is crucial for IoT companies to obtain this permission correctly. However, this can be really challenging in the IoT world because people often find it difficult to interact with and manage multiple IoT devices under their control. Also, the rules about privacy are not always clear. As such, this paper proposes a new model to improve how consent is managed in the world of IoT. The model seeks to minimize “consent fatigue” (when people get tired of always being asked for permission) and give DS more control over how their data are shared. This includes having default permission settings, being able to compare similar devices, and, in the future, using AI to give personalized advice. The model allows users to easily review and change their IoT device permissions if previous conditions are not met. It also emphasizes the need for easily understandable privacy rules, clear communication with users, and robust tracking of consent for data usage. By using this model, companies that provide IoT services can do a better job of protecting user privacy and managing DS consent. In addition, companies can more easily comply with data protection laws and build stronger relationships with their customers.

Heterogeneous decentralised machine unlearning with seed model distillation

AbstractAs some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalised IoT service providers have to put unlearning functionality into their consideration. The most straightforward method to unlearn users' contribution is to retrain the model from the initial state, which is not realistic in high throughput applications with frequent unlearning requests. Though some machine unlearning frameworks have been proposed to speed up the retraining process, they fail to match decentralised learning scenarios. A decentralised unlearning framework called heterogeneous decentralised unlearning framework with seed (HDUS) is designed, which uses distilled seed models to construct erasable ensembles for all clients. Moreover, the framework is compatible with heterogeneous on‐device models, representing stronger scalability in real‐world applications. Extensive experiments on three real‐world datasets show that our HDUS achieves state‐of‐the‐art performance.

A SWOT-Based Framework for Personalized Ranking of IoT Service Providers With Generalized Fuzzy Data for Sustainable Transport in Urban Regions

Sustainable transport in cities has been gaining a lot of attraction recently and a core focus of engineering management. Internet of Things (IoT) is seen as a widely accepted technology that promotes sustainability through the interconnection of diverse computing sources for solving environmental problems. Previous studies on IoT have discussed interesting factors toward its adoption, but selecting a suitable IoT service provider (IoTSP) is an open challenge due to a diverse set of factors in practice. Driven by the challenge, in this article, a generalized fuzzy-based decision model is put forward for IoTSP selection, which is the prime objective of the study. Initially, a strength, weakness, opportunity, threat (SWOT) analysis is adopted to identify the crucial challenges in IoT adoption. Later, the relative significance of these challenges is calculated by adopting the regret/rejoice approach. Due to uncertainty, certain rating information of IoTSPs is missing that are rationally imputed by proposing an analytical approach. Rating matrices from experts are transformed into opinion vectors, and a prioritization algorithm is developed with query vector for rational personalized ordering of IoTSPs. Data for the study are acquired via questionnaire, which is filled by experts. The efficacy of the developed model is exemplified by using a real case study of IoTSP selection for pollution management in Chennai. Concerning the findings, mobility, security, and connectivity are the most vital factors for IoTSP selection. Results show that the proposed model is a viable tool for IoTSP selection and it is robust, unique, and stable compared to its counterparts.

Function traffic-aware VNF migration for service restoration in an NFV-enabled IoT system

Maintaining a service continuum is crucial for providing a reliable service in an Internet-of-Things (IoT) system based on Network Function Virtualization (NFV) due to the distribution of Virtual Network Functions (VNF). We address the optimization problem of VNF migration in an NFV-enabled IoT system (NIoT), considering the dynamics of traffic volume processed by an individual IoT function. We develop an optimization model based on integer linear programming and a function traffic-aware heuristic algorithm to solve the problem. The evaluation results show that our proposed heuristic algorithm offers an approximation solution very close to the optimal VNF migration in significantly reduced time. It outperforms a benchmark solution based on Simulated Annealing in both the service restoration cost and computation time. Furthermore, our findings provide valuable insights into an investment strategy for an IoT service provider seeking to enhance the system performance in the presence of a failure.

A Seamless Integration Solution for LoRaWAN Into 5G System

The Internet of Things (IoT) has succeeded to be one of the important future communication technologies. The evolution of IoT has accelerated along with the emergence of 5G considered as a leading IoT service provider. In this context, the Low Power Wide Area Network (LPWAN) has recently attracted attention as it provides an impeccable infrastructure for massive Machine-Type Communications (mMTC). Long Range Wide Area Network (LoRaWAN) is one of the most adopted LPWAN technologies in the world. However, an efficient integration of LoRaWAN technology into the 5G System (5GS) is required. In this paper, we review briefly related work trying to integrate LoRaWAN into the 5GS. Then we present our solution for the integration and we detail the adopted network architecture. We propose new authentication methods based on the Extensible Authentication Protocol (EAP) providing secure access, and an adaptation function to attain seamless and efficient integration. In addition, we evaluate our solution in terms of performance and security. Moreover, a comparison of our solution with related work confirms the efficiency of our solution.

Five Common Misconceptions About Privacy-Preserving Internet of Things

Billions of devices in the Internet of Things (IoT) collect sensitive data about people, creating data privacy risks and breach vulnerabilities. Accordingly, data privacy preservation is vital for sustaining the proliferation of IoT services. In particular, privacy-preserving IoT connects devices embedded with sensors and maintains the data privacy of people. However, common misconceptions exist among IoT researchers, service providers, and users about privacy-preserving IoT. This article refutes five common misconceptions about privacy-preserving IoT concerning data sensing and innovation, regulations, and privacy safeguards. For example, IoT users have a common misconception that no data collection is permitted in data privacy regulations. On the other hand, IoT service providers often think data privacy impedes IoT sensing and innovation. Addressing these misconceptions is essential for making progress in privacy-preserving IoT. This article refutes such common misconceptions using real-world experiments and online survey research. First, the experiments indicate that data privacy should not be perceived as an impediment in IoT but as an opportunity to increase customer retention and trust. Second, privacy-preserving IoT is not exclusively a regulatory problem but also a functional necessity that must be incorporated in the early stages of any IoT design. Third, people do not trust services that lack sufficient privacy measures. Fourth, conventional data security principles do not guarantee data privacy protection, and data privacy can be exposed even if data is securely stored. Fifth, IoT decentralization does not attain absolute privacy preservation.

A Multi-Objective Crowding Optimization Solution for Efficient Sensing as a Service in Virtualized Wireless Sensor Networks

The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs.

A mechanism to ensure identity-based anonymity and authentication for IoT infrastructure using cryptography

The spread of the Internet of Things (IoT) has caused a variety of devices to communicate with one another, producing enormous volumes of data and nurturing a linked ecosystem. However, the privacy of users, the integrity of the data, and secure authentication are seriously jeopardised by this link. In this context, we suggest a unique technique that makes use of identity-based anonymity and cryptographic authentication in IoT infrastructure in order to solve these problems.In order to facilitate safe communications between IoT devices, users, and service providers, our proposed mechanism creates a third-party authenticated protocol. This anonymity protection reduces the danger of unauthorised tracking and data exploitation by securing user names and usage patterns from possible attackers.The proposed system ensures the integrity and confidentiality of data transmitted between IoT devices and service providers by utilising sophisticated encryption algorithms. Additionally, it simplifies the authentication procedure, improving system efficiency overall without sacrificing security. The proposed technique into practise and assessed it using real-world IoT infrastructure scenarios, showcasing how well it protects user privacy and ensures safe communication. The findings show that the mechanism has a low computing cost and can withstand a range of security threats, making it a viable option for identity-based anonymity and authentication in IoT networks. Our approach makes a significant contribution to protecting user data and encouraging trustworthy interactions in IoT infrastructure as the IoT environment continues to grow.

Workflow task scheduling for homogeneous environments on multiprocessing based on IoT variant of beta artificial bee colony

AbstractIn the present era of supercomputing, the efficient scheduling of tasks is the first priority of the IoT and cloud service provider. Their objective is to utilize the maximum resource in order to fulfill various clients' requests. The scheduling of task on multiple processors increases change of underutilization of resources. Hence, this demand‐efficient scheduling algorithm that fulfills computer service provider's demands. This paper proposes a modified artificial bee colony algorithm for scheduling of multiple tasks on multiple processors by introducing crossover operation in employee bee phase to reduce the slow convergence rate. This proposed approach is tested on various scenarios in which there exist condition dependency among the tasks exists. The experimental results reduced the proposed approach to reduce the overall makes pan on an average by 15.37%.

Selection of IoT service provider for sustainable transport using q-rung orthopair fuzzy CRADIS and unknown weights

Internet-of-things (IoT) is a fast-growing technology and sustainable transportation is a crucial application area of IoTs. Selecting an appropriate IoT service provider (IoTSP) is cumbersome and viewed as a multi-criteria decision problem. Recent studies on IoTSP selection inferred that uncertainty and subjectivity during preference elicitation are not flexibly handled. Moreover, they mentioned hesitation and interrelationship are crucial behaviors to be modeled. Motivated by the inferences, this research aims to develop an integrated framework with q-rung orthopair fuzzy sets (q-ROFSs) to evaluate IoTSPs. In this line, the CRITIC method is presented for experts’ importance determination under the q-ROF context. Later, Cronbach’s measure is used for criteria weight calculation with q-ROFSs. Also, an algorithm for prioritization is developed by extending the CRADIS formulation. Lastly, the proposed framework is testified through a case example of IoTSP selection for smart city utilities in India. Results infer that availability, total cost, and security/privacy are crucial criteria for evaluating IoTSPs. Sensitivity and comparison analysis is further conducted to determine the framework’s robustness. This study can assist urban planners, politicians, and other stakeholders in selecting proper IoTSPs when forming sustainable smart cities.

A social smart city for public and private mobility: A real case study

Nowadays, smart city paradigm plays a primary role in the fulfillment of sustainable solutions in the field of urban mobility, both public and private. At the same time, the Internet of Things (IoT) is allowing the development of increasingly advanced solutions for real-time management of collected information related to the management and coexistence of vehicles (i.e., buses, cars, trains, bicycles, etc.) immersed in urban and sub-urban traffic. The Social IoT (SIoT) paradigm adds a relational connotation between objects typical of human relationships. Objects operate as equals and request/provide information among them in the perspective of providing IoT services to users while maintaining their individuality. Social object relationship enables the design of solutions aiming to improve the exchange of information among network nodes in terms of security from malicious attacks external to the so-called social network of objects. In this context, a new SIoT smart city solution is presented in this article: private and public vehicles together with pedestrians are involved in a real-time collection of data to improve the viability of the city in order to suggest new directions and information to citizens to better organize how to live the city. The developed architecture presented in this article is equipped with an artificial intelligence that process collected traffic data and, thanks to machine learning techniques, evaluate the directions and flows undertaken by vehicles and pedestrians on a daily basis. The authors are also presenting an application that allow both citizens to live the city in a better way and municipal authorities to promptly manage traffic flows. The proposed system was installed in a specific area of Cagliari (Italy) and the traffic flows have been compared with daily traffic data monitored before the installation, observing an average gain of up to 35 percent in daily traffic reduction.

Why Do People Use IoT-Enabled Devices?

The purpose of this paper is to identify factors predicting perceived intention of using IoT by Indians. To fulfill the purpose, basing on literature review and on diffusion theory of planned behavior (DTPB) model, factors predicting perceived intention of using IoT by the Indian consumers have been identified and hypotheses have been developed which were tested by collecting data using questionnaire from 400 concerned respondents. The data were analyzed and tested using different standard tools. The study revealed that: perceived usefulness, perceived ease of use, compatibility impact attitude, influence of family members, influence of peers, influence of others impact subjective norms, self-efficacy, and facilitating conditions impact perceived behavioral control. Again, attitude and perceived behavioral control influence intention though subjective norms significantly do not influence intention. Again, it is found that intention positively influences the actual use of IoT by the consumers. The outcome would help the IoT service providers to improve their businesses.

3rdParTEE: Securing Third-Party IoT Services Using the Trusted Execution Environment

Advancements in the Internet of Things (IoT) have resulted in the connection and deployment of numerous smart and embedded devices. Although such devices enable various services such as smart grids, they attract more attackers to the IoT world. A trusted execution environment (TEE), which can be created by using TrustZone technology, is a promising security artifact for protecting critical operations and sensitive data of IoT devices. Unfortunately, although TrustZone is available in most ARM architecture-based devices ranging from microcontrollers to high-end smart devices, it has not been widely adopted by third-party IoT service providers because of its limited access. That is, because the TEE is maintained by the TEE platform vendors to preserve its security. Therefore, third parties must adhere to strict policies and procedures to ensure the deployment of trusted services in the TEE. This aspect hinders the fast development and deployment of IoT services. In this work, we propose 3rdParTEE to address this problem by enabling third-party IoT service providers to readily run their trusted services, thereby minimizing their dependency on the TEE maintainers. Specifically, 3rdParTEE facilitates the secure running of the third-party’s native kernel driver in the TEE without hampering the security of the existing TEE components. To demonstrate the effectiveness of our approach, we ran three kernel drivers for maintaining the IoT services platform (e.g., kernel integrity check) in the TEE. Additionally, during the performance evaluation, we observed a reasonable performance overhead of up to 7% when running the kernel drivers in such a secure manner.

Role and challenge of technology toward a smart sustainable city: Topic modeling, classification, and time series analysis using information and communication technology patent data

This research defines and classifies essential technology groups that comprise a smart sustainable city (SSC) and explores patterns of technological growth by analyzing patents. First, topic modeling and network analysis are conducted using patent text data and international patent classification code, respectively. Patents are classified and defined based on the technology theme and the type of technology provider. Second, hardware-oriented and service application-oriented technologies are classified and predicted using machine learning. Third, the similarities in growth patterns of different technology groups are explored using dynamic time warping (DTW). Moreover, lagging or preceding relationships are examined using multivariate time series analysis. Network infrastructure and Internet-of-things (IoT) application technologies have short-term lagging and preceding relationships along with long-term equilibrium relationships. In terms of player groups, long-term relationships between device vendors, chipset makers, network operator and IoT service provider, and network equipment vendors are confirmed. Technologies for realizing a sustainable city must be prioritized, and the strategic direction of technology must be established to realize truly smart sustainable cities. The supplier of technologies along with possible collaboration opportunities must also be selected. This study proposes the role and direction of technology for SSC and presents a comprehensive methodology for analyzing technology using patents.

Reinforcement learning for cost-effective IoT service caching at the edge

In the edge computing environment, Internet of Things (IoT) application service providers can rent resources from edge servers to cache their service items such as datasets and code libraries, and thus significantly reducing the service request latency and the core network traffic. Since IoT service providers need to pay for the rented edge computing resources, it is essential to find a dynamical service caching strategy to minimize the service cost while optimizing the performance objective such as service latency reduction. However, most of the existing studies either overlooked the problem of collaborative service caching or failed to consider the system's long-term service cost and latency. In this paper, to address such a problem, we coordinate multiple edge servers to cache service items and formulate the collaborative service caching problem using a multi-agent multi-armed bandit model. Furthermore, we propose a utility-aware collaborative service caching (UACSC) scheme based on a multi-agent reinforcement learning. The UACSC scheme can coordinate multiple edge servers to make a dynamic joint caching decision, aiming at maximizing the system's long-term utility. To evaluate the performance of our proposed scheme, we implement four representative baseline algorithms and compare them with six different performance metrics. In addition, a real-world case study is also presented to demonstrate the effectiveness of the UACSC scheme. Comprehensive experimental results show that the UACSC scheme can effectively coordinate multiple edge servers to cache service items, and achieve higher service latency reduction and lower service cost compared with other baseline algorithms.

Post-Quantum Blockchain-Based Data Sharing for IoT Service Providers

Post-Quantum Blockchain-Based Data Sharing for IoT Service Providers

Data Security Challenges with its Defence Strategies of Internet of Things: Critical Review Study

Due to the sensitivity of the data security issue and the lack of studies focused on data security in the Internet of Things (IoT) environment; this paper-through the methodology of critical review of relevant previous and recent studies-aimed to extract the most important challenges or problems and technical and defence strategies and solutions related to securing data in IoT. That is all In order to serve as a practical guide to benefit the concerned ones. As for the results of the studies, there were several things, including: that the most common security issues and challenges within the previous studies were: confidentiality, privacy and encryption effectiveness, and authentication methods are the best methods for securing data by nearly 60%, and the most important of these methods is lightweight authentication, multi-factor authentication, then mutual authentication. In addition, it is necessary to pay attention to securing all layers of the IoT. Especially the perception layer; as it is the main interface for the attack for easy physical access to the end nodes and web interfaces. Moreover, there are several modern technologies and methods that contribute to securing data such as: lightweight systems, digital twins, Software-Defined Network (SDN), edge computing and engineering, fog computing, trust computing, Context aware of accessibility. As for the recommendations, the study recommended all the following. IoT service providers should adopt several security methods, techniques and protocols in order to raise the level of security during data transfer and storage. The concerned organizations and bodies should issue unified laws and frameworks in order to standardize the work in IoT environment and In order to protect the confidentiality and privacy of data.