Architecting IoT Systems That Scale Securely

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Wireless communication in Internet of Things (IoT) systems involves several devices communicating in the limited available frequency spectrum using different wireless communication standards. To achieve efficient spectrum usage in the IoT systems, it is desired to have the ability of dynamic spectrum allocation. Cognitive radios (CR) enable efficient spectrum utilization by performing spectrum sensing to gain the knowledge of current spectrum occupancy and dynamically reconfiguring the communication parameters, making them highly suitable for IoT systems. In this paper, we present the efficient hardware implementation of a spectrum sensing scheme for CR based IoT applications that require large bandwidth. We show that the proposed implementation on field programmable gate arrays (FPGAs) can achieve over 1.56x improvement in performance, while consuming 77% less generic resources and at a fractionally higher power consumption.

LoRaWAN based internet of things (IoT) system for precision irrigation in plasticulture fresh-market tomato

<abstract><p>With the widespread application of smart devices, the security of internet of things (IoT) systems faces entirely new challenges. The IoT data stream operates in a non-stationary, dynamic environment, making it prone to concept drift. This paper focused on addressing the issue of concept drift in data streams, with a key emphasis on introducing an innovative drift detection method-ensemble multiple non-parametric concept localization detectors, abbreviated as EMNCD. EMNCD employs an ensemble of non-parametric statistical methods, including the Kolmogorov-Smirnov, Wilcoxon rank sum and Mann-Kendall tests. By comparing sample distributions within a sliding window, EMNCD accurately detects concept drift, achieving precise localization of drift points, and enhancing overall detection reliability. Experimental results demonstrated the superior performance of EMNCD compared to classical methods on artificial datasets. Simultaneously, to enhance the robustness of data stream processing, we presented an online anomaly detection method based on the isolation forest (iForest). Additionally, we proposedwhale optimization algorithm (WOA)-extreme gradient boosting (XGBoost), a drift adaptation model employing XGBoost as a base classifier. This model dynamically updates using drift points detected by EMNCD and fine-tunes parameters through the WOA. Real-world applications on the edge-industrial IoTset (IIoTset) intrusion dataset explore the impact of concept drift on intrusion detection, where IIoT is a subclass of IoT. In summary, this paper focused on EMNCD, introducing innovative approaches for drift detection, anomaly detection, and drift adaptation. The research provided practical and viable solutions to address concept drift in data streams, enhancing security in IoT systems.</p></abstract>

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.

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

Internet of Things (IoT) systems are characterized by being highly heterogeneous, distributed, and scalable, made up of a variety of devices that operate in uncertain and ubiquitous settings. In this sense, models@runtime emerges as a promising software development approach to address IoT systems’ intrinsic characteristics. This study presents a systematic review to know the state of the art using of models@runtime to support IoT systems’ operation. The methodology proposed by Kitchenham has been used to carry out this research. The systematic review answers the following research questions: i) How are models@runtime being used in the IoT domain? And ii) How is addressed the research in studies related to the use of models@runtime in the IoT domain? Initially, 692 primary studies were retrieved, where 50 relevant studies related to approaches based on models@runtime for IoT were selected after applying the corresponding inclusion and exclusion criteria. Those primary studies were classified based on the extraction criteria. Among review’s main results are that the models@runtime have been widely used to support the monitoring and self-adaptation mechanisms in IoT systems, as opposed to the few studies found aimed at interoperability, quality assurance, self-organization, and self-optimization of IoT systems, consisting of essential research opportunities.

The Internet of Things (IoT) has assumed a pivotal role in the advancement of communication technology and in our daily lives. However, an IoT system such as a smart grid with poorly designed topology and weak security protocols might be vulnerable to cybercrimes. Exploits may arise from sensor data interception en route to the intended consumer within an IoT system. The increasing integration of electronic devices interconnected via the internet has galvanized the acceptance of this technology. Nonetheless, as the number of users of this technology surges, there must be an aligned concern to ensure that security measures are diligently enforced within IoT communication systems, such as in smart homes, smart cities, smart factories, smart hospitals, and smart grids. This research addresses security lacunae in the topology and configuration of IoT energy monitoring systems using post-quantum cryptographic techniques. We propose tailored implementations of the Rivest–Shamir–Adleman (RSA), N-th degree Truncated Polynomial Ring Units (NTRU), and a suite of cryptographic primitives based on Module Learning With Rounding (Saber) as post-quantum cryptographic candidate algorithms for IoT devices. These aim to secure publisher–subscriber end-to-end communication in energy system monitoring. Additionally, we offer a comparative analysis of these tailored implementations on low-resource devices, such as the Raspberry Pi, during data transmission using the Message Queuing Telemetry Transport (MQTT) protocol. Results indicate that the customized implementation of NTRU outperforms both SABER and RSA in terms of CPU and memory usage, while Light SABER emerges as the front-runner when considering encryption and decryption delays.

Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions

The purpose of this paper is to explore the role of Machine Learning (ML) in fortifying the security of cloudbased Internet of Things (IoT) systems, using a comprehensive security management approach. The methodological approach involved comparing different ML techniques such as Decision Trees, Random Forest, Support Vector Machines, and Convolutional Neural Networks. Their effectiveness was evaluated based on the accuracy of threat detection in cloud-based IoT systems. The findings revealed that Convolutional Neural Networks demonstrated the highest accuracy rate (98%) in threat detection, thereby significantly enhancing the security of IoT systems. It also identified improvements in threat detection, prevention, response, and system recovery across all ML techniques. Research limitations were primarily the rapidly evolving nature of both ML and IoT technologies, necessitating continual reassessments. The scope was also limited to cloud-based IoT systems, leaving room for further research on other types of IoT systems. The practical implications included improved system security, which could lead to increased trust and wider adoption of IoT technology in various sectors, from healthcare to home security. The social implications entail a safer digital environment, contributing to data privacy and reducing the risk of cyber threats for individuals and communities. The originality of this paper lies in its comprehensive approach to IoT security management using ML, providing valuable insights into the effectiveness of different ML techniques in enhancing threat detection accuracy.

This research paper introduces a novel paradigm that synergizes innovative algorithms, namely efficient data encryption, the Quondam Signature Algorithm (QSA), and federated learning, to effectively counteract random attacks targeting Internet of Things (IoT) systems. The incorporation of federated learning not only fosters continuous learning but also upholds data privacy, bolsters security measures, and provides a robust defence mechanism against evolving threats. The Quondam Signature Algorithm (QSA) emerges as a formidable solution, adept at mitigating vulnerabilities linked to man-in-the-middle attacks. Remarkably, the QSA algorithm achieves noteworthy cost savings in IoT communication by optimizing communication bit requirements. By seamlessly integrating federated learning, IoT systems attain the ability to harmoniously aggregate and analyse data from an array of devices while zealously guarding data privacy. The decentralized approach of federated learning orchestrates local machine-learning model training on individual devices, subsequently amalgamating these models into a global one. Such a mechanism not only nurtures data privacy but also empowers the system to harness diverse data sources, enhancing its analytical capabilities. A thorough comparative analysis scrutinizes varied cost-in-communication schemes, meticulously weighing both encryption and federated learning facets. The proposed approach shines by virtue of its optimization of time complexity through the synergy of offline phase computations and online phase signature generation, hinged on an elliptic curve digital signature algorithm-based online/offline scheme. In contrast, the Slow Block Move (SBM) scheme lags behind, necessitating over 25 rounds, 1500 signature generations, and an equal number of verifications. The proposed scheme, fortified by its marriage of federated learning and efficient encryption techniques, emerges as an embodiment of improved efficiency and reduced communication costs. The culmination of this research underscores the intrinsic benefits of the proposed approach: marked reduction in communication costs, elevated analytical prowess, and heightened resilience against the spectrum of attacks that IoT systems confront.

A survey of trust computation models for service management in internet of things systems

PurposeThe purpose of this paper is to answer “Why do Chinese consumers use IOT systems?” and “Do consumers’ cognitive and affect experiences moderate the relationship between psychological perception factors and perceived usefulness?”Design/methodology/approachIn-depth interview with consumers and experts had been conducted and then the perceived psychological factors that influence perceived usefulness had been summarized. Based on a survey test of 337 smart home users, this study applies partial least squares technique analysis to test the research model.FindingsThe research results show that perceived psychological factors (perceived ease of use, perceived intelligence, perceived convenience and perceived privacy risk) have significant effect on the intention and behavior of IOT systems usage through perceived usefulness. Cognitive experience moderates the relationship between perceived ease of use and perceived usefulness, perceived privacy risk and perceived usefulness; affect experience moderates the relationship between perceived ease of use and perceived usefulness.Research limitations/implicationsThe current study mainly tested the antecedents of consumers’ usage of IOT systems, and the outcome of using the system was not investigated. Future research can examine the outcome (e.g. satisfaction, perceived value) by using the expectation-confirmation theory.Originality/valueThis study provides a useful insight into the key driving factors in consumers’ intention and behavior of using IOT systems. The previous studies over IOT systems have not observed consumers’ perception of IOT systems, but in fact IOT systems are being applied to more and more personal users.

with the rapid development in computing and networking capabilities, a new technology has been emerged known as internet of things (IoT). IoT is a giant network of connected and interacted devices, which collect data and send it using the internet. Today, IoT is playing important role in daily life; it adds intelligence to several domains, such as health care, smart homes and cities, and smart transportation.Nevertheless, several challenges are introduced by the new IoT system requirements, which cannot be adequately addressed by current models and architectures. Security and privacy present the main challenges of IoT, which are more important now than ever, due to the explosive growth of connected devices and the huge amount of generated data. All these challenges really should be taken in consideration. Good reference architecture is the main key to ensure security, privacy and many other emergent requirements, because it presents the functional view of IoT. So, IoT requires new architectures to address these new challenges and face these problems. Therefore, the aim of this paper is to suggest powerful architecture to build secure IoT system, and which guarantees the privacy of all its users. It tries to fulfill the new emergent requirements, and solve the previous challenges. In addition, it explains the role of each part to achieve a robust secure IoT system, and how it contributes in the integration of other technologies.

This paper presents an implementation of a Wireless Internet of Things (IoT) system applied to the traction motor drive condition monitoring in electric vehicles (EVs). The design and testing of the prototype using an ESP8266 microcontroller module to acquire motor's vibration, current, and temperature information for the motor condition monitoring application is presented. This IoT system has been designed and developed from the ground up using commercially off-the-shelf components and open-source software platform for fast, reliable data acquisitions, low power consumption, and data gathered by the IoT system get reported to the cloud server in real time. The experimental results reveal that the IoT system is capable of capturing and reporting vital motor's parameters to cloud server and an automatic notification is sent to operators when motor's abnormality is detected in real time. Thanks to IoT technology, the preventive maintenance of traction motor can be effectively and remotely planned with rich data collection and analysis. With advanced power consumption reduction techniques, a sensor node consumes extremely low amount of battery power at which ideal for mobile applications.