Dynamic Internet Of Things Environments Research Articles

QS-Trust: An IoT ecosystem security model incorporating quality of service and social factors for trust assessment

In the rapidly growing and increasingly complex Internet of Things (IoT) ecosystem, securing communication and data exchanges between devices is a major concern. To address this, we proposed QS-Trust, a trust-based security model considering both Quality of Service (QoS) and social parameters. QS-Trust uses a trust value to determine the trust level between devices and employs a QoS-aware trust-based algorithm to improve the security of data transmissions. Additionally, the model incorporates intelligence parameters such as computing power, memory capacity, device behavior and context information to enhance the accuracy of trust evaluation. Our simulation results demonstrated that QS-Trust effectively improved the security of the IoT ecosystem while maintaining the high level of QoS. The execution time of QS-Trust was in the range of 21 to 128 milliseconds, which is efficient for real-time IoT applications. QS-Trust offers a promising solution for securing the IoT ecosystem. The QS-Trust model effectively addresses the challenges of maintaining accurate and up-to-date trust levels in dynamic IoT environments through its decentralized approach, multi-factor evaluations, and adaptive algorithms. By continuously monitoring device performance and interactions and dynamically adjusting trust scores, QS-Trust ensures that the IoT network remains secure and reliable.

Policy-Based Contextual Access Control Mechanism for Smart IoT Devices

Access control remained an important aspect of computer security, and it has been the focus of extensive research over the past several decades. Access control mechanisms generally composed of two fundamental components: authentication and authorization. Authentication refers to verifying the identity of an entity, and authorization guarantees that only authenticated entity or devices can access the permitted devices or other resources. Various traditional access control schemes, such as Access Control Lists (ACLs), Role-Based Access Control (RBAC), and Attribute-Based Access Control (ABAC) exist, but these have certain limitations that hinder their direct implementation in the Internet of Things (IoT). For instance, ACLs maintain user-specific access privilege lists, which are feasible for environments with limited users and devices, but impractical for large scale systems like IoT. RBAC assigns devices access through roles associated with permissions, however role management in dynamic IoT environments poses significant challenges. ABAC grants access based on user and devices attributes that requires certain attribute criteria for authorization. We advocate that IoT environments are dynamic in nature and consist of very large volumes of smart IoT devices (such as smart sensors, smart phones and gadgets) that which introduce unique access control challenges. One significant challenge is providing dynamic access to smart IoT devices, as opposed to relying on static rules, roles, or attributes. Considering these challenges, this research advocates for a novel access control scheme tailored for accessing smart IoT devices in internet of things environments. The prototype implementation of the proposed approach is carried out along with conducting the usability study to evaluate the performance and suitability of the proposed system for real world internet of things (IoT) scenarios.

Innovative IoT Threat Detection: Weighted Variational Autoencoder-Based Hunter Prey Search Algorithm for Strengthening Cybersecurity

Cybersecurity threat detection in the Internet of Things (IoT) identifies and mitigates risks associated with connected devices. The IoT devices are vulnerable to attacks as they do not contain any robust security mechanism. Traditional methods for cybersecurity-based threat detection face significant problems such as scalability, privacy concerns, and resource constraints when applied to the dynamic IoT environment. To tackle these challenges, this paper proposed a novel Weighted Variational Autoencoder-based Hunter Prey Search (WVA-HPS) algorithm for enhancing cybersecurity threat detection in IoT. In this study, a weighted Variational Autoencoder is employed for regulating weight mechanism using weight regularization and the weight average ensemble method. The Hunter Prey Search optimization (HPSO) algorithm is utilized for minimizing overfitting issues to enhance the efficiency of the WVA method. The proposed WVA-HPS model comprises five different stages such as data collection, data preprocessing, threat detection, model evaluation, and output. The study is validated on diverse datasets namely BoT-IoT, MQTTset, and IoT-23. The WVA-HPS method's performance is analyzed using the metrics namely precision, accuracy, specificity, F-measure, and recall and its performance is compared with existing methods. The experimental results illustrate the performance of the WVA-HPS method for cybersecurity threat detection in an IoT environment.

Innovative IoT Threat Detection: Weighted Variational Autoencoder-Based Hunter Prey Search Algorithm for Strengthening Cybersecurity

Cybersecurity threat detection in the Internet of Things (IoT) identifies and mitigates risks associated with connected devices. The IoT devices are vulnerable to attacks as they do not contain any robust security mechanism. Traditional methods for cybersecurity-based threat detection face significant problems such as scalability, privacy concerns, and resource constraints when applied to the dynamic IoT environment. To tackle these challenges, this paper proposed a novel Weighted Variational Autoencoder-based Hunter Prey Search (WVA-HPS) algorithm for enhancing cybersecurity threat detection in IoT. In this study, a weighted Variational Autoencoder is employed for regulating weight mechanism using weight regularization and the weight average ensemble method. The Hunter Prey Search optimization (HPSO) algorithm is utilized for minimizing overfitting issues to enhance the efficiency of the WVA method. The proposed WVA-HPS model comprises five different stages such as data collection, data preprocessing, threat detection, model evaluation, and output. The study is validated on diverse datasets namely BoT-IoT, MQTTset, and IoT-23. The WVA-HPS method's performance is analyzed using the metrics namely precision, accuracy, specificity, F-measure, and recall and its performance is compared with existing methods. The experimental results illustrate the performance of the WVA-HPS method for cybersecurity threat detection in an IoT environment.

Enhancing Security and Trust in Internet of Things through Meshtastic Protocol Utilising Low-Range Technology

The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, a dynamic mesh networking protocol that offers robustness and adaptability, important characteristics for the dynamic and heterogeneous IoT environment. LoRaWAN (Low-Range Wide Area Network), a low-power, long-range wireless communication standard, introduces energy efficiency and extends the reach of IoT networks, enabling secure communication over extended distances. To improve the trustworthiness of IoT devices, we present an integrated approach that leverages the strengths of Meshstastic’s dynamic mesh networking capabilities and LoRa’s low-power, long-range communication, along with the integration of a reputation model specifically designed for IoT. We evaluated the performance of the proposed solution through several simulations and real-world experiments. The results show that the devices’ measured values of trust reflect the real behaviour of the devices. These findings underscore the viability and applicability of the Meshtastic protocol utilising LoRa technology as a pivotal step towards establishing resilient and trustworthy IoT infrastructures in the face of evolving security challenges.

Risk and threat mitigation techniques in internet of things (IoT) environments: a survey

Security in the Internet of Things (IoT) remains a predominant area of concern. Although several other surveys have been published on this topic in recent years, the broad spectrum that this area aims to cover, the rapid developments and the variety of concerns make it impossible to cover the topic adequately. This survey updates the state of the art covered in previous surveys and focuses on defences and mitigations against threats rather than on the threats alone, an area that is less extensively covered by other surveys. This survey has collated current research considering the dynamicity of the IoT environment, a topic missed in other surveys and warrants particular attention. To consider the IoT mobility, a life-cycle approach is adopted to the study of dynamic and mobile IoT environments and means of deploying defences against malicious actors aiming to compromise an IoT network and to evolve their attack laterally within it and from it. This survey takes a more comprehensive and detailed step by analysing a broad variety of methods for accomplishing each of the mitigation steps, presenting these uniquely by introducing a “defence-in-depth” approach that could significantly slow down the progress of an attack in the dynamic IoT environment. This survey sheds a light on leveraging redundancy as an inherent nature of multi-sensor IoT applications, to improve integrity and recovery. This study highlights the challenges of each mitigation step, emphasises novel perspectives, and reconnects the discussed mitigation steps to the ground principles they seek to implement.

Optimal Computation Resource Allocation in Energy-Efficient Edge IoT Systems With Deep Reinforcement Learning

This paper investigates a computation resource optimization problem of mobile edge computing (MEC)-aided Internet-of-Things (IoT) devices with a reinforcement learning (RL) solution. Specifically, we leverage the stochastic optimization method and formulate the Lyapunov optimization technique to maximize the long-term energy efficiency, taking into account the transmission power, network stability, and transmission latency. Based on the Markov decision process and model-free deep RL (DRL) approach, we propose a double DRL-based online computation offloading method to implement a deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. Furthermore, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading tasks. The proposed real-time Lyapunov optimization and DRL algorithms achieve low computational complexity and optimal processing time. Simulation results demonstrate that the proposed algorithm can achieve near-optimal control performance with enhanced energy efficiency performance compared to the baseline policy control algorithms.

Anomaly Detection for IOT Systems Using Active Learning

The prevalence of Internet of Things (IoT) technologies is on the rise, making the identification of anomalies in IoT systems crucial for ensuring their security and reliability. However, many existing approaches rely on static classifiers and immutable datasets, limiting their effectiveness. In this paper, we have utilized the UNSW-NB15 dataset, which contains 45 variables including multi- and binary-target variables, to determine the most relevant properties for detecting abnormalities in IoT systems. To address this issue, our research has investigated the use of active learning-based algorithms for anomaly detection in IoT systems. Active learning is a powerful technique that improves precision and productivity by eliminating the need for labeling and adapting to dynamic IoT environments. Additionally, our study has combined feature engineering methods, active learning approaches, and a random forest classifier to construct a resilient anomaly detection model for IoT devices. The proposed model has outperformed several state-of-the-art techniques, achieving an impressive accuracy rate of 99.7%. By implementing a rigorous sampling procedure and leveraging the collaborative nature of the random forest technique, our model has demonstrated a notable level of precision with a weighted average accuracy of 0.995. The findings of the study offered empirical evidence, supporting the efficacy of our active learning methodology in identifying abnormalities in IoT systems. Moreover, our study provides valuable insights and recommendations for future research and development activities in this field. Overall, this research contributes to the advancement of anomaly detection techniques in IoT systems, further enhancing their security and reliability.

A Lightweight Model-Based Evolutionary Consensus Protocol in Blockchain as a Service for IoT

Internet of Things (IoT) is experiencing fast proliferation with emerging trends in autonomy and local decision-making to avoid the explosive burden on network infrastructure between cloud and edge. Thereby, blockchain as a Service (BaaS) for IoT, as an emerging distributed services computing paradigm, has drawn intense attention due to its decentralization, auditability, and tamper-resistance. However, the primary challenge is to design a tailor-made consensus protocol that is applicable to BaaS for IoT. Existing consensus protocols generally focus on power-intensive environments, which is not feasible for power-constrained BaaS-enabled IoT systems. In this paper, to fully exploit BaaS’s superiority (e.g. to sharing data securely), we propose a lightweight model-based evolutionary consensus protocol called Proof of Evolutionary Model (PoEM) that can improve the quality of BaaS in IoT environments. Beyond existing rule-based consensus protocols, PoEM iteratively trains a machine learning model to achieve consensus. In this way, PoEM enhances consensus efficiency and enables low-performance IoT devices to be involved. Moreover, considering IoT environments’ dynamics, a novel mechanism is designed to manage nodes joining and exiting dynamically. Extensive analytical and experimental results show PoEM’s improved consensus efficiency and applicability in dynamic BaaS-based IoT environments while providing high-level security guarantees.

BLAST-IoT: BLockchain Assisted Scalable Trust in Internet of Things

Internet of Things (IoT) paved the way to realize ubiquitous access towards manifold services and applications by enabling connectivity between different physical devices. The devices, having heterogeneous computation power, protocol stack, or storage power, can send or receive various services by communicating through the internet. These services inevitably demand a secure and reliable network system to interchange the data or information for their impressive utilization. One proven solution to attain this objective is embedding Trust Management System (TMS) that aims to reduce risk or uncertainty associated with interacting adversary devices by capturing their dynamic behavior in the system. With TMS, devices compute and maintain trust value on the interacting devices by observing their behavior with different interaction instances. This trust information is exchanged between the devices to assess the global reputation of devices and facilitate devices’ decisions when there is a lack of direct interactions. For systems having enormous low-power devices, one crucial aspect of TMS is secure and timely dissemination, management, and storage of trust information to accurately assess devices’ dynamic behavior. Addressing this issue, we present a blockchain-assisted scalable trust model, BLAST-IoT, for IoT systems of heterogeneous devices in this paper. BLAST-IoT intends to ensure secure dissemination and storage of trust information leveraging on the functionalities provided by blockchain technology. It adopts a semi-distributed approach where direct trust information, computed by IoT end devices, is processed and stored by some high-power edge devices running public blockchain networks to handle impediments caused by power-deficient IoT devices. The effectiveness of BLAST-IoT has been evaluated through implementation and simulation-based results for trust convergence and assessment accuracy in the dynamic IoT environment. Further, the throughput performance of BLAST-IoT has been examined with an increase in network load.

Artificial intelligence implication on energy sustainability in Internet of Things: A survey

The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges.

Synchronizing UAV Teams for Timely Data Collection and Energy Transfer by Deep Reinforcement Learning

Due to their promising applications and intriguing characteristics, Unmanned Aerial Vehicles (UAVs) can be dispatched as flying base stations to serve multiple energy-constrained Internet-of-Things (IoT) sensors. Moreover, to ensure fresh data collection while providing sustainable energy support to a large set of IoT devices, a required number of UAVs should be deployed to carry out these two tasks efficiently and promptly. Indeed, the data collection requires that UAVs first make Wireless Energy Transfer (WET) to supply IoT devices with the necessary energy in the downlink. Then, IoT devices perform Wireless Information Transmission (WIT) to UAVs in the uplink based on the harvested energy. However, it turns out that when the same UAV performs WIT and WET, its energy usage and the data collection time are severely penalized. Worse yet, it is difficult to efficiently coordinate between UAVs to improve the performance in terms of WET and WIT. This work proposes to divide UAVs into two teams to behave as data collectors and energy transmitters, respectively. A Multi-Agent Deep Reinforcement Learning (MADRL) method, called TEAM, is leveraged to jointly optimize both teams’ trajectories, minimize the expected Age of Information (AoI), maximize the throughput of IoT devices, minimize the energy utilization of UAVs, and enhance the energy transfer. Simulation results depict that TEAM can effectively synchronize UAV teams and adapt their trajectories while serving a large-scale dynamic IoT environment.

Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing

Our world is moving fast towards the era of the Internet of Things (IoT), which connects all kinds of devices to digital services and brings significant convenience to our lives. With the rapid increase in the number of devices connected to the IoT, there may exist more network vulnerabilities, resulting in more network attacks. Under this dynamic IoT environment, an effective intrusion detection system (IDS) is urgently needed to detect attacks with low-latency and high accuracy. A number of promising IDSs have been proposed based on deep learning (DL) techniques, but they need to do parameter tuning under different environments, which is very time-consuming. To alleviate this problem, this paper proposes a multi-objective evolutionary convolutional neural network for intrusion detection system, called MECNN, which is run on the fog nodes of Fog computing on IoT. In this approach, convolutional neural network (CNN) is used as the classifier to detect intrusions and the multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm is modified to evolve the CNN model, which greatly simplifies the parameter tuning process of DL. To be specific, a novel encoding scheme is first proposed to transform the topological architecture of CNN into a chromosome of MOEA/D and then the two conflicting objectives, i.e., detection performance and model complexity of the CNN model, are simultaneously optimized by MOEA/D, which can obtain a number of IDSs with various detection performance and model complexities. Then, the most suitable MECNN model can be deployed in different fog nodes of Fog computing, providing low-latency and high-accuracy intrusion detection for IoT. Finally, the experimental studies are conducted on two popular datasets (AWID and CIC-IDS2107), which have validated that our MECNN model can improve detection performance and robustness to better protect the IoT when compared to other state-of-the-art IDSs.

IoTSAX: A Dynamic Abstractive Entity Summarization Approach With Approximation and Embedding-Based Reasoning Rules in Publish/Subscribe Systems

Users are interested in entity information and may use paradigms like publish/subscribe systems in Internet of Things (IoT), where entity-centric data comes from multiple sources. The IoT application demands contributed to the data integration and semantic interoperability need while maintaining high usability and low processing system resources usage. Existing approaches dealt with semantic interoperability by using/extending ontologies with strict schemata/semantics and difficult to be updated. Other approaches tackled high usability by providing sensor data abstractions that infer high-level knowledge, but they are either complex for real-time processing or have semantic/syntactic coupling making them nonflexible. Therefore, our key question is: can a dynamic entity summarization system that provides high-level abstractions while ensuring semantic/syntactic decoupling, and low use of processing system resources, be defined? In this article, we address this question by proposing the abstractive publish/subscribe summarization system that provides abstractive summaries of numerical IoT data for user-friendly subscriptions by using IoTSAX, an enhanced symbolic aggregate approximation (SAX) methodology for dynamic IoT environments, and approximate rule-based reasoning by using embedding models. Our results for two use cases, medicine and smart cities, show that although abstraction can be 2 to 3 times slower in latency, it achieves up to 98% reduction in the notifications’ number. IoTSAX outperforms SAX in approximation error up to 2 to 3 times more and in compression space-saving percentage when data redundancy occurs, while it has a similar or better latency and throughput performance. Finally, concept hierarchy-based embedding models can achieve F-scores of up to 0.87 for approximate rule-based reasoning.

Scalable Access Control Scheme of Internet of Things Based on Blockchain

With the development of Internet of things (IoT) technology, a large-scale, heterogeneous and dynamic distributed IoT environment has gradually formed between different IoTs. In order to solve the scalability problem of restricted device access management in the Internet of things, a distributed access control system model of the Internet of things based on blockchain technology is proposed. The system model adopts a single smart contract, which simplifies the whole process in the blockchain network and reduces the communication overhead between nodes. According to the simulation results and evaluation, it is proved that the solution has good scalability.

A bio-inspired adaptive model for search and selection in the Internet of Things environment.

The Internet of Things (IoT) is a paradigm that can connect an enormous number of intelligent objects, share large amounts of data, and produce new services. However, it is a challenge to select the proper sensors for a given request due to the number of devices in use, the available resources, the restrictions on resource utilization, the nature of IoT networks, and the number of similar services. Previous studies have suggested how to best address this challenge, but suffer from low accuracy and high execution times. We propose a new distributed model to efficiently deal with heterogeneous sensors and select accurate ones in a dynamic IoT environment. The model’s server uses and manages multiple gateways to respond to the request requirements. First, sensors were grouped into three semantic categories and several semantic sensor network types in order to define the space of interest. Second, each type’s sensors were clustered using the Whale-based Sensor Clustering (WhaleCLUST) algorithm according to the context properties. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was improved to search and select the most adequate sensor matching users’ requirements. Experimental results from real data sets demonstrate that our proposal outperforms state-of-the-art approaches in terms of accuracy (96%), execution time, quality of clustering, and scalability of clustering.

Gateway based Trust Management System for Internet of Things

Internet of Things (IoT) a growing phenomenon, refers to the seamless integration of things into the information network. The security in IoT is tampered because of the various attacks which happen due to resource constrained nature of the devices in the network. Thus, although IoT is evolving as an attractive next generation networking paradigm, it can be adopted only when the security issues are resolved. This implies that, in a dynamic and collaborative IoT environment, the devices need to be trustworthy. This paper proposes a gateway based trust management system and an algorithm for computation of trust for the devices. The system focuses on making the computations on the devices lightweight and the network robust. The proposed system is tested against various IoT attacks and results demonstrate that it can clearly identify the malicious device if any, in the IoT network.

A trust aware security mechanism to detect sinkhole attack in RPL-based IoT environment using random forest – RFTRUST

The Internet of Things (IoT) plays a vital role in many application domains like battlefield surveillance, wildlife monitoring, disaster response, medical care, transportation, industry, smart home, smart cities, etc. However, this network is susceptible to various types of attacks due to its special features like sensing, intelligence, large scale, self-configuring, connectivity, heterogeneity, open and dynamic environment. It is significant to ensure security in the IoT network. In the scalable and dynamic IoT environment, conventional security mechanisms such as cryptography techniques, key management, intrusion detection system, anomaly detection, etc cannot be applicable, because it consumes more energy. Therefore, the IoT network requires a lightweight security mechanism for reliable and secure data transmission. A trust-based security solution solves many security-related problems. The proposed RFTrust model provides a trust-based lightweight solution for ensuring security in the IoT network. It is primarily designed to address the sinkhole attack in Routing Protocol for Low power and Lossy networks (RPL) based IoT environments. It enhances the trusted routing in the IoT environment by finding and removing sinkhole nodes in the network. The proposed model uses Random Forest (RF) and Subjective Logic (SL) to improve the network performance by identifying sinkhole attack. The mathematical analysis shows the applicability of the proposed model. The merits of the proposed work are highlighted by comparing performance with the existing similar protocols in terms of delivery ratio, throughput, average delay, energy consumption, false-positive rate, false-negative rate, and detection accuracy.

Efficient, Flexible and Secure Group Key Management Protocol for Dynamic IoT Settings

For many Internet of Things (IoT) scenarios, group communication in the form of multicasting and broadcasting has proven to be effective. Classical Group Key Management (GKM) schemes perform inefficiently in dynamic IoT environments, wherein nodes frequently leave or join a network or migrate from one group to another. In this paper, we present a highly efficient and secure GKM protocol for dynamic IoT settings, which maintains forward and backward secrecy at all times and is completely resistant to collusion attacks. Also, our protocol is highly flexible and can handle several new scenarios in which device or user dynamics may take place, e.g., allowing a device group to join or leave the network or creation or dissolution of a user group, which are not handled by schemes proposed in prior work. We evaluate the performance of the proposed protocol via extensive mathematical analysis and numerical computations.

Devote: Criticality-Aware Federated Service Provisioning in Fog-Based IoT Environments

In this article, we present an efficient criticality-aware decision-making system, named Devote, for fog-based Internet of Things (IoT) environment. Devote introduces an intelligent algorithm for the service of data based on the criticality, while considering the current availability of the resources at the fog node (FN). To cope with the dynamic IoT environment, we adopt a reinforcement-learning-based algorithm for the processing of the IoT data based on time-varying conditions. Additionally, we propose an efficient online secretary-based algorithm for choosing the best suitable candidate FN for offloading the data. To show the effectiveness of Devote, we obtained the numerical results for assessing its performance, while collating it with the benchmark schemes. We analyze different performance metrics, such as service delay, economy, and user satisfaction, which show that Devote incurs less service delay, as compared to other systems, while achieving user satisfaction of 88.4%.