Heterogeneous Internet Of Things Research Articles

An Energy-Efficient Multilevel Secure Routing Protocol in IoT Networks

In Internet of Things (IoT) applications with multihop networking, not only traditional energy efficiency but also many distinct features should be considered when designing routing protocols, including different security requirements, heterogeneity, and scalability. In this article, an energy-efficient multilevel secure routing (EEMSR) protocol in IoT networks is proposed. Considering that clustering is a reasonable solution of conserving energy, a cluster-based multihop routing protocol is utilized to reduce the high communication overhead due to the scalability of IoT networks. In particular, more reasonable analytic hierarchy process and genetic algorithms are adopted to assign accurate weight and optimize intercluster routing in which heterogeneous IoT networks are considered to support large amount of heterogeneous IoT entities and services. Moreover, multiple trust levels are adopted to defend the different attacks by calculating the trust factor on the clustering and routing, including data perception trust, data fusion trust, and communication trust. It is shown that the proposed algorithm outperforms the existing algorithms in terms of network lifetime, throughput, packet delivery ratio, network energy balance, and adaptability.

A Reliable and Lightweight Trust Inference Model for Service Recommendation in SIoT

In the era of Internet of Things (IoT), millions of heterogeneous IoT devices generate an explosion of data and services waiting to be discovered. The convergence of IoT with social networks (SIoT) interconnects multiple IoT applications and alleviates the common data sparsity and cold start problems in traditional recommendation systems. However, the social trust relationships may also be very sparse, which affects the accuracy of trust-based recommendation systems. Meanwhile, mobile devices have limited resources and are more vulnerable to malicious attacks in the IoT environment. In order to complete the trust relationship and further improve the trust-based recommendation performance, we propose a reliable and lightweight trust inference model for service recommendation in SIoT, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> . First, we obtain a comprehensive weighted centrality metric (LGWC) considering both local and global contexts. Based on this, we propose a corresponding lightweight trust path selection algorithm. Then, we present a reliable trust inference calculation algorithm consist of trust propagation and aggregation strategy, which can efficiently resist two common malicious attacks. Finally, we incorporate the rating, direct trust, and indirect trust together into the matrix factorization model, and integrate the influence of truster and trustee to obtain the synthetic model for rating predication. To the best of our knowledge, this article is the first to integrate trust inference algorithm into the trust-based recommendation systems. The extensive experiments are conducted on three real-world data sets, and the results show that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIRec</i> model performs better than other advanced recommendation models in both “all users” view and “cold start users” view.

Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment.

In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to a high failure rate for task offloading and considerable latency due to congestion. To improve the quality of services (QoS) and performance due to the dynamic flow of requests from devices, numerous task offloading strategies in the area of multi-access edge computing (MEC) have been proposed in previous studies. Nevertheless, the neighboring edge servers, where computational resources are in excess, have not been considered, leading to unbalanced loads among edge servers in the same network tier. Therefore, in this paper, we propose a collaboration algorithm between a fuzzy-logic-based mobile edge orchestrator (MEO) and state-action-reward-state-action (SARSA) reinforcement learning, which we call the Fu-SARSA algorithm. We aim to minimize the failure rate and service time of tasks and decide on the optimal resource allocation for offloading, such as a local edge server, cloud server, or the best neighboring edge server in the MEC network. Four typical application types, healthcare, AR, infotainment, and compute-intensive applications, were used for the simulation. The performance results demonstrate that our proposed Fu-SARSA framework outperformed other algorithms in terms of service time and the task failure rate, especially when the system was overloaded.

Advanced encryption schemes in multi-tier heterogeneous internet of things: taxonomy, capabilities, and objectives

The Internet of Things (IoT) is increasingly becoming widespread in different areas such as healthcare, transportation, and manufacturing. IoT networks comprise many diverse entities, including smart small devices for capturing sensitive information, which may be attainable targets for malicious parties. Thus security and privacy are of utmost importance. To protect the confidentiality of data handled by IoT devices, conventional cryptographic primitives have generally been used in various IoT security solutions. While these primitives provide just an acceptable level of security, they typically neither preserve privacy nor support advanced functionalities. Also, they overly count on trusted third parties because of some limitations by design. This multidisciplinary survey paper connects the dots and explains how some advanced cryptosystems can achieve ambitious goals. We begin by describing a multi-tiered heterogeneous IoT architecture that supports the cloud, edge, fog, and blockchain technologies and assumptions and capabilities for each layer. We then elucidate advanced encryption primitives, namely wildcarded, break-glass, proxy re-encryption, and registration-based encryption schemes, as well as IoT-friendly cryptographic accumulators. Our paper illustrates how they can augment the features mentioned above while simultaneously satisfying the architectural IoT requirements. We provide comparison tables and diverse IoT-based use cases for each advanced cryptosystem as well as a guideline for selecting the best one in different scenarios and depict how they can be integrated.

A novel federated learning based lightweight sustainable IoT approach to identify abnormal traffic

Purpose This strategy significantly reduces the computational overhead and storage overhead required when using the kernel density estimation method to calculate the abnormal evaluation value of the test sample. Design/methodology/approach To effectively deal with the security threats of botnets to the home and personal Internet of Things (IoT), especially for the objective problem of insufficient resources for anomaly detection in the home environment, a novel kernel density estimation-based federated learning-based lightweight Internet of Things anomaly traffic detection based on nuclear density estimation (KDE-LIATD) method. First, the KDE-LIATD method uses Gaussian kernel density estimation method to estimate every normal sample in the training set. The eigenvalue probability density function of the dimensional feature and the corresponding probability density; then, a feature selection algorithm based on kernel density estimation, obtained features that make outstanding contributions to anomaly detection, thereby reducing the feature dimension while improving the accuracy of anomaly detection; finally, the anomaly evaluation value of the test sample is calculated by the cubic spine interpolation method and anomaly detection is performed. Findings The simulation experiment results show that the proposed KDE-LIATD method is relatively strong in the detection of abnormal traffic for heterogeneous IoT devices. Originality/value With its robustness and compatibility, it can effectively detect abnormal traffic of household and personal IoT botnets.

Guest Editorial Special Issue on Blockchain-Enabled Internet of Things

Blockchain, as a constantly evolving Peer-to-Peer (P2P) distributed ledger technology with characteristics, such as decentralization, security, interoperation, and trust establishment, can potentially lower the costs of the underpinning infrastructure and maintenance compared with conventional centralized systems. Consequently, the distributed structure of blockchain is naturally suitable for the Internet of Things (IoT), which can be used to build secure and trusted IoT. Despite the advances made in applying blockchain to IoT in the past few years, some research challenges remain to be addressed, including the poor scalability, heterogeneous IoT devices, and the impact of integration on network performance.

Cognitive Long‐range: Towards efficient public communication infrastructure for Internet of Things

SummaryThe most common Internet of Things (IoT) scenarios entail devices with limited energy resources and need to be connected to the Internet via wireless networks. This has driven the recent development of low‐power wide‐area networks (LPWANs) and the rise of the Long Range (LoRa) technology. The LoRa protocol has a simple modulation scheme that ensures low power consumption, high convergence, and resistance against interference. In most LPWAN technologies, several physical layer challenges arise, such as low data rates, spectral inefficiency, and increased interference. As a physical layer solution, the cognitive radio (CR) offers a possible way of resolving these challenges. CR allows wireless networks to operate without the need for a dedicated spectrum. Regarding the variety of end‐user requirements, developing a public communication network that can support such diverse and heterogeneous applications is necessary to reduce the implementation costs than developing a dedicated communication network for each application. This paper proposes a Cognitive LoRa (C‐LoRa) protocol that utilizes unlicensed and licensed frequencies as well as interference mitigation to improve the QoS of LoRa. To extract the priority list of traffic patterns, C‐LoRa incorporates the Analytic Hierarchy Process (AHP) algorithm. The priority list enables real‐time applications to receive optimal spectrum allocation. C‐LoRa can be efficiently implemented as a public communication infrastructure for heterogeneous IoT devices. The addition of licensed channels improves the overall QoS and decreases the average waiting time in queues. The platform layer of C‐LoRa consists of a cognitive engine that sends traffic priority lists to cognitive spectrum allocators. The IoT application servers are connected to the cloud platform layer via SNMP, HTTP, and other desired protocols. Access gateways equipped with a cognitive spectrum allocator are always connected to a power supply and serve as a transparent bridge to the cognitive engine at the platform layer, converting RF packets to IP packets and vice versa.

Heterogeneous IoT platform in Network Architecture

Heterogeneous Internet of Things (HetIoT) is an emerging research field that has strong potential to transform both our understanding of fundamental computer science principles and our future living. This paper proposes a four-layer HetIoT architecture consisting of sensing, networking, cloud computing, and applications. This paper also suggests several potential solutions to address the challenges facing future HetIoT, including data integration and processing in large-scale HetIoT

Resource identifier interoperability among heterogeneous IoT platforms

Many standards, projects, and platforms are being developed as the Internet of Things (IoT) is adopted in a wide range of fields. However, because each IoT platform is based on a different resource identifier (ID), it is difficult to identify each device and use the service among heterogeneous IoT platforms. To solve this problem, we propose an interoperability framework that includes an IoT resource name system (RNS) based on analysis of the resource IDs (i.e., device ID and resource request formats) of five selected IoT platforms: oneM2M, Oliot, Watson IoT, IoTivity, and FIWARE. The IoT RNS converts a specific resource path into a resource request format for each platform. The converted resource path is shared among IoT RNSs for each platform, and users can request services from other platforms using converted resource paths. We also present an example of interoperability scenario among heterogeneous IoT platforms using the proposed IoT RNS in a smart city. The scenario includes each stage, such as resource registration and deletion, sharing mapping tables, converting resource addresses, and service requests. Furthermore, to prove the aims of the proposed approach, we implemented the resource interoperability scenario between oneM2M and FIWARE. In the experiments, resources can interwork in the two platforms through resource path conversion. Based on the results, we performed a qualitative evaluation of the IoT RNS with the current studies. In conclusion, our proposal overcomes the issues of building an existing integrated platform or specific central ontology and duplicating resources inside the platform. In addition, we separate the functions of the root and local IoT RNSs to solve communication traffic and memory capability issues.

Zero knowledge proofs based authenticated key agreement protocol for sustainable healthcare

Upgradation of technologies for sustainable smart cities has led to rapid growth in Internet of Things (IoT) applications, including e-healthcare services wherein smart devices collect patient data and deliver it remotely to the servers in real-time. Despite its enormous benefits, IoT in healthcare has not received much attention primarily due to the risk of unauthorized access to confidential medical information enabled by the vulnerable wireless channel for communication. Besides, tiny IoT devices have limited computing power and storage capabilities that prevent administrators from using complex and resource-hungry security protocols. The cyber attacks on the Internet of Healthcare applications (IoHA) could result in fatalities, decreased revenue, and reputation loss, hence endangering sustainability. The existing security protocols are unsuitable due to the cost complexities that necessitate developing new security protocols for resource-constrained and heterogeneous IoT networks. We introduce a confidentiality and anonymity-preserving scheme for critical infrastructures of IoT to conquer cyber threats for sustainable healthcare. This paper proposes Zero-Knowledge Proofs (ZKP) based Authenticated Key Agreement (AKA) protocol for IoHA. ZKP-AKA uses zero-knowledge proofs, physically unclonable function, biometrics, symmetric cryptography, message digest, etc., for accomplishing the protocol’s objective at minimal computation, storage, and communication expenses. ZKP-AKA retains data integrity, confidentiality, anonymity, and safety from significant cyber threats. • This article introduces a privacy-preserving mutual authentication and key agreement protocol to protect IoT healthcare networks from unauthorized abuses. • The protocol has been implemented using Zero Knowledge Proof (ZKP) and Physically Unclonable Function (PUF) to safeguard confidentiality and prevent physical attacks. • The security protocol’s robustness against cyber-attacks has been investigated through formal (AVISPA) and informal (logical rules) analysis. • The protocol’s performance has been analyzed and compared with existing security protocols.

Evaluation and Selection Models for Ensemble Intrusion Detection Systems in IoT

Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infrastructure from adversary activities. Several network systems suffer from drawbacks that allow intruders to use malicious traffic to obtain unauthorized access. Attacks such as Distributed Denial of Service attacks (DDoS), Denial of Service attacks (DoS), and Service Scans demand a unique automatic system capable of identifying traffic abnormality at the earliest stage to avoid system damage. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not only the issue with current Intrusion Detection Systems (IDS), but the efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed Integrated Evaluation Metrics (IEM) to determine the best performance of IDS models. The automated Ranking and Best Selection Method (RBSM) is performed using the proposed IEM to select the best model for the ensemble classifier to detect highly accurate attacks using machine learning and deep learning techniques. Three datasets of real IoT traffic were merged to extend the proposed approach’s ability to detect attack traffic from heterogeneous IoT networks. The results show that the performance of the proposed model achieved the highest accuracy of 99.45% and 97.81% for binary and multi-classification, respectively.

A decentralized blockchain-based key management protocol for heterogeneous and dynamic IoT devices

Secure communication is one of the main challenges that are slowing down the development of the Internet of Things (IoT). Key Management (KM) is particularly a difficult security issue, mainly because of the lack of resources of the IoT devices. Most of the existing solutions do not consider the heterogeneous and dynamic nature of the IoT. They do not regard the difference in capability of its components and impose equal loads on them. Moreover, they store keys in device memories before deployment, which makes adding devices difficult afterwards. We propose a novel decentralized blockchain-based KM protocol for the IoT. Our solution balances the loads between nodes according to their capabilities. We prove that this makes it efficient and scalable. Furthermore, our solution securely rekey the network upon a change. To decentralize the KM, we use the blockchain technology and smart contracts. We show that the system continues to operate when an entity fails and that the compromise of an entity does not jeopardize the whole network. We also prove that our solution fulfills the IoT requirements in terms of security and performance. Finally, we propose an implementation on IoT platforms to validate our theoretical analysis and simulation results.

A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Internet of things (IoT) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for IoT applications. Data is the foundation and core of cloud IoT platform applications, and massive multisource heterogeneous IoT data aggregation and storage have basic requirements such as real-time, security, and scalability. This paper focuses on the aggregation and storage methods of massive heterogeneous cloud IoT data, solving the multisource data aggregation problem caused by inconsistent protocols and the heterogeneous data storage problem caused by inconsistent data types. A heterogeneous network protocol adaptation and data aggregation method is proposed for the multisource data aggregation problem caused by protocol inconsistency. A protocol adaptation layer is set up in the IoT virtual gateway to achieve compatibility with multiple types of data aggregation protocols, ensuring adaptive access to different types of IoT nodes, and on this basis, data is transmitted to the cloud IoT platform through a unified interface, shielding the variability of IoT sensing devices. Given the problems of device forgery and malicious tampering in the data aggregation process, we propose a fast authentication and data storage method for IoT devices based on “API key” and implant the device authentication API key into the protocol adaptation layer of the virtual gateway to realize the source authentication of IoT nodes and ensure the authenticity of data.

Cascading-Failures Effect on Heterogeneous Internet of Things Systems under Targeted Selective Attack

With the rapid development of the Internet of Things (IoT), the physical system and the network space are further deeply integrated, forming a larger-scale IoT heterogeneous fusion system. The attack mode considered in the security mechanism research of traditional large-scale complex systems is relatively simple; only simple attack types such as random attacks on physical systems or network systems are considered. In addition, existing attack modalities such as selectivity, locality, and distribution cannot fully consider the characteristics of security threats in the IoT system. In this paper, for large-scale heterogeneous IoT system scenarios, attackers can attack network systems or physical systems through cyberspace. We conduct situational awareness analysis on important traffic nodes or backbone nodes and study the cascading failures of two interdependent heterogeneous space systems. In view of the existence of such targeted attack threats in large-scale IoT heterogeneous systems, we focus on security assessment and risk prediction issues. First, this paper analyzes and models different IoT heterogeneous systems. Then using the penetration theory, we analyze the cascading failure process step by step and obtain the critical threshold for system collapse failure. Finally, we further verify the correctness of the theoretical values through simulation to effectively analyze and illustrate the reliability of the parameters affecting the system risk. The experimental results show that the large-scale IoT heterogeneous system presents a first-order discontinuous transition value near the critical threshold and the power-law index of the SF network has little effect on the system security.

IoT Platforms and Security: An Analysis of the Leading Industrial/Commercial Solutions.

For simplifying and speeding up the development of the Internet of Things (IoT) ecosystem, there has been a proliferation of IoT platforms, built up according to different design principles, computing paradigms, technologies, and targets. This paper proposes a review of main examples populating the wide landscape of IoT platforms and their comparison based on the IoT-A reference architecture. In such a way, heterogeneous IoT platforms (both current and future) can be analyzed regardless of their low-level specifications but exclusively through the lens of those key functionalities and architectural building blocks that enable the interplay among devices, data flow, software, and stakeholders within the IoT ecosystem. Among these, security by design (i.e., the inclusion of security design principles, technology, and governance at every level) must be integrated into every tier, component, and application to minimize the risk of cyber threats and preserve the integrity of the IoT platforms, not only within individual components but also for all the components working together as a whole.

EdgeLoc: A Robust and Real-Time Localization System Toward Heterogeneous IoT Devices

Indoor localization has become an essential demand driven by indoor location-based services (ILBSs) for mobile users. With the rising of Internet of Things (IoT), heterogeneous smartphones and wearables have become ubiquitous. However, the ILBSs for heterogeneous IoT devices confront significant challenges, such as received signal strength (RSS) variances caused by hardware heterogeneity, multipath reflections from complex environments, and localization time restricted by computation resources. This article proposes EdgeLoc, a robust and real-time indoor localization system toward heterogeneous IoT devices to solve the above challenges. In particular, the RSS fingerprinting data of Wi-Fi is employed for localization and tackling the heterogeneity of IoT devices in twofold. First, feature-level and signal-level solutions are presented to address the random RSS variances. At the feature level, this work proposes a novel capsule neural network model to efficiently extract incremental features from RSS fingerprinting data. At the signal level, a multistep dataflow is further devised to process RSS fingerprints into image-like data, which utilizes the feature matrix to reduce absolute sensing errors introduced by hardware heterogeneity. Second, an edge-IoT framework is designed to utilize the edge server to train the deep learning model and further supports real-time localization for heterogeneous IoT devices. Extensive field experiments with over 33 600 data points are conducted to validate the effectiveness of EdgeLoc with a large-scale Wi-Fi fingerprint data set. The results show that EdgeLoc outperforms the state-of-the-art SAE-CNN method in localization accuracy by up to 14.4%, with an average error of 0.68 m and an average positioning time of 2.05 ms.

Creating a Sensor Tier for the EMULSION IoT Platform with Low-Cost Electronic Modules

This paper presents some of the designed and experimentally-tested low-cost electronic modules, utilized for the creation of a sensor tier for the generic, multi-service, cloud-based operational platform EMULSION, which is being elaborated for rapid building of mobile Internet of Things (IoT) systems and roll-out of corresponding IoT services. The next step is to achieve full integration of the designed modules with various other existing components with different processing and communication capabilities, as to allow 1,000,000 heterogeneous IoT nodes, deployed at the sensor tier of the platform, to communicate simultaneously online within a single EMULSION cluster.

Clouds Proportionate Medical Data Stream Analytics for Internet of Things-Based Healthcare Systems

Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.

IRECOVer: Patch your IoT on-the-fly

In this paper, we propose a holistic solution for the security management of the Internet of Things (IoT) devices. Following the “security-by-design” principle, we propose RECOnfigurable microserVices-based IoT (iRECOVer), where each IoT device has a fully modular software adopting a Microservices-based architecture. This architecture at the device level leads to a new generation of IoT devices, where an IoT device is composed of a set of programmable functional modules that can be plugged-in/out of the device dynamically. iRECOVer enables patching vulnerable modules of devices by unplugging and replacing them with secure and patched modules on-the-fly, with zero or minimal device downtime. Furthermore, the security management of deployed IoT devices is automated by supporting the remote activation/deactivation of modules inside the device. We also discuss how iRECOVer supports heterogeneous IoT devices by incorporating a custom-designed mitigation solution for each device. These mitigation solutions may consist of modules provided by different vendors or publicly available and verified modules, therefore reducing the development efforts as well as catering for the prevailing lack of security expertise in the IoT industry. We present an experimental evaluation of our solution by measuring the benefits in terms of reduced mitigation time, performance, and impact on device functionality. Our results, when compared with existing approaches, indicate that the proposed solution is able to achieve on-the-fly mitigation of deployed IoT devices in negligible time, while incurring a low CPU load and additionally requiring, on average, only 4.5% of the device’s memory.

Managing Heterogeneous and Time-Sensitive IoT Applications through Collaborative and Energy-Aware Resource Allocation

In the Internet of Things (IoT) environment, the computing resources available in the cloud are often unable to meet the latency constraints of time critical applications due to the large distance between the cloud and data sources (IoT devices). The adoption of edge computing can help the cloud deliver services that meet time critical application requirements. However, it is challenging to meet the IoT application demands while using the resources smartly to reduce energy consumption at the edge of the network. In this context, we propose a fully distributed resource allocation algorithm for the IoT-edge-cloud environment, which (i) increases the infrastructure resource usage by promoting the collaboration between edge nodes, (ii) supports the heterogeneity and generic requirements of applications, and (iii) reduces the application latency and increases the energy efficiency of the edge. We compare our algorithm with a non-collaborative vertical offloading and with a horizontal approach based on edge collaboration. Results of simulations showed that the proposed algorithm is able to reduce 49.95% of the IoT application request end-to-end latency, increase 95.35% of the edge node utilization, and enhance the energy efficiency in terms of the edge node power consumption by 92.63% in comparison to the best performances of vertical and collaboration approaches.