Heterogeneous Internet Of Things Systems Research Articles

Efficient hybrid centralized and blockchain-based authentication architecture for heterogeneous IoT systems

With the rapid increase in the number of Internet of Things (IoT) devices in recent years, massive amounts of sensitive IoT data are being generated and transmitted over the Internet. Despite its growing adoption in various fields, IoT security remains a major challenge requiring further research. IoT authentication is an essential security mechanism for building trust in IoT systems. However, conventional authentication approaches use expensive cryptographic primitives that do not align with the resource-constrained nature of IoT devices. Furthermore, centralized authentication schemes have proven to be inapplicable for cross-domain authentication and do not limit the scalability of IoT networks. Recently, blockchain technology has been applied to building decentralized authentication between IoT devices. Nevertheless, most existing blockchain-based authentication approaches incur high overhead in IoT computation, storage, and energy consumption. Authentication time is another critical issue in real-time IoT systems. When numerous IoT authentication requests are transferred to the blockchain, an additional time delay is imposed, in addition to the high computational cost of the blockchain caused by the consensus mechanism. This study proposes a hybrid centralized and blockchain-based authentication architecture for IoT systems. Edge servers are deployed to provide centralized authentication for associated IoT devices. A blockchain network of centralized edge servers is then established to ensure decentralized authentication and verification of IoT devices that belong to different and heterogeneous IoT systems. Lightweight cryptographic methods are implemented to achieve efficient authentication, in which limiting the consumption of IoT resources is required. The architecture is demonstrated using a local Ethereum blockchain network. The results indicate that the proposed method achieves significant improvements in terms of computation cost, execution time, and power consumption for IoT compared with centralized and blockchain-based authentication schemes. A security analysis proves the ability of our architecture to mitigate attacks and satisfy the IoT security requirements.

PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT Systems

Federated learning (FL) has been recognized as a promising collaborative on-device machine learning method in the design of Internet of Things (IoT) systems. However, most existing FL methods fail to deal with IoT applications that contain a variety of IoT devices equipped with different types of neural network (NN) models. This is because traditional FL methods assume that local models on devices should have the same architecture as the global model on cloud. To address this problem, we propose a novel framework named PervasiveFL that enables efficient and effective FL among heterogeneous IoT devices. Without modifying original local models, PervasiveFL installs one lightweight NN model named modellet on each device. By using the deep mutual learning (DML) and our entropy-based decision gating (EDG) method, modellets and local models can selectively learn from each other through soft labels using locally captured data. Meanwhile, since modellets are of the same architecture, the learned knowledge by modellets can be shared among devices in a traditional FL manner. In this way, PervasiveFL can be pervasively applied to any heterogeneous IoT system. Comprehensive experimental results on four well-known datasets show that PervasiveFL can not only pervasively enable FL among heterogeneous devices within a large-scale IoT system, but also significantly enhance the inference accuracy of heterogeneous IoT devices with low communication overhead.

Improving Fault Tolerance and Reliability of Heterogeneous Multi-Agent IoT Systems Using Intelligence Transfer

Driven by the ever-growing diversity of software and hardware agents available on the market, Internet-of-Things (IoT) systems, functioning as heterogeneous multi-agent systems (MASs), are increasingly required to provide a level of reliability and fault tolerance. In this paper, we develop an approach to generalized quantifiable modeling of fault-tolerant and reliable MAS. We propose a novel software architectural model, the Intelligence Transfer Model (ITM), by which intelligence can be transferred between agents in a heterogeneous MAS. In the ITM, we propose a novel mechanism, the latent acceptable state, which enables it to achieve improved levels of fault tolerance and reliability in task-based redundancy systems, as used in the ITM, in comparison with existing agent-based redundancy approaches. We demonstrate these improvements through experimental testing of the ITM using an open-source candidate implementation of the model, developed in Python, and through an open-source simulator that tested the behavior of ITM-based MASs at scale. The results of these experiments demonstrated improvements in fault tolerance and reliability across all MAS configurations we tested. Fault tolerance was observed to improve by a factor of between 1.27 and 6.34 in comparison with the control group, depending on the ITM configuration tested. Similarly, reliability was observed to improve by a factor of between 1.00 and 4.73. Our proposed model has broad applicability to various IoT applications and generally in MASs that have fault tolerance or reliability requirements, such as in cloud computing and autonomous vehicles.

Multi-Armed-Bandit Based Channel Selection Algorithm for Massive Heterogeneous Internet of Things Networks

In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method is necessary. Although some channel allocation methods have already been studied, as far as we know, there is no research focusing on the implementation phase of Internet of Things devices while considering massive heterogeneous Internet of Things systems where different kinds of Internet of Things devices coexist in the same Internet of Things system. This paper focuses on the multi-armed-bandit-based channel allocation method that can be implemented on resource-constrained Internet of Things devices with low computational processing ability while avoiding congestion in massive Internet of Things systems. This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in most cases. Specifically, the frame success rate can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices. In addition, the performance in terms of frame success rate can be improved by 20% compared to the equality channel allocation. Moreover, the multi-armed-bandit-based channel allocation methods is implemented on 50 Wi-SUN Internet of Things devices that support IEEE 802.15.4g/4e communication and evaluate the performance in frame success rate in an actual wood house coexisting with LoRa devices. The experimental results show that the modified multi-armed-bandit method can achieve the highest frame success rate compared to other well-known frame success rate-based channel selection methods.

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.

Performance analysis of multi-access edge computing networks for heterogeneous IoT systems

The emergence of Multi-access Edge Computing (MEC) has drastically transformed the computing capabilities of communication systems. By facilitating the convenience of low latency and on-demand services for delay-sensitive tasks, these platforms have also contributed towards mitigating the communication overheads to a larger extent. An emerging application area which has largely benefitted with the adoption of MEC is the Internet of Things (IoT) paradigms. However, with the exponential rise in interconnected IoT devices requesting provisioning of resources to the MEC may lead to traffic bottlenecks in the MEC networks. This further leads to lower response times and degradation in the network’s Quality of Service (QoS). In view of modeling request processing mechanism and monitoring of network QoS, a Markov process driven framework to model MEC-based IoT systems is proposed. By exploiting the analytical inference from pure birth–death processes and queue theoretic models the steady-state solutions for MEC networks is derived. Further, critical performance metrics like mean number of requests in the network, traffic intensity of requests, fluctuations in number of requests, and the mean response time of the network are provided. The corresponding results obtained for the above analysis are validated through numerical illustrations to prove the convergence of the framework and its impact on different network parameters.

Energy-Efficient Multivariate Privacy-Aware RF Spectrum Reservation in Wireless Virtualization for Wireless Internet of Things

Next generation communication systems are expected to be heterogeneous and to have varying Quality of Service (QoS) requirements for different Internet of Things (IoT) applications. Wireless virtualization is regarded as an emerging technology capable of handling the ever growing resource demands by those heterogeneous IoT applications and systems by enabling wireless resource sharing through slicing. Scarce wireless resources and expensive physical infrastructure are leased to business units. The business units do not own the infrastructure, but rather gain access through a subleasing process. However, due to the lack of efficient management of wireless resources, energy efficiency of the physical infrastructure cannot be guaranteed. A viable solution to this problem is to anticipate resource needs and proactively implement energy efficient measures to ensure QoS requirements of users are met using a reservation scheme. Previous spectrum reservation approaches do not consider energy efficiency in the reservation process. In this paper, we present an adaptive resource reservation scheme for wireless network virtualization. The proposed scheme relies on an informed estimate of resource needs by a combination of aggregated event data from multiple contributors, and prediction based on previous allocation data. We present a Privacy aware Aggregation Model (PrivAgg) that relies on the truthfulness of contributors by providing secure enrollment and communication. We also present a prediction algorithm, Volume and Bandwidth-conditioned Spectrum Selective Moving Average (VBSSMA), that enforces a multivariate filter for the allocation history in order to increase the accuracy of prediction. We evaluate the performance of the reservation scheme and algorithms using theoretical analysis and numerical results from extensive simulations. Numerical results over multiple configurations for the weight of the aggregator and predictor components shows that VBSSMA results in up to 27% less allocation cost and 4% less error than existing Volume-conditioned Spectrum Selective Moving Average reservation approach.

ORCA: Enabling an Owner-Centric and Data-Driven Management Paradigm for Future Heterogeneous Edge-IoT Systems

Integrating the Internet of Things (IoT) and edge computing in Edge-IoT systems, converged with machine intelligence, has the potential to enable a wide range of applications in smart homes, factories, and cities. Edge-IoT can connect many diverse devices, and IoT asset owners can run heterogeneous IoT systems supported by various vendors or service providers (SPs), using either cloud or local edge computing (or both) for resource assistance. The existing methods typically manage the systems as separate vertical silos, or in a vendor-/SP-centric way, which suffers from significant challenges. In this article, we present a novel owner-centric management paradigm named ORCA to address the gaps left by the owner-centric paradigm and empower IoT asset owners to effectively identify and mitigate potential issues in their own network premises, regardless of vendors'/SPs' situations. ORCA aims to be scalable and extensible in assisting IoT owners to perform intelligent management through a behavior-oriented and data-driven approach. ORCA is an ongoing project, and the preliminary results indicate that it can significantly empower IoT system owners to better manage their IoT assets.

Modeling-framework for model-based software engineering of complex Internet of things systems.

Internet of things (IoT) systems are composed of variety of units from different domains. While developing a complete IoT system, different professionals from different domains may have to work in collaboration. In this paper we provide a framework which allows using discrete and continuous time modeling and simulation approaches in combination for IoT systems. The proposed framework demonstrates on how to model Ad-hoc and general IoT systems for software engineering purpose. We demonstrate that model-based software engineering on one hand can provide a common platform to overcome communication gaps among collaborating stakeholders whereas, on the other hand can model and integrate heterogeneous components of IoT systems. While modeling heterogeneous IoT systems, one of the major challenges is to apply continuous and discrete time modeling on intrinsically varying components of the system. Another difficulty may be how to compose these heterogeneous components into one whole system. The proposed framework provides a road-map to model discrete, continuous, Ad-hoc, general systems along with composition mechanism of heterogeneous subsystems. The framework uses a combination of Agent-based modeling, Aspect-oriented modeling, contract-based modeling and services-oriented modeling concepts. We used this framework to model a scenario example of a service-oriented IoT system as proof of concept. We analyzed our framework with existing systems and discussed it in details. Our framework provides a mechanism to model different viewpoints. The framework also enhances the completeness and consistency of the IoT software models.

Characterizing DNS Behaviors of Internet of Things in Edge Networks

The recent spate of cyber attacks and security threats toward Internet-of-Things (IoT) systems in smart cities, smart homes, and industry 4.0 calls for effective techniques to understand if, when, who, what IoT systems are exploited and compromised by Internet attackers. Toward this end, this article attempts to study DNS behavioral patterns of IoT systems in edge networks as a first step of characterizing their communication patterns and their interactions with IoT users, cloud servers, and other IoT or non-IoT devices in the same edge networks. Specifically, we analyze the temporal–spatial patterns of DNS behaviors of a variety of IoT systems in two dozens of edge networks and develop a simple yet effective Bloom filter mechanism for detecting anomalous traffic patterns based on unusual DNS queries and answers. To the best of our knowledge, this article is the first effort to systematically measure and monitor IoT network traffic from a DNS perspective for providing the security of heterogeneous IoT systems and ensuring IoT user privacy.

Message-Based Communication for Heterogeneous Internet of Things Systems.

The Internet of Things (IoT) domain presents a wide spectrum of technologies for building IoT applications. The requirements are varying from one application to another granting uniqueness to each IoT system. Each application demands custom implementations to achieve efficient, secure and cost-effective environments. They pose a set of properties that cannot be addressed by a single-based protocol IoT network. Such properties are achievable by designing a heterogeneous IoT system, which integrates diverse IoT protocols and provides a network management solution to efficiently manage the system components. This paper proposes an IoT message-based communication model applied atop the IoT protocols in order to achieve functional scalability and network management transparency agnostic to the employed communication protocol. The paper evaluates the proposed communication model and proves its functional scalability in a heterogeneous IoT system. The experimental assessment compares the payload size of the proposed system with respect to the LwM2M standard, a protocol designed specifically for IoT applications. In addition, the paper discusses the energy consumption introduced by the proposed model as well as the options available to reduce such impact.

Energy Efficiency Characterization in Heterogeneous IoT System With UAV Swarms Based on Wireless Power Transfer

An unmanned aerial vehicle (UAV) swarm together with a large-scale heterogeneous Internet of Things (IoT) network consisting of macrocells and energy-constrained IoT transmitters (IoT-Ts) is investigated. The UAVs are utilized as flying robot swarms that intelligently transfer energy to the energy-constrained IoT-Ts on the ground. Each IoT-T has an associated IoT device (IoT-D) that is placed at a fixed distance in a random direction. The transmission probability of the energy-constrained IoT-Ts is derived by considering one-slot charging and two-slot charging according to three dimensional (3D) locations, respectively. The coverage probability of each type of IoT-D is investigated. The energy efficiency is derived by considering the transmission power of the active IoT-Ts and the effect of the association biasing factor, and the energy efficiency is also maximized by deploying the optimal density of IoT-Ts. Simulation results are examined to validate the accuracy of our theoretical analysis. Results illustrate the insightful effects of the network parameters, and the helpful guidelines for practical UAV swarms and IoT system design.

RNN-Assisted Network Coding for Secure Heterogeneous Internet of Things With Unreliable Storage

With the rapid growth of Internet of Things (IoT), integrating a variety of IoT can result in novel applications. However, IoT devices are often deployed in an open environment where IoT are inclined to be malfunctioned. Although data reliability can be achieved by data recovery with conventional replication, the communication between IoT is susceptible to eavesdropping. Therefore, in this paper, we study the eavesdropping prevention of data repair in IoT environments based on network coding. We theoretically derive the relation between security level and storage in heterogeneous IoT systems. To further reduce the repair bandwidth, we exploit recurrent neural network for the storage failure prediction. Under the condition when failure probability and workloads of storage devices are considered, two allocation algorithms are proposed to avoid data repair. Finally, we show the relation between storage cost and reliability with different numbers of IoT devices. Experimental results manifest that the proposed allocation algorithms can outperform the baseline case by 18.4% in terms of the security level.

A Machine Learning Enabled MAC Framework for Heterogeneous Internet-of-Things Networks

Nowadays, an Internet-of-Things (IoT) connected system brings a tremendous paradigm shift into the medium access control (MAC) design. In this paper, we present a distributed MAC framework assisted by machine learning for the Heterogeneous IoT system, where the IoT devices coexist with the WiFi users in the unlicensed industrial, scientific, and medical (ISM) spectrum. Specifically, the superframe is divided into two phases: a rendezvous phase and a transmission phase. During the rendezvous phase, the gateway that is capable of machine learning predicts the number of WiFi users and the IoT devices by performing a triangular handshake on the primary channel. The prediction takes advantage of the deep neural network (DNN) model which is pretrained on our universal software radio peripheral (USRP2) testbed offline. The gateway allocates the frequency channels to the WiFi and IoT systems based on the inference results. Then, the IoT devices and WiFi users initiate data transmissions during the transmission phase. Furthermore, system throughput is analyzed and optimized in two typical scenarios, respectively. An optimized MAC framework is proposed to maximize the total system throughput by finding the key design parameters. The analytical and simulation results that are conducted using the ns-2 demonstrate the effectiveness of the proposed MAC framework.

IoT Architectural Framework: Connection and Integration Framework for IoT Systems

The proliferation of the Internet of Things (IoT) has since seen a growing interest in architectural design and adaptive frameworks to promote the connection between heterogeneous IoT devices and IoT systems. The most widely favoured software architecture in IoT is the Service Oriented Architecture (SOA), which aims to provide a loosely coupled systems to leverage the use and reuse of IoT services at the middle-ware layer, to minimise system integration problems. However, despite the flexibility offered by SOA, the challenges of integrating, scaling and ensuring resilience in IoT systems persist. One of the key causes of poor integration in IoT systems is the lack of an intelligent, connection-aware framework to support interaction in IoT systems. This paper reviews existing architectural frameworks for integrating IoT devices and identifies the key areas that require further research improvements. The paper concludes by proposing a possible solution based on microservice. The proposed IoT integration framework benefits from an intelligent API layer that employs an external service assembler, service auditor, service monitor and service router component to coordinate service publishing, subscription, decoupling and service combination within the architecture.

From WSN towards WoT: Open API Scheme Based on oneM2M Platforms.

Conventional computing systems have been able to be integrated into daily objects and connected to each other due to advances in computing and network technologies, such as wireless sensor networks (WSNs), forming a global network infrastructure, called the Internet of Things (IoT). To support the interconnection and interoperability between heterogeneous IoT systems, the availability of standardized, open application programming interfaces (APIs) is one of the key features of common software platforms for IoT devices, gateways, and servers. In this paper, we present a standardized way of extending previously-existing WSNs towards IoT systems, building the world of the Web of Things (WoT). Based on the oneM2M software platforms developed in the previous project, we introduce a well-designed open API scheme and device-specific thing adaptation software (TAS) enabling WSN elements, such as a wireless sensor node, to be accessed in a standardized way on a global scale. Three pilot services are implemented (i.e., a WiFi-enabled smart flowerpot, voice-based control for ZigBee-connected home appliances, and WiFi-connected AR.Drone control) to demonstrate the practical usability of the open API scheme and TAS modules. Full details on the method of integrating WSN elements into three example systems are described at the programming code level, which is expected to help future researchers in integrating their WSN systems in IoT platforms, such as oneM2M. We hope that the flexibly-deployable, easily-reusable common open API scheme and TAS-based integration method working with the oneM2M platforms will help the conventional WSNs in diverse industries evolve into the emerging WoT solutions.

A Novel Multi-Feature Representation of Images for Heterogeneous IoTs

With the applications heterogeneous of Internet of Things (IoT) technology, the heterogeneous IoT systems generate a large number of heterogeneous datas, including videos and images. How to efficiently represent these images is an important and challenging task. As a local descriptor, the texton analysis has attracted wide attentions in the field of image processing. A variety of texton-based methods have been proposed in the past few years, which have achieved excellent performance. But, there still exists some problems to be solved, especially, it is difficult to describe the images with complex scenes from IoT. To address this problem, this paper proposes a multi-feature representation method called diagonal structure descriptor. It is more suitable for intermediate feature extraction and conducive to multi-feature fusion. Based on visual attention mechanism, five kinds of diagonal structure textons are defined by the color differences of diagonal pixels. Then, four types of visual features are extracted from the mapping sub-graphs and integrated into 1-D vector. Various experiments on three Corel-datasets demonstrate that the proposed method performs better than several state-of-the-art methods.