Distributed Internet Of Things Systems Research Articles

Improving a procedure of load balancing in distributed IoT systems

The object of this research is the process of load balancing in distributed Internet of Things (IoT) systems. Within this work, a complex of problems related to efficient load distribution has been addressed. The authors conducted an analysis of existing load-balancing approaches and their drawbacks and proposed an enhanced architecture for the MQTT broker. Additionally, methods and algorithms for load balancing were developed based on multi-criteria server monitoring. Furthermore, the authors created a mathematical model to assess the uniformity of load distribution in the system and introduced a corresponding metric – the load distribution coefficient. In order to evaluate the proposed load balancing methods, a series of experiments were conducted, including the simulation of a distributed IoT system with non-deterministic load. The main goal of these experiments was to assess the uniformity of MQTT load distribution by the broker. The results of the experiments confirmed the hypothesis of improved load distribution efficiency through multi-criteria monitoring-based balancing. The utilization of the proposed load-balancing methods allowed for a more efficient utilization of computational resources. It was found that when using the proposed methods, in the case of non-deterministic load in the IoT system, the load distribution coefficient on average exceeded the corresponding indicator of existing methods by 70 %. In addition, the value of this coefficient for the proposed methods remains virtually unchanged throughout the experiment, which is evidence of the stable operation of the system as a whole. The results obtained can be useful in the development of modern IoT systems.

High-performance hybrid nanogenerator for self-powered wireless multi-sensing microsystems

Wireless sensor network nodes are widely used in wearable devices, consumer electronics, and industrial electronics and are a crucial component of the Internet of Things (IoT). Recently, advanced power technology with sustainable energy supply and pollution-free characteristics has become a popular research focus. Herein, to realize an unattended and reliable power supply unit suitable for distributed IoT systems, we develop a high-performance triboelectric-electromagnetic hybrid nanogenerator (TEHNG) to harvest mechanical energy. The TEHNG achieves a high load power of 21.8 mW by implementing improvements of material optimization, configuration optimization and pyramid microstructure design. To realize a self-powered integrated microsystem, a power management module, energy storage module, sensing signal processing module, and microcontroller unit are integrated into the TEHNG. Furthermore, an all-in-one wireless multisensing microsystem comprising the TEHNG, the abovementioned integrated functional circuit and three sensors (temperature, pressure, and ultraviolet) is built. The milliwatt microsystem operates continuously with the TEHNG as the only power supply, achieving self-powered operations of sensing environmental variables and transmitting wireless data to a terminal in real time. This shows tremendous application potential in the IoT field.

Edge-Based Federated Deep Reinforcement Learning for IoT Traffic Management

The wide adoption of large-scale Internet of Things (IoT) systems has led to an unprecedented increase in backhaul (BH) traffic congestion, making it critical to optimize traffic management at the network edge. In IoT systems, the BH network is supported by various backhauling technologies that have different characteristics. Also, the characteristics of the BH links can be sometimes time varying and have an unknown state, due to external factors such as having the resources shared with other systems. It is the responsibility of the edge devices to be able to forward IoT traffic through the unknown-state BH network by selecting the suitable BH link for each collected data flow. To the best of our knowledge, this type of BH selection problem is not addressed in the literature. Therefore, there is a crucial need to develop intelligent approaches enabling edge devices to learn how to deal with unknown-state (partially observable) components of the BH network, which is the primary goal of this article. We propose an edge-based BH selection technique for improving traffic delivery by exploiting multiobjective feedback on delivery performance. The proposed approach relies on the advantage-actor–critic deep reinforcement learning (DRL) methods. Moreover, to improve the DRL training performance in large-scale deployments of distributed IoT systems, federated learning (FL) is applied to enable multiple edge devices to collaborate in training a shared BH selection policy. The proposed federated DRL (F-DRL) approach is able to solve the BH selection problem as verified and demonstrated through extensive simulations.

An Integrated Collaborative Filtering Framework With Location-Aware Graph Embedding in Intelligent Internet of Things Systems

Reliable Internet of Things (IoT) service discovery is a significant task in intelligent service-oriented IoT systems. Collaborative filtering (CF) turns out to be an effective solution to IoT service discovery. However, the traditional CF framework is facing the following challenges: inefficiency in learning the high-order interactions between users and IoT services and ineffectively making use of geographical location information. Moreover, deploying a CF framework in real-world distributed IoT systems poses another significant challenge in terms of reliability assurance and privacy protection. For bridging this gap, we propose an innovative integrated collaborative filtering framework (ICF) with incorporating the location-aware quality of IoT services into a heterogeneous graph embedding model. Meanwhile, a Geohash-based privacy-preservation mechanism is introduced to encoding the location information into a short string for protecting the sensitive location information. The proposed ICF framework is an integrated architecture combining advanced graph embedding learning and heterogeneous side information. And a joint objective optimization function is designed in the graph embedding learning to qualify automatic IoT service features. In this way, the hidden user-service interaction information and location-aware quality semantic features can be explored in an effective and reliable way. Furthermore, the learned location-aware quality embedding vector is incorporated to discover the reliable services among all IoT services with the mini-batch online clustering-based CF algorithm. The experimental findings illustrate the significant efficacy and reliability of our proposed ICF framework in large scale IoT service discovery scenarios.

Variational Few-Shot Learning for Microservice-Oriented Intrusion Detection in Distributed Industrial IoT

Along with the popularity of the Internet of Things (IoT) techniques with several computational paradigms, such as cloud and edge computing, microservice has been viewed as a promising architecture in large-scale application design and deployment. Due to the limited computing ability of edge devices in distributed IoT, only a small scale of data can be used for model training. In addition, most of the machine-learning-based intrusion detection methods are insufficient when dealing with imbalanced dataset under limited computing resources. In this article, we propose an optimized intra/inter-class-structure-based variational few-shot learning (OICS-VFSL) model to overcome a specific out-of-distribution problem in imbalanced learning, and to improve the microservice-oriented intrusion detection in distributed IoT systems. Following a newly designed VFSL framework, an intra/inter-class optimization scheme is developed using reconstructed feature embeddings, in which the intra-class distance is optimized based on the approximation during a variation Bayesian process, while the inter-class distance is optimized based on the maximization of similarities during a feature concatenation process. An intelligent intrusion detection algorithm is, then, introduced to improve the multiclass classification via a nonlinear neural network. Evaluation experiments are conducted using two public datasets to demonstrate the effectiveness of our proposed model, especially in detecting novel attacks with extremely imbalanced data, compared with four baseline methods.

Embedding Blockchain Technology Into IoT for Security: A Survey

In recent years, the Internet of Things (IoT) has made great progress. The interconnection between IoT and the Internet enables real-time information processing and transaction implementation through heterogeneous intelligent devices. But the security, the privacy, and the reliability of IoT are key challenges that limit its development. The features of the blockchain, such as decentralization, consensus mechanism, data encryption, and smart contracts, are suitable for building distributed IoT systems to prevent potential attacks and to reduce transaction costs. As a decentralized and transparent database platform, blockchain has the potential to raise the performance of IoT security to a higher level. This article systematically analyzes state of the art of IoT security based on the blockchain, paying special attention to the security features, issues, technologies, approaches, and related scenarios in blockchain-embedded IoT. The integration and interoperation of blockchain and IoT is an important and foreseeable development in the computational communication system.

Application-Oriented Block Generation for Consortium Blockchain-Based IoT Systems With Dynamic Device Management

Due to its salient features, such as immutability and auditability, blockchain is becoming more integrated into the Internet of Things (IoT) for enhancing security and developing a decentralized IoT framework. However, different IoT applications require different transaction processing performance, which brings challenges to the convergence of blockchains in IoT. Moreover, the membership of a distributed IoT system may fluctuate when an IoT device joins or leaves the system. The dynamic nature of IoT systems also introduces new challenges for device management. Accordingly, we propose an application-oriented block generation (AOBG) scheme for blockchain-enabled IoT with dynamic device management and conditional traceability. Specifically, we first construct a framework for a consortium blockchain-based IoT system, including structures for application-oriented transactions and blocks, and consensus mechanism. We present different miners, respectively, for processing urgent and ordinary transactions adaptively with applications. Then, an AOBG protocol is proposed for this framework based on group signature. The group signature is used to achieve anonymity, traceability, and nonframeability. Combining time-bound keys in group signature with node accounts in blockchain, the proposed scheme can realize efficient transaction verification, dynamic device management, conditional traceability with data security, and privacy preservation. Extensive experiments demonstrate high efficiency of the proposed scheme.

Toward Secure and Efficient Deep Learning Inference in Dependable IoT Systems

The rapid development of deep learning (DL) enables resource-constrained systems and devices [e.g., Internet of Things (IoT)] to perform sophisticated artificial intelligence (AI) applications. However, AI models, such as deep neural networks (DNNs), are known to be vulnerable to adversarial examples (AEs). Past works on defending against AEs require heavy computations in the model training or inference processes, making them impractical to be applied in IoT systems. In this article, we propose a novel method, Super-IoT, to enhance the security and efficiency of AI applications in distributed IoT systems. Specifically, Super-IoT utilizes a pixel drop operation to eliminate adversarial perturbations from the input and reduce network transmission throughput. Then, it adopts a sparse signal recovery method to reconstruct the dropped pixels and wavelet-based denoising method to reduce the artificial noise. Super-IoT is a lightweight method with negligible computation cost to IoT devices and little impact on the DNN model performance. Extensive evaluations show that it can outperform three existing AE defensive solutions against most of the AE attacks with better transmission efficiency.

BacS: A blockchain-based access control scheme in distributed internet of things

Distributed Internet of Things (Distributed IoT) is a large-scale, heterogeneous, dynamic distributed architecture environment which is gradually formed based on Internet of Things (IoT) technology. In order to cope with the large number access requirements for IoT data brought by application expansion, the data of IoT devices are usually stored in the management server (DMS) of current domain, and adopt a centralized access control mechanism to user. This centrally approach can easily cause data to be tampered with and leaked. Moreover, registering different identities when user accesses different domains increases the difficulty to manage his identities. Therefore, this paper proposes a blockchain-based access control scheme called BacS for Distributed IoT. In BacS, firstly, we use account address of the node in blockchain as the identity to access DMS, redefine the access control permission of data of devices and store on blockchain. Then we design processes of authorization, authorization revocation, access control and audit in BacS. Finally, we use a lightweight symmetric encryption algorithm (SEA) to achieve privacy-preserving for Distributed IoT system. We build a credible experimental model on Ethereum private chain, results show that BacS is feasible and effective that it can achieve secure access in Distributed IoT environment while protecting privacy.

Attack and Defense Strategies for Intrusion Detection in Autonomous Distributed IoT Systems

In this paper, we develop a methodology to capture and analyze the interplay of attack-defense strategies for intrusion detection in an autonomous distributed Internet of Things (IoT) system. In our formulation, every node must participate in lightweight intrusion detection of a neighbor target node. Consequently, every good node would play a set of defense strategies to faithfully defend the system while every bad node would play a set of attack strategies for achieving their own goals. We develop an analytical model based on Stochastic Petri Net (SPN) modeling techniques. Our methodology allows the optimal defense strategies to be played by good nodes to maximize the system lifetime when given a set of parameter values characterizing the distributed IoT system operational environment. We conduct a detailed performance evaluation based on an experiment dataset deriving from a reference autonomous distributed IoT system comprising 128 sensor-carrying mobile nodes and show how IDS defense mechanisms can counter malicious attack mechanisms under the ADIoTS system while considering multiple failure conditions.

A Decentralized Blockchain-Based Trust Management Protocol for the Internet of Things

The Internet of Things (IoT) is a network that integrates a variety of heterogeneous nodes, such as connected devices (sensors, robots, smart phones…), connected cars, smart homes, etc. These smart objects communicate and collaborate between each other in distributed and dynamic environments that face several security challenges. Trust management is one of the most important challenges in such environments. Existing trust management solutions do not meet the new requirements of IoT such as heterogeneity, mobility and scalability. In this paper, we propose a hierarchical and scalable based blockchain trust management protocol with mobility support in massively distributed IoT systems. In our protocol, mobile smart objects disseminate trust information about service providers to the blockchain. Thus, all the objects will have a global view about each service provider in the architecture which speeds up the trust decision making. In addition, our protocol is resilient against known malicious attacks such as \textit{bad-mouthing}, \textit{ballot-stuffing} and \textit{cooperative attacks}. We confirm the efficiency of our proposal through theoretical analysis and extensive simulations. Finally, we show that it outperforms existing solutions especially in terms of scalability, mobility support, communication and computation.

A Hybrid BlockChain-Based Identity Authentication Scheme for Multi-WSN

Internet of Things (IoT) equipment is usually in a harsh environment, and its security has always been a widely concerned issue. Node identity authentication is an important means to ensure its security. Traditional IoT identity authentication protocols usually rely on trusted third parties. However, many IoT environments do not allow such conditions, and are prone to single point failure. Blockchain technology with decentralization features provides a new solution for distributed IoT system. In this paper, a blockchain based multi-WSN authentication scheme for IoT is proposed. The nodes of IoT are divided into base stations, cluster head nodes and ordinary nodes according to their capability differences, which are formed to a hierarchical network. A blockchain network is constructed among different types of nodes to form a hybrid blockchain model, including local chain and public chain. In this hybrid model, nodes identity mutual authentication in various communication scenarios is realized, ordinary node identity authentication operation is accomplished by local blockchain, and cluster head node identity authentication are realized in public blockchain. The analysis of security and performance shows that the scheme has comprehensive security and better performance.

Scalable Learning-Based Heterogeneous Multi-Band Multi-User Cooperative Spectrum Sensing for Distributed IoT Systems

The emerge of Internet of Things (IoT) brings up revolutionary changes to wireless communications. Cognitive radio (CR) can be seen as one of the prominent solutions to spectrum scarcity in IoT, where multi-band cooperative spectrum sensing (CSS) is the key. However, lack of centralized control and increase in number of devices place a room for many challenges. One of the main challenges is secondary users' (SUs') scheduling to sense a subset of channels in heterogeneous distributed CR networks (CRNs). To overcome the aforementioned challenge, in this paper, we propose a novel heterogeneous multi-band multi-user CSS (HM2CSS) scheme. The proposed scheme allows heterogeneous SUs to sense multiple channels and consists of two stages. We formulate a mathematical model to optimize leader-selection for each channel in the first stage. We then formulate another optimization problem to determine corresponding cooperative SUs to sense these channels in the second stage. After that, diffusion learning is used to decide on the availability of channels. Simulations illustrate that the proposed scheme improves detection performance and CRN throughput, is scalable in terms of detection performance, and provides fair energy consumption for CSS on all channels compared to existing multi-band CSS schemes.

IoT Testing-as-a-Service: A New Dimension of Automation

Internet of Things (IoT) systems has become a global trend enhancing the capabilities smart computing era involving a variety of distributed end-devices and multi- scalable applications. The collaborative nature of IoT systems connected through the Internet increases the heterogeneity of coming data streams that need to be processed for correct decision making in a real-time environment. The processing of huge data streams for remotely distributed IoT systems create loops for data breaches and open new challenges for security and scalability of system testing. Thus, the testing of IoT systems is becoming the necessity, requires automated testing framework due to the amount of IoT devices and processing of data events is prone to error by traditional software testing. An automated IoT testing service based framework is purposed in this paper, to test the distributed IoT systems by reducing cost and scalability issues of software testing. The infrastructure of IoT systems demands a large number of platforms be developed which requires systematic testing approach. Therefore, the purposed automated IoT testing as a service model performs distributed interoperability testing, oneM2M based conformance testing, security testing of distributed systems and validating semantics/syntactic testing of IoT devices in a systematic approach. Lastly, to provide more strength to the work we discussed and analyze existing IoT testing models to evaluate our proposed model.

Emerging Edge Computing Technologies for Distributed IoT Systems

The ever-increasing growth in the number of connected smart devices and various Internet of Things (IoT) verticals is leading to a crucial challenge of handling massive amount of raw data generated from distributed IoT systems and providing real-time feedback to the end-users. Although existing cloud-computing paradigm has an enormous amount of virtual computing power and storage capacity, it is not suitable for latency-sensitive applications and distributed systems due to the involved latency and its centralized mode of operation. To this end, edge/fog computing has recently emerged as the next generation of computing systems for extending cloud-computing functions to the edges of the network. Despite several benefits of edge computing such as geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed in realizing edge computing technologies for future IoT systems. In this regard, this paper provides a holistic view on the current issues and effective solutions by classifying the emerging technologies in regard to the joint coordination of radio and computing resources, system optimization and intelligent resource management. Furthermore, an optimization framework for edge-IoT systems is proposed to enhance various performance metrics such as throughput, delay, resource utilization and energy consumption. Finally, a Machine Learning (ML) based case study is presented along with some numerical results to illustrate the significance of edge computing.

Web Fusion Model of Internet of Things Based on Hierarchy Requirement Analysis

Each item of sensory information is effectively integrated by Internet of things (IoT) system. However, due to different framework and interface, software and hardware platform and communication protocol are mutually independent, which makes distributed IoT system unable to fulfill uniform operation and management. By aiming at issues difficultly fulfilled by IoT system, a kind of framework model of IoT system based on Web Service is put forward. Firstly, function and framework analysis is conducted for local sensor service system, integrated sensor service system and integrated application service system required by model; next, an introduction about design of Web Service interface is made; finally, services and functions are abstracted to normalize the relation among three varieties of service systems of model. This scheme is proved to be with good applicability, flexibility and expansibility in cases.