Heterogeneous Devices Research Articles

Hybrid User Grouping With Heterogeneous Devices in NOMA-Enabled IoT Networks

In this paper, we consider an uplink Internet of Things (IoT) network consisting of time-sensitive devices and throughput-sensitive devices, focusing on effective coordination between different performance requirements. To improve the spectral efficiency, Non-Orthogonal Multiple Access (NOMA) is introduced to obtain a potential performance gain by assigning the spectrum resources to different types of devices with diverse performance requirements in a coordinative and resource-sharing manner. Considering the performance diversity of heterogeneous devices, we provide two NOMA user grouping strategies: Mixed Block Strategy (MBS) and Uniform Block Strategy (UBS) which configure NOMA user groups as those consisting of different types of devices and those containing only a single type of device, respectively. Then, we propose a hybrid resource allocation protocol by switching between these two strategies. The performance of a special case with two time-sensitive devices and two throughput-sensitive devices is first optimized to evaluate the proposed strategies, and then extended to compare with traditional strategy in the multi-user scenarios. Simulation results illustrate that MBS and UBS outperform the traditional strategy, i.e., lower Age of Information (AoI) and higher throughput, and have their own advantages at different system parameters. On the basis, the hybrid protocol can further improve the system performance.

Scalable and Interactive Simulation for IoT Applications With TinySim

Recent years, the rapid development of IoT (Internet of Things) technologies and applications have been witnessed. Three important features are characterized in modern IoT applications, i.e., device heterogeneity, long-range communication and cloud/edge-device integration. Difficulties are raised by the above features towards IoT application developers, e.g., predicting and evaluating the performance of the entire IoT application system. To deal with the above difficulties, we design and implement an IoT simulator, TinySim, which satisfies the requirements of high fidelity, high scalability, and seamless transplantation. TinySim takes advantage of the hardware-independent features of TinyLink programming language. Hence, a similar code can be used for both simulation and execution on real hardware platforms. Many virtual IoT devices can be simulated by TinySim at the PC end. These IoT devices can send or receive messages from the cloud or smartphones, making it possible for the developers to evaluate the entire system without the actual IoT hardware. We connect TinySim with Unity 3D to provide high interactivity. To reduce the event synchronization overhead between TinySim and Unity 3D, a dependence graph-based approach is proposed. We design an approximation-based approach to reduce the number of simulation events, greatly speeding up the simulation process. We carefully evaluate TinySim using benchmarks and two concrete case studies. TinySim can simulate representative IoT applications such as smart flowerspot and shared bikes. We conduct extensive experiments to evaluate the performance of TinySim. Results show that TinySim can achieve high accuracy with an error ratio lower than 9.52% in terms of energy and latency. Further, TinySim can simulate 4,000 devices within 11.2 physical-minutes for 10 simulation-minutes, which is about 3× faster than the state-of-art approach.

BonsaiKV: Towards Fast, Scalable, and Persistent Key-Value Stores with Tiered, Heterogeneous Memory System

Emerging NUMA/CXL-based tiered memory systems with heterogeneous memory devices such as DRAM and NVMM deliver ultrafast speed, large capacity, and data persistence all at once, offering great promise to high-performance in-memory key-value stores. To fully unleash the performance potential of such memory systems, this paper presents BonsaiKV, a key-value store that makes the best use of different components in a tiered memory system. The core of BonsaiKV is a tri-layer hierarchical storage architecture that separates data indexing, persistence, and scalability from each other and realizes each of them within a specialized software-hardware layer. We design BonsaiKV with a set of novel techniques, including collaborative tiered indexing, NVMM congestion control mechanisms, fine-grained data striping, and NUMA-aware data management, to leverage hardware strengths and tackle device deficiencies. We compare BonsaiKV with state-of-the-art NVMM-optimized key-value stores and persistent index structures using a variety of YCSB workloads. Evaluation results demonstrate that BonsaiKV outperforms others by up to 7.69×, 19.59×, and 12.86× in read-, write- and scan-intensive scenarios, respectively.

DISTRIBUTED ARTIFICIAL INTELLIGENCE MODELS IN PERSPECTIVE COMMUNICATION NETWORKS

6G technology will create an intelligent, highly scalable, dynamic and programmable wireless network capable of serving a variety of heterogeneous wireless devices. Various 6G modules and devices will generate colossal amounts of distributed data, so post-NGN (New Generation Networks) will need to implement a number of machine learning methods that will solve significantly complicated network problems. To overcome these problems, distributed learning methods can be used, allowing devices to train models jointly, without exchanging raw data, which reduces communication costs, delays, and increases data privacy level as well. Distributed machine learning models will play an important role in 6G networks, since they have a number of advantages over centralized methods, however, the implementation of distributed algorithms in resource-constrained wireless environments can be challenging. It is important to take into account the wireless environment uncertainty associated with various disturbing factors and limited wireless (transmission power, radio frequency spectrum) and hardware resources (computing power). Consequently, it is important to choose the suitable machine learning algorithm based on the wireless environment characteristics and the resource requirements of the learning process. The article reviews the application of distributed artificial intelligence models in new generation communication networks for resource management and data processing purposes. The general algorithms and approaches of distributed machine learning, applications, methods and models are described. The article analyzes the ways in which distributed artificial intelligence models can solve various problems in communication networks, including optimizing resource use and ensuring high performance and availability of network services.

Toward Cooperative Federated Learning Over Heterogeneous Edge/Fog Networks

Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation, treating edge/fog devices and other infrastructure participating in ML as separate processing elements. Consequently, FL has been vulnerable to several dimensions of network heterogeneity, such as varying computation capabilities, communication resources, data qualities, and privacy demands. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-toserver (D2S) interactions. Through D2D and D2S cooperation, CFL counteracts network heterogeneity in edge/fog networks through enabling a model/data/resource pooling mechanism, which will yield substantial improvements in ML model training quality and network resource consumption. We propose a set of core methodologies that form the foundation of D2D and D2S cooperation and present preliminary experiments that demonstrate their benefits. We also discuss new FL functionalities enabled by this cooperative framework such as the integration of unlabeled data and heterogeneous device privacy into ML model training. Finally, we describe some open research directions at the intersection of cooperative edge/fog and FL.

Vertical Layering of Quantized Neural Networks for Heterogeneous Inference.

Although considerable progress has been obtained in neural network quantization for efficient inference, existing methods are not scalable to heterogeneous devices as one dedicated model needs to be trained, transmitted, and stored for one specific hardware setting, incurring considerable costs in model training and maintenance. In this paper, we study a new vertical-layered representation of neural network weights for encapsulating all quantized models into a single one. It represents weights as a group of bits (vertical layers) organized from the most significant bit (also called the basic layer) to less significant bits (enhance layers). Hence, a neural network with an arbitrary quantization precision can be obtained by adding corresponding enhance layers to the basic layer. However, we empirically find that models obtained with existing quantization methods suffer severe performance degradation if adapted to vertical-layered weight representation. To this end, we propose a simple once quantization-aware training (QAT) scheme for obtaining high-performance vertical-layered models. Our design incorporates a cascade downsampling mechanism with the multi-objective optimization employed to train the shared source model weights such that they can be updated simultaneously, considering the performance of all networks. After the model is trained, to construct a vertical-layered network, the lowest bit-width quantized weights become the basic layer, and every bit dropped along the downsampling process act as an enhance layer. Our design is extensively evaluated on CIFAR-100 and ImageNet datasets. Experiments show that the proposed vertical-layered representation and developed once QAT scheme are effective in embodying multiple quantized networks into a single one and allow one-time training, and it delivers comparable performance as that of quantized models tailored to any specific bit-width.

Intelligent Reflecting Surface-Assisted Wireless Powered Heterogeneous Networks

In this paper, we introduce an intelligent reflecting surface (IRS)-assisted wireless powered heterogeneous network (WPHN) consisting of two heterogeneous groups of devices. Specifically, one group of devices, i.e., energy-harvesting devices (EHDs), are charged by external energy supplies, while the other group of devices, i.e., non-energy-harvesting devices (NEHDs), are powered by internal energy supplies. An IRS aims to participate in the wireless energy transfer (WET) in downlink and the wireless information transfer (WIT) in the uplink. A sum throughput maximization problem is formulated subject to the constraints of individual energy consumption, transmission time scheduling, and IRS phase shifts. To cope with the non-convexity of the problem, we first derive the optimal IRS phase shifts of the uplink WIT independently. Next, the semi-definite programming (SDP) relaxation is adopted to recast this non-convex problem into the convex one, which can be numerically solved. Then, a novel low-complexity scheme is developed to gain more insights and mitigate the computational complexity induced by the SDP relaxation. In particular, the dual problem and Karush-Kuhn-Tucker conditions are first utilized to obtain the optimal transmission time scheduling. Then, we propose a method based on Riemannian manifold optimization to compute the optimal IRS phase shifts of the downlink WET in closed-form. Finally, simulation results are presented to verify the optimality of our proposed scheme, and highlight the benefits induced by the IRS to coordinate these heterogeneous devices.

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems’ architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective.

A Softwarized Intrusion Detection System for IoT-Enabled Smart Healthcare System

The Internet of Things-enabled Smart Healthcare System (IoT-SHS) is a networked infrastructure of intelligent wearables, software applications, health systems, and services that continuously monitors and transmits patient-sensitive data using an open wireless channel. The conventional security mechanisms are unsuitable for detecting attacks in the dynamic IoT-SHS context due to resource limitations and heterogeneity in low-cost healthcare devices. Deep Learning (DL) solutions for Intrusion Detection System (IDS) and softwarization of the network has the potential to achieve secure network services in the IoT-SHS environment. Motivated by the aforementioned discussion, we propose an intelligent softwarized IDS for protecting the critical infrastructure of the IoT-SHS ecosystem. Specifically, the DL-based IDS is designed using a hybrid cuda Long Short-Term Memory Deep Neural Network (cuLSTM-DNN) algorithm to assist network administrators in efficient decision-making for the generated intrusions. To further bolster the system’s resilience, we suggest a deployment architecture for the proposed CUDA-powered IDS using OpenStack Tacker in a real SDN environment, ensuring that virtual machines can directly utilize the host’s NVIDIA GPU, thereby streamlining and enhancing the network’s operational efficiency. The experimental results using the CICDDoS2019 dataset confirm the effectiveness of the proposed framework over some baseline and recent state-of-the-art techniques.

Time Synchronization Based on Cross-Technology Communication for IoT Networks

Time synchronization is a fundamental requirement for wireless communication systems to work properly. Most of the existing studies focus on time synchronization among homogeneous devices. This work investigates time synchronization with heterogeneous technologies (e.g., WiFi, ZigBee and Bluetooth) which is important for the rising Internet of Thing (IoT) scenarios where heterogeneous devices coexist. Recent advances in Cross-Technology Communication (CTC) break the wall between heterogeneous wireless devices. In this work, we propose a new time synchronization strategy based on the CTC technique and provide a technique called TimeBee, which takes the advantage of coordination from a WiFi device to assist ZigBee devices for time synchronization. An effective method is employed so that ZigBee nodes are coordinated for time synchronization based on the received timestamps from WiFi devices. The experimental results show that TimeBee achieves global time synchronization with low time errors.

Web Platform for Interconnecting Body Sensors and Improving Health Care

The Internet of Things (IoT) is a paradigm in which smart objects actively collaborate among them and with other physical and virtual objects available in the Web in order to perform high-level tasks for the benefit of end-users. In the e-health scenario, these communicating smart objects can be body sensors that enable a continuous real-time monitoring of vital signs of patients. Data produced by such sensors can be used for several purposes and by different actors, such as doctors, patients, relatives, and health care centers, in order to provide remote assistance to users. However, major challenges arise mainly in terms of the interoperabil- ity among several heterogeneous devices from a variety of manufacturers. In this context, we introduce Eco Health (Ecosystem of Health Care Devices), a Web middleware platform for connecting doctors and patients using attached body sensors, thus aiming to provide improved health monitoring and diagnosis for patients. This platform is able to integrate information obtained from heterogeneous sensors in order to provide mechanisms to monitor, process, visualize, store, and send notifications regarding patients’ conditions and vital signs at real-time by using Internet standards. In this paper, we present blueprints of our proposal to Eco Health and its logical architecture and implementation, as well as an e-health motivational scenario where such a platform would be useful.

Energy Proficient Secure Clustered Protocol in Mobile Wireless Sensor Network Utilizing Blue Brain Technology

Mobile Wireless Sensor Network (MWSN) guarantees copious applications in daily life of ordinary people, government bodies, commerce, defense and culture etc. Association along with the devices in blue brain to fetch diverse appliances in the real world is an exigent assignment. Several existed protocols proficiently indulgence both homogenous and heterogeneous devices. This research work conquers the gap between the physical wireless blue brain network surroundings and the factual heterogeneous network. This research work intention not only provide a proficient energy responsive clustering broadcasting protocol for blue brain network, excluding a real blue brain network design for probing the proposed protocol contrasted to existed tactics. Proficient cluster-head election amplifies the deployment of the blue brain nodes energy stuffing and therefore amplifies the network life-span and the packets relaying rate to the base station. Energy Proficient Secure Clustered (EPSC) exploits special weighted selection possibilities for electing a Cluster-head based on regions heterogeneity intensity. NS2 simulation demonstrates prolongs the network life-span, diminishes reclustering, enhanced security and amplifies throughput contrasted with BM-BWO. The results will be beneficial for the design process in WSN for blue brain application.

GNN-based Advanced Feature Integration for ICS Anomaly Detection

Recent adversaries targeting the Industrial Control Systems (ICSs) have started exploiting their sophisticated inherent contextual semantics such as the data associativity among heterogeneous field devices. In light of the subtlety rendered in these semantics, anomalies triggered by such interactions tend to be extremely covert, hence giving rise to extensive challenges in their detection. Driven by the critical demands of securing ICS processes, a Graph-Neural-Network (GNN) based method is presented to tackle these subtle hostilities by leveraging an ICS’s advanced contextual features refined from a universal perspective, rather than exclusively following GNN’s conventional local aggregation paradigm. Specifically, we design and implement the Graph Sample-and-Integrate Network (GSIN), a general chained framework performing node-level anomaly detection via advanced feature integration, which combines a node’s local awareness with the graph’s prominent global properties extracted via process-oriented pooling. The proposed GSIN is evaluated on multiple well-known datasets with different kinds of integration configurations, and results demonstrate its superiority consistently on not only anomaly detection performance (e.g., F1 score and AUPRC) but also runtime efficiency over recent representative baselines.

Taxonomic Exploration of Healthcare IoT: Challenges, Solutions, and Future Frontiers

An Internet of things (IoT) ecosystem is a fast-developing network in which users can connect a heterogeneity of physical and virtual devices, including customized healthcare areas. As medical resources are scarce, populations are aging with chronic diseases and require remote monitoring, medical expenses are rising, and telemedicine is being demanded in developing nations, the IoT is an attractive topic in healthcare. Through the IoT, people can enjoy better health and diminish pressure on sanitary systems. In this study, previously published studies in Healthcare IoT (HIoT) systems are detailed, analyzed, and taxonomically classified. By categorizing the articles according to the types of HIoT systems, we dispense a detailed taxonomical study. In addition, different evaluation methodologies, tools, and metrics are discussed, along with their advantages and disadvantages. The studies indicate that power management, trust, privacy, fog computing, and resource management are among the open issues. The future of the Internet includes tactile networks, social networks, big data analytics, software-defined networking, network function virtualization, the Internet of nano things (IoNT), and blockchain. It would be beneficial to study and research HioT systems further in terms of interoperability, the implementation of real-world test beds, scalability, and mobility.

Group key management in the Internet of Things: Handling asynchronicity

Internet of Things (IoT) devices have permeated our immediate surroundings, becoming ubiquitous. These devices engage in continuous communication, with a significant aspect being group communications. Indeed, the latter are often more efficient than one-to-one interactions, rendering them well-suited for applications like e-health, federated learning, and military systems. However, security plays a pivotal role in these group communications, especially in safeguarding data confidentiality and user privacy. In this context, group key management protocols offer a mechanism for establishing shared group keys and updating them when needed. Nonetheless, the inherently asynchronous nature of IoT devices has proven to be a challenge. IoT devices are susceptible to unexpectedly entering an offline mode due to factors such as battery depletion, energy conservation, or loss of wireless connectivity. Consequently, the establishment of a group key for secure communications becomes a complex task. In this paper, we introduce an innovative approach to establishing group credentials asynchronously while ensuring robust security attributes, including Perfect Forward Secrecy (PFS) and Post-compromise Security (PCS). Our protocol is designed around blockchain technology, specifically smart contracts to embrace the distributed nature of IoT. Additionally, we incorporate a reputation-based mechanism to address the heterogeneity of IoT devices in terms of resource disparity. Evaluation results demonstrate the feasibility of our approach, along with reasonable performances.

Time-Constrained Ensemble Sensing With Heterogeneous IoT Devices in Intelligent Transportation Systems

Recently we have witnessed the rise of Artificial Intelligence of Things (AIoT) and the shift of sensing paradigm from cloud-centric to the edge-centric, which effectively improves the sensing capability of intelligence transportation systems. To improve the real-time sensing performance, in this work we propose an ensemble sensing based scheme to solve the time-constraint synchronized inference problem and achieve robust inference with heterogeneous IoT devices in intelligence transportation systems. We design and implement Ensen, which incorporates various novel techniques such as customized DNN model design, KD-based model training, and dynamic deep ensemble management, etc., to achieve improved accuracy and maximize the computational resource usage of the whole sensing group. Extensive evaluations on different types of common IoT devices have shown that Ensen achieves a robust performance and can be easily extended to different types of convolutional neural networks.

AIoTML: A Unified Modeling Language for AIoT-Based Cyber–Physical Systems

Due to deeply intertwined physical and hardware/software components together with increasing number of interconnected heterogeneous devices powered by Artificial Intelligence (AI) techniques, the design complexity of Cyber-Physical Systems (CPSs) becomes skyrocketing. Model-Driven Engineering (MDE) methods have been proven to be effective in increasing the productivity of CPS design. However, there is still a lack of MDE approaches that enable design space exploration as well as the code generation for the design of AI IoT (AIoT)-based CPSs. To mitigate the situation, this paper presents a unified modeling language named AIoTML for AIoT-based CPSs, which enables the construction of AI-based components across different modeling levels for the purposes of intelligent sensing and control. By extending the constructs of state-of-the-art Domain-Specific Language (DSL) ThingML, AIoTML can seamlessly unify the modeling of both autonomous executions of AIoT devices and their surrounding physical environment, which facilitates both platform-independent simulation and control optimization for platform-specific CPSs. The compiler developed for AIoTML provides a family of code generators to support the construction of digital twins on various heterogeneous target AIoT platforms. Comprehensive evaluations on two complex real-world designs demonstrate the effectiveness of our AIoTML approach in fast development of AIoT-based CPSs with high control quality.

Prototypes of devices for heterogeneous hybrid semiconductor electronics with an embedded biomolecular domain

A macromolecular system embedded in a semiconductor microelectronic device is considered as a biomolecular nano- or micro-sized domain that performs the functions of converting acoustic and electromagnetic signals. The issues of the choice of substances, the dynamic and structural-functional state of the domain, as well as the physical foundations of its interaction with matrix elements are discussed. The process of excitation of forced vibrations in amino acid molecules (for example, glycine, tryptophan, diphenyl-L-alanine) under the influence of short (10–100 ps) packets of electrical signals in the IR range with a frequency in the range of 1–125 THz was studied by the method of supercomputer nonequilibrium modeling of molecular dynamics. The acoustoelectric interpretation of oscillation generation was carried out using a unified equivalent circuit of the peptide group. Examples of developed prototypes of heterogeneous devices are given. It is concluded that embedded biomolecular domains, presented as a multifunctional element base, are promising for signal conversion in hybrid microelectronics.

FOG-Empowered Augmented-Intelligence-Based Proactive Defensive Mechanism for IoT-Enabled Smart Industries

The recent innovations in the network communication domain have entirely revolutionized the conventional industrial sector by introducing a new era of automatic communication. The Industrial Internet of Things (IIoT) is acknowledged as an exclusive space where IoT seems to divulge expressive manifestations. However, the expanded connectivity, more openness, and the widespread use of low-power communication devices of IIoT makes them vulnerable to malicious attacks, and criminal activities. Moreover, the heterogeneous and prevalent nature of the IIoT devices makes it very difficult to come up with a centralized threat detection mechanism, thus its security remains a major concern. Motivated by this, we propose a fog-empowered Augmented Intelligence (IA)-based defensive mechanism to ensure secure communication in IoT-enabled smart industries. The designed framework incorporates the aggregated potentials of two highly acclaimed Deep Learning (DL) classifiers: Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM). Where the DL-based scheme detects anomalies in such industrial networks and the FOG-based scenario promotes the routing flexibility and interoperability of the heterogeneous devices of the IoT-enabled smart industrial network. The validity of the proposed mechanism is analytically investigated by conducting a comparison with the benchmark threat detection techniques. The proposed solution is also generically examined parallel to some state-of-the-art approaches. The Cu-GRU-BiLSTM framework achieved up to 99.91% accuracy with a low false alarm rate and outperforms the baseline and recent threat detection approaches. The simulation and comparison results validate the effectiveness of the proposed mechanism and advocate it as a phenomenal choice to ensure efficacious and secure communication in IoT-based smart industries.

HPCchain: A Consortium Blockchain System Based on CPU-FPGA Hybrid-PUF for Industrial Internet of Things

Industrial Internet of Things (IIoT) is experiencing rapid developments in the era of Industry 4.0. However, the ever-increasing applications put forwards higher requirements for authentication. Facing such a problem, researchers combine two cutting-edge techniques, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> Physical Unclonable Function (PUF) and blockchain. In detail, PUF can generate multiple Challenge-Response Pairs (CRPs) for IIoT devices by leveraging their unique physical features. Moreover, blockchain platforms are employed for storing/synchronizing CRPs, thereby resisting the single-point-failure. Although realizing the unclonable authentications, the existing works ignore the device heterogeneity of IIoT and fail to develop the specified blockchain platform for supporting PUF. In this article, we present a Hybrid PUF-based consortium blockchain for IIoT authentication, named HPCchain. Specifically, we first present the notion of Hybrid-PUF, which assigns different devices to generate different types of PUFs, and then employs them to play different roles in HPCchain. In this way, we can overcome the IIoT heterogeneity. Moreover, we propose the PUF-empowered credit scheme for HPCchain and realize the dynamic endorsement, with which we develop a PUF-based consensus mechanism for HPCchain. Finally, we design the registration and authentication schemes for IIoT nodes, atop HPCchain. Extensive experiments demonstrate the validity of our proposals.