Consumer Internet Of Things Research Articles

Strong reasons make strong actions: What Shakespeare’s ‘King John’ can teach us about the Internet of Things

The rapid proliferation of Internet of Things (IoT) devices in modern societies brings forth unprecedented opportunities for convenience and connectivity but also poses significant cyber security challenges. This paper examines the risks associated with these devices and the regulatory frameworks governing them in key regions including the US, the EU, China and India. Through a comprehensive analysis, it becomes evident that while efforts are being made to address IoT cyber security concerns, discrepancies in approaches and regulations hinder global harmonisation and create obstacles for industry compliance. Drawing from insights into existing cyber security frameworks and industry practices, the paper proposes actionable recommendations to enhance consumer IoT cyber security. These recommendations include defining baseline security requirements, promoting expertise within IoT workforces, advocating for the independent involvement of conformity assessment bodies (CABs), leveraging the quality infrastructure ecosystem, and launching an international awareness campaign. By implementing these measures, stakeholders can foster a safer and more secure IoT environment, mitigating the risks posed by cyber threats and ensuring the trust and resilience of connected devices. As society continues to navigate the complexities of IoT adoption, it is imperative to recognise the urgency of addressing cyber security challenges. By heeding the lessons from Shakespeare’s ‘King John’ — ‘Strong reasons make strong actions’ — and taking decisive steps to fortify IoT cyber security, we can safeguard individuals, businesses and critical infrastructure from the evolving threat landscape.

TXAI-ADV: Trustworthy XAI for Defending AI Models against Adversarial Attacks in Realistic CIoT

Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT and countering emerging risks require constant updates and monitoring of these devices. Machine learning (ML), in combination with Explainable Artificial Intelligence (XAI), has become an essential component of the CIoT ecosystem due to its rapid advancement and impressive results across several application domains for attack detection, prevention, mitigation, and providing explanations of such decisions. These attacks exploit and steal sensitive data, disrupt the devices’ functionality, or gain unauthorized access to connected networks. This research generates a novel dataset by injecting adversarial attacks into the CICIoT2023 dataset. It presents an adversarial attack detection approach named TXAI-ADV that utilizes deep learning (Mutli-Layer Perceptron (MLP) and Deep Neural Network (DNN)) and machine learning classifiers (K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Gaussian Naive Bayes (GNB), ensemble voting, and Meta Classifier) to detect attacks and avert such situations rapidly in a CIoT. This study utilized Shapley Additive Explanations (SHAP) techniques, an XAI technique, to analyze the average impact of each class feature on the proposed models and select optimal features for the adversarial attacks dataset. The results revealed that, with a 96% accuracy rate, the proposed approach effectively detects adversarial attacks in a CIoT.

Guest Editorial of the Special Section on Intelligent Computing for Big Data in Consumer Internet of Things

Guest Editorial of the Special Section on Intelligent Computing for Big Data in Consumer Internet of Things

Two-Level Privacy-Preserving Framework: Federated Learning for Attack Detection in the Consumer Internet of Things

Two-Level Privacy-Preserving Framework: Federated Learning for Attack Detection in the Consumer Internet of Things

Towards a right to repair for the Internet of Things: A review of legal and policy aspects

The way in which consumers engage with, utilise, or discard the technologies in their lives is constantly being reassessed and changed. This paper questions what role the emergent “right to repair” could play in resolving issues posed by the increasing ubiquity of the Internet of Things (IoT). The right gives consumers the ability and freedom to fix their devices, or to fair access to appropriate services that can carry out repair on their behalf. In this paper, firstly we establish the problem space surrounding consumer IoT – i.e., devices that are interconnected via the internet, enabling them to send and receive data. We reflect on hardware, software, and data components that pose legal and policy challenges for data protection, security, and sustainability. Through a literature review we then reflect on the current socio-legal developments that support or oppose changes in the consumer IoT market in regards to repair. We then highlight gaps in the existing literature that should inform future research trajectories in this area. This includes exploring disparities between environmental and consumer autonomy approaches, assessing consistency in regulatory developments, and market prioritisation. Finally, the paper concludes with a series of key insights and recommendations from our analysis including: recognition of the growing e-Waste problem and the inequalities it exacerbates and perpetuates; the need for identification and argumentation for different formulations of “repair” and how these may impact the implementation of a right going forward; the need for identification of the reasoning behind disparities in governmental approaches to the right to repair; and the need to practically translate better IoT design practices into reality.

Competition of multiplatform firms: Implications for the Internet of Things.

The Internet of Things (IoT) technology trend is transforming business and society. This creates a need to understand strategic behavior in the consumer IoT, where firms tend to offer multiple platform devices, and new generations of devices are introduced frequently. We propose a novel analytical model that formalizes the concept of a multiplatform firm that offers a system of platforms, such as a smartphone, and a new platform device, such as a smartwatch, and orchestrates a multiplatform ecosystem. The analysis shows how a platform design decision, like offering a new standalone device, affects consumer choices and market outcomes. We identify two classes of new devices that matter, and show when a new platform device may disrupt the smartphone market. Moreover, we characterize conditions under which it is profitable for a vendor to make its new platform device look and feel more like its smartphone. Overall, we provide insights into how multiplatform firms differ from platform firms. We identify future research opportunities on the economics of consumer IoT and multiplatform ecosystems.

An Adversarial Attack on Artificial Intelligence Malware Detection in Consumer Internet of Things

An Adversarial Attack on Artificial Intelligence Malware Detection in Consumer Internet of Things

Secure Energy Aware Power Control in Consumer Internet of Things With Semi Grant Free NOMA

Secure Energy Aware Power Control in Consumer Internet of Things With Semi Grant Free NOMA

Multi-Feature Fusion Based Convolutional Neural Networks for EEG Epileptic Seizure Prediction in Consumer Internet of Things

Multi-Feature Fusion Based Convolutional Neural Networks for EEG Epileptic Seizure Prediction in Consumer Internet of Things

An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT

Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers’ needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing gateway peer (GWP) in the blockchain network to address scalability, ledger optimization, and secure model transmission issues. In the architecture, we replace the centralized aggregator with the blockchain network, while GWP limits the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we incorporated differential privacy and advanced normalization techniques into ML processes. These approaches enhance the cybersecurity of end-users and promote the adoption of technological innovation standards by service providers. The proposed approach has undergone extensive testing using the well-respected Stanford (CARS) dataset. We experimentally demonstrate that the proposed architecture enhances network scalability and significantly optimizes the ledger. In addition, the normalization technique outperforms batch normalization when features are under DP protection.

In principle vs in practice: User, expert and policymaker attitudes towards the right to data portability in the internet of things

The right to data portability (RtDP) was enshrined in law with the introduction of the EU's General Data Orotection Regulation (GDPR, Article 20) in 2018. RtDP gives a user the right to obtain and transfer their data to a different service, and the data controller the obligation to facilitate this transfer. Since GDPR's implementation, RtDP has been highlighted in the Digital Markets Act (DMA; 2022) and the proposed Data Act. Despite these reinforcements, there are gaps in understanding of RtDP amongst digital service users. Additionally, many organisations struggle to facilitate data transfer, particularly when it comes to the Internet of Things (IoT). This study examines the attitudes towards IoT data portability by conducting semi-structured interviews with users of consumer IoT devices (n = 28), academics/industry experts (n = 11) and policymakers (n = 8). Results indicate that whilst policymakers and consumers value this right in principle, it is rendered meaningless without a data subject's ability to exercise it in practice. A lack of guidance for data controllers and consumers has created an atmosphere of uncertainty which urgently needs to be addressed.

Time-Sensitive Networking-Driven Deterministic Low-Latency Communication for Real-Time Telemedicine and e-Health Services

With the convergence of telemedicine and electronic health (e-health) services, Consumer Internet of Things (CIoT) enabled smart healthcare has attracted much attention from researchers. Telemedicine and e-health care can help to eliminate geographical barriers and provide high-quality medicine services. Whereas, deterministic low-latency communication is a major problem in smart healthcare system with commercial network, since the latency can affect the process of diagnosis and treatment while producing inaccuracy. To address this issue, Time Sensitive Networking (TSN) is introduced in this article for bounded lowlatency network services. First, we present a Digital Twin based TSN (DTTSN) framework where digital modeling and intelligent simulation are performed under a virtual environment. Second, a digital twin scheduling model is created to predict the forwarding delay. In order to enhance model adaption in different scenarios, cycle generative adversarial network (CycleGAN) is adopted to train the scheduling model. Finally, the optimal routing strategy deduced in digital twin network is fed back to physical network for automated configuration. Simulation results verify that our digital twin network can promote visibility of operations and predictions of physical network scheduling and routing, while optimizing delay and balancing link load.

A-DAFTO: Artificial Cap Deferred Acceptance-Based Fair Task Offloading in Complex IoT-Fog Networks

The rapid growth of the Consumer Internet of Things (CIoTs) has led to its adoption in next-generation applications, including interactive gaming, healthcare services, video streaming, etc. The limited computational resources of CIoTs often hinder providing timely responses to real-time consumer applications. A sensible option in this regard is to offload computations to nearby fog nodes (FNs) with enough resources to meet the application quality-of-service (QoS) requirements. However, an inconsiderate offloading policy can result in unusual traffic at the FNs, leading to congestion that can negatively impact the latency requirements of applications in a complex IoT-Fog network. Therefore, developing offloading policies that distribute the network load and effectively utilize the FN resources is essential. This paper proposes a matching theory-based protocol A-DAFTO, addressing the challenges above. A-DAFTO outputs an offloading plan which distributes the network and computational load respecting the application’s deadline. The offloading is modeled as matching and is solved using the artificial cap deferred acceptance (ACDA) algorithm. The generated offloading schedule is fair, as none of the stakeholders are incentivized to deviate from their assignments. Further, experimentation via simulation shows that A-DAFTO achieves zero outages with a 15.32% reduction in total offloading delay compared to the baselines.

Highly Flexible and Traffic Isolating RAN Slicing: A Consumer IoT-Based Use Case

Future networks are envisaged to support an increasingly wide range of diverse services, with distinct service requirements. Network slicing presents an economically sustainable solution to properly handle these stringent requirements, on top of a common physical infrastructure. The support of network slicing at different ran resource management tasks is particularly challenging, as it involves the dynamic split of the scarce radio resources among the slices, aiming to achieve optimal resource share with traffic isolation of slices. To tackle those challenges, we propose an efficient low-complexity dynamic ran slicing scheme supported jointly at the ac and the ps levels. The scheme relies on a fair ac module to regulate the admission of users from the slices according to a penalty expression considering the expected sla with the contracted tenants’ priority. We also develop a ps algorithm based on the tunable slices’ weight, to efficiently fulfill kpi of services. Consumer Internet-of-Things (CIoT)-based healthcare services are considered a use case, effectively representing multi-service network slicing applications. Performance evaluation based on extensive simulations shows that the proposed scheme outperforms the referenced baseline schemes, achieving optimal trade-off towards flexible, fair, and efficient resource sharing with sufficient traffic isolation of slices.

Network Externality Effects on Behavioral Intention to Use Consumer Internet of Things Among Urban Citizens in Indonesia

Abstract One of the most popular technologies is the internet of things (IoT). It refers to the number of users and penetration in the industry (I-IoT) and consumer (C-IoT) sectors. The previous stud-ies indicated that the usage rate of the C-IoT is outperforming the I-IoT worldwide. However, the contrary indication occurred in Indonesia. Among developing countries, the spending level of IoT in Indonesia is significant, but the use level of the technology is less developed. This survey study purposed to predict what factors influence the behavior intention to use C-IoT. The researchers extended the unified theory of acceptance and use of technology (UTAUT) model by adopting the network externality aspects. Around 400 valid data were collected from urban communities in the six most populous provinces in the country. The scholars used the partial least squares structural equation modeling (PLS-SEM) method using SmartPLS 3.3 in the data analysis stage. The findings expressed that the number of users and social influence factors are not influential factors influencing behavior intention to use IoT. Besides that, the UTAUT model extension may also be one of the theoretical references for future similar studies. Practically, the findings may also be one of the considerations for the stakeholders of C-IoT implementation in Indonesia.

Decentralized ME-Centric Framework—A Futuristic Architecture for Consumer IoT

The Internet of Things (IoT) landscape is synergistic with various participating domains like smart healthcare, Industrial IoT, and smart cities. The Consumer Internet of Things (CIoT) landscape with its massive potential occupies a sizable chunk of the future internet and is still immature due to its lack of interoperability and data sustainability among IoT applications. Most of the existing CIoT deployments are vertical solutions comprising CIoT devices, gateways, centralized cloud services, resulting in fragmented silos of data. To overcome the integration issues and provide a global ecosystem for CIoT applications, we need a contemporary, decentralized, semantic, data-centric, and interoperable approach. To enhance seamless integration among the user and CIoT applications, a holistic approach that is flexible, robust with reliable performance is indispensable. In this article, we introduce a futuristic decentralized MECentric state-of-the-art architecture for CIoT that can overcome the challenges of and enhance the agility, scalability, resiliency, and security of CIoT systems. In a decentralized ME-Centric prototype, we integrate edge computing with decentralized storage solutions and new cutting technologies like digital twins, and blockchain with semantic ontologies to design a comprehensive beneficial outlook for consumers.

Joint Active and Passive Beamforming Design for RIS-Aided IBFD IoT Communications: QoS and Power Efficiency Considerations

In a reconfigurable intelligent surface (RIS) aided communication network, a RIS equipped with multiple passive reflecting elements assists in extending the coverage of wireless networks by overcoming the effects of non-line-of-sight propagation. Accordingly, to present the case for utilizing RIS panels in future consumer internet of things (IoT) networks, in this paper, we focus on a RIS-aided IoT network operating in in-band full-duplex (IBFD) mode. Since all the IoT node pairs communicate simultaneously through individual IBFD links at the same time-frequency resources, the nodes suffer from strong self-interference (SI) and inter-node interference. Consequently, in this paper, we investigate a joint active and passive beamforming design at each IoT node and the RIS, respectively, by minimizing the total transmission power of the network, subject to a minimum rate constraint for each IoT node and a constraint that guarantees a unit modulus phase shift at the RIS. As the original joint active and passive beamforming design problem is non-convex and intractable, we decompose the problem into two sub-problems. For a given passive beamformer design, we solve the original problem to obtain the optimal beamforming vectors at each IoT node. Next, for the obtained active beamformer, we propose two algorithms based on i) gradient descent (GD) search and ii) Riemannian conjugate gradient (RCG) to obtain the optimal passive beamformer at the RIS. Through extensive numerical analysis, we verify the effectiveness of the proposed algorithms and also show the effects of using RIS and transmitting in IBFD mode on the average rate and power of the considered consumer IoT network. We investigate the impact of several system parameters such as RIS elements, node pairs, level of hardware impairments at the transmitter and receiver, etc. We also highlight the trade-off between performance improvement and complexity due to elements used at RIS.

Graph Federated Learning for CIoT Devices in Smart Home Applications

This article deals with the problem of statistical and system heterogeneity in a cross-silo federated learning (FL) framework where there exist a limited number of Consumer Internet of Things (CIoT) devices in a smart building. We propose a novel graph signal processing (GSP)-inspired aggregation rule based on graph filtering dubbed “G-Fedfilt.” The proposed aggregator enables a structured flow of information based on the graph’s topology. This behavior allows capturing the interconnection of CIoT devices and training domain-specific models. The embedded graph filter is equipped with a tunable parameter which enables a continuous tradeoff between domain-agnostic and domain-specific FL. In the case of domain-agnostic, it forces G-Fedfilt to act similar to the conventional federated averaging (FedAvg) aggregation rule. The proposed G-Fedfilt also enables an intrinsic smooth clustering based on the graph connectivity without explicitly specified which further boosts the personalization of the models in the framework. In addition, the proposed scheme enjoys a communication-efficient time scheduling to alleviate the system heterogeneity. This is accomplished by adaptively adjusting the amount of training data samples and sparsity of the models’ gradients to reduce communication desynchronization and latency. Simulation results show that the proposed G-Fedfilt achieves up to 3.99% better classification accuracy than the conventional FedAvg when concerning model personalization on the statistically heterogeneous local data sets, while it is capable of yielding up to 2.41% higher accuracy than FedAvg in the case of testing the generalization of the models. Furthermore, the proposed communication optimization scheme can boost the framework’s efficiency by reducing the computation, communication desynchronization, and latency up to 70.21%, 99.65%, and 44.61%, respectively, at the cost of 0.36% accuracy and under the system heterogeneity.

Combining Stochastic and Deterministic Modeling of IPFIX Records to Infer Connected IoT Devices in Residential ISP Networks

Residential Internet service providers (ISPs) today have limited device-level visibility into subscriber houses, primarily due to the network address translation (NAT) technology. The continuous growth of “unmanaged” consumer Internet of Things (IoT) devices combined with the rise of work-from-home makes home networks attractive targets to sophisticated cyber attackers. Volumetric attacks sourced from a distributed set of vulnerable IoT devices can impact ISPs by deteriorating the performance of their network, or even making them liable for being a carrier of malicious traffic. This article explains how ISPs can employ IP Flow Information eXport (IPFIX), a flow-level telemetry protocol available on their network, to infer connected IoT devices and ensure their cyber health without making changes to home networks. Our contributions are threefold: 1) we analyze more than nine million IPFIX records of 26 IoT devices collected from a residential testbed over three months and identify 28 flow features pertinent to their network activity that characterize the network behavior of IoT devices—we release our IPFIX records as open data to the public; 2) we train a multiclass classifier on stochastic attributes of IPFIX flows to infer the presence of certain IoT device types in a home network with an average accuracy of 96%. On top of the machine learning (ML) model, we develop a trust metric to track network activity of detected devices over time; and 3) finally, we develop deterministic models (DTs) of specific and shared cloud services consumed by IoTs, yielding an average accuracy of 92%. We show a combination of stochastic and DTs mitigates false positives in 75% of incidents at the expense of an average 7% reduction in true positives.

A 16-nm SoC for Noise-Robust Speech and NLP Edge AI Inference With Bayesian Sound Source Separation and Attention-Based DNNs

The proliferation of personal artificial intelligence (AI) -assistant technologies with speech-based conversational AI interfaces is driving the exponential growth in the consumer Internet of Things (IoT) market. As these technologies are being applied to keyword spotting (KWS), automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS) applications, it is of paramount importance that they provide uncompromising performance for context learning in long sequences, which is a key benefit of the attention mechanism, and that they work seamlessly in polyphonic environments. In this work, we present a 25-mm2 system-on-chip (SoC) in 16-nm FinFET technology, codenamed SM6, which executes end-to-end speech-enhancing attention-based ASR and NLP workloads. The SoC includes: 1) FlexASR, a highly reconfigurable NLP inference processor optimized for whole-model acceleration of bidirectional attention-based sequence-to-sequence (seq2seq) deep neural networks (DNNs); 2) a Markov random field source separation engine (MSSE), a probabilistic graphical model accelerator for unsupervised inference via Gibbs sampling, used for sound source separation; 3) a dual-core Arm Cortex A53 CPU cluster, which provides on-demand single Instruction/multiple data (SIMD) fast fourier transform (FFT) processing and performs various application logic (e.g., expectation–maximization (EM) algorithm and 8-bit floating-point (FP8) quantization); and 4) an always-ON M0 subsystem for audio detection and power management. Measurement results demonstrate the efficiency ranges of 2.6–7.8 TFLOPs/W and 4.33–17.6 Gsamples/s/W for FlexASR and MSSE, respectively; MSSE denoising performance allowing 6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> smaller ASR model to be stored on-chip with negligible accuracy loss; and 2.24-mJ energy consumption while achieving real-time throughput, end-to-end, and per-frame ASR latencies of 18 ms.