Heterogeneity Of Internet Of Things Devices Research Articles

A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors.

The Internet of Things (IoT) includes billions of sensors and actuators (which we refer to as IoT devices) that harvest data from the physical world and send it via the Internet to IoT applications to provide smart IoT services and products. Deploying, managing, and maintaining IoT devices for the exclusive use of an individual IoT application is inefficient and involves significant costs and effort that often outweigh the benefits. On the other hand, enabling large numbers of IoT applications to share available third-party IoT devices, which are deployed and maintained independently by a variety of IoT device providers, reduces IoT application development costs, time, and effort. To achieve a positive cost/benefit ratio, there is a need to support the sharing of third-party IoT devices globally by providing effective IoT device discovery, use, and pay between IoT applications and third-party IoT devices. A solution for global IoT device sharing must be the following: (1) scalable to support a vast number of third-party IoT devices, (2) interoperable to deal with the heterogeneity of IoT devices and their data, and (3) IoT-owned, i.e., not owned by a specific individual or organization. This paper surveys existing techniques that support discovering, using, and paying for third-party IoT devices. To ensure that this survey is comprehensive, this paper presents our methodology, which is inspired by Systematic Literature Network Analysis (SLNA), combining the Systematic Literature Review (SLR) methodology with Citation Network Analysis (CNA). Finally, this paper outlines the research gaps and directions for novel research to realize global IoT device sharing.

T-FedHA: A Trusted Hierarchical Asynchronous Federated Learning Framework for Internet of Things

Federated Learning (FL) is a distributed machine learning system designed to effectively address potential data privacy concerns, making it particularly promising for the Internet of Things (IoT). However, challenges arise due to the trust and heterogeneity of IoT devices. FL often assumes devices are trustworthy and curious, which is impractical in the realistic IoT environment where the trustworthiness of participating devices becomes an intractable problem. Additionally, the diverse computational power and communication efficiency across IoT devices impact the efficiency and performance of FL. To address these challenges and enable the effective implementation of FL in the IoT environment, this article proposes a Trusted Hierarchical Asynchronous Federated Learning Framework (T-FedHA). The framework introduces a “Device-Edge-Central” hierarchy and employs a distributed trust management model. This model manages the trust of IoT devices, selecting devices that meet trust requirements for FL training. This enhances the security and trustworthiness of the overall FL system. Furthermore, T-FedHA integrates standard FL methods with a hierarchical asynchronous strategy that includes edge servers. This approach improves model aggregation and update processes, employing an asynchronous technique that considers the staleness of model updates to enhance overall performance and effectiveness. Extensive experiments confirm the efficacy of T-FedHA. The distributed trust management model efficiently calculates and manages IoT device trust, allowing only trusted devices to participate in FL training. The hierarchical asynchronous approach proves more feasible and efficient, as well as robust and flexible, contributing to the reliability of the proposed framework.

Resource-Aware Knowledge Distillation for Federated Learning

The rise of deep learning and the Internet of Things (IoT) has driven a number of smart-world applications, which are mostly deployed in distributed environments. Federated learning, a privacy-preserving collaborative learning paradigm, has shown considerable potential to leverage the rich distributed data at network edges. Nonetheless, the heterogeneity of IoT devices and their connected network environment impedes federated learning applications in IoT systems. Particularly, the stale gradients updated by slower local learners impact the effectiveness of federated learning. Transmitting weight updates with a large number of users leads to network congestion at the edge of the network and incurs unaffordable communication costs. To overcome these challenges, we propose a transfer knowledge based federated learning framework under a resource-limited distributed system. We formulate a knowledge distillation based federated learning optimization problem with the consideration of dynamic local resource. The proposed approach carries out federated learning with the help of knowledge distillation to avoid occupying the expensive network bandwidth or bringing a heavy burden to the network. Theoretical analysis demonstrates convergence of the learning process. The experimental results on three public datasets illustrate that the proposed framework is capable of substantially improving the efficiency of federated learning and outperforming state-of-the-art schemes.

Partition Placement and Resource Allocation for Multiple DNN-Based Applications in Heterogeneous IoT Environments

The evolution of the Internet of Things (IoT) has been driving the explosive growth of deep neural network (DNN)-based applications and processing demands. Hence, edge computing has emerged as a potential solution to meet these processing requirements. However, emerging IoT applications have increasingly demanded to run multiple DNNs to extract multifaceted knowledge, requiring more computational resources and increasing response time. Consequently, edge nodes cannot act as a complete substitute for the previous cloud paradigm, owing to their relatively limited resources. To address this problem, we propose to incorporate nearby IoT devices when allocating resources to multiple DNN models. Furthermore, the optimization of resource allocation can be hindered by the heterogeneity of IoT devices, which affects the delay performance of DNN-based computing. In this context, we propose a DNN partition placement and resource allocation strategy that considers different processing powers, memory, and battery levels for heterogeneous IoT devices. We evaluate the performance of the proposed strategy through extensive simulations. Simulation results reveal that the proposed strategy outperforms other existing solutions in terms of end-to-end delay, service probability, and energy consumption. The proposed solution was further simulated in a Kubernetes testbed consisting of actual devices to assess its feasibility.

Network anomaly detection methods in IoT environments via deep learning: A Fair comparison of performance and robustness

The Internet of Things (IoT) is a key enabler in closing the loop in Cyber-Physical Systems, providing “smartness” and thus additional value to each monitored/controlled physical asset. Unfortunately, these devices are more and more targeted by cyberattacks because of their diffusion and of the usually limited hardware and software resources. This calls for designing and evaluating new effective approaches for protecting IoT systems at the network level (Network Intrusion Detection Systems, NIDSs). These in turn are challenged by the heterogeneity of IoT devices and the growing volume of transmitted data.To tackle this challenge, we select a Deep Learning architecture to perform unsupervised early anomaly detection. With a data-driven approach, we explore in-depth multiple design choices and exploit the appealing structural properties of the selected architecture to enhance its performance. The experimental evaluation is performed on two recent and publicly available IoT datasets (IoT-23 and Kitsune). Finally, we adopt an adversarial approach to investigate the robustness of our solution in the presence of Label Flipping poisoning attacks. The experimental results highlight the improved performance of the proposed architecture, in comparison to both well-known baselines and previous proposals.

A core IoT ontology for automation support in edge computing

Service providers provision more and more Internet-of-Things (IoT) services in the cloud for dynamicity and cost-effectiveness purposes. This is made possible thanks to the introduction of edge computing that brings additional computing and resources for analytics close to the data sources and thus enables meeting the low latency requirement. Edge nodes should support (i) the heterogeneity of IoT devices (e.g., sensor, actuator) and (ii) characteristics (e.g., mobility, location awareness). IoT is already integrated to the hybrid cloud/edge environment. However, the ecosystem lacks of automation due to the previously mentioned characteristics. Indeed, edge nodes are often manually selected during deployment time, and most of the regular quality-of-service (QoS) management procedures remain difficult to implement. This paper introduces a comprehensive semantic model called EdgeOnto. It encompasses all concepts related to IoT applied in the context of edge computing. The ultimate goal of EdgeOnto is to automate the several steps that make up the IoT services lifecycle in hybrid cloud/edge environment. On the one hand, semantics enable an automatic discovery of the relevant edge nodes that are suitable to host and execute IoT services considering their requirements. On the other hand, it allows supporting the specific QoS procedures that are related to such setting (e.g., low latency, mobility, jitter). The core ontology was designed with the Protégé open-source tool. A smart strawberry farming use case was implemented and evaluated for illustration purposes. The results validate the accuracy and the precision of the designed semantic matchmaker.

Ransomware-based Cyber Attacks: A Comprehensive Survey

<p>Internet of Things (IoT) and sensor devices have been connected due to the development of the IoT and Information Communication Technology (ICT). It offers automatic environments in smart city and IoT scenarios and describes investments in advanced resources in futuristic human lives as sustainable growth of quality-wise life with intelligent infrastructure. Nowadays, IoT devices are continuously increasing and utilized in advanced IoT applications, including Smart Homes, Smart Farming, Smart Enterprises, and others. However, security and privacy are significant challenges with Ransomware-based Cyber-attack detection in IoT due to the lack of security design and heterogeneity of IoT devices. In the last few years, various advanced paradigms and technologies have been utilized to mitigate the security issues with Ransomware attack detection in IoT devices and data. This paper comprehensively surveys Ransomware-based Cyber Attacks and discusses solutions based on advanced technologies such as Artificial Intelligence (AI), Blockchain, and Software Defined Networks (SDN). Then, we design service scenarios for ransomware-based cyber-attack detection. Finally, we summarize the open research challenges and future directions for ransomware in IoT.</p> <p> </p>

IoT Forensics: Amazon Echo as a Use Case

Internet of Things (IoT) are increasingly common in our society, and can be found in civilian settings as well as sensitive applications, such as battlefields and national security. Given the potential of these devices to be targeted by attackers, they are a valuable source in digital forensic investigations. In addition, incriminating evidence may be stored on an IoT device (e.g., Amazon Echo in a home environment and Fitbit worn by the victim or an accused person). In comparison to IoT security and privacy literature, IoT forensics is relatively under-studied. IoT forensics is also challenging in practice, particularly due to the complexity, diversity, and heterogeneity of IoT devices and ecosystems. In this paper, we present an IoT-based forensic model that supports the identification, acquisition, analysis, and presentation of potential artifacts of forensic interest from IoT devices and the underpinning infrastructure. Specifically, we use the popular Amazon Echo as a use case to demonstrate how our proposed model can be used to guide forensics analysis of IoT devices.

Software-Defined architecture for QoS-Aware IoT deployments in 5G systems

Internet of Things (IoT), a ubiquitous network of interconnected objects, harvests information from the environments, interacts with the physical world, and uses the existing Internet infrastructure to provide services for information transfer and emerging applications. However, the scalability and Internet access fundamentally challenge the realization of a wide range of IoT applications. Based on recent developments of 5G system architecture, namely SoftAir, this paper introduces a new software-defined platform that enables dynamic and flexible infrastructure for 5G IoT communication. A corresponding sum-rate analysis is also carried out via an optimization approach for efficient data transmissions. First, the SoftAir decouples control plane and data plane for a software-defined wireless architecture and enables effective coordination among remote radio heads (RRHs), equipped with millimeter-wave (mmWave) frontend, for IoT access. Next, by introducing an innovative design of software-defined gateways (SD-GWs) as local IoT controllers in SoftAir, the wide diversity of IoT applications and the heterogeneity of IoT devices are easily accommodated. These SD-GWs aggregate the traffic from heterogeneous IoT devices and perform protocol conversions between IoT networks and radio access networks. Moreover, a cross-domain optimization framework is proposed in this extended SoftAir architecture concerning both upstream and downstream communication, where the respective sum-rates are maximized and system-level constraints are guaranteed, including (i) quality-of-service requirements of IoT transmissions, (ii) total power limit of mmWave RRHs, and (iii) fronthaul network capacities. Simulation results validate the efficacy of our solutions, where the extended SoftAir solution surpasses existing IoT schemes in spectral efficiency and achieves optimal data rates for next-generation IoT communication.

Software Architecture Solution Based on SDN for an Industrial IoT Scenario

The Industry 4.0 (I4.0) adoption comprises the change of traditional factories into smart using the ICTs. The goal is to monitor processes, objects, machinery, and workers in order to have real‐time knowledge about what is going on in the factory and for achieving an efficient data collection, management, and decision‐making that help improve the businesses in terms of product quality, productivity, and efficiency. Internet of Things (IoT) will have an important role in the I4.0 adoption because future smart factories are expected to rely on IoT infrastructures composed of constellations of hundreds or thousands of sensor devices spread all over the industrial facilities. However, some problems could arise in the massive IoT deployment in a medium‐high factory: thousands of IoT devices to cope from different technologies and vendors could mean dozens of vendor tools and user interfaces to manage them. Moreover, the heterogeneity of IoT devices could entail different communication protocols, languages, and data formats, which can result in lack of interoperability. On the other hand, conventional IT networks and industrial machinery are expected to be managed together with the IoT infrastructure, maybe using a tool or a set of tools, for orchestrating the whole smart factory. This work meets these challenges presenting an open‐source software architecture solution based on OpenDaylight (ODL), a Software Defined Network (SDN) controller, for orchestrating an industrial IoT scenario. This work is addressed by shedding light on critical aspects from the SDN controller architectural choices, to specific IoT interfaces and the difficulties for covering the wide range of communication protocols, popular in industrial contexts. Such a global view of the process gives light to practical difficulties appearing in introducing SDN in industrial contexts, providing an open‐source architecture solution that guarantees devices and networks interoperability and scalability, breaking the vendor lock‐in barriers and providing a vendor‐agnostic solution for orchestrating all actor of an I4.0 smart factory.