Internet Of Things Device Data Research Articles

PIoT-blockchain-enabled security framework for strengthening IoT device identity and data access

The Internet of Things (IoT) connects billions of devices, but concerns about data security and device identification have grown. This study introduces Proof of IoT (PIoT), a novel consensus method using blockchain-based smart contracts to ensure secure data transfer and device authentication in IoT networks. PIoT employs NuCypher-based security computations for authentication and data updating, ensuring only authorized parties access blockchain-recorded information. A reward-based filtering mechanism identifies malicious nodes, bolstering consensus integrity. Simulations demonstrate PIoT's superior performance compared to conventional methods, showing a 74.7% and 96.8% improvement over LPBFT and DAC4SH Consensus, respectively. PIoT enhances blockchain security under IoT by 15% compared to DAC4SH. Beyond facilitating secure IoT device communication, this approach mitigates various IoT attacks, offering a comprehensive solution to emerging IoT security challenges. ABBREVIATION: DAC4SH, Data access control scheme for smart home; DDoS, Distributed denial of service; DoS, Denial of Service; DTLS, Datagram Transport Layer Security; IoT, Internet of Things; LPBFT, Lightweight Particle Byzantine fault tolerance; MAC, Message authentication code; MITM, Man in the Middle; PBFT, Practical Byzantine Fault Tolerance; PIoT, Proof of IoT; TLS, Transport Layer Security

AN ANALYSIS OF THE HETEROGENEOUS IOT DEVICE NETWORK INTERACTION IN A CYBER-PHYSICAL SYSTEM

The article is devoted to the study of existing technologies regarding Internet of Things (IoT) device interaction in a heterogeneous network. Since each smart home appliance can be controlled by a customer who aims to find a cost-effective and easy-to-connect product for their own connected home, there are certain functional limitations for devices from distinct manufacturers that may decrease the intention to merge them all into a single network. A variety of proprietary protocols and communication standards embedded by vendors make their products unable to interact with other vendor devices if the connection standard used is not identical. Also, an IoT product design refers to its own functionality, mainly excluding the possibility of integration into other existing infrastructure. As IoT equipment emerges on the market, the complexity of its connection to a heterogeneous network corresponds to the firmware and the standard unification according to modern demands. It means that potential users might face the necessity of overcoming these issues to achieve high performance in terms of network interoperability. In general, an IoT gateway operating as a middleware might have the potential to enable a network with distinct communication models to operate without failure or data loss. This task requires the received data to be converted into the format in which the data is intended. This paper includes a comparative analysis of existing IoT device interaction standards, connection protocols, and data transfer technologies, evaluating their features for an effective adoption of the proposed network architecture, which can be used to improve the interoperability of heterogeneous IoT devices.

An autonomous mixed data oversampling method for AIOT-based churn recognition and personalized recommendations using behavioral segmentation.

The telecom sector is currently undergoing a digital transformation by integrating artificial intelligence (AI) and Internet of Things (IoT) technologies. Customer retention in this context relies on the application of autonomous AI methods for analyzing IoT device data patterns in relation to the offered service packages. One significant challenge in existing studies is treating churn recognition and customer segmentation as separate tasks, which diminishes overall system accuracy. This study introduces an innovative approach by leveraging a unified customer analytics platform that treats churn recognition and segmentation as a bi-level optimization problem. The proposed framework includes an Auto Machine Learning (AutoML) oversampling method, effectively handling three mixed datasets of customer churn features while addressing imbalanced-class distribution issues. To enhance performance, the study utilizes the strength of oversampling methods like synthetic minority oversampling technique for nominal and continuous features (SMOTE-NC) and synthetic minority oversampling with encoded nominal and continuous features (SMOTE-ENC). Performance evaluation, using 10-fold cross-validation, measures accuracy and F1-score. Simulation results demonstrate that the proposed strategy, particularly Random Forest (RF) with SMOTE-NC, outperforms standard methods with SMOTE. It achieves accuracy rates of 79.24%, 94.54%, and 69.57%, and F1-scores of 65.25%, 81.87%, and 45.62% for the IBM, Kaggle Telco and Cell2Cell datasets, respectively. The proposed method autonomously determines the number and density of clusters. Factor analysis employing Bayesian logistic regression identifies influential factors for accurate customer segmentation. Furthermore, the study segments consumers behaviorally and generates targeted recommendations for personalized service packages, benefiting decision-makers.

Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices.

Blockchain-Enabled Internet of Things (IoT) Applications in Healthcare: A Systematic Review of Current Trends and Future Opportunities

The use of advanced computer technology in the healthcare industry has the potential to improve patient care and therapeutic results. The goal of this project is to improve data security, privacy, and decentralisation in healthcare by integrating blockchain and Internet of Things (IoT) technologies. The adoption of IoT devices makes it possible to gather and analyse patient sensory data in real–time; however centralised processing and storage present problems such as data manipulation and privacy issues. The study investigates the creation of a decentralised IoT-based e-healthcare system that takes these issues into account by utilising blockchain technology. In addition, the paper also emphasises how blockchain use has advanced smart contract technologies. Smart contracts provide safe user authentication for IoT device access, assuring responsibility, traceability, and data integrity. The study investigates the potentially game-changing applications of blockchain technology in healthcare, such as enhanced data interoperability, patient-cantered care, reduced administrative procedures, and increased transaction transparency. The report also highlights the significance of blockchain in managing pharmaceutical supply chains, considering the essential influence on patient welfare and safety. Effective management is essential in the healthcare business because supply chain interruptions or breaches can have serious implications. The present level of research in blockchain-enabled IoT applications for healthcare is examined comprehensively using the PRISMA framework and records from the Scopus database. The three most important research topics are cloud computing, fog computing, and medical services. The results highlight the important role that blockchain-enabled IoT applications have played in enhancing data security and privacy in the healthcare industry. Real-time data gathering, precise diagnoses, individualised treatments, and simplified administrative procedures are all made possible by the integration of blockchain and IoT. Additionally, scalable solutions and insightful data for healthcare decision-making are provided via fog computing, cloud computing, machine learning, and smart contracts.

Blockchain-Based Distributed Computing Consistency Verification for IoT Mobile Applications

The maturation of wireless connectivity, blockchain (distributed ledger technologies), and intelligent systems has fostered a comprehensive ecosystem for the Internet of Things (IoT). However, the growing volume of data generated by IoT devices creates substantial pressure on blockchain storage and computation capabilities, impeding the further development of the IoT ecosystem. Decentralizing data storage across multiple chains and utilizing cross-chain technology for data exchange eliminates the need for expensive centralized infrastructure, lowers data transfer costs, and improves accessibility. Hence, the issue of computational and storage pressure in blockchain can be improved. Nonetheless, the data of IoT devices are constantly updating, and ensuring consistency for dynamic data across heterogeneous chains remains a significant challenge. To address the aforementioned challenge, we propose a blockchain-based distributed and lightweight data consistency verification model (BDCA), which leverages a batch verification dynamic Merkle hash tree (BV-MHT) and an advanced gamma multi-signature scheme (AGMS) to enable consistent verification of dynamic data while ensuring secure and private data transmission. The AGMS scheme is reliable and robust based on security analysis while the dependability and consistency of BDCA are verified through inductive reasoning. Experimental results indicate that BDCA outperforms CPVPA and Fortress in communication and computation overhead for data preprocessing and auditing in a similar condition, and the AGMS scheme exhibits superior performance when compared to other widely adopted multi-signature schemes such as Cosi, BLS, and RSA. Furthermore, BDCA provides up to 99% data consistency guarantees, demonstrating its practicality.

Secure Internet of Thing based data communication in blockchain model using novel teaching‐learning optimized fuzzy approach

AbstractThe potential uses in the Internet of Things (IoT), blockchain is gaining prominence. It is particularly skillful at storing data in immutable blocks, which is connected to its protected peer‐to‐peer architecture in the face of a growing challenge of transaction authorization in industrial and service‐provider applications. IoT is prolonged to several additional applications as a result of its integration with various technologies, allowing direct engagement with our individual and professional lives. The major aim of this study is to provide the security based IoT data communication in blockchain using novel techniques. The device's security is a significant concern. However, on‐demand security solutions have issues since devices have limited processing and energy resources. IoT devices do not allow for the use of large, costly systems. In order to address these issues, we present a novel optimized fuzzy architecture for a blockchain‐based IoT device data exchange and communication in this research. The TLBO method is used to improve decision‐making capabilities, while the Mamdani fuzzy inference system makes the majority of the decisions. To construct the two input variables (drop ratio and integrated trust level) and reduce the dimensionality of the input information, the TLBO method was used. We examine and illustrate the efficacy of our method with another state‐of‐art method. Based on the experiment, we have obtained 1.8, 2.1, 2.8, 3.2 and 3.8 ms computational time based on the number of users from 2 to 10, respectively. The proposed method attains 96% accuracy and 95.4% F‐score results than other previous methods.

Federated Learning for IoT Devices With Domain Generalization

Federated Learning (FL) is a distributed machine learning technique that allows numerous Internet of Things (IoT) devices to jointly train a machine learning model using a centralized server for help. Local data never leaves each IoT device in FL, so the local data of IoT devices are protected. In FL, distributed IoT devices usually collect their local data independently, so the dataset of each IoT device may naturally form a distinct source domain. In real-world applications, the model trained over multi-source domains may have poor generalization performance on unseen target domains. To address this issue, we propose FedADG to equip federated learning with domain generalization capability. FedADG employs the federated adversarial learning approach to measure and align the distributions among different source domains via matching each distribution to a reference distribution. The reference distribution is adaptively generated (by accommodating all source domains) to minimize the domain shift distance during alignment. Therefore, the learned feature representation tends to be universal, and thus, it has good generalization performance over the unseen target domains while protecting local data privacy. Intensive experiments on various datasets demonstrate that FedADG has comparable performance with the state-of-the-art.

Securing a Smart Home with a Transformer-Based IoT Intrusion Detection System

Machine learning (ML)-based Network Intrusion Detection Systems (NIDSs) can classify each network’s flow behavior as benign or malicious by detecting heterogeneous features, including both categorical and numerical features. However, the present ML-based NIDSs are deemed insufficient in terms of their ability to generalize, particularly in changing network environments such as the Internet of Things (IoT)-based smart home. Although IoT devices add so much to home comforts, they also introduce potential risks and vulnerabilities. Recently, many NIDS studies on other IoT scenarios, such as the Internet of Vehicles (IoV) and smart cities, focus on utilizing the telemetry data of IoT devices for IoT intrusion detection. Because when IoT devices are under attack, their abnormal telemetry data values can reflect the anomaly state of those devices. Those telemetry data-based IoT NIDS methods detect intrusion events from a different view, focusing on the attack impact, from the traditional network traffic-based NIDS, which focuses on analyzing attack behavior. The telemetry data-based NIDS is more suitable for IoT devices without built-in security mechanisms. Considering the smart home IoT scenario, which has a smaller scope and a limited number of IoT devices compared to other IoT scenarios, both NIDS views can work independently. This motivated us to propose a novel ML-based NIDS to combine the network traffic-based and telemetry data-based NIDS together. In this paper, we propose a Transformer-based IoT NIDS method to learn the behaviors and effects of attacks from different types of data that are generated in the heterogeneous IoT environment. The proposed method utilizes a self-attention mechanism to learn contextual embeddings for input network features. Based on the contextual embeddings, our method can solve the feature set challenge, including both continuous and categorical features. Our method is the first to utilize both network traffic data and IoT sensors’ telemetry data at the same time for intrusion detection. Experiments reveal the effectiveness of our method on a realistic network traffic intrusion detection dataset named ToN_IoT, with an accuracy of 97.95% for binary classification and 95.78% for multiple classifications on pure network data. With the extra IoT information, the performance of our method has been improved to 98.39% and 97.06%, respectively. A comparative study with existing works shows that our method can achieve state-of-the-art performance on the ToN_IoT dataset.

Topology identification method of low-voltage distribution network based on measurement data of IoT devices

Accurate network topology information serves as the foundation for the operation of active control and active management, which have become indispensable requirements for the development of distribution networks with the growth of intelligence and information technology. The access of user-side Internet of Things (IoT) devices makes the topology of distribution network increasingly complex. IoT devices provide a considerable amount of information, which serves as the data basis for identifying the distribution network’s structure. The cyber–physical distribution system (CPDS) and the entire IoT distribution architecture are initially introduced in this study. Next, we enumerate the communication requirements of certain IoT services to illustrate the feasibility of IoT devices acquiring measurement data. Then, based on the measurement data that can be collected in real time by the user-side IoT devices, a data-driven low-voltage (LV) distribution network topology identification method is proposed through the similarity analysis of the measurement data. Finally, a real LV distribution network is used to validate the effectiveness and practicability of the proposed method, and the case study proves that the topology identification scheme is accurate and efficient. The method can provide correct physical connection information of LV distribution network, solving the problems of inflexibility and inefficiency of existing LV distribution network topology identification methods.

A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks

The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method.

ORTHOGONAL REGRESSED STEEPEST DESCENT DEEP PERCEPTIVE NEURAL LEARNING FOR IoT- AWARE SECURED BIG DATA COMMUNICATION

The Internet of Things (IoT) is a collection of interconnected intelligent devices that exists within the larger network known as the Internet. With the increasing popularity of IoT devices, massive data is generated day by day. The collected data need to be continuously uploaded to the cloud server. Besides, the transmission of data in the cloud environment is performed via the internet, which faces numerous threats. However, the security issue always lacks an effective big data communication. Therefore, a novel technique called Orthogonal Regressed Steepest Descent Deep Structured Perceptive Neural Learning based Secured Data Communication (ORSDDSPNL-SDC) is introduced with higher accuracy and lesser time consumption. The ORSDDSPNL-SDC technique comprises three phases, namely registration, user authentication, and secure data communication. In the ORSDDSPNL-SDC technique, the registration phase is carried out for creating the new ID, and password for each user in the cloud. The IoT device's data is then sent to a cloud server by the cloud user for storage. After that, the orthogonal regressed steepest descent multilayer deep perceptive neural learning is applied to examine the user_ ID with already registered ID based on Szymkiewicz–Simpson coefficient. Then the Maxout activation function is to classify the user as authorized or unauthorized. Finally, the steepest descent function is applied for minimizing the classification error and increasing the classification accuracy. In this way, the authorized or unauthorized user is identified. Then the secured communication is performed with the authorized cloud users. Experimental evaluation is carried out on the factors such as classification accuracy, classification time and error rate, and space complexity with respect to a number of users. The qualitative results and discussion indicate that the proposed ORSDDSPNL-SDC offers elevated performance with regard to achieving higher classification accuracy and minimum error as well as computation time when compared to the existing methods.

FedBKD: Heterogenous Federated Learning via Bidirectional Knowledge Distillation for Modulation Classification in IoT-Edge System

Benefit from the rapid evolution of artificial intelligence and wireless communication technology, diverse Internet of Things (IoT) devices with edge computing ability have widely penetrated every aspect of daily human life. However, the deviations of private datasets and the heterogeneity of local models caused by the difference in device composition and application scenarios have hampering the aggregation of global recognition model in modulation classification task, thus constraining the classification performance of intelligent IoT-edge devices severely. To address this problem, we propose a heterogenous Federated learning framework based on Bidirectional Knowledge Distillation (FedBKD) for IoT system, which integrates knowledge distillation into the local model upload (client-to-cloud) and global model download (cloud-to-client) steps of federated learning. The client-to-cloud distillation is regarded as a process of multi-teacher knowledge distillation and the global network is regarded as a student network that unifies the heterogeneous knowledge from multiple local teacher networks. A public dataset is generated by conditional variational autoencoder (CVAE) and stored in the cloud server for supporting the obtaining of heterogeneous knowledge without sharing the private data of IoT devices. The cloud-to-client distillation is single-teacher-multiple-students process, which distills the knowledge from the single global model back to multiple heterogeneous local networks and partial knowledge distillation is used in this process. We implement our FedBKD method in the modulation classification task and the simulation results have proven the effectiveness of our proposed method.

BLS-identification: A device fingerprint classification mechanism based on broad learning for Internet of Things

The popularity of the Internet of Things (IoT) has enabled a large number of vulnerable devices to connect to the Internet, bringing huge security risks. As a network-level security authentication method, device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases. However, flexible and diversified IoT devices with limited resources increase difficulty of the device fingerprint authentication method executed in IoT, because it needs to retrain the model network to deal with incremental features or types. To address this problem, a device fingerprinting mechanism based on a Broad Learning System (BLS) is proposed in this paper. The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices, and extracts feature parameters of the traffic packets. A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset. The complexity of the dataset is reduced using Principal Component Analysis (PCA) and the device type is identified by training weights using BLS. The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.

Comprehensive Survey Over Security and Protection Issues of IoT Devices

The Internet of Things (IoT) technology has been involved nearly in each sector of daily life from home to industries level. It is another modern worldview pictured as a standard employer of interconnected machines. IoT technology brought easiness and increased the efficiency of communication between human and devices as compared to manual devices. Due to the increment in the usage of IoT devices in residential and commercial areas, these devices attracted the attackers for the security breaches. The main function of IoT security is to promise to provide the security and protection of private data of IoT users, data and devices and infrastructure. The end users are like a layman and do not have enough knowledge of these security holes that is why they are the easy target for the attackers. Various methods are acquainted with lift out and make use of IoT ideas. IoT technology is playing a critical role in every field like savvy properties and intelligent city areas, elevating safety concerns. This paper presents a review on security threats, the reason and order of these threats, and the preparations that had been given towards these IoT protection issues.

Internet of Things in the Healthcare System: A Study

Today's health care is increasingly focused on technology. Unexpected deaths among humans are a problem that arises from delayed medical attention. The development of a body health monitoring system is therefore necessary. The Internet of Things (IoT) enables the integration of numerous devices capable of connecting to the internet, providing information on the state of a patient's health and providing information in real time to doctors who can help. The term "Internet of Things" (IoT) refers to a system or methodology that enables physical objects to connect and communicate with one another through networking technology. The in-depth analysis of developments in IoT-based healthcare methods is presented in this paper. It also categorises a prevailing IoT-based healthcare net and provides a summary of all prospective systems. In this context, study of IoT health care protocols offers a thorough overview of the topic. A thorough survey of IoT healthcare services and applications is also launched. IoT device data can assist medical professionals in choosing the most effective course of therapy for patients and achieving desired results. For achieving requirements like Data and device security, encompassing the confidentiality and integrity of data as well as the authentication of devices, IoT has been established in healthcare system.

SAKAP: SGX-Based Authentication Key Agreement Protocol in IoT-Enabled Cloud Computing

With the rapid development of the Internet, Internet of Things (IoT) technology is widely used in people’s daily lives. As the number of IoT devices increases, the amount of data to be processed also increases. The emergence of cloud computing can process the data of IoT devices in a timely manner, and it provides robust storage and computing capabilities to facilitate data resource sharing. Since wireless communication networks are unstable and open, it is easy for attackers to eavesdrop, intercept, and tamper with the messages sent. In addition, authentication protocols designed for IoT-enabled cloud computing environments still face many security challenges. Therefore, to address these security issues, we propose an Intel software-guard-extensions (SGX)-based authentication key agreement protocol in an IoT-enabled cloud computing environment. The goal is to ensure data privacy and sustainable communication between the entities. Moreover, SGX can resist several well-known attacks. Finally, we show the security using the real-or-random model, ProVerif, and informal analysis. We also compare the security and performance of the proposed protocol with existing protocols. The comparison results show that our proposed protocol reduces the communication cost by 7.07% compared to the best one among the current protocols and ensures sufficient security.

3rdParTEE: Securing Third-Party IoT Services Using the Trusted Execution Environment

Advancements in the Internet of Things (IoT) have resulted in the connection and deployment of numerous smart and embedded devices. Although such devices enable various services such as smart grids, they attract more attackers to the IoT world. A trusted execution environment (TEE), which can be created by using TrustZone technology, is a promising security artifact for protecting critical operations and sensitive data of IoT devices. Unfortunately, although TrustZone is available in most ARM architecture-based devices ranging from microcontrollers to high-end smart devices, it has not been widely adopted by third-party IoT service providers because of its limited access. That is, because the TEE is maintained by the TEE platform vendors to preserve its security. Therefore, third parties must adhere to strict policies and procedures to ensure the deployment of trusted services in the TEE. This aspect hinders the fast development and deployment of IoT services. In this work, we propose 3rdParTEE to address this problem by enabling third-party IoT service providers to readily run their trusted services, thereby minimizing their dependency on the TEE maintainers. Specifically, 3rdParTEE facilitates the secure running of the third-party’s native kernel driver in the TEE without hampering the security of the existing TEE components. To demonstrate the effectiveness of our approach, we ran three kernel drivers for maintaining the IoT services platform (e.g., kernel integrity check) in the TEE. Additionally, during the performance evaluation, we observed a reasonable performance overhead of up to 7% when running the kernel drivers in such a secure manner.

Design and Implementation of SEMAR IoT Server Platform with Applications.

Nowadays, rapid developments of Internet of Things (IoT) technologies have increased possibilities of realizing smart cities where collaborations and integrations of various IoT application systems are essential. However, IoT application systems have often been designed and deployed independently without considering the standards of devices, logics, and data communications. In this paper, we present the design and implementation of the IoT server platform called Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) for integrating IoT application systems using standards. SEMAR offers Big Data environments with built-in functions for data aggregations, synchronizations, and classifications with machine learning. Moreover, plug-in functions can be easily implemented. Data from devices for different sensors can be accepted directly and through network connections, which will be used in real-time for user interfaces, text files, and access to other systems through Representational State Transfer Application Programming Interface (REST API) services. For evaluations of SEMAR, we implemented the platform and integrated five IoT application systems, namely, the air-conditioning guidance system, the fingerprint-based indoor localization system, the water quality monitoring system, the environment monitoring system, and the air quality monitoring system. When compared with existing research on IoT platforms, the proposed SEMAR IoT application server platform offers higher flexibility and interoperability with the functions for IoT device managements, data communications, decision making, synchronizations, and filters that can be easily integrated with external programs or IoT applications without changing the codes. The results confirm the effectiveness and efficiency of the proposal.

Hypergraph-Based Active Minimum Delay Data Aggregation Scheduling in Wireless-Powered IoT

Thanks to the promising wireless power transmission (WPT) technology, wireless-powered Internet of Things (WPIoT) can significantly improve the sustainable service ability of Internet of Things (IoT) with low personnel maintenance costs, and thus, shows remarkable and broad prospects in many applications, especially under the abominable and dangerous environment. Minimum delay data aggregation scheduling (MAS) is a problem of cardinal significance in WPIoT with the objective of timely collecting the data of IoT devices. However, due to the residual energy limitation of IoT devices, WPIoT shows the special feature of adopting the store-charge-and-forward communication mode, which brings new research challenges on designing efficient solutions to the MAS problem. We show that the MAS problem under the physical interference model in WPIoT is NP-hard. To tackle this problem, we propose a delay-efficient data aggregation scheduling algorithm called HADA based on an active data aggregation tree construction method and a novel hypergraph-based link scheduling method. Extensive numerical experiments are conducted to evaluate the performance of our proposed algorithm. The results demonstrate that our HADA algorithm can efficiently improve the performance compared with the existing baseline algorithms.