IoT Users Research Articles

EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things

Decreasing energy consumption in networks with limited resources, such as the Internet of Things, has always been one of the main challenges in guaranteeing network performance. In this article, cooperative game theory is employed to improve the cooperation patterns of fog computing resources. The EDLIoT method consists of two main steps: “Topology Construction” and “Determining Optimal Fog Computing Resources to Process IoT Object Tasks”. In the first step of the proposed method, the set of reliable communications in the network is identified to establish connections between IoT objects and fog computing resources in the form of a tree structure. Then, in the second step, a model based on cooperative game theory and the cost function is used to determine the optimal computing resources in the fog layer for outsourcing the processing tasks of IoT objects. In EDLIoT, active IoT objects perform computation in the fog layer instead of locally, to conserve energy. This is done so that IoT objects, if possible, discover the most suitable processing resources in the fog based on characteristics such as energy consumption, delay, and processing power of the computing resource. The efficiency of the proposed method has been evaluated in a simulated environment, and the results have been compared with those of previous algorithms. The results demonstrate that using the EDLIoT method, in addition to decreasing energy consumption and delay, more computing tasks can be processed through fog resources, thereby increasing the quality of service for IoT users.

Forensically analyzing IoT smart camera using MAoIDFF-IoT framework

IoT devices spread over a wide range of applications these days, and their vast amount of data generated becomes a target for intruders. IoT digital forensics, which involves extracting the digital evidence from the IoT device itself and/or its network traffic using a framework is important and challenging. The challenges include the diversity of types of IoT devices, resource constraints, and users’ privacy. In this article, we focus on network forensics investigations of smart camera traffic as a case study. The investigation process followed the MAoIDFF-IoT framework, a comprehensive and effective framework for IoT devices, and focusing on the locations of potential Artifacts of Interest (AoI). In addition, a few scenarios in using the camera are investigated to obtain a valuable artifact. The results show that it is possible to extract a few artifacts from the network captured traffic even though the traffic is encrypted. Moreover, this research offers guidelines for digital investigators to conduct network forensics on smart camera devices, with detailed results provided.

Utility-Driven End-to-End Network Slicing for Diverse IoT Users in MEC: A Multi-Agent Deep Reinforcement Learning Approach.

Mobile Edge Computing (MEC) is crucial for reducing latency by bringing computational resources closer to the network edge, thereby enhancing the quality of services (QoS). However, the broad deployment of cloudlets poses challenges in efficient network slicing, particularly when traffic distribution is uneven. Therefore, these challenges include managing diverse resource requirements across widely distributed cloudlets, minimizing resource conflicts and delays, and maintaining service quality amid fluctuating request rates. Addressing this requires intelligent strategies to predict request types (common or urgent), assess resource needs, and allocate resources efficiently. Emerging technologies like edge computing and 5G with network slicing can handle delay-sensitive IoT requests rapidly, but a robust mechanism for real-time resource and utility optimization remains necessary. To address these challenges, we designed an end-to-end network slicing approach that predicts common and urgent user requests through T distribution. We formulated our problem as a multi-agent Markov decision process (MDP) and introduced a multi-agent soft actor-critic (MAgSAC) algorithm. This algorithm prevents the wastage of scarce resources by intelligently activating and deactivating virtual network function (VNF) instances, thereby balancing the allocation process. Our approach aims to optimize overall utility, balancing trade-offs between revenue, energy consumption costs, and latency. We evaluated our method, MAgSAC, through simulations, comparing it with the following six benchmark schemes: MAA3C, SACT, DDPG, S2Vec, Random, and Greedy. The results demonstrate that our approach, MAgSAC, optimizes utility by 30%, minimizes energy consumption costs by 12.4%, and reduces execution time by 21.7% compared to the closest related multi-agent approach named MAA3C.

Analyzing Opportunities and Challenges in Sino-Korean Collaboration within the Intelligent Operation and Maintenance Sector Amid China’s Telecommunications Boom

This study examines the development of the Chinese communications industry and the status of cooperation between China and Korea in smart operations and maintenance (O&M). It highlights China’s rapid advancements in network infrastructure, communication revenue, and cellular IoT users, offering new opportunities for Sino-Korean collaboration in smart O&M. However, challenges such as technology gaps, standards differences, and cultural barriers exist. Strategies proposed include promoting technology exchange, joint R&D, standard harmonization, and market expansion. Despite limitations in data availability and challenge analysis, this study aims to inform policymakers, corporate decision-makers, and industry experts to address challenges and promote sustainable development in the communication industry. Future research will integrate quantitative studies to identify key challenges objectively and aid stakeholders in clarifying management priorities.

"Revolutionizing Lawn Care: AI-Driven Solar-Powered Humorless Grassland Mower With IoT Integration"

The primary objective of an AI-driven solar-powered humorless grassland mower with IoT integration is to provide an innovative, sustainable, and efficient solution for grassland maintenance, addressing the challenges of traditional lawn care methods while promoting environmental stewardship and technological advancement. Methods: Developing an AI-driven solar-powered humorless grassland mower with IoT integration involves integrating various technologies and methodologies such as AI Algorithm, Solar Power System Design, Robotics and Automation, IoT Integration, User Interface and Control System. Findings: It can vary depending on the specific objectives, implementation, and testing conducted. It can provide valuable insights into its performance, sustainability, reliability, and user acceptance, helping to inform further development and optimization efforts. Novelty: By introducing novel features and approaches in these areas, an AI-driven solar-powered grassland mower with IoT integration can offer unique capabilities and benefits that set it apart from conventional lawn care equipment and contribute to advancements in sustainable landscaping practices. This paper proposes an innovative AI-Driven Solar-Powered Humorless Grassland Mower with IoT Integration. Traditional manual lawn mowing not only demands labor but also contributes to environmental pollution through nonrenewable resource consumption. To address these challenges, our solution leverages advanced technologies to transform grassland upkeep. The mower is equipped with a range of IoT sensors including ultrasonic, proximity, GPS, and cameras. These sensors enable real-time data collection and decision-making, allowing the mower to adjust its mowing schedule based on forecasted data and navigate around obstacles. Furthermore, during periods of poor sunlight or low battery levels, the mower autonomously returns to its self-charging station for recharging. Data collected by the mower can be sent to the cloud for further analysis and storage. Users have the convenience of remotely controlling the mower through a smartphone app or web interface, enabling initiation, termination, or scheduling of mowing sessions from anywhere. Our AI-integrated IoT solution offers a sustainable and efficient approach to grassland maintenance, reducing labuor requirements and environmental impact while maximizing operational autonomy.

Firm social management system for supporting IoT users in application environment

Internet of Things (IoT) applications connect various services and communication channels to support social user interaction. User management impedes the smooth operation of these networked services by causing interference and delayed responses. The suggested FMS provides a solution by resolving connection- and interference-related problems brought on by application switchovers and social effects. This article introduces a Firm Management System (FMS) for Heterogeneous Application Support (HAS) to solve the problems connected IoT applications encounter in delivering social user assistance. The FMS uses state learning methods and software-controlled networks for effective user and application administration. The system categorizes Application services based on user migration and social management effects like visibility and popularity. Considering the application's utilization condition and user migration patterns, it then assigns resources to the designated services. Through efficient user management infrastructures, this strategy guarantees support for varied users.The proposed algorithm improves high resource allocation by 10.65%, resulting in effective resource use. Additionally, it improves 9.73% service responsiveness, 7.9% latency, 10.89% service failures, and 7.59% system complexity. The results demonstrate how well the suggested system performs regarding resource allocation optimization, service response latency management, and service failure mitigation. The user experience and system effectiveness are improved due to these enhancements, which also increase the overall performance and dependability of IoT applications.

Low-latency partial resource offloading in cloud-edge elastic optical networks

In the context of the rapid deployment of IoT, 5G, and cloud computing, numerous emerging applications demand efficient networked computing capacity for task offloading from mobile and IoT users. This paper focuses on the optimization of network resource allocation and reduction of end-to-end (E2E) latency through the strategic decision of whether and where to offload user requests in a cloud-edge elastic optical network (CE-EON). To address this problem, we first formulate the problem into an integer linear programming (ILP) model as an initial solution. Additionally, we introduce several heuristic approaches that leverage the concept of partial resource offloading, specifically based on proportional segmentation (PRO_PS), partial resource offloading based on average segmentation (PRO_AS), all resource offloading (ARO), and all local processing (ALP). Furthermore, we implement a collaborative cloud-edge (CCE) offloading approach as a baseline for comparison. Our results demonstrate that the PRO_PS approach closely approximates the optimal solutions obtained from the ILP model in static scenarios. Moreover, the PRO_PS approach achieves the lowest E2E latency, blocking probability, and optimized network resource allocation in dynamic scenarios. This highlights the effectiveness of the proposed approach in improving system performance and addressing the challenges of CE-EONs.

Recommendation and Distillation of IoT Multi-Environments

ABSTRACT The Internet of Things enhances the quality of life by automating tasks and streamlining human-device interactions. However, manual device management remains time-consuming, especially in multiple or new environments that demand new settings and interactions. Learning systems aid in automating task management, but their learning times hinder personalization and struggle when the system has to interact with multiple IoT environments, impacting user experience. This paper aims to optimize knowledge sharing for IoT environments, proposing a framework that utilizes recommender systems to find optimal and reusable configurations among IoT environments and users. To that end, this work leverages teacher-student relationships in Knowledge Distillation, facilitating knowledge sharing and enhancing knowledge reuse in learning models. In addition, real-time processing eliminates training time. This approach achieves a remarkable 93.15% accuracy.

Intelligent Resource Allocation and Task Offloading Model for IoT Applications in Fog Networks: A Game-Theoretic Approach

Fog computing is an emerging paradigm that allows IoT devices and applications to process their data at the network’s edge. However, fog servers have limited computational resources compared to the cloud system; therefore, they cannot accommodate the ever-increasing computational demand from IoT devices. In such a challenging environment, IoT users’ decision to offload their tasks to the fog node or the central cloud server is affected by the environment’s current dynamics as other IoT users are also competing to maximize their resource utilization. In this paper, we propose a computational model that considers energy consumption and transmission latency as decision parameters for task offloading of IoT applications. Second, we model the competition as a game where IoT devices’ decision for the optimal distribution of tasks is captured in a joint optimization problem for energy consumption and latency. Third, we propose a decentralized task distribution algorithm where the players learn to update their strategy based on other players’ actions. We also prove that the solution of the proposed algorithm converges to Nash equilibrium (NE). Finally, we carry out extensive evaluations and compare our computational model and results with existing studies.

Pair Outage Probability Minimization for NOMA Among Resource-Constrained IoT Users

Pair Outage Probability Minimization for NOMA Among Resource-Constrained IoT Users

Intelligent Reflecting Surface Aided Simultaneous Wireless Information and Energy Transfer to IoT Users Under Spatial Correlation

Intelligent Reflecting Surface Aided Simultaneous Wireless Information and Energy Transfer to IoT Users Under Spatial Correlation

A Model for Predicting IoT User Behavior Based on Bayesian Learning and Neural Networks

To facilitate the allocation of energy and resources in the Internet of Things system, this paper presents a model for predicting user behavior in Internet of Things environments. The model is based on Bayesian learning and neural networks and is designed to provide insights into the future behavior of users, allowing for the allocation of resources in advance. In this paper, the data are preprocessed by data merging and format processing, and then the association rules are mined by association rules analysis. Finally, the data are utilized to train the behavioral prediction model of the short‐duration memory network via Bayesian optimization. The experimental results showed that the average running time of the research model was 1.682 s, the average accuracy was 96.77%, the average root‐mean‐square error was 0.382, and the average absolute error was 0.315. The designed behavior prediction model is capable of effectively predicting the user behavior of the Internet of Things, thereby enabling the reasonable allocation of energy and resources in the Internet of Things system.

An Efficient NB-IoT Compatible GF-NOMA PHY Mechanism for mMTC

The third generation partnership project (3GPP) has proposed the narrowband Internet of things (NB-IoT) standard for serving IoT users. Improvements to the NB-IoT standard are being considered to efficiently support massive machine-type communication (mMTC) IoT applications. Subsequent releases of the NB-IoT standard envision to support mMTC using different access mechanisms. In this work, a grant-free (GF) non-orthogonal multiple access (NOMA) system compatible with the current NB-IoT standard is proposed, where the resources are divided into transmission opportunities (TOs). The TOs are further divided into multiple groups based on the repetitions adopted by the users in the system. Physical (PHY) layer procedures for preamble transmission/detection, channel estimation, data transmission/decoding, and acknowledgment (ACK) transmission/reception are described in detail for the proposed GF-NOMA system. Specifically, the preamble sequences are also used as spreading sequences for data transmission and three types of sequences -Zadoff-Chu, random Gaussian and Golay sequences are explored. Results are presented for per-user success probability, latency and energy consumption considering two NB-IoT compatible settings along with repetitions and retransmissions. It is demonstrated that the proposed GF-NOMA scheme meets the mMTC requirement recommended by the 3GPP with more than 99% per-user success probability and with the energy consumption reduced by at least 80% compared to NB-IoT, while being readily compatible with the current NB-IoT standard.

Joint DoA and CFO Estimation Scheme With Received Beam Scanned Leaky Wave Antenna for Industrial Internet of Things (IIoT) Systems

Direction-of-arrival (DoA) estimation is essential in many industrial internet of things (IIoT) applications where IoT users may require a high-resolution estimation algorithm for features limited to positioning and device localization. The conventional DoA estimation is challenging as the number of antenna arrays increases leading to an increase in design complexity and cost in RF circuitry. As a result, the conventional DoA estimation algorithms may not be suited for IIoT applications. In this work, a joint DOA and carrier frequency offset (CFO) schemes is introduced for typical uplink IIoT systems by equipping the receiver with a leaky-wave antenna (LWA). By considering that the LWA can simultaneously steer its beam direction towards the active user. As a result, each user is estimated independently. The joint algorithm is developed based on the high-resolution MUSIC scheme, which utilizes the LWA properties to compute the DoA spectrum. Furthermore, we develop an iterative CFO estimation approach as the desired solution to a multi-dimensional search. Subsequently, we further formulate the Cramer–Rao lower bounds (CRLB) to serve as a performance benchmark for the accuracy of the proposed scheme. The SDR platform is provided to validate the proposed algorithm in real-time. Finally, the simulation results and the SDR experiments justify that the proposed scheme offers more robustness than conventional counterparts with linear array antenna, which achieves a root mean square error (RMSE) approach to the derived CLRB, especially at a medium signal-to-noise ratio (SNR) range which suited for IIoT applications.

Integration of the Internet of Things and Cloud

The integration of IoT and cloud poses increased security challenges. Implementing security mechanisms in IoT systems is challenging due to the availability of limited resources, large number of devices, heterogeneity of devices, generation of bulk data, etc. Likewise, cloud resources are also vulnerable to security issues due to virtualization, insider threats, data loss, data breaches, insecure APIs, etc. Security is of major concern with the integration of IoT and cloud. The primary objective of this review is to highlight the security issues associated with an IoT system and cloud system and with the integration of the two, as well as to highlight solutions in each case. The secondary objective is to describe popular IoT-cloud platforms and also to highlight how such platforms facilitate secure integration. Ultimately a highlight on a shared responsibility model of implementing security is emphasized as both IoT users and cloud service providers have to cooperatively share the responsibility to deploy secure cloud-based IoT applications.

Algebraic Signature-Based Public Data Integrity Batch Verification for Cloud-IoT

With the rapid development of Internet of Things, the related data are growing explosively. However, IoT devices have limited storage and computing capabilities so that they cannot deal with massive data storage and computing locally. The integration of IoT and cloud is regarded as an effective solution to the above issue, i.e., IoT devices outsource collected data to cloud to enjoy powerful storage and computing resources. Because the data stored in cloud are out of the control of IoT users, some security risks need to be addressed in advance. In this paper, we propose a public data integrity verification scheme, called AIVCI, to check the data integrity for Cloud-IoT scenarios. Firstly, based on algebraic signature and homomorphic hash function, AIVCI can efficiently complete data auditing. Secondly, AIVCI adopts blind technology to prevent the privacy leakage of IoT data and further protect the privacy of IoT users. Thirdly, batch auditing is implemented to improve auditing efficiency and meet realistic demands for Cloud-IoT scenarios. And a new data structure named Improved Divide and Conquer Table (ID&CT) is designed to realize efficient data dynamics. Finally, the security and performance analysis demonstrates that AIVCI is more secure and efficient.

IoTBeholder

With the deployment of a growing number of smart home IoT devices, privacy leakage has become a growing concern. Prior work on privacy-invasive device localization, classification, and activity identification have proven the existence of various privacy leakage risks in smart home environments. However, they only demonstrate limited threats in real world due to many impractical assumptions, such as having privileged access to the user's home network. In this paper, we identify a new end-to-end attack surface using IoTBeholder, a system that performs device localization, classification, and user activity identification. IoTBeholder can be easily run and replicated on commercial off-the-shelf (COTS) devices such as mobile phones or personal computers, enabling attackers to infer user's habitual behaviors from smart home Wi-Fi traffic alone. We set up a testbed with 23 IoT devices for evaluation in the real world. The result shows that IoTBeholder has good device classification and device activity identification performance. In addition, IoTBeholder can infer the users' habitual behaviors and automation rules with high accuracy and interpretability. It can even accurately predict the users' future actions, highlighting a significant threat to user privacy that IoT vendors and users should highly concern.

Smart Security in Smart City Using Naïve Bayes and RSA

As all major cities adopt the concept of smart cities, concerns arise among the public regarding data security and privacy. The constant threat of attacks on confidentiality, integrity and accessibility of data makes it vulnerable to cyber attacks. The increasing use of IoT devices also increases the potential for cyber attacks that can harm all IoT users. Therefore, it is crucial for city governments to be aware of data security issues related to smart spaces, services, and citizen security, and to provide solutions to existing problems by making maximum policies related to the implementation of smart city concepts. From the above explanation, the author is taking the analysis step with the title "Analysis of Naive Bayes Classifier and Rsa (Rivest Shamir Adleman) Combination in Smart Security in the Implementation of Smart City in Pemko Medan" where the benefits that can be obtained are to gain deeper understanding of Smart Security level, obtain information about the Smart Securty level, and classify the stage of Smart Securty using the combination of Naive Bayes Classifier and Rsa (Rivest Shamir Adleman) in the implementation of Smart City in Pemko Medan.

SmartPatch: Verifying the Authenticity of the Trigger-Event in the IoT Platform

Emerging IoT clouds are playing a more important role in modern lives, enabling users/developers to program applications to make better use of smart devices. However, preliminary research has shown IoT cloud vulnerabilities could expose IoT users to security risks. To better understand the problem, we studied the SmartThings cloud, one of the most popular IoT cloud platforms that support user-defined device automation (SmartApps). Specifically, we found new vulnerabilities in SmartThings that allow attackers to fake events to trigger the SmartApps to operate devices (e.g., open a lock). Exploiting such vulnerabilities, we successfully faked 7 different types of events, which impact 138 (out of 187) SmartThings’ official open-sourced SmartApps. To defeat such attacks, we propose an authenticity-verification-based scheme to deny fake events. Moreover, we designed a tool, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmartPatch</monospace> , to help users secure their SmartThings systems. In specific, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmartPatch</monospace> automatically patches the vulnerable SmartApps and Device Handlers (input) and outputs the flawless programs, which are ready for users to deploy in their SmartThings systems. We have made <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmartPatch</monospace> publicly available. With the help of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SmartPatch</monospace> , we patched all the vulnerable SmartThings’ official open-sourced programs (146 SmartApps and 321 Device Handlers). Experiments have shown the compatibility, effectiveness, and efficiency of our proposed approach.

Outage Probability of Aerial Base Station NOMA MIMO Wireless Communication With RF Energy Harvesting

In this work, we consider a base station (BS) mounted on an unmanned aerial vehicle (UAV) called as an aerial BS (ABS). The ABS is equipped with multiple antennas to assist two pairs of users in nonorthogonal multiple access (NOMA) scheme, and it serves as a relay station to forward the data from the uplink to the downlink of ground IoT users in half-duplex mode. Moreover, the communication power of ABS is harvested from ambient radio-frequency (RF) sources, such as television (TV) and radio signals via the time switching mechanism. In order to improve the efficiency of harvested energy, multiple antennas at the TV/radio tower and the maximal-ratio transmission (MRT) scheme are applied. The proposed ABS RF energy harvesting system overcomes the problem of connectivity of ground IoT users and the energy limitation of devices. The system performance is evaluated in terms of the outage probability of each pair of IoT users in various urban communication environments. Monte Carlo simulation results are provided to validate the accuracy of the developed analytical results and also demonstrate the performance of the proposed system. The results show that the proposed system achieves better performance than that of benchmark systems and the performance can be improved significantly by adjusting the UAV’s altitude.