Mobile Internet Of Things Devices Research Articles

Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices.

The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm's data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes.

Minimize average tasks processing time in satellite mobile edge computing systems via a deep reinforcement learning method

Recently, the development of Low Earth Orbit (LEO) satellites and the advancement of the Mobile Edge Computing (MEC) paradigm have driven the emergence of the Satellite Mobile Edge Computing (Sat-MEC). Sat-MEC has been developed to support communication and task computation for Internet of Things (IoT) Mobile Devices (IMDs) in the absence of terrestrial networks. However, due to the heterogeneity of tasks and Sat-MEC servers, it is still a great challenge to efficiently schedule tasks in Sat-MEC servers. Here, we propose a scheduling algorithm based on the Deep Reinforcement Learning (DRL) method in the Sat-MEC architecture to minimize the average task processing time. We consider multiple factors, including the cooperation between LEO satellites, the concurrency and heterogeneity of tasks, the dynamics of LEO satellites, the heterogeneity of the computational capacity of Sat-MEC servers, and the heterogeneity of the initial queue for task computation. Further, we use the self-attention mechanism to act as a Q-network to extract high-dimensional dynamic information of tasks and Sat-MEC servers. In this work, we model the Sat-MEC environment simulation at the application level and propose a DRL-based task scheduling algorithm. The simulation results confirm the effectiveness of our proposed scheduling algorithm, which reduces the average task processing time by 22.1%, 30.6%, and 41.3%, compared to the genetic algorithm(GA), the greedy algorithm, and the random algorithm, respectively.

Learning-Based Reconfigurable-Intelligent-Surface-Aided Rate-Splitting Multiple Access Networks

Rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) techniques show promise in enhancing spectral efficiency in sixth-generation Internet of Things (IoT) networks. However, optimizing the synergy between these two methods is challenging due to the complex and dynamic environment. This study focuses on maximizing the sum-rate metric in RIS-assisted uplink multiantenna RSMA IoT networks to address this problem. We jointly optimized the base station beamforming design, power allocation, and RIS phase shifts to enhance the spectral efficiency with multiple mobile IoT devices present. The controlled parameters are continuous variables and the mathematical problem is non-concave, Therefore, we formulated the problem as a Markov decision process and used the deep deterministic policy gradient (DDPG) to determine the optimal joint actions. We proposed a safe action shaping process for the decision-making actor network to address constraint violations. Through a rigorous performance evaluation, we demonstrated that the DDPG approach with action shaping outperforms the current DDPG algorithm regarding the maximum achievable sum rate.

English

The ubiquitous nature of wireless sensor networks (WSNs) means that their underlying network architecture may be used to serve a broad range of use cases, from "smart cities" to "smart homes" and beyond. Other serious problems, such as energy use and security, have persisted. When it comes to WSN security, the blockchain might be the answer. Traditional blockchains have limitations, such as inefficient transaction processing and limited scalability. When Wireless Sensor Networks (WSNs) are deployed, most of the time, the nodes are left unmanaged and vulnerable to various security threats. Reliable data distribution in WSNs is nontrivial because of the limited resources and dynamic behavior of sensor nodes. Traditional cryptography and authentication-based methods have been shown to be unsuitable for countering node misbehavior attacks due to their high cost and incapability. The lightweight authentication encryption system was developed to solve the problems of sensor resource scarcity and data security (data integrity and secrecy). Current trust-based solutions, on the other hand, increase traffic congestion and decrease the network's lifespan due to the high costs associated with trust estimates and network-wide dissemination. As a result, the objective of this study is to present an intrusion prevention architecture for mobile Internet of Things devices, along with its integration into WSN, to ensure data security while simultaneously improving the network delivery ratio. The architecture that has been suggested is made up of two different discrete components. To begin, the ambiguity principle is used to construct non-overlapping and independently structured clusters, which are then used to preserve the clusters' stability. Second, based on the architecture of the blockchain, encrypted multi-hop routing pathways with end-to-end coverage are being established. By reducing end-to-end latency, time consumption, packet loss rate, and energy usage while increasing throughput, our recommended model's simulation results reveal good results and security gains.

Meta-Learning Based Classification for Moving Object Trajectories in Mobile IoT

The proliferation and ubiquity of GPS-enabled mobile Internet of Things (IoT) devices (e.g., drones and unmanned robotic devices) across many disciplines has generated substantial interest in the analysis and mining of trajectory data for enhancing public services such as air pollution monitoring and road safety. Among all trajectory analysis techniques in mobile IoT, classifying moving device trajectories is a fundamental research problem due to its importance for numerous important mobile IoT applications such as travel demand analysis and animal migration patterns. However, existing classification methods for moving object trajectories either are dynamic programming problems (such as similarity-based methods with dynamic time wrapping or Fréchet distance) with a quadratic time complexity in all cases, or need abundant labeled samples to training classification model, thus limit the scalability of these methods for given resource-constrained mobile IoT devices. In this study, we propose a deep learning-based approach for trajectory classification in mobile IoT with linear time complexity by firstly encoding a trajectory as a vector via deep representation learning, then utilizing a meta-learning based approach to learn the ability of classifying trajectory with few annotated samples. Experiments on a massive trajectory dataset show that the proposed approach outperforms state-of-the-art baselines consistently and significantly.

CPU Frequency Scaling Optimization in Sustainable Edge Computing

Sustainable edge computing (SEC) is a promising technology that can reduce energy consumption and computing latency for the mobile Internet of things (IoT). By collecting renewable energy such as solar or wind energy from the environment, a sustainable cloudlet outside the electric grid can provide powerful computing capabilities for resource-constrained mobile IoT devices. In the real world, the density of sustainable energy can vary significantly over time. Therefore, the SEC cloudlet needs to dynamically adjust the clock frequency to balance energy consumption and computing latency. In this paper, we consider the limited energy storage of the cloudlet and the dynamic intensity of renewable energy, and then develop offline optimal CPU frequency scaling policies that (a) maximize the computing power of the cloudlet within a certain period of time, and (b) minimize the execution time given tasks offloaded to the cloudlet. An optimal tightest string policy is proposed to solve the optimization problem. In addition, a dynamic programming (DP) based suboptimal solution is introduced to simplify the practical implementation. How to design an online CPU frequency management strategy is also briefly discussed.

Resource Management in UAV Enabled MEC Networks

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem. According to simulation results, both local and offloading schemes give optimal solution.

Decentralized Blockchain Network for Resisting Side-Channel Attacks in Mobility-Based IoT

The inclusion of mobility-based Internet-of-Things (IoT) devices accelerates the data transmission process, thereby catering to IoT users’ demands; however, securing the data transmission in mobility-based IoT is one complex and challenging concern. The adoption of unified security architecture has been identified to prevent side-channel attacks in the IoT, which has been discussed extensively in developing security solutions. Despite blockchain’s apparent superiority in withstanding a wide range of security threats, a careful examination of the relevant literature reveals that some common pitfalls are associated with these methods. Therefore, the proposed scheme introduces a novel computational security framework wherein a branched and decentralized blockchain network is formulated to facilitate coverage from different variants of side-channel IoT attacks that are yet to be adequately reported. A unique blockchain-based authentication approach is designed to secure communication among mobile IoT devices using multiple stages of security implementation with Smart Agreement and physically unclonable functions. Analytical modeling with lightweight finite field encryption is used to create this framework in Python. The study’s benchmark results show that the proposed scheme offers 4% less processing time, 5% less computational overhead, 1% more throughput, 12% less latency, and 30% less energy consumption compared to existing blockchain methods.

A practical approach for high-resolution air quality mapping using IoT mobile devices

A practical approach for high-resolution air quality mapping using IoT mobile devices

Joint Device Association, Resource Allocation, and Computation Offloading in Ultradense Multidevice and Multitask IoT Networks

With the emergence of more and more applications of Internet of Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultradense IoT networks, the ultradensely deployed small base stations (SBSs) will consume a large amount of energy. To reduce the network-wide energy consumption and prolong the standby time of IMDs and SBSs, under the proportional computation resource allocation and devices’ latency constraints, we jointly perform the device association, computation offloading, and resource allocation to minimize the network-wide energy consumption for ultradense multidevice and multitask IoT networks. To further balance the network loads and fully utilize the computation resources, we take account of multistep computation offloading. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we develop an improved hierarchical adaptive search (IHAS) algorithm to find its solution. Then, we give the convergence, computational complexity, and parallel implementation analyses for such an algorithm. By comparing with other algorithms, we can easily find that such an algorithm can greatly reduce the network-wide energy consumption under devices’ latency constraints.

Adaptive Federated Learning With Non-IID Data

Abstract With the widespread use of Internet of things(IoT) devices, it generates an enormous volume of data, and it is a challenge to mine the IoT data value while ensuring security and privacy. Federated learning is a decentralized approach for training data located on edge devices, such as mobile phones and IoT devices, while keeping privacy, efficiency, and security. However, the Non-IID (non-independent and identically distributed) data, always greatly impacts the performance of the global model. In this paper, we propose a FedDynamic algorithm to solve the statistical challenge of federated learning caused by Non-IID. As Non-IID data can lead to significant differences in model parameters between edge devices, we set different weights for different devices during model aggregation to get a high-performance global model. We analyze and exact key indices (local model accuracy, local data quality, and model difference between local models and the global model), which can reflect the quality of the model, and calculate the aggregation weight for edge devices based on the key indices. Furthermore, we dynamically adjust aggregation weight based on accuracy’s variety to solve weight staleness during the training process. Experiments on the MNIST, FMNIST, EMNIST, CINIC-10 and CIFAR-10 datasets show that the FedDynamic algorithm has better accuracy and convergence performance, compared to the FedAvg, FedProx and Scaffold algorithms.

ELIoT: enhancing LiFi for next-generation Internet of things

Communication for the Internet of things (IoT) currently is predominantly narrowband and cannot always guarantee low latency and high reliability. Future IoT applications such as flexible manufacturing, augmented reality and self-driving vehicles rely on sophisticated real-time processing in the cloud to which mobile IoT devices are connected. High-capacity links that meet the requirements of the upcoming 6G systems cannot easily be provided by the current radio-based communication infrastructure. Light communication, which is also denoted as LiFi, offers huge amounts of spectrum, extra security and low-latency transmission free of interference even in dense reuse settings. We present the current state-of-the-art of LiFi systems and introduce new features needed for future IoT applications. We discuss results from a distributed multiple-input multiple-output topology with a fronthaul using plastic optical fibre. We evaluate seamless mobility between the light access points and also handovers to 5G, besides low-power transmission and integrated positioning. Future LiFi development, implementation and efforts towards standardization are addressed in the EU ELIoT project which is presented here.

A Novel Path Planning and Node Selection Method Using Reinforcement Learning in NTN IoT Networks

With the rapid deployment of 5G networks in recent years, the characteristics of high bandwidth, low latency, and low energy consumption of 5G networks have enabled the rapid development of IoT (Internet of things) technology. However, 5G networks cannot provide high-quality wireless coverage for many IoT devices in border areas and hotspots with a high signal density that lack fixed infrastructure. Therefore, this paper uses the UAV (unmanned aerial vehicle) to carry the communication platform to build the NTN (nonterrestrial network) to provide wireless coverage for terrestrial fixed and mobile IoT devices. Meanwhile, since the NTN needs to provide wireless coverage for many IoT devices, we use deep reinforcement learning to provide path planning for the UAV communication platform to improve the efficiency of wireless coverage. We build a simulation environment to evaluate the performance of the NTN network for wireless coverage of IoT devices in urban hotspot areas. Experimental results show that the method proposed in this paper can provide higher downlink rates for more IoT devices than NB-IoT (narrowband Internet of things).

Global IoT Mobility: A Path Based Forwarding Approach

With the huge proliferation of mobile Internet of Things (IoT) devices such as connected vehicles, drones, and healthcare wearables, IoT networks are promising mobile connectivity capacity far beyond the conventional computing platforms. The success of this service provisioning is highly dependent on the flexibility offered by such enabling technologies to support IoT mobility using different devices and protocol stacks. Many of the connected mobile IoT devices are autonomous, and resource constrained, which poses additional challenges for mobile IoT communication. Therefore, given the unique mobility requirements of IoT devices and applications, many challenges are still to be addressed. This paper presents a global mobility management solution for IoT networks that can handle both micro and macro mobility scenarios. The solution exploits a path-based forwarding fabric together with mechanisms from Information-Centric Networking. The solution is equally suitable for legacy session-based mobile devices and emerging information-based IoT devices such as mobile sensors. Simulation evaluations have shown minimum overhead in terms of packet delivery and signalling costs to support macro mobility handover across different IoT domains.

Stochastic Geometric Analysis of Handover Based on Fuzzy Logic in MIMO IoT Systems

High-density base station (BS) will bring the problem of frequent handover to mobile Internet of Things (IoT) devices. Frequent handover will lead to Quality-of-Service (QoS) problems, such as increased handover delay and reduced data transmission rate. In order to overcome the limitation of mobile IoT devices’ QoS caused by frequent handover BSs, the multiple-input–multiple-output (MIMO) systems can be used to improve the channel capacity and reduce the BS density. However, because the handover decision of mobile IoT devices will affect the data rate and network stability, how to implement the handover to reduce the impact of handover on mobile IoT devices is a problem worthy of study. Therefore, this article proposes a handover strategy based on fuzzy logic in MIMO systems, which are used to reduce the handover frequency and improve the average data rate of mobile IoT devices. First, the mobile IoT devices convert the devices’s speed, the distance from the devices to the MIMO systems, and the transmission power of the MIMO systems to fuzzy values. Then, the fuzzy values obtained in the first step are taken as the input, and the fuzzy-logic-based handover algorithm is implemented through the fuzzy logic controller. Finally, according to the results of the handover algorithm and the theoretical results about the average throughput of mobile IoT devices derived in this article, the number of antennas used to provide transmission services for IoT devices with different data rate requirements and different mobile speeds can be allocated more flexibly. Simulation results show that the proposed scheme achieves a better performance.

Use QR Code and IoT Mobile Devices to Prevent the Spread of an Epidemic (COVID-19)

The spread of new pandemic novel Corona-Virus (COVID-19) brought violent changes in livelihood. It was difficult to control and cause Curfew in many countries, disrupted life, Imposing conditions for social distancing, masks because of unavailable of drugs, and treatments. The aim of this paper is using QR code and IoT for prevention transmission of COVID-19 by making electronic medical register for each human stored in cloud database included periodic Corona-Virus test and generate unique QR code for each one on the other side two software applications designed one for monitoring and update information of the medical register in the cloud and the second software read QR code and control the gates of public locations via IoT, as the results only healthy people can travel among public locations (markets, malls, Universities, Subways,…etc) and the infected persons can be monitored.

Secure Throughput Optimization for Cache-Enabled Multi-UAVs Networks

This article considers an ultradense heterogeneous network (UDHN) consisting of cache-enabled unmanned aerial vehicles (UAVs) and Internet of Things mobile devices (IMDs) receiving their requested contents via the power domain nonorthogonal multiple access (PD-NOMA) protocol. Employing the fast global <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means (FGKMs) algorithm, IMDs are partitioned into several clusters connecting to either other IMDs or UAV belonging to the same cluster in the presence of untrusted users, known as nonlegitimate eavesdroppers. It is assumed that users located in the cluster edge area communicate with multiple UAVs to obtain their requested contents. The main goal for such a network is to jointly optimize the number of UAVs, their 3-D placements, and the cache placement probability of contents stored in UAVs and IMDs by maximizing the secure cache throughput. Toward this goal, we prove that the objective function is nonconcave. Therefore, we decompose the optimization problem into multiple subproblems. We first employ the FGKM algorithm to optimally determine the number of employed UAVs and their horizontal placements. Then, the UAVs’ altitudes are optimized by employing the interior-point method (IPM), while the convex approximation for the objective function and its constraints are substituted. Then, we optimize the secure cache throughput of IMDs by proposing a caching placement strategy for contents stored in UAVs and IMDs via Device to Device (D2D) and UAV to Device (U2D) links, given illegal eavesdroppers’ presence. Then, we propose an iterative algorithm to achieve the near-optimal solution for the cache throughput of IMDs. Different from existing works, the closed-form expressions for the achievable secrecy rate and the successful probability of D2D communications and U2D transmissions, as well as the secure cache throughput are derived. Finally, simulation results are presented to validate the proposed caching placement strategy. It is shown that the proposed scheme outperforms the conventional most popular caching (MPC) strategy substantially.

Reliable data delivery in ICN-IoT environments

In an IoT environment, which is characterized by a multitude of interconnected smart devices with sensing and computational capabilities, many applications are (i) content-based, that is, they are only interested in finding a given type of content rather than the location of data, and (ii) contextualized, that is, the content is generated and consumed in the proximity of the user. In this context, the Information-Centric Networking (ICN) paradigm is an appealing model for efficiently retrieving application data, and the service decentralization towards the network edge helps to reduce the core network load being the data produced by IoT devices mainly confined in the area where they are generated. MobCCN is an ICN-based data delivery protocol that we designed for operating efficiently in such context (Borgia et al., 2016, 2018), where static and mobile IoT devices are enriched with ICN functions. Specifically, MobCCN leverages an efficient routing and forwarding protocol, exploiting opportunistic contacts among IoT mobile devices, to fill the Forwarding Interest Base (FIB) tables so as to correctly forward Interest packets towards the intended data producers. In this paper, we aim to enhance the reliability of MobCCN by exploring different retransmissions mechanisms, such as retransmissions based on number of duplicate Interests that are received for the same requested content, periodic retransmissions, single path versus disjoint multi-path forwarding, hysteresis mechanism and combinations of them. Extensive simulation results show that, among the analysed MobCCN variants, the one that implements both periodic retransmissions and a hysteresis-based retransmission process ensures the highest delivery rates (up to 95%) and the shortest network path, with a very limited traffic overhead due to retransmissions.

Adaptive Rate NOMA for Cellular IoT Networks

Internet-of-Things (IoT) technology is envisioned to enable a variety of real-time applications by interconnecting billions of sensors/devices. These IoT devices rely on low-power wide-area wireless connectivity for transmitting, mostly fixed- but small-size, status updates of the random processes observed by them. Owing to their ubiquity, cellular networks are seen as a natural candidate for providing reliable wireless connectivity to IoT devices. Given the massive number of IoT devices, enabling non-orthogonal multiple access (NOMA) for the mobile users and IoT devices is appealing in terms of the efficient utilization of spectrum compared to the orthogonal multiple access (OMA). For instance, the uplink NOMA can also be configured such that the mobile users adapt their transmission rates depending upon the channel conditions while the IoT devices transmit at a fixed rate. For this setting, we analyze the ergodic capacity of the mobile users and the mean local delay of IoT devices using stochastic geometry. Our analysis demonstrates that the aforementioned NOMA configuration provides better ergodic capacity for mobile users compared to OMA when delay constraint of IoT devices is strict. We also show that NOMA supports a larger packet size at IoT devices than OMA under the same delay constraint.

Mobility-Aware Controller Orchestration in Multi-Tier Service-Oriented Architecture for IoT

This work addresses the problem of controller orchestration in Internet of Things (IoT)-based Service-Oriented Architecture. It focuses on the fundamental issues of heterogeneity and dynamism of hybrid edge-cloud networks involving both static and mobile IoT devices for provisioning IoT-based services. Due to the limited capacity of IoT devices, IoT-cloud services require the support of edge networks, termed as ‘Service Edge,’ for meeting their ultra-low latency requirements. In such complex and dynamic networks, software-defined networking (SDN) paradigm is utilized for efficient resource utilization and service-oriented network management. A significant problem in SDN is the controller placement problem. Although researchers proposed several controller placement schemes for SDN, the problem of multi-tier controller placement in hybrid cloud-edge networks has not yet been addressed. In this work, this problem is formulated mathematically and shown to be a variant of the well-known <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two-level capacitated facility location problem</i> , which is NP-hard. Thereafter, COMET, a scheme based on coalitional game and social choice theory, is proposed to solve the problem in polynomial time. COMET is designed while considering varying loads on the switches due to the presence of mobile IoT devices. Simulations depict that COMET reduces network delay by 49.02% with a significant increase in resiliency compared to the state-of-the-art.