Power-Domain Collision-Tolerant Random Access Method with Auxiliary Beam for Satellite Internet of Things:A New Solution

There are numerous terminals in the satellite Internet of Things(IoT).To save cost and reduce power consumption,the system needs terminals to catch the characteristics of low power consumption and light control.The regular random access(RA)protocols may generate large amounts of collisions,which degrade the system throughout severally.The near-far effect and power control technologies are not applicable in capture effect to obtain power difference,resulting in the collisions that cannot be separated.In fact,the optimal design at the receiving end can also realize the condition of packet power domain separation,but there are few relevant researches.In this paper,an auxiliary beamforming scheme is proposed for power domain signal separation.It adds an auxiliary reception beam based on the conventional beam,utilizing the correlation of packets in time-frequency domain between the main and auxiliary beam to complete signal separation.The roll-off belt of auxiliary beam is used to create the carrier-to-noise ratio(CNR)difference.This paper uses the genetic algorithm to optimize the auxiliary beam direction.Simulation results show that the proposed scheme outperforms slotted ALOHA(SA)in terms of system throughput per-formance and without bringing terminals additional control burden.

Optimization of the Internet of Remote Things Data Acquisition Based on Satellite UAV Integrated Network

In order to achieve dependable and efficient data acquisition and transmission in the Internet of Remote Things(IoRT),we investigate the optimization scheme of IoRT data acquisition under the unmanned aerial vehicle(UAV)-low earth orbit(LEO)satellite integrated space-air-ground network,in which the UAV acquires data from massive Internet of Things(IoT)devices in special scenarios.To combine with the actual scenario,we consider two different data types,that is,delay-sensitive data and delay-tolerant data,the transmission mode is accordingly divided into two types.For delay-sensitive data,the data will be transmitted via the LEO satellite relay to the data center(DC)in real-time.For delay-tolerant data,the UAV will store and carry the data until the acquisition is completed,and then return to DC.Due to nonconvexity and complexity of the formulated problem,a multi-dimensional optimization Rate Demand based Joint Optimization(RDJO)algorithm is proposed.The algorithm first uses successive convex approximation(SCA)technology to solve the non-convexity,and then based on the block coordinate descent(BCD)method,the data acquisition efficiency is maximized by jointly optimizing UAV deployment,the bandwidth allocation of IoRT devices,and the transmission power of the UAV.Finally,the proposed RDJO algorithm is compared with the conventional algorithms.Simulation consequences demonstrate that the efficiency of IoRT data acquisition can be greatly improved by multi-parameter optimization of the bandwidth allocation,UAV deployment and the transmission power.

Challenges,Opportunities,and Future Research in the Integration of 5G/6G Networks,LEO Satellites,and IoT for Environmental Protection and Sustainable Development at ECSTAR

The rapid proliferation of connected IoT(Internet of Things)devices,along with the increasing demand for 5G mobile networks and ubiquitous high-speed connectivity,poses significant challenges in the telecommunications sector.To address these challenges,a comprehensive understanding of the integration of 5G/6G networks and LEO(Low Earth Orbit)satellite networks is required,forming the concept of“integrated networks”.Integration offers valuable advantages,including service continuity,wide-area coverage,and support for critical communications and emerging applications.This paper provides a high-level overview of the convergence of 5G/6G,LEO satellites,and IoT devices,shedding light on the technological challenges and standardization issues associated with the transition from 5G to 6G networks using NTNs(Non-Terrestrial Networks)based on LEO satellites.Furthermore,this research delves into the emerging social issues,potential possibilities,and the paradigm shift from the IoT to the IoI(Internet of Intelligence),which is poised to revolutionize the landscape of 6G wireless networks.By highlighting the interconnectedness of 5G/6G networks,LEO satellite systems,and IoT devices,it underscores the importance of leveraging these converging technologies to address environmental protection and achieve the United Nations SDGs(Sustainable Development Goals).In addition to providing valuable insights for readers seeking to comprehend the convergence of 5G/6G networks,LEO satellite systems,and IoT devices,this paper represents the outcomes of a comprehensive analysis conducted at the ECSTAR(Excellence Center of Space Technology and Research).Through an examination of technological challenges and advancements,it identifies future research directions and potential avenues for exploration at ECSTAR,thereby contributing to a broader understanding of integrated networks and their profound impact on future telecommunications systems.This research serves as a significant resource for advancing the knowledge and discourse surrounding the linkages between the convergence of these technologies,environmental protection,and the pursuit of the SDGs.

一种基于NB-IoT的智能井盖监测系统设计

针对城市井盖保有量大、安全事故频发、人工巡检困难、管理组织混乱等问题,提出一种基于窄带物联网(NB-IoT)技术的窨井盖自动监测系统。该系统以STM32作为主控模块,包含倾角、水位等传感器,实现对井盖状态以及井内重点数据的采集,配合云端服务器和客户端完成井盖数据远程可视化显示。结果表明,所提系统可以实现故障井盖自动报警、精确定位,降低人工巡检的难度,让城市井盖管理更加智能化,让故障检修更加便捷化,提高城市管理的智能化水平。

From Earth to Space:A First Deployment of 5G Core Network on Satellite

Recent developments in the aerospace industry have led to a dramatic reduction in the manufacturing and launch costs of low Earth orbit satellites.The new trend enables the paradigm shift of satelliteterrestrial integrated networks with global coverage.In particular,the integration of 5G communication systems and satellites has the potential to restructure nextgeneration mobile networks.By leveraging the network function virtualization and network slicing,the satellite 5G core networks will facilitate the coordination and management of network functions in satellite-terrestrial integrated networks.We are the first to deploy a 5G core network on a real-world satellite to investigate its feasibility.We conducted experiments to validate the satellite 5G core network functions.The validated procedures include registration and session setup procedures.The results show that the satellite 5G core network can function normally and generate correct signaling.

面向物联网的NB-IoT信号优化方法研究

随着物联网技术的飞速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗、广覆盖、大连接的无线通信技术,逐渐成为连接物理世界与数字世界的桥梁。然而,在实际应用中,NB-IoT信号面临着诸如信号衰减、干扰、覆盖不均等挑战。这些挑战不仅影响用户体验,还限制了物联网应用的进一步发展。因此,研究面向物联网的NB-IoT信号优化方法具有重要意义。文章深入研究面向物联网的NB-IoT信号优化方法,提出多种有效的优化策略和技术手段。

低轨卫星物联网下NB⁃IoT时延功耗研究

【目的】在低轨(LEO)卫星物联网(IoT)场景下,广域覆盖范围内窄带物联网(NB⁃IoT)体制终端因为业务场景不同造成时延功耗需求也不相同。然而,地面NB⁃IoT体制终端业务场景单一,采用固定式工作状态定时器参数配置方法,无法满足LEO卫星IoT大尺度地理范围内多场景终端时延功耗的不同需求。【方法】针对LEO卫星IoT广覆盖特性,文章基于简化后随机接入交互流程终端工作状态切换的马尔科夫链模型,提出了以系统下行延迟和终端功耗作为非支配排序遗传算法(NS⁃GA⁃Ⅱ)的目标函数得到多场景帕累托前沿最优解集的方法,进一步从帕累托前沿中选择满足不同场景时延功耗需求的工作状态定时器参数在线配置终端。【结果】仿真结果表明,文章提出的多目标优化方法对工作状态定时器参数进行全局寻优,克服了穷举法等传统方法可能陷入局部最优的不足,并验证了简化随机接入交互流程可以有效降低终端时延功耗。【结论】文章所提多目标优化方法可以得到终端在LEO卫星IoT多场景下的工作状态定时器参数。

Dynamic Uplink Transmission Scheduling for Satellite Internet of Things Applications

Satellite communication systems provide a cost-effective solution for global internet of things(IoT)applications due to its large coverage and easy deployment.This paper mainly focuses on Satellite networks system,in which low earth orbit(LEO)satellites network collect sensing data from the user terminals(UTs)and then forward the data to ground station through geostationary earth orbit(GEO)satellites network.Considering the limited uplink transmission resources,this paper optimizes the uplink transmission scheduling scheme over LEO satellites.A novel transmission scheduling algorithm,which combined Algorithms of Simulated Annealing and Monte Carlo(SA-MC),is proposed to achieve the dynamic optimal scheduling scheme.Simulation results show the effectiveness of the proposed SA-MC algorithm in terms of cost value reduction and fast convergence.

A Novel Orthogonal LoRa Multiple Access Algorithm for Satellite Internet of Things

In recent years,LoRa has been extensively researched in the satellite Internet of Things(IoT).However,the multiple access technology of LoRa is still one of the bottlenecks of satellite IoT.To improve the multiple access performance of LoRa satellite IoT,based on the orthogonality of LoRa symbols in the fractional domain,this paper proposes a low complexity Orthogonal LoRa Multiple Access(OLMA)algorithm for multiple LoRa users occupying the same frequency bandwidth.The algorithm introduces the address code to divide the fractional bandwidth into multiple parts,and the OLMA users with different address codes occupy different parts to transmit the information code,thus avoiding mutual interference caused by collisions in the same frequency bandwidth.The multiple access capability of OLMA can be flexibly configured only by simply adjusting the length of the address code according to the actual application requirements of data transmission.Theoretical analysis and simulation results show that the OLMA algorithm can greatly improve the multiple access capability and the total transmission bit rate of LoRa IoT without changing the existing LoRa modulation parameters and process.

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.

Help from space:grant-free massive access for satellite-based IoT in the 6G era

The Sixth-Generation(6G)standard for wireless communications is expected to realize ubiquitous coverage for massive Internet of Things(IoT)networks by 2030.Satellite-based communications are recognized as a highly promising technical enabler to satisfy IoT service requirements in the 6G era.This study analyzes multiple access technologies,which are essential for the effective deployment of satellite-based IoT.First,we thoroughly investigate the existing research related to massive access,including information-theory considerations as well as Non-Orthogonal Multiple Access(NOMA)and Random Access(RA)technologies.Then,we explore the influence of the satellite transmission environment on multiple access technologies.Based on this study,a Non-orthogonal Massive Grant-Free Access(NoMaGFA)scheme,which reaps the joint benefits of RA and NOMA,is proposed for asynchronous transmissions in satellite-based IoT to achieve improved system throughput and enhance the system robustness under varying traffics.Finally,we identify some important and interesting future developments for satellite-based IoT,including waveform design,transceiver design,resource allocation,and artificial intelligence-enhanced design.

基于双模式端址跳变SD-IoT主动防御方法

由于地址跳变是物联网主动防御的一种有效手段,但因跳变资源匮乏、可预见性以及数据包混淆度低已经成为制约物联网地址跳变的主要问题。为此,提出一种基于双模式端址跳变的主动防御方法。该方法设计了双模式端址选择算法,通过动态确定虚拟端址生成策略,以通信时间为阈值,扩大端址跳变空间,从而解决地址池资源受限问题。同时,还构建了双虚拟端址跳变方法,通过动态分配和同步虚拟接收和发送地址,提升数据包混淆度,增强跳变的不可预见性。并且基于SDN(Software Defined Network)设计了流表双向同步机制,实现流表的动态下发和同步,以保证端址跳变的一致性。实验结果表明,该方法能有效提升地址跳变的多样性和不可预测性,显著增强抵御嗅探攻击的能力。

Non-Orthogonal Multi-Carrier Transmission for Internet via Satellite

As the important complementary to terrestrial mobile communications, Internet via satellite can extend the coverage of communication and improve the continuity of data services. To build a space-terrestrial integrated communication system is the inevitable trend in the future. Taking into account combination of 5th generation(5G) terrestrial mobile communication system and satellite communication system, it is necessary to evaluate the promising 5G air interface waveform which can be adopted by satellite. In this paper, several non-orthogonal multi-carrier transmission schemes are evaluated and generalized frequency division multiplexing(GFDM) is advised as potential scheme of space-terrestrial integrated communication system. After the overview of GFDM, the implementation of GFDM transceiver is discussed respectively in time-domain and in frequency-domain. By deriving and comparing implementation complexity, GFDM modulation in time-domain is more suitable for satellite communication system. Then the properties of demodulation algorithms are specified. Based on designed pilot subcarriers, a new improved receiving algorithm is proposed in the end of the paper. The improved algorithm solves the problem of inter subcarriers interference(ICI) in matched filtering(MF) receiver and improves the re-ceiving symbol error rate(SER) obviously. The simulation and analysis prove that the proposed algorithm is effective.

Intelligent Internet of Things with Reliable Communication and Collaboration Technologies

The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.

Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights.The rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and connectivity.These IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of data.However,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges.However,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability issues.This paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data analysis.The proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic data.SLItFC addresses the intricate task of efficiently managing and analyzing IoT data traffic at the edge.It employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over time.This adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve rapidly.With the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational time.SLItFC can reduce computational time while maintaining high classification accuracy.This efficiency is paramount in edge computing,where resource constraints demand streamlined data processing.Additionally,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insights and decision-making.With the Self-Learning process,the SLItFC model monitors the network traffic data acquired from the IoT Devices.The Sugeno fuzzy model is implemented within the edge computing environment for improved classification accuracy.Simulation analysis stated that the proposed SLItFC achieves 94.5%classification accuracy with reduced classification time.

Delta Compression Correction Method for Covert Communication in IoT

Covert communication can conceal the existence of wireless transmission and thus has the ability to address information security transfer issue in many applications of the booming Internet of Things(IoT).However,the proliferation of sensing devices has generated massive amounts of data,which has increased the burden of covert communication.Considering the spatiotemporal correlation of data collection causing redundancy between data,eliminating duplicate data before transmission is beneficial for shortening transmission time,reducing the average received signal power of warden,and ultimately realizing covert communication.In this paper,we propose to apply delta compression technology in the gateway to reduce the amount of data generated by IoT devices,and then sent it to the cloud server.To this end,a cost model and evaluation method that is closer to the actual storage mode of computer systems is been constructed.Based on which,the delta version sequence obtained by existing delta compression algorithms is no longer compact,manifested by the still high cost.In this situation,we designed the correction scheme based on instructions merging(CSIM)correction to save costs by merging instructions.Firstly,the delta version sequence is divided into five categories and corresponding merge rules were derived.Then,for any COPY/ADD class delta compression algorithm,merge according to strict to relaxed to selection rules while generating instructions.Finally,a more cost-effective delta version sequence can be gained.The experimental results on random data show that the delta version sequences output by the CSIM corrected 1.5-pass and greedy algorithms have better performance in cost reducing.

Ultra reliability and massive connectivity provision in integrated internet of military things(IoMT)based on tactical datalink

One of the major challenges arising in internet of military things(IoMT)is accommodating massive connectivity while providing guaranteed quality of service(QoS)in terms of ultra-high reliability.In this regard,this paper presents a class of code-domain nonorthogonal multiple accesses(NOMAs)for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system(JTIDS).In the considered scenario,a satellite equipped with Nr antennas servers K devices including vehicles,drones,ships,sensors,handset radios,etc.Nonorthogonal coded modulation,a special form of multiple input multiple output(MIMO)-NOMA is proposed.The discussion starts with evaluating the output signal to interference-plus-noise(SINR)of receiver filter,leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered.The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved.The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying(MSK)and Gaussian MSK(GMSK),two pivotal modulation formats in IoMT standards such as Link-16 and JITDS.Numerical results show that near capacity performance is offered.Fortunately,the performance is obtained using simple forward error corrections(FECs)of higher coding rate than existing schemes do,while the transmit power is reduced by 6 dB.The proposed design finds wide applications not only in IoMT but also in deep space communications,where ultra reliability and massive connectivity is a keen concern.

Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer processes.However,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy costs.This paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research process.The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data centers.These sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for analysis.The data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center environment.Through the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT sensors.The model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power costs.Furthermore,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and AlexNet.The NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark algorithms.These findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive capabilities of cloud computing systems.The IoT plays a critical role in driving these advancements by providing real-time data insights into the operational aspects of data centers.

Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

Vector Dominance with Threshold Searchable Encryption (VDTSE) for the Internet of Things

The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.