Energy and Latency-Aware Scheduling Under Channel Uncertainties in LTE Networks for Massive IoT

The machine-to-machine (M2M) communication is an enabler technology for internet of things (IoT) that provides communication between machines and devices without human intervention. One of the main challenges in IoT is managing a large number of machine-type communications co-existing with the human to human (H2H) or human type communications. Long term evolution (LTE) and LTE-advanced (LTE-A) technologies due to their inherent characteristics like high capacity and flexibility in data access management are appropriate choices for M2M/IoT systems. In this paper, a two-phase intelligent scheduling mechanism based on interval type-2 fuzzy logic to (1) satisfy QoS requirements, (2) ensure fair resource allocation and (3) control energy level of devices for coexistence of M2M/H2H traffics in LTE-A networks, is presented. The proposed interval type-2 fuzzy Logic mechanism enhances data traffic efficiency by predicting and handling the network uncertainties. The performance of the proposed algorithm is evaluated in terms of various metrics such as delay, throughput, and bandwidth utilization.     

Securing Internet of Things (IoT) with machine learning

Advances in hardware, software, communication, embedding computing technologies along with their decreasing costs and increasing performance have led to the emergence of the Internet of Things (IoT) paradigm. Today, several billions of Internet‐connected devices are part of the IoT ecosystem. IoT devices have become an integral part of the information and communication technology (ICT) infrastructure that supports many of our daily activities. The security of these IoT devices has been receiving a lot of attention in recent years. Another major recent trend is the amount of data that is being produced every day which has reignited interest in technologies such as machine learning and artificial intelligence. We investigate the potential of machine learning techniques in enhancing the security of IoT devices. We focus on the deployment of supervised, unsupervised learning techniques, and reinforcement learning for both host‐based and network‐based security solutions in the IoT environment. Finally, we discuss some of the challenges of machine learning techniques that need to be addressed in order to effectively implement and deploy them so that they can better protect IoT devices.     

Leveraging AI-enabled 6G-driven IoT for sustainable smart cities

Many scholastic researches have begun around the globe about the competitive technological interventions like 5G communication networks and its challenges. The incipient technology of 6G networks has emerged to facilitate ultrareliable and low-latency applications for sustainable smart cities which are infeasible with the existing 4G/5G standards. Therefore, the advanced technologies like machine learning (ML), block chain, and Internet of Things (IoT) utilizing 6G network are leveraged to develop cost-efficient mechanisms to address the issues of excess communication overhead in the present state of the art. Initially, the authors discussed the key vision of 6G communication technologies, its core technologies (such as visible light communication [VLC] and THz), and the existing issues with the existing network generations (such as 5G and 4G). A detailed analysis of benefits, challenges, and applications of blockchain-enabled IoT devices with application verticals like Smart city, smart factory plus, automation, and XR that form the key highlights for 6G wireless communication network is also presented. In addition, the key applications and latest research of artificial intelligence (AI) in 6G are discussed facilitating the dynamic spectrum allocation mechanism and mobile edge computing. Lastly, an in-depth study of the existing open issues and challenges in green 6G communication network technology, as well as review of solutions and potential research recommendations are also presented.     

Survey on recent advances in IoT application layer protocols and machine learning scope for research directions

The Internet of Things (IoT) has been growing over the past few years due to its flexibility and ease of use in real-time applications. The IoT's foremost task is ensuring that there is proper communication among different types of applications and devices, and that the application layer protocols fulfill this necessity. However, as the number of applications grows, it is necessary to modify or enhance the application layer protocols according to specific IoT applications, allowing specific issues to be addressed, such as dynamic adaption to network conditions and interoperability. Recently, several IoT application layer protocols have been enhanced and modified according to application requirements. However, no existing survey articles focus on these protocols. In this article, we survey traditional and recent advances in IoT application layer protocols, as well as relevant real-time applications and their adapted application layer protocols for improving performance. As changing the nature of protocols for each application is unrealistic, machine learning offers means of making protocols intelligent and is able to adapt dynamically. In this context, we focus on providing open challenges to drive IoT application layer protocols in such a direction.     

M2M的业务范畴及其技术发展

指出M2M是使用电信运营商提供的网络和业务支撑平台并提供给其他各种规模垂直行业的公共物联网,它是物联网应用的一个子集。M2M的主要技术包括Thingbased M2M技术、机器对机器直接通信技术和M2M通信网技术。M2M市场潜力巨大,但还存在很多不确定因素,需要我们开展更多研究。     

Data analytics in wireless systems and IoT issues and challenges

The emergence of data engineering along with the support of Online Social Networks is growing by millions every day due to the introduction of wireless systems and Internet of Things. The rapid growth of usage of smart devices helps to create new generation knowledge sharing platforms. Data Analytics has a major role to play in the growth and success of wireless and IoT applications. The growth of data has become exponential and is difficult to analyze. Many researchers depend on the data available on Wireless Systems and IoT for developing new generation services and applications. With the opportunity of information and communication technologies like heterogeneous networking, cloud computing, web services, crowd sensing and data mining, ubiquitous and asynchronous information sharing is feasible. But this also brings out a lot of provocations with respect to conflicting standards, data portability, data aggregation, data distribution, differential context and communication overhead. The smart information and communication technology has changed many features of human lifestyle: personal and work places.     

物联网技术与应用研究

 在解析物联网两层基本涵义的基础上,提出了包括底层网络分布、汇聚网关接入、互联网络融合、终端用户应用四部分的物联网系统架构;设计了由网络通信协议、网络控制平台、应用终端平台组成的面向物联网的网络协议体系,并从硬件和软件两个层面讨论了实现物联网的关键技术;在分析当前物联网标准、技术、安全以及应用方面存在问题的基础上提出了未来物联网发展的六个重要理念.     

Wormhole Attack Detection System for IoT Network: A Hybrid Approach

Many errors in data communication cause security attacks in Internet of Things (IoT). Routing errors at network layer are prominent errors in IoT which degrade the quality of data communication. Many attacks like sinkhole attack, blackhole attack, selective forwarding attack and wormhole attack enter the network through the network layer of the IoT. This paper has an emphasis on the detection of a wormhole attack because it is one of the most uncompromising attacks at the network layer of IoT protocol stack. The wormhole attack is the most disruptive attack out of all the other attacks mentioned above. The wormhole attack inserts information on incorrect routes in the network; it also alters the network information by causing a failure of location-dependent protocols thus defeating the purpose of routing algorithms. This paper covers the design and implementation of an innovative intrusion detection system for the IoT that detects a wormhole attack and the attacker nodes. The presence of a wormhole attack is identified using location information of any node and its neighbor with the help of Received Signal Strength Indicator (RSSI) values and the hop-count. The proposed system is energy efficient hence it is beneficial for a resource-constrained environment of IoT. It also provides precise true-positive (TPR) and false-positive detection rate (FPR).

     

POE在物联网中的应用与发展趋势

随着物联网时代的来临,物联网的各种应用正在不断增加和完善,对基础电源的需求更加迫切,本文主要探讨POE技术在物联网中的应用场景及其发展趋势。     

机器学习在物联网虚假用户识别中的运用

随着通信技术的发展,物联网卡和5G技术将得到大规模应用,但存在个别企业利用物联网卡资费便宜、没有实名制等特点从中非法牟利、破坏社会稳定的问题,不利于行业健康发展。因此如何识别虚假用户成为物联网行业研究的重要课题。主要研究了在实时海量的物联网终端数据中,如何运用机器学习模型高效地识别疑似虚假用户。具体来看,通过研究相关数据的特征,采用基于正样本和未标记样本的半监督式学习模型建立实时监控异常行为的模型,达到识别物联网行业中潜在虚假用户的目的。本研究成果体现在节约大量人力物力的同时,可以帮助相关部门、人员及时发现用户的异常行为,采取相应的措施避免产生较大损失,具有广泛的行业应用前景。     

Cognitive Radio Engine Design for IoT Using Real-Coded Biogeography-Based Optimization and Fuzzy Decision Making

The Internet of Things (IoT) paradigm expands the current Internet and enables communication through machine to machine, while posing new challenges. Cognitive radio (CR) Systems have received much attention over the last decade, because of their ability to flexibly adapt their transmission parameters to their changing environment. Current technology trends are shifting to the adaptability of cognitive radio networks into IoT. The determination of the appropriate transmission parameters for a given wireless channel environment is the main feature of a cognitive radio engine. For wireless multicarrier transceivers, the problem becomes high dimensional due to the large number of decision variables required. Evolutionary algorithms are suitable techniques to solve the above-mentioned problem. In this paper, we design a CR engine for wireless multicarrier transceivers using real-coded biogeography-based optimization (RCBBO). The CR engine also uses a fuzzy decision maker for obtaining the best compromised solution. RCBBO uses a mutation operator in order to improve the diversity of the population and enhance the exploration ability of the original BBO algorithm. The simulation results show that the RCBBO driven CR engine can obtain better results than the original BBO and outperform results from the literature. Moreover, RCBBO is more efficient when applied to high-dimensional problems in cases of multicarrier system.     

SPSIC: Semi-Physical Simulation for IoT Clouds

Recent years have witnessed various successful demonstrations of the emerging IoT technologies, while the researchers still need to face a lot of technological challenges. It is necessary to verify and evaluate the relevant theories and calculations before the application of IoT, and that is why building a simulation platform for the IoT becomes so important, especially for a large scale IoT to meet the requirement of a scale perception in a large scope. The IoT which always contains a complicated network and communication system has made the network simulation software OPNET Modeler to be a good choice for it. Moreover, the IoT has transfer all kinds of “objects” that humans need into the form of data by various sensing equipment and intelligent devices, and those data will be stored, analyzed and processed by cloud computing finally. The paper presents an innovative method to establish an intelligent, independent and expandable data driven IoT service platform by OPNET’s Semi-Physical Simulation to combine the simulated network for IoT with the real Cloud computing(SPSIC) which applies real network to achieve the long-term surveillance, management, sharing and analysis of the collected data at any time.     

物联网安全研究

物联网是计算机、互联网与移动通信网等相关技术的演进和延伸,其安全问题愈来愈受到关注。该文对物联网安全进行深入研究,对其体系结构进行分析,并将其应用模型抽象为“物、网、应用”;将其安全需求概括为“谁家的物归谁管,谁家的物归谁用”。该文将“物”抽象为四类,即感知类物、被感知类物、被控制类物及感知和被控制类物,并针对每一类“物”的应用提出了具体的解决方案,同时也提出了“网”与“应用”的安全解决方案。     

Cross-Layer QoS Enabled SDN-Like Publish/Subscribe Communication Infrastructure for IoT

Publish/subscribe paradigm is often adopted to create the communication infrastructure of the Internet of Things(IoT)for many clients to access enormous real-time sensor data.However,most current publish/subscribe middlewares are based on traditional ossified IP networks,which are difficult to enable Quality of Service(QoS).How to design the next generation publish/subscribe middleware has become an urgent problem.The emerging Software Defined Networking(SDN)provides new opportunities to improve the QoS of publish/subscribe facilities for delivering events in IoT owing to its customized programmability and centralized control.We can encode event topics,priorities and security policies into flow entries of SDN-enabled switches to satisfy personalized QoS needs.In this paper,we propose a cross-layer QoS enabled SDN-like publish/subscribe communication infrastructure,aiming at building an IoT platform to seamlessly connect IoT services with SDN networks and improving the QoS of delivering events.We first present an SDN-like topic-oriented publish/subscribe middleware architecture with a cross-layer QoS control framework.Then we discuss prototype implementation,including topic management,topology maintenance,event routing and policy management.In the end,we use differentiated services and cross-layer access control as cross-layer QoS scenarios to verify the prototype.Experimental results show that our middleware is effective.     

Performance evaluation of a priority‐based resource allocation scheme for multiclass services in IoT

Recently, the deployment of novel smart network concepts, such as the Internet of things (IoT) or machine‐to‐machine communication, has gained more attention owing to its role in providing communication among various smart devices. The IoT involves a set of IoT devices (IoTDs) such as actuators and sensors that communicate with IoT applications via IoT gateways without human intervention. The IoTDs have different traffic types with various delay requirements, and we can classify them into two main groups: critical and massive IoTDs. The fundamental promising technology in the IoT is the advanced long‐term evolution (LTE‐A). In the future, the number of IoTDs attempting to access an LTE‐A network in a short period will increase rapidly and, thus, significantly reduce the performance of the LTE‐A network and affect the QoS required by variant IoT traffic. Therefore, efficient resource allocation is required. In this paper, we propose a priority‐based allocation scheme for multiclass service in IoT to efficiently share resources between critical and massive IoTD traffic based on their specific characteristics while protecting the critical IoTDs, which have a higher priority over the massive IoTDs. The performance of the proposed scheme is analyzed using the Geo/G/1 queuing system focusing on QoS guarantees and resource utilization of both critical and massive IoTDs. The distribution of service time of the proposed system is determined and, thus, the average waiting and service times are derived. The results indicate that the performance of the massive IoTDs depends on the data traffic characteristics of the critical IoTDs. Furthermore, the results emphasize the importance of the system delay analysis and demonstrate its effects on IoT configurations.     

Fog-Sec: Secure end-to-end communication in fog-enabled IoT network using permissioned blockchain system

The technological integration of the Internet of Things (IoT)-Cloud paradigm has enabled intelligent linkages of things, data, processes, and people for efficient decision making without human intervention. However, it poses various challenges for IoT networks that cannot handle large amounts of operation technology (OT) data due to physical storage shortages, excessive latency, higher transfer costs, a lack of context awareness, impractical resiliency, and so on. As a result, the fog network emerged as a new computing model for providing computing capacity closer to IoT edge devices. The IoT-Fog-Cloud network, on the other hand, is more vulnerable to multiple security flaws, such as missing key management problems, inappropriate access control, inadequate software update mechanism, insecure configuration files and default passwords, missing communication security, and secure key exchange algorithms over unsecured channels. Therefore, these networks cannot make good security decisions, which are significantly easier to hack than to defend the fog-enabled IoT environment. This paper proposes the cooperative flow for securing edge devices in fog-enabled IoT networks using a permissioned blockchain system (pBCS). The proposed fog-enabled IoT network provides efficient security solutions for key management issues, communication security, and secure key exchange mechanism using a blockchain system. To secure the fog-based IoT network, we proposed a mechanism for identification and authentication among fog, gateway, and edge nodes that should register with the blockchain network. The fog nodes maintain the blockchain system and hold a shared smart contract for validating edge devices. The participating fog nodes serve as validators and maintain a distributed ledger/blockchain to authenticate and validate the request of the edge nodes. The network services can only be accessed by nodes that have been authenticated against the blockchain system. We implemented the proposed pBCS network using the private Ethereum 2.0 that enables secure device-to-device communication and demonstrated performance metrics such as throughput, transaction delay, block creation response time, communication, and computation overhead using state-of-the-art techniques. Finally, we conducted a security analysis of the communication network to protect the IoT edge devices from unauthorized malicious nodes without data loss.     

Area,energy, and time assessment for a distributed TPM for distributed trust in IoT clusters

IoT clusters arise from natural human societal clusters such as a house, an airport, and a highway. IoT clusters are heterogeneous with a need for device to device as well as device to user trust. The IoT devices are likely to be thin computing clients. Due to low cost, an individual IoT device is not built to be fault tolerant through redundancy. Hence the trust protocols cannot take the liveness of a device for granted. In fact, the differentiation between a failing device and a malicious device is difficult from the trust protocol perspective. We present a minimal distributed trust layer based on distributed consensus like operations. These distributed primitives are cast in the context of the APIs supported by a trusted platform module (TPM). TPM with its 1024 bit RSA is a significant burden on a thin IoT design. We use RNS based slicing of a TPM where in each slice resides within a single IoT device. The overall TPM functionality is distributed among several IoT devices within a cluster. The VLSI area, energy, and time savings of such a distributed TMP implementation is assessed. A sliced/distributed TPM is better suited for an IoT environment based on its resource needs. We demonstrate over 90% time reduction, over 3% area reduction, and over 90% energy reduction per IoT node in order to support TPM protocols.     

A Self-Optimizing QoS-Based Access for IoT Environments

Nowadays, providing Internet of Things (IoT) environments with service level guarantee is a challenging task. Moreover, IoT services should be autonomous in order to minimize human intervention and thus to reduce the operational management cost of the corresponding big scale infrastructure. We describe in this paper a service level-based IoT architecture enabling the establishment of an IoT Service Level Agreement (iSLA) between an IoT Service Provider (IoT-SP) and an IoT Client (IoT-C). The proposed iSLA specifies the requirements of an IoT service, used in a specific application domain (e-health, smart cities, etc.), in terms of different measurable Quality of Service (QoS) parameters. In order to achieve this agreement, several QoS mechanisms are to be implemented within each layer of the IoT architecture. In this context, we propose an adaptation of the IEEE 802.15.4 slotted CSMA/CA mechanism to provide different IoT services with QoS guarantee. Our proposal called QBAIoT (QoS-based Access for IoT) creates different Contention Access Periods (CAP) according to different traffic types of the IoT environment. These CAPs are QoS-based and enable traffic differentiation. Thus, a QoS CAP is configured with several slots during which only IoT objects belonging to the same QoS class can send their data. Furthermore, we specify a self-management closed control loop in order to provide our IoT architecture with a self-optimizing capability concerning QoS CAPs slots allocation. This capability takes into account the actual usage of QoS CAPs as well as the characteristics of the corresponding traffic class.

     

A random access channel resources allocation approach to control machine-to-machine communication congestion over LTE-advanced networks

The Internet of Things (IoT) is becoming a reality, and one of the core elements to make this reality come true is machine-to-machine (M2M) communication. With the fast-growing rate in which devices are being deployed, communication among them has become crucial to support the development of IoT applications. The Long-Term Evolution-Advanced (LTE-A) is a potential access network for these M2M devices. However, the LTE-A inherited characteristics from older cellular network standards, which were designed for human-to-human (H2H) and human-to-machine (H2M) communication. Accordingly, supporting M2M communication poses some challenges to LTE-A, with a highlight of the congestion and overload problems in the radio access network (RAN) during the random access channel (RACH) procedure. Such a problem arises due to the large number of devices sending access-request messages to the network and the limited number of resources to satisfy this new demand. In this paper, we introduce a solution to mitigate the impact of M2M communication in the LTE-A network. Precisely, we model how to divide the random access resources into different types of devices as a bankruptcy problem. Then, we propose two solutions: (i) A game theory approach to formulate the bankruptcy problem as a transferable utility game, and (ii) an axiomatic method where some elements are considered for judging the amount of resources each class should receive. The simulation results show that our approaches present improvements in terms of energy efficiency, impact control of M2M over H2H accesses, and priority respect among the different classes of devices.     

A review on evolving domains of Internet of Things: Architecture,applications, and technical challenges

The Internet of Things (IoT) is a system that includes smart items with different sensors, advanced technologies, analytics, cloud servers, and other wireless devices that integrate and work together to create an intelligent environment that benefits end users. With its wide spectrum of applications, IoT is revolutionizing both the current and future generations of the Internet. IoT systems can be employed for broad-ranging real applications, such as agriculture, the environment, cities, healthcare, and the industrial sector. In this paper, we briefly discuss the three-tier architectural view of IoT, its different communication technologies, and the smart sensors. Moreover, we study various application areas of IoT such as the environmental domain, healthcare, agriculture, smart cities, and industrial, commercial, and general aspects. A critical analysis is shown for the existing schemes and techniques related to this work. Further, this paper addresses the basic context, tools and evaluation approaches, future scope, and the advantages and disadvantages of the aforestated IoT applications. A comprehensive analysis is provided for each domain along with its fundamental parameters like the quality of service (QoS), network longevity, scalability, energy efficiency, accuracy, and cost. Finally, this study highlights the technical challenges and open research problems existing in different IoT applications.