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.     

A survey on communication protocols and performance evaluations for Internet of Things

The Internet of Things (IoT) is a large-scale network of devices capable of sensing, data processing, and communicating with each other through different communication protocols. In today's technology ecosystem, IoT interacts with many application areas such as smart city, smart building, security, traffic, remote monitoring, health, energy, disaster, agriculture, industry. The IoT network in these scenarios comprises tiny devices, gateways, and cloud platforms. An IoT network is able to keep these fundamental components in transmission under many conditions with lightweight communication protocols taking into account the limited hardware features (memory, processor, energy, etc.) of tiny devices. These lightweight communication protocols affect the network traffic, reliability, bandwidth, and energy consumption of the IoT application. Therefore, determining the most proper communication protocol for application developers emerges as an important engineering problem. This paper presents a straightforward overview of the lightweight communication protocols, technological advancements in application layer for the IoT ecosystem. The survey then analyzes various recent lightweight communication protocols and reviews their strengths and limitations. In addition, the paper explains the experimental comparison of Constrained Applications Protocol (CoAP), Message Queuing Telemetry (MQTT), and WebSocket protocols, more convenient for tiny IoT devices. Finally, we discuss future research directions of communication protocols for IoT.     

IoTScal‐H : Hybrid monitoring solution based on cloud computing for autonomic middleware‐level scalability management within IoT systems and different SLA traffic requirements

Wireless sensor network (WSN) technologies have enabled ubiquitous sensing to intersect many areas of modern day living. The creation of these devices offers the ability to get, gather, exchange, and consume environmental measurement from the physical world in a communicating‐actuating network, called the Internet of Things (IoT). As the number of physical world objects from heterogeneous network environments grows, the data produced by these objects raise uncontrollably, bringing a delicate challenge into scalability management in the IoT networks. Cloud computing is a much more mature technology, offering unlimited virtual capabilities in terms of storage capacity and processing power. Ostensibly, it seems that cloud computing and IoT are evolving independently on their own paths, but in reality, the integration of clouds with IoT will lead to deal with the inability to scale automatically depending on the overload caused by the drastic growth of the number of connected devices and/or by the huge amount of exchanged data in the IoT networks. In this paper, our objective is to promote the scalability management, using hybrid mechanism that will combine traffic‐oriented mechanism and resources‐oriented mechanism, with adaption actions. By the use of autonomic middleware within IoT systems, we seek to improve the monitoring components's architectural design, based on cloud computing‐oriented scalability solution. The intention is to maximize the number of satisfied requests, while maintaining at an acceptable QoS level of the system performances (RTT of the system, RAM, and CPU of the middleware). In order to evaluate our solution performance, we have performed different scenarios testbed experiments. Generally, our proposed results are better than those mentioned as reference.     

Research on scheduling method based on traffic matrix for IoT security

With the increasing number of sensors in the Internet of Things (IoT), network nodes are prone to load imbalance, which seriously affects the reliability and scalability of the IoT. In this paper, a method of load balancing is proposed to reduce the pressure of switches in IoT and improve the efficiency of switch. The method is based on software defined network (SDN) technology using dynamic traffic scheduling of server cluster (DTSSC) model. It obtains the traffic and load matrices for each server and achieves load balancing on each of them. This method has high efficiency and low overhead and is easy to deploy. The validity of this method is verified through experiments; ie, the load ratio of each server is balanced.     

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.     

基于高性能网络处理器的NIPS设计与实现

在高速网络环境下,利用入侵防御系统（IPS）对全部的网络流量进行检测是一项十分巨大挑战。网络处理器是专门处理和转发网络数据流的高速可编程处理器,在网络交换及通信设备中有着十分广泛的应用。论文首先介绍IPS的特点,及其在网络安全中的重要作用;接着,详细介绍Intel高性能网络处理器的硬件组成和框架,并给出一种基于Intel高性能网络处理器的NIPS的具体设计与实现方案。     

Blockchain-envisioned access control for internet of things applications: a comprehensive survey and future directions

Telecommunication Systems - With rapid advancements in the technology, almost all the devices around are becoming smart and contribute to the Internet of Things (IoT) network. When a new IoT device...     

基于流量预测的物联网卫星节点动态缓存分配路由策略

针对低轨物联网卫星系统的路由问题,提出了基于流量预测的物联网卫星节点动态缓存分配路由策略。首先,分析低轨卫星覆盖区域内业务分布的时空特性,提出了端到端流量预测方法。然后,根据流量预测结果,提出了动态缓存分配路由策略。卫星节点通过对星间链路的流量负载进行周期性监测,动态分配与邻居节点间各条星间链路的缓存资源,分为初始化和系统运行2个阶段。同时,提出了节点拥塞时的业务分流及数据分组转发策略,通过比较排队时延和转发时延的大小,决定数据分组是否需要进行重路由。仿真结果表明,所提路由策略有效地降低了分组丢失率及平均端到端时延,改善了业务在全网的分布情况。     

A New Method to Find a High Reliable Route in IoT by Using Reinforcement Learning and Fuzzy Logic

Recently, Internet is moving quickly toward the interaction of objects, computing devices, sensors, and which are usually indicated as the Internet of things (IoT). The main monitoring infrastructure of IoT systems main monitoring infrastructure of IoT systems is wireless sensor networks. A wireless sensor network is composed of a large number of sensor nodes. Each sensor node has sensing, computing, and wireless communication capability. The sensor nodes send the data to a sink or a base station by using wireless transmission techniques However, sensor network systems require suitable routing structure to optimizing the lifetime. For providing reasonable energy consumption and optimizing the lifetime of WSNs, novel, efficient and economical schemes should be developed. In this paper, for enhancing network lifetime, a novel energy-efficient mechanism is proposed based on fuzzy logic and reinforcement learning. The fuzzy logic system and reinforcement learning is based on the remained energies of the nodes on the routes, the available bandwidth and the distance to the sink. This study also compares the performance of the proposed method with the fuzzy logic method and IEEE 802.15.4 protocol. The simulations of the proposed method which were carried out by OPNET (Optimum Network performance) indicated that the proposed method performed better than other protocols such as fuzzy logic and IEEE802.15.4 in terms of power consumption and network lifetime.

     

CoFEE: Context-aware futuristic energy estimation model for sensor nodes using Markov model and autoregression

Nowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth. Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs). It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internet-connected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series.     

基于多层模式匹配技术的高速以太网NIDS实现方案

目前多数基于网络的入侵检测系统(NIDS)无法适用于对高速以太网链路的实时流量分析和入侵检测任务.本文在传统模式匹配方法的基础上,引入了基于协议分析的多层模式匹配概念:采用FPGA硬件逻辑对长度和偏移量相对固定的数据包包首部分进行模式匹配;采用核心态软件逻辑对长度和偏移量变化的数据包负载部分进行模式匹配.新的模式匹配技术有效提高了NIDS的整体性能.最后,本文给出了一种基于多层模式匹配的高速以太网NIDS实现方案.并对FPGA硬件逻辑和核心态软件逻辑采用的检测策略进行了详细说明.     

UAV-based LoRaWAN flying gateway for the internet of flying things

The Internet of Things (IoT) is one of the paradigms related to the evolution of telecommunication networks which is contributing to the evolution of numerous use cases, such as smart city and smart agriculture. However, the current communication infrastructure and wireless communication technologies are not always able to guarantee a proper service for these IoT scenarios. Smart solutions are needed to overcome current terrestrial network limitations offering a cost-effective way to extend the current terrestrial network coverage. For example, temporary extensions “on-request” of the terrestrial infrastructure may be a viable solution to allow collecting data generated by nodes outside the current network coverage. Flying objects can help achieve this goal. Various studies supported the use of unmanned aerial vehicles (UAVs) as intermediate nodes between IoT devices and the network. However, such solutions have not been exhaustively tested yet in real-case scenarios. This paper proposes an efficient solution to collect data from multiple IoT sensors in rural and remote areas based on UAVs. It describes the implementation of the proposed UAV-based Long RangeWide Area Network (LoRaWAN) flying gateway able to collect data directly from LoRaWAN sensors during its flight, keep them stored in an onboard memory, and forward them at the end of its flying path to a platform where the authorized users can access them. A prototype of the gateway has been developed to assess the proposed solution through both indoor and outdoor tests aiming to test its feasibility both in terms of communication performance and UAV-required hardware resources.     

Challenges and research directions for Internet of Things

The emergence of Internet of Things (IoT) is empowered by the availability of the high volume of smart sensors, Radio Frequency Identification, a suitable communication technologies and protocols. In the near future, the Internet will be full of heterogeneous connected devices. In recent years, the IoT has drawn significant attention as it can solve difficult problems. However, the heterogeneity of devices and the large scale networks expose the IoT to many challenges that must be addressed; otherwise, the systems performance will deteriorate. As an attempt to identify these challenges, this paper comprehensibly cites the main IoT concepts, the serious IoT challenges and the quality of services presented in the recent literature. It also investigates the corresponding main research directions and the proposed solutions. This paper can increase the knowledge of the reader since it is the first IoT survey that presents load balancing algorithms utilized in solving the extreme data storage challenge.     

An elastic intrusion detection system for software networks

Internal users are the main causes of anomalous and suspicious behaviors in a communication network. Even when traditional security middleboxes are present, internal attacks may lead the network to outages or to leakage of sensitive information. In this article, we propose BroFlow, an Intrusion Detection and Prevention System based on Bro traffic analyzer and on the global network view of the software-defined networks (SDN) which is provided by the OpenFlow. BroFlow main contributions are (i) dynamic and elastic resource provision of traffic-analyzing machines under demand; (ii) real-time detection of DoS attacks through simple algorithms implemented in a policy language for network events; (iii) immediate reaction to DoS attacks, dropping malicious flows close of their sources, and (iv) near-optimal placement of sensors through a proposed heuristic for strategically positioning sensors in the network infrastructure, which is shared by multi-tenants, with a minimum number of sensors. We developed a prototype of the proposed system, and we evaluated it in a virtual environment of the Future Internet Testbed with Security (FITS). An evaluation of the system under attack shows that BroFlow guarantees the forwarding of legitimate packets at the maximal link rate, reducing up to 90 % of the maximal network delay caused by the attack. BroFlow reaches 50 % of bandwidth gain when compared with conventional firewalls approaches, even when the attackers are legitimate tenants acting in collusion. In addition, the system reduces the sensors number, while keeping full coverage of network flows.     

Fuzzy logic–based distributed clustering protocol to improve energy efficiency and stability of wireless smart sensor networks for farmland monitoring systems

Wireless smart sensor networks (WSSNs) are emerging as the physical backbone of the internet of things (IoT) technology. On the basis of the IoT platform, web‐based systems and services are been developing such as e‐surveillance, industrial‐IoT, and precision agriculture. For farmland monitoring systems, WSSNs need to be scalable in terms of coverage area. Sensor nodes are energy‐constrained devices, and hence, many energy‐efficient clustering protocols are developed in the literature. But these methods overload the cluster leaders (CLs) with cluster computation and data communication costs. An improper CL selection may lead to the early death of such nodes and hence does not prolong the network lifetime stability. We propose a fuzzy logic (FL)–based distributed clustering protocol to enhance the energy efficiency of WSSN while maximizing the coverage area. The load of CLs is shared by originators and super‐CLs (SCLs) selected in the network. The wireless link and received signal strength (RSS) are greatly affected by environmental conditions and thus cannot be considered as ideal network parameters. We use FL systems to tackle the uncertainty of such network parameters. The proposed protocol is simulated for different scalable WSSNs. The results indicate that the proposed protocol provides better lifetime stability than the recent conventional protocols. The functionalities of the protocol are proposed considering the recent wireless standards. Hence, the proposed protocol can be suitably implemented for farmland monitoring systems.     

低轨卫星星座物联网业务量建模

随着物联网(IoT)规模的不断发展,其业务需求呈现出多样化、全球化的趋势。针对地面物联网无法覆盖全球的缺点,卫星物联网尤其是低轨卫星星座(LEOSC)物联网可以有效地为地面物联网提供覆盖性能上的补充和延伸。由于低轨卫星星座物联网系统广覆盖、高动态的特点,其业务量统计特性需要考虑到环境因素造成的影响,这导致其业务量分布与地面物联网存在显著差异。从合理高效利用星上有限资源角度出发,该文研究基于低轨卫星星座的全球物联网业务模型。结合多样化的业务特点以及卫星通信系统特性,采用统计建模理论,得出了全球物联网业务模型框架。并且初步提出了一种基于最高优先级的接入策略,以供设备节点实时选择接入的卫星。仿真结果表明：泊松过程可以用于近似模拟低轨卫星物联网中大量存在的异步流量的叠加过程;由于低轨卫星具有高动态性,其业务源高速变化,导致了卫星业务忙闲不均,峰均比(PAR)较高。     

Group and hierarchical key management for secure communications in internet of things

Different devices with different characteristics form a network to communicate among themselves in Internet of Things (IoT). Thus, IoT is of heterogeneous in nature. Also, Internet plays a major role in IoT. So, issues related to security in Internet become issues of IoT also. Hence, the group and hierarchical management scheme for solving security issues in Internet of Things is proposed in this paper. The devices in the network are formed into groups. One of the devices is selected as a leader of each group. The communication of the devices from each group takes place with the help of the leader of the corresponding group using encrypted key to enhance the security in the network. Blom's key predistribution technique is used to establish secure communication among any nodes of group. The hierarchy is maintained such that the security can be increased further, but the delay is increased as it takes time to encrypt at every level of hierarchy. Hence, the numbers of levels of hierarchy need to be optimized such that delay is balanced. Hence, this algorithm is more suitable for delay‐tolerant applications. The performance of the proposed Algorithm is evaluated and is proved to perform better when compared with the legacy systems like Decentralized Batch‐based Group Key Management Protocol for Mobile Internet of Things (DBGK).     

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.     

Coverage and Connectivity-Based 3D Wireless Sensor Deployment Optimization

The wireless sensor network technology of Internet of Things (IoT) senses, collects and processes the data from its interconnected intelligent sensors to the base station. These sensors help the IoT to understand the environmental change and respond towards it. Thus sensor placement is a crucial device of IoT for efficient coverage and connectivity in the network. Many existing works focus on optimal sensor placement for two dimensional terrain but in various real-time applications sensors are often deployed over three-dimensional ambience. Therefore, this paper proposes a vertex coloring based sensor deployment algorithm for 3D terrain to determine the sensor requirement and its optimal spot and to obtain 100% target coverage. Further, the quality of the connectivity of sensors in the network is determined using Breadth first search algorithm. The results obtained from the proposed algorithm reveal that it provides efficient coverage and connectivity when compared with the existing methods.

     

SVELTE: Real-time intrusion detection in the Internet of Things

In the Internet of Things (IoT), resource-constrained things are connected to the unreliable and untrusted Internet via IPv6 and 6LoWPAN networks. Even when they are secured with encryption and authentication, these things are exposed both to wireless attacks from inside the 6LoWPAN network and from the Internet. Since these attacks may succeed, Intrusion Detection Systems (IDS) are necessary. Currently, there are no IDSs that meet the requirements of the IPv6-connected IoT since the available approaches are either customized for Wireless Sensor Networks (WSN) or for the conventional Internet.In this paper we design, implement, and evaluate a novel intrusion detection system for the IoT that we call SVELTE. In our implementation and evaluation we primarily target routing attacks such as spoofed or altered information, sinkhole, and selective-forwarding. However, our approach can be extended to detect other attacks. We implement SVELTE in the Contiki OS and thoroughly evaluate it. Our evaluation shows that in the simulated scenarios, SVELTE detects all malicious nodes that launch our implemented sinkhole and/or selective forwarding attacks. However, the true positive rate is not 100%, i.e., we have some false alarms during the detection of malicious nodes. Also, SVELTE’s overhead is small enough to deploy it on constrained nodes with limited energy and memory capacity.