Load Balancing Based on Multi-Agent Framework to Enhance CloudEnvironment

According to the advances in users’ service requirements, physical hardware accessibility, and speed of resource delivery, Cloud Computing (CC) is an essential technology to be used in many fields. Moreover, the Internet of Things (IoT) is employed for more communication flexibility and richness that are required to obtain fruitful services. A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents. This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number of IoT devices with low resources. Different agents are integrated based on relevant features of behavioral interaction using classification techniques to balance the workload. A load balancing algorithm is developed to serve users’ requests to improve the solution of workload problems with an efficient distribution. The activity task from IoT devices has been classified by feature selection methods in the preparatory phase to optimize the scalability of CC. Then, the server’s availability is checked and the classified task is assigned to its suitable server in the main phase to enhance the cloud environment performance. Multi-agent load balancing framework is succeeded to cope with the importance of using large-scale requirements of CC and (low resources and large number) of IoT.     

基于物联网的爆破振动无线监测系统

针对工程爆破振动监测系统不断提出的新要求,基于新兴的物联网概念,结合无线网络技术,设计并研制了基于物联网的无线爆破振动监测系统。采用ARM为主控芯片的嵌入式操作系统,内嵌无线传输模块实时传输爆破数据,具有RFID设备认证功能和GPS全球卫星定位和精确定时功能,用户仅需登陆终端爆破监测管理系统即可获取爆破现场实时数据。基于物联网的爆破振动无线监测系统,能够大大地降低人工现场布线工作量、组网灵活、综合成本低,可广泛应用于各个领域的工程爆破振动监测,极具应用前景和市场推广价值。     

ECC: Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing

Due to the explosion of network data traffic and IoT devices, edge servers are overloaded and slow to respond to the massive volume of online requests. A large number of studies have shown that edge caching can solve this problem effectively. This paper proposes a distributed edge collaborative caching mechanism for Internet online request services scenario. It solves the problem of large average access delay caused by unbalanced load of edge servers, meets users’ differentiated service demands and improves user experience. In particular, the edge cache node selection algorithm is optimized, and a novel edge cache replacement strategy considering the differentiated user requests is proposed. This mechanism can shorten the response time to a large number of user requests. Experimental results show that, compared with the current advanced online edge caching algorithm, the proposed edge collaborative caching strategy in this paper can reduce the average response delay by 9%. It also increases the user utility by 4.5 times in differentiated service scenarios, and significantly reduces the time complexity of the edge caching algorithm.     

基于物联网的爆破振动无线监测系统

针对工程爆破振动监测系统不断提出的新要求,基于新兴的物联网概念,结合无线网络技术,设计并研制了基于物联网的无线爆破振动监测系统。采用ARM为主控芯片的嵌入式操作系统,内嵌无线传输模块实时传输爆破数据,具有RFID设备认证功能和GPS全球卫星定位和精确定时功能,用户仅需登陆终端爆破监测管理系统即可获取爆破现场实时数据。基于物联网的爆破振动无线监测系统,能够大大地降低人工现场布线工作量、组网灵活、综合成本低,可广泛应用于各个领域的工程爆破振动监测,极具应用前景和市场推广价值。     

Intelligent Real-Time IoT Traffic Steering in 5G Edge Networks

In the Next Generation Radio Networks (NGRN), there will be extreme massive connectivity with the Heterogeneous Internet of Things (HetIoT) devices. The millimeter-Wave (mmWave) communications will become a potential core technology to increase the capacity of Radio Networks (RN) and enable Multiple-Input and Multiple-Output (MIMO) of Radio Remote Head (RRH) technology. However, the challenging key issues in unfair radio resource handling remain unsolved when massive requests are occurring concurrently. The imbalance of resource utilization is one of the main issues occurs when there is overloaded connectivity to the closest RRH receiving exceeding requests. To handle this issue effectively, Machine Learning (ML) algorithm plays an important role to tackle the requests of massive IoT devices to RRH with its obvious capacity conditions. This paper proposed a dynamic RRH gateways steering based on a lightweight supervised learning algorithm, namely K-Nearest Neighbor (KNN), to improve the communication Quality of Service (QoS) in real-time IoT networks. KNN supervises the model to classify and recommend the user’s requests to optimal RRHs which preserves higher power. The experimental dataset was generated by using computer software and the simulation results illustrated a remarkable outperformance of the proposed scheme over the conventional methods in terms of multiple significant QoS parameters, including communication reliability, latency, and throughput.     

An Internet of Energy Things Based on Wireless LPWAN

Under intense environmental pressure, the global energy sector is promoting the integration of renewable energy into interconnected energy systems. The demand-side management (DSM) of energy systems has drawn considerable industrial and academic attention in attempts to form new flexibilities to respond to variations in renewable energy inputs to the system. However, many DSM concepts are still in the experimental demonstration phase. One of the obstacles to DSM usage is that the current information infrastructure was mainly designed for centralized systems, and does not meet DSM requirements. To overcome this barrier, this paper proposes a novel information infrastructure named the Internet of Energy Things (IoET) in order to make DSM practicable by basing it on the latest wireless communication technology: the low-power wide-area network (LPWAN). The primary advantage of LPWAN over general packet radio service (GPRS) and area Internet of Things (IoT) is its wide-area coverage, which comes with minimum power consumption and maintenance costs. Against this background, this paper briefly reviews the representative LPWAN technologies of narrow-band Internet of Things (NB-IoT) and Long Range (LoRa) technology, and compares them with GPRS and area IoT technology. Next, a wireless-to-cloud architecture is proposed for the IoET, based on the main technical features of LPWAN. Finally, this paper looks forward to the potential of IoET in various DSM application scenarios.     

Dynamic trust management for secure communications in social internet of things (SIoT)

The world has faced three Information and Communication Technology (ICT) revolutions and the third ICT wave led to Internet of Things, the notion of anything, everything, anytime and everywhere. Out of the many visions of IoT, one revolutionary concept is to make IoT sociable i.e., incorporating social networking within Internet of Things. This revolution has led to the notion of Social Internet of Things (SIoT). Establishing a SIoT network or community is not so simple and requires integration of heterogeneous technology and communication solutions. This paper focuses on establishing a secure and reliable communication over nodes in SIoT by computing trust dynamically among neighboring nodes. Trust Management is an important area that has attracted numerous researchers over the past few years. The proposed DTrustInfer computes trust based on first hand observation, second hand observation, centrality and dependability factor of a node. Properties of trust such as honesty, cooperativeness, community interest and energy of a node are considered for computing trust. Also, this paper ensures secure communication among SIoT nodes through simple secret codes. Experimental results show that the proposed DTrustInfer outperforms the existing trust models significantly.     

物联网技术在物流包装应用中的问题及对策

目的研究物联网技术在物流包装中的应用进展,提出待解决问题的对策。方法运用物联网和包装管理的基本原理,采用探究性、描述性和因果性研究方法系统阐释其工作机理。结果应提高芯片技术研发和生产能力,创建可视化智能管理系统,加强物流包装EDI技术的开发和应用,提高包装信息响应,加强电子产品代码(EPC)网络管理,提高网络安全技术水平,从而加强物联网技术在物流包装领域中的推广和应用。结论物流包装领域可通过实施物联网技术实现持续改进的目标。     

Networked Predictive Control Over the Internet Using Round-Trip Delay Measurement

With the rapid development of network technology, the potential use of networked real-time control and automation is enormous and appealing. However, closed-loop control systems via the Internet are very difficult to implement practically due to their stochastic nature. The large and random time delay and data dropout caused by data transmission impact the performance of the control system and even result in system instability. To address this problem, this paper proposes a methodology to modify and enhance the conventional system so that it can be used over the Internet. This paper describes a model-based networked predictive control scheme based on round-trip time delay measurement rather than separate consideration of the feedback channel delay (between the sensor and controller) and the forward channel delay (between the controller and actuator), which successfully avoids the requirement of synchronization. It is required by many schemes but is actually hard to achieve over the Internet. Stability for both fixed and random network transmission delays is also investigated in this paper. To illustrate the control performance improvement of the proposed control scheme, the results ofoff-line simulations and real-time experiments via the Internet are presented.      

天津市农业物联网标准体系研究

近年来,随着物联网技术在农业生产中的广泛应用,农业物联网技术的标准问题已引起了社会的普遍关注。为了保证此项技术在天津市农业生产中运用得当,开展农业物联网标准化工作势在必行。本文针对目前这种情况,构建了一整套科学完整的农业物联网标准体系,用于强化标准实施监督,夯实标准化技术基础,促进农业物联网有序发展,为天津在农业现代化产业发展方面提供重要的标准技术支撑。     

A Lightweight Three-Factor User Authentication Protocol for the Information Perception of IoT

With the development of computer hardware technology and network technology, the Internet of Things as the extension and expansion of traditional computing network has played an increasingly important role in all professions and trades and has had a tremendous impact on people lifestyle. The information perception of the Internet of Things plays a key role as a link between the computer world and the real world. However, there are potential security threats in the Perceptual Layer Network applied for information perception because Perceptual Layer Network consists of a large number of sensor nodes with weak computing power, limited power supply, and open communication links. We proposed a novel lightweight authentication protocol based on password, smart card and biometric identification that achieves mutual authentication among User, GWN and sensor node. Biometric identification can increase the non-repudiation feature that increases security. After security analysis and logical proof, the proposed protocol is proven to have a higher reliability and practicality.     

Network Traffic Obfuscation System for IIoT-Cloud Control Systems

One of the latest technologies enabling remote control, operational efficiency upgrades, and real-time big-data monitoring in an industrial control system (ICS) is the IIoT-Cloud ICS, which integrates the Industrial Internet of Things (IIoT) and the cloud into the ICS. Although an ICS benefits from the application of IIoT and the cloud in terms of cost reduction, efficiency improvement, and real-time monitoring, the application of this technology to an ICS poses an unprecedented security risk by exposing its terminal devices to the outside world. An adversary can collect information regarding senders, recipients, and prime-time slots through traffic analysis and use it as a linchpin for the next attack, posing a potential threat to the ICS. To address this problem, we designed a network traffic obfuscation system (NTOS) for the IIoT-Cloud ICS, based on the requirements derived from the ICS characteristics and limitations of existing NTOS models. As a strategy to solve this problem wherein a decrease in the traffic volume facilitates traffic analysis or reduces the packet transmission speed, we proposed an NTOS based on packet scrambling, wherein a packet is split into multiple pieces before transmission, thus obfuscating network analysis. To minimize the ICS modification and downtime, the proposed NTOS was designed using an agent-based model. In addition, for the ICS network traffic analyzer to operate normally in an environment wherein the NTOS is applied, a rule-based NTOS was adopted such that the actual traffic flow is known only to the device that is aware of the rule and is blocked for attackers. The experimental results verified that the same time requested for response and level of difficulty of analysis were maintained by the application of an NTOS based on packet scrambling, even when the number of requests received by the server per second was reduced. The network traffic analyzer of the ICS can capture the packet flow by using the pre-communicated NTOS rule. In addition, by designing an NTOS using an agent-based model, the impact on the ICS was minimized such that the system could be applied with short downtime.     

Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data, collected from various data providers. Then, ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process. In this study, the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers, where IoT data is encrypted and recorded in a distributed ledger. The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts. To examine the performance of the proposed method, it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD) from UCI AI repository. The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.     

Energy-Efficient and Blockchain-Enabled Model for Internet of Things (IoT) in Smart Cities

Wireless sensor networks (WSNs) and Internet of Things (IoT) have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart cities. The data generated from these sensors are used by smart cities to strengthen their infrastructure, utilities, and public services. WSNs are suitable for long periods of data acquisition in smart cities. To make the networks of smart cities more reliable for sensitive information, the blockchain mechanism has been proposed. The key issues and challenges of WSNs in smart cities is efficiently scheduling the resources; leading to extending the network lifetime of sensors. In this paper, a linear network coding (LNC) for WSNs with blockchain-enabled IoT devices has been proposed. The consumption of energy is reduced for each node by applying LNC. The efficiency and the reliability of the proposed model are evaluated and compared to those of the existing models. Results from the simulation demonstrate that the proposed model increases the efficiency in terms of the number of live nodes, packet delivery ratio, throughput, and the optimized residual energy compared to other current techniques.     

一种P2P文件共享系统汇聚拥塞控制机制

针对现有P2P文件共享系统采用并发多连接的文件传输方式,过分占用网络带宽资源,导致其它传统互联网业务性能低下的问题,提出了一种P2P文件共享系统汇聚拥塞控制机制(ACCM).ACCM采用应用层网络测量技术感知节点接入网链路拥塞状况,依据网络拥塞状况动态地调整P2P文件共享系统并发文件传输连接窗口,在最大化网络带宽利用率的基础上实现对传统互联网应用的友好性.网络实验结果表明,在网络拥塞发生时,ACCM能够促使P2P文件共享系统并发连接窗口主动退避,实现和传统互联网应用的和平共处;在网络空闲时,ACCM能够促使P2P文件共享系统扩大并发连接窗口,提高网络带宽资源的利用率.     

Service Scheduling Based on Edge Computing for Power Distribution IoT

With the growing amounts of multi-micro grids, electric vehicles, smart home, smart cities connected to the Power Distribution Internet of Things (PD-IoT) system, greater computing resource and communication bandwidth are required for power distribution. It probably leads to extreme service delay and data congestion when a large number of data and business occur in emergence. This paper presents a service scheduling method based on edge computing to balance the business load of PD-IoT. The architecture, components and functional requirements of the PD-IoT with edge computing platform are proposed. Then, the structure of the service scheduling system is presented. Further, a novel load balancing strategy and ant colony algorithm are investigated in the service scheduling method. The validity of the method is evaluated by simulation tests. Results indicate that the mean load balancing ratio is reduced by 99.16% and the optimized offloading links can be acquired within 1.8 iterations. Computing load of the nodes in edge computing platform can be effectively balanced through the service scheduling.     

Network Invulnerability Enhancement Algorithm Based on WSN ClosenessCentrality

Wireless Sensor Network (WSN) is an important part of the Internet of Things (IoT), which are used for information exchange and communication between smart objects. In practical applications, WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption, etc. In order to overcome these problems, a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network. Also, a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics. Simulation results show that the life cycle and data transmission volume of the network can be improved with a lower network construction cost, and the invulnerability of the network is effectively enhanced.     

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.     

Exploring self-organization and self-adaption for smart manufacturing complex networks

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.     

A Fair Blind Signature Scheme to Revoke Malicious Vehicles in VANETs

With the rapid development of IoT (Internet of Things), VANETs (Vehicular Ad-Hoc Networks) have become an attractive ad-hoc network that brings convenience into people’s lives. Vehicles can be informed of the position, direction, speed and other real-time information of nearby vehicles to avoid traffic jams and accidents. However, VANET environments could be dangerous in the absence of security protection. Because of the openness and self-organization of VANETs, there are plenty of malicious pathways. To guarantee vehicle security, the research aims to provide an effective VANET security mechanism that can track malicious vehicles as necessary. Therefore, this work focuses on malicious vehicles and proposes an anonymous authentication scheme in VANETs based on the fair blind signature to protect vehicle security.