Mobile edge computing (MEC) provides users with abundant wireless resources and cloud computing capabilities to meet their computing demand. Existing works tend to consider caching and computing offloading separately, so it is difficult to achieve overall optimization of system performance. To further improve system performance in smart home scenario, a novel collaborative caching and computing offloading scheme (CCCO) was proposed in this paper. First, a new collaborative caching strategy is designed in this paper to improve the cache hit rate, i.e., smart devices cache the task's computation results and edge servers collaboratively cache the related data of the sub-tasks after task division, Then, sub-tasks are collaboratively offloaded to servers for processing. Finally, Deep Q Network algorithm is used to obtain the optimal offloading and caching decisions for minimizing system latency. Simulation results show that the proposed algorithm significantly outperforms the traditional computing offloading scheme in terms of latency.

With the increasing number of user terminals and the development of 5G technology,a network has been formed where macro base stations and small base stations co-exist.Meanwhile,applications such as ultra-high resolution video and cloud VR/AR have higher requirements for latency.In order to reduce the latency in 5G networks,a cooperative multicast proactive caching scheme based on adversarial automatic coding is proposed in this paper.In this scheme,firstly,users are divided into different groups based on their characteristics.And then the content that the group may request will be predicated by using AAE.To reduce the redundancy of cached contents,the ant colony algorithm is used to pre-deploy the predicted contents to each small base station.Finally,in the content distribution phase,if a user requests a content with high popularity,the content will be proactively cached in a multicast manner to other users in this group who don't send the request,otherwise it is distributed in a normal manner.Simulation results show that the CMPCAAE scheme outperforms the classical caching scheme in terms of average delay and missing ratio of the system.

针对边缘计算中数据共享的信任关系难建立、隐私难保证和中心框架的单点故障等问题,提出一种基于区块链的访问控制机制.利用区块链的智能合约来管理访问权限和审计数据,依赖于区块链的去中心化和不可篡改特性解决单点故障和节点信任问题.为了保证数据的隐私性,上传到第三方服务器上的数据采用AES-128算法加密,而解密密钥则通过SGX技术构建安全程序进行共享.性能分析表明,该方案满足扩展性要求,能进行大型数据共享.

With the continuous development of deep learning, the use of deep learning methods for intelligent violent behavior recognition has become an active research field in computer vision. However, there are relatively few studies on violent behavior recognition in dark light environment. In this paper, we propose the DarkFight network model for violent behavior recognition in dark light environment. In the proposed DarkFight model, Resnet50 is used as the basic network model to extract features. In order to obtain multi-scale spatiotemporal features, DarkFight adds a multi-scale attention module PSA to the network. The DarkFight model uses Zero-DCE for low-light image enhancement. In order not to lose the spatiotemporal characteristics of the low-light environment, DarkFight uses the initial dark-light data and the enhanced data as the input to the two paths of the model. In addition, this paper researches the replacement of the traditional temporal global average pooling layer of the network with the BERT module to better utilize the temporal information when fusing the two path features. We construct a violent behavior dataset VDID (Violence Detection in Dark) in a dark-light environment, and conduct experimental verification on this dataset. The verification results show that the recognition precision of the DarkFight model increases by 2.51%, the accuracy increases by 3.4%, and the F1-score improves by 0.032.

Software-Defined Networking (SDN) technology provides higher programmability and centralized management in Industrial Internet of Things (IIoT)/industrial networks. Besides, it renders dynamic reconfiguration in order to optimize routing between controlled devices. Consideration of multiple metrics for Quality of Service (QoS) routing offers a precise network scheme and improves the performance compared to a single-metric. By applying the SDN platform, there is a possibility to build the forwarding paths based on multiple metrics such as delay, loss probability, and bandwidth, which are the paramount QoS requirements for many IIoT applications. This means that the performance is affected remarkably if the packet loss and delay exceed a definite limit and may become unsuitable for the endpoint. In this paper, an optimum path routing is used to ensure multiple constraints in Software-Defined-IIoT (SD-IIoT). With the use of this optimum path, the Reactive Flow Creation (RFC) approach increases the delay in the data transmission. This does not guarantee the delay-sensitive requirements for IIoT applications. To solve this issue, we propose dual solutions for flow creation: the optimized Proactive Flow Creation (PFC) approach and the combination of Reactive and Proactive Flow Creation (RPFC) approach. The improvement and capacity of the newly proposed approaches are demonstrated through the performances of reducing packet loss and delay with the consideration of both wired and wireless networks in a testbed setup.

边缘计算(Edge Computing,EC)作为云计算的补充,在处理lOT设备产生的计算任务时可以保证计算的延时符合系统的要求.针对在传统卸载场景中,由于计算任务到达存在空窗期导致异地边缘云存在空闲状态,造成异地边缘云利用不充分的问题,文中提出了一种基于遗传算法的多边缘与云端协同计算卸载模型(Genetic Algorithm-based Multi-edge Collaborative Computing Offloading Model,GAMCCOM).该计算卸载方案联合本地边缘和异地边缘进行任务卸载,并采用遗传算法进行求解,从而得到同时考虑时延和能耗的最小的系统代价.通过仿真实验结果可知,在综合考虑卸载系统的时延消耗和能量消耗的情况下,该方案相比基本的三层卸载方案系统整体代价降低了23％,在只考虑时延消耗和只考虑能量消耗的情况下依然分别能够降低系统代价17％ 和15％.因此针对边缘计算的不同卸载目标,GAMCCOM卸载方案对系统代价均有比较优秀的降低效果.

将思政教育引入到"交换技术与通信网"课程教学很有必要.本文根据课程特点,分别从矛盾、联系、发展的角度详细论述了"交换技术与通信网"课程如何进行辩证法教学,并简要介绍了政治意识教育、心理素质教育、创新精神教育、法制观念教育、职业道德教育的实施方法.在思政教育方法论述完成以后,本文从问卷调查的角度对引入思政教育的教学改革效果进行了分析和总结.

The current study constructed social network using player positions and passing process based on available literature. Multiple indicators were used to measure the importance of positions comprehensively. The results showed that in the football passing process, the attacking midfielder was the most important position, followed by the central defending midfielder. Based on two-sample difference tests, the results showed that the winning teams usually had better performance on positions of forward on left, central forward, defender on left and defender on right. To analyze the effects of playing positions on the whole network and test the sensitivity of passing networks, we deleted n (n = 1, 2, 3..., 10) positions of a team, and then tested the efficiency of the networks based on positions left. (C) 2020 Elsevier Ltd. All rights reserved.

The RPL routing protocol is a lightweight distance vector routing protocol in Internet of Things(IoT),which is vulnerable to Rank attacks,causing normal communication between nodes to be significantly affected by serious network packet loss.In order to detect and isolate the malicious Rank attack nodes in the RPL routing protocol,this paper proposes a security RPL routing protocol based on trust mechanism and Rank threshold,Sec-RPL,which introduces the detection and isolation technology of malicious nodes.Based on the fact that malicious attacks on nodes will lead to a decrease in the trust value,Sec-RPL filters the normal nodes and suspected malicious nodes preliminarily.Then the Rank values of suspected malicious nodes are compared with the threshold of Rank,and the nodes with a Rank value lower than the threshold are isolated as attack nodes to achieve optimal routing decisions.Simulation results show that the Sec-RPL routing protocol has excellent performance in the success rate of detection,packet loss rate,and false alarm rate.Also,it consumes fewer computing resources and has higher security than the OF0-RPL and original RPL routing protocol.

Environmental monitoring systems are often designed to measure and log the current status of an environment or to establish trends in environmental parameters. In this paper, We proposed an autonomous robotic system that is designed and implemented to monitor environmental parameters such as temperature, humidity, air quality, and harmful gas concentration. The robot has GPS coordinates, and it can store data on the ThingSpeak IoT platform. The mobile robot is controlled by a smartphone which runs an app built on the Android platform. The whole system is realized using a cost-effective ARM-based embedded system called Arduino and Raspberry Pi which communicates through a wireless network to the IoT platform, where data are stored, processed and can be accessed using a computer or any smart device from anywhere. The system can update sensor data to IoT server every 15 seconds. The stored data can be used for further analysis of the reduction of pollution, save energy and provide an overall living environment enhancement. The robotic system has designed for cost-effective remote monitoring environmental parameters without any human intervention to avoid health risk efficiently. A proof-of-concept prototype has been developed to illustrate the effectiveness of the proposed system.

In the network of poor conditions and high error ratio, the stored data may be lost. If the lost data cannot be recovered effectively, it will make serious effects. Therefore, guaranteeing the availability and integrity of data is very important in any storage system, especially in cloud storage. The existing data redundancy strategies in cloud storage are unable to adapt to dynamic changes in the network environment. ADRS( Adaptive Data Redundancy Strategy), which combines fragment replication and LT(Luby Transform) code, is proposed. ADRS can adjust its parameters according to the current network state to optimize the performance of cloud storage networks. When the amount of source packets sent by the server nodes is smaller than the threshold, fragment replication will be the main storage mode and supplemented by LT code. When the amount of source packets is larger than the threshold, LT code is the main storage mode and supplemented by fragment replication. The simulation results show that ADRS fully integrates the advantages of fragment replication and LT code, which can reduce average delay and improve the reliability and stability of the system at the cost of increasing some storage space.

针对数据中心网络中传统多径路由方法易造成大象流(Elephant flow)冲突,进而导致网络链路拥塞等问题,文中提出了一种基于粒子群优化的动态负载均衡机制(Dynamic load balancingmechanism based on particle swarm optimization,DLB-PSO).该机制结合SDN具有全局网络拓扑信息可视化等技术优势,基于粒子群优化算法,根据当前网络链路资源状态等信息,以最小化最大链路利用率为优化目标,为大象流计算出最佳传输路径.实验结果表明,相比于传统的等价多径路由(Equal-cost multi-path routing,ECMP)、全局优先匹配(Global first fit,GFF)及面向SDN的LABERIO机制,文中提出的机制能够更加有效地改善网络吞吐量,降低流的时延抖动.

As the amount of data increases in data center networks, the traffic load of some links is excessive. The tradi-tional ECMP mechanism is no longer applicable to the data center network because it does not consider the link status and traffic characteristics. Meanwhile ECMP mechanism may hash some large flows to the same path. It may cause large flows collision and bring the problem of link bottleneck when scheduling flows. Based on the Software Defined Network (SDN)architecture, this paper proposes a dynamic Load Balancing scheme based on Flow Classification(LBFC)for the Fat-Tree topology, and considers link state information and traffic characteristics. LBFC mechanism dynamically adjusts the flow classification threshold to classify flows into the large and small flows. LBFC selects the forwarding path for the large and small flows in different ways to meet the different transmission bandwidth requirements of the large and small flows. The simulation results show that the LBFC can dynamically determine the large flows and small flows according to the network link status and traffic characteristics and achieve load balancing. Compared with ECMP, GFF and DLB algo-rithms, the LBFC mechanism improves network throughput and link utilization. The transmission delay is also reduced.

Software Defined Network(SDN)is an emerging network architecture. Its separation of control and forward-ing architecture brings great convenience and flexibility to network management, but it also brings new security threats and challenges. By performing the Distributed Denial of Service(DDoS)attack on the centralized controller of the SDN, the attacker will make the information unreachable and cause network congestion. In order to detect the DDoS attack, a detection method based on the C4.5 decision tree is proposed. It extracts information from each switch flow entry, then generates a decision tree to classify traffic to realize the detection of DDoS attacks. Finally, the experimental results show that the method has higher detection success rate, lower false alarm rate and less detection time.

无线传感器网络WSN(Wireless Sensor Networks)被广泛应用于军事、生产、医疗等各个方面,而当下的许多传感器网络都部署在恶劣、开放的环境中,存在各种各样的威胁.黑洞攻击是一种典型的路由攻击,在这种攻击中恶意节点声称自己是剩余能量多、能够一跳到达目的节点的节点或者声称自己就是目的节点,因此很多节点会把要发送的数据发给该恶意节点,而恶意节点吸引数据包后,并不将数据包转发而是丢弃,这就造成传输空洞.针对这种特性,文中提出了一种基于位置信息的诱捕检测算法BTCOLI(Blackhole Attack Detection Algorithm Based on Location In-formation),以实际不存在的目的节点为诱饵,找到黑洞节点,对其进行身份验证以及位置检测,从而剔除该恶意节点.同时,还提出了相应的防御方案:在网络中加入预共享对称密钥,并提供HMAC(Hash-based Message Authentication Code)消息验证机制,以防止恶意节点加入网络.最后通过搭建NS-2下的仿真平台,验证了该算法在检测率方面的优越性.

Sensor nodes in wireless sensor network(WSN) are prone to failure due to energy depletion and the effects of harsh environments.The failure of the node will make the network to be disconnected.This paper studies connectivity restoration of WSN,analyzes and summarizes the related main direction and the research results in recent years.According to the scale of network failure,the restoration algorithms are divided into two types,one is for small-scale failure and the other is for large-scale failure.In addition,the repair algorithms are further classified from other aspects,such as triggering conditions,realization viewpoint,and etc.Finally,the shortcomings of the current research and future research directions are pointed out.

For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.

基于现有的两阶段虚拟网络映射算法,分析节点综合资源计算标准,改进选取节点映射的评估方法。该虚拟网络映射算法基于虚拟网络中节点的多种属性,即节点CPU计算资源、链路带宽资源、节点相邻节点的数目及拓扑属性。节点映射阶段采用改进之后的资源评估方式进行节点映射,同时采用可重用技术,实现物理网络节点可重复映射,即物理网络中的同一个节点可以被虚拟网络中的多个节点多次映射。该算法不仅有效地减少了映射过程中出现的资源瓶颈问题,而且降低了链路映射的成本,节约了部分映射带宽资源,从而使得网络基础设施提供商可以接受到更多的虚拟网络服务,提高网络运营的网络收益。仿真实验数据表明,该虚拟网络映射算法在虚拟网络请求接受率和网络开销比等参数指标上具有显著提升。

Opportunistic routing achieves high throughput in the face of lossy wireless links.The current opportunistic routing protocol,MORE,is the first opportunistic routing work to adopt network coding,but in the selection of candidate forward node set,it only considers the quality of the link between nodes,which leads to premature dead for some nodes because of energy depletion,affecting the integrity of wireless sensor networks.Aiming at this outstanding issue,an improved routing protocol based on MORE is proposed,which balances energy consumption of wireless sensor nodes.According to transmission mechanism of opportunistic routing,considering the expected transmission count (ETX) and residual energy (RE),a new routing metric expected life time (ELT) and a selection strategy of candidate forward node set based on it is proposed.Finally,simulations are performed by NS2.The results show that in the basis of taking the residual energy into account,the improved MORE protocol not only ensures the reliability of data transmission,but also balances the energy consumption of nodes,improving survival of nodes and prolonging the network life cycle.

With the extensive application of the wireless sensor network,the security problem has been more and more prominent,so it needs more attention.In wireless sensor networks,the wormhole attack is one of attacks against the WSN routing protocol and is a coordinated attack by two nodes usually.The malicious nodes establish communication through a secret high quality and wide band link which called "Tunnel".Attack nodes catch the routing packet in network and then transmit it to colluding node through "Tunnel".Thus it can destroy the network topology and the network routing.According to the features of wormhole attacks,using the existing scheme of detection with connectivity information and adding locating device,a comprehensive algorithm for detecting and locating wormhole attacks in WSN has been proposed to improve detection rate in the low density.The results of simulation with NS2-based platform show that the proposed algorithm is superior to other ones with less detection rate and position errors.