超密集物联网中的多蜂窝网络选择算法

随着物联网终端爆炸式的增加,物联网致密化成为发展的必然。在海量终端数量和资源严重受限的情况下,如何确定物联网终端设备的接入基站以及如何有效地为接入的用户进行资源分配,成为超密集物联网系统所需解决的重要问题。文章在吞吐量和系统能耗之间进行折中,以系统能效为优化目标,设计了一种超密集物联网中的多蜂窝网络选择算法。该算法为用户和基站设置效用函数,基于匹配理论完成用户的选择过程。最后对该算法和对比算法进行仿真,结果表明,多蜂窝网络选择算法在能耗和能效性能上均优于对比算法。     

走出家用物联网设备网络安全设计中的传统路径依赖

Q/CVC 0028-2019《智能家电(IoT设备、互联设备)网络安全评价及测试规范》标准针对当前市场上物联网设备网络安全现状,规定了不同等级的安全要求,对提升家用物联网产品网络应用规范具有重要作用。本文从Q/CVC 0028-2019标准中选取了数个技术要点来剖析编制思路和应用实践,对家电企业在产品设计过程中如何通过该标准改进和提升就可避免陷入传统产品设计的路径依赖进行研究。本文中还攫取数项常见网络安全缺陷问题,解读Q/CVC 0028-2019标准的架构及其背后的技术脉络。     

面向IoT的边缘节点平台架构

针对边缘节点体系结构支持功能单一、模块划分混乱、向云端传输数据中的带宽开销大以及数据传输安全问题,设计开发了一套面向IoT的边缘节点平台架构.该平台架构包括设备管理、数据管理、资源管理、应用管理以及导出服务管理5个模块,使用最小二乘法等算法在边缘侧实现数据采集、数据过滤、数据加密、指标分析等功能.测试结果表明,该平台能...     

利用改进ILP和二进制穷举择优法的低成本物联网流量多目标路由感知方法

针对无线网络不能为多样化应用需求提供支持及卸载移动通信核心成本较高的问题,提出了一种改进整数线性规划模型(IILP)结合二进制穷举择优法的低成本混合物联网流量多目标路由感知方法。首先,基于IILP对混合物联网流量路由感知进行建模,获得准确的能量感知模型;其次,采用多目标MAXI路由感知算法对多目标路由感知模型进行了求解,降低了流量路由求解的延时;最后,采用二进制穷举择优法对流量路由感知的吞吐量进行扩展。仿真实验表明,与现有算法相比,提出方法降低了求解的延时,提高了流量的吞吐量,减少了流量的丢包率,同时还降低了混合物联网多目标路由感知的成本。     

Achieving concurrency in cloud-orchestrated Internet of Things for resource sharing through multiple concurrent access

Cloud-orchestrated Internet of Things (IoT) facilitates proper utilization of network resources and placating user demands in smart communications. Multiple concurrent access (MCA) techniques designed for cloud-assisted communication helps to achieve better resource sharing features with fault tolerance ability. A multi-objective resource allocation and sharing (RAS) for balancing MCA in cloud-orchestrated IoT is presented in this article. The RAS constraints are modeled through linear programming (LP) as an optimization approach. The constraints are resolved using genetic representations (GR) for reducing the unserviced requests and failed resource allocations. Conventional genetic stages are inherited by the LP model to solve resource allocation and access issues reducing latency. The combined LP and GR jointly resolve resource allocation and MCA stagnation in cloud network. A fair outcome of LP-GR is estimation using the metrics response latency, resource utilization, request handled, and average latency.     

面向IoT的两级多接入边缘计算节能卸载策略

多接入边缘计算（multi-access edge computing,MEC）技术将计算和存储资源下沉到网络边缘,可大幅提高物联网（Internet of things,IoT）系统的计算能力和实时性。然而,MEC往往面临计算需求增长和能量受限的约束,高效的计算卸载及能耗优化机制是MEC技术中重要的研究领域。为保证计算效率的同时最大程度提升计算过程中的能效,提出了两级边缘节点（edge nodes,ENs）中继网络模型,并设计了一种计算资源及信道资源联合优化的最优能耗卸载策略算法（optimal energy consumption algorithm,OECA）。将MEC中的能效建模为0-1背包问题;以最小化系统总体能耗为目标,系统自适应地选择计算模式和分配无线信道资源;在Python环境下仿真验证了算法性能。仿真结果表明,相比于基于有向无环图的卸载策略算法（directed acyclic graph algorithm,DAGA）,OECA可将网络容量提升18.3%,能耗缩减13.1%。     

带智能预测缓存的物联网标识解析器

针对现有物联网标识解析存在的性能缺陷, 提出一种带智能预测缓存的物联网标识解析器, 以DNS为基础, 支持各种物联网标识编码类型的解析, 并具有智能预测的缓存功能。预测时采用关联规则方法对日志数据进行挖掘, 根据当前的查询请求预测下一个可能解析的标识, 提前解析, 缓存解析结果。同时结合TTL策略以及LRU策略作为缓存置换方法, 具有高效性和时效性。实验表明, 该解析器具有较高的命中率和较小的响应时间, 且当缓存大小为62时获得最优性能。     

Household flow detection using FEAT (flow estimating accelerometer-thermometer) device

The use of IoT devices in water end use disaggregation verification is an emerging field which offers benefits over conventional approaches, in terms of cost, accuracy and scalability. Having reliably disaggregated water appliance consumption data will enable smart water meter data to be used in household water conservation approaches and for understanding water consumption behaviours. The FEAT device provides a low cost, easily applied and scalable solution that is demonstrated to work even for very low flow conditions of 0.03 l/s. The FEAT device is a combination of a battery, Wi-fi board and MPU6050 sensors providing multi-modal accelerometer and thermometer data. The study places 7 of these FEAT devices onto hot and cold water pipes leading to a shower, which is operated 4 times in a high flow situation, 0.13 l/s, and 4 times in a low flow situation, 0.03 l/s. The data is then analysed and compared with a flow logger to determine if the FEAT device can detect when a domestic appliance is using water. There are limiting cases where the level of noise or external interference limits distorts the data, obscuring the distinguishable peaks in the data due to the similarity of the values. By using high and low pass filtering methods it was possible to enhance the peaks but there are still situations where peaks cannot be detected: for example, if a rigid pipe is not able to vibrate easily or if a hot water boiler is not triggered due to the low flow rate. However, the results show it should be possible to overcome these limiting cases, as it is much less likely for both the vibration and temperature data to be adversely affected by noise or external influences simultaneously, therefore decreasing the effect of noise and external influences. In conclusion, this research paper demonstrates that FEAT devices are a low cost, easily applied and scalable solution for detecting flow. By using high and low pass filtering, placing sensors on freely moving pipes and through the use of multi-modal verification, the FEAT device is shown to work on both metal and plastic pipes even in the lowest flow situations of 0.03 l/s. Therefore the FEAT device is a suitable solution for appliance identification in disaggregation verification datasets.     

A Traceable Capability-based Access Control for IoT

Delegation mechanism in Internet of Things (IoT) allows users to share some of their permissions with others. Cloud-based delegation solutions require that only the user who has registered in the cloud can be delegated permissions. It is not convenient when a permission is delegated to a large number of temporarily users. Therefore, some works like CapBAC delegate permissions locally in an offline way. However, this is difficult to revoke and modify the offline delegated permissions. In this work, we propose a traceable capability-based access control approach (TCAC) that can revoke and modify permissions by tracking the trajectories of permissions delegation. We define a time capability tree (TCT) that can automatically extract permissions trajectories, and we also design a new capability token to improve the permission verification, revocation and modification efficiency. The experiment results show that TCAC has less token verification and revocation/modification time than those of CapBAC and xDBAuth. TCAC can discover 73.3% unvisited users in the case of delegating and accessing randomly. This provides more information about the permissions delegation relationships, and opens up new possibilities to guarantee the global security in IoT delegation system. To the best of our knowledge, TCAC is the first work to capture the unvisited permissions.