电信科学 ›› 2021, Vol. 37 ›› Issue (8): 1-17.doi: 10.11959/j.issn.1000-0801.2021204
• 综述 • 下一篇
吴吉义1, 李文娟2, 曹健3, 钱诗友3, 张启飞4, BUYYA Rajkumar5
修回日期:
2021-08-15
出版日期:
2021-08-20
发布日期:
2021-08-01
作者简介:
吴吉义(1979− ),男,博士,浙江省人工智能学会秘书长、高级工程师,主要研究方向为服务计算、人工智能基金资助:
Jiyi WU1, Wenjuan LI2, Jian CAO3, Shiyou QIAN3, Qifei ZHANG4, Rajkumar BUYYA5
Revised:
2021-08-15
Online:
2021-08-20
Published:
2021-08-01
Supported by:
摘要:
智能物联网(artificial intelligence of things,AIoT)是人工智能与物联网技术相融合的产物,这一新兴概念在智慧城市、智能家居、智慧制造、无人驾驶等领域得到了广泛应用。然而AIoT相关技术研究仍处于初级阶段, 存在大量问题和挑战。首先简述了AIoT技术产生的背景,明晰其定义和应用场景。以此为契机,构建了一个新型的面向智能信息处理的云边端融合AIoT架构。在给出AIoT研究体系的基础上, 详细探讨并比较了其各组成技术模块,包括AI融合IoT数据采集、复杂事件处理及协同、云边端融合研究、AI融合IoT安全及隐私保护和AI融合应用服务等方面的研究现状和解决方案。最后, 论述了AIoT未来的研究方向和发展趋势。
中图分类号:
吴吉义, 李文娟, 曹健, 钱诗友, 张启飞, BUYYA Rajkumar. 智能物联网AIoT研究综述[J]. 电信科学, 2021, 37(8): 1-17.
Jiyi WU, Wenjuan LI, Jian CAO, Shiyou QIAN, Qifei ZHANG, Rajkumar BUYYA. AIoT: a taxonomy, review and future directions[J]. Telecommunications Science, 2021, 37(8): 1-17.
表1
国内主要IT企业AIoT应用领域"
数据来源时间 | 公司名称 | 战略重点 | 应用场景 |
2018年3月 | 阿里巴巴 | IoT成为第5个主赛道 | 智能人居、智慧城市、工业物联网 |
2018年4月 | 百度 | 与硬蛋签订AIoT合作协议 | 无人驾驶、智能家居 |
2018年5月 | 腾讯 | 布局人联网、物联网和智联网 | 腾讯超级大脑 |
2018年11月 | 小米 | 成为核心战略 | 智能家居、硬件平台研发 |
2018年12月 | 华为 | 首次公布AIoT战略 | 全场景应用 |
2018年12月 | 京东 | 退出“京鱼座”生态品牌 | 智能硬件、智能家居、智慧出行等 |
2019年1月 | 云知声 | 公布多模态AI芯片战略 | 智慧城市、智慧家居、智慧出行等 |
2019年1月 | 思必驰 | 发布首款AI芯片TAIHANG | 智能家居、智能车载、企业服务 |
2019年1月 | OPPO | 成立新兴移动事业部、注意AIoT技术研发 | 智慧生活 |
2019年1月 | 旷世 | 宣布打造AIoT操作系统 | 制造业、智慧物流等 |
表3
AIoT复杂事件处理领域研究方案比较"
文献 | 应用场景 | 主要贡献 | 性能检测 |
参考文献[ | 物联网环境大规模异构网元互联、分布式自组织和动态协作 | 提出基于生物启发算法的物联网网元信息交互机制,基于人体血糖调节模型的物联网动态自组织机制和物联网智能服务模型 | 通过仿真实验证明了算法的稳定性、目标跟踪能力和服务处理能力 |
参考文献[ | 5G支持的IoT中的通信问题 | 提出基于遗传算法的小区负载均衡策略 | 采用仿真实验验证吞吐率、时延、接入成功率 |
参考文献[ | 制造物联云平台复杂事件处理 | 设计分布式复杂事件处理机制 | 通过两个案例对平台进行功能测试和性能评估 |
参考文献[ | 物联网智能网关 | 设计了包含视频监控、数据采集和智能控制的IoT网关 | 通过仿真系统对智能网关的功能进行测试,同时在智慧农业平台进行了部署和识别视频检测 |
参考文献[ | 基于软件定义网络的IoT系统 | 提出基于深度学习的数据流量预测算法和信道分配算法提高传输质量 | 通过仿真验证了预测和信道分配算法的准确度和性能(吞吐量和丢包率) |
参考文献[ | IoT 组件的智能集成、配置和监测 | 提出语义增强型智能控制系统(SEMIoTICS)架构,支持IoT组件的语义建模及在线配置 | 以场景案例分析方式对架构的可用性进行了讨论 |
表4
云雾边融合研究方案比较"
文献 | 应用场景 | 主要贡献 | 性能检测 |
参考文献[ | 基于IoT的5G网络数据决策 | 对AI技术在基于IoT的5G网络中应用现状进行了综述 | 对各种现存方案做了理论分析和比较 |
参考文献[ | 5G 支持的 IoT 网络中的通信和数据挖掘 | 提出5G智能物联网范式以实现智慧大数据处理和信道优化 | 采用仿真实验验证系统性能 |
参考文献[ | AIoT支持的能源管理 | 提出AIoT进行智能能源管理的理论模型 | 理论分析和建模 |
参考文献[ | 融合边缘和雾计算的AIoT应用 | 对智能算法在云-雾-边混合框架的应用进行了综述 | 对各种现存方案做了理论分析和比较 |
参考文献[ | 智慧城市基于IoT的能源管理 | 提出具备深度学习能力的边缘辅助 IoT 能源管理模型 | 通过仿真验证了策略的性能(回报率和时延) |
参考文献[ | 云边混合计算系统智能决策 | 提出云边智能框架的落地模型 | 理论分析和建模 |
参考文献[ | 车载网 | 提出车载网智能推荐算法 | 通过仿真实验验证了推荐算法的精确度和时间效率 |
参考文献[ | 智能制造 | 提出基于云的智能制造模型 | 构建了原型系统验证制造设备智能感知和获取方法的有效性 |
参考文献[ | 工业物联网 | 提出云-边协同工业物联网架构和智能任务卸载模型 | 通过仿真实验检测了模型的服务精确度和服务流量 |
表5
AIoT安全与隐私保护研究方案比较"
文献 | 应用场景 | 主要贡献 | 性能检测 |
参考文献[ | 无线传感器网络 | 提出基于区块链的海量IoT数据采集框架以提高数据采集的安全、可信和有效性 | 通过仿真验证了数据采集的时延、信任模型的准确性和数据聚合的计算开销 |
参考文献[ | 车载无线网 | 提出可信智能协同网络框架以提高数据采集的安全性和准确性 | 通过仿真验证了协同数据采集方法的成本、时间和准确度 |
参考文献[ | 基于 IoT 的智能电力服务系统隐私保护 | 提出基于k-means和差分隐私的隐私和可用性数据聚类方案 | 通过理论分析了策略的安全性,通过仿真验证了聚类算法的效率 |
参考文献[ | 安全审计系统 | 提出基于区块链架构、符合ISO标准的安全审计系统,提升AIoT应用的安全性 | 通过理论论证了方案的可行性(性能和隐私保护能力) |
参考文献[ | 物联网安全防护 | 提出基于机器学习的安全框架,实现对安全威胁的快速诊断和预防 | 构建了安全框架的理论模型,通过仿真实验验证了模型的性能 |
参考文献[ | 物联网安全及隐私保护 | 设计IoT攻击模型,对基于机器学习的IoT安全策略进行了综述 | 采用理论分析和仿真对比了不同策略的有效性 |
参考文献[ | 数字交易市场 | 分析了边缘智能在IoT数字交易中应用的安全和隐私问题 | 对存在问题及可行处理方案进行了理论分析 |
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