A proof system of the CaIT calculus

The Internet of Things (IoT) can realize the interconnection of people, machines, and things anytime, anywhere. Most of the existing research mainly focuses on the practical applications of IoT, and there is a lack of research on modeling and reasoning about IoT systems from the perspective of formal methods. Thus, the Calculus of the Internet of Things (CaIT) has been proposed to specify and analyze IoT systems before the actual implementation, which can effectively improve development efficiency, and enhance system quality and reliability. To verify the correctness of IoT systems described by CaIT, this paper presents a proof system for CaIT, in which specifications and verifications are based on the extended Hoare Logic with time. Furthermore, we explore the cooperation between isolated proofs to validate the postconditions of the communication actions occurring in these proofs, with a particular focus on broadcast communication. We also demonstrate the soundness of our proof system. A simple “smart home” is given to illustrate the availability of our proof system.     

基于深度强化学习的双层无人机边缘卸载策略

移动边缘计算(mobile edge computing, MEC)已逐渐成为有效缓解数据过载问题的手段, 而在高人流密集的场景中, 固定在基站上的边缘服务器可能会因网络过载而无法提供有效的服务. 考虑到时延敏感型的通信需求, 双层无人机(unmanned aerial vehicle, UAV)的高机动性和易部署性成为任务计算卸载的理想选择, 其中配备计算资源的顶层无人机(top-UAV, T-UAV)可以为抓拍现场画面的底层UAV (bottom-UAV, B-UAV)提供卸载服务. B-UAV搭载拍摄装置, 可以选择本地计算或将部分任务卸载给T-UAV进行计算. 文中构建了双层UAV辅助的MEC系统模型, 并提出了一种DDPG-CPER (deep deterministic policy gradient offloading algorithm based on composite prioritized experience replay)新型计算卸载算法. 该算法综合考虑了决策变量的连续性以及在T-UAV资源调度和机动性等约束条件下优化了任务执行时延, 提高了处理效率和响应速度, 以保证现场观众对比赛的实时观看体验. 仿真实验结果表明, 所提算法表现出了比DDPG等基线算法更快的收敛速度, 能够显著降低处理延迟.     

Efficient Deep Learning Framework for Fire Detection in Complex Surveillance Environment

To prevent economic, social, and ecological damage, fire detection and management at an early stage are significant yet challenging. Although computationally complex networks have been developed, attention has been largely focused on improving accuracy, rather than focusing on real-time fire detection. Hence, in this study, the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment. The proposed model architecture is inspired by the VGG16 network, with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4. This results in higher performance with a reduced number of parameters and inference time. Moreover, smaller convolutional kernels are utilized, which are particularly designed to obtain the optimal details from input images, with numerous channels to assist in feature discrimination. In E-FireNet, three steps are involved: preprocessing of collected data, detection of fires using the proposed technique, and, if there is a fire, alarms are generated and transmitted to law enforcement, healthcare, and management departments. Moreover, E-FireNet achieves 0.98 accuracy, 1 precision, 0.99 recall, and 0.99 F1-score. A comprehensive investigation of various Convolutional Neural Network (CNN) models is conducted using the newly created Fire Surveillance SV-Fire dataset. The empirical results and comparison of numerous parameters establish that the proposed model shows convincing performance in terms of accuracy, model size, and execution time.     

An Anti-Physical Attack Scheme of ARX Lightweight Algorithms for IoT Applications

The lightweight encryption algorithm based on Add-Rotation-XOR (ARX) operation has attracted much attention due to its high software affinity and fast operation speed. However, lacking an effective defense scheme for physical attacks limits the applications of the ARX algorithm. The critical challenge is how to weaken the direct dependence between the physical information and the secret key of the algorithm at a low cost. This study attempts to explore how to improve its physical security in practical application scenarios by analyzing the masking countermeasures of ARX algorithms and the leakage causes. Firstly, we specify a hierarchical security framework by quantitatively evaluating the indicators based on side-channel attacks. Then, optimize the masking algorithm to achieve a trade-off balance by leveraging the software-based local masking strategies and non-full-round masking strategies. Finally, refactor the assembly instruction to improve the leaks by exploring the leakage cause at assembly instruction. To illustrate the feasibility of the proposed scheme, we further conducted a case study by designing a software-based masking method for Chaskey. The experimental results show that the proposed method can effectively weaken the impact of physical attacks.     

面向文本描述的CPS资源能力知识图谱构建

信息物理融合系统(cyber-physical system, CPS)在社会生活中发挥越来越广泛的作用. CPS资源的按需编排建立在CPS资源的软件定义基础上,软件接口的定义则依赖对CPS资源能力的充分描述.目前, CPS领域内缺少一个能规范表示资源及其能力的知识库和构建该知识库的有效方法.面向CPS资源的文本描述,提出构建CPS资源能力知识图谱并设计一种自底向上的自动构建方法.给定资源,所提方法先从其代码和文档中提取资源能力的文本描述信息,并基于预定义的表示模式生成规范化表示的能力短语.然后,基于动宾结构的关键成分对能力短语进行划分、聚合与抽象,生成不同类型资源的能力层次化抽象描述.最后,构建资源能力知识图谱.面向Home Assistant平台,构建了包含32个资源类别、957个资源能力的知识图谱.图谱构建实验从不同维度对比分析了手工构建和所提方法自动构建的结果.实验表明,所提方法为CPS资源能力知识图谱的自动化构建提供可行途径,有助于减少人工构建工作量,补充CPS领域内资源服务与能力的描述,并提高图谱的知识完备性.     

智慧景观背景下物联网技术研究进展与展望

[目的]智慧景观旨在通过数据驱动将景观项目数字化、网络化、可视化。明确智能景观背景下物联网技术的相关研究进展，对促进景观项目的精准化和科学化具有重要意义。[方法]以“景观”“物联网”为主题，检索Web of Science（WoS）数据库2013—2022年的相关英文文献，采用科学计量方法，利用COOC13.4及VOSviewer1.6.13计量软件，对文献数量、作者、国家、期刊、关键词进行可视化知识图谱分析。[结果]结果表明，物联网技术与景观项目周期密切相关，研究集中于勘察与测绘、规划设计、环境监测、信息管理四大方面。[结论]分析得出物联网技术的具体应用，能有效促进其在空间信息化、规划智能化、治理智慧化三大方面的发展，以期为今后景观项目周期中物联网技术的应用发展提供参考。     

基于深度强化学习的无人机辅助物联网多目标优化

无人机辅助无线供电物联网是一种创新的网络架构,利用无人机作为能量传输中介,能够解决物联网设备电力供应 的限制和局限性。针对无人机辅助无线供电物联网网络中多目标控制策略学习的问题,提出了一种基于深度强化学习的多 目标双延迟深度确定性策略梯度(MOTD3) 算法,旨在满足偏航角、飞行速度以及发射功率约束条件下,实现总数据速率、总 收获能量最大化以及能耗和悬停时间最小化的多目标联合优化,同时因需求动态变化无人机进行在线路径规划。仿真结果 表明,该算法在保证良好的收敛情况和稳定性前提下,较其他算法在总数据速率、总收获能量、能耗与悬停时间方面分别提高 14.7%、10.6%、6.1%和10.3%,且具有较强泛化能力,可适用于实际中不同通信场景。     

UAV-IoT collaboration: Energy and time-saving task scheduling scheme

UAVs are capable of providing significant potential to IoT devices through sensors, cameras, GPS systems, and so forth. Therefore, the smart UAV-IoT collaborative system has become a current hot research topic. However, other concerns require in-depth investigation and study, such as resource allocation, security, privacy preservation, trajectory optimization, intelligent decision-making, energy harvesting, and so forth. Here, we suggest a task-scheduling method that splits IoT devices into distinct clusters based on physical proximity and saves time and energy. Cluster heads can apply an auto regressive moving average (ARMA) model to predict intelligently the timestamp of the arrival of the next task and associated estimated payments. Based on the overall expected payment, a cluster head can smartly advise the UAV about its time of next arrival. According to the findings of the simulation, the proposed ETTS algorithm significantly outperforms Task TSIE and TDMA-WS in terms of energy use (67%) and delays (36%).     

Classification of Request-Based Mobility Load Balancing in Fog Computing

Every day, more and more data is being produced by the Internet of Things (IoT) applications. IoT data differ in amount, diversity, veracity, and velocity. Because of latency, various types of data handling in cloud computing are not suitable for many time-sensitive applications. When users move from one site to another, mobility also adds to the latency. By placing computing close to IoT devices with mobility support, fog computing addresses these problems. An efficient Load Balancing Algorithm (LBA) improves user experience and Quality of Service (QoS). Classification of Request (CoR) based Resource Adaptive LBA is suggested in this research. This technique clusters fog nodes using an efficient K-means clustering algorithm and then uses a Decision Tree approach to categorize the request. The decision-making process for time-sensitive and delay-tolerable requests is facilitated by the classification of requests. LBA does the operation based on these classifications. The MobFogSim simulation program is utilized to assess how well the algorithm with mobility features performs. The outcome demonstrates that the LBA algorithm’s performance enhances the total system performance, which was attained by (90.8%). Using LBA, several metrics may be examined, including Response Time (RT), delay (d), Energy Consumption (EC), and latency. Through the on-demand provisioning of necessary resources to IoT users, our suggested LBA assures effective resource usage.     

An efficient certificateless key exchange protocol for heterogeneous networks in human-centered IoT systems

Human-centered Internet of things (IoT) systems enable human beings to enjoy the ubiquitous services and play more and more important roles in our life. A common application scenario in human-centered IoT systems is that two distributed wireless devices from heterogeneous networks want to communicate with each other. However, key generation centers (KGCs) from different networks usually use independent security parameters. It is difficult for two users with different security parameters to establish a common session key. We propose a certificateless key exchange protocol for two different devices managed by different KGCs to address the issue. The security of the proposed protocol is conducted in the random oracle model with the hardness assumption of elliptic curve computational Diffie-Hellman (ECDH) problem. The main merits of our protocol include the following: (a) it enables users from heterogeneous networks to establish upon a shared session key, (b) it can solve the key escrow problem, (c) it does not use bilinear pairings and obtains computational efficiency, and (d) it achieves stronger security compared with other related protocols.