Large scale Internet of Things (IoT) applications in the 5G era pose sever challenges on the network architecture in terms of heterogeneity,scalability,mobility and security.Due to the identification and location overloading problem of IP,TCP/IP based network architecture appears inefficient in addressing the challenges mentioned above.Named Data Networking (NDN) makes named content as the primary sematic and has consistency in logical topologies between network layer and application la-yer.The advantages of NDN in addressing these four challenges are reflected in the fact that naming shields the underlying hete-rogeneity,end-to-end decoupling and network layer caching provide native support for many-to-many communication and multicast,consumer mobility is supported natively by consumer driven communication pattern and content-based security is more lightweight.In this paper,future research directions of NDN based IoT were summarized.Especially,the combination of NDN and technologies including edge computing,blockchain and Software Defined Networking (SDN) to construct edge storage and computing model,centralized and distributed control model,distributed security model were proposed.

The large-scale deployment of the Internet of Things makes it possible for vulnerable IoT devices to be connected to the network.When an attacker uses a vulnerable device to access the target internal network,it can lurk to wait for an attack.To prevent such attacks,it is necessary to develop a security mechanism for access control of suspicious devices and management of internal devices.Firstly,in order to realize the access control of suspicious devices,a device identification method is given in this paper.By setting a white list,a communication traffic feature fingerprint is constructed,and a random forest method is used to train the device identification model.Secondly,to manage internal devices,an intelligent security management model is proposed to build an ontology threat model based on assets,vulnerabilities and security mechanisms.Finally,the experimental results verify the detection performance of the device recognition model,the recognition accuracy rate is above 96%.Compared with theexisting similar methods,the results prove that the proposed method has better detection stability.

For indoor positioning scenarios,there is often a need to obtain the order in which certain items are placed.RFID(Radio Frequency Identification) is one of the solutions that can be selected because of its light weight and low cost.To solve the problem of relative positioning of items by studying the ARIMA based on the phase and time series prediction model,this paper proposes an indoor relative position positioning algorithm based on UHF (Ultra-High Frequency) RFID tags.By using passive RFID tags and readers,moving the RFID antenna to obtain the phase value,the ARIMA model is used to predict the sequence of the phase change during the movement of the antenna,the time series is predicted to reach a certain time stamp,and then the prediction time is given.The weights are assigned to the time stamps of some special phase points in the process of stamping and phase change,and the final time stamps are obtained to sort relative positions.Experiments show that this RFID indoor relative position positioning system can achieve recognition accuracy rateby 96.67% for book sequence detection in a library environment.Compared with the classical STPP algorithm and HMRL algorithm,its performance is greatly improved.

Internet of Things systems,such as wireless sensor networks,usually have the characteristics of the high restriction of the resources and coupling with the physical world,which makes it difficult to debug the equipment after deployed.Therefore,it is especially important to thoroughly test and profile the systems before deploying to the real world.Due to the intrusiveness,traditional debugging methods based on the serial port are incompetent for detailed tracing on resource-constrained devices.This paper studies the application of hardware assisted tracing technology in the embedded network sensor systems’ test and evaluation.Then,it designs and realizes a Hardware Assisted Tracing testBed (HATBED).HATBED consists of a controller,observers,and targets.It can provide three services,network-wide remote debugging,flexible software tracing and non-invasive software profiling.HATBED can support non-intrusive tracing and profiling without relying on operating systems and applications.In the experiment,this paper benchmarks time and power consumption,time accuracy,and code coverage under bare-metal and FreeRTOS.Then,it tests the RIOT-OS examples and completes the ping6 command high time accuracy feature profiling and UDP communication function coverage and basic block coverage.With the help of hardware assisted tracing technology,HATBED caneva-luate the resource-constrained Internet of Things systems more efficiently and adequately.

In recent years,with the rapid development of technology,smart mobile devices have become a part of people’s lives.The popularity of smart mobile devices provides sufficient physical support for the realization of sonar perception theory.When the sonar signal is propagated,it is modulated by the propagation space and life activities,so it carries a wealth of life state and space information.The popularity of smartphones,the maturity of communication technology,and the innovative use of acoustic signals have enabled sonar sensing devices to achieve low-cost,fine-grained sensing collection and calculation.Utilizing acoustic signals in sonar sensing technology does not require the support of special hardware.With the unique concealment and its typical feature of high accuracy,the acoustic signals can calculate the surrounding space information.This article elaborates on the research history of acoustic signals in the field of spatial positioning and sensing technology,summarizes the basic principles of the main technologies,and finally analyzes the problems and future development trends of acoustic signals in mobile sensing application technology.

With the rapid development of Internet of Things (IoT) and 5G technology,there are more and more applications of artificial intelligence based on deep learning,which makes medical imaging,urban security,autonomous driving and other visual fields based on temporal-spatial data become research hotpots in the direction of IoT.At the same time,the video data,picture data,temperature and humidity data and gas concentration data collected by the IoT system also grow rapidly,which eventually makes the processing speed and feedback speed of the IoT system slower and slower.In view of the large amount of temporal-spatial data collected by IoT nodes and the problem of transient anomalies,this paper designs an EPLSN (Exception Processing Long and Short Memory Network) algorithm based on long and short memory network.This paper designs logic structure of the input gate and improves the network model structure,solving the problem of the classification of transient abnormal data and temporal-spatial data,improving the classification accuracy of the IoT temporal-spatial data,and cleaning the abnormal data.According to the characteristics of the temporal-spatial data collected by the IoT sensor,the data is stored in different data blocks.At the same time,a time-series database is used to temporarily store temporal-spatial data,and an IoT search architecture based on temporal-spatial data is proposed.The architecture is suitable for the real-time search system in IoT environment and accele-rates the search speed of the IoT system.

With the rapid development of the Internet of Things (IoT) technology,there are a large number of devices with different functions (such as a variety of smart home equipment,mobile intelligent transportation devices,intelligent logistics or warehouse management equipment,etc.,with different sensors),which are connected to each other and widely used in intelligent cities,smart factories and other fields.However,the limited processing power of these IoT devices makes it difficult to meet the demand for delay-sensitive,computation-intensive applications.The emergence of mobile edge computing (MEC) effectively solves this problem.IoT devices can offload tasks to edge servers and use them to perform computing tasks.These servers are usually deployed by the network operator at the edge of the network,that is,the network access layer close to the client,which is used to aggregate the user network.At a certain time,IoT devices may be in the coverage area of multiple edge servers,and they share the limited computing and communication resources of the servers.In this complex environment,it is an NP-hard problem to formulate a task offloading and resource allocation scheme to optimize the delay of task completion or the energy consumption of IoT devices.At present,lots of work has been done on this issue and make some progress,but some problems still exist in the practical application.In order to further promote the research in this field,this paper analyzes and summarizes the latest achievements in recent years,compares their advantages and disadvantages,and looks forward to the future work.

With the rapid development of Internet of things technology,radio frequency identification(RFID) system,with its advantages of non-contact and rapid identification,has become the first choice to solve the problem of Internet of things.RFID network planning should consider multiple objectives,which has been proved to be a multi-objective optimization problem.In this paper,an improved grey wolf optimizer is proposed,which uses Gauss mutation operator and inertia constant strategy to realize RFID network planning.Through the establishment of the optimization model,on the basis of satisfying the four objectives of 100% coverage of tags,deploying fewer readers,avoiding signal interference and consuming less power,this paper makes a comparative analysis with particle swarm optimization(PSO),genetic algorithm(GA) and monarch butterfly algorithm(MMBO).The experimental results show that grey wolf algorithm performs better in RFID network planning.In the same experimental environment,compared with other algorithms,the fitness value of IGWO is 20.2% higher than GA,13.5% higher than PSO,9.66% higher than MMBO,and the number of tags covered is more,so the optimization scheme can be found more effectively.

With the development of new generation,direct-current transmission,electric energy storage and other technologies,flexible load such as new energy generation and electric vehicles and energy storage devices with charge-discharge ability are constantly integrated into the power grid,which makes the traditional distribution network architecture change greatly.Due to the great instability of the new type of source grid load storage,it brings great challenges to the distribution network dispatching,especially the extra power loss in scheduling which is difficult to control.With the construction of Ubiquitous Power Internet of Things (UPIoT),real-time information collection and data analysis of source grid load storage can be realized,which provides an opportunity for real-time data-driven collaborative scheduling of Source-Grid-Load-Storage.The collaborative scheduling of Source-Grid-Load-Storage in distribution network has a natural distributed characteristic.Therefore,a distributed state awareness system can be built which can bring low latency and high precision for the collaborative real-time scheduling of Source-Grid-Load-Storage.The distribution network structure under the background of UPIoT is analyzed in this paper,then the source grid load storage and their interaction methods in a distributed environment are modeled.This model is based on the premise that the feeder nodes have certain computing and communication capabilities,and it stipulates the data interaction method of all the nodes in entire distribution network,which can effectively reflect the effect of collaborative scheduling in the distribution network.A collaborative scheduling mechanism of Source-Grid-Load-Storage with distributed state awareness under Power Internet of Things is proposed,and the response strategy of each end of source grid load storage is defined in this paper,thus realizing the goal of peak load shifting and scheduling loss reduction.Based on some real data of the power grid,a simulation verification experiment is designed.The experimental results verify the effectiveness of the collaborative scheduling mechanism of Source-Grid-Load-Storage.

In most of the edge computing applications including Internet of Things (IoT) devices,the application programming interface (API) based on Internet application technologies,which are commonly known as Web Technologies,is the core of information interaction between devices and remote servers.Compared with traditional web applications,most users cannot directly access APIs used by edge devices,which makes them suffer fewer attacks.However,with the popularity of edge computing,the attack based on API has gradually become a hot spot.Therefore,this paper proposes a web attack vector detection method for IoT service providers.It can be utilized to detect malicious traffic against its API services and provide security intelligence for the security operation center (SOC).Based on the feature extraction of text sequence requested by hypertext transfer protocol (HTTP),this method combines bidirectional long short-term memory (BLSTM) to detect the attack vector of web traffic according to the relatively fixed format of API request message.Experimental results show that,compared with the rule-based Web application firewall (WAF) and traditional machine learning methods,the proposed method has better recognition ability for attacks on IoT service APIs.