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Multiuser multiple input multiple output (MU-MIMO) wireless communication system provides substantial downlink throughput in millimeter wave (mmWave) communication by allowing multiple users to communicate at the same frequency and time slots. However, the design of the optimum beam-vector for each user to minimise interference from other users is challenging. In this paper, based on the concept of signal-to-leakage plus noise ratio (SLNR), we analyze the ergodic sum-rate capacity using statistical Eigen-mode (SE) and zero-forcing (ZF) models with Ricean fading channel. In the analysis, the orthogonality of channel vectors between users is assumed to guarantee interference cancelation from other cochannel users. The impact of the number of antenna elements on the achievable sum-rate capacity obtained by dirty paper coding (DPC) method considered as a nonlinear scheme for approximating average system capacity is studied. A power iterative precoding scheme that iteratively finds the most dominant eigenvector (optimum weight vector) for minimising cochannel interference (CCI), that is, maximising the SLNR for all users simultaneously, is designed resulting in enhancement of average system capacity. The average system capacities achieved by the proposed power iterative technique in this study compared with the singular value decomposition (SVD) method are in the ranges of 5–11 bps/Hz and 1–6 bps/Hz, respectively. Therefore, the proposed power iterative method achieves higher performance than the SVD regarding achievable sum-rate capacity.

We study the energy harvesting problem in the Internet of Things with heterogeneous users, where there are three types of single-antenna users: ID users that only receive information, EH users that can only receive energy, and ID/EH users that receive information and energy simultaneously from a multiantenna base station via power splitting. We aim to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of the ID users and ID/EH users by jointly designing the power allocation at the transmitter and the power splitting strategy at the ID/EH receivers under the maximum transmit power and the minimum energy harvesting constraints. Specifically, we first apply the semidefinite relaxation (SDR), zero-forcing (ZF), and maximum ratio transmission (MRT) techniques to solve the nonconvex problems. We then apply the zero-forcing dirty paper coding (ZF-DPC) technique to eliminate the multiuser interference and derive the closed-form optimal solution. Numerical results show that ZF-DPC provides higher achievable minimum SINR than SDR and ZF in most cases.

In recent years, the public’s demand for location services has increased significantly. As outdoor positioning has matured, indoor positioning has become a focus area for researchers. Various indoor positioning methods have emerged. Pedestrian dead reckoning (PDR) has become a research hotspot since it does not require a positioning infrastructure. An integral equation is used in PDR positioning; thus, errors accumulate during long-term operation. To eliminate the accumulated errors in PDR localisation, this paper proposes a PDR localisation system applied to complex scenarios with multiple buildings and large areas. The system is based on the pedestrian movement behavior recognition algorithm proposed in this paper, which recognises the behavior of pedestrians for each gait and improves the stride length estimation for PDR localisation based on the recognition results to reduce the accumulation of errors in the PDR localisation algorithm itself. At the same time, the system uses self-researched hardware to modify the audio equipment used for broadcasting within the indoor environment, to locate the acoustic source through a Hamming distance-based localisation algorithm, and to correct the estimated acoustic source estimated location based on the known source location in order to eliminate the accumulated error in PDR localisation. Through analysis and experimental verification, the recognition accuracy of pedestrian movement behavior recognition proposed in this paper reaches 95% and the acoustic source localisation accuracy of 0.32 m during movement, thus, producing an excellent effect on eliminating the cumulative error of PDR localisation.

In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

In recent years, the rapid development of cloud computing, mobile Internet, Internet of Things, and other technologies has accelerated the explosive growth of the number and types of data in various industries. As one of the current hot research fields, the multidimensional data fusion system has received widespread attention all over the world. Road traffic and traffic management involve a wide range of participants; a variety of traffic behavior and traffic management means have produced a huge volume of traffic management data and have a very high application value. Therefore, it is of great significance to study the application mode of the traffic management multidimensional data fusion system and realize the asset value transformation of the multidimensional data fusion system. Starting from the safety supervision service of operating vehicles, this paper elaborates the traffic management multidimensional data fusion system platform for the safety supervision service of operating vehicles around the application requirements and architecture of the traffic management multidimensional data fusion system and the results of this research team. Paper with city comprehensive transport hub as the research object uses the Internet of Things technology; establishes a set of BIM model-based intelligent Internet operation management platform, multidimensional collection personnel, and vehicles, such as traffic, equipment, and business data information; realizes the integration of data, correlation, and instructions issued by ability, to support the building intelligence operation management needs of the business; and realizes traffic and transportation status detection.

As people’s awareness of the issue of privacy leakage continues to increase, and the demand for privacy protection continues to increase, there is an urgent need for some effective methods or means to achieve the purpose of protecting privacy. So far, there have been many achievements in the research of location-based privacy services, and it can effectively protect the location privacy of users. However, there are few research results that require privacy protection, and the privacy protection system needs to be improved. Aiming at the shortcomings of traditional differential privacy protection, this paper designs a differential privacy protection mechanism based on interactive social networks. Under this mechanism, we have proved that it meets the protection conditions of differential privacy and prevents the leakage of private information with the greatest possibility. In this paper, we establish a network evolution game model, in which users only play games with connected users. Then, based on the game model, a dynamic equation is derived to express the trend of the proportion of users adopting privacy protection settings in the network over time, and the impact of the benefit-cost ratio on the evolutionarily stable state is analyzed. A real data set is used to verify the feasibility of the model. Experimental results show that the model can effectively describe the dynamic evolution of social network users’ privacy protection behaviors. This model can help social platforms design effective security services and incentive mechanisms, encourage users to adopt privacy protection settings, and promote the deployment of privacy protection mechanisms in the network.

With the continuous deepening of enterprise management and market competition, the pressure of production and operation of enterprises is increasing, and it is urgent to improve the management level of enterprises. This paper mainly studies the decision optimization design of enterprise standardization management planning based on mobile network system. In this paper, the idea of clustering is used to integrate the customers into a customer clustering mode, which will greatly reduce the number of customers studied in the model and simplify the optimization process of the model. Through online and offline processes, these can achieve dynamic and rapid processing of data flow and can well meet the needs of users for data flow analysis. In the construction of enterprise standardization strategic performance evaluation index system, it is necessary to decompose, combine, and converge the index system. According to the construction principles and methods discussed above, the influencing factors of standardized strategic performance evaluation indicators of construction enterprises are preliminarily stratified by using the expert survey method and cluster analysis method, so as to construct the initial evaluation system. For the mobile network system testing, we use the black box test and white box test. The test content mainly includes two modules: area calculation and label calculation. Experimental data show that when the time threshold Ts is set to 1 hour, the prediction accuracy of TBM increases from 46.9% to 47.7%. The results show that the mobile network system realizes the whole life cycle management of enterprise standards and improves the management level of enterprise standardization work.

Computation and data sensitivity are the metrics of the current Internet of Things (IoT). In cloud data centers, current analytics are often hosted and reported on suffering from high congestion, limited bandwidth, and security mechanisms. Various platforms are developed in the area of fog computing and thus implemented and assessed to run analytics on multiple devices, including IoT devices, in a distributed way. Fog computing advances the paradigm of cloud computing on the network edge, introducing a number of options and facilities. Fog computing enhances the processing, verdicts, and interventions to occur through IoT devices and spreads only the necessary details. The ideas of fog computing based on IoT in healthcare frameworks are exploited by shaping the disseminated delegate layer of insight between sensor hubs and the cloud. The cloud proposed a system adapted to overcome various challenges in omnipresent medical services frameworks, such as portability, energy efficiency, adaptability, and unwavering quality issues, by accepting the right to take care of certain weights of the sensor network and a distant medical service group. An overview of e-health monitoring system in the context of testing and quality assurance of fog computing is presented in this paper. Relevant papers were analyzed in a comprehensive way for the identification of relevant information. The study has compiled contributions of the existing methodologies, methods, and approaches in fog computing e-healthcare.

With the development of the economy, people’s living standards continue to improve, and the management awareness of their own health and safety has gradually increased. The human body usually has a long process of transition from being healthy to the appearance of disease, and people’s lifestyle and daily behavior will affect the health of the body to a large extent. This paper studies the national sports AI health management service system based on edge computing. This paper organically combines edge computing, artificial intelligence, and health management service systems. Through detection devices with smart sensors and smart health systems, a comprehensive health management service platform for the masses is constructed to promote the development of sports health management services. Upgrade and transform to make it more in line with the modern intelligent environment. The advancement of modern technology has gradually transformed my country’s traditional health record management service to an intelligent electronic health record management system. From the experimental data, it can be seen that the filing rate and pass rate of residents’ electronic files have reached 88.275% and 87.435%, but the utilization rate of health files is only 31.685%. The establishment of a supporting health management service system is precisely to effectively increase the utilization rate of health files, so that users can truly enjoy intelligent, professional, and real-time health services.

Activity selection is critical for the smart environment and Cyber-Physical Systems (CPSs) that can provide timely and intelligent services, especially as the number of connected devices is increasing at an unprecedented speed. As it is important to collect labels by various agents in the CPSs, crowdsourcing inference algorithms are designed to help acquire accurate labels that involve high-level knowledge. However, there are some limitations in the algorithm in the existing literature such as incurring extra budget for the existing algorithms, inability to scale appropriately, requiring the knowledge of prior distribution, difficulties to implement these algorithms, or generating local optima. In this paper, we provide a crowdsourcing inference method with variational tempering that obtains ground truth as well as considers both the reliability of workers and the difficulty level of the tasks and ensure a local optimum. The numerical experiments of the real-world data indicate that our novel variational tempering inference algorithm performs better than the existing advancing algorithms. Therefore, this paper provides a new efficient algorithm in CPSs and machine learning, and thus, it makes a new contribution to the literature.

In order to improve the efficiency of customization and reduce the cost of customization under Big data environment, this paper uses cost-sharing contract, pricing mechanism, Hotelling model, and game theory tools and research methods, for C2B Electronic Commerce (e-commerce) mode of Supply Chain Pricing Strategy for in-depth discussion. This paper first gives the architecture of the customization service system based on big data. The paper studies the game equilibrium of supply chain members under four scenarios: centralized decision-making, decentralized decision-making, C2B-dominated decision-making, and traditional enterprise-dominated decision-making in a supply chain composed of a supplier and C2B e-commerce enterprises with horizontal price competition, and examines the cross-price. Important parameters such as impact coefficient, impact coefficient of effort degree of personalized customization, and so on have an impact on variables such as effort degree of personalized customization, retail price, and profit of supply chain members of C2B e-commerce enterprises. Research shows that with the increase of cross-price impact coefficient, C2B e-commerce will enhance its personalized customization efforts in different situations in order to pursue higher profits.

With the advancement of solid-state devices for lighting, illumination is on the verge of being completely restructured. This revolution comes with numerous advantages and viable opportunities that can transform the world of wireless communications for the better. Solid-state LEDs are rapidly replacing the contemporary incandescent and fluorescent lamps. In addition to their high energy efficiency, LEDs are desirable for their low heat generation, long lifespan, and their capability to switch on and off at an extremely high rate. The ability of switching between different levels of luminous intensity at such a rate has enabled the inception of a new communication technology referred to as visible light communication (VLC). With this technology, the LED lamps are additionally being used for data transmission. This paper provides a tutorial and a survey of VLC in terms of the design, development, and evaluation techniques as well as current challenges and their envisioned solutions. The focus of this paper is mainly directed towards an indoor setup. An overview of VLC, theory of illumination, system receivers, system architecture, and ongoing developments are provided. We further provide some baseline simulation results to give a technical background on the performance of VLC systems. Moreover, we provide the potential of incorporating VLC techniques in the current and upcoming technologies such as fifth-generation (5G), beyond fifth-generation (B5G) wireless communication trends including sixth-generation (6G), and intelligent reflective surfaces (IRSs) among others.

The SDN controller uses the OpenFlow Discovery Protocol (OFDP) to collect network topology status. OFDP detects the link between OpenFlow switches by generating Link Layer Discovery Protocol (LLDP) packets. However, OFDP is not a completely secure protocol and can be used by attackers to perform topology discovery injection attacks, topology discovery man-in-the-middle attacks, and topology discovery flood attacks, thereby confusing the network topology. This paper proposes a Correlation-based Topology Anomaly Detection (CTAD) mechanism to run in a software-defined network controller. Spearman’s rank correlation is used to analyze the correlation between network traffic between links and measure the time difference between the round trip time of each LLDP frame to determine whether the topology man-in-the-middle attack exists in the network. This paper also adds a dynamic authentication key and counting mechanism in the LLDP frame to prevent attackers from using the topology discovery injection attack to generate fake links and topology discovery flooding attacks, causing network routing or switching abnormalities.

Garbage classification is difficult to supervise in the stage of collection and transportation. This paper proposes a computer vision-based method for intelligent supervision and workload statistics of garbage trucks. In terms of hardware, this paper deploys a camera and an image processing unit with NPU based on the original on-board computing and communication equipment. In terms of software, this paper uses the YOLOv3-tiny algorithm on the image processing unit to perform real-time target detection on garbage truck work, collects statistics on the color, specifications, and quantity of garbage bins cleaned by the garbage truck, and uploads the results to the server for recording and display. The proposed method has low deployment and maintenance costs while maintaining excellent accuracy and real-time performance, which makes it have good commercial application value.

Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.

The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, and search and rescue operations among others. The current technologies are using fixed base stations to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. This open gate for the UAV technology is to be used as a mobile alternative to increase accessibility with fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general and provides detailed steps that are involved in the multiarmed bandit (MAB) approach in solving the UAV biomedical engineering technology device and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal performance and efficiency of medical engineered devices. The simulated results depicted that a multiarmed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technology systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.

Sensor-cloud is a developing technology and popular paradigm for various applications. It integrates wireless sensor into a cloud computing environment. On the one hand, the cloud offers extensive data storage and analytical and processing capabilities not available in sensor nodes. On the other hand, data distribution (such as time synchronization and configuration files) is always an important topic in such sensor-cloud systems, which leads to a rapid increase in energy consumption by sensors. In this paper, we aim to reduce the energy consumption of data dissemination in sensor-cloud systems and study the optimization of energy consumption with time-varying channel quality when multiple nodes use the same channel to transmit data. Suppose that there is a certain probability that the nodes send data for competing channel. And then, they decide to distribute data in terms of channel quality for saving energy after getting the channel successfully whether or not. Firstly, we construct the maximization problem of average energy efficiency for distributing data with delay demand. Then, this maximization problem transferred an optimal stopping problem which generates the optimal stopping rule. At last, the thresholds of the optimal transmission rate in each period are solved by using the optimal stopping theory, and the optimal energy efficiency for data distribution is achieved. Simulation results indicate that the strategy proposed in this paper can to some extent improve average energy efficiency and delivery ratio and enhance energy optimization effect and network performance compared with other strategies.

In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.

The projected rise in wireless communication traffic has necessitated the advancement of energy-efficient (EE) techniques for the design of wireless communication systems, given the high operating costs of conventional wireless cellular networks, and the scarcity of energy resources in low-power applications. The objective of this paper is to examine the paradigm shifts in EE approaches in recent times by reviewing traditional approaches to EE, analyzing recent trends, and identifying future challenges and opportunities. Considering the current energy concerns, nodes in emerging wireless networks range from limited-energy nodes (LENs) to high-energy nodes (HENs) with entirely different constraints in either case. In view of these extremes, this paper examines the principles behind energy-efficient wireless communication network design. We then present a broad taxonomy that tracks the areas of impact of these techniques in the network. We specifically discuss the preponderance of prediction-based energy-efficient techniques and their limits, and then discuss the trends in renewable energy supply systems for future networks. Finally, we recommend more context-specific energy-efficient research efforts and cross-vendor collaborations to push the frontiers of energy efficiency in the design of wireless communication networks.

This paper presents an artificial spider-web topology model inspired by the structure and invulnerability of a spider web. A hierarchical clustering routing rule is accordingly established using the vibration transmission features of the natural spider web as a reference. Furthermore, the end-to-end delay is applied as the quantitative indicator of invulnerability for analyzing the communication performance and characteristics of the artificial spider-web topology. The simulation tests of a one-layer and 3-layer artificial spider-web model are implemented to obtain the importance and destructive tolerance of network components based on OPNET, with the change of communication conditions and fault types. This paper can provide a practical analysis method for the invulnerability of the artificial spider-web topology and offer important implications for the construction and maintenance of wireless sensor networks based on the topology.

In this paper, we propose a data dissemination protocol for asynchronous duty-cycling wireless sensor networks. In an asynchronous duty-cycling network, each node independently selects its wake-up time. In this environment, data dissemination becomes energy consuming, because broadcasting a packet does reach all neighbors but only the neighbors that are awake at the time. A node can forward its packet to all neighbors by continuously transmitting the packet for a whole wake-up interval, but it leads to high energy consumption and high dissemination delay. The idea proposed in this paper is to use opportunistic forwarding, where each node forwards the packet to a neighbor that wakes up early and receives the packet. Each node forwards the packet, as long as there is a neighboring node that has not received the packet yet. The main benefit of this opportunistic forwarding-based dissemination is that every time a packet is disseminated, it may take a different path to reach the nodes. At the beginning of dissemination, a sender needs to transmit for a very short duration of time because there are plenty of neighboring nodes to receive the packet. As more nodes receive the packet, the transmit duration of the sender becomes longer, thus consuming more energy. Since the order of dissemination is different every time, energy consumption is naturally balanced among the nodes, without explicit measures. Through extensive simulations, we show that the proposed protocol achieves longer network lifetime and shorter dissemination delay compared to other dissemination protocols in various network environments.

Federal Aviation Administration (FAA) of the United States is considering Remote ID systems for unmanned aerial vehicles (UAVs). These systems act as license plates used on automobiles, but they transmit information using radio waves. To be useful, the transmissions in such systems need to reach long distances to minimize the number of ground stations to capture these transmissions. LoRaWAN is designed as a cheap long-range technology to be used for long-range communication for the Internet of Things. Several manufacturers make LoRaWAN modules, which are readily available on the market and are, therefore, ideal for the UAVs Remote IDs at a low cost. In this paper, we present our experiences in using LoRaWAN technology as a communication technology. Our experiments to identify and locate the UAV systems uncovered several issues of using LoRaWAN in such systems that are documented in this paper. Using several ground stations, we can determine the location of a UAV equipped with a LoRaWAN module that transmits the UAV Remote ID. Hence, it can help identify UAVs that unintentionally, or intentionally, fly into restricted zones., Comment: 11 pages

Recently, vehicular ad hoc networks (VANETs) embark a great deal of attention in the area of wireless and communication technology and are becoming one of the prominent research areas in the intelligent transportation system (ITS) because they provide safety and precautionary measures to the drivers and passengers, respectively. VANETs are quite different from the mobile ad hoc networks (MANETs) in terms of characteristics, challenges, system architecture, and their application. In this paper, we summarize the recent state-of-the-art methods of VANETs by discussing their architecture, security, and challenges. Secondly, we discuss the detailed analysis of security schemes and the possible measures to provide secure communication in VANETs. Then, we comprehensively cover the authentication schemes, which is able to protect the vehicular network from malicious nodes and fake messages. Thus, it provides security in VANETs. Thirdly, we cover the mobility and network simulators, as well as other simulation tools, followed by the performance of authentication schemes. Finally, we discuss the comfort and safety applications of VANETs. In sum, this paper comprehensively covers the entire VANET system and its applications by filling the gaps of existing surveys and incorporating the latest trends in VANETs.

This paper presents a concise overview of current public safety communication networks known as LMR (Land Mobile Radio) and emerging LTE- (Long-Term Evolution-) based broadband public safety networks to be deployed in the 700 MHz band. A broadband nationwide network for public safety based on LTE is inevitable where shared or dedicated types of LTE-based public safety networks are possible. Current LTE services do not meet mission-critical requirements and several enhancements have been defined by 3GPP to address this in Releases 12 and 13. First responders are familiar with LMR and consider it to be a reliable technology with massive deployment everywhere. Therefore, it is expected that LMR will continue to exist alongside any new LTE-based broadband public safety network. Recent LTE releases (particularly Release 15) addressed the LMR-LTE interoperability issue and described comprehensive interworking facilities. New and upcoming features and services of LTE in Releases 14 and 15, such as mission-critical data, mission-critical video, and aerial user equipments, are also directly applicable to public safety. The paper endeavours to provide a quick yet meaningful review of all these issues. It also offers a look ahead at the new and rapidly advancing virtualization technologies, such as software-defined radio access network, and radio access network slicing, as enablers for future public safety networks.

In this paper, we propose robust power allocation strategies to improve the localization performance in cooperative wireless sensor localization systems when suffering interference of jammer nodes. In wireless sensor localization systems, transmitting power strategies will affect the localization accuracy and determine the lifetime of wireless sensor networks. At the same time, the power allocation problem will be evolution to a new challenge when there are jammed nodes. So in this paper, we first present the optimization framework in jammed cooperative localization systems. Moreover, the imperfect parameter estimations of agent and jammer nodes are considered to develop robust power allocation strategies. In particular, this problem can be transformed into second-order cone programs (SOCPs) to obtain the end solution. Numerical results show the proposed power allocation strategies can achieve better performance than uniform power allocation and the robust schemes can ensure lower localization error than nonrobust power control when systems are subject to uncertainty.

This paper presents an energy-efficient technique to handle redundant traffic (EEHRT) in the zone-based routing for wireless sensor networks. In this technique, multihop routing is performed based on the remaining energy of the nodes. Afterwards, it performs position-based routing without the need for the nodes to know their respective position. The main objective of this paper is to handle the redundant packets generated in zone-based routing using short beacon messages. Nodes of lower zones route the data of the higher zone to base station (BS) with a minimum number of hops and utilize only those nodes on the path which are energy efficient and located closer to BS. Moreover, the source node is acknowledged by the relaying node using a wireless broadcast advantage (WBA) without sending any special ACK packet to the sender, which reduces the control overhead in the routing process. The EEHRT technique improves the routing against RARZ by ensuring only one copy of the packet is propagated at each hop along the routing path to BS. Simulation results show that EEHRT achieved 28% improvement in energy efficiency, 10% and 25% improvements in data throughput against total and distinct packet reception at BS respectively, 35% increase in overall network lifetime, and 100% reduction in redundant packets generation and propagation in the network against RARZ routing.

The ROLL working group proposed the RPL (IPv6 routing protocol for low-power and lossy networks) to respond to the requirements of low-power and lossy networks (LLNs). In this paper, we propose a new approach to assess the RPL performances based on its main components, namely, the objective function (OF) and the trickle algorithm. To this end, we compare between the RPL-EC (RPL based combined ETX and energy consumption) and the RPL-FL (RPL based on the flexible trickle algorithm). This paper compares the two implementations to assess the weight of each proposed improvement against the standard RPL. The results show that RPL performances are greatly influenced by the change of both the objective function and trickle algorithm. RPL-FL provides best values in terms of overhead, packet delivery ratio (PDR), and energy consumption, while RPL-EC acts better with the convergence time and the network lifetime compared to the standard RPL.

In recent times, optical wireless communications (OWC) have become attractive research interest in mobile communication for its inexpensiveness and high-speed data transmission capability and it is already recognized as complementary to radio-frequency (RF) based technologies. Light fidelity (LiFi) and optical camera communication (OCC) are two promising OWC technologies that use a photo detector (PD) and a camera, respectively, to receive optical pulses. These communication systems can be implemented in all kinds of environments using existing light-emitting diode (LED) infrastructures to transmit data. However, both networking layers suffer from several limitations. An excellent solution to overcoming these limitations is the integration of OCC and LiFi. In this paper, we propose a hybrid OCC and LiFi architecture to improve the quality-of-service (QoS) of users. A network assignment mechanism is developed for the hybrid system. A dynamic link-switching technique for efficient handover management between networks is proposed afterward which includes switching provisioning based on user mobility and detailed network switching flow analysis. Fuzzy logic (FL) is used to develop the proposed mechanisms. A time-division multiple access (TDMA) based approach, called round-robin scheduling (RRS), is also adopted to ensure fairness in time resource allocation while serving multiple users using the same LED in the hybrid system. Furthermore, simulation results are presented taking different practical application scenarios into consideration. The performance analysis of the network assignment mechanism, which is provided at the end of the paper, demonstrates the importance and feasibility of the proposed scheme.

Edge computing is an emerging computational model that enables efficient offloading of service requests to edge servers. By leveraging the well-developed technologies of cloud computing, the computing capabilities of mobile devices can be significantly enhanced in edge computing paradigm. However, upon the arrival of user requests, whether to dispatch them to the edge servers or cloud servers in order to guarantee the quality of service (QoS), i.e., the QoS-aware service selection problem, still remains an open problem. Due to the dynamic mobility of users and the variation of task arrivals and service processes, it is extremely costly to obtain the global optimal solution by both mathematical approaches and simulation-based schemes. To attack this challenge, this paper proposes a simulation-based approach of QoS-aware dynamic service selection for mobile edge computing systems. Stochastic system models are presented and mathematical analyses are provided. Based on the analytical results, the QoS-aware service selection problem is formulated by a dynamic optimization problem. Goal softening is applied to the original problem, and service selection algorithms are designed using ordinal optimization techniques. Simulation experiments are conducted to validate the efficacy of the approach presented in this paper.

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

In recent years, the application of wireless sensor networks (WSN) in power systems has received a great deal of attention. As we all know, the most important issue for the power system is security and stability, especially due to the massive outages caused by cascading trips. Therefore, in today’s era, from the perspective of cascading trips, how to effectively use WSN to analyze and evaluate the security level of the power grid is an important direction for future power development. In this paper, an algorithm based on the WSN collection of online data to calculate the corresponding security level of the system is proposed for the cascading trip phenomenon, to achieve the online evaluation of the cascading trips. First, this paper proposes a hybrid layered network structure based on WSN for monitoring system and details the acquisition of power grid parameters by its acquisition layer. Secondly, combined with the manifestation of cascading trips and the action equation of current-type line backup protection, the mathematical representation of the grid cascading trips is given, and the mathematical form corresponding to the critical situation is strictly proved, and an index for evaluating the security level of the power grid is proposed and then further combined with the actual physical constraints of the power grid and the establishment of a mathematical model for calculating the security level of the grid cascading trips. For this model, this paper relies on evolution particle swarm optimization (EPSO) to give specific ideas for solving the model. Finally, a case analysis is performed by the IEEE39 node system and the results of the case show the effectiveness of the model and method.

Orthogonal frequency division multiplexing (OFDM) is the key technique of the communication system. In this paper, coprime array is designed in OFDM system to obtain joint direction of arrival (DOA) and carrier frequency offset (CFO) estimates. The proposed system can achieve beamforming and adaptive signal processing, reduce the multipath fading, and attain angle diversity. Meanwhile, coprime array has the advantages of extended array aperture, increased degrees of freedom, and reduced mutual coupling effect, which is adaptable in OFDM system. Moreover, this paper proposes a low-complexity parameter algorithm with superior performance, which first exploits propagator method (PM) as the initialization and then parallel factor (PARAFAC) method is employed for the accurate DOA and CFO estimation. Simulation results verify the effectiveness of the OFDM coprime array system and the proposed low-complexity multiparameter algorithm.

Artificial intelligence (AI) is progressively changing techniques of teaching and learning. In the past, the objective was to provide an intelligent tutoring system without intervention from a human teacher to enhance skills, control, knowledge construction, and intellectual engagement. This paper proposes a definition of AI focusing on enhancing the humanoid agent Nao’s learning capabilities and interactions. The aim is to increase Nao intelligence using big data by activating multisensory perceptions such as visual and auditory stimuli modules and speech-related stimuli, as well as being in various movements. The method is to develop a toolkit by enabling Arabic speech recognition and implementing the Haar algorithm for robust image recognition to improve the capabilities of Nao during interactions with a child in a mixed reality system using big data. The experiment design and testing processes were conducted by implementing an AI principle design, namely, the three-constituent principle. Four experiments were conducted to boost Nao’s intelligence level using 100 children, different environments (class, lab, home, and mixed reality Leap Motion Controller (LMC). An objective function and an operational time cost function are developed to improve Nao’s learning experience in different environments accomplishing the best results in 4.2 seconds for each number recognition. The experiments’ results showed an increase in Nao’s intelligence from 3 to 7 years old compared with a child’s intelligence in learning simple mathematics with the best communication using a kappa ratio value of 90.8%, having a corpus that exceeded 390,000 segments, and scoring 93% of success rate when activating both auditory and vision modules for the agent Nao. The developed toolkit uses Arabic speech recognition and the Haar algorithm in a mixed reality system using big data enabling Nao to achieve a 94% success learning rate at a distance of 0.09 m; when using LMC in mixed reality, the hand sign gestures recorded the highest accuracy of 98.50% using Haar algorithm. The work shows that the current work enabled Nao to gradually achieve a higher learning success rate as the environment changes and multisensory perception increases. This paper also proposes a cutting-edge research work direction for fostering child-robots education in real time.

In recent years, a series of researches have revealed that the Deep Neural Network (DNN) is vulnerable to adversarial attack, and a number of attack methods have been proposed. Among those methods, an extremely sly type of attack named the one-pixel attack can mislead DNNs to misclassify an image via only modifying one pixel of the image, leading to severe security threats to DNN-based information systems. Currently, no method can really detect the one-pixel attack, for which the blank will be filled by this paper. This paper proposes two detection methods, including trigger detection and candidate detection. The trigger detection method analyzes the vulnerability of DNN models and gives the most suspected pixel that is modified by the one-pixel attack. The candidate detection method identifies a set of most suspected pixels using a differential evolution-based heuristic algorithm. The real-data experiments show that the trigger detection method has a detection success rate of 9.1%, and the candidate detection method achieves a detection success rate of 30.1%, which can validate the effectiveness of our methods.

This paper selects an unlicensed relay among available self-powered relays to not only remain but also secure information transmission from an unlicensed source to an unlicensed destination. The relays harvest energy in signals of the unlicensed source and the licensed transmitter. Then, they spend the harvested energy for their relaying operation. Conditioned on the licensed outage restriction, the peak transmission power restriction, Rayleigh fading, and the licensed interference, the current paper proposes an exact closed-form formula of the secrecy outage probability to quickly evaluate the secrecy performance of the proposed relay selection method in cognitive radio networks with energy harvesting. The proposed formula is corroborated by computer simulations. Several results illustrate the effectiveness of the relay selection in securing information transmission. Additionally, the security capability is saturated at large peak transmission powers or large preset outage probabilities of licensed users. Furthermore, the security capability depends on many specifications among which the power splitting ratio, the relays’ positions, and the time switching ratio can be optimally selected to obtain the best security performance.

In this paper, we propose and evaluate performance of multihop multipath underlay cognitive radio networks. In a primary network, an uplink nonorthogonal multiple access method is employed to allow primary transmitters to simultaneously transmit their data to a primary receiver. Using an underlay spectrum-sharing method, secondary source and secondary relays must adjust their transmit power to guarantee quality of service of the primary network. Under the limited transmit power, cochannel interference from the primary transmitters, and hardware noises caused by impairments, we propose best-path selection methods to improve the end-to-end performance for the secondary network. Moreover, both multihop decode-and-forward and amplify-and-forward relaying protocols are considered in this paper. We derive expressions of outage probability for the primary and secondary networks and propose an efficient method to calculate the transmit power of the secondary transmitters. Then, computer simulations employing the Monte-Carlo approach are realized to validate the derivations.

Limited by the insufficiency of single UAV’s load and flight time capabilities, the multi-UAV (unmanned aerial vehicle) collaboration to improve mission efficiency and expand mission functions has become the focus of current UAV theory and application research. In this paper, the research on UAV global path planning is carried out using the ant colony algorithm, and an indoor UAV path planning model based on the ant colony algorithm is constructed. In order to improve the efficiency of the algorithm, enhance the adaptability and robustness of the algorithm, a distributed path planning algorithm based on the dual decomposition UAV communication chain is proposed. This algorithm improves the basic ant colony algorithm from the aspects of path selection, pheromone update, and rollback strategy in view of the inherent shortcomings of the ant colony algorithm. In order to achieve the best performance of the algorithm, this paper analyzes each parameter in the ant colony algorithm in depth and obtains the optimal combination of parameters. The construction method of the Voronoi diagram was improved, and the method was simulated to verify that the method can obtain a Voronoi diagram path that is safer than the original method under certain time conditions. Through the principle analysis and simulation verification of the Dijkstra algorithm and the dual decomposition ant colony algorithm, it is concluded that the dual decomposition ant colony algorithm is more efficient in pathfinding. Finally, through simulation, it was verified that the dual decomposition ant colony algorithm can plan a safe and reasonable flight path for multiple UAV formation flights in an offline state and achieve offline global obstacle avoidance for multiple UAVs.

Security incidents such as natural disasters and power outages can cause inter-domain routing system regional failures, significantly impact the Internet’s safety. Reducing the impact of security incidents is essential for maintaining the stability of the Internet. One of the major impacts of security incidents is that many UPDATE messages will generate, which may easily cause network oscillations. This paper presents the UPDATE messages analysis during the six security incidents and finds that many duplicates and invalid messages are the leading cause of network instability. To effectively process these UPDATE messages, this paper proposes an UPDATE message preprocessing algorithm by analyzing the UPDATE operating mechanism to remove duplicate and invalid messages. Aiming at the problem of slow route search in existing route update methods using software-defined networking (SDN), this paper designs a RIB hierarchical structure for multi-level retrieval and proposes SMSEI-SDN combination with current route update strategies. Experimental results show that when a security incident occurs, by removing duplicate and invalid messages, SMSEI-SDN can reduce the total number of messages by an average of 19% and a maximum of 34.9% within the 60 s of caching time. Besides, SMSEI-SDN can reduce the routing update time by more than 99.98% compared to existing methods. This work provides insights for network operators and researchers interested in security incident impact suppression in the inter-domain routing system.

This paper implements a deep learning-based modulation pattern recognition algorithm for communication signals using a convolutional neural network architecture as a modulation recognizer. In this paper, a multiple-parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all-digital communication signals. The architecture learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output, which realizes the fitting process to the complex numerical mapping. By comparing and analyzing several commonly used time-frequency analysis methods, a time-frequency analysis method that can well highlight the differences between different signal modulation patterns is selected to convert the time-frequency map into a digital image that can be processed by a deep network. In order to fully extract the spatial and temporal characteristics of the signal, the CLP algorithm of the CNN network and LSTM network in parallel is proposed. The CNN network and LSTM network are used to extract the spatial features and temporal features of the signal, respectively, and the fusion of the two features as well as the classification is performed. Finally, the optimal model and parameters are obtained through the design of the modulation recognizer based on the convolutional neural network and the performance analysis of the convolutional neural network model. The simulation experimental results show that the improved convolutional neural network can produce certain performance gains in radio signal modulation style recognition. This promotes the application of machine learning algorithms in the field of radio signal modulation pattern recognition.

As an information carrier, face images contain abundant sensitive information. Due to its natural weak privacy, direct publishing may divulge privacy. Anonymization Technology and Data Encryption Technology are limited by the background knowledge and attack means of attackers, which cannot completely content the needs of face image privacy protection. Therefore, this paper proposes a face image publishing SWP (sliding window publication) algorithm, which satisfies the differential privacy. Firstly, the SWP translates the image gray matrix into a one-dimensional ordered data stream by using image segmentation technology. The purpose of this step is to transform the image privacy protection problem into the data stream privacy protection problem. Then, the sliding window model is used to model the data flow. By comparing the similarity of data in adjacent sliding windows, the privacy budget is dynamically allocated, and Laplace noise is added. In SWP, the data in the sliding window comes from the image. To present the image features contained in the data more comprehensively and use the privacy budget more reasonably, this paper proposes a fusion similarity measurement EM (exact mechanism) mechanism and a dynamic privacy budget allocation DA (dynamic allocation) mechanism. Also, for further improving the usability of human face images and reducing the impact of noise, a sort-SWP algorithm based on the SWP method is proposed in the paper. Through the analysis, it can be seen that ordered input can further improve the usability of the SWP algorithm, but direct sorting of data will destroy the ε -differential privacy. Therefore, this paper proposes a sorting method-SAS method, which satisfies the ε -differential privacy; SAS obtain an initial sort by using an exponential mechanism firstly. And then an approximate correct sort is obtained by using the Annealing algorithm to optimize the initial sort. Compared with LAP algorithm and SWP algorithm, the average accuracy rate of sort-SWP algorithm in ORL, Yale is increased by 56.63% and 21.55%, the recall rate is increased by 6.85% and 3.32%, and F1-sroce is improved by 55.62% and 16.55%.

With the rapid development of communication and microelectronic technology, the vehicular ad hoc network (VANET) has received extensive attention. However, due to the open nature of wireless communication links, it will cause VANET to generate many network security issues such as data leakage, network hijacking, and eavesdropping. To solve the above problem, this paper proposes a new authentication protocol which uses bilinear pairings and temporary pseudonyms. The proposed authentication protocol can realize functions such as the identity authentication of the vehicle and the verification of the message sent by the vehicle. Moreover, the proposed authentication protocol is capable of preventing any party (peer vehicles, service providers, etc.) from tracking the vehicle. To improve the efficiency of message verification, this paper also presents a batch authentication method for the vehicle to verify all messages received within a certain period of time. Finally, through security and performance analysis, it is actually easy to find that the proposed authentication protocol can not only resist various security threats but also have good computing and communication performance in the VANET environment.

This paper builds an intelligent E-Government platform based on the Internet of Things. It adopts a three-layer architecture model, including the government collection layer, the government network layer, and the government application layer. By making full use of data technology and information technology, government services can be realized in real-time perception, efficient operation, scientific decision-making, active service, and intelligent supervision and can be open and collaborative, so as to provide the public with better quality and more efficient and more responsive government services. In addition, this paper has realized optimization in the aspects of government service mode, Internet of Things application mode, and database structure. Through the use of big data technology, the government service information will be digitized and integrated, and through the data sharing and exchange platform, the government information resources will be intercommunicated and shared across departments, levels, and regions. The application mode has been upgraded. Compared with the traditional E-Government, the government has made a qualitative leap in the degree of automation of control, the intelligence of service and decision, the remote support ability, and the space-time scope that government can control. The distributed management mode of the SQL Server is adopted to realize the exchange of requested data and process the data content, which can greatly improve the working efficiency of the system. Finally, through testing, the government affairs management system has good stability; there is no congestion and delay when multiple users access the system, so the response speed and efficiency of the system basically meet the requirements.

There is inevitable polarization angle deviation between the target echo signal and the direct path signal of illuminator of opportunity (IO) in passive radar. In order to investigate the potential performance loss in target detection induced by the random deviation, small unmanned aerial vehicle (UAV) detection experiments with digital television terrestrial multimedia broadcasting- (DTMB-) based passive radar are conducted in this paper. Experimental results show that the polarization angles of the clutter signal and target echo signal are inconsistent. When the polarization diversity technology is used to suppress the clutter signal, the processing performance of the target echo signal may be reduced. On the premise that clutter is effectively suppressed by the processing algorithm, polarization synthesis can maximize the target echo signal processing gain. The effectiveness of the target localization algorithm combining time difference of arrival (TDOA) and direction of arrival (DOA) is also verified with polarization diversity reception in this paper.

This paper provides an indepth analysis and research on the intelligent management and monitoring system of sports training hall through the Internet of Things. Firstly, it introduces the project background of the sports training base stadium service system, analyses the significance of the system development, and describes the related technology and the method of development. The paper then analyses the requirements of the management business of the system and describes the main business processes of the system with activity diagrams; the paper then analyses the functional requirements and data requirements of the system and describes the system with use case diagrams and thumbnail class diagrams, respectively. Based on this, the thesis designs the overall aspect of the system, giving the corresponding total package diagram, and designs all functions one by one, giving the corresponding class diagram, the corresponding sequence diagram, and the corresponding processing flow diagram. The thesis gives the design of database tables by designing entity property diagrams and entity-to-entity relationship diagrams according to the data requirements and system functions. Finally, the research of the thesis is summarized accordingly, stating what has been done, what results have been achieved, what problems still exist, and what further work needs to be done to make the outlook of the work.

In this paper, we propose an AHP-WKNN method for indoor localization which combines the Analytic Hierarchy Process (AHP) technique and the Weighted K -nearest Neighbor (WKNN) algorithm. AHP serves to assign weights when WKNN is employed to select fingerprints for indoor positioning. The AHP technique can reasonably enlarge the influence that the received signal strength (RSS) gap between reference points has on the weights, achieving better performance in positioning. This paper also modifies the adaptive Kalman filter (AKF) noise reduction method by correcting the output based on the error between the RSS measurement and the expected output. The modified AKF can track the changes of RSS more effectively and achieve better performance of noise reduction. The simulation result shows that the proposed AHP-WKNN method and the modified AKF can improve positioning accuracy effectively.

Aiming at the problems in the social governance structure level under the management mode of the Internet of Things, this paper designs a network agent based on the idea of service grid construction that has the functions of service resource configuration discovery, load balancing, and protocol conversion. Using it as a service governance infrastructure reduces the coupling between business services and service governance, and the network agent will intercept the service call traffic within the platform to achieve cross-protocol and cross-frame service communication. Through the proposed global query decomposition strategy and algorithm, a global query mapping is realized, which solves the problem of global query decomposition based on the global data model. We designed the unified management mechanism of two commonly used IoT databases based on XML. This process involves the realization of heterogeneous Internet of Things database resource configuration, data mode conversion, and global query mapping. Among them, in the process of data schema conversion, the conflict of the data in the schema mapping is resolved, the XML Schema is generated, the schema mapping metadata is established, and the XML document of the data is generated. We build a framework for optimal allocation of cloud computing resources based on load forecasting and propose an adaptive elastic control system for resource allocation based on resource status monitoring and demand forecasting. In view of the low resource utilization problem caused by the resource management mode of single virtual machine serving single user adopted by Internet of Things service providers, this paper constructs a new public cloud architecture with a five-layer structure. On the basis of this architecture, an adaptive configuration mode of virtualized resources based on a single virtual machine serving multiple users is proposed. This mode can automatically search for optimal virtualization resources for application resource requests made by different users and run different applications on the same virtual machine without affecting the quality of service, so that IoT providers can guarantee services. At the same time, it improves the utilization efficiency of IoT resources and reduces energy consumption.

In order to improve the stability of the electrical control system, based on the Internet of Things technology, this paper constructs an online monitoring system for electrical control based on the Internet of Things. Based on actual needs, communication requirements, and common communication methods in the remote monitoring system, this paper combines the requirements of actual remote communication to conduct a detailed comparative analysis of the principles, performance characteristics, reliability, cost, maintenance, and other aspects of various communication methods, proposes a plan that can meet the communication requirements of the system, and completes the design of the wireless linear link communication network. In addition, according to the three-tier architecture of the Internet of Things, this paper comprehensively uses the technologies related to the Internet of Things to design and develop the perception layer, transmission layer, and application layer. Finally, this paper designs experiments to test the performance of the system constructed in this paper. The research results show that the online monitoring system based on the Internet of Things constructed in this paper meets the needs of intelligent monitoring of the electrical control system.

The purpose of this paper is to improve the efficiency of performance creative choreography (PCC). Our research work shows that we can realize the model integration and data optimization for PCC in complex environments based on the combined architecture of sensor network (SN) and machine-learning algorithm (MLA). In order to explain the process and content of this research better, this paper designs a specific problem description framework for PCC, which mainly includes the following content: (1) a twin sensor network (TSN) architecture based on digital twin information interaction is proposed, which defines and describes the acquisition method, classification (creative data, rehearsal data, and live data), and temporal and spatial features of performance data. (2) Proposed a mobile computing method based on director semantic annotation (DSA) as the core computing module of TSN. (3) A spatial dynamic line (SDL) model and a creative activation mechanism (CAM) based on DSA are proposed to realize fast and efficient PCC of dance with the TSN architecture. Experimental results show that the TSN architecture proposed in this article is reasonable and effective. The SDL model achieved significantly better performance with little time increase and improved the computability and aesthetics of PCC. New research ideas are proposed to solve the computational problem of PCC in complex environments.

With the popularization of mobile terminals and the rapid development of mobile communication technology, many PC-based services have placed high demands on data processing and storage functions. Cloud laptops that transfer data processing tasks to the cloud cannot meet the needs of users due to low latency and high-quality services. In view of this, some researchers have proposed the concept of mobile edge computing. Mobile edge computing (MEC) is based on the 5G evolution architecture. By deploying multiple service servers on the base station side near the edge of the user’s mobile core network, it provides nearby computing and processing services for user business. This article is aimed at studying the use of caching and MEC processing functions to design an effective caching and distribution mechanism across the network edge and apply it to civil aviation express marketing. This paper proposes to focus on mobile edge computing technology, combining it with data warehouse technology, clustering algorithm, and other methods to build an experimental model of MEC-based caching mechanism applied to civil aviation express marketing. The experimental results in this paper show that when the cache space and the number of service contents are constant, the LECC mechanism among the five cache mechanisms is more effective than LENC, LRU, and RR in cache hit rate, average content transmission delay, and transmission overhead. For example, with the same cache space, ATC under the LECC mechanism is about 4%~9%, 8%~13%, and 18%~22% lower than that of LENC, LRU, and RR, respectively.

Root cause identification of performance degradation within distributed systems is often a difficult and time-consuming task, yet it is crucial for maintaining high performance. In this paper, we present an execution trace-driven solution that reduces the efforts required to investigate, debug, and solve performance problems found in multinode distributed systems. The proposed approach employs a unified analysis method to represent trace data collected from the user-space level to the hardware level of involved nodes, allowing for efficient and effective root cause analysis. This solution works by extracting performance metrics and state information from trace data collected at user-space, kernel, and network levels. The multisource trace data is then synchronized and structured in a multidimensional data store, which is designed specifically for this kind of data. A posteriori analysis using a top-down approach is then used to investigate performance problems and detect their root causes. In this paper, we apply this generic framework to analyze trace data collected from the execution of the web server, database server, and application servers in a distributed LAMP (Linux, Apache, MySQL, and PHP) Stack. Using industrial level use cases, we show that the proposed approach is capable of investigating the root cause of performance issues, addressing unusual latency, and improving base latency by 70%. This is achieved with minimal tracing overhead that does not significantly impact performance, as well as O log n query response times for efficient analysis.