Actual Deployment Research Articles

Research and Application on Smart Courtyard Monitoring System

Residential safety has always been a basic need for people’s lives. In the vast suburbs and villages of China, quantities of courtyard houses are distributed, and the level of security monitoring is related to the people’s security and happiness. This study takes the characteristics of the human body with a large aspect ratio into account. By improving the structure of the Faster-RCNN network, a method for enhancing human body features is designed to enhance the network’s ability to recognize the human body, and covers various human postures such as standing and lying down, and eventually improves the accuracy of the original human body recognition by 7%. In addition, this study designs and deploys a complete set of real-time monitoring system, which continuously saves images when detecting human intrusion, and sends alarm messages to the user’s mobile phone WeChat in real time, which meets the real-time and adds convenience. The actual deployment shows that the system can find the target within 120 milliseconds when the intruder enters the sight and send out alarm messages to meet the realtime design requirements.

Acoustic localization in ocean reverberation via matrix completion with sensor failure

As one of the most important physical phenomena of underwater acoustics, ocean reverberation is a common and strong interference which significantly degrades the performance of target bearing estimation. Meanwhile, sensor failure is inevitable in actual sonar deployment as the underwater scene is complicated. Therefore, it is a challenge for acoustic localization in the ocean reverberation with sensor failure. To address this issue, we propose an improved approach based on the principle of low rank characteristics of matrix in this paper. Firstly, we utilize Hankel structured matrix to counteract the problem of sensor failure. Then, the algorithm of matrix completion (MC) based on ℓ1-norm and ℓ2-norm are exploited to recover the true signal matrix from the corrupted received matrix. Numerical results demonstrate that compared with other related methods, the ℓ1-norm provides better capability in the probability of recovery and shows the best robustness of bearing estimation with the narrow beam width and low sidelobe. Moreover, the proposed approach verifies the performances of reverberation suppression and achieves high resolution of localization.

Safeguarding unmanned aerial systems: an approach for identifying malicious aerial nodes

The coordination between aerial and ground nodes has enhanced the versatility and quality of the traditional networks. The application of aerial systems in mission-critical operations, as well as civilian applications, brings in the context of safeguarding unmanned aerial systems (UAS) from malicious attackers. This study discusses the threats and attacks mounted on UAS, alongside the challenges introduced by the unmanned aerial vehicle (UAV) network structure itself. A framework for safeguarding UAS against malicious attackers and recovering the rogue UAVs is proposed in the study. The proposed framework enforces a dynamic conceptual grid-based layout over the actual geographical deployment. The dynamically shuffling grid ascertains the security of transmission channels, as every time the grid is shuffled periodically or based on abnormal behaviour, the safety paradigm is reinitiated. Public key cryptographic algorithms are deployed for securing the communication links. Neural networks-based predictions are used for detecting abnormality in behavioural, statistical, and mobility patterns. Principal component analysis based on multivariate statistical analysis is used for detecting outliers in the aerial network environment. The behaviour prediction and outlier detection algorithms significantly improve the overall performance of the network and provide immunity against the intruders with reduced false positives, high accuracy, and better detection rate.

IPoE based campus network IPv4/IPv6 dual-stack admission control design and implementation

With the expansion of the campus network, various security risks have appeared. Most campus networks have implemented AAA, but seldom implemented admission control, which decreases the efficiency of dynamic allocation of IP addresses and makes it possible for users to avoid traffic billing. In order to realize the admission control of IPv4/IPv6 dual-stack network in large-scale campus network, we design and implement IPoE-based dual-stack admission control for the campus network in Tsinghua University. In IPoE, users can obtain IP address by DHCP with Ethernet physical link access. Authentication is achieved through a variety of user authentication methods. IP packets are encapsulated in Ethernet and pass through the access network to BRAS devices. BRAS devices find the user's address assignment information based on the client hardware address and the current Vlan ID after receiving the client's DHCP confirmation request message. Our main contributions include: for the various application scenarios under unified IPv4/IPv6 network, session-level IPoE technology is proposed; with the application of different configuration template in different protocol stack, various account control policy is submitted to AAA servers to achieve multi-type, multi-scenario requirements; to deal with the risk in large scale campus network, BRAS, AAA server, DHCP server can cooperate to realize that only authorized users are permitted to obtain addresses, thus avoid the security threats from unauthorized users; to cope with the high load pressure under large-scale deployment, the whitelist of webportal configured on the BRAS side reduces the load on webportal servers. The actual deployment shows that the dual-stack access authentication system has achieved the design objectives and runs smoothly.

Classification of Actual Sensor Network Deployments in Research Studies from 2013 to 2017

Technologies, such as Wireless Sensor Networks (WSN) and Internet of Things (IoT), have captured the imagination of researchers, businesses, and general public, due to breakthroughs in embedded system development, sensing technologies, and ubiquitous connectivity in recent years. That resulted in the emergence of an enormous, difficult-to-navigate body of work related to WSN and IoT. In an ongoing research effort to highlight trends and developments in these technologies and to see whether they are actually deployed rather than subjects of theoretical research with presumed potential use cases, we gathered and codified a dataset of scientific publications from a five-year period from 2013 to 2017 involving actual sensor network deployments, which will serve as a basis for future in-depth analysis of the field. In the first iteration, 15,010 potentially relevant articles were identified in SCOPUS and Web of Science databases; after two iterations, 3059 actual sensor network deployments were extracted from those articles and classified in a consistent way according to different categories, such as type of nodes, field of application, communication types, etc. We publish the resulting dataset with the intent that its further analysis may identify prospective research fields and future trends in WSN and IoT.

SIP Network Secure Communication Model Based on Improved SIP Protocol

The hot topics of session initiation protocol (SIP) authentication are mainly hypertext transfer protocol (HTTP) digest authentication mechanism and public-key cryptography standards (PKI) authentication mechanism. Among them, the PKI authentication mechanism encounters problems such as long encryption and decryption time and difficult actual deployment. Aiming at the shortcomings of the existing identity authentication mechanism of SIP, start with the chaotic sequence generated by chaotic equations, the dynamic password generation is optimised during the authentication process. A SIP security authentication mechanism based on chaotic equations is proposed. Firstly, improved security authentication scheme is applied in the video intercom system, then the chaotic signal generator on the microcontroller is designed, and finally the improved security authentication scheme is applied into the system. The results show that the system can realise all the functions at design time based on the improved SIP certification.

AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training.

Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.

Research on Pedestrian Detection Algorithm Based on Deep Learning

Pedestrian detection is a kind of computer vision technology which is used to judge whether there is a pedestrian in a given image or video and estimate its position accurately. Pedestrian detection technology is widely used in intelligent security monitoring, driverless and robot fields. Pedestrian detection technology has nearly 30 years of development. Although it has greatly improved in accuracy and speed, there are problems such as lighting, pedestrian individual differences, occlusion and multi-scale pedestrian targets in the actual application scenarios, which lead to more missed and false detection in pedestrian detection. The algorithm is not robust enough, which makes the actual deployment difficult.

Trends of developing training in advanced countries and direction of the Army

This study examines the history of LVCG training, which is attracting attention as the army’s education and training method in the era of the 4th industrial revolution, and the recent trends in advanced countries, and the direction of the army’s LVCG training development. LVCG training is an effective means for soldiers to improve their survival rate and combat skills by experiencing realistic virtual battles iteratively prior to actual deployment, thereby ultimately improving their readiness. The LVCG training system is classified into four categories: live, virtual training, war game training, and game, collectively referred to as LVCG. In the 1980s, advanced countries began to use the LVCG training system in various areas of education and training, and after the 1990s, they promoted a synthetic environment incorporating the LVCG training system. Recently, the synthetic training environment (STE) that integrates LVCG into a three-dimensional virtual environment. This is attracting attention and developed countries are rapidly transitioning to STE. The Army is also promoting education and training innovation based on big data and artificial intelligence by establishing a low-cost, highly efficient LVCG training environment with LVCG integrated around the synthetic training environment platform and securing education and training data using STE.

A Local Environment Model Based on Multi-Sensor Perception for Intelligent Vehicles

Accurate perception of the driving environment is a key technology for intelligent vehicles. Given some critical problems such as low robustness, low detection precision, difficulty in actual deployment, we propose a local environment model (LEM) based on multi-sensor fusion technology through Lidar, millimeter-wave (MMW) radar, camera, and ultrasonic radar. The local environment model mainly consists of the drivable area and the dynamic target list. The drivable area is extracted by the ground gradient threshold algorithm. Based on it, we propose an effective trim algorithm to make the drivable area model more practical. Furthermore, low-cost ultrasonic radars are deployed to compensate for the blind area of Lidar. The dynamic target list is established by local tracking and global tracking in the forward area. Kalman filter and converted measurement Kalman filter (CMKF) are adopted in the local tracking of Lidar, camera, and MMW radar. In the global tracking, the global nearest neighbor (GNN) algorithm is used for data association and the optimal distributed estimation fusion (ODEF) algorithm is used for sensor fusion. To improve the robustness of tracking, we use an assignment method to better exploit sensor performance. Finally, the vehicle experiment is carried out in the campus environment. Experimental results indicate that the proposed algorithm can avoid the false detection of the drivable area and realize real-time multi-target dynamic tracking. Therefore, the robustness and accuracy of the local environment model is verified.

INTERNATIONAL ECONOMIC COOPERATION OF CENTRAL ASIAN COUNTRIES ON ENERGY EFFICIENCY AND USE OF RENEWABLE ENERGY SOURCES

The purpose of this study is to identify priority areas for international cooperation of Central Asian countries to realize the potential of renewable energy sources and increase the efficiency of energy systems. Such scientific methods as literature analysis, comparative analysis, retrospective analysis, as well as the expert assessment method have been used in the study. The results of the study show that the countries of Central Asia have significant potential for the development of renewable energy. In recent years, interest in energy efficiency measures in Central Asia has been growing. Kazakhstan, Uzbekistan, and Turkmenistan consider renewable energy as a way to increase their oil and gas exports, while Tajikistan and Kyrgyzstan hope to reduce their dependence on energy imports from neighboring countries. At the same time, despite the enormous potential of renewable energy sources (RES), the actual deployment of renewable energy in the region remains low. The transition of the Central Asian countries' energy sector to new forms of energy production with a focus on renewable energy should be carried out in cooperation with countries with the best practices in the field of RES development. International cooperation will support the Central Asian countries in realizing their huge potential for RES and accelerating their pace of transition to renewable energy.Keywords: renewable energy, cooperation, energy resources, program, project.JEL Classifications: Q42, Q43, Q48, O19, R19, F59.DOI: https://doi.org/10.32479/ijeep.9962

Exploring the Impact of Wind Loads on Tower Crane Operation

The construction industry continues to be one of the industries with the highest accident rate. Tower cranes have always been the central component of many construction operations. With recent advancements in tower cranes, more efforts have been made for their safe operation. The impact of natural environmental factors on the operation of tower cranes cannot be ignored, especially the impact of the wind load, which can be found in construction regulations worldwide. Therefore, a large number of recent studies have focused on exploring different approaches to improving the impact of loads, such as improved feedback controllers and collocated sensors. However, there are few studies on the impact of the flexible cable pendulum system under wind load. To quantify the impact in this paper, first, the swing model of the hanging and the model of sling fluctuation are proposed as the base. Second, the flexible cable pendulum model is designed for the impact of tower cranes under wind load. By field experiments, the applicability of the model in practical use is analyzed. A case of the Crane Beach Hydropower Station is investigated as a demonstration. With MATLAB programming, the variation in the hanging impulse with wind speed is shown, indicating that the maximum wind speed should be 12 m/s if the factors that people can control are considered. When considering the factors that can be harmful to humans, the maximum wind speed is 17 m/s. With the proposed model, the impacts of wind load on tower crane operation can be quantified without actual deployment, offering a more straightforward quantitative tool for safe regulations and engineering management.

Modelling within-day ridehailing service provision with limited data

This paper proposes a holistic, within-day ridehailing service provision modelling approach, focusing on large-scale services with human-driven fleets. The model is powered by a full, two-year record of ridehailing trips in Toronto. Canada. Despite the abundant data, operations and fleet-related data still lacks, and uncertainty still exists concerning actual service provision deployment. Hence, a novel approach dealing with limited data availability is developed, which extracts implicit supply levels from trip-based demand data. The model is time-step driven, agent-based, and uses list-based data structures. It focuses on three key components: matching mechanisms, time intervals, and (several features of) driver activity. After testing various component combinations, a Hungarian matching algorithm, a normal distribution of driver work hours, and 5-minute time intervals allow close replication of observed wait time distributions and unique drivers per hour. Emergent model outputs also include VKT by vehicle states (idling, en-route, in-service), vehicle shifts and trip chains.

Real-World Spatio–Temporal Behavior Aware D2D Multicast Networks

Studies show that real-world user mobility has certain spatio-temporal correlations. However, most of the existing works on device-to-device multicast (D2MD) networks use randomly generated user locations, thus fail to capture the real-world spatio-temporal behavior of the users. In this work, we propose a realistic performance evaluation of the D2MD network that takes into account the joint spatio-temporal correlations of the users in the network. The proposed performance evaluation will be beneficial in assessing the viability of the D2MD networks before their actual deployment. Further, in order to acquire the required joint spatio-temporal behavior, we propose two novel methods namely observed mobility exploitation (OME) and expected mobility exploitation (EME). In OME, the joint spatio-temporal behavior is extracted from the past location information of the users. On the other hand, in EME, the past location information of a set (i.e., the training set) of users, following an average spatial-temporal behavior similar to the intended users, is utilized to estimate the intended users’ joint spatio-temporal behavior. The realistic performance of two D2MD networks is evaluated for a tractable campus set-up in terms of content delivery time and data offload ratio. It has been shown that for the D2MD networks the data offloading varies spatio-temporally and goes up to 35%. Further, a comparison is drawn between the conventional cellular unicast and multicast networks, and the two D2MD networks.

Game Theory-Based Planning of Nodes in Wireless Sensor Networks for Optimum Coverage With Maximum Battery Life

Maximize the coverage is one of the important aspects, in designing the wireless sensor network. One should aim at a nearly optimum coverage rate without reducing the life span of the network. Numerous solutions were suggested for this from which most try to select the least number of nodes as active, while the others remain in the sleep mode to save energy. This approach extends the life span of the network also. Nevertheless, these methods suffer from a lack of mathematical bases for the initial seed node choice in selecting active nodes. In this manuscript, a distributed method is suggested to address the challenge of maximum coverage with maximum network life. The exploitation of the game theory as a mathematical basis for the choice of active nodes is demonstrated in the current manuscript. The basis of the game theory in node planning, is node compete with each other to become active. Node tries to avoid coverage redundancy using the costs of the activation. This cost use number of the active neighbors and the uncovered area. The comparison of the simulation results with the results of the actual network deployed with nodeMCUs is done. We found that the network lifetimes almost doubles from 52 Hours to 98 hours with actual deployment on two nodeMCUs. The suggested method works well for maximum coverage, life span, and energy efficiency. The proposed method also causes a reduction in the redundancy rate.

Analysis of Acquired Indoor LTE-A Data from an Actual HetNet Cellular Deployment

Increasing the user downlink throughput is always a task of utmost importance for any cellular network service provider. However, most of the research is only focused on system simulations due to environmental simplicity. In this paper, instead of focusing on theoretical simulations, we aim to find the best practical solution to increase user throughput in an actual HetNet deployment where many small cells are deployed inside a building. An inter-frequency deployment is implemented in the actual LTE-A HetNet environment. A series of data collections were conducted in an actual indoor LTE-Advanced HetNet (heterogeneous network) environment with optimization strategies such as ABS (Almost Blank Subframe) and turning off/on interfering antennas. Detailed findings and analysis are provided to better understand the complicated actual HetNet environment. This work is also important to understand the future 5G network since the small cells and HetNet will continue to play an important role in the 5G network.

Agent Based Modelling and Simulation to estimate movement time of pilgrims from one place to another at Allahabad Jn. Railway Station during Kumbh Mela-2019

Kumbh Mela festival of India is one of the largest mass gathering event of huge religious importance all over the world. Large gatherings in these kind of religious events require rigorous monitoring and attention. Successful organization of such events requires synchronization among officials of different public departments such as police, health, security, communication, railways etc. The railway department plays a significant role in handling huge surge of passengers and their transportation during such events. Every 12-years Kumbh Mela is organized in the city of Prayagraj (formerly Allahabad) in northern India. The Allahabad Jn. railway station experiences huge inflow and outflow of pilgrims during Kumbh Mela. The railway authorities deploy predefined crowd movement strategies and boarding procedures for smooth transportation of pilgrims. However, these strategies are outlined based on previous experiences and ground knowledge of stakeholders. The strategies followed by railway authorities are needed to be evaluated and tested for realistic assessment and possible refinement before actual deployment. Our model is able to capture and simulate the real time behaviour of entities such as pilgrims and trains by programming them as synthetic agents. This model is helpful in analyzing the time taken by a group of pilgrims to move from a designated place to their target platform and board the train. The consumed time is calculated by simulating different movement and boarding procedures including the actual plans followed by the railway authorities. In this way it is possible to assess the efficiency of their movement plans and reasons about possible refinement.

Application Research of “Edge-Cloud Collaboration” Architecture in Security Protection of Ubiquitous Power Internet of Things

With the planning and construction of the ubiquitous power Internet of Things(UPIOT), how to achieve secure connection and interaction between the platform and the terminal becomes a crucial issue. Based on cloud computing and edge computing, this paper constructs the “edge-cloud collaboration” architecture of UPIOT to meet the functional requirements of grid-side intelligent services and core-side data analysis and low-latency, high-bandwidth, and large-access performance. According to the actual deployment situation of Jiangsu electric power, the firewall and VPN application scenario security protection scheme is proposed to provide a theoretical basis for the UPIOT security protection.

Synchronization system with symmetric optical fiber links

In most synchronization schemes based on fiber, the compensation on fiber link asymmetry significantly affects the synchronization accuracy, thus complicated calibration may be needed to achieve high performance synchronization. Moreover, as the fiber temperature varies, fiber link asymmetry is not fixed, leading to synchronization deterioration, especially in long distance applications. This paper proposes a new synchronization scheme named LBSYNC, which achieves complete symmetry fiber link with new loopback structure. LBSYNC is based on White Rabbit system, a high performance synchronization technology with sub-nanosecond accuracy and tens of picoseconds precision, but adopts customized protocol. Theoretical analysis and experiments show that LBSYNC can achieve better synchronization performance than White Rabbit system in the fiber interconnection situation or the fiber temperature fluctuation situation, which is expected to have wider applicability in actual deployment environment.

Co-registration of pre- and post-stent intravascular OCT images for validation of finite element model simulation of stent expansion.

Intravascular optical coherence tomography (IVOCT) provides high-resolution images of coronary calcifications and detailed measurements of acute stent deployment following stent implantation. Since pre- and post-stent IVOCT image "pull-back" acquisitions start from different locations, registration of corresponding pullbacks is needed for assessing treatment outcomes. In particular, we are interested in assessing finite element model (FEM) prediction of lumen gain following stenting, requiring registration. We used deep learning to segment calcifications in corresponding pre- and post-stent IVOCT pullbacks. We created 1D representations of calcium thickness as a function of the angle of the helical IVOCT scans. Registration of two scans was done by maximizing the cross correlation of these two 1D representations. Registration was accurate, as determined by visual comparisons of 2D image frames. We used our pre-stent calcification segmentations to create a lesion-specific FEM, which took into account balloon size, balloon pressure, and stent measurements. We then compared simulated lumen gain from FEM analysis to actual stent deployment results. Actual lumen gain across ~200 registered pre and post-stent images was 1.52 ± 0.51, while FEM prediction was 1.43 ± 0.41. Comparison between actual and FEM results showed no significant difference (p < 0.001), suggesting accurate prediction of FEM modeling. Registered image data showed good visual agreement regarding lumen gain and stent strut malapposition. Hence, we have developed a platform for evaluation of FEM prediction of lumen gain. This platform can be used to guide development of FEM prediction software, which could ultimately help physicians with stent treatment planning of calcified lesions.