RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.

Distributed intrusion detection for mobile ad hoc networks

Mobile ad hoc networking （MANET） has become an exciting and important technology in recent years, because of the rapid proliferation of wireless devices. Mobile ad hoc networks is highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective for those features. A distributed intrusion detection approach based on timed automata is given. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then the timed automata is constructed by the way of manually abstracting the correct behaviours of the node according to the routing protocol of dynamic source routing （DSR）. The monitor nodes can verify the behaviour of every nodes by timed automata, and validly detect real-time attacks without signatures of intrusion or trained data. Compared with the architecture where each node is its own IDS agent, the approach is much more efficient while maintaining the same level of effectiveness. Finally, the intrusion detection method is evaluated through simulation experiments.

3D obstacle detection of indoor mobile robots by floor detection and rejection

Obstacle detection is essential for mobile robots to avoid collision with obstacles.Mobile robots usually operate in indoor environments,where they encounter various kinds of obstacles;however,2D range sensor can sense obstacles only in 2D plane.In contrast,by using 3D range sensor,it is possible to detect ground and aerial obstacles that 2D range sensor cannot sense.In this paper,we present a 3D obstacle detection method that will help overcome the limitations of 2D range sensor with regard to obstacle detection.The indoor environment typically consists of a flat floor.The position of the floor can be determined by estimating the plane using the least squares method.Having determined the position of the floor,the points of obstacles can be known by rejecting the points of the floor.In the experimental section,we show the results of this approach using a Kinect sensor.

Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road Networks

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

Methodology for Road Defect Detection and Administration Based on Mobile Mapping Data

A detailed inspection of roads requires highly detailed spatial data with sufficient precision to deliver an accurate geometry and to describe road defects visually.This paper presents a novel method for the detection of road defects.The input data for road defect detection included point clouds and orthomosaics gathered by mobile mapping technology.The defects were categorized in three major groups with the following geometric primitives:points,lines and polygons.The method suggests the detection of point objects from matched point clouds,panoramic images and ortho photos.Defects were mapped as point,line or polygon geometries,directly derived from orthomosaics and panoramic images.Besides the geometric position of road defects,all objects were assigned to a variety of attributes:defect type,surface material,center-of-gravity,area,length,corresponding image of the defect and degree of damage.A spatial dataset comprising defect values with a matching data type was created to perform the attribute analysis quickly and correctly.The final product is a spatial vector data set,consisting of points,lines and polygons,which contains attributes with further information and geometry.This paper demonstrates that mobile mapping suits a large-scale feature extraction of road infrastructure defects.By its simplicity and flexibility,the presented methodology allows it to be easily adapted to extract further feature types with their attributes.This makes the proposed approach a vital tool for data extraction settings with multiple mobile mapping data analysts,e.g.,offline crowdsourcing.

Cognitive Radio Sensor Node Empowered Mobile Phone for Explosive Trace Detection

Usefulness of sensor network applications in human life is increasing day by day and the concept of wireless connection promises new application areas. Sensor network can be very beneficial in saving human life from terrorist attacks causing explosion in certain areas leading to casualties. But realization of the sensor network application in explosive detection requires high scalability of the sensor network and fast transmission of the information through real time monitoring and control. In this paper a novel mechanism for explosive trace detection in any populated area by the use of mobile telephony has been described. The aim is to create a system that will assure common men, local population and above all the nation a secured environment, without disturbing their freedom of movement. It would further help the police in detection of explosives more quickly, isolation of suicide bombers, remediation of explosives manufacturing sites, and forensic and criminal investigation. To achieve this, the paper has projected an idea that can combine the strength of the mobile phones, the polymer sensor and existing cellular network. The idea is to design and embed a tiny cog-nitive radio sensor node into the mobile phone that adapts to the changing environment by analyzing the RF surroundings and adjusting the spectrum use appropriately. The system would be capable of detecting explo-sives within a defined territory. It would communicate the location of the detected explosives to the respec-tive service provider, which in turn would inform the law and enforcement agency or Police.

Analysis of Global System for Mobile Communication (GSM) Subscription Fraud Detection System

This work is concerned with GSM subscription fraud detection system using some network techniques. Fraud is a hitch around the globe with huge loss of income. Fraud has an effect on the credibility and performance of telecommunication companies. The most difficult problem that faces the industry is the fact that fraud is dynamic, which means that whenever fraudsters feel that they will be detected, they devise other ways to circumvent security measures. In such cases, the perpetrators’ intention is to completely avoid or at least reduce the charges for using the services. Subscription fraud is one of the major types of telecommunication fraud in which a customer obtains an account without intention to pay the bill. Thus at the level of a phone number, all transactions from the number will be fraudulent. In such cases abnormal usage occurs throughout the active period of the account;which is usually used for call selling or intensive self usage. This provides a means for illegal high profit business for fraudsters requiring minimal investment and relatively low risk of getting caught. A system to prevent subscription fraud in GSM telecommunications with high impact on long distance carriers is proposed to detect fraud. The system employs adaptive flexible techniques using advanced data analysis like Artificial Neural Networks (ANN). This study aims at developing a fraud detection model occurrence in GSM Network. The paper also gives analysis of the fraud detection Systems, fraud detection and prevention, fraud prevention methods etc. Fraud affects us all and is of particular concern to those who manage large government and business organisations where the potential losses are greatest. The operation of a mobile network is complex, and fraudsters invest a lot of energy to find and exploit every weakness of the system. A typical example would be subscription fraud, where a fraudster acquires a subscription to the mobile network under a false identity;and start reselling the use of his phone to unscrupulous customers (typically for international calls to distant foreign countries) at rate less than the regular tariff.

Intelligent Mobile Drone System Based on Real-Time Object Detection

Drone also known as unmanned aerial vehicle(UAV)has drawn lots of attention in recent years.Quadcopter as one of the most popular drones has great potential in both industrial and academic fields.Quadcopter drones are capable of taking off vertically and flying towards any direction.Traditional researches of drones mainly focus on their mechanical structures and movement control.The aircraft movement is usually controlled by a remote controller manually or the trajectory is pre-programmed with specific algorithms.Consumer drones typically use mobile device together with remote controllers to realize flight control and video transmission.Implementing different functions on mobile devices can result in different behaviors of drones indirectly.With the development of deep learning in computer vision field,commercial drones equipped with camera can be much more intelligent and even realize autonomous flight.In the past,running deep learning based algorithms on mobile devices is highly computational intensive and time consuming.This paper utilizes a novel real-time object detection method and deploys the deep learning model on the modern mobile device to realize autonomous object detection and object tracking of drones.

General Study of Mobile Agent Based Intrusion Detection System (IDS)

The extensive access of network interaction has made present networks more responsive to earlier intrusions. In distributed network intrusions, there are many computing nodes that are assisted by intruders. The evidence of intrusions is to be associated from all the held up nodes. From the last few years, mobile agent based technique in intrusion detection system (IDS) has been widely used to detect intrusion over distributed network. This paper presented survey of several existing mobile agent based intrusion detection system and comparative analysis report between them. Furthermore we have focused on each attribute of analysis, for example technique (NIDS, HIDS or Hybrid), behavior layer, detection techniques for analysis, uses of mobile agent and technology used by existing IDS, strength and issues. Their strengths and issues are situational wherever appropriate. We have observed that some of the existing techniques are used in IDS which causes low detection rate, behavior layers like TCP connection for packet capturing which is most important activity in NIDS and response time (technology execution time) with memory consumption by mobile agent as major issues.

Obstacle detection of mobile robot based on data fusion

To study the problem of obstacle detection based on multi-sensors data fusion,the multi-target tracking theory and techniques are introduced into obstacle detection systems,and the exact position of obstacle can be determined.Data fusion problems are discussed directly based on achievable data from some sensors without considering the specific structure of each individual sensor.With respect to normal linear systems and nonlinear systems,the corresponding algorithms are proposed.The validity of the method is confirmed by simulation results.

A distributed deadlock detection algorithm for mobile computing system

The mode of mobile computing originated from distributed computing and it has the un-idempotent operation property, therefore the deadlock detection algorithm designed for mobile computing systems will face challenges with regard to correctness and high efficiency. This paper attempts a fundamental study of deadlock detection for the AND model of mobile computing systems. First, the existing deadlock detection algorithms for distributed systems are classified into the resource node dependent (RD) and the resource node independent (RI) categories, and their corresponding weaknesses are discussed. Afterwards a new RI algorithm based on the AND model of mobile computing system is presented. The novelties of our algorithm are that: 1) the blocked nodes inform their predecessors and successors simultaneously; 2) the detection messages (agents) hold the predecessors information of their originator; 3) no agent is stored midway. Additionally, the quit-inform scheme is introduced to treat the excessive victim quitting problem raised by the overlapped cycles. By these methods the proposed algorithm can detect a cycle of size n within n-2 steps and with (n^2-n-2)/2 agents. The performance of our algorithm is compared with the most competitive RD and RI algorithms for distributed systems on a mobile agent simulation platform. Experiment results point out that our algorithm outperforms the two algorithms under the vast majority of resource configurations and concurrent workloads. The correctness of the proposed algorithm is formally proven by the invariant verification technique.

Decentralized content sharing in mobile ad-hoc networks:A survey

The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.

WOA-DNN for Intelligent Intrusion Detection and Classification in MANET Services

Mobile ad-hoc networks(MANET)are garnering a lot of attention because of their potential to provide low-cost solutions to real-world communica-tions.MANETs are more vulnerable to security threats.Changes in nodes,band-width limits,and centralized control and management are some of the characteristics.IDS(Intrusion Detection System)are the aid for detection,deter-mination,and identification of illegal system activity such as use,copying,mod-ification,and destruction of data.To address the identified issues,academics have begun to concentrate on building IDS-based machine learning algorithms.Deep learning is a type of machine learning that can produce exceptional outcomes.This study proposes that WOA-DNN be used to detect and classify incursions in MANET(Whale Optimized Deep Neural Network Model)WOA(Whale Opti-mization Algorithm)and DNN(Deep Neural Network)are used to optimize the preprocessed data to construct a system for classifying and predicting unantici-pated cyber-attacks that are both effective and efficient.As a result,secure data transport to other nodes is provided,preventing intruder attacks.The invaders are found using the(Machine Learning)ML-IDS and WOA-DNN methods.The data is reduced in dimensionality using Principal Component Analysis(PCA),which improves the accuracy of the outputs.A classifier is used in forward propagation to predict whether a result is normal or malicious.To compare the traditional and proposed models’effectiveness,the accuracy of classification,detection of the attack rate,precision rate,and F-Measure,Recall are utilized.The proposed WOA-DNN model has higher assessment metrics and a 99.1%accuracy rate.WOA-DNN also has a greater assault detection rate than others,resulting in fewer false alarms.The classification accuracy of the proposed WOA-DNN model is 99.1%.

A Collaborative Approach for Secured Routing in Mobile Ad-Hoc Network

Mobile computing is the most powerful application for network com-munication and connectivity,given recent breakthroughs in thefield of wireless networks or Mobile Ad-hoc networks(MANETs).There are several obstacles that effective networks confront and the networks must be able to transport data from one system to another with adequate precision.For most applications,a frame-work must ensure that the retrieved data reflects the transmitted data.Before driv-ing to other nodes,if the frame between the two nodes is deformed in the data-link layer,it must be repaired.Most link-layer protocols immediately disregard the frame and enable the high-layer protocols to transmit it down.In other words,because of asset information must be secured from threats,information is a valu-able resource.In MANETs,some applications necessitate the use of a network method for detecting and blocking these assaults.Building a secure intrusion detection system in the network,which provides security to the nodes and route paths in the network,is a major difficulty in MANET.Attacks on the network can jeopardize security issues discovered by the intrusion detection system engine,which are then blocked by the network’s intrusion prevention engine.By bringing the Secure Intrusion Detection System(S-IDS)into the network,a new technique for implementing security goals and preventing attacks will be developed.The Secure Energy Routing(SER)protocol for MANETs is introduced in this study.The protocol addresses the issue of network security by detecting and preventing attacks in the network.The data transmission in the MANET is forwarded using Elliptical Curve Cryptography(ECC)with an objective to improve the level of security.Network Simulator–2 is used to simulate the network and experiments are compared with existing methods.

A Model Training Method for DDoS Detection Using CTGAN under 5GC Traffic

With the commercialization of 5th-generation mobile communications(5G)networks,a large-scale internet of things(IoT)environment is being built.Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service(DDoS)attacks across vast IoT devices.Recently,research on automated intrusion detection using machine learning(ML)for 5G environments has been actively conducted.However,5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data.If this data is used to train an ML model,it will likely suffer from generalization errors due to not training enough different features on the attack data.Therefore,this paper aims to study a training method to mitigate the generalization error problem of the ML model that classifies IoT DDoS attacks even under conditions of insufficient and imbalanced 5G traffic.We built a 5G testbed to construct a 5G dataset for training to solve the problem of insufficient data.To solve the imbalance problem,synthetic minority oversampling technique(SMOTE)and generative adversarial network(GAN)-based conditional tabular GAN(CTGAN)of data augmentation were used.The performance of the trained ML models was compared and meaningfully analyzed regarding the generalization error problem.The experimental results showed that CTGAN decreased the accuracy and f1-score compared to the Baseline.Still,regarding the generalization error,the difference between the validation and test results was reduced by at least 1.7 and up to 22.88 times,indicating an improvement in the problem.This result suggests that the ML model training method that utilizes CTGANs to augment attack data for training data in the 5G environment mitigates the generalization error problem.

BLIND ADAPTIVE MULTIUSER DETECTOR FOR NONLINEARLY MODULATED SATELLITE MOBILE CDMA SYSTEMS

This paper presents a novel blind adaptive noncoherent decorrelative multiuser detector for nonlinearly modulated satellite mobile Code Division Multiple Access (CDMA) systems. By using the known signature waveforms of the counterpart earth station in the blind adaptive multiuser detector, the system performance has been improved obviously. The computation results about the convergence properties of the new detector and the previous detectors demonstrate that the proposed multiuser detector has better performance than previous multiuser detectors for nonlinearly modulated CDMA systems.

Detecting and adaptive responding mechanism for mobile WSN

Mobile wireless sensor network(WSN)composed by mobile terminals has a dynamic topology and can be widely used in various fields.However,the lack of centralized control,dynamic topology and limited energy supply make the network layer of mobile WSN be vulnerable to multiple attacks,such as black hole(BH),gray hole(GH),flooding attacks(FA)and rushing attacks(RU).Existing researches on intrusion attacks against mobile WSN,currently,tend to focus on targeted detection of certain types of attacks.The defense methods also have clear directionality and is unable to deal with indeterminate intrusion attacks.Therefore,this work will design an indeterminate intrusion attack oriented detecting and adaptive responding mechanism for mobile WSN.The proposed mechanism first uses a test sliding window(TSW)to improve the detecting accuracy,then constructs parameter models of confidence on attack(COA),network performance degradation(NPD)and adaptive responding behaviors list,finally adaptively responds according to the decision table,so as to improve the universality and flexibility of the detecting and adaptive responding mechanism.The simulation results show that the proposed mechanism can achieve multiple types of intrusion detecting in multiple attack scenarios,and can achieve effective response under low network consumption.

Highly Sensitive Detection of Deoxyribonucleic Acid Hybridization Using Au-Gated AlInN/GaN High Electron Mobility Transistor-Based Sensors

Gallium nitride- （GaN） based high electron mobility transistors （HEMTs） provide a good platform for biological detection. In this work, both Au-gated AlInN/GaN HEMT and AlGaN/GaN HEMT biosensors are fabricated for the detection of deoxyribonucleic acid （DNA） hybridization. The Au-gated AIInN/GaN HEMT biosensor exhibits higher sensitivity in comparison with the AlGaN/GaN HEMT biosensor. For the former, the drain-source current （VDS = 0.5 V） shows a clear decrease of 69μA upon the introduction of 1μmolL^-1 （μM） complimentary DNA to the probe DNA at the sensor area, while for the latter it is only 38 μA. This current reduction is a notable indication of the hybridization. The high sensitivity can be attributed to the thinner barrier of the AlInN/GaN heterostructure, which makes the two-dimensional electron gas channel more susceptible to a slight change of the surface charge.