Event-Triggered Fixed-Time Consensus of Second-Order Nonlinear Multi-Agent Systems with Delay and Switching Topologies

To address fixed-time consensus problems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturbances,the event-triggered fixed-time consensus protocol is proposed.First,the virtual velocity is designed based on the backstepping control method to achieve the system consensus and the bound on convergence time only depending on the system parameters.Second,an event-triggered mechanism is presented to solve the problem of frequent communication between agents,and triggered condition based on state information is given for each follower.It is available to save communication resources,and the Zeno behaviors are excluded.Then,the delay and switching topologies of the system are also discussed.Next,the system stabilization is analyzed by Lyapunov stability theory.Finally,simulation results demonstrate the validity of the presented method.

Adaptive Fuzzy Dynamic Surface Control of Flexible-Joint Robot Systems With Input Saturation

In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.

Locomotion Optimization and Manipulation Planning of a Tetrahedron-Based Mobile Mechanism with Binary Control

Locomotion and manipulation optimization is essential for the performance of tetrahedron-based mobile mechanism. Most of current optimization methods are constrained to the continuous actuated system with limited degree of freedom(DOF), which is infeasible to the optimization of binary control multi-DOF system. A novel optimization method using for the locomotion and manipulation of an 18 DOFs tetrahedron-based mechanism called 5-TET is proposed. The optimization objective is to realize the required locomotion by executing the least number of struts.Binary control strategy is adopted, and forward kinematic and tipping dynamic analyses are performed, respectively.Based on a developed genetic algorithm(GA), the optimal number of alternative struts between two adjacent steps is obtained as 5. Finally, a potential manipulation function is proposed, and the energy consumption comparison between optimal 5-TET and the traditional wheeled robot is carried out. The presented locomotion optimization and manipulation planning enrich the research of tetrahedron-based mechanisms and provide the instruction to the successive locomotion and operation planning of multi-DOF mechanisms.

A cooperative user selection scheme based on energy efficiency and interference factor in cooperative communication systems

With the rapid development of green communications,energy consumption issue plays more and more important role in cooperative communication strategies and communication systems.Based on cooperative transmission model,a cooperative user selection scheme is proposed in consideration of both energy efficiency and interference factor.With the proposed scheme,the selected cooperative user consumes less energy and receives less interference.Furthermore,the main factor is analyzed to affect system performance,including signal-to-noise ratio(SNR)of source user and cooperative user,distance between source user and cooperative user or base station(BS),and fading factor in the transmission model.Through the proposed scheme,energy consumption and influence of interference are jointly taken into account during the cooperative user selection process.Besides,bit error rate(BER)in proposed scheme is also superior to existing schemes.Simulation results are presented to show the performance improvement of the proposed scheme.

Path prediction of flexible needles based on Fokker-Planck equation and disjunctive Kriging model

Path prediction of flexible needles based on the Fokker-Planck equation and disjunctive Kriging model is proposed to improve accuracy and consider the nonlinearity and anisotropy of soft tissues.The stochastic differential equation is developed into the Fokker-Planck equation with Gaussian noise,and the position and orientation probability density function of flexible needles are then optimized by the stochastic differential equation.The probability density function obtains the mean and covariance of flexible needle movement and helps plan puncture paths by combining with the probabilistic path algorithm.The weight coefficients of the ordinary Kriging are extended to nonlinear functions to optimize the planned puncture path,and the Hermite expansion is used to calculate nonlinear parameter values of the disjunctive Kriging optimization model.Finally,simulation experiments are performed.Detailed comparison results under different path planning maps show that the kinematics model can plan optimal puncture paths under clinical requirements with an error far less than 2 mm.It can effectively optimize the path prediction model and help improve the target rate of soft tissue puncture with flexible needles through data analysis and processing of the mean value and covariance parameters derived by the probability density and disjunctive Kriging algorithms.

A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of research.As the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also increases.In addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient.Thus,there is a need to augment them with intelligent network intrusion detection techniques.Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times.However,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection methods.Deep learning solutions are lucrative options as they remove the necessity for feature selection.Therefore,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more heightened.This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers.Vehicular data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper.The proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing works.By running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.

Finite-time Adaptive Fault-tolerant Control for Nonlinear Systems With Multiple Faults

This paper focuses on the problem of adaptive finitetime fault-tolerant control for a class of non-lower-triangular nonlinear systems.The faults encountered in the control system include the actuator faults and the abrupt system fault.By applying backstepping design and neural networks approximation,an adaptive finite-time fault-tolerant control scheme is developed.It is shown that the proposed controller ensures that all signals in the closed-loop system are semi-globally practically finite-time stable and the track-ing error converges to a small neighborhood around the origin within finite time.The simulation is carried out to explain the validity of the developed strategy.

Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing

To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services,the mobile edge computing(MEC)has been drawing increased attention from both industry and academia recently.This paper focuses on mobile users’computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy.Since wireless network states and computing requests have stochastic properties and the environment’s dynamics are unknown,we use the modelfree reinforcement learning(RL)framework to formulate and tackle the computation offloading problem.Each mobile user learns through interactions with the environment and the estimate of its performance in the form of value function,then it chooses the overhead-aware optimal computation offloading action(local computing or edge computing)based on its state.The state spaces are high-dimensional in our work and value function is unrealistic to estimate.Consequently,we use deep reinforcement learning algorithm,which combines RL method Q-learning with the deep neural network(DNN)to approximate the value functions for complicated control applications,and the optimal policy will be obtained when the value function reaches convergence.Simulation results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.

Resource Allocation and User Association for HTTP Adaptive Streaming in Heterogeneous Cellular Networks with Small Cells

Video streaming,especially hypertext transfer protocol based(HTTP) adaptive streaming(HAS) of video,has been expected to be a dominant application over mobile networks in the near future,which brings huge challenge for the mobile networks.Although some works have been done for video streaming delivery in heterogeneous cellular networks,most of them focus on the video streaming scheduling or the caching strategy design.The problem of joint user association and rate allocation to maximize the system utility while satisfying the requirement of the quality of experience of users is largely ignored.In this paper,the problem of joint user association and rate allocation for HTTP adaptive streaming in heterogeneous cellular networks is studied,we model the optimization problem as a mixed integer programming problem.And to reduce the computational complexity,an optimal rate allocation using the Lagrangian dual method under the assumption of knowing user association for BSs is first solved.Then we use the many-to-one matching model to analyze the user association problem,and the joint user association and rate allocation based on the distributed greedy matching algorithm is proposed.Finally,extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.

A Novel Low Density Parity Check Coded Differential Amplitude and Pulse Position Modulation Free-Space Optical System for Turbulent Channel

Based on the proposed partly equidifferent mapping and its specific Differential Amplitude and Pulse Position Modulation(DAPPM) demodulation, a modified FSO scheme for turbulent channel is designed and analyzed. The novel Low Density Parity Check(LDPC) coded 4×4 and 4×8 DAPPM Free-Space Optical communication(FSO) system is constructed. The Monte Carlo simulation results show approximately 2d B transmit power reduction against classical LDPC-DAPPM at the identical Bit-Error-Rate in strong turbulent channel. The proposed partly equidifferent mapping is compatible with other modulations, so it enables widespread adoption in other coded FSO systems.

Optimal Transmission Behaviour Policies of Secondary Users in Proactive-Optimization Cognitive Radio Networks

In Cognitive Radio(CR)networks,there is a common assumption that secondary devices always obey commands and are under full control.However,this assumption may become unrealistic for future CR networks with more intelligent,sophisticated and autonomous devices.Imperfect spectrum sensing and illegal behaviour of secondary users can result in harmful interference to primary users.In this paper,we propose a novel concept of Proactive-Optimization CR(POCR)networks,in which highly intelligent secondary users always try to proactively consider potentially harmful interference when making their behaviour decision.Furthermore,we propose an optimal transmission behaviour decision scheme for secondary users in POCR networks considering the possible harmful interference and penalties from primary users.Specifically,we formulate the system as a Partially-Observable Markov Decision Process(POMDP)problem.With this formulation,a low-complexity dynamic programming framework is presented to obtain the optimal behaviour policy.Extensive simulation results are presented to illustrate the significant performance improvement of the proposed scheme compared with the existing one that ignores the proactive-optimization of secondary users.

Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning

Device-to-Device(D2D)communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity.In this paper,we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput.Firstly,we treat each D2D pair as an independent agent.Each agent makes decisions based on the local channel states information observed by itself.The multi-agent Reinforcement Learning(RL)algorithm is proposed for our multi-user system.We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,so the problem is modeled as a stochastic non-cooperative game.Hence,each agent becomes a player and they make decisions together to achieve global optimization.Thereby,the multi-agent Q-learning algorithm based on game theory is established.Secondly,in order to accelerate the convergence rate of multi-agent Q-learning,we consider a power allocation strategy based on Fuzzy C-means(FCM)algorithm.The strategy firstly groups the D2D users by FCM,and treats each group as an agent,and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users.The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system.In particular,FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.

Error propagation determined iterative channel estimation with ICI mitigation for fast time-varying OFDM channels

The intersubcarrier interference(ICI) degrades the performance of the pilot-aided channel estimation in fast time-varying orthogonal frequency division multiplexing(OFDM) systems.To solve the error propagation in joint channel estimation and data detection due to this ICI,a scheme of error propagation determined iterative estimation is proposed,where in the first iteration,Kalman filter based on signal to interference and noise is designed with ICI transformed to be part of the noise,and for the later iterations,a determined iterative estimation algorithm obtains an optimal output from all iterations using the iterative updating strategy.Simulation results present the significant improvement in the performance of the proposed scheme in high-mobility situation in comparison with the existing ones.

Prediction-Based Resource Assignment Scheme to Maximize the Net Profit of Cloud Service Providers

In a cloud computing environment, users using the pay-as-you-go billing model can relinquish their services at any point in time and pay accordingly. From the perspective of the Cloud Service Providers (CSPs), this is not beneficial as they may lose the opportunity to earn from the relinquished resources. Therefore, this paper tackles the resource assignment problem while considering users relinquishment and its impact on the net profit of CSPs. As a solution, we first compare different ways to predict user behavior (i.e. how likely a user will leave the system before its scheduled end time) and deduce a better prediction technique based on linear regression. Then, based on the RACE (Relinquishment-Aware Cloud Economics) model proposed in [1], we develop a relinquishment-aware resource optimization model to estimate the amount of resources to assign on the basis of predicted user behavior. Simulations performed with CloudSim show that cloud service providers can gain more by estimating the amount of resources using better prediction techniques rather than blindly assigning resources to users. They also show that the proposed prediction-based resource assignment scheme typically generates more profit for a lower or similar utilization.

Towards Optimal Planning and Scheduling in Smart Homes

This research addresses the planning and scheduling problem in and among the smart homes in a community microgrid. We develop a bi-linear algorithm, named ECO-Trade to generate the near-optimal schedules of the households’ loads, storage and energy sources. The algorithm also facilitates Peer-to-Peer (P2P) energy trading among the smart homes in a community microgrid. However, P2P trading potentially results in an unfair cost distribution among the participating households. To the best of our knowledge, the ECO-Trade algorithm is the first near-optimal cost optimization algorithm which considers the unfair cost distribution problem for a Demand Side Management (DSM) system coordinated with P2P energy trading. It also solves the time complexity problem of our previously proposed optimal model. Our results show that the solution time of the ECO-Trade algorithm is mostly less than a minute. It also shows that 97% of the solutions generated by the ECO-Trade algorithm are optimal solutions. Furthermore, we analyze the solutions and identify that the algorithm sometimes gets trapped at a local minimum because it alternately sets the microgrid price and quantity as constants. Finally, we describe the reasons of the cost increase by a local minimum and analyze its impact on cost optimization.

Using Hybrid and Diversity-Based Adaptive Ensemble Method for Binary Classification

This paper proposes an adaptive and diverse hybrid-based ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and the application of adaptive selection of the most suitable model for each data instance. Ensemble method, an important machine learning technique uses multiple single models to construct a hybrid model. A hybrid model generally performs better compared to a single individual model. In a given dataset the application of diverse single models trained with different machine learning algorithms will have different capabilities in recognizing patterns in the given training sample. The proposed approach has been validated on Repeat Buyers Prediction dataset and Census Income Prediction dataset. The experiment results indicate up to 18.5% improvement on F1 score for the Repeat Buyers dataset compared to the best individual model. This improvement also indicates that the proposed ensemble method has an exceptional ability of dealing with imbalanced datasets. In addition, the proposed method outperforms two other commonly used ensemble methods (Averaging and Stacking) in terms of improved F1 score. Finally, our results produced a slightly higher AUC score of 0.718 compared to the previous result of AUC score of 0.712 in the Repeat Buyers competition. This roughly 1% increase AUC score in performance is significant considering a very big dataset such as Repeat Buyers.

Using Heuristics to the Controller Placement Problem in Software-Defined Multihop Wireless Networking

Solving the controller placement problem (CPP) in an SDN architecture with multiple controllers has a significant impact on control overhead in the network, especially in multihop wireless networks (MWNs). The generated control overhead consists of controller-device and inter-controller communications to discover the network topology, exchange configurations, and set up and modify flow tables in the control plane. However, due to the high complexity of the proposed optimization model to the CPP, heuristic algorithms have been reported to find near-optimal solutions faster for large-scale wired networks. In this paper, the objective is to extend those existing heuristic algorithms to solve a proposed optimization model to the CPP in software-defined multihop wireless networking (SDMWN).Our results demonstrate that using ranking degrees assigned to the possible controller placements, including the average distance to other devices as a degree or the connectivity degree of each placement, the extended heuristic algorithms are able to achieve the optimal solution in small-scale networks in terms of the generated control overhead and the number of controllers selected in the network. As a result, using extended heuristic algorithms, the average number of hops among devices and their assigned controllers as well as among controllers will be reduced. Moreover, these algorithms are able tolower the control overhead in large-scale networks and select fewer controllers compared to an extended algorithm that solves the CPP in SDMWN based on a randomly selected controller placement approach.

Vertical Handover Decision for Mobile IoT Edge Gateway Using Multi-Criteria and Fuzzy Logic Techniques

Internet of Things (IoT) is ubiquitous, including objects or devices communicating through heterogenous wireless networks. One of the major challenges in mobile IoT is an efficient vertical handover decision (VHD) technique between heterogenous networks for seamless connectivity with constrained resources. The conventional VHD approach is mainly based on received signal strength (RSS). The approach is inefficient for vertical handover, since it always selects the target network with the strongest signal without taking into consideration of factors such as quality of service (QoS), cost, delay, etc. In this paper, we present a hybrid approach by integrating the multi-cri- teria based VHD (MCVHD) technique and an algorithm based on fuzzy logic for efficient VHD among Wi-Fi, Radio and Satellite networks. The MCVHD provides a lightweight solution that aims to achieving seamless connectivity for mobile IoT Edge Gateway over a set of heterogeneous networks. The proposed solution is evaluated in real time using a testbed containing real IoT devices. Further, the testbed is integrated with lightweight and efficient software techniques, e.g., microservices, containers, broker, and Edge/Cloud techniques. The experimental results show that the proposed approach is suitable for an IoT environment and it outperforms the conventional RSS Quality based VHD by minimizing handover failures, unnecessary handovers, handover time and cost of service.

Automatic Derivation of Fault Tree Models from SysML Models for Safety Analysis

Safety Critical Systems (SCS) are those systems that may cause harm to the user(s) and/or the environment if operating outside of their prescribed specifications. Such systems are used in a wide variety of domains, such as aerospace, automotive, railway transportation and healthcare. In this paper, we propose an approach to integrate safety analysis of SCSs within the Model Driven Engineering (MDE) system development process. The approach is based on model transformation and uses standard well-known techniques and open source tools for the modeling and analysis of SCSs. More specifically, the system modeled with the OMG’s standard systems modeling language, SysML, is automatically transformed in Fault Tree (FT) models, that can be analyzed with existing FT tools. The proposed model transformation takes place in two steps: a) generate FTs at the component level, in order to tackle complexity and enable reuse;and b) generate system level FTs by composing the components and their FTs. The approach is illustrated by applying it to a simplified industry-inspired case study.