An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms

In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.

A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

Machine Learning for 5G and Beyond:From ModelBased to Data-Driven Mobile Wireless Networks

During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.

Editorial: Special Topic on Machine Learning for Wireless Networks

Success in applying deep learning in speech recognition had triggered a resurgence of interests in deep learning,starting around 2010.Since then,deep learning has been applied with remarkable results to many problems,such as image recognition,speech and image synthesis,natural language processing,finance and artificial intelligence such as game playing.The success owes much to the availability of large amount of data,improved computational capabilities,and advances in algorithms.

Preliminary Network Centric Therapy for Machine Learning Classification of Deep Brain Stimulation Status for the Treatment of Parkinson’s Disease with a Conformal Wearable and Wireless Inertial Sensor

The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.

Empirical Study of Tree Parity Machine and Its Applicationon Key Management for Wireless Sensor Networks

In this paper,a model with two mutual learning neural networks named Tree Parity Machine(TPM) is firstly introduced,as well as its cryptographic property of weight synchronization with that of chaos cryptography is comparatively discussed. A full empirical study on the stability and security of the TPM weight synchronization is conducted in detail. Then two improvement methods for the weight synchronization are proposed. Experiment results show that the improved TPM synchronization model can be efficiently against the third party attack. At last,a lightweight TPM-based key management scheme is proposed for TinySec on wireless sensor networks,which is full implemented on the Mica2 node and the performance test result is acceptable.

Intrusion Detection for Wireless Mesh Networks using Finite State Machine

Wireless Mesh Networks is vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, Lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer suffi- cient and effective for those features. In this paper, we propose a distributed intrusion detection ap- proach based on timed automata. 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 we con- struct the Finite State Machine （FSM） by the way of manually abstracting the correct behaviors of the node according to the routing protocol of Dynamic Source Routing （DSR）. The monitor nodes can verify every node＇s behavior by the Finite State Ma- chine （FSM）, 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, our approach is much more efficient while maintaining the same level of effectiveness. Finally, we evaluate the intrusion detection method through simulation experiments.

Scope of machine learning applications for addressing the challenges in next-generation wireless networks

The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users.Due to this popularity,there has been a huge rise in mobile data volume,applications,types of services,and number of customers.Furthermore,due to the COVID-19 pandemic,the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home.This massive increase in demand for multi-class services has posed numerous challenges to wireless network frameworks.The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic,such as real-time live streaming of videos,audios,text,images etc.,at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers.Next-generation wireless networks(NGWNs,i.e.5G networks and beyond)are being developed to accommodate the service qualities mentioned above and many more.However,achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers.It requires the analysis of a huge volume of network data(structured and unstructured)received or collected from heterogeneous devices,applications,services,and customers and the effective and dynamic management of network parameters based on this analysis in real time.In the ever-increasing network heterogeneity and complexity,machine learning(ML)techniques may become an efficient tool for effectively managing these issues.In recent days,the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain.This study discusses current wireless network research,brief discussions on ML methods that can be effectively applied to the wireless networking domain,some tools available to support and customise efficient mobile system design,and some unresolved issues for future research directions.

Application of Machine-Learning Based Prediction Techniques in Wireless Networks

Wireless networks are key enablers of ubiquitous communication. With the evolution of networking technologies and the need for these to inter-operate and dynamically adapt to user requirements, intelligent networks are the need of the hour. Use of machine learning techniques allows these networks to adapt to changing environments and enables them to make decisions while continuing to learn about their environment. In this paper, we survey the various problems of wireless networks that have been solved using machine-learning based prediction techniques and identify additional problems to which prediction can be applied. We also look at the gaps in the research done in this area till date.

An Auto-configuration 4M Group Management using Wireless Sensor Networks for Reconfigurable Manufacturing System

The first tier of automotive manufacturers has faced to pressures about move,modify,updating tasks for manufacturing resources in production processes from demand response of production order sequence for motor company and process innovation purpose for productivity. For meets this requirements,it has to require absolutely lead time to re-wiring of physical interface for production equipment,needs for change existing program and test over again.For prepare this constraints,it needs studying an auto-configuration functions that build for both visibility and flexibility based on the 4M(Man,Machine,Material, Method)group management which is supports from WSN (Wireless Sensor Network)of the open embedded device called M2M(Machine to Machine)and major functions of middleware including point manager for real-time device communication,real-time data management,Standard API (Application Program Interface)and application template management.To be application system to RMS (Reconfigurable Manufacturing System)for rapidly response from various orders and model from motor company that is beginning to establishing the mapping of manufacturing resources of 4M using WSN.

Distinction of an Assortment of Deep Brain Stimulation Parameter Configurations for Treating Parkinson’s Disease Using Machine Learning with Quantification of Tremor Response through a Conformal Wearable and Wireless Inertial Sensor

Deep brain stimulation offers an advanced means of treating Parkinson’s disease in a patient specific context. However, a considerable challenge is the process of ascertaining an optimal parameter configuration. Imperative for the deep brain stimulation parameter optimization process is the quantification of response feedback. As a significant improvement to traditional ordinal scale techniques is the advent of wearable and wireless systems. Recently conformal wearable and wireless systems with a profile on the order of a bandage have been developed. Previous research endeavors have successfully differentiated between deep brain stimulation “On” and “Off” status through quantification using wearable and wireless inertial sensor systems. However, the opportunity exists to further evolve to an objectively quantified response to an assortment of parameter configurations, such as the variation of amplitude, for the deep brain stimulation system. Multiple deep brain stimulation amplitude settings are considered inclusive of “Off” status as a baseline, 1.0 mA, 2.5 mA, and 4.0 mA. The quantified response of this assortment of amplitude settings is acquired through a conformal wearable and wireless inertial sensor system and consolidated using Python software automation to a feature set amenable for machine learning. Five machine learning algorithms are evaluated: J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest. The performance of these machine learning algorithms is established based on the classification accuracy to distinguish between the deep brain stimulation amplitude settings and the time to develop the machine learning model. The support vector machine achieves the greatest classification accuracy, which is the primary performance parameter, and K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.

ZTE Communications Special Issue on Machine Learning for Wireless Networks

Recent development in machine learning has stimulated growing interests in applying machine learning to communication system design.While some researchers have advocated applying machine learning and deep learning tools to communication system design,others are doubtful as to how

An Auto-Configuration of 4M Group Management Using Wireless Sensor Networks

The first tier of automotive manufacturers has faced to pressures about move, modify, updating tasks for manufacturing resources in production processes from demand response of production order sequence for motor company and process innovation purpose for productivity. For meets this requirements, it has to require absolutely lead time to re-wiring of physical interface for production equipment, needs for change existing program and test over again. For prepare this constraints, it needs studying an auto-configuration functions that build for both visibility and flexibility based on the 4M (Man, Machine, Material, Method) group management which is supports from WSN (Wireless Sensor Network) of the open embedded device called M2M (Machine to Machine) and major functions of middleware including point manager for real-time device communication, real-time data management, Standard API (Application Program Interface) and application template management. To be application system to RMS (Reconfigurable Manufacturing System) for rapidly response from various orders and model from motor company that is beginning to establishing the mapping of manufacturing resources of 4M using WSN.

Implementation of Machine Learning Classification Regarding Hemiplegic Gait Using an Assortment of Machine Learning Algorithms with Quantification from Conformal Wearable and Wireless Inertial Sensor System

The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quantify hemiplegic gait in consideration of the hemiplegic affected leg and unaffected leg. The recorded inertial sensor data, which is inclusive of the gyroscope signal, can be readily transmitted by wireless means to a secure Cloud. Incorporating Python to automate the post-processing of the gyroscope signal data can enable the development of a feature set suitable for a machine learning platform, such as the Waikato Environment for Knowledge Analysis (WEKA). An assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and na&#239ve Bayes, were evaluated in terms of classification accuracy and time to develop the machine learning model. The K-nearest neighbors achieved optimal performance based on classification accuracy achieved for differentiating between the hemiplegic affected leg and unaffected leg for gait and the time to establish the machine learning model. The achievements of this research endeavor demonstrate the utility of amalgamating the conformal wearable and wireless inertial sensor with machine learning algorithms for distinguishing the hemiplegic affected leg and unaffected leg during gait.

Distance Estimation and Material Classification of a Compliant Tactile Sensor Using Vibration Modes and Support Vector Machine

Many animals possess actively movable tactile sensors in their heads,to explore the near-range space.During locomotion,an antenna is used in near range orientation,for example,in detecting,localizing,probing,and negotiating obstacles.A bionic tactile sensor used in the present work was inspired by the antenna of the stick insects.The sensor is able to detect an obstacle and its location in 3 D(Three dimensional) space.The vibration signals are analyzed in the frequency domain using Fast Fourier Transform(FFT) to estimate the distances.Signal processing algorithms,Artificial Neural Network(ANN) and Support Vector Machine(SVM) are used for the analysis and prediction processes.These three prediction techniques are compared for both distance estimation and material classification processes.When estimating the distances,the accuracy of estimation is deteriorated towards the tip of the probe due to the change in the vibration modes.Since the vibration data within that region have high a variance,the accuracy in distance estimation and material classification are lower towards the tip.The change in vibration mode is mathematically analyzed and a solution is proposed to estimate the distance along the full range of the probe.

An improved Machine Learning Approach to Classify Sleep Stages and Apnea Events

Sleep apnea(SA)is a common sleep disorder.Identifying patients at risk by means of comprehensive monitoring that requires overnight stay at professional sleep clinics are costly and inconvenient and can lead to unreliable results in view of the unfamiliar sleep environment.Existing wearable devices for sleep monitoring,which can be used in a familiar home environment,do not provide the same comprehensive monitoring as through clinical monitoring.The larger objective of the present work is to develop a sleep monitoring system for home use,which can provide comprehensive monitoring.In the development in this paper,machine learning(ML)models are explored for the classification of SA and sleep stages using multisensory data,without neglecting any of the required signals.The data acquired through the sensors are normalized,their features are extracted using Composite Multiscale Sample Entropy(CMSE)and are standardized using a robust scaling algorithm.Processed features are classified using a Neural Network(NN)and the obtained results for the SA classification are compared with those obtained by using a Support Vector Machine(SVM)approach.The impact of neglecting signals when classifying sleep stages is analyzed as well.The results are presented in the paper and observations are made.The NN model trained with the Bayesian regularization algorithm has provided an overall average accuracy of 94.5%and performed slightly better than when trained using the scaled conjugate gradient backpropagation algorithm(93.2%).The SVMs have yielded lower accuracy levels compared to the NNs(<92%).It is observed that the use of all 14 signals for SS classification yields an overall test accuracy of 72.3%,which is higher than that when one or few signals are used.It is concluded that ML models are effective in classifying sleep data from multiple sensors.Accuracy levels are higher when fused multisensory data are used as inputs.Furthermore,NN models are found to be better suitable in practical application and can be incorporated into an inexpensive and convenient wearable device that can carry out comprehensive monitoring.

Implementation of an Assortment of Machine Learning Classification Algorithms Regarding Diadochokinesia for Hemiparesis with Quantification from Conformal Wearable and Wireless System

Diadochokinesia pertains to a standard aspect of the conventional neurological examination, which involves the oscillation between muscle groups with an agonist and antagonist relationship. A representative example is the pronation and supination of the forearm. Hemiparesis visibly demonstrates disparity of diadochokinesia, and clinical quantification is achieved through the use of an ordinal scale, which is inherently subjective. A conformal wearable and wireless inertial sensor equipped with a gyroscope mounted about the dorsum of the hand can objectively quantify diadochokinesia respective of forearm pronation and supination. The objective of the research endeavor was to apply an assortment of machine learning algorithms to distinguish between a hemiplegic affected and unaffected upper limb pair based on diadochokinesia with respect to pronation and supination of the forearm. Performance of the machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes, were evaluated in consideration of classification accuracy and time to develop the machine learning model. The machine learning feature set was derived from the acquired gyroscope signal data. Using the gyroscope signal data from the conformal wearable and wireless inertial sensor the logistic regression and naïve Bayes machine learning algorithms achieved considerable performance capability with respect to both time to converge the machine learning model and classification accuracy for distinguishing between a hemiplegic upper limb pair for diadochokinesia in consideration of pronation and supination.

A network condition classification scheme for supporting video delivery over wireless Internet*

Real-time video transport over wireless Internet faces many challenges due to the heterogeneous environment including wireline and wireless networks. A robust network condition classification algorithm using multiple end-to-end metrics and Support Vector Machine (SVM) is proposed to classify different network events and model the transition pattern of network conditions. End-to-end Quality-of-Service (QoS) mechanisms like congestion control, error control, and power control can benefit from the network condition information and react to different network situations appropriately. The proposed network condition classifica- tion algorithm uses SVM as a classifier to cluster different end-to-end metrics such as end-to-end delay, delay jitter, throughput and packet loss-rate for the UDP traffic with TCP-friendly Rate Control (TFRC), which is used for video transport. The algorithm is also flexible for classifying different numbers of states representing different levels of network events such as wireline congestion and wireless channel loss. Simulation results using network simulator 2 (ns2) showed the effectiveness of the proposed scheme.

Data Fusion-Based Machine Learning Architecture for Intrusion Detection

In recent years,the infrastructure of Wireless Internet of Sensor Networks(WIoSNs)has been more complicated owing to developments in the internet and devices’connectivity.To effectively prepare,control,hold and optimize wireless sensor networks,a better assessment needs to be conducted.The field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data analysis.This study investigates the methodology of Real Time Sequential Deep Extreme LearningMachine(RTS-DELM)implemented to wireless Internet of Things(IoT)enabled sensor networks for the detection of any intrusion activity.Data fusion is awell-knownmethodology that can be beneficial for the improvement of data accuracy,as well as for the maximizing of wireless sensor networks lifespan.We also suggested an approach that not only makes the casting of parallel data fusion network but also render their computations more effective.By using the Real Time Sequential Deep Extreme Learning Machine(RTSDELM)methodology,an excessive degree of reliability with a minimal error rate of any intrusion activity in wireless sensor networks is accomplished.Simulation results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed approach.Eventually,threats and a more general outlook are explored.

Energy Prediction in IoT Systems Using Machine Learning Models

The Internet of Things (IoT) technology has been developed fordirecting and maintaining the atmosphere in smart buildings in real time. Inorder to optimise the power generation sector and schedule routine maintenance,it is crucial to predict future energy demand. Electricity demandforecasting is difficult because of the complexity of the available demandpatterns. Establishing a perfect prediction of energy consumption at thebuilding’s level is vital and significant to efficiently managing the consumedenergy by utilising a strong predictive model. Low forecast accuracy is justone of the reasons why energy consumption and prediction models havefailed to advance. Therefore, the purpose of this study is to create an IoTbasedenergy prediction (IoT-EP) model that can reliably estimate the energyconsumption of smart buildings. A real-world test case on power predictionsis conducted on a local electricity grid to test the practicality of the approach.The proposed (IoT-EP) model selects the significant features as input neurons,the predictable data is selected as output nodes, and a multi-layer perceptronis constructed along with the features of the Convolution Neural Network(CNN) algorithm. The analysis of the proposed IoT-EP model has higheraccuracy of 90%, correlation of 89%, and variance of 16% in less training timeof 29.2 s, and with a higher prediction speed of 396 (observation/sec). Whencompared to existing models, the results showed that the proposed (IoT-EP)model outperforms with a satisfactory level of accuracy in predicting energyconsumption in smart buildings.