The future Wireless Cloud Networks (WCNs) are required to satisfy both extremely high levels of service resilience and security assurance (i.e., Blue criteria) by overproviding backup network resources and cryptograph...The future Wireless Cloud Networks (WCNs) are required to satisfy both extremely high levels of service resilience and security assurance (i.e., Blue criteria) by overproviding backup network resources and cryptographic protection on wireless communication respectively, as well as minimizing energy consumption (i.e., Green criteria) by switching off unnecessary resources as much as possible. There is a contradiction to satisfy both Blue and Green design criteria simultaneously. In this paper, we propose a new BlueGreen topological control scheme to leverage the wireless link connectivity for WCNs using an adaptive encryption key allocation mechanism, named as Shared Backup Path Keys (SBPK). The BlueGreen SBPK can take into account the network dependable requirements such as service resilience, security assurance and energy efficiency as a whole, so as trading off between them to find an optimal solution. Actually, this challenging problem can be modeled as a global optimization problem, where the network working and backup elements such as nodes, links, encryption keys and their energy consumption are considered as a resource, and their utilization should be minimized. The case studies confirm that there is a trade-off optimal solution between the capacity efficiency and energy efficiency to achieve the dependable WCNs.展开更多
Emerging wireless community cloud enables usergenerated video content to be shared and consumed in a social context. However, the nature of shared wireless medium and timevarying channels seriously limits the quality ...Emerging wireless community cloud enables usergenerated video content to be shared and consumed in a social context. However, the nature of shared wireless medium and timevarying channels seriously limits the quality of service(QoS), partially owing to the lack of mechanisms for effectively utilizing multi-rate channel resources. In this paper, the joint optimization of admission control and rate adaptation is proposed, resulting in a bandwidth-aware rate-adaptive admission control(BRAC) scheme to provide bandwidth guarantee for sharing social multimedia contents. The analytical approach leads to the following major contributions:(1) a bandwidth-aware rate selection(BRS) algorithm to optimally meet the bandwidth requirement of the data session and channel conditions at the physical layer;(2) a routing-coupled rate adaption and admission control algorithm to admit data sessions with bandwidth guarantee. Moreover, extensive numerical simulations suggest that BRAC is efficient and effective in meeting the bandwidth requirements for sharing social multimedia contents. These insights will shed light on communication system implementation for multimedia content sharing over multirate wireless community cloud.展开更多
The current IT cloud computing is playing a vital role in most of the areas such as Education, Research, Health care, etc. The cloud computing technology involving in sensor networks embedded system and IOT (Inte...The current IT cloud computing is playing a vital role in most of the areas such as Education, Research, Health care, etc. The cloud computing technology involving in sensor networks embedded system and IOT (Internet of Things). At present scenario, the sensors collected the information from the particular environment, where the sensors are fixed and transfer the collected information to cloud storage, here the challenge is the data transmission i.e. data that traverse from sensor to cloud environment are the big issue and maximum number of data loss is very high especially in dynamic routing environment. If data loss is identified in any routing path then automatically the information will transfer to alternate routing path. In this paper, we introduce a new algorithm for automatic routing path selection that can be integrated with cloud technology. This algorithm supports when data loss is found in the particular path of a network, then it selects an alternate route to transfer the data. The proposed model is comparatively more efficient than the prior methodologies. The implementation of the proposed work is done on NS3 simulator, and the performance metric is analyzed.展开更多
This paper presents a prototype of an Integrated Cloud-Based Wireless Sensor Network (WSN) developed to monitor pH, conductivity and dissolved oxygen parameters from wastewater discharged into water sources. To provid...This paper presents a prototype of an Integrated Cloud-Based Wireless Sensor Network (WSN) developed to monitor pH, conductivity and dissolved oxygen parameters from wastewater discharged into water sources. To provide realtime online monitoring and Internet of Things (IoT) capability, the system collects and uploads sensor data to ThingSpeak cloud via GPRS internet connectivity with the help of AT commands in combination with HTTP GET method. Moreover, the system sends message alert to the responsible organ through GSM/GPRS network and an SMS gateway service implemented by Telerivet mobile messaging platform. In this prototype, Telerivet messaging platform gives surrounding communities a means of reporting observed or identified water pollution events via SMS notifications.展开更多
Internet of Car, resulting from the Internet of Things, is a key point for the forthcoming smart city. In this article, GPS technology, 3G wireless technology and cloud-processing technology are employed to construct ...Internet of Car, resulting from the Internet of Things, is a key point for the forthcoming smart city. In this article, GPS technology, 3G wireless technology and cloud-processing technology are employed to construct a cloud-processing network platform based on the Internet of Car. By this platform, positions and velocity of the running cars, information of traffic flow from fixed monitoring points and transportation videos are combined to be a virtual traffic flow data platform, which is a parallel system with real traffic flow and is able to supply basic data for analysis and decision of intelligent transportation system.展开更多
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 Thera...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.展开更多
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. Impe...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 <span style="font-family:Verdana;">K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.</span>展开更多
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 quant...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 naïve 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.展开更多
文摘The future Wireless Cloud Networks (WCNs) are required to satisfy both extremely high levels of service resilience and security assurance (i.e., Blue criteria) by overproviding backup network resources and cryptographic protection on wireless communication respectively, as well as minimizing energy consumption (i.e., Green criteria) by switching off unnecessary resources as much as possible. There is a contradiction to satisfy both Blue and Green design criteria simultaneously. In this paper, we propose a new BlueGreen topological control scheme to leverage the wireless link connectivity for WCNs using an adaptive encryption key allocation mechanism, named as Shared Backup Path Keys (SBPK). The BlueGreen SBPK can take into account the network dependable requirements such as service resilience, security assurance and energy efficiency as a whole, so as trading off between them to find an optimal solution. Actually, this challenging problem can be modeled as a global optimization problem, where the network working and backup elements such as nodes, links, encryption keys and their energy consumption are considered as a resource, and their utilization should be minimized. The case studies confirm that there is a trade-off optimal solution between the capacity efficiency and energy efficiency to achieve the dependable WCNs.
基金sponsored by the following funds:the National Natural Science Foundation of China(No.61502381)the Fundamental Research Funds for the Central Universities(No.xjj2015065)the China Post Doctoral Science Foundation(No.2015M570836)
文摘Emerging wireless community cloud enables usergenerated video content to be shared and consumed in a social context. However, the nature of shared wireless medium and timevarying channels seriously limits the quality of service(QoS), partially owing to the lack of mechanisms for effectively utilizing multi-rate channel resources. In this paper, the joint optimization of admission control and rate adaptation is proposed, resulting in a bandwidth-aware rate-adaptive admission control(BRAC) scheme to provide bandwidth guarantee for sharing social multimedia contents. The analytical approach leads to the following major contributions:(1) a bandwidth-aware rate selection(BRS) algorithm to optimally meet the bandwidth requirement of the data session and channel conditions at the physical layer;(2) a routing-coupled rate adaption and admission control algorithm to admit data sessions with bandwidth guarantee. Moreover, extensive numerical simulations suggest that BRAC is efficient and effective in meeting the bandwidth requirements for sharing social multimedia contents. These insights will shed light on communication system implementation for multimedia content sharing over multirate wireless community cloud.
文摘The current IT cloud computing is playing a vital role in most of the areas such as Education, Research, Health care, etc. The cloud computing technology involving in sensor networks embedded system and IOT (Internet of Things). At present scenario, the sensors collected the information from the particular environment, where the sensors are fixed and transfer the collected information to cloud storage, here the challenge is the data transmission i.e. data that traverse from sensor to cloud environment are the big issue and maximum number of data loss is very high especially in dynamic routing environment. If data loss is identified in any routing path then automatically the information will transfer to alternate routing path. In this paper, we introduce a new algorithm for automatic routing path selection that can be integrated with cloud technology. This algorithm supports when data loss is found in the particular path of a network, then it selects an alternate route to transfer the data. The proposed model is comparatively more efficient than the prior methodologies. The implementation of the proposed work is done on NS3 simulator, and the performance metric is analyzed.
文摘This paper presents a prototype of an Integrated Cloud-Based Wireless Sensor Network (WSN) developed to monitor pH, conductivity and dissolved oxygen parameters from wastewater discharged into water sources. To provide realtime online monitoring and Internet of Things (IoT) capability, the system collects and uploads sensor data to ThingSpeak cloud via GPRS internet connectivity with the help of AT commands in combination with HTTP GET method. Moreover, the system sends message alert to the responsible organ through GSM/GPRS network and an SMS gateway service implemented by Telerivet mobile messaging platform. In this prototype, Telerivet messaging platform gives surrounding communities a means of reporting observed or identified water pollution events via SMS notifications.
基金supported by National Basic Research Program of China (973 Program) 2012CB821200 (2012CB821206)National Natural Science Foundation under Grant No. 61170113, No.91024001, No.61070142+1 种基金Beijing Natural Science Foundation(No.4111002)KM201010011006, PHR201008242
文摘Internet of Car, resulting from the Internet of Things, is a key point for the forthcoming smart city. In this article, GPS technology, 3G wireless technology and cloud-processing technology are employed to construct a cloud-processing network platform based on the Internet of Car. By this platform, positions and velocity of the running cars, information of traffic flow from fixed monitoring points and transportation videos are combined to be a virtual traffic flow data platform, which is a parallel system with real traffic flow and is able to supply basic data for analysis and decision of intelligent transportation system.
文摘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.
文摘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 <span style="font-family:Verdana;">K-nearest neighbors achieves considerable classification accuracy with minimal time to develop the machine learning model.</span>
文摘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 naïve 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.
