Neal N. Xiong

Robust Cyber-Physical Systems

In this paper we comprehensively survey the concept and strategies for building a resilient and integrated cyber-physical system (CPS). Here resilience refers to a 3S-oriented design, that is, stability, security, and systematicness: Stability means the CPS can achieve a stable sensing-actuation close-loop control even though the inputs (sensing data) have noise or attacks; Security means that the system can overcome the cyber-physical interaction attacks; and Systematicness means that the system has a seamless integration of sensors and actuators. We will also explain the CPS modeling issues since they serve as the basics of 3S design. We will use two detailed examples from our achieved projects to explain how to achieve arobust, systematic CPS design: Case study 1 is on the design of a rehabilitation system with cyber (sensors) and physical (robots) integration. Case Study 2 is on the implantable medical device design. It illustrates the nature of CPS security principle. The dominant feature of this survey is that it has both principle discussions and practical cyber-physical coupling design. Comprehensive survey on entire CPS design process.Qualitative and quantitative descriptions on CPS resilience.From basic concepts to case studies.Point out the future research trends.

Context-aware QoS prediction for web service recommendation and selection

Abstract QoS prediction is one of the key problems in Web service recommendation and selection. The context information is a dominant factor affecting QoS, but is ignored by most of existing works. In this paper, we employ the context information, from both the user side and service side, to achieve superior QoS prediction accuracy. We propose two novel prediction models, which are capable of using the context information of users and services respectively. In the user side, we use the geographical information as the user context, and identify similar neighbors for each user based on the similarity of their context. We study the mapping relationship between the similarity value and the geographical distance. In the service side, we use the affiliation information as the service context, including the company affiliation and country affiliation. In the two models, the prediction value is learned by the QoS records of a user (or a service) and the neighbors. Also, we propose an ensemble model to combine the results of the two models. We conduct comprehensive experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of our models.

Analysis and Design of Digital Chaotic Systems With Desirable Performance via Feedback Control

The dynamical degradation of digital chaotic systems (DCSs) often has serious negative influences on some digital chaos-based systems and then becomes one of the bottleneck problems stopping chaos from some applications. In this paper, we first restrict the Devaneys chaos definition to finite state sets to describe the chaotic motion of digital systems. Then, we propose a novel control method for DCSs based on the differential mean value theorem and state feedback technology. Simulation results show the effectiveness, robustness, and superiority of the proposed method. Finally, we construct a new pseudorandom number generator (PRNG) and evaluate its randomness via NIST SP800-22 and TestU01 test suites. Statistical test results show that the proposed PRNG has high reliability of randomness, thus it can be used for cryptography and other potential applications.

An adaptive virtual relaying set scheme for loss-and-delay sensitive WSNs

Abstract In loss-and-delay sensitive wireless sensor networks (WSNs), especially when the duty cycles of nodes are extremely low, it is a challenge to ensure that data can be transmitted to sink nodes with high reliability and low delay. To address this problem, in this paper, we propose a data collection scheme named Adaptive Virtual Relaying Set (AVRS) where a set of relay nodes with more reliable connections to the sender node is selected to form its Virtual Relaying Set (VRS) to help transmit packets. In ring-based WSNs, each node in a VRS helps send packets in turn to the upper ring before the transmission is successful, or the packet is dropped if they all fail. Therefore, the larger the VRS (implying more retransmission chances), the higher the packet transmission reliability and the lower the delay will be. On the other hand, as the sender node has to stay active during the transmission stage, having a large VRS will cause huge energy consumption. Combined with the fact that the energy consumption of different parts of WSNs is unbalanced, nodes in the near-sink area (i.e., hotspots) have extremely high energy cost while nodes in the far-sink area (i.e., non-hotspots) still have ample energy remaining when the network dies. The main idea of the AVRS is the following rule. The size of the VRS of nodes is determined adaptively according to its energy usage pattern, making the size small in hotspots and relatively large in non-hotspots. The AVRS takes advantage of the residual energy of nodes in far-sink areas to achieve improved network data collection performance. Meanwhile, as nodes in near-sink areas have small VRS, the network can maintain a long lifespan without any reductions. Both theoretical analyses and simulative results demonstrate that the AVRS can improve data transmission reliability by more than 50% and reduce network delay by at least 33%.

A Trust-Based Secure Routing Scheme Using the Traceback Approach for Energy-Harvesting Wireless Sensor Networks

The Internet of things (IoT) is composed of billions of sensing devices that are subject to threats stemming from increasing reliance on communications technologies. A Trust-Based Secure Routing (TBSR) scheme using the traceback approach is proposed to improve the security of data routing and maximize the use of available energy in Energy-Harvesting Wireless Sensor Networks (EHWSNs). The main contributions of a TBSR are (a) the source nodes send data and notification to sinks through disjoint paths, separately; in such a mechanism, the data and notification can be verified independently to ensure their security. (b) Furthermore, the data and notification adopt a dynamic probability of marking and logging approach during the routing. Therefore, when attacked, the network will adopt the traceback approach to locate and clear malicious nodes to ensure security. The probability of marking is determined based on the level of battery remaining; when nodes harvest more energy, the probability of marking is higher, which can improve network security. Because if the probability of marking is higher, the number of marked nodes on the data packet routing path will be more, and the sink will be more likely to trace back the data packet routing path and find malicious nodes according to this notification. When data packets are routed again, they tend to bypass these malicious nodes, which make the success rate of routing higher and lead to improved network security. When the battery level is low, the probability of marking will be decreased, which is able to save energy. For logging, when the battery level is high, the network adopts a larger probability of marking and smaller probability of logging to transmit notification to the sink, which can reserve enough storage space to meet the storage demand for the period of the battery on low level; when the battery level is low, increasing the probability of logging can reduce energy consumption. After the level of battery remaining is high enough, nodes then send the notification which was logged before to the sink. Compared with past solutions, our results indicate that the performance of the TBSR scheme has been improved comprehensively; it can effectively increase the quantity of notification received by the sink by 20%, increase energy efficiency by 11%, reduce the maximum storage capacity needed by nodes by 33.3% and improve the success rate of routing by approximately 16.30%.

Construction Low Complexity and Low Delay CDS for Big Data Code Dissemination

The diffusion of codes is an important processing technology for big data networks. In previous scheme, data analysis was conducted for small samples of big data and complex problems that cannot be processed by big data technology. Due to the limited capacity of intelligence device, a better method is to select a set of nodes (intelligence device) to form a connected dominating set (CDS) to save energy, and constructing CDS is proved to be a complete NP problem. However, it is a challenge to reduce the communication delay and complexity for urgent data transmission in big data. In this paper, an appropriate duty cycle control (ADCC) scheme is proposed to reduce communication delay and complexity while improving energy efficient in CDS-based WSNs. In ADCC scheme, the method for constructing CDS is proposed at lower complexity. Nodes in CDS are selected according to the degree of nodes. Then, duty cycle of dominator nodes in CDS is higher than that of dominated nodes, so the communication delay in the proposed scheme is far less than that of previous scheme. The duty cycle of dominated nodes is small to save energy. This is because the number of dominator nodes in CDS is far less than the number of dominated nodes whose duty cycle is small; thus, the total energy consumption of the network is less than that of the previous scheme. As a result, the performance of energy consumption and communication delay and complex have been improved. Its complexity is reduced a lot for big data. The theoretical analysis shows that compared to the previous scheme, the transmission delay can be reduced 25–92% and the energy efficiency is improved by about 80% while retaining network lifetime.

Anonymity-Based Privacy-Preserving Data Reporting for Participatory Sensing

In this paper, we propose an efficient anonymous data reporting protocol for participatory sensing, which provide strong privacy protection, data accuracy, and generality. The protocol consists of two stages: 1) slot reservation and 2) message submission. In the slot reservation stage, a group of N participants cooperate to assign each member a message slot in a vector which is essentially a message submission schedule, in such a manner that each participants slot is oblivious to other members and the application server. In the message submission stage, each participant transmits an encoded data to the application server based on the slot information known only to herself, in such a way that the application server cannot link a data to a specific participant. With such a data reporting protocol, the link between the data and the participants is broken, and as a result, participants privacy is protected. We conduct theoretical analysis of the correctness and anonymity of our protocol, as well as experiments to demonstrate the efficiency in small-scale applications with periodic data sampling.

Construction of Large-Scale Low-Cost Delivery Infrastructure Using Vehicular Networks

Vehicular ad-hoc networks, as one of the major components of Internet of Things in smart cities, have enormous idle transportation capability due to the huge number of vehicles. This paper argues that it is feasible to employ a large number of mobile vehicles in smart city to construct a large-scale low-cost delivery infrastructure using opportunistic routing to improve the city’s social welfare. To this end, a low-cost good delivery system (LCGDS) with opportunistic relay is proposed to meet the requirements of good delivery. In LCGDS, an enormous number of mobile vehicles constitute a stable, highly reliable, low-cost infrastructure with large transportation capabilities, called the mobile Internet of mission-critical things (M-IoMCT). The main contributions of the LCGDS are: 1) demonstrate the technological feasibility of M-IoMCT for delivering goods through mobile vehicles in a “ride-sharing” fashion with low costs and 2) in order to increase the reliability of goods transmission, the LCGDS proposes mobile vehicle selection algorithm which effectively selects a vehicle with higher probability of arriving at the destination. Both theoretical and experimental simulation results show that the performance of the LCGDS outperforms those of other solutions. Compared with other solutions, our results demonstrate that the costs in the LCGDS are only about 24% of the costs in the origin goods transmission scheme.

System resource utilization analysis and prediction for cloud based applications under bursty workloads

Abstract Performance analysis and prediction need a solid understanding of the system workload. As a salient workload characteristic, burstiness has critical impact on resource provisioning and performance of cloud based applications. Thus performance analysis and prediction under bursty workloads are of crucial importance to cloud based applications. However, it is yet challenging for such analysis and prediction, since no accurate and effective bursty workload generator exists, as well as the fine-grained bursty workload analysis and prediction method. In this article, to deal with these challenges, a bursty workload generator has been proposed for Cloudstone (a cloud benchmark) based on 2-state Markovian Arrival Process (MAP2). Then based on this generator, a fine-grained performance analysis method, which can be used to predict the probability density function of CPU utilization, has been suggested for cloud based applications, to support better resource provisioning decision making and system performance optimization. Finally, extensive experiments are conducted in a Xen-based virtualized environment to evaluate the accuracy and effectiveness of the two methods. By comparing the actual value of Indices of Dispersion for Count with the target value deduced from MAP2 model, the experiments show the precision of our method is superior to existing works. By comparing the real and predicted system resource utilization under a variety of bursty workloads generated by the proposed generator, the experiments also demonstrate the effectiveness and accuracy of the proposed fine-grained system resource utilization prediction method.

Adaptive Aggregation Routing to Reduce Delay for Multi-Layer Wireless Sensor Networks

The quality of service (QoS) regarding delay, lifetime and reliability is the key to the application of wireless sensor networks (WSNs). Data aggregation is a method to effectively reduce the data transmission volume and improve the lifetime of a network. In the previous study, a common strategy required that data wait in the queue. When the length of the queue is greater than or equal to the predetermined aggregation threshold (Nt) or the waiting time is equal to the aggregation timer (Tt), data are forwarded at the expense of an increase in the delay. The primary contributions of the proposed Adaptive Aggregation Routing (AAR) scheme are the following: (a) the senders select the forwarding node dynamically according to the length of the data queue, which effectively reduces the delay. In the AAR scheme, the senders send data to the nodes with a long data queue. The advantages are that first, the nodes with a long data queue need a small amount of data to perform aggregation; therefore, the transmitted data can be fully utilized to make these nodes aggregate. Second, this scheme balances the aggregating and data sending load; thus, the lifetime increases. (b) An improved AAR scheme is proposed to improve the QoS. The aggregation deadline (Tt) and the aggregation threshold (Nt) are dynamically changed in the network. In WSNs, nodes far from the sink have residual energy because these nodes transmit less data than the other nodes. In the improved AAR scheme, the nodes far from the sink have a small value of Tt and Nt to reduce delay, and the nodes near the sink are set to a large value of Tt and Nt to reduce energy consumption. Thus, the end to end delay is reduced, a longer lifetime is achieved, and the residual energy is fully used. Simulation results demonstrate that compared with the previous scheme, the performance of the AAR scheme is improved. This scheme reduces the delay by 14.91%, improves the lifetime by 30.91%, and increases energy efficiency by 76.40%.