Generating Contour Maps for Dynamic Fields Monitored by Sensor Networks

Wireless sensor networks provide a new tool that enables researchers and scientists to efficiently monitor dynamic fields. In order to extend the lifetime of the network, it is important for us to minimize network data transmission as much as possible. Previous work proposed many useful aggregation techniques to answer max, min and average questions, and some of them have been employed in real applications. But we cannot get spatial information from these aggregation techniques. This thesis presents an efficient aggregation technique for continuous generation of contour maps for a dynamic field monitored by a wireless sensor network. A contour map is a useful data representation schema that provides an efficient way to visualize an approximation to the monitored field. In this thesis, we discuss an energy-efficient technique, which we call Isovector Aggregation, for generating such contours using an in-network approach. Our technique achieves energy efficiency in two principal ways. Firstly, only a selection of nodes close to contours are chosen to report, and each reported message contains information about a part or all of the contours, rather than any single node’s ID and value pair. Secondly, contours are progressively merged and simplified along the data routing tree, which eliminates many unnecessary contour points from contour vectors before they are transmitted back to the base station. Using Isovector Aggregation, the base station receives a complete representation of the contours that requires no further processing. Analysis shows that for region-related monitoring, Isovector Aggregation is the only technique that has O( n) traffic generation and that considers in-network traffic reduction at the same time. These two factors make Isovector Aggregation highly scalable. Experimental results using simulations also show that Isovector Aggregation involves considerably less data transmission compared to other approaches, such as the no-aggregation approach and the Isolines Aggregation technique, without compromising the accuracy of representations of the baseline maps

Graph Theoretical Analysis of the Dynamic Lines of Collaboration Model for Disruption Response

The Dynamic Lines of Collaboration (DLOC) model was developed to address the Network-to-Network (N2N) service challenge found in e-Work networks with pervasive connectivity. A variant of the N2N service challenge found in emerging Cyber-Physical Infrastructures (CPI) networks is the collaborative disruption response (CDR) operation under cascading failures. The DLOC model has been validated as an appropriate modelling tool to aid the design of disruption responders in CPIs by eliciting the dynamic relation among the service team when handling service requests from clients in the CPI network

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Data Dissemination And Information Diffusion In Social Networks

Data dissemination problem is a challenging issue in social networks, especially in mobile social networks, which grows rapidly in recent years worldwide with a significant increasing number of hand-on mobile devices such as smart phones and pads. Short-range radio communications equipped in mobile devices enable mobile users to access their interested contents not only from access points of Internet but also from other mobile users. Through proper data dissemination among mobile users, the bandwidth of the short-range communications can be better utilized and alleviate the stress on the bandwidth of the cellular networks. In this dissertation proposal, data dissemination problem in mobile social networks is studied. Before data dissemination emerges in the research of mobile social networks, routing protocol of finding efficient routing path in mobile social networks was the focus, which later became the pavement for the study of the efficient data dissemination. Data dissemination priorities on packet dissemination from multiple sources to multiple destinations while routing protocol simply focus on finding routing path between two ends in the networks. The first works in the literature of data dissemination problem were based on the modification and improvement of routing protocols in mobile social networks. Therefore, we first studied and proposed a prediction-based routing protocol in delay tolerant networks. Delay tolerant network appears earlier than mobile social networks. With respect to delay tolerant networks, mobile social networks also consider social patterns as well as mobility patterns. In our work, we simply come up with the prediction-based routing protocol through analysis of user mobility patterns. We can also apply our proposed protocol in mobile social networks. Secondly, in literature, efficient data dissemination schemes are proposed to improve the data dissemination ratio and with reasonable overhead in the networks. However, the overhead may be not well controlled in the existing works. A social-aware data dissemination scheme is proposed in this dissertation proposal to study efficient data dissemination problem with controlled overhead in mobile social networks. The data dissemination scheme is based on the study on both mobility patterns and social patterns of mobile social networks. Thirdly, in real world cases, an efficient data dissemination in mobile social networks can never be realized if mobile users are selfish, which is true unfortunately in fact. Therefore, how to strengthen nodal cooperation for data dissemination is studied and a credit-based incentive data dissemination protocol is also proposed in this dissertation. Data dissemination problem was primarily researched on mobile social networks. When consider large social networks like online social networks, another similar problem was researched, namely, information diffusion problem. One specific problem is influence maximization problem in online social networks, which maximize the result of information diffusion process. In this dissertation proposal, we proposed a new information diffusion model, namely, sustaining cascading (SC) model to study the influence maximization problem and based on the SC model, we further plan our research work on the information diffusion problem aiming at minimizing the influence diffusion time with subject to an estimated influence coverage