A microbial inspired routing protocol for VANETs

We present a bio-inspired unicast routing protocol for vehicular Ad Hoc Networks which uses the cellular attractor selection mechanism to select next hops. The proposed unicast routing protocol based on attractor selecting (URAS) is an opportunistic routing protocol, which is able to change itself adaptively to the complex and dynamic environment by routing feedback packets. We further employ a multi-attribute decision-making strategy, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), to reduce the number of redundant candidates for next-hop selection, so as to enhance the performance of attractor selection mechanism. Once the routing path is found, URAS maintains the current path or finds another better path adaptively based on the performance of current path, that is, it can self-evolution until the best routing path is found. Our simulation study compares the proposed solution with the state-of-the-art schemes, and shows the robustness and effectiveness of the proposed routing protocol and the significant performance improvement, in terms of packet delivery, end-to-end delay, and congestion, over the conventional method

Prediction of Disease Using Machine Learning over Big Data-Survey

With massive information development in medical specialty and aid community, precise analysis of medical information advantages premature disease detection, patient care and community services. although, the analysis accuracy is reduced once the standard of medical information is incomplete. moreover, completely different regions exhibit distinctive characteristics of bound regional diseases, which can weaken the prediction of illness outbreaks. during this paper, we tend to contour machine learning algorithms for effective prediction of chronic malady eruption in disease-frequent communities. we tend to experiment the tailored prediction models over real-life hospital information collected from central China in 2013-2015. to beat the problem of incomplete information, we tend to use a latent issue model to build the missing information. we tend to experiment on a regional chronic illness of cerebral infarction. we tend to propose a replacement convolutional neural network based multimodal disease risk prediction (CNN-MDRP) algorithmic program victimisation structured and unstructured information from hospital. To the simplest of our data, none of the prevailing work targeted on each information varieties within the space of medical massive information analytics. Compared to many typical prediction algorithms, the prediction accuracy of our projected algorithmic program reaches ninety four.8% with a convergence speed that is faster than that of the CNN-based unimodal disease risk prediction (CNN-UDRP) algorithmic program

Future Trends and Challenges for Mobile and Convergent Networks

Some traffic characteristics like real-time, location-based, and community-inspired, as well as the exponential increase on the data traffic in mobile networks, are challenging the academia and standardization communities to manage these networks in completely novel and intelligent ways, otherwise, current network infrastructures can not offer a connection service with an acceptable quality for both emergent traffic demand and application requisites. In this way, a very relevant research problem that needs to be addressed is how a heterogeneous wireless access infrastructure should be controlled to offer a network access with a proper level of quality for diverse flows ending at multi-mode devices in mobile scenarios. The current chapter reviews recent research and standardization work developed under the most used wireless access technologies and mobile access proposals. It comprehensively outlines the impact on the deployment of those technologies in future networking environments, not only on the network performance but also in how the most important requirements of several relevant players, such as, content providers, network operators, and users/terminals can be addressed. Finally, the chapter concludes referring the most notable aspects in how the environment of future networks are expected to evolve like technology convergence, service convergence, terminal convergence, market convergence, environmental awareness, energy-efficiency, self-organized and intelligent infrastructure, as well as the most important functional requisites to be addressed through that infrastructure such as flow mobility, data offloading, load balancing and vertical multihoming.Comment: In book 4G & Beyond: The Convergence of Networks, Devices and Services, Nova Science Publishers, 201

From cellular attractor selection to adaptive signal control for traffic networks

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains

Reliability-optimal cooperative communication and computing in connected vehicle systems

The emergence of vehicular networking enables distributed cooperative computation among nearby vehicles and infrastructures to achieve various applications that may need to handle mass data by a short deadline. In this paper, we investigate the fundamental problems of a cooperative vehicle-infrastructure system (CVIS): how does vehicular communication and networking affect the benefit gained from cooperative computation in the CVIS and what should a reliability-optimal cooperation be? We develop an analytical framework of reliability-oriented cooperative computation optimization, considering the dynamics of vehicular communication and computation. To be specific, we propose stochastic modeling of V2V and V2I communications, incorporating effects of the vehicle mobility, channel contentions and fading, and theoretically derive the probability of successful data transmission. We also formulate and solve an execution time minimization model to obtain the success probability of application completion with the constrained computation capacity and application requirements. By combining these models, we develop constrained optimizations to maximize the coupled reliability of communication and computation by optimizing the data partitions among different cooperators. Numerical results confirm that vehicular applications with a short deadline and large processing data size can better benefit from the cooperative computation rather than non-cooperative solutions

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008