An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-007155.pdf

Limits of predictability in human mobility

Vibrating flip-flow screens have been widely used in the mineral processing industry. As the key components, flip-flow screen panels strongly affect the dynamics of vibrating flip-flow screens. However, there is no systematic research into this problem. In this study, the dynamic characteristics of flip-flow screen panel are identified by the finite element analysis. Furthermore, a nonlinear coupling dynamic model of vibrating flip-flow screens that considers the effects of flip-flow screen panels is proposed, and the analytical and numerical solutions of its governing equation are deduced via the equivalent linearization method and the time marching method. Also, experimental results show that the proposed model describes the movements of vibrating flip-flow screens more accurately than the classical mechanical model which does not consider flip-flow screen panels. Finally, the effects of the screen panel’s parameters, such as the slack length and the stiffness, on the dynamics of vibrating flip-flow screens are investigated.

Nonlinear Model of Vibrating Flip-flow Screens that Considers the Effects of Screen Panels (February 2022)

In recent times, the vehicle industry has started to equip vehicles with advanced technologies and features to provide communication connections to nearby vehicles and roadside infrastructure. This advanced technology in the domain of Intelligent Transport Systems (ITSs) is usually called Cooperative Intelligent Transportation Systems (C-ITS), and it is designed to improve road safety, comfort of drivers, transportation efficiency, and other factors. The advancement of C-ITS includes the use of different wireless technologies such as IEEE 802.11, ITS-G5/DSRC (Dedicated short-range communications) and 4G/5G. In the C-ITS domain, researchers are not only working on passenger-based vehicles but also heavy vehicles, as well as autonomous cars. Heavy vehicles considerably contribute to the financial growth and return on invested capital (ROI). To make heavy vehicle systems safe and efficient, the C-ITS system needs to provide a platform offering different sets of pilot use-cases, where each use-case has a specific set of requirements. In this chapter, some of the critical pilot use-cases are examined, considering Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) scenarios by exploiting road traffic and weather data. Important constraints are also studied for C-ITS pilot use-cases to establish a comprehensive qualitative assessment of the ITS-G5 protocol stack. This chapter also evaluates the performance of ITS-G5 in an operational environment in various realistic pilot field measurements considering safety-critical messages (alerts). The collected results show that the implementation of the ITS-G5 protocol in real time offers a successful delivery of safety-critical messages (90%–99%) on frequency channels of 10 MHz. Moreover, ITS-G5 transmits safety-critical messages having a low latency (< 100 ms) that meets the C-ITS standard for real-time operations. Discussed scenarios in this chapter are (a) road-work warning, (b) emergency vehicle, (c) road weather, (d) slow vehicle, and (e) hazard warning. The operational performance of these pilot scenarios makes this system highly suitable to exchange data packets by maintaining a reasonable distance, having low latency as well as the lost packets in V2V and V2I scenarios. This pilot platform has been designed and developed at the Finnish Meteorological Institute (FMI), in Sodankylä, Finland. The overall operational presentation of the C-ITS pilot platform in real time makes the system suitable to be used not only for heavy vehicles but also for automobiles. 

Implementation of a cooperative intelligent transport system utilizing weather and road observation data

Cooperative Intelligent Systems development is growing and the data they produce is increasing exponentially. This amount of data will soon be large enough to fall in big data paradigm. We propose to exploit these data as data stream. We aim to detect anomaly on the road using concept drift detection methods over data stream. To achieve this purpose, we create a data generation tool to obtain large data-sets of vehicles taking an avoiding behavior and detect obstacles through crowdsensing. We use two scenarios that we aim to detect: a stopped car and a growing pothole. We focus our study on the vehicle orientation information on which we apply Page-Hinkley and ADWIN methods. We obtain interesting detection results with ADWIN on the stopped car scenario. The Page-Hinkley algorithm is obtaining good results but with a latency that makes it unexploitable in real context. But for the pothole detection, both approaches are not providing significant results.

Obstacle Detection based on Cooperative-Intelligent Transport System Data

The area coverage is a non-trivial problem in Wireless Sensors Network (WSN) due to the lack of knowledge in the minimum sensor nodes set that can cover an area of interest and limitation of energy reserve, monitoring and communication ranges. Coverage protocols address two fundamental questions: how can the coverage performance of deployed sensor nodes in a monitoring region be evaluated; and how can the coverage performance be improved when wireless sensor network cannot effectively satisfy quality of service requirements? Constructing sets of minimal nodes to optimize the coverage problem push researchers to use different techniques from different domains, such as Voronoi Diagram, Connected Dominating Set, Clustering, and others. Each technique deals with the problem by its own philosophy. Our approach aims to use a hybrid model combining Diagram of Voronoi, Clustering, and Connected Dominating Set in order to benefit from the advantages of these three models to optimize area coverage, keep connectivity, and minimize energy consumption. The simulation showed the ability of our approach to ensure optimal coverage with minimal power consumption for a longtime.

Hybrid Model Approach for Wireless Sensor Networks Coverage Improvement

Connected vehicles is a growing field of research that will produce great amount of data in near future These data can be mined to generate traffic prediction, detect driver profile, find alternative route, etc This will help car manufacturers, road operators, telecom operators and other actors in the sector to improve road safety and drivers comfort But nowadays few data are collected to create these toolsIn this paper we compare different completion approach on data extracted from real experimentation on road to perform efficient driving profile detection We analyze the deviations of the driver headings along a defined trajectory on specific Points of Interest (POI) to extract the driving profiles

C-ITS data completion to improve unsupervised driving profile detection

A majority of influence maximization models in social networks in literature are based on a seminal work by Kempe et al., in which two classic influence models were proposed i.e Linear Threshold Model and Independent Cascade Model. However, these two models use assumed values to model influence and influence propagation in social networks. This may lead to inaccurate approximation of influence. In this work, we model influence from actual social actions among members of a social network through a proposed algorithm - Selective Breadth First Traversal - that efficiently generates an optimal seed set for influence maximization. Experimental results on real data show that our approach provides an improvement over a number of traditional influence maximization algorithms.

Influence maximization through user interaction modeling

The study of coverage problem in uncertain WSN environment requires the consideration of this uncertainty by taking the best possible decisions, since it is impossible to explicitly represent all the combinatorics to produce a conditional active/passive state nodes' planning in the area of interest, and allow reasoning on various environmental states of the partially known physical world. This paper addresses the problem of area coverage based on the Dempster-Shafer theory. The authors aim to ensure the full area coverage while using a subset of connected nodes, with minimal costs using a minimal number of dominant nodes regardless of the type of used deployment (random or deterministic). This is ensured by activating a single node in each subset of each geographic sub-area, thus extending the lifetime of the wireless sensor network to its maximum. The comparison of the proposed model denoted evidential approach for area coverage (EAAC) with two well-known protocols and with a recent one showed a better performance and a slight improvement in the covered area.

Cyril de Runz

Papers

Hybrid Model Approach for Wireless Sensor Networks Coverage Improvement

C-ITS data completion to improve unsupervised driving profile detection

Influence maximization through user interaction modeling

An Evidential Approach for Area Coverage in Mobile Wireless Sensor Networks

Obstacle Detection based on Cooperative-Intelligent Transport System Data