Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction

A credibility score algorithm for malicious data detection in urban vehicular networks

This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control

QUANTUM ROAD TRAFFIC MODEL FOR AMBULANCE TRAVEL TIME ESTIMATION

Efficient management of ambulance utilisation is a vital issue for life saving. Knowledge of the amount of time needed for an ambulance to get to the hospital and when it will be available for a new task, can be estimated using modern Intelligent Transport Systems. Their main feature is an ability to simulate the state of traffic not only in long term, but also the real time events like accidents or high congestion, using microscopic models. The paper introduces usage of Quantum Computing paradigm to propose a quantum model of road traffic, which can track the state of traffic and estimate the travel time of vehicles. Model, if run on quantum computer can simulate the traffic in vast areas in real time. Proposed model was verified against the cellular automata model. Finally, application of quantum microscopic traffic models for ambulance vehicles was taken into consideration

SEM-EDS and X-ray micro computed tomography studies of skeletal surface pattern and body structure in the freshwater sponge Spongilla lacustris collected from Goczalkowice reservoir habit (Southern Poland)

Introduction. Freshwater sponges are common animals of most aquatic ecosystems. They feed by filtering small particles from the water, and so are thought to be sensitive indicators of pollution. Sponges are strongly associated with the abiotic environment and are therefore used as bioindicators for monitoring of water quality in water habitats. Among the freshwater sponges, Spongilla lacustris is one of the classic models used to study evolution, gene regulation, development, physiology and structural biology in animal water systems. It is also important in diagnostic of aquatic environments. The aim of this study was to characterize and visualize three-dimensional architecture of sponge body and measure skeleton elements of S. lacustris from Goczalkowice reservoir for identification purposes. Material and methods. The scanning electron microscopy with an energy dispersive X-ray microanalysis (SEM- -EDS) and X-ray micro computed tomography (micro-CT) were used to provide non-invasive visualization of the three-dimensional architecture of Spongilla lacustris body. Results. We showed that sponge skeleton was not homogeneous in composition and comprised several forms of skeleton organization. Ectosomal skeleton occurred as spicular brushes at apices of primary fibres with cementing spongin material. Choanosomal skeletal architecture was alveolate with pauci- to multispicular primary fibres connected by paucispicular transverse fibres, made by megascleres embedded in a scanty spongin matrix both in the choanosome and at the sponge surface. In contrast, microscleres were irregularly scattered in choanosome and skeletal surface. Furthermore, SEM-EDS studies showed that the distribution of silica in megascleres and microscleres was observed along the spicules and sponge surface areas. Conclusions. In conclusion, we showed that the combination of SEM-EDS and micro-CT microscopy techniques allowed obtaining a complete picture of the sponge spatial architecture

Wpływ cyrkulacji atmosferycznej na występowanie ekstremalnych opadów w Poznaniu w latach 1920-2010

The aim of this work is to define the influence of atmospheric circulation on the occurrence of extreme precipitation in Poznań in the years 1920–2010. The daily totals for atmospheric precipitation taken at the IMGW Poznań Ławica meteorological station were used. The research also uses data collected from the NCEP/NCAR Reanalysis (Kalnay et al. 1996) concerning the distribution of atmospheric pressure at sea level, the geopotential height of 500 hPa as well as an indicator for the availability of precipitable water (PW). Data on the frequency of occurrence of Grosswetterlagen atmospheric circulation from 1920–2000 was also used

Self-organizing Traffic Signal Control with Prioritization Strategy Aided by Vehicular Sensor Network

Part 6: Modelling and OptimizationInternational audiencePreemption strategies are necessary for traffic signal control at intersections in a road network to ensure minimum delay of priority vehicles, such as ambulances or police cars. This paper introduces a decentralized algorithm, which extends the self-organizing signal control to provide preemption for the priority vehicles. The introduced algorithm enables effective utilisation of real-time data collected in vehicular sensor network (VSN). Results of simulation experiments show that the proposed approach ensures a quick passage of the priority vehicles and minimizes the negative effect of signal preemption on delays of non-priority vehicles. The new VSN-aided preemption strategy improves performance of the state-of-the-art methods that are based on road-side vehicle detectors and simple vehicle-to-infrastructure communication systems

A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network

The paper introduces an artificial neural network ensemble for decentralized control of traffic signals based on data from sensor network. According to the decentralized approach, traffic signals at each intersection are controlled independently using real-time data obtained from sensor nodes installed along traffic lanes. In the proposed ensemble, a neural network, which reflects design of signalized intersection, is combined with fully connected neural networks to enable evaluation of signal group priorities. Based on the evaluated priorities, control decisions are taken about switching traffic signals. A neuroevolution strategy is used to optimize configuration of the introduced neural network ensemble. The proposed solution was compared against state-of-the-art decentralized traffic control algorithms during extensive simulation experiments. The experiments confirmed that the proposed solution provides better results in terms of reduced vehicle delay, shorter travel time, and increased average velocity of vehicles