The Trap Coverage Area Protocol for Scalable Vehicular Target Tracking

Vehicle target tracking is a sub-field of increasing and increasing interest in the vehicular networking research area, in particular for its potential application in dense urban areas with low associated costs, e.g., by exploiting existing monitoring infrastructures and cooperative collaboration of regular vehicles. Inspired by the concept of trap coverage area, we have originally designed and implemented an original protocol for vehicle tracking in wide-scale urban scenarios, called TCAP. TCAP is capable of achieving the needed performance while exploiting a limited number of inexpensive sensors (e.g., public-authority cameras already installed at intersections for traffic monitoring), and opportunistic vehicle collaboration, with high scalability and low overhead if compared with state-of-the-art literature. In particular, the wide set of reported results show i) the suitability of our TCAP tracking in the challenging urban conditions of high density of vehicles, ii) the very weak dependency of TCAP performance from topology changes/constraints (e.g., street lengths and speed limits), iii) the TCAP capability of self-adapting to differentiated runtime conditions

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Non-cooperating vehicle tracking in VANETs using the conditional logit model

Vehicular Ad Hoc Networks (VANETs) are widely considered as indispensable elements of the future intelligent transportation systems that are aiming to apply information and communications technologies to improve transportation safety and quality of experience. We present our take on a relatively unexplored problem, exploiting VANETs for on-road surveillance. The proposal is inspired by multi-agent systems intended for surveillance, e.g., a distributed camera network. We propose a tracking system composed of three operational modules, namely, localization, tracking data collection and prediction of future locations of a target. Camera equipped onboard units (OBUs) act as remote mobile sensors. Tracking messages are communicated among the OBUs and roadside units (RSUs). These messages are also triggered in the possible locations of the target in a timely manner. Therefore, it is imperative to scope the search to limit the number of OBUs and RSUs involved in the tracking operation, thus, minimizing the number of tracking messages. To this end, a movement modeling technique utilizes the OBU-observations to classify the target's movement pattern to aid future trajectory prediction. In our previous work, we proposed a Dirichlet-multinomial (D-M) model under the Bayesian estimation framework. In this paper, we present newly identified cues towards improving the movement estimation model. The D-M model is constrained to the assumption that all the choice sets are identical across trials. We demonstrate that this is almost never the case. The improved model exploits a choice model, called the conditional logit. The conditional logit model is attractive when choice sets vary across trials. Additionally, we weight outcome of each trial according to the given choice sets to achieve higher estimation accuracy. We evaluate the new model by means of an experimental analysis and compare results with the D-M model

Molecular phylogeny of horseshoe crab using mitochondrial Cox1 gene as a benchmark sequence

An effort to assess the utility of 650 bp Cytochrome C oxidase subunit I (DNA barcode) gene in delineating the members horseshoe crabs (Family: xiphosura) with closely related sister taxa was made. A total of 33 sequences were extracted from National Center for Biotechnological Information (NCBI) which include horseshoe crabs, beetles, common crabs and scorpion sequences. Constructed phylogram showed beetles are closely related with horseshoe crabs than common crabs. Scorpion spp were distantly related to xiphosurans. Phylogram and observed genetic distance (GD) date were also revealed that Limulus polyphemus was closely related with Tachypleus tridentatus than with T.gigas. Carcinoscorpius rotundicauda was distantly related with L.polyphemus. The observed mean Genetic Distance (GD) value was higher in 3rd codon position in all the selected group of organisms. Among the horseshoe crabs high GC content was observed in L.polyphemus (38.32%) and lowest was observed in T.tridentatus (32.35%). We conclude that COI sequencing (barcoding) could be used in identifying and delineating evolutionary relatedness with closely related specie

Crab and cockle shells as heterogeneous catalysts in the production of biodiesel

In the present study, the waste crab and cockle shells were utilized as source of calcium oxide to transesterify palm olein into methyl esters (biodiesel). Characterization results revealed that the main component of the shells are calcium carbonate which transformed into calcium oxide upon activated above 700 °C for 2 h. Parametric studies have been investigated and optimal conditions were found to be catalyst amount, 5 wt.% and methanol/oil mass ratio, 0.5:1. The waste catalysts perform equally well as laboratory CaO, thus creating another low-cost catalyst source for producing biodiesel. Reusability results confirmed that the prepared catalyst is able to be reemployed up to five times. Statistical analysis has been performed using a Central Composite Design to evaluate the contribution and performance of the parameters on biodiesel purity