Implementation of a new replacement method in WMN-PSO simulation system and its performance evaluation

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.With the fast development of wireless technologies, Wireless Mesh Networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless Internet connectivity. In our previous work, we implemented the Linearly Decreasing Vmax Method (LDVM) for our WMN-PSO simulation system. In this paper, we implement a new replacement method for mesh routers called Rational Decrement of Vmax Method (RDVM). We use Size of Giant Component (SGC) and Number of Covered Mesh Clients (NCMC) as metrics for optimization. From the simulation results, we found that RDVM converges faster to best solution than LDVM.Peer ReviewedPostprint (author's final draft

Routing Protocols for Underwater Acoustic Sensor Networks: A Survey from an Application Perspective

Underwater acoustic communications are different from terrestrial radio communications; acoustic channel is asymmetric and has large and variable end‐to‐end propagation delays, distance‐dependent limited bandwidth, high bit error rates, and multi‐path fading. Besides, nodes’ mobility and limited battery power also cause problems for networking protocol design. Among them, routing in underwater acoustic networks is a challenging task, and many protocols have been proposed. In this chapter, we first classify the routing protocols according to application scenarios, which are classified according to the number of sinks that an underwater acoustic sensor network (UASN) may use, namely single‐sink, multi‐sink, and no‐sink. We review some typical routing strategies proposed for these application scenarios, such as cross‐layer and reinforcement learning as well as opportunistic routing. Finally, some remaining key issues are highlighted

A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management

The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of “citizens as sensors” as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications

サーバクラスタでの低消費電力化のための移行モデルの研究

博士(工学)法政大学 (Hosei University

Key Requirements for the Detection and Sharing of Behavioral Indicators of Compromise

[EN] Cyber threat intelligence feeds the focus on atomic and computed indicators of compromise. These indicators are the main source of tactical cyber intelligence most organizations benefit from. They are expressed in machine-readable formats, and they are easily loaded into security devices in order to protect infrastructures. However, their usefulness is very limited, specially in terms of time of life. These indicators can be useful when dealing with non-advanced actors, but they are easily avoided by advanced ones. To detect advanced actor¿s activities, an analyst must deal with behavioral indicators of compromise, which represent tactics, techniques and procedures that are not as common as the atomic and computed ones. In this paper, we analyze why these indicators are not widely used, and we identify key requirements for successful behavioral IOC detection, specification and sharing. We follow the intelligence cycle as the arranged sequence of steps for a defensive team to work, thereby providing a common reference for these teams to identify gaps in their capabilities.Villalón-Huerta, A.; Ripoll-Ripoll, I.; Marco-Gisbert, H. (2022). Key Requirements for the Detection and Sharing of Behavioral Indicators of Compromise. Electronics. 11(3):1-20. https://doi.org/10.3390/electronics1103041612011

Comparison of WLAN Probe and Light Sensor-Based Estimators of Bus Occupancy Using Live Deployment Data

Bus company operators are interested in obtaining knowledge about the number of passengers on their buses—preferably doing so at low deployment costs and in an automated manner, while keeping accuracy high. One solution, widely used in practice, involves deploying a light sensor-based system, counting the people entering and leaving the bus. The light sensor system is simple, but errors accumulate over time, because it is not capable of error correcting. For this reason, the light sensor-based system is compared to a WLAN probe-based system, which has entirely different characteristics. Inaccuracy with the WLAN estimator comes from a need to filter out mobile devices outside the bus and to map the number of detected devices to a number of people. The comparison is performed based on data collected from a real-life deployment in a medium sized German city. The comparison shows the trade-off in selecting either of the two methods. Furthermore, a novel approach for fusion of the light sensor and WLAN estimators is proposed which has a big potential in improving accuracy of both estimators. A fusion approach is proposed that utilizes the different error characteristics for error compensation by calculating compensation terms. The knowledge of Ground Truth is not required as part of this fusion approach for calibration; results show that the approach can find the optimal parameter settings and that it makes this occupancy estimation approach scalable and automated

Opinion Mining for Software Development: A Systematic Literature Review

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present 1) well-defined categories of opinion mining-related software development activities, 2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, 3) available datasets for performance evaluation and tool customization, and 4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities, and provide critical insights for the further development of opinion mining techniques in the SE domain

How to develop IoT cloud e-health systems based on fiware: A lesson learnt

Nowadays, the penetration of sensors and actuators in different application fields is revolutionizing all aspects of our daily life. One of the major sectors that is taking advantage of such cutting-edge cheap smart devices is healthcare. In this context, Remote Patient Monitoring (RPM) at home represents a tempting opportunity for hospitals to reduce clinical costs and to improve the quality of life of both patients and their families. It allows patients to be monitored remotely by means networks of Internet of Things (IoT) medical devices equipped with sensors and actuators that collect healthcare data from patients and send them to a Cloud-based Hospital Information System (HIS) for processing. Up to now, many different proprietary software systems have been developed as stand-along expensive solutions, presenting interoperability, extensibility, and scalability issues. In recent years, the European Commission (EC) has promoted the wide adoption of FIWARE technology, launching 16 Industrial Accelerators focusing on different application fields. One of these, i.e., FICHe, is specialized in healthcare, providing the guidelines on how to develop eHealth systems. This paper focuses on how to compose new cutting-edge IoT and Cloud-based Cyber Physical Health Sytem (CPHS) services and applications interconnected with remote medical sensors and actuators using FIWARE technology in the context envisioned by FICHe. In particular, we discuss the design and development of an RPM system implemented through the collaboration between the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) “Bonino Pulejo” (i.e., a clinical and research healthcare centre specialized in the treatment of neuro lesions), University of Messina, IBM Research, Telefónica, and the University of the Western Cape in South Africa. The description of our best practice provides a model and guidelines for the development of lightweight and low cost RPM services for rural and isolated areas, with the expectation of expanding healthcare to the developing world and in general allows us to outline how to deal with the real adoption of the FIWARE technology in an e-health project

Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Machine Learning in Image Analysis and Pattern Recognition

This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition