Ant-Fuzzy Meta Heuristic Genetic Sensor Network System for Multi Sink Aggregated Data Transmission

Wireless sensor network with the hierarchical organization of sensors aggregate the tasks into groups. The sensor nodes broadcast the aggregated data directly to the distant base station. Existing Mixed Integer Programming (MIP) formulation obtain the good solutions for multi-action processes but not effectual in developing the hybrid genetic algorithms with the Tabu search meta-heuristics ant colony optimization. Another existing work developed for security purpose named as Dynamic secure end-to-end Data Aggregation with Privacy function (DyDAP) decrease the network load but topological configurations with multiple sinks are not addressed. To develop the hybrid genetic algorithm on ant-fuzzy system, Hybrid (i.e.,) ant-fuzzy Meta-heuristic Genetic method (HMG) based on the Tabu search is proposed in this paper. Ant-fuzzy Meta heuristic Genetic method carries out the classification process on the aggregated data. The classification based on the genetic method uses the Tabu search based mathematical operation to attain the feasible solution on multiple sinks. Initially, Ant-fuzzy Meta-heuristic Genetic method classifies the data record based on the weighted meta-heuristic distance. The classified records perform the Tabu search operation to transmit the aggregated data to the multiple sink nodes. HMG method achieves approximately 19 % improved transmitted message rate. Experiment is conducted in the network simulator on the factor such as classification time and transmission rate

A Survey on Intelligent Traffic Management System

Intelligent road traffic flow control is one of the major area of research in transportation and city traffic management in recent times. It is found in studies that most of the pollution is attributed by vehicles waiting at the traffic signal than driving vehicles in the peak up time. The main purpose of this work is to reduce the pollution level which is emitted by vehicles at the Traffic signal. In order to reduce the city traffic pollution and at control the traffic flow effectively, we have proposed a novel technique of traffic light management based on pollution sensing

Congestion adaptive traffic light control and notification architecture using Google maps APIs

Mishra, S., Bhattacharya, D., & Gupta, A. (2018). Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs. Data, 3(4), [67]. DOI: 10.3390/data3040067Traffic jams can be avoided by controlling traffic signals according to quickly building congestion with steep gradients on short temporal and small spatial scales. With the rising standards of computational technology, single-board computers, software packages, platforms, and APIs (Application Program Interfaces), it has become relatively easy for developers to create systems for controlling signals and informative systems. Hence, for enhancing the power of Intelligent Transport Systems in automotive telematics, in this study, we used crowdsourced traffic congestion data from Google to adjust traffic light cycle times with a system that is adaptable to congestion. One aim of the system proposed here is to inform drivers about the status of the upcoming traffic light on their route. Since crowdsourced data are used, the system does not entail the high infrastructure cost associated with sensing networks. A full system module-level analysis is presented for implementation. The system proposed is fail-safe against temporal communication failure. Along with a case study for examining congestion levels, generic information processing for the cycle time decision and status delivery system was tested and confirmed to be viable and quick for a restricted prototype model. The information required was delivered correctly over sustained trials, with an average time delay of 1.5 s and a maximum of 3 s.publishersversionpublishe

A taxonomy for planning and designing smart mobility services

The development of smart mobility initiatives requires specialized and contextualized policies addressing the needs and interests of many stakeholders involved. Since the development of such policies is challenging, there is a need to learn from the experience of many cities around the world offering efficient and successfully adopted smart mobility services. However, in practice, the information provided about such initiatives is shallow and unstructured. To address this issue, we study the state of the art in mobility services, reviewing scientific publications and 42 smart mobility services delivered by nine smart cities around the world, and we propose a taxonomy for planning and designing smart mobility services. The taxonomy provides a common vocabulary to discuss and share information about such services. It comprises eight dimensions: type of services, maturity level, users, applied technologies, delivery channels, benefits, beneficiaries, and common functionality. The contribution of the proposed taxonomy is to serve as a tool for guiding policy makers by identifying a spectrum of mobility services that can be provided, to whom, what technologies can be used to deliver them, and what is the delivered public value so to justify their implementation. In addition, the taxonomy can also assist researchers in further developing the domain. By identifying common functionality, it could also help Information Technology (IT) teams in building and maintaining smart mobility services. Finally, we further discuss usage scenarios of the taxonomy by policy makers, IT staff and researchers.NORTE-01-0145-FEDER-000037, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (EFDR). The first author is also supported by the Portuguese funding agency, FCT, under grant PD/BD/52238/201

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Wireless communication technologies for the Internet of Things

Internet of Things (IoT) is the inter-networking paradigm based on many processes such as identifying, sensing, networking and computation. An IoT technology stack provides seamless connectivity between various physical and virtual objects. The increasing number of IoT applications leads to the issue of transmitting, storing, and processing a large amount of data. Therefore, it is necessary to enable a system capable to handle the growing traffic requirements with the required level of QoS (Quality of Service). IoT devices become more complex due to the various components such as sensors and network interfaces. The IoT environment is often demanding for mobile power source, QoS, mobility, reliability, security, and other requirements. Therefore, new IoT technologies are required to overcome some of these issues. In recent years new wireless communication technologies are being developed to support the development of new IoT applications. This paper provides an overview of some of the most widely used wireless communication technologies used for IoT applications

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Open Platforms for Connected Vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen

Cyber-Attack Drone Payload Development and Geolocation via Directional Antennae

The increasing capabilities of commercial drones have led to blossoming drone usage in private sector industries ranging from agriculture to mining to cinema. Commercial drones have made amazing improvements in flight time, flight distance, and payload weight. These same features also offer a unique and unprecedented commodity for wireless hackers -- the ability to gain ‘physical’ proximity to a target without personally having to be anywhere near it. This capability is called Remote Physical Proximity (RPP). By their nature, wireless devices are largely susceptible to sniffing and injection attacks, but only if the attacker can interact with the device via physical proximity. A properly outfitted drone can increase the attack surface with RPP (adding a range of over 7 km using off-the-shelf drones), allowing full interactivity with wireless targets while the attacker can remain distant and hidden. Combined with the novel approach of using a directional antenna, these drones could also provide the means to collect targeted geolocation information of wireless devices from long distances passively, which is of significant value from an offensive cyberwarfare standpoint. This research develops skypie, a software and hardware framework designed for performing remote, directional drone-based collections. The prototype is inexpensive, lightweight, and totally independent of drone architecture, meaning it can be strapped to most medium to large commercial drones. The prototype effectively simulates the type of device that could be built by a motivated threat actor, and the development process evaluates strengths and shortcoming posed by these devices. This research also experimentally evaluates the ability of a drone-based attack system to track its targets by passively sniffing Wi-Fi signals from distances of 300 and 600 meters using a directional antenna. Additionally, it identifies collection techniques and processing algorithms for minimizing geolocation errors. Results show geolocation via 802.11 emissions (Wi-Fi) using a portable directional antenna is possible, but difficult to achieve the accuracy that GPS delivers (errors less than 5 m with 95% confidence). This research shows that geolocation predictions of a target cell phone acting as a Wi-Fi access point in a field from 300 m away is accurate within 70.1 m from 300 m away and within 76 meters from 600 m away. Three of the four main tests exceed the hypothesized geolocation error of 15% of the sensor-to-target distance, with tests 300 m away averaging 25.5% and tests 600 m away averaging at 34%. Improvements in bearing prediction are needed to reduce error to more tolerable quantities, and this thesis discusses several recommendations to do so. This research ultimately assists in developing operational drone-borne cyber-attack and reconnaissance capabilities, identifying limitations, and enlightening the public of countermeasures to mitigate the privacy threats posed by the inevitable rise of the cyber-attack drone