Archives in DNA:Exploring implications of an emerging bio-digital technology through design fiction

Continuing developments in DNA-based digital data storage systems promise us a sustainable, techno-utopian future; propositioning bio-digital solutions addressing the ever-increasing global data production, and inadequacies of conventional storage infrastructure to meet the demand. Distinct attributes of DNA make it an attractive archival medium. With its ability to retain high density of digital information cheaply, and to do so over multi-lifespans, DNA-based storage systems are seen as able to radically shape how we archive and use data, across wide-ranging applications. However, while the stakeholders continue to refine and race towards commercialization of the emerging technology, its sociocultural and ethical implications remain unexplored, limiting opportunities to generate insights on how such systems could be better designed and experienced. This workshop begins to explore what our DNA-mediated archival futures may hold. We learn about the fundamental principles governing the new technology and create stories about its pervasion in our lives, mediated through design fiction and structured discourse

Internet of Things: smart ubiquitous architecture of intelligent transport system

By 2020, there will be more than 24 billion smart devices connected in the Internet of Things (IoT). Tremendously augmented motorization, population, and urbanization has not only brought us many amenities but also has increased traffic congestion to its limits. In this paper, we used IOT to design an efficient and congestion free Intelligent Transport System (ITS). A lot of research is done to either improve or change any one aspect of ITS at one time. This paper demonstrates every aspect or features of an efficient ITS. The purpose of this research is to provide developing countries a detailed and easy to follow ITS architecture so that they can create an ITS for their populace

BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images

Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using multi-view camera images. Specifically, a visual BEV (Birds-Eye-View) encoder extracts and flattens the multi-view images into BEV space. While the semantic map features are structurally embedded as map queries sequence. Then a cross-model transformer associates the BEV features and semantic map queries. The localization information of ego-car is recursively queried out by cross-attention modules. Finally, the ego pose can be inferred by decoding the transformer outputs. We evaluate the proposed method in large-scale nuScenes and Qcraft datasets. The experimental results show that the BEV-locator is capable to estimate the vehicle poses under versatile scenarios, which effectively associates the cross-model information from multi-view images and global semantic maps. The experiments report satisfactory accuracy with mean absolute errors of 0.052m, 0.135m and 0.251$^\circ$ in lateral, longitudinal translation and heading angle degree

A Semantic loT Early Warning System for Natural Environment Crisis Management

An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-type EWS include easier sensor and data source plug-and-play, simpler, richer, and more dynamic metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model.We use lightweight semantics for metadata to enhance rich sensor data acquisition.We use heavyweight semantics for top level W3CWeb Ontology Language ontology models describing multileveled knowledge-bases and semantically driven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a reployed EWS infrastructure

Using a Smart City IoT to Incentivise and Target Shifts in Mobility Behaviour-Is It a Piece of Pie?

The work presented in this paper is a central part of the research and development in the SUNSET project (contract No. 270228), supported by the 7th Framework Research Program funded by the European Commission. The authors also acknowledge the support of other SUNSET consortium members in helping to create and evaluate the SUNSET tripzoom system

The EDEN-IW ontology model for sharing knowledge and water quality data between heterogeneous databases

Abstract The Environmental Data Exchange Network for Inland Water (EDEN-IW) project's main aim is to develop a system for making disparate and heterogeneous databases of Inland Water quality more accessible to users. The core technology is based upon a combination of: ontological model to represent a Semantic Web based data model for IW; software agents as an infrastructure to share and reason about the IW semantic data model and XML to make the information accessible to Web portals and mainstream Web services. This presentation focuses on the Semantic Web or Ontological model. Currently, we have successfully demonstrated the use of our systems to semantically integrate two main database resources from IOW and NERI -these are available on-line. We are in the process of adding further databases and supporting a wider variety of user queries such as Decision Support System queries

A Fuzzy Logic Module to Estimate a Driver’s Fuel Consumption for Reality-Enhanced Serious Games

Reality-enhanced gaming is an emerging serious game genre, that could contextualize a game within its real instruction-target environment. A key module for such games is the evaluator, that senses a user performance and provides consequent input to the game. In this project, we have explored an application in the automotive field, estimating driver performance in terms of fuel consumption, based on three key vehicular signals, that are directly controllable by the driver: throttle position sensor (TPS), engine rotation speed (RPM) and car speed. We focused on Fuzzy Logic, given its ability to embody expert knowledge and deal with incomplete information availability. The fuzzy models – that we iteratively defined based on literature expertise and data analysis – can be easily plugged into a reality-enhanced gaming architecture. We studied four models with all the possible combinations of the chosen variables (TPS and RPM; RPM and speed; TPS and speed; TPS, speed and RPM). Input data were taken from the enviroCar database, and our fuel consumption predictions compared with their estimated values. Results indicate that the model with the three inputs outperforms the other models giving a higher coefficient of determination (R2), and lower error. Our study also shows that RPM is the most important fuel consumption predictor, followed by TPS and speed

The Agentcities Network Architecture

Agentcities is a worldwide initiative designed to help realize the commercial and research potential of agent-based applications by constructing an open distributed network of platforms hosting diverse agents and services. The ultimate aim of the Agentcities initiative is to enable the dynamic, intelligent and autonomous composition of services to achieve user and business goals, thereby creating compound services to address changing needs. In this paper, we present the progress and current status of the Agentcities Network, six months after the launch of the project. The architecture of the Network, consisting of agents, services and platforms, is described. Finally, the plans and challenges for enhancing the Agentcities Network in the next phase of development are also discussed

How IoT-Driven Citizen Science Coupled with Data Satisficing Can Promote Deep Citizen Science

To study and understand the importance of Internet of Things-driven citizen science (IoTCS) combined with data satisficing, we set up and undertook a citizen science experiment for air quality (AQ) in four Pakistan cities using twenty-one volunteers. We used quantitative methods to analyse the AQ data. Three research questions (RQ) were posed as follows: Which factors affect CS IoT-CS AQ data quality (RQ1)? How can we make science more inclusive by dealing with the lack of scientists, training and high-quality equipment (RQ2)? Can a lack of calibrated data readings be overcome to yield otherwise useful results for IoT-CS AQ data analysis (RQ3)? To address RQ1, an analysis of related work revealed that multiple causal factors exist. Good practice guidelines were adopted to promote higher data quality in CS studies. Additionally, we also proposed a classification of CS instruments to help better understand the data quality challenges. To answer RQ2, user engagement workshops were undertaken as an effective method to make CS more inclusive and also to train users to operate IoT-CS AQ devices more understandably. To address RQ3, it was proposed that a more feasible objective is that citizens leverage data satisficing such that AQ measurements can detect relevant local variations. Additionally, we proposed several recommendations. Our top recommendations are that: a deep (citizen) science approach should be fostered to support a more inclusive, knowledgeable application of science en masse for the greater good; It may not be useful or feasible to cross-check measurements from cheaper versus more expensive calibrated instrument sensors in situ. Hence, data satisficing may be more feasible; additional cross-checks that go beyond checking if co-located low-cost and calibrated AQ measurements correlate under equivalent conditions should be leveraged