OppNet: Enabling citizen-centric urban IoT data collection through opportunistic connectivity service

Urban IoT data collection is challenging due to the limitations of the fixed sensing infrastructures. Instead of transmitting data directly through expensive cellular networks, citizen-centric data collection scheme through opportunistic network takes advantage of human mobility as well as cheap WiFi and D2D communication. In this paper, we present OppNet, which implements a context aware data forwarding algorithm and fills the gap between theoretical modelling of opportunistic networking and real deployment of citizen-centric data collection system. According to the results from a 3-day real-life experiment, OppNet shows consistent performance in terms of number of hops and time delay. Moreover, the underlying social structure can be clearly identified by analysing social contact data collected through OppNet

Game theoretic and auction-based algorithms towards opportunistic communications in LPWA LoRa networks

Low Power Wide Area (LPWA) networks have been the enabling technology for large-scale sensor and actuator networks. Low cost, energy-efficiency and longevity of such networks make them perfect candidates for smart city applications. LoRa is a new LPWA standard based on spread spectrum technology, which is suitable for sensor nodes enabling long battery life and bi-directional communication but with low data rates. In this paper, we will demonstrate a use-case inspired model in which, end-nodes with multiple radio transceivers (LoRa/WiFi/BLE) have the option to interconnect via multiple networks to improve communications resilience under the diverse conditions of a smart city of a billion devices. To facilitate this, each node has the ability to switch radio communications opportunistically and adaptively, and this is based on the application requirements and dynamic radio parameters

Frequency-Domain Transient Analysis of Multitime Partial Differential Equation Systems

Abstract-Multitime partial differential equations (MPDEs) provide an efficient method to simulate circuits with widely separated rates of inputs. This paper proposes a fast and accurate frequency-domain multitime transient analysis method for MPDE systems, which fills in the gap for the lack of general frequency-domain solver for MPDE systems. A blockpulse function-based multidimensional inverse Laplace transform strategy is adopted. The method can be applied to discrete input systems. Numerical examples then confirm its superior accuracy, under similar efficiency, over time-domain solvers

Co-creating the cities of the future

In recent years, the evolution of urban environments, jointly with the progress of the Information and Communication sector, have enabled the rapid adoption of new solutions that contribute to the growth in popularity of Smart Cities. Currently, the majority of the world population lives in cities encouraging different stakeholders within these innovative ecosystems to seek new solutions guaranteeing the sustainability and efficiency of such complex environments. In this work, it is discussed how the experimentation with IoT technologies and other data sources form the cities can be utilized to co-create in the OrganiCity project, where key actors like citizens, researchers and other stakeholders shape smart city services and applications in a collaborative fashion. Furthermore, a novel architecture is proposed that enables this organic growth of the future cities, facilitating the experimentation that tailors the adoption of new technologies and services for a better quality of life, as well as agile and dynamic mechanisms for managing cities. In this work, the different components and enablers of the OrganiCity platform are presented and discussed in detail and include, among others, a portal to manage the experiment life cycle, an Urban Data Observatory to explore data assets, and an annotations component to indicate quality of data, with a particular focus on the city-scale opportunistic data collection service operating as an alternative to traditional communications.This work has been partially funded by the research project OrganiCity, under the grant agreement No. 645198 of the European Union’s Horizon 2020 research and innovation program

Quality-aware incentivisation for mobile crowd services

With the proliferation of mobile devices, mobile crowd participants have become one of the important providers for data and services. For example, mobile crowd sensing systems are designed to perform various tasks from environmental monitoring to traffic management by using the data collected through consumer mobile devices. In addition, the recent development of device to device (D2D) communications enables another kind of mobile crowd systems that offer data and computation offloading services in mobile peer to peer (P2P) networks. To provide mobile crowd services (MCS), mobile crowd participants may have to contribute their personal mobile resources, such as data, communication and computation resources. This could result in excessive resource consumption on their mobile devices and cause potential privacy issues. As a result, incentive mechanisms must be carefully designed into MCS systems in order to encourage the participation of mobile users despite these negative factors. Besides, quality of service (QoS) is also an essential driver to the practical and sustainable deployment and operation of MCS and building QoS awareness into the incentive mechanisms ensures that the limited monetary resources could be used to elicit the best QoS from mobile crowd participants. Therefore, in this thesis, we approach the incentivisation problems for MCS from a quality-aware perspective. We focus on two typical MCS systems - mobile crowd sensing services (MCSS) and mobile P2P crowd services (MPCS). Although heterogeneous services are offered through these MCS systems, their incentive mechanisms can be built systematically since they all pose similar challenges in terms of quality model design, economic activity modelling, and system deployment. For MCSS, the focus of this thesis is on data quality issues and we propose incentive mechanisms that incorporate data quality models into cooperative game theoretical frameworks. As for MPCS, communication and execution quality are modeled and built into specially structured data trading and offloading incentive mechanisms. This thesis emphasises both theoretical and practical aspects of MCS systems. From the theoretical point of view, the incentive mechanisms proposed in this thesis are designed based on various game theoretical and economic models. From the practical point of view, system prototypes are deployed to evaluate the performance of MCS and corresponding incentive mechanisms based on both QoS and economic impact.Open Acces

Effective truth discovery and fair reward distribution for mobile crowdsensing

By leveraging the sensing capabilities of consumer mobile devices, mobile crowdsensing (MCS) systems enable a number of new applications for Internet of Things (IoT), such as traffic management, environmental monitoring, and localisation. However, the sensing data collected from the crowd workers are of various qualities, making it difficult to discover the ground truth and maintain the fairness of incentivisation schemes. In this paper, we propose a truth discovery algorithm based on a two-stage Maximum Likelihood Estimator (MLE), which explicitly characterises the heterogeneous sensing capabilities of the crowd and is able to estimate ground truth accurately using only a small amount of data from IoT infrastructures. Moreover, based on the truth discovery algorithm, two reward distribution schemes, LRDS and MRDS, are proposed to ensure fairness of rewarding the crowd according to their effort levels. We evaluate the estimation accuracy of the truth discovery algorithm and the fairness of the reward distribution schemes using both simulations and real-world MCS campaigns. The evaluation results indicate that the proposed methods achieve superior performance compared with state-of-the-art methods in terms of estimation accuracy and fairness of reward distribution. © 2018 Elsevier B.V

Human as a service : towards resilient parking search system with sensorless sensing

The high demand for ubiquitous availability of reliable parking spaces in cities faces challenges on timely information sharing and low-cost infrastructure deployment. In this paper, we propose a mobile crowdsensing system, namely ParkHop, to aggregate on-street and roadside parking space information through sensorless sensing, and disseminate this information to urban drivers in a resilient manner. ParkHop targets special social groups that have stable work routines to serve as crowd workers. We propose a crowdsensing algorithm employing a joint estimator to process crowdsensed data, and evaluate the reliability of crowd workers based on the veracity of their answers to a series of control questions. In addition, the specific worker selection method to speed up the crowdsensing process and incentive scheme to achieve fair reward distribution have been carefully designed in ParkHop. Our system disseminates the availability of parking spaces and their up-to-date price information to drivers with on-demand needs via a publish-subscribe messaging pattern. The efficacy of ParkHop for aggregation and dissemination of parking space information has been evaluated in both real-world tests and simulations. Our results show the system is robust and agile enough to cope with different crowdsensing scenarios

Acidic and Alkaline Conditions Affect the Growth of Tree Peony Plants via Altering Photosynthetic Characteristics, Limiting Nutrient Assimilation, and Impairing ROS Balance

Exposure to acidic and alkaline conditions were found to cause the excess accumulation of reactive oxygen species in tree peony, thereby causing damage and inhibiting plant growth and development. The activities of antioxidant enzymes were also found to be significantly up-regulated, especially under alkaline conditions; this explained why tree peony is better adapted to alkaline than to acidic conditions. Through pairwise comparisons, 144 differentially expressed genes (DEGs) associated with plant growth, photosynthesis, and stress were identified. The DEGs related to stress were up-regulated, whereas the remaining DEGs were almost all down-regulated after acid and alkaline treatments. The nutrient assimilation was greatly inhibited. Chlorophyll synthesis genes were suppressed, and chlorophyll content was reduced. The development and structures of stomata and chloroplasts and the transcription of related genes were also influenced. Among photosynthesis-related DEGs, electron transport chains were the most sensitive. The suppressed expression of photosynthesis genes and the reduced light-harvesting capacity, together with the impairment of chloroplasts and stomata, finally led to a sharp decrease in the net photosynthetic rate. Carbohydrate accumulation and plant biomass were also reduced. The present study provides a theoretical basis for the response mechanisms of tree peony to adverse pH conditions and enriches knowledge of plant adaptation to alkaline conditions

A multifunctional hierarchical porous SiO2/GO membrane for high efficiency oil/water separation and dye removal

2020 Elsevier Ltd Removing contaminants from wastewater is critical to secure the global water supply. Membrane technologies for water purification are exceptionally attractive due to their high efficiency and low energy consumption. The traditional porous polymer films, however, are easy to be fouled by the organic pollutants, causing pore blockage and deteriorated separation performance. We herein report the rational design of a porous SiO2/GO hybrid membrane by coupling graphene oxide (GO) nanosheets with SiO2 nanoparticles and using ethylenediamine to crosslink them, for efficient oil/water separation and dye removal. The SiO2 nanoparticles provide an excellent hydrophilicity and underwater superoleophobicity interface, resulting in efficient and antifouling oil/water separation with an outstanding rejection rate over 99.4% for different types of oil; and the hierarchical scaffold, formed from the hydrophilic GO nanosheets embedded with SiO2 nanoparticles, greatly facilitates the rapid permeation of water with a high flux rate of up to 2387 L m−2 h−1 for pure water and 470 L m−2 h−1 for oil/water separation. Moreover, the abundant functional groups on the GO surface also render this membrane with a high removal capability for dye blocking, enabling it to remove soluble pollutants in molecular dimensions as well. This design strategy not only provides an outstanding membrane for water purification but also sheds light on the design of multi-purpose functional membranes for a variety of energy and environment-related applications