On-demand Transmission Interval Control Method for Spatio-Temporal Data Retention

With the development and the spread of Internet of Things (IoT) technologies, various types of data are generated for IoT applications anywhere and anytime. We defined such data that depends heavily on generation time and location as Spatio-Temporal Data (STD). In the previous works, we have proposed the data retention system using vehicular networks to achieve the paradigm of “local production and consumption of STD.” The system can provide STDs quickly for users within a specific location by retaining the STD within the area. However, the system does not consider that each STD has different requirements for the data retention. In particular, the lifetime of the STD and the diffusion time to the whole area directly influence to the performance of data retention. Therefore, we propose a dynamic control of data transmission interval for the data retention system by considering the requirements. Through the simulation evaluation, we found that our proposed method can satisfy the requirements of STD and maintain a high coverage rate in the area.11th International Conference on Intelligent Networking and Collaborative Systems(INCoS 2019), September 5-7, 2019, Oita, Japa

Beacon-less Autonomous Transmission Control Method for Spatio-Temporal Data Retention

With the development and spread of Internet of Things (IoT) technology, the number of devices connected to the Internet is increasing, and various kinds of data are now being generated from IoT devices. Some data generated from IoT devices depends on geographical location and time. We refer to such data as spatio-temporal data (STD). Since the “local production and consumption” of STD is effective for location-dependent applications, we have proposed a STD retention system using vehicles equipped with storage modules, computing resources, and short-range wireless communication equipment. In this previous system, each vehicle controls the data transmission probability based on the neighboring vehicle density in order to achieve effective data retention. However, since the overhead of beacon messages required for estimation of the neighboring vehicle density becomes a critical problem with the increase in the number of vehicles, thereby preventing the effective data retention. In this paper, we propose a new data transmission control method to realize effective and reliable STD retention without beacon. Simulation results showed that our proposed scheme can achieve effective data retention.12th International Workshop on Information Network Design (WIND-2020), in conjunction with 12th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2020), August 31st - September 2nd, 2020, University of Victoria, Canada（新型コロナ感染拡大に伴い、現地開催中止

Efficient Data Diffusion and Elimination Control Method for Spatio-Temporal Data Retention System

With the development and spread of Internet of Things technologies, various types of data for IoT applications can be generated anywhere and at any time. Among such data, there are data that depend heavily on generation time and location. We define these data as spatio-temporal data (STD). In previous studies, we proposed a STD retention system using vehicular networks to achieve the “Local production and consumption of STD” paradigm. The system can quickly provide STD for users within a specific location by retaining the STD within the area. However, this system does not take into account that each type of STD has different requirements for STD retention. In particular, the lifetime of STD and the diffusion time to the entire area directly influence the performance of STD retention. Therefore, we propose an efficient diffusion and elimination control method for retention based on the requirements of STD. The results of simulation evaluation demonstrated that the proposed method can satisfy the requirements of STD, while maintaining a high coverage rate in the area

A multivariate framework to study spatio-temporal dependency of electricity load and wind power

With massive wind power integration, the spatial distribution of electricity load centers and wind power plants make it plausible to study the inter-spatial dependence and temporal correlation for the effective working of the power system. In this paper, a novel multivariate framework is developed to study the spatio-temporal dependency using vine copula. Hourly resolution of load and wind power data obtained from a US regional transmission operator spanning 3 years and spatially distributed in 19 load and two wind power zones are considered in this study. Data collection, in terms of dimension, tends to increase in future, and to tackle this high-dimensional data, a reproducible sampling algorithm using vine copula is developed. The sampling algorithm employs k-means clustering along with singular value decomposition technique to ease the computational burden. Selection of appropriate clustering technique and copula family is realized by the goodness of clustering and goodness of fit tests. The paper concludes with a discussion on the importance of spatio-temporal modeling of load and wind power and the advantage of the proposed multivariate sampling algorithm using vine copula

Geolocation-centric Information Platform for Resilient Spatio-temporal Content Management

In IoT era, the growth of data variety is driven by cross-domain data fusion. In this paper, we advocate that “local production for local consumption (LPLC) paradigm” can be an innovative approach in cross-domain data fusion, and propose a new framework, geolocation-centric information platform (GCIP) that can produce and deliver diverse spatio-temporal content (STC). In the GCIP, (1) infrastructure-based geographic hierarchy edge network and (2) adhoc-based STC retention system are interplayed to provide both of geolocation-awareness and resiliency. Then, we discussed the concepts and the technical challenges of the GCIP. Finally, we implemented a proof-of-concepts of GCIP and demonstrated its efficacy through practical experiments on campus IPv6 network and simulation experiments

Multi-scale controls on spatial patterns of soil water storage in the hummocky regions of North America

The intensification of land-water management due to agriculture, forestry, and urbanization is a global phenomenon increasing the pressure on world’s water resources and threatening water security in North America. The Prairie Pothole Region of North America covers approximately 775,000 km2 and contains millions of wetlands that serve important hydrological and ecological functions. The unique hummocky topography and the variable effect of different processes contribute to high spatio-temporal variability in soil water, posing major challenges in hydrological studies. The objectives of this study were to a) examine the spatial pattern of soil water storage and its scale and location characteristics; and b) to identify its controls at multiple scales. Soil water content at 20 cm intervals down to 140 cm was measured along a transect extending over several knoll–depression cycles in a hummocky landscape. High water storage in depressions and low water storage on the knolls created a spatial pattern that was inversely related to elevation. Spatial patterns were strongly similar within any given season (intra-season rank correlation coefficient as high as 0.99), moreso than between the same season over different years (inter-annual rank correlation coefficient as high as 0.97). Less similar spatial patterns were observed between different seasons (inter-season rank correlation coefficients as high as 0.90). While the intra-season and inter-annual spatial patterns were similar at scales >18 m, the inter-season spatial patterns were similar at much large scales (>72 m). This may be due to the variations in landform elements and micro-topography. The similarity at scales >72 m were present at any time and depth. However, small- and medium-scale spatial patterns changed with depth and with season due to a change in the hydrological processes. The relative dominance of a given set of processes operating both within a season and for the same season over different years yielded strong intra-season and inter-annual similarity at scales >18 m. Moreover, similarity was stronger with increasing depth, and was thought to be due to the dampening effect of overlying soil layers that are more dynamic. Similarity of spatial patterns over time helps to identify the location that best represents the field averaged soil water and improves sampling efficiency. Change in the similarity of scales of spatial pattern helps identify the change in sampling domain as controlled by hydrological processes. The scale information can be used to improve prediction for use in environmental management and modeling of different surface and subsurface hydrological processes. The similarity of spatial pattern between the surface and subsurface layers help make inferences on deep layer hydrological processes as well as groundwater dynamics from surface water measurements