Autonomous Data Transmission Control Based on Node Density for Multiple Spatio-temporal Data Retention

Although countless services can now be accessed via the Internet, some specific services such as local traffic conditions and limited-time sale advertisements are strongly dependent on geographical locations and times. Information of this type, which is commonly referred to as spatio-temporal data (STD), is not readily available online. Since the paradigm of local production and consumption of STDs can be effective for location-dependent applications, we previously proposed an STD data retention system that uses vehicular networks to create a mobile cloud. Unfortunately, effective data retention is difficult when multiple STDs exist in the same area because channel interference will result from the increased number of data transmissions. To resolve this issue, we herein propose an autonomous data transmission control method based on node density for multiple STD retention that facilitates a highly reliable coverage rate while limiting the individual data transmissions for each STD. Then, through simulations, we show that our proposed method is effective for simultaneously retaining multiple STDs.7th IEEE International Conference on Cloud Networking (CloudNet 2018), 22-24 October, 2018, Tokyo, Japa

Adaptive Data Transmission Control for Spatio-temporal Data Retention over Crowds of Vehicles

Some specific services for Internet of Things, such as real-time map and providing local weather information, depend strongly on geographical time and location. We refer to the data for such service as spatio-temporal data (STD). When STD is used in a query response system similar to conventional Internet services, users not only need to acquire data actively as required, they must also have functions for retrieving data available STD. Therefore, we propose an STD retention system that uses vehicles as information hubs (InfoHubs) for disseminating and retaining the data in a specific area. In our system, InfoHubs diffuse, maintain, and advertise STD over places and times where the STD are strongly dependent, thereby allowing users to receive such data passively within the specific area. Additionally, because STD are associated with a particular space, the system can reduce search costs. We also propose an adaptive transmission control method that each vehicle effectively operates its wireless resources autonomously and STD are retained and distributed efficiently. Finally, we evaluated our proposed method using simulations and clarified that our proposed system is capable of achieving a coverage rate of nearly 100% for STD while reducing the number of data transmissions compared to existing systems

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Data Completeness-aware Transmission Control for Large Spatio-Temporal Data Retention

With the development of IoT technology, various kinds of data are generated by IoT devices. Some of this data contains information 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 an STD retention system that utilizes vehicles as data sources. STD sources ranges from simple sensory data to large video data. However, since previous methods assumed to retain an STD consisting of only one packet, retention of large STD sources may suffer from frequent packet collisions due to the increase in the number of packet transmissions. In this paper, we propose a data completeness-aware transmission control mechanism for large STD retention. Our simulation results showed that the proposed scheme reduces channel collision by suppressing the forwarding of incomplete data, and achieved a nearly 100% coverage rate.IEEE 40th International Conference on Consumer Electronics (ICCE 2022), 7-9 January 2022, Virtual Online Conferenc

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

A Review of the Enviro-Net Project

Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems and the development of new techniques to analyze sensor data. The Enviro-Net Project encompasses several of such sensor system deployments across five countries in the Americas. These deployments use a few different ground-based sensor systems, installed at different heights monitoring the conditions in tropical dry forests over long periods of time. This paper presents our experience in deploying and maintaining these systems, retrieving and pre-processing the data, and describes the Web portal developed to help with data management, visualization and analysis.Comment: v2: 29 pages, 5 figures, reflects changes addressing reviewers' comments v1: 38 pages, 8 figure

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

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community