PulseSS: A Pulse-Coupled Synchronization and Scheduling Protocol for Clustered Wireless Sensor Networks
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The Arctic region is a complex and dynamic system, where changes in temperature and partitioning between water in solid, liquid and vapor phase are fundamentally reshaping hydro-biogeochemical fluxes from the bedrock to the atmosphere. The increasing temperature and frequency of extreme heat, precipitation and fire events (Wang et al., 2017; Meredith et al., 2019) have critical consequences for the Arctic ecosystems and people, as well as for the global climate system. Although many observational and remote sensing activities have been on-going, detailed ecosystem processes – particularly related to controls on and impacts of permafrost thaw – are difficult to detect or measure. To improve the understanding and prediction of these extreme events and their consequences, we need a revolution in the way we collect ground-based observations from hillslope to pan-Arctic scales and couple them with satellite imagery and models to deliver rich, actionable, and scale-appropriate data.
Collaborative strategies for mobile sensor nodes ensure the efficiency and the robustness of data processing, while limiting the required communication bandwidth. In order to solve the problem of pipeline inspection and oil leakage monitoring, a collaborative weighted mobile sensing scheme is proposed. By adopting a weighted mobile sensing scheme, the adaptive collaborative clustering protocol can realize an even distribution of energy load among the mobile sensor nodes in each round, and make the best use of battery energy. A detailed theoretical analysis and experimental results revealed that the proposed protocol is an energy efficient collaborative strategy such that the sensor nodes can communicate with a fusion center and produce high power gain.
This viewgraph presentation provides information on hardware and software configurations for a network architecture for sensors. The hardware configuration uses a central station and remote stations. The software configuration uses the 'lost station' software algorithm. The presentation profiles a couple current examples of this network architecture in use.
Sensor nodes composed of three basic components... radio module: COTS radio module implementing standardized WSN protocol; treated as WSN modem by main board main board: contains application processor (TI MSP430 microcontroller), memory, power supply; responsible for sensor data acquisition, pre-processing, and task scheduling; re-used in every application with growing library of embedded C code sensor card: contains application-specific sensors, data conditioning hardware, and any advanced hardware not built into main board (DSPs, faster A/D, etc.); requires (re-) development for each application.
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Idaho National Laboratory (INL), Next Generation Wireless Test Bed (NGWTB) program, is part of the Critical Infrastructure Protection (CIP) initiative. Our nation’s reliance on wireless and Internet technology is rapidly evolving as more corporations and government agencies, employees, and handheld devices include mobile connectivity for both voice and data. The competitive advantage offered by a mobile workforce is leading many corporations to make wireless integration and convergence a top business priority. Similarly, military systems worldwide are rapidly embracing next-generation commercial technologies to accelerate network-centric capabilities and provide enhanced situational awareness. While handheld devices such as laptops, BlackBerrys, and cell phones once had limited mobility and range, they are rapidly incorporating new and multiple protocols such as Bluetooth, Wi-Fi, Satellite, and broadband cellular (3G/4G) on a single platform to increase their effectiveness and agility. Worldwide there is exponential growth in public Wi-Fi and/or Satellite connectivity access nodes, and 3G/4G cellular continues to evolve and expand the range and speed of these devices. Additionally, critical infrastructure networks, previously isolated or connected with dedicated wireline circuits, are incorporating Zigbee for wireless sensor networks, wireless LANs for maintenance functions, and cellular or Internet based backhaul to manage control centers. Yet with all the build-up surrounding wireless technology, few understand the complexities surrounding wireless protocols and security, the risks of converged network infrastructures, need for interoperability of communication systems, or mitigation measures to safely use and improve new technologies in both commercial and military environments. With no single vendor source for all the telecommunications equipment needed for wireless infrastructure upgrades, and no one entity providing end-to-end testing or independent validation, the INL's NGWTB program fulfills a niche for facilitating integrated testing of multiple technologies, across multiple wireless domains, of next generation wireless telecommunications systems. NGWTB engineers and technicians have constructed and operate domain infrastructure in the Wireless Personal Area Network (WPAN), Wireless Local Area Network (WLAN), Wireless Metropolitan Area Networks (WMAN), and Wireless Wide Area Networks (WWAN). Advantages that make the INL's NGWTB the place to conduct project and programmatic testing include: • NTIA Experimental Station authority • Geographically isolated and low-noise radio frequency (RF) test areas located on a 890 sm DOE reservation • Access to isolated high-speed LAN and FOT networks The rollout of next-generation networks will involve deploying new technologies at every level including new: handsets, frequencies, antenna arrays, cell configurations that are optimized for both data and voice, massive upgrade of cell controllers’ “land-line” phone links from low bandwidth connections to higher capacity fiber or wireless backhaul, and integration of voice switching and Internet/data network protocols (Voice-Over-IP, Web phones). These systems are expected to greatly transform our nation's communications infrastructure and the types of services it provides.
Research in wireless sensor network technology has exploded in the last decade. Promises of complex and ubiquitous control of the physical environment by these networks open avenues for new kinds of science and business. Due to the small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors working in concert. Although the reduction in size has been phenomenal it results in severe limitations on the computing, communicating, and power capabilities of these devices. Under these constraints, research efforts have concentrated on developing techniques for performing relatively simple tasks with minimal energy expense assuming some form of centralized control. Unfortunately, centralized control does not scale to massive size networks and execution of simple tasks in sparsely populated networks will not lead to the sophisticated applications predicted. These must be enabled by new techniques dependent on local and autonomous cooperation between sensors to effect global functions. As a step in that direction, in this work we detail a technique whereby a large population of sensors can attain a global goal using only local information and by making only local decisions without any form of centralized control.
Devices, systems, and techniques for data collecting network such as wireless sensors are disclosed. A described technique includes detecting one or more remote nodes included in the wireless sensor network using a local power level that controls a radio range of the local node. The technique includes transmitting a local outdegree. The local outdegree can be based on a quantity of the one or more remote nodes. The technique includes receiving one or more remote outdegrees from the one or more remote nodes. The technique includes determining a local node type of the local node based on detecting a node type of the one or more remote nodes, using the one or more remote outdegrees, and using the local outdegree. The technique includes adjusting characteristics, including an energy usage characteristic and a data compression characteristic, of the wireless sensor network by selectively modifying the local power level and selectively changing the local node type.
Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R 2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m 3 /m 3 ). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.
This paper describes the design and deployment of a functional Wireless Underground Sensor Network (WUSN) on a university campus. This is an unique system that is operational over a large area and over a long period of time, providing real time monitoring of the environment with the data being curated and displayed on an open website. The system details, including the architecture, node design, user interface, and deployment are provided. Initial measurement results are analyzed to show the functionality and performance of the system.
Wireless sensors and sensor networks is an emerging technology area with many applications within the aerospace industry. Integrated vehicle health monitoring (IVHM) of aerospace vehicles is needed to ensure the safety of the crew and the vehicle, yet often high costs, weight, size and other constraints prevent the incorporation of instrumentation onto spacecraft. This paper presents a few of the areas such as IVHM, where new wireless sensing technology is needed on both existing vehicles as well as future spacecraft. From ground tests to inflatable structures to the International Space Station, many applications could receive benefits from small, low power, wireless sensors. This paper also highlights some of the challenges that need to overcome when implementing wireless sensor networks for aerospace vehicles.
This dissertation discusses several machine learning techniques to improve routing in delay tolerant networks (DTNs). These are networks in which there may be long one-way trip times, asymmetric links, high error rates, and deterministic as well as non-deterministic loss of contact between network nodes, such as interplanetary satellite networks, mobile ad hoc networks and wireless sensor networks. This work uses historical network statistics to train a multi-label classifier to predict reliable paths through the network. In addition, a clustering technique is used to predict future mobile node locations. Both of these techniques are used to reduce the consumption of resources such as network bandwidth, memory and data storage that is required by replication routing methods often used in opportunistic DTN environments. Thesis contributions include: an emulation tool chain developed to create a DTN test bed for machine learning, the network and software architecture for a machine learning based routing method, the development and implementation of classification and clustering techniques and performance evaluation in terms of machine learning and routing metrics.