The Deployment in the Wireless Sensor Networks: Methodologies, Recent Works and Applications

International audienceThe wireless sensor networks (WSN) is a research area in continuous evolution with a variety of application contexts. Wireless sensor networks pose many optimization problems, particularly because sensors have limited capacity in terms of energy, processing and memory. The deployment of sensor nodes is a critical phase that significantly affects the functioning and performance of the network. Often, the sensors constituting the network cannot be accurately positioned, and are scattered erratically. To compensate the randomness character of their placement, a large number of sensors is typically deployed, which also helps to increase the fault tolerance of the network. In this paper, we are interested in studying the positioning and placement of sensor nodes in a WSN. First, we introduce the problem of deployment and then we present the latest research works about the different proposed methods to solve this problem. Finally, we mention some similar issues related to the deployment and some of its interesting applications

Bodacious-instance coverage mechanism for wireless sensor network

Copyright © 2020 Shahzad Ashraf et al. Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA

Novel Approach using Robust Routing Protocol in Underwater Acoustic Wireless Sensor Network with Network Simulator 2: A Review

In recent year wireless sensor network has been an emerging technology and promising technology in unveiling the riddle of the marine life and other underwater applications. As it is a permutation of computation, sensing and communication. In the 70% of the earth a huge amount of unexploited resources lies covered by oceans. To coordinate interact and share information among themselves to carry out sensing and monitoring function underwater sensor network consists number of various sensors and autonomous underwater vehicles deployed underwater. The two most fundamental problems in underwater sensor network are sensing coverage and network connectivity. The coverage problem reflects how well a sensor network is tracked or monitored by sensors. An underwater wireless sensor networks is the emerging field that is having the challenges in each field such as the deployment of nodes, routing, floating movement of sensors etc. This paper is concerned about the underwater acoustic wireless sensor network of routing protocol applications and UW-ASNs deployments for monitoring and control of underwater domains

Decision-Making for Search and Classification using Multiple Autonomous Vehicles over Large-Scale Domains

This dissertation focuses on real-time decision-making for large-scale domain search and object classification using Multiple Autonomous Vehicles (MAV). In recent years, MAV systems have attracted considerable attention and have been widely utilized. Of particular interest is their application to search and classification under limited sensory capabilities. Since search requires sensor mobility and classification requires a sensor to stay within the vicinity of an object, search and classification are two competing tasks. Therefore, there is a need to develop real-time sensor allocation decision-making strategies to guarantee task accomplishment. These decisions are especially crucial when the domain is much larger than the field-of-view of a sensor, or when the number of objects to be found and classified is much larger than that of available sensors. In this work, the search problem is formulated as a coverage control problem, which aims at collecting enough data at every point within the domain to construct an awareness map. The object classification problem seeks to satisfactorily categorize the property of each found object of interest. The decision-making strategies include both sensor allocation decisions and vehicle motion control. The awareness-, Bayesian-, and risk-based decision-making strategies are developed in sequence. The awareness-based approach is developed under a deterministic framework, while the latter two are developed under a probabilistic framework where uncertainty in sensor measurement is taken into account. The risk-based decision-making strategy also analyzes the effect of measurement cost. It is further extended to an integrated detection and estimation problem with applications in optimal sensor management. Simulation-based studies are performed to confirm the effectiveness of the proposed algorithms

Factors that May Influence the Performance of Wireless Sensor Networks

International audienc

Autonomous & adaptive oceanographic front tracking on board autonomous underwater vehicles

Oceanic fronts, similar to atmospheric fronts, occur at the interface of two fluid (water) masses of varying characteristics. In regions such as these where there are quantifiable physical, chemical, or biological changes in the ocean environment, it is possible - with the proper instrumentation - to track, or map, the front boundary. In this paper, the front is approximated as an isotherm that is tracked autonomously and adaptively in 2D (horizontal) and 3D space by an autonomous underwater vehicle (AUV) running MOOS-IvP autonomy. The basic, 2D (constant depth) front tracking method developed in this work has three phases: detection, classification, and tracking, and results in the AUV tracing a zigzag path along and across the front. The 3D AUV front tracking method presented here results in a helical motion around a central axis that is aligned along the front in the horizontal plane, tracing a 3D path that resembles a slinky stretched out along the front. To test and evaluate these front tracking methods (implemented as autonomy behaviors), virtual experiments were conducted with simulated AUVs in a spatiotemporally dynamic MIT MSEAS ocean model environment of the Mid-Atlantic Bight region, where a distinct temperature front is present along the shelfbreak. A number of performance metrics were developed to evaluate the performance of the AUVs running these front tracking behaviors, and the results are presented herein.United States. Office of Naval Research (Awards N00014-11-1-0097 and N00014-14-1-0214

Design, Testing and Evaluation of Robotic Mechanisms and Systems for Environmental Monitoring and Interaction

Unmanned Aerial Vehicles (UAVs) have significantly lowered the cost of remote aerial data collection. The next generation of UAVs, however, will transform the way that scientists and practitioners interact with the environment. In this thesis, we address the challenges of flying low over water to collect water samples and temperature data. We also develop a system that allows UAVs to ignite prescribed fires. Specifically, this thesis contributes a new peristaltic pump designed for use on a UAV for collecting water samples from up to 3m depth and capable of pumping over 6m above the water. Next, temperature sensors and their deployment on UAVs, which have successfully created a 3D thermal structure map of a lake, contributes to mobile sensors. A sub-surface sampler, the “Waterbug” which can sample from 10m deep and vary buoyancy for longer in-situ analysis contributes to robotics and mobile sensors. Finally, we designed and built an Unmanned Aerial System for Fire Fighting (UAS-FF), which successfully ignited over 150 acres of prescribed fire during two field tests and is the first autonomous robot system for this application. Advisers: Carrick Detweiler and Carl Nelso

Environment Aware Connectivity for Wireless Underground Sensor Networks

Wireless underground sensor networks (WUSNs) consist of sensors that are buried in and communicate through soil. The channel quality of WUSNs is strongly impacted by environmental parameters such soil moisture. Thus, the communication range of the nodes and the network connectivity vary over time. To address the challenges in underground communication, above ground nodes are deployed to maintain connectivity. In this paper, the connectivity of WUSNs under varying environmental conditions is captured by modeling the cluster size distribution under sub-critical conditions and through a novel aboveground communication coverage model for underground clusters. The resulting connectivity model is utilized to analyze two communication schemes: transmit power control and environmentaware routing, which maintain connectivity while reducing energy consumption. It is shown that transmit power control can maintain network connectivity under all soil moisture values at the cost of energy consumption. Utilizing relays based on soil moisture levels can decrease this energy consumption. A composite of both approaches is also considered to analyze the tradeoff between connectivity and energy consumption

SeisCORK meeting report

SeisCORK meeting, November 15 and 16, 2004, Stress/Mohr Engineering, Houston, Texas 77041-1205The purpose of this meeting was to explore design options to simultaneously acquire borehole seismic data and hydro-geological data (pressure, temperature, fluid sampling and microbiological sampling) on a single CORK system. The scientific focus was to add a seismic component to the Juan de Fuca Hydrogeology program. By permanently installing a sensor string in the borehole our goal was to enable: l) time-lapse VSP's and offset VSP's with sufficient data quality to study amplitude versus offset, shear wave anisotropy, and lateral heterogeneity; 2) monitoring of micro- and nano- earthquake activity around the site for correlation with pressure transients. Because of the difficulty in ensuring adequate coupling through multiple casing strings we concluded that it was impractical to install the vertical seismic array with 10m spacing (50-60 nodes) that would be necessary for VSP's and time-lapse VSP's. We did describe a scenario for a vertical seismic array with approximately 100m spacing (5-6 nodes) that could be used for offset-VSP's and seismic monitoring. This uses some unique technology and involves two seismic strings: one in the annulus between the 4- 1/2" and 10-3/4" casings and one in the middle of the 4-1/2" casing.Funding was provided by the National Science Foundation under Grant No. OCE-0450318

The Ocean Observatories Initiative

Author Posting. © The Oceanography Society, 2018. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 31, no. 1 (2018): 16–35, doi:10.5670/oceanog.2018.105.The Ocean Observatories Initiative (OOI) is an integrated suite of instrumented platforms and discrete instruments that measure physical, chemical, geological, and biological properties from the seafloor to the sea surface. The OOI provides data to address large-scale scientific challenges such as coastal ocean dynamics, climate and ecosystem health, the global carbon cycle, and linkages among seafloor volcanism and life. The OOI Cyberinfrastructure currently serves over 250 terabytes of data from the arrays. These data are freely available to users worldwide, changing the way scientists and the broader community interact with the ocean, and permitting ocean research and inquiry at scales of centimeters to kilometers and seconds to decades.Funding for the OOI is provided by the National Science Foundation through a Cooperative Support Agreement with the Consortium for Ocean Leadership (OCE-1026342)