scholarly journals Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 307
Author(s):  
Winfred Ingabire ◽  
Hadi Larijani ◽  
Ryan M. Gibson ◽  
Ayyaz-UI-Haq Qureshi
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Urban Areas ◽  
Large Scale ◽  
Low Cost ◽  
Wireless Sensor ◽  
Node Localization ◽  
Positioning Systems ◽  
Power Characteristics ◽  
Random Neural Networks

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.

Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network

2019 ◽  
Vol 10 (1) ◽  
pp. 82-109 ◽  
Author(s):  
Mihoubi Miloud ◽  
Rahmoun Abdellatif ◽  
Pascal Lorenz
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Dimensional Space ◽  
Fitness Function ◽  
Low Cost ◽  
Bat Algorithm ◽  
Wireless Sensor ◽  
Flower Pollination Algorithm ◽  
Node Localization ◽  
Moth Flame Optimization Algorithm

Recently developments in wireless sensor networks (WSNs) have raised numerous challenges, node localization is one of these issues. The main goal in of node localization is to find accurate position of sensors with low cost. Moreover, very few works in the literature addressed this issue. Recent approaches for localization issues rely on swarm intelligence techniques for optimization in a multi-dimensional space. In this article, we propose an algorithm for node localization, namely Moth Flame Optimization Algorithm (MFOA). Nodes are located using Euclidean distance, thus set as a fitness function in the optimization algorithm. Deploying this algorithm on a large WSN with hundreds of sensors shows pretty good performance in terms of node localization. Computer simulations show that MFOA converge rapidly to an optimal node position. Moreover, compared to other swarm intelligence techniques such as Bat algorithm (BAT), particle swarm optimization (PSO), Differential Evolution (DE) and Flower Pollination Algorithm (FPA), MFOA is shown to perform much better in node localization task.

scholarly journals Evaluation of an Easy-to-Install, Low-Cost Dendrometer Band for Citizen-Science Tree Research

2019 ◽  
Vol 117 (4) ◽  
pp. 317-322
Author(s):  
Michael G Just ◽  
Steven D Frank
Keyword(s):  
Citizen Science ◽  
Urban Areas ◽  
Large Scale ◽  
Low Cost ◽  
Acer Rubrum ◽  
Cost Effective ◽  
Stem Growth ◽  
Growth Monitoring ◽  
Eastern United States ◽  
Highly Correlated

AbstractTree-stem growth is an important metric for evaluating many ecological and silvicultural research questions. However, answering these questions may require monitoring growth on many individual trees that span changing environments and geographies, which can incur significant costs. Recently, citizen science has been successfully employed as a cost-effective approach to collect data for large-scale projects that also increases scientific awareness. Still, citizen-science-led tree-growth monitoring requires the use of tools that are affordable, understandable, and accurate. Here, we compare an inexpensive, easy-to-install dendrometer band to two other bands that are more expensive with more complex installations. We installed a series of three dendrometers on 31 red maples (Acer rubrum) in two urban areas in the eastern United States. We found that the stem-growth measurements reported by these dendrometers were highly correlated and, thus, validate the utility of the inexpensive band.

Localization of Distributed Wireless Sensor Networks using Two Sage SDP Optimization

2017 ◽  
Vol 7 (3) ◽  
pp. 1255
Author(s):  
Reza Shahbazian ◽  
Seyed Ali Ghorashi
Keyword(s):  
Optimization Problem ◽  
Low Cost ◽  
Reference Points ◽  
Wireless Sensor ◽  
Relaxation Techniques ◽  
Problem Size ◽  
Distance Measurements ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Cost Efficient

<span class="fontstyle0">A wireless sensor network (WSN) may comprise a large distributed set of low cost, low power sensing nodes. In many applications, the location of sensors is a necessity to evaluate the sensed data and it is not energy and cost efficient to equip all sensors with global positioning systems such as GPS. In this paper, we focus on the localization of sensors in a WSN by solving an optimization problem. In WSN localization, some sensors (called anchors) are aware of their location. Then, the distance measurements between sensors and anchors locations are used to localize the whole sensors in the network. WSN localization is a non-convex optimization problem, however, relaxation techniques such as semi-definite programming (SDP) are used to relax the optimization. To solve the optimization problem, all constraints should be considered simultaneously and the solution complexity order is O(n2) </span><span class="fontstyle0">where </span><span class="fontstyle2">n </span><span class="fontstyle0">is the number of sensors. The complexity of SDP prevents solving large size problems. Therefore, it would be beneficial to reduce the problem size in large and distributed WSNs. In this paper, we propose a two stage optimization to reduce the solution time, while provide better accuracy compared with original SDP method. We first select some sensors that have the maximum connection with anchors and perform the SDP localization. Then, we select some of these sensors as virtual anchors. By adding the virtual anchors, we add more reference points and decrease the number of constraints. We propose an algorithm to select and add virtual anchors so that the total solution complexity and time decrease considerably, while improving the localization accuracy.</span>

scholarly journals Current research on Internet of Things (IoT) in Agriculture

2020 ◽  
Vol 9 (8) ◽  
pp. 882-886
Keyword(s):  
Power Supply ◽  
Precision Agriculture ◽  
Large Scale ◽  
Low Cost ◽  
Low Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Major Application ◽  
Sensing Time

IoT, a sensation in modern-day technology, has a major impact on the rapidly growing technological aspects. It’s making people’s life easier and also enabling us to do things that were previously seen as miracles. It helps in solving many complex real-time problems. One such major application in the field of agriculture can turn out to be productive and profitable. This paper explains a variety of techniques infusing IoT in agriculture, that helps in productive and predictive results in that field, thereby leading towards precision agriculture. A low-cost power supply and unambiguous farming can be possible with using IoT system. Wireless Sensor Networks (WSN) in which sensor nodes learn and adopt over the sensing time, gives simplicity, low energy usage. This is aimed to be deployed on a large scale by predicting using big data techniques from centralized data analysis.

scholarly journals Anchor-Free Localization Using a Deep Neural Network in Wireless Sensor Networks with Multiple Sinks

2021 ◽  
Author(s):  
Arunanshu Mahapatro ◽  
V CH Sekhar Rao Rayavarapu
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Urban Areas ◽  
Deep Neural Network ◽  
Wireless Sensor ◽  
Range Estimation ◽  
Node Localization ◽  
Free Node ◽  
Channel Classification

<div>Wireless sensor networks (WSNs) is one of the vital part of the Internet of Things (IoT) that allow to acquire and provide information from interconnected sensors. Localization-based services are among the most appealing applications associated to the IoT. The deployment of WSNs in the indoor environments and urban areas creates obstacles that lead to the Non-Line-of-Sight (NLOS) propagation. Additionally, the localization accuracy is minimized by the NLOS propagation. The main intention of this paper is to develop an anchor-free node localization approach in multi-sink WSN under NLOS conditions using three key phases such as LOS/NLOS channel classification, range estimation, and anchor-free node localization. The first phase adopts Heuristicbased Deep Neural Network (H-DNN) for LOS/NLOS channel classification. Further, the same H-DNN s used for the range estimation. The hidden neurons of DNN are optimized using the proposed Adaptive Separating Operator-based Elephant Herding Optimization (ASO-EHO) algorithm. The node localization is formulated as a multi-objective optimization problem. The objectives such as localization error, hardware cost, and energy overhead are taken into consideration. ASO-EHO is used for node localization. The suitability of the proposed anchor-free node localization model is validated by comparing over the existing models with diverse counts of nodes. </div>

scholarly journals Temperature Stability Investigations of Neural Network Models for Graphene-Based Gas Sensor Devices

2021 ◽  
Vol 10 (1) ◽  
pp. 19
Author(s):  
Yosra Bahri ◽  
Sebastian A. Schober ◽  
Cecilia Carbonelli ◽  
Robert Wille
Keyword(s):  
Neural Networks ◽  
Gas Sensors ◽  
Large Scale ◽  
Heating Temperature ◽  
Low Cost ◽  
Temperature Stability ◽  
Training Data ◽  
Neural Network Models ◽  
Temperature Deviation ◽  
Algorithm Performance

Chemiresistive gas sensors are a crucial tool for monitoring gases on a large scale. For the estimation of gas concentrations based on the signals provided by such sensors, pattern recognition tools, such as neural networks, are widely used after training them on data measured by sample sensors and reference devices. However, in the production process of low-cost sensor technologies, small variations in their physical properties can occur, which can alter the measuring conditions of the devices and make them less comparable to the sample sensors, leading to less adapted algorithms. In this work, we study the influence of such variations with a focus on changes in the operating and heating temperature of graphene-based gas sensors in particular. To this end, we trained machine learning models on synthetic data provided by a sensor simulation model. By varying the operation temperatures between −15% and +15% from the original values, we could observe a steady decline in algorithm performance, if the temperature deviation exceeds 10%. Furthermore, we were able to substantiate the effectiveness of training the neural networks with several temperature parameters by conducting a second, comparative experiment. A well-balanced training set has shown to improve the prediction accuracy metrics significantly in the scope of our measurement setup. Overall, our results provide insights into the influence of different operating temperatures on the algorithm performance and how the choice of training data can increase the robustness of the prediction algorithms.

scholarly journals A Design of Irregular Grid Map for Large-Scale Wi-Fi LAN Fingerprint Positioning Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Jae-Hoon Kim ◽  
Kyoung Sik Min ◽  
Woon-Young Yeo
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Position Estimation ◽  
Location Based Services ◽  
Positioning Systems ◽  
Irregular Grid ◽  
Positioning Method ◽  
Accurate Position ◽  
Radio Fingerprint ◽  
Received Signal Strength Indication

The rapid growth of mobile communication and the proliferation of smartphones have drawn significant attention to location-based services (LBSs). One of the most important factors in the vitalization of LBSs is the accurate position estimation of a mobile device. The Wi-Fi positioning system (WPS) is a new positioning method that measures received signal strength indication (RSSI) data from all Wi-Fi access points (APs) and stores them in a large database as a form of radio fingerprint map. Because of the millions of APs in urban areas, radio fingerprints are seriously contaminated and confused. Moreover, the algorithmic advances for positioning face computational limitation. Therefore, we present a novel irregular grid structure and data analytics for efficient fingerprint map management. The usefulness of the proposed methodology is presented using the actual radio fingerprint measurements taken throughout Seoul, Korea.

scholarly journals Empirical mapping for evaluating an LPWAN (LoRa) wireless network sensor prior to installation in a vineyard

OENO One ◽  
2021 ◽  
Vol 55 (2) ◽  
pp. 301-313
Author(s):  
Guilhem Brunel ◽  
Simon Moinard ◽  
Arnaud Ducanchez ◽  
Thomas Crestey ◽  
Léo Pichon ◽  
...  
Keyword(s):  
Wireless Network ◽  
Urban Areas ◽  
Low Cost ◽  
Wireless Sensor ◽  
Area Network ◽  
Noise Power ◽  
Signal Power ◽  
Wide Area Network ◽  
Power Maps

The main aim of this study was to use Empirical mapping to test the efficiency of local low cost wireless network sensors (LPWAN - Low-Power Wide Area Network) before being applied in real wine-growing conditions. The second aim was to obtain information on the communication distances to be expected from a LPWAN, taking into account the specific needs and real conditions of a vineyard. A hand-held autonomous end-device was specifically built to simulate short messages sent by sensors via a locally designed LPWAN. This device was used to test the quality of the network from different locations within an entire vineyard and also inside the cellar. Two parameters were used to test the quality of reception of the messages: i) The Received Signal Strength Indication (RSSI), which is the received signal power measured in decibels (dB or dBm), and ii) the Signal-to-Noise Ratio (SNR), which is the ratio of the received signal power to the ambient noise power. Maps of signal reception and errors between the observed and the theoretical signal highlighted how vineyard environment (e.g., hedges, topography, and buildings) affects the signal. The results show that the maximum communication distance differed considerably from distances published in the literature. In the open field, the signal, although attenuated by the distance, was received up to 600 meters away, or even more in favourable conditions. Meanwhile, in urban areas the signal was attenuated by buildings and the electro-magnetic environment and therefore communication distances were very short (< 50 m). Empirical mapping has great potential for determining how the local environment affects signal quality and as a decision support tool for identifying the optimal location for the sensors and gateway. With a single well-positioned gateway, such low cost wireless sensor networks (LPWAN-LoRa) could be used by small to medium-sized vineyards to collect information from sensors either outside in the fields or indoors in the vineyard cellar. This paper proposes a very cheap method (< 40 €) for testing and spatialising the quality of a low cost wireless sensor network before its implementation, and it also provides information on zones with low quality reception.

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