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5.8 GHz wireless localization based weighted algorithm for home network applications
(2020-07-01)
Mankong, Thanadon
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Maw, Myo Myint
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Promwong, Sathaporn
New wireless network solution, the distributed network nodes short range area are unknown. This paper, consider the accuracy with weighted algorithm (WA) based on wireless network received signal strength is evaluated for localization. The wireless transmission model using wireless ISM band with four reference nodes and one target node operated at frequency of 5.8 GHz. Distance error is used to evaluate the precision of location approximation with respected to the CDF. The received signal strength in measurement field is presented in this paper. The contributes of this paper is useful for a study in an indoor positioning with 5.8 GHz for future home network applications.
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Analysis of multi-hop wireless sensor networks using probability propagation models
(2019-01-01)
Jaksukam, Komgrit
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Tongloy, Teerawat
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Chuwongin, Santad
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Boonsang, Siridech
This paper presents a formula for estimating the probability of collecting a given amount of data from a propagation model and multi-hop wireless sensor networks (WSNs) based on Monte Carlo simulation with cluster-tree topology. The probabilistic model is based on an analytical model of the IEEE 802.15.4 MAC protocol. The probability of successful node transmission is extended to the probabilities of successful collection at the cluster P(X=k) and sink node P(X ≥ k). A numerical example has been provided for comparing the probabilities. We propose a model to calculate the probability from the ratio of the collection rate to the total number of nodes and therefore provide the likeliness of complete data collection. Finally, the results from our analysis provide an estimation of the probability of achieving successful transmission in WSNs.
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Soft-clustering technique for fingerprint-based localization
(2018-01-01)
Cherntanomwong, Panarat
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Sooraksa, Pitikhate
In this paper, the soft-clustering algorithm for the fingerprint-based localization technique is proposed. In an indoor environment, the fingerprint-based localization technique is usually employed since it can deal with signal fluctuation. Its basic principle is to find the target location by comparing its signal parameters with a previously recorded database of knownlocation- signal parameters. Here, the received signal strength indicator (RSSI) provided by the wireless sensor network (WSN) is used as the signal parameter. The high accuracy of location estimation requires a very fine spatial resolution of the database, corresponding to the time consumed for pattern matching. To reduce the calculation time, clustering can be applied because it can reduce the database size by grouping similar data in the same cluster. The accuracy of the algorithm to cluster the target location and fingerprint locations is the main concern. The result shows that the clustering technique used can successfully cluster the target sensing node into an appropriate cluster. This implies that, by using soft clustering with the fingerprint technique, the target location can be estimated faster than by using classical fingerprint techniques since the target location can be estimated within a small set of fingerprints in the cluster, not with all fingerprints in the database.
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Asymmetry regional boundary localization for indoor robot navigation
(2018-01-01)
Wanadecha, N.
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Nilas, P.
This paper presents the new methodology of indoor localization based-on Received Signal Strength Indicator (RSSI) of Wireless Sensor Network (WSN) using Asymmetry Regional Boundary Localization (ARBL) which is designed to support mobile robot navigation. since many types of obstruction such as walls, poles, and traps in the environment may confuse robot trajectory and avoidance strategy, our main objective is to analyze our desire test bed in the form of asymmetry region with the benefit of RSSI map fingerprinting technique. The regions are created from multiple anchor nodes’ boundary intersection which is predetermined by the working range that provide the finest repeatability value. Each region is classified by supervised neural network. When the robot senses the existence of obstacle, that particular region is declared as occupied zone which is not allowed to enter during path planning process. Once the entire virtual map is acquired, suitable path is obtained by Q-learning algorithm, thus the robot can travel through the field. The experiments are performed with 2 different types of test bed. Each of which shows region classification performance of 71.9% and 85.8%, respectively. Finally, to demonstrate the effective of our method, dealing with obstacles in the field, the comparison between conventional Q-learning algorithm and our proposed method is shown. The proposed method not only gives better learning progress but also spends less computational resources, reducing the complexity of the navigation problem.
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Efficient Route Update and Maintenance for Reliable Routing in Large-Scale Sensor Networks
(2017-02-01)
Pradittasnee, Lapas
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Camtepe, Seyit
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Tian, Yu Chu
Reliable data transmissions are challenging in industrial wireless sensor networks as channel conditions change over time. Rapid changes in channel conditions require accurate estimation of the routing path performance and timely update of the routing information. However, this is not well fulfilled in existing routing approaches. Addressing this problem, this paper presents combined global and local update processes for efficient route update and maintenance, and incorporates them with a hierarchical proactive routing framework. While the global process updates the routing path with a relatively long period, the local process with a shorter period checks potential routing path problems. A theoretical modeling is developed to describe the processes. Through simulations, the presented approach is shown to reduce end-to-end delay up to 30 times for large networks, while improving packet reception ratio (PRR) in comparison with hierarchical and proactive routing protocols ROL/NDC, DSDV, and DSDV with IPv6 Routing Protocol for Low-Power and Lossy Networks' Trickle algorithm. Compared with reactive routing protocols AODV and Ad Hoc On-demand Multipath Distance Vector, it provides similar PRR while reducing end-to-end delay over 15 times.
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Investigating of antenna selection for the adaptive centroid localization systems
(2015-09-01)
Yuangyai, Chumpol
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Pan, Chung Yu
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Lin, Yi Jou
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Cheng, Chen Yang
Location-based services are widely integrated in our daily lives, and they are used in various ways such as for investigating inventory goods and personal tracking in healthcare. With increasing applications of wireless localization, accuracy of location estimation requirements has become more critical. However, indoor localization suffers from multi-path interference that affects the traditional algorithmic calculation methods based on radio signal strength. The strength of radio signal depends significantly on antenna types and the deployment of wireless sensor network. Therefore, the aim of this paper is to investigate a robust deployment of wireless sensor network considering antennas and optimal signal range to increase signal strength and to reduce receiving signal missing. A bi-response design approach was taken to evaluate antenna selection, signal range, and antenna power rate. The experiment result was applied with existing algorithm to prove effectives. Further, to avoid wireless sensors collision which may result in low accuracy of receiving radio signal, adaptive weight center of gravity localization (AWCG) were proposed. AWCG is based on an assumption of the dynamic relationship between the radio signal strength and the distance in different environments at different times. In the proposed localization algorithm, the error distance was approximately one meter. It is expected to significantly improve the location estimation accuracy with the suggested deployment and proposed algorithm.
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