Distance measurement based on RSSI is a low-cost and low complexity of the distance measurement technique, and it is widely applied in the range-based localization of the Wireless Sensor Networks. Because t there is a non line of sight and multipath transmission effects as the indoor environment, the ranging error is relatively large. In the method of eliminating ranging error, using linear regression analysis of the RF signal transmission parameters A and n to optimize the constants obtains to meet specific environmental parameters. Proposed two kinds of filtering model for filtering collected RSSI. As the experimental results show, ranging error has been significantly improved through the parameter optimization and filtering processing.

Research on model of indoor distance measurement based on receiving signal strength

The traditional indoor location algorithm based on distance-loss model mostly turn received signal strength indicator RSSI into distance, and then through the location-distance algorithm to achieve positioning. These algorithms need fit the wireless signal propagation model parameters A and N through experience or large amounts of data, so they are dependent on experience and are not strong universal algorithms for location of the different environment, also low accuracy. After lots of research and analysis of radio signal propagation model and the traditional indoor location algorithm, a new indoor location algorithm uses BP neural network to fit the distance-loss model is proposed. From a number of distances between reference nodes and blind node, Taylor series expansion algorithm is used to determine the coordinates of the blind node. Finally, the experiment result shows that the new algorithm improves the positioning accuracy and universality, compared with the traditional positioning algorithms.

Research on indoor location technology based on back propagation neural network and Taylor series

After lots of research and analysis of the RSSI-d(received signal strength indicator RSSI and distanced) relationship between the reference nodes and blind nodes, A new indoor WLAN location technology using BP neural network to fit the RSSI-d curve is proposed. Firstly, establish a three-layer BP neural network, the input layer of the network is RSSI, through the hidden layer processing, the final output from the output layer is distance d between the reference node and blind node. Once get three more such distance d, according to the known coordinates of the reference nodes, Taylor series expansion algorithm is used to determine the coordinates of the blind node. Finally, the experiment result shows that the new algorithm improves the positioning accuracy and universality, compared with the traditional positioning algorithms.

A new indoor location technology using back propagation neural network to fit the RSSI-d curve

The traditional indoor wireless location algorithm based on distance-loss model mostly need fit the parameters A and n of the wireless signal propagation model through experience or large amounts of experiment data, so they do not fully reflect the real volatile environment, also result in low accuracy. After lots of research and analysis of radio signal propagation model and the traditional indoor location algorithm, a new indoor location algorithm using BP neural network to fit the distance-loss model is proposed. From a number of distances between reference nodes and blind node, a more accurate six-point centroid algorithm is used to estimate the position of the blind node instead of using the traditional three-point centroid algorithm. Finally, the experiment result shows that the new algorithm improves the positioning accuracy and universality, compared with the traditional positioning algorithms.

A new indoor location technology using back propagation neural network and improved centroid algorithm

Secara umum, pengukuran jarak berdasarkan waktu tempuh data memberikan nilai waktu tempuh yang didapat akan selalu berubah secara signifikan dan tidak dapat ditentukan perubahannya. Oleh karena itu, dalam paper ini pengukuran dilakukan berdasarkan kekuatan sinyal yang diterima. Hal ini bertujuan untuk mengetahui tingkat keakurasian pengukuran dengan menggunakan wireless. Dalam penelitian ini, pertama adalah menganalisa teori tentang pengukuran jarak menggunakan RSSI dan pengaruhnya, kemudian merancang sistem untuk pengukuran. Nilai RSSI yang didapat diproses oleh mikrokontroller ATMega328P dan kemudian dikirim ke Visual Basic pada PC untuk dianalisa ke jarak. Di dalam visual basic digunakan model nilai rata-rata untuk memproses dan mengambil data akhir RSSI dari Xbee Pro ZB. Setelah penelitian dilakukan, diperoleh kesimpulan bahwa pengukuran kesalahan rata-rata adalah 2,35 meter dengan jarak sebenarnya 5-90 meter. Katakunci : RSSI , Visual basic,  Xbee pro ZB

Perancangan Sistem Pengukur Jarak Antara 2 Titik Wireless Xbee Pro Berdasarkan Nilai RSSI

Sensor networks is an Ad-Hoc network consist of large amount of sensors. These sensors are distributed in a huge area. Each of the small, cheap, intelligent sensors has processor, memory and wireless transmission ability. These sensors collect or monitor the surroundings in real-time and process these data to obtain the detailed and accurate information from their covered area. Information from sensors must be combined with its location to make sense, so the location of sensors is very important in sensor network applications. Localization of sensors becomes one of the key techniques in sensor networks. A power-efficient localization algorithm is proposed in this paper. It uses few anchors(the sensors whose location is known) to implement the localization of other nodes without special devices such as GPS. This algorithm not only has the lower time cost and communication cost, but also needs only a few anchor nodes whose distribution is independent. Experimental results and analysis show that the localization algorithm proposed in this paper has higher accuracy, lower power cost and better expansibility. It is very suitable for large scale sensor networks.

Power-Efficient node localization algorithm in wireless sensor networks

Sensory data in Wireless Sensor Networks have many redundancies. For this problem, the paper presents a sparse data gathering framework, which adopts Compressive Sensing(CS) to reduce data redundancies, and utilizes Unmanned Aerial Vehicle (UAV) as the mobile relay to collect the compressed data. In this framework, the UAV is only required to collect data of the fewer nodes to generate the measurement matrix, and joins the covariance matrix to design the sensing matrix in CS. To reduce energy consumption, we use greedy algorithm to plan the route of the UAV. The experiments demonstrate that the proposed framework can make data be sparser and the recovery performance be better.

An Unmanned Aerial Vehicle-aided sparse data gathering in Wireless Sensor Networks

Joint power control and user scheduling for backbone-assisted industrial wireless networks with successive interference cancellation

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Power-Efficient node localization algorithm in wireless sensor networks

An Unmanned Aerial Vehicle-aided sparse data gathering in Wireless Sensor Networks

Joint power control and user scheduling for backbone-assisted industrial wireless networks with successive interference cancellation