Distance Estimation by Sequential Rearrangement of Signal Strength in Wireless Sensor Networks

2011 ◽  
Vol E94-B (9) ◽  
pp. 2634-2637
Author(s):  
Seung-Hwan JIN ◽  
Jae-Kark CHOI ◽  
Nan HAO ◽  
Sang-Jo YOO
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Distance Estimation ◽  
Signal Strength ◽  
Wireless Sensor

scholarly journals Novel Distance Estimation based Localization Scheme for Wireless Sensor Networks using Modified Swarm Intelligence Algorithm

2021 ◽  
Vol 2 (4) ◽  
pp. 171-176
Author(s):  
Pasumpon Pandian A
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Radio Signal ◽  
Distance Estimation ◽  
Signal Strength ◽  
Wireless Sensor ◽  
Localization Scheme ◽  
Localization Algorithms ◽  
Range Free

Wireless sensor networks (WSN) consists of a huge number of nodes that are positioned randomly to obtain information regarding the environment and communicate with each other. On detection of an event, obtaining information regarding the geographical location of the sensor is beneficial in most applications. Range-free and range-based localization schemes are the major categories of localization algorithms available. Range-free localization algorithms utilize the connectivity information to provide a cost efficient localization solution. On the other hand, range-based localization schemes use radio signal strength and distance from anchor nodes for estimating the unknown node location. Several swarm intelligence algorithms are used for reducing the noise while optimizing localization and distance estimation while using these schemes. In this paper, we propose an enhanced swarm intelligence scheme that provides better performance when compared to the existing algorithms in terms of noise level, signal strength, number of anchors, number of nodes, radio signal strength and localization error. Surrogate based optimization (SBO), firefly algorithm (FA), butterfly optimization algorithm (BOA), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are compared with the proposed scheme.

Testing the feasibility of wireless sensor networks and the use of radio signal strength indicator to track the movements of wild animals

2018 ◽  
Vol 45 (8) ◽  
pp. 659 ◽  
Author(s):  
C. R. Krull ◽  
L. F. McMillan ◽  
R. M. Fewster ◽  
R. van der Ree ◽  
R. Pech ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Signal Strength ◽  
The Body ◽  
Data Uncertainty ◽  
Wireless Sensor ◽  
Small Animals ◽  
Significant Difference

Context Wireless sensor networks (WSNs) are revolutionising areas of animal behaviour research and are advantageous based on their ability to be deployed remotely and unobtrusively, for long time periods in inaccessible areas. Aims We aimed to determine the feasibility of using a WSN to track detailed movement paths of small animals, e.g. rats (Rattus spp.) 100–400g, too small for current GPS technology, by calibrating active Radio Frequency Identification (RFID) tags and loggers using Radio Frequency Signal Strength Indicator (RSSI) as a proxy for distance. Active RFIDs are also called Wireless Identification (WID) tags. Methods Calibration tests were conducted using a grid of loggers (n=16) spaced at 45-m intervals in clear line-of-sight conditions. WID tags (n=16) were placed between the loggers at 45-m intervals. Eight ‘walks’ were also conducted through the grid using a single WID tag. This involved attaching the tag to a small bottle of water (to simulate the body of an animal), towed around the grid using a 1-m long tow line attached to a volunteer walker. The volunteer also held a GPS device that logged their track. Models were constructed to test the effects of distance, tag movement and individual differences in loggers and tags on the reliability of movement data. Key results Loggers were most successful at detecting tags at distances <50m. However, there was a significant difference in the detection probabilities of individual loggers and also the transmission performance of individual tags. Static tags were less likely to be detected than the mobile tag; and although RSSI was somewhat related to distance, the reliability of this parameter was highly variable. Implications We recommend caution in the future use of current radio frequency ID tags in wireless sensor networks to track the movement of small animals, and in the use of RSSI as an indicator of individual distance values, as extensive in situ calibration is required. ‘Off the shelf’ devices may vary in performance, rendering data unreliable. We emphasise the importance of calibrating all equipment in animal tracking studies to reduce data uncertainty and error.

scholarly journals Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4179 ◽  
Author(s):  
Stelian Dolha ◽  
Paul Negirla ◽  
Florin Alexa ◽  
Ioan Silea
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal Strength ◽  
Data Access ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Wireless Sensor ◽  
Node Localization ◽  
Received Signal Strength Indicator ◽  
Starting Point

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

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