Localization Algorithm Based on Difference Estimation for Wireless Sensor Networks

Su, Fugen; Ren, Weizheng; Jin, Hongli

doi:10.1109/iccsn.2009.129

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…There is a considerable error in the methods which use the MMSE method in the localization progress such as [14], [15]. These methods, consider lines between the intersection points of the circles and the intersection point of these lines gives the estimated point for unknown node.…”

Section: F Error Sourcementioning

confidence: 99%

“…In this case we can see that the localization error of the proposed algorithm is zero while the localization error of the Adaptive Weighted Centroid algorithm is about 20% of the average radio range of the nodes. According to [9], [10] and [15] the maximum fluctuation on the received powers and distances are usually less than ±7dB and 50% of the average radio range respectively. In our simulation these amplitudes of fluctuations happen when RPLECR is equal to 0.13.…”

Section: Simulationsmentioning

confidence: 99%

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An adaptive environmental modeling localization method in Wireless Sensor Networks

Solhjoo

Tinati

2012

20th Iranian Conference on Electrical Engineering (ICEE2012)

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This paper presents a new localization algorithm in Wireless Sensor Networks. The RSSI (Received Signal Strength Indicator) is a localization method which provides an inexpensive and accurate solution for localization. However the time and location based fluctuations on the RSSI values and improper models cause a considerable localization error in the real applications. Most of the RSSI models suppose the radio range to be circular. In the proposed method the algorithm tries to find a relation between RSSI and distance for each anchor node in each direction adaptively and produce a noncircular model. The simulation results show that this method can reduce amount of the localization error considerably. Also unlike to the range free methods the proposed method can work properly in the low node densities.

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Section: F Error Sourcementioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

An adaptive environmental modeling localization method in Wireless Sensor Networks

Solhjoo

Tinati

2012

20th Iranian Conference on Electrical Engineering (ICEE2012)

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“…Sensor data without location information is not useful especially in large-scale applications with hundreds to thousands of nodes. SDN in WSNs provides the possibility of obtaining location information with a good level of accuracy by implementing proposed localization algorithms [ 94 , 95 , 96 , 97 , 98 , 99 ]. The localization algorithm to be implemented and managed can either be centralized or distributed depending on the mapping information and level of accuracy provided by the algorithm.…”

Section: Management Of Wsns Based On Sdnmentioning

confidence: 99%

Software Defined Networking for Improved Wireless Sensor Network Management: A Survey

Ndiaye

Hancke

Abu‐Mahfouz³

2017

Sensors

183

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Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the Internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment of thousands or maybe hundreds of thousands of sensor nodes/actuators. To ensure proper working order and network efficiency of such a network of sensor nodes, an effective WSN management system has to be integrated. However, the inherent challenges of WSNs such as sensor/actuator heterogeneity, application dependency and resource constraints have led to challenges in implementing effective traditional WSN management. This difficulty in management increases as the WSN becomes larger. Software Defined Networking (SDN) provides a promising solution in flexible management WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based management in WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. This paper highlights some of the recent work on traditional WSN management in brief and reviews SDN-based management techniques for WSNs in greater detail while drawing attention to the advantages that SDN brings to traditional WSN management. This paper also investigates open research challenges in coming up with mechanisms for flexible and easier SDN-based WSN configuration and management.

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“…Other authors propose new range based algorithms, like Su [38] that uses a likelihood calculation for determining the distance.…”

Section: Range-based Techniquesmentioning

confidence: 99%

LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN

et al. 2012

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Localization Algorithm Based on Difference Estimation for Wireless Sensor Networks

Cited by 9 publications

References 7 publications

An adaptive environmental modeling localization method in Wireless Sensor Networks

An adaptive environmental modeling localization method in Wireless Sensor Networks

Software Defined Networking for Improved Wireless Sensor Network Management: A Survey

LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN

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