Localization in cooperative Wireless Sensor Networks: A review

Bal, Mert; Liu, Min; Shen, Weiming; Ghenniwa, Hamada

doi:10.1109/cscwd.2009.4968098

Cited by 57 publications

(37 citation statements)

References 30 publications

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“…A plethora of localization techniques have been proposed that are broadly classified in two categories: range-based and range-free [5]. Range-based localization estimates the distance or angle between two nodes to calculate their relative location by employing Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) or using RSSI to calculate the distance.…”

Section: Related Workmentioning

confidence: 99%

Embracing localization inaccuracy: A case study

Raza

Murphy

Picco

2013

2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing

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Abstract-In recent years, indoor localization has become a hot research topic with some sophisticated solutions reaching accuracy on the order of ten centimeters. While certain classes of applications can justify the corresponding costs that come with these solutions, a wealth of applications have requirements that can be met at much lower cost by accepting lower accuracy. This paper explores one specific application for monitoring patients in a nursing home, showing that sufficient accuracy can be achieved with a carefully designed deployment of low-cost wireless sensor network nodes in combination with a simple RSSI-based localization technique. Notably our solution uses a single radio sample per period, a number that is much lower than similar approaches. This greatly eases the power burden of the nodes, resulting in a significant lifetime increase. This paper evaluates a concrete deployment from summer 2012 composed of fixed anchor motes throughout one floor of a nursing home and mobile units carried by patients. We show how two localization algorithms perform and demonstrate a clear improvement by following a set of simple guidelines to tune the anchor node placement. We show both quantitatively and qualitatively that the results meet the functional and non-functional system requirements.

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Section: Related Workmentioning

confidence: 99%

Embracing localization inaccuracy: A case study

Raza

Murphy

Picco

2013

2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing

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“…Similarly, localizations are used to identify the location of intruder,i.e., enemies in battlefield, find the optimum coverage of a network and optimal routes in geographic routing [10]. Existing localization approaches use Global Positioning Systems (GPS) in sensor nodes which is expensive to equip with all nodes.…”

Section: Sensors Localizationmentioning

confidence: 99%

“…APIT requires a high density of anchor node to be effective. Coordinate [10,83] [125,75,32] to eliminate the above mentioned problems. The work done by Lu Q. et al [75] proposes NDV-hop localization algorithm, where an unknown node calculates its location based on the distance between itself and all other anchor nodes.…”

Section: Range-based and Range Free Localization Techniquesmentioning

confidence: 99%

An integrated framework for wireless sensor network management

Karim

Mahmoud

Khan

2012

Wirel. Commun. Mob. Comput.

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Wireless Sensor Networks (WSNs) have significant potential in many application domains, and are poised for growth in many markets ranging from agriculture and animal welfare to home and office automation. Although sensor network deployments have only begun to appear, the industry still awaits the maturing of this technology to realize its full benefits. The main constraints to large scale commercial adoption of sensor networks are the lack of available network management and control tools for determining the degree of data aggregation prior to transforming it into useful information, localizing the sensors accurately so that timely emergency actions can be taken at exact location, and scheduling data packets so that data are sent based on their priority and fairness. Moreover, due to the limited communication range of sensors, a large geographical area cannot be covered, which limits sensors application domain. Thus, we investigate a scalable and flexible WSN architecture that relies on multi-modal nodes equipped with IEEE 802.15.4 and IEEE 802.11 in order to use a Wi-Fi overlay as a seamless gateway to the Internet through WiMAX networks. We focus on network management approaches such as sensors localization, data scheduling, routing, and data aggregation for the WSN plane of this large scale multimodal network architecture and find that most existing approaches are not scalable, energy efficient, and fault tolerant. Thus, we introduce an efficient approach for each of localization, data scheduling, routing, and data aggregation; and compare the performance of proposed approaches with existing ones in terms of network energy consumptions, localization error, end-to-end data transmission delay and packet delivery ratio. Simulation results, theoretical and statistical analysis show that each of these approaches outperforms the existing approaches. To the best of our knowledge, no integrated network management solution comprising efficient localization, data scheduling, routing, and data aggregation approaches exists in the literature for a large scale WSN. Hence, we efficiently integrate all network management components so that it can be used as a single network management solution for a large scale WSN, perform experimentations to evaluate the performance of the proposed framework, and validate the results through statistical analysis. Experimental results show that our proposed framework outperforms existing approaches in terms of localization energy consumptions, localization accuracy, network energy consumptions and end-to-end data transmission delay.

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“…The demerits of RSSI method is that it is easily affected by multi-path fading, shadowing, scattering and also in the non-line of sight condition [12].…”

Section: Received Signal Strength Indication (Rssi)mentioning

confidence: 99%

“…It is important when there is an uncertainty of the exact location of some fixed or mobile devices. An effective localization algorithm can then use all the available information from the wireless sensor nodes to infer the position of the individual nodes [12].…”

Section: Localizationmentioning

confidence: 99%