Information fusion for efficient target detection in large-scale surveillance Wireless Sensor Networks

Abrardo, Andrea; Martal, M.; Ferrari, Gianluigi

doi:10.1016/j.inffus.2017.02.002

Cited by 30 publications

(7 citation statements)

References 19 publications

(29 reference statements)

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“…This would mean monitoring crowds within close proximity or having multiple devises deployed. (2) In addition, scanning in bluetooth is resource intensive and given that users devices are multipurpose, users will always disable the bluetooth feature whenever they see it irrelevant. (3) The fear of receiving unsolicited requests and software bugs means users will only turn on their devices when security is guaranteed.…”

Section: Estimating Crowd Densities and Pedestrian Flows Using Wifi Amentioning

confidence: 99%

“…Sensor based algorithms provide a step by step description of how sensor networks counter some of the problems they try to mitigate while addressing the generic WSN issues of the need for increased battery life spans, robustness and fault tolerance among others. And depending on the objectives of the sensor networks, these algorithms have been deployed as centralized, hierarchical, distributed or hybrid [2] [1]. Fig 1 below is an illustration of major WSN components.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds

Obbo¹,

Nabaasa²

2019

IJCA

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In this paper a survey is carried out on crowd detection algorithms, highlighting challenges and gaps visa -avis people crowd detection. The research identifies some of the key capabilities considered invaluable in effective people crowd detection and compares some of the current crowd detection algorithms with regard to these. The results reveal that most algorithms are primarily non-people crowd detection algorithms. While people crowds are intelligent and can easily bypass most of the current crowd detection algorithms. Given the need for detection and management of people crowds, there is great need for specialized algorithms for people crowd detection using effective and non resource intensive methods .

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Section: Estimating Crowd Densities and Pedestrian Flows Using Wifi Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds

Obbo¹,

Nabaasa²

2019

IJCA

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“…A Decision Support System (DSS) has been proposed, which is based on multisensor data fusion for greenhouse application. An energy-efficient information fusion approaches prompted by the exposure of a mobile object has been presented in the work of Abrardo et al 25 In this work, an electronic-signals intellect setup is used by the author(s), in which the event detection is established on the radio signal emanated by the mobile target. 22 A fuzzy-based model (FBM) for data fusion has been proposed to solve the quality-of-service (QoS) issue in WSNs.…”

Section: Related Workmentioning

confidence: 99%

“…In the second approach, an optimization algorithm is used to train the sensor models in terms of accuracy and cost. An energy‐efficient information fusion approaches prompted by the exposure of a mobile object has been presented in the work of Abrardo et al In this work, an electronic‐signals intellect setup is used by the author(s), in which the event detection is established on the radio signal emanated by the mobile target. In the work of Zhang et al, a heuristic algorithm for path selection for WSN is designed.…”

Section: Related Workmentioning

confidence: 99%

A multisensor data fusion strategy for path selection in Internet‐of‐Things oriented wireless sensor network (WSN)

Kumar

Chaurasiya

2018

Concurrency and Computation

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To give the complete description of an environment or to take a robust decision, a number of observations are collected and combined from multiple sensor nodes. The process of combining and analyzing the observations is called multisensor data fusion. The fusion is used to produce more consistent, accurate, and useful information rather than provided by any individual sensor node. Data fusion finds wide application in many areas of such as object recognition, wireless sensor network, image processing, environment mapping, and localization. Nowadays, the Internet of Things (IoT) utilizes wireless sensor network (WSN) as a necessary platform for data sensing and communication. For efficiency, data fusion is performed on the sensed sample collected by the sensor nodes. However, fusion of the network parameters is also essential to select an appropriate sensor node for the forwarding of data. Therefore, in this paper, we propose a multisensor data fusion (MDF) strategy that performs fusion of the collected network parameters like bandwidth and centrality for the selection of an appropriate path. Extensive simulations were made, and related results showed that our proposed scheme has a better performance compared with other schemes in our simulated scenarios. KEYWORDS data fusion, Internet-of-Things, performance analysis, wireless sensor network INTRODUCTIONA wireless sensor network (WSN) is a type of network that contains large number of nodes having sensing capability such that they can easily detect any changes in the surrounding real-world environment. The nodes in WSN are used to carry sensed information from one to another desired location for further processing. 1,2 Technological advances are being developed daily, and a WSN plays a rapid contribution for providing smart communication with the use of smart devices. These smart devices perform communication at the different locations by providing a level of transparency among users and maintained an interconnected smart network. A number of sensor nodes are constituted by the smart network, which is used to send sensed information and termed as the Internet of Things (IoT). 3 In this paper, we called this network as an IoT oriented WSN. In this network, the users communicate with each other by exchanging of sensed data, monitoring of events, and surrounding and reacting autonomously. 4 The difference between the WSN and IoT oriented WSN is only the few sensors are deployed in WSN rather than along with smart devices for applications.However, the IoT oriented WSN consists of devices having sensors with full network connectivity. The network of IoT oriented WSN is concerned with the loss of packets and performance of the links for achieving high throughput in less delay.The services of IoT oriented WSN are provided by using a standard interface such that users can create a query, retrieve information, and change their states accordingly. An Internet link is used to provide the standard interface between users and IoT devices. The hardware components of a sensor no...

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“…But until now, there are still considerable uncertainties associated with danger understanding and recognition, especially for engineering-critical applications [3][4][5], which can be partly attributed to implications of environment conditions to video recognition efficiency of the surveillance system. It has been demonstrated that suitable model parameters in online algorithms and difficulty level of object detection tasks in different environments can be much different [6].…”

Section: Introductionmentioning

confidence: 99%

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

et al. 2018

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Video surveillance plays a vital role in maintaining the social security although, until now, large uncertainty still exists in danger understanding and recognition, which can be partly attributed to intractable environment changes in the backgrounds. This article presents a brain-inspired computing of attention value of surrounding environment changes (EC) with a processes-based cognition model by introducing a ratio value of EC-implications within considered periods. Theoretical models for computation of warning level of EC-implications to the universal video recognition efficiency (quantified as time cost of implication-ratio variations from to +1 , = 1, 2, . . .) are further established. Imbedding proposed models into the online algorithms is suggested as a future research priority towards precision security for critical applications and, furthermore, schemes for a practical implementation of such integration are also preliminarily discussed.

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Information fusion for efficient target detection in large-scale surveillance Wireless Sensor Networks

Cited by 30 publications

References 19 publications

Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds

Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds

A multisensor data fusion strategy for path selection in Internet‐of‐Things oriented wireless sensor network (WSN)

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

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