A Zero-Configuration Tracking System for First Responders Networks

Femminella, Mauro; Reali, Gianluca

doi:10.1109/jsyst.2015.2462742

Cited by 12 publications

(14 citation statements)

References 41 publications

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“…In particular emergency situations, when the transmission of the GPS signal was obstructed, the localization process is impossible with GPS signal only, the Wi-Fi combination will be helpful in both indoor/outdoor detection and localization. Reference [15] provides a robust system for tracking GPS in such situation, that the Navigation System can be based on the Wi-Fi signal to improve the accuracy of uses. The same concept can be applied in the I/O detection scheme and improve its performance in the emergency situation, when the GPS signal becomes unstable and weaker.…”

Section: Related Workmentioning

confidence: 99%

GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques

Bui

et al. 2020

Applied Sciences

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Recent advances in mobile communication require that indoor/outdoor environment information be available for both individual applications and wireless signal transmission in order to improve interference control and serve upper-layer applications. In this paper, we present a scheme to identify the indoor/outdoor environment using GPS signals combined with machine learning classification techniques. Compared to traditional schemes, which are based on received signal strength indicator (RSSI), the proposed scheme promises a robust approach with high accuracy, smooth operation when moving between indoor and outdoor environments, as well as easy implementation and training. The proposed scheme combined information from a certain number of GPS satellites, using the GPS sensor on mobile devices. Then, data are collected, preprocessed, and classified as indoor or outdoor environment using a machine learning model that is optimized for the best performance. The GPS input data were collected in the Kookmin University area and included 850 training samples and 170 test samples. The overall accuracy reached 97%.

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Section: Related Workmentioning

confidence: 99%

GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques

Bui

et al. 2020

Applied Sciences

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“…In [25], a localization and tracking system based on the usage of IEEE 802.11 technology is provided for augmenting the GPS coverage in emergency scenarios. Likewise, [26] proposes a localization and tracking system based on the combined usage of WiFi and GPS for emergency scenarios, and this system is characterized by ease of deployment and usage, since it does not require any particular configurations.…”

Section: Related Workmentioning

confidence: 99%

A Data Forwarding Approach for Fire-Rescue Scenario with Multi-Type Mobile Nodes

Liu

Zhou

et al. 2019

Wireless Communications and Mobile Computing

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The opportunistic mobile sensor network has been extensively applied in various public safety applications such as the fire rescue and earthquake rescue, since it can provide a surveillance range with an inexpensive cost and avoid the dangers of humans staying in risk zones. However, due to some environmental events such as building structure damage, airflow push, and fire explosions, the sensor nodes sprinkled into the fire-rescue scenario may be kept moving. Thus, the contacts between nodes become momentary, and the data packets cannot be forwarded along stable communication paths. To this end, the opportunistic forwarding manner is adopted in the fire-rescue scenario to enable the data packets to be transferred to the rescue control center (RCC) through some discrete hops. The contributions of this paper are threefold. First, the nodes in the fire-rescue scenario are carefully investigated and classified into four types: small-range mobile nodes (SRNs), large-range mobile nodes (LRNs), firefighter nodes (FNs), and robot nodes (RNs). Second, we formulate the data forwarding problem, and the optimal proportions of SRNs, LRNs, and FNs in data holders are mathematically analyzed to obtain the maximum delivery ratio. Third, a data forwarding approach for fire-rescue scenario (DFAFR) is proposed. In DFAFR, the optimal proportions of SRNs, LRNs, and FNs in data holders are maintained as far as possible through selecting different types of data holder candidates, and then the new data holders are determined from these data holder candidates and the adjacent RNs on basis of their expected delivery delay. Finally, the performance of DFAFR is analyzed through simulations of the fire-rescue scenario, and the results indicate that DFAFR can enhance the delivery ratio and shorten the delivery delay while the forwarding overhead is restricted.

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“…Reference [24] has already showed that the complexity of each iteration of hybrid PSO is higher than SPSO. Based on (3), (5), and (8), CPSO and SPSO have the same time complexity in each iteration.…”

Section: Complexity Analysismentioning

confidence: 99%

“…Because distributed localization approach allows each sensor node to estimate its own location without any central control or computation unit, which alleviates large communication overhead, so they are more suitable for localization of large-scale WSNs than centralized methods. Further, because many applications require absolute positions such as longitude and latitude, the localization algorithm should be anchors based [5,6], where anchors are the sensor nodes knowing their global positions, and the rest sensor nodes, referred to as unknown nodes, are localized based on beacon messages broadcasted by anchors. The localization procedure is often iterative because anchors are significantly less than unknown nodes, so some unknown nodes are localized by their neighbour localized unknown 2 Wireless Communications and Mobile Computing nodes instead of anchors.…”

Section: Introductionmentioning

confidence: 99%

Localization of Large‐Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

Cui

Liang

Zhou

et al. 2018

Wireless Communications and Mobile Computing

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Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.

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A Zero-Configuration Tracking System for First Responders Networks

Cited by 12 publications

References 41 publications

GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques

GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques

A Data Forwarding Approach for Fire-Rescue Scenario with Multi-Type Mobile Nodes

Localization of Large‐Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

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