Experiments with aLS-Coop-Loc cooperative combined localization and time-synchronization

Kleunen, Wouter van; Moseley, N.A.; Meratnia, Nirvana; Havinga, Paul J.M.

doi:10.1109/wiopt.2014.6850283

Cited by 7 publications

(9 citation statements)

References 6 publications

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“…If the transmit power P = [p e1 , p e2 , ...p em ] meets the condition (13), all the transmission can be received successfully. The matrix F m×m in (13) is named interference matrix.…”

Section: B Power Control Schemementioning

confidence: 99%

“…A random and minimum schedule delay scheduling order are proposed. In addition, although TDMA-based protocols require time synchronization for transmission scheduling, in recent years, several works [12], [13], [14] have provided the time synchronization mechanisms for UWSNs. Hence, the time synchronization issue is out the scope of this paper.…”

Section: Introductionmentioning

confidence: 99%

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Link Scheduling Method for Underwater Acoustic Sensor Networks Based on Correlation Matrix

et al. 2016

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The unique characteristic of the underwater acoustic communication channel force development of new protocols for underwater acoustic sensor networks. Especially, the pretty slower propagation speed of acoustic signals in underwater should be taken into consideration. This paper constructs a link scheduling model on MAC layer for multi-hop underwater acoustic sensor networks. The proposed model employs correlation matrix to describe the conflicts relationship among links and uses propagation delay to generate conflict matrix for collision detection. In order to minimum the frame length, a power control strategy is introduced and aimed to reduce the effect of link interference. Then, a heuristic algorithm is presented to solve the conflict-free problem based on the model. Simulation results show that the link scheduling method and the power control strategy can effectively improve network performance with respect to throughput and end to end delay.

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“…If the transmit power P = [p e1 , p e2 , ...p em ] meets the condition (13), all the transmission can be received successfully. The matrix F m×m in (13) is named interference matrix.…”

Section: B Power Control Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Link Scheduling Method for Underwater Acoustic Sensor Networks Based on Correlation Matrix

et al. 2016

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“…The third part of link scheduling algorithm is for the last hop (lines [11][12][13][14][15]. The link with higher propagation delay will be scheduled firstly.…”

Section: Link Schedulingmentioning

confidence: 99%

Minimum Delay Multipath Routing Based on TDMA for Underwater Acoustic Sensor Network

Bai

Wang

Shen

et al. 2016

International Journal of Distributed Sensor Networks

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Multipath routing is an alternative routing technique, which uses redundant paths to deliver data from source to destination. Compared to single path routing protocols, it can address reliability, delay, and energy consumption issues. Thus, multipath routing is a potential technique to overcome the long propagation delay and adverse link condition in underwater environment. However, there are still some problems in multipath routing. For example, the multiple paths may interfere with each other and arouse large end-to-end delay difference amongst multiple paths. This paper proposes a novel multipath routing structure and a conflict-free algorithm based on TDMA scheme. The forwarding nodes are selected based on the propagation delay and location information. This special multiple routing structure not only can ensure parallel multiple transmission without collision, but also can get a small end-to-end delay difference amongst multiple paths. Simulation results show that the multipath routing scheme proposed in this paper outperforms the traditional strategy.

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“…Range-based localization with known locations of sensors can be applied on either the received signal strength [5], the time of arrival (TOA) [6], the TDOA, or a hybrid [7]. Of such ranging measurements, the TDOA is the most suited and common for natural sound source localization, in which the time of sound emission is unknown.…”

Section: Related Workmentioning

confidence: 99%

“…given by (6). For each loop, a random subset C j is selected without replacement from all possible combinations of anchor nodes in N (k) taken p i at a time.…”

Section: Distributed Nondeterministic Estimationmentioning

confidence: 99%

Nondeterministic sound source localization with smartphones in crowdsensing

Kamminga

Scholten

et al. 2016

2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

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Abstract-The proliferation of smartphones nowadays has enabled many crowd assisted applications including audiobased sensing. In such applications, detected sound sources are meaningless without location information. However, it is challenging to localize sound sources accurately in a crowd using only microphones integrated in smartphones without existing infrastructures, such as dedicated microphone sensor systems. The main reason is that a smartphone is a nondeterministic platform that produces large and unpredictable variance in data measurements. Most existing localization methods are deterministic algorithms that are ill suited or cannot be applied to sound source localization using only smartphones. In this paper, we propose a distributed localization scheme using nondeterministic algorithms. We use the multiple possible outcomes of nondeterministic algorithms to weed out the effect of outliers in data measurements and improve the accuracy of sound localization. We then proposed to optimize the cost function using least absolute deviations rather than ordinary least squares to lessen the influence of the outliers. To evaluate our proposal, we conduct a testbed experiment with a set of 16 Android devices and 9 sound sources. The experiment results show that our nondeterministic localization algorithm achieves a root mean square error (RMSE) of 1.19 m, which is close to the Cramer-Rao bound (0.8 m). Meanwhile, the best RMSE of compared deterministic algorithms is 2.64 m.

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Experiments with aLS-Coop-Loc cooperative combined localization and time-synchronization

Cited by 7 publications

References 6 publications

Link Scheduling Method for Underwater Acoustic Sensor Networks Based on Correlation Matrix

Link Scheduling Method for Underwater Acoustic Sensor Networks Based on Correlation Matrix

Minimum Delay Multipath Routing Based on TDMA for Underwater Acoustic Sensor Network

Nondeterministic sound source localization with smartphones in crowdsensing

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