Time Delay Estimation in Mobile Sensors for Underwater Networking

The time synchronization between any two sensor nodes in an Ad-hoc Underwater Sensor Networks (UWSNs) could be destroyed due to motion of these wireless sensors which induced Doppler shift. This synchronization obstacle can be sorted out by exploiting the mobility between sensor nodes. In the proposed system, the time delay between sensor nodes in both divergence and convergence scenarios are estimated based on estimating the time scaling factor. An improvement is introduced in terms of packet structure in order to challenge the channel effect and accurate estimation over the speed up to ±2 m/s. To verify the proposed system robustness, different levels of the nodes speeds have been considered in the simulation. Obtained results show that the proposed system is robust against severs channel conditions. Keywords: UWSNs, time delay, time synchronization

Wireless Sensor Networks for Underwater Localization: A Survey

Autonomous Underwater Vehicles (AUVs) have widely deployed in marine investigation and ocean exploration in recent years. As the fundamental information, their position information is not only for data validity but also for many real-world applications. Therefore, it is critical for the AUV to have the underwater localization capability. This report is mainly devoted to outline the recent advance- ment of Wireless Sensor Networks (WSN) based underwater localization. Several classic architectures designed for Underwater Acoustic Sensor Network (UASN) are brie y introduced. Acoustic propa- gation and channel models are described and several ranging techniques are then explained. Many state-of-the-art underwater localization algorithms are introduced, followed by the outline of some existing underwater localization systems

Underwater localization and node mobility estimation

In this paper, localizing a moving node in the context of underwater wireless sensor networks (UWSNs) is considered. Most existing algorithms have had designed to work with a static node in the networks. However, in practical case, the node is dynamic due to relative motion between the transmitter and receiver. The main idea is to record the time of arrival message (ToA) stamp and estimating the drift in the sampling frequency accordingly. It should be emphasized that, the channel conditions such as multipath and delay spread, and ambient noise is considered to make the system pragmatic. A joint prediction of the node mobility and speed are estimated based on the sampling frequency offset estimation. This sampling frequency offset drift is detected based on correlating an anticipated window in the orthogonal frequency division multiplexing (OFDM) of the received packet. The range and the distance of the mobile node is predicted from estimating the speed at the received packet and reused in the position estimation algorithm. The underwater acoustic channel is considered in this paper with 8 paths and maximum delay spread of 48 ms to simulate a pragmatic case. The performance is evaluated by adopting different nodes speeds in the simulation in two scenarios of expansion and compression. The results show that the proposed algorithm has a stable profile in the presence of severe channel conditions. Also, the result shows that the maximum speed that can be adopted in this algorithm is 9 km/h and the expansion case profile is more stable than the compression scenario. In addition, a comparison with a dynamic triangular algorithm (DTN) is presented in order to evaluate the proposed system

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Fast distributed multi-hop relative time synchronization protocol and estimators for wireless sensor networks

The challenging problem of time synchronization in wireless sensor networks is considered in this paper, where a new distributed protocol is proposed for both local and multi-hop synchronization. The receiver-to-receiver paradigm is used, which has the advantage of reducing the time-critical-path and thus improving the accuracy compared to common sender-to-receiver protocols. The protocol is fully distributed and does not rely on any fixed reference. The role of the reference is divided amongst all nodes, while timestamp exchange is integrated with synchronization signals (beacons). This enables fast acquisition of timestamps that are used as samples to estimate relative synchronization parameters. An appropriate model is used to derive maximum likelihood estimators (MLE) and the Cramer-Rao lower bounds (CRLB) for both the offset-only, and the joint offset/skew estimation. The model permits to directly estimating relative parameters without using or referring to a reference' clock. The proposed protocol is extended to multi-hop environment, where local synchronization is performed proactively and the resulted estimates are transferred to the intermediate/end-point nodes on-demand, i.e. as soon as a multi-hop communication that needs synchronization is initiated. On-demand synchronization is targeted for multi-hop synchronization instead of the always-on global synchronization model, which avoids periodic and continuous propagation of synchronization signals beyond a single-hop. Extension of local MLE estimators is proposed to derive relative multi-hop estimators. The protocol is compared by simulation to some state-of-the-art protocols, and results show much faster convergence of the proposed protocol. The difference has been on the order of more than twice compared to CS-MNS, more than ten times compared to RBS, and more than twenty times compared to TPSN. Results also show scalability of the proposed protocol concerning the multi-hop synchronization. The error does not exceed few microseconds for as much as 10 hops in R4Syn, while in CS-MNS, and TPSN, it reaches few tens of microseconds. Implementation and tests of the protocol on real sensor motes confirm microsecond level precision even in multi-hop scenarios, and high stability (long lifetime) of the skew/offset model

Low cost underwater acoustic localization

Over the course of the last decade, the cost of marine robotic platforms has significantly decreased. In part this has lowered the barriers to entry of exploring and monitoring larger areas of the earth's oceans. However, these advances have been mostly focused on autonomous surface vehicles (ASVs) or shallow water autonomous underwater vehicles (AUVs). One of the main drivers for high cost in the deep water domain is the challenge of localizing such vehicles using acoustics. A low cost one-way travel time underwater ranging system is proposed to assist in localizing deep water submersibles. The system consists of location aware anchor buoys at the surface and underwater nodes. This paper presents a comparison of methods together with details on the physical implementation to allow its integration into a deep sea micro AUV currently in development. Additional simulation results show error reductions by a factor of three.Comment: 73rd Meeting of the Acoustical Society of Americ

Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments; where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance