Data gathering problem with the data importance consideration in Underwater Wireless Sensor Networks

Cheng, Chien-Fu; Li, Lung-Hao

doi:10.1016/j.jnca.2016.10.010

Cited by 60 publications

(46 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these works, the objective functions of the proposed optimization models are generally defined as (a) the minimization of end-to-end delay (eg, previous studies 4,5,[13][14][15][16][17][18] ), (b) the minimization of energy consumption (eg, previous studies 2,5,7,15,[18][19][20], and (c) the maximization of network lifetime (eg, previous studies 4, [21][22][23] ). Some constraints of these optimization models include the per-node flow-balance constraint (ie, flows are balanced at each node), the end-to-end flow conservation constraint (ie, every generated data are terminated at the sink node), the energy capacity constraint (ie, amount of energy consumed by nodes are limited), data-capacity constraint (ie, the capacity of each link is bounded), and the delay constraint (ie, end-to-end delay of the network is limited), which are either defined as separate constraints 2,7,9,20,[22][23][24] or incorporated into some constraints (eg, per-flow balance). [25][26][27] Some applications of optimization methods used in time-critical UASNs include routing protocol development 2,10,14,24,25 and optimal relay-node deployment.…”

Section: Related Workmentioning

confidence: 99%

“…5,15,18,28 The objective can be also be the maximization of network lifetime as in previous research. 4,21 Similar to the surface-level gateway deployment problem, there are also several studies in the literature that aim to achieve an optimal deployment strategy of relay nodes, 16 mobile-sink based data collectors, 6,19,22,27 and autonomous underwater vehicles 17,23 for time-critical UASNs.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Yildiz

2019

Int J Communication

View full text Add to dashboard Cite

Summary Underwater acoustic sensor networks (UASNs) are subjected to harsh characteristics of underwater acoustic channel such as severe path losses, noise, and high propagation delays. Among these constraints, propagation delay (more generally, end‐to‐end delay) is the most dominating limitation especially for time‐critical UASN applications. Although the minimization of end‐to‐end delay can be achieved by using the minimum hop routing, this solution cannot lead prolonged lifetimes since nodes consume excessive energy for transmission over long links. On the other hand, the maximization of network lifetime is possible by using energy efficient paths, which consist of relatively short links but high number of hops. However, this solution results in long end‐to‐end delays. Hence, there is a trade‐off between maximizing the network lifetime and minimizing the end‐to‐end delay in UASNs. In this work, we develop a novel multi‐objective–optimization (MOO) model that jointly maximizes the network lifetime while minimizing the end‐to‐end delay. We systematically analyze the effects of limiting the end‐to‐end delay on UASN lifetime. Our results reveal that the minimum end‐to‐end delay routing solution results in at most 72.93% reduction in maximum network lifetimes obtained without any restrictions on the end‐to‐end delay. Nevertheless, relaxing the minimum end‐to‐end delay constraint at least by 30.91% yields negligible reductions in maximum network lifetimes.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Yildiz

2019

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In [25], data related to abnormal events are considered important in an underwater wireless sensor network. Zhag et al [26] apply the data importance concept to eliminate redundant observations made by multiple surveillance cameras.…”

Section: Data Importancementioning

confidence: 99%

“…• Application-agnostic data importance: The data similarity concept [25,29] is not tied to a specific application. In general, consecutive sensor data, e.g., temperature/pressure readings or surveillance images, might be similar.…”

Section: Data Importancementioning

confidence: 99%

“…Although notion of data importance has been considered in various contexts [2,[25][26][27][28]30], our work is different from them in that we (1) enhance the total utility via adaptive sensor data transfer from IoT devices to the edge server based on data importance and (2) provide a real-time sensor data analytics framework at the network edge, while supporting generic data importance metrics.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Information Derivation from Big Sensor Data via Edge Computing

Kang

Chen

et al. 2017

BDCC

View full text Add to dashboard Cite

Abstract:In data-intensive real-time applications, e.g., cognitive assistance and mobile health (mHealth), the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., mental or physical health conditions, from sensor data streams in real-time rather than overloading users with massive raw data. However, achieving the objective is challenging due to the data volume and complex data analysis tasks with stringent timing constraints. Most existing big data management systems, e.g., Hadoop, are not directly applicable to real-time sensor data analytics, since they are timing agnostic and focus on batch processing of previously stored data that are potentially outdated and subject to I/O overheads. Moreover, embedded sensors and IoT devices lack enough resources to perform sophisticated data analytics. To address the problem, we design a new real-time big data management framework to support periodic in-memory real-time sensor data analytics at the network edge by extending the map-reduce model originated in functional programming, while providing adaptive sensor data transfer to the edge server based on data importance. In this paper, a prototype system is designed and implemented as a proof of concept. In the performance evaluation, it is empirically shown that important sensor data are delivered in a preferred manner and they are analyzed in a timely fashion.

show abstract

A systematic literature review on mobility in terrestrial and underwater wireless sensor networks

Ghanem

Mansoor

Ahmad

2021

Int J Communication

View full text Add to dashboard Cite

Wireless sensor network (WSN) is one of the most significant technologies that gets much attention from researchers due to its promising applications in various environments. Mobility in wireless sensor networks contributes significantly to improving the performance of terrestrial wireless sensor networks (TWSNs) and underwater wireless sensor networks (UWSNs). This research's primary goal is to highlight the roles of mobility and mobile element tasks in enhancing terrestrial and underwater wireless sensor networks. Our systematic literature review (SLR) analyzed studies published between the periods of 2003 and January 2020. Out of 1762 studies retrieved by searching six online digital libraries and other resources, only 107 of them were selected for primary studies based on selection criteria and relevant to three identified research questions. This was followed by data extraction from the primary studies for answering the three SLR questions. The study reveals that mobility plays an essential role in improving network performance in TWSNs and UWSNs, especially in the aspects of coverage, energy efficiency, throughput, and connectivity. The study also identifies the tasks of the mobile elements in TWSNs and UWSNs, including data collection, localization, and charging. Moreover, it demonstrates the parameters of the mobile elements that should be considered when building networks. Finally, this study concludes by highlighting some open research issues that can be considered for future research.

show abstract

Data gathering problem with the data importance consideration in Underwater Wireless Sensor Networks

Cited by 60 publications

References 24 publications

Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Real-Time Information Derivation from Big Sensor Data via Edge Computing

A systematic literature review on mobility in terrestrial and underwater wireless sensor networks

Contact Info

Product

Resources

About