Study of multiple-source data collection in wireless sensor networks

<p>A detailed survey on the process of data collection from multiple sources in Wireless Sensor Networks (WSNs) is introduced. The topologies that determine the location of the network components with respect to each other are presented. These topologies are often referred to as Mobility topologies. The performance of the overall WSN architecture significantly depends on these topologies. As a consequence, these topologies are elaborately compared and discussed. The most common network components that efficiently collaborate in data collection process are explained. To highlight the data collection process as a subject of our concern, the phases that describe the stages of the data collection are illustrated. These phases consist of three successive stages: discovery, data transfer, and routing. To sum up, the most recent approaches for developing the process of data collection in multiple-source WSNs are also presented.</p>

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Enhancement of Data Collection Performance in Wireless Sensor Networks Based on Path-Constrained Mobile Sink

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2021.0228.37 ◽

2021 ◽

Vol 14 (1) ◽

pp. 400-409

Author(s):

Mohamed Borham ◽

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Ghada Khoriba ◽

Mostafa-Sami Mostafa ◽

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...

Keyword(s):

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Data Collection ◽

Routing Protocol ◽

Maximum Amount ◽

Wireless Sensor ◽

Collection Process ◽

Data Collection Process ◽

Energy Limitation

Due to the energy limitation in Wireless Sensor Networks (WSNs), most researches related to data collection in WSNs focus on how to collect the maximum amount of data from the network with minimizing the energy consumption as much as possible. Many types of research that are related to data collection are proposed to overcome this issue by using mobility with path constrained as Maximum Amount Shortest Path routing Protocol (MASP) and zone-based algorithms. Recently, Zone-based Energy-Aware Data Collection Protocol (ZEAL) and Enhanced ZEAL have been presented to reduce energy consumption and provide an acceptable data delivery rate. However, the time spent on data collection operations should be taken into account, especially concerning real-time systems, as time is the most critical factor for these systems' performance. In this paper, a routing protocol is proposed to improve the time needed for the data collection process considering less energy consumption. The presented protocol uses a novel path with a communication time-slot assignment algorithm to reduce the count of cycles that are needed for the data collection process with reduction of 50% of the number of cycles needed for other protocols. Therefore, the time and energy needed for data collection are reduced by approximately 25%and 6% respectively, which prolongs the network lifetime. The proposed protocol is called Energy-Time Aware Data Collection Protocol (ETCL).

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Cross-Layer Routing for a Mobility Support Protocol Based on Handover Mechanism in Cluster-Based Wireless Sensor Networks with Mobile Sink

Sensors ◽

10.3390/s19132843 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2843 ◽

Cited By ~ 1

Author(s):

Maamar Zahra ◽

Yulin Wang ◽

Wenjia Ding

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Collection ◽

Mobile Sink ◽

Wireless Sensor ◽

Hierarchical Architecture ◽

Cross Layer ◽

Collection Process ◽

Data Collection Process ◽

Handover Mechanism

Wireless sensor networks with mobile collectors or sinks face some challenges regarding the data collection process and the continuous connectivity and delivering of data while the mobile sink is moving throughout the network. These challenges increase as the network grows. For this aim, we propose in this paper a cross-layer routing protocol which supports mobility for large-scale wireless sensor networks, which we name CLR-MSPH. We adapt CLR-MSPH for the hierarchical architecture of the network, and it performs on cluster-based wireless sensor networks where the network is organized in clusters. Our proposed protocol deals with the problem of handover data after the mobile sink leaves the radio range of cluster head without sending all data stored in the cluster head’s buffer. We also introduce a mobility model for the mobile sink for a better data collection process. CLR-MSPH is considered as an extending implementation of BMAC protocol with handover mechanism (BMAC-H). In order to prove the efficiency of the proposed protocol, we compare CLR-MSPH to BMAC-H, where we adapted BMAC-H to perform in cluster-based wireless sensor networks. The simulation results show that CLR-MSPH performs better than BMAC-H in terms of packets reception rate, energy, and latency.

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Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks

International Journal of Distributed Sensor Networks ◽

10.1155/2012/592471 ◽

2012 ◽

Vol 8 (1) ◽

pp. 592471 ◽

Cited By ~ 5

Author(s):

Lei Liu ◽

Jin-Song Chong ◽

Xiao-Qing Wang ◽

Wen Hong

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Compressed Sensing ◽

Source Parameters ◽

Location Estimation ◽

Multiple Source ◽

Wireless Sensor ◽

Multiple Sources ◽

Redundant Dictionary ◽

Speed Up

Source localization is an important problem in wireless sensor networks (WSNs). An exciting state-of-the-art algorithm for this problem is maximum likelihood (ML), which has sufficient spatial samples and consumes much energy. In this paper, an effective method based on compressed sensing (CS) is proposed for multiple source locations in received signal strength-wireless sensor networks (RSS-WSNs). This algorithm models unknown multiple source positions as a sparse vector by constructing redundant dictionaries. Thus, source parameters, such as source positions and energy, can be estimated by [Formula: see text]-norm minimization. To speed up the algorithm, an effective construction of multiresolution dictionary is introduced. Furthermore, to improve the capacity of resolving two sources that are close to each other, the adaptive dictionary refinement and the optimization of the redundant dictionary arrangement (RDA) are utilized. Compared to ML methods, such as alternating projection, the CS algorithm can improve the resolution of multiple sources and reduce spatial samples of WSNs. The simulations results demonstrate the performance of this algorithm.

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Nodes and sensors for multipoint data collection

Manuali – Scienze Tecnologiche - SPARKLE - Entrepreneurship for Sustainable Precision Agriculture ◽

10.36253/978-88-5518-044-3.16 ◽

2020 ◽

pp. 16

Author(s):

Pilar Barreiro ◽

Eva Cristina Correa ◽

Belén Diezma Iglesias

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Collection ◽

Data Transfer ◽

Wireless Sensor ◽

Soil Humidity ◽

Network Nodes ◽

Crop Physiology ◽

Pest Detection ◽

Different Parts

In this topic the technologies realted to wireless sensor networks (WSN) will be presented. The different parts of the network (nodes, gateway, data transfer protocols...) will be explained, as well as the sensors themselves (sensors for soil humidity, temperature, presure, precipitation, crop physiology, pest detection, etc.)

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