An Efficient Mobile Data Collector Based Data Aggregation Scheme for Wireless Sensor Networks

Sharma, Upasana; Krishna, C. Rama; Sharma, Teek Parval

doi:10.1109/cict.2015.78

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…In WSNs, TSP is usually employed to find the best path for MDCs [17][18][19][20][21][22]. The typical approach is identifying a set of points to visit and collect data from nearby sensors and then applying TSP on the data collection points.…”

Section: Related Workmentioning

confidence: 99%

“…The center of a circumcircle is identified as an SP if there are any sensors within the transmission range. [19] groups sensors into clusters and selects a cluster head from each cluster based on the distance to other sensors in the group. The sensor with the minimum distance is selected as the cluster head.…”

Section: Related Workmentioning

confidence: 99%

“…The second baseline is a cluster head (CH) selection technique for MDC-based data aggregation in WSNs [19]. In this approach, sensors broadcast their coordinates and a CH is selected in such a way that the total distance from sensors to the CH is minimized.…”

Section: Baselinesmentioning

confidence: 99%

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Steiner Zone Yaklaşımı İle Bölünmüş Kablosuz Algılayıcı Ağlarda Hareketli Veri Toplama

Senturk

2020

Bilişim Teknolojileri Dergisi

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Wireless sensor networks (WSNs) typically operate in harsh environmental conditions. Hardware constraints and external damage from inhospitable surroundings leave the nodes susceptible to node failures. Depending on the damage scale, the network can be subject to partitioning which segments the network into multiple isolated connected components. A prompt reactive approach to restore network connectivity is to employ a mobile data collector (MDC) that visits and collects data from partitions periodically. Availability of the wireless data communication through multi-hop routing in a partitioned network complicates designating the shortest possible route for data collection. This paper regards the mentioned data collection problem as the Close-enough Traveling Salesman Problem (CETSP) and employs Steiner zone approach to designate respective data collection points for corresponding partitions. We have assessed the proposed approach in terms of the number of points visited for data collection and the total travel distance of the MDC. Obtained results indicate that the proposed approach can reduce the number of data collection points up to 67% and total travel distance up to 42%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Steiner Zone Yaklaşımı İle Bölünmüş Kablosuz Algılayıcı Ağlarda Hareketli Veri Toplama

Senturk

2020

Bilişim Teknolojileri Dergisi

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“…In order to ensure the relative equilibrium of the energy consumption among the nodes, an energy-aware sink relocation method is proposed [23], in which the sensor nodes are divided into hierarchies and the data direction of the relay nodes is determined according to the residual energy. In [24], an intelligent mobile data gathering scheme is presented to implement dynamical changes of data gathering tour of cluster's neighbor information table (NIT). And the mobile data collector decides to choose the optimal path to traverse between cluster heads based on the NIT information.…”

Section: Related Workmentioning

confidence: 99%

Mobile Sink-Based Path Optimization Strategy in Wireless Sensor Networks Using Artificial Bee Colony Algorithm

Sun

Pan

2019

IEEE Access

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In traditional static wireless sensor networks (WSNs), the unbalanced communication overhead in different regions will result in premature death of some monitoring nodes. The introduction of mobile sink in WSNs can not only balance the node traffic load, but also obtain even energy consumption of nodes, thus effectively avoiding the ''hot spot'' problem and prolonging the network lifetime. However, the mobility of the sink will lead to frequent changes in the aspect of network topology, which can aggravate the overhead of the node's reorganization in hierarchical WSNs. Therefore, it is essential to obtain the optimal trajectory design of the mobile sink so as to improve the ability of data gathering. In this paper, a mobile sink-based path optimization strategy in WSNs using artificial bee colony algorithm is proposed. First, the problem of overall energy consumption in the network can be transformed into the minimization of the total hops between all subnodes and the rendezvous points of the mobile sink. The objective function and the constraint criterion should be established. Second, an improved artificial bee colony algorithm is proposed to solve the problem. On the one hand, the cumulative factor is introduced to the position update of the employed bee stage to speed up the convergence of the algorithm. On the other hand, the Cauchy mutation operator is presented to increase the diversity of the feasible solution and enhance the global search ability of the algorithm. The simulation results show that the proposed algorithm is better than the traditional methods in the aspects of energy efficiency and the real-time performance of data collection.INDEX TERMS Wireless sensor networks, mobile sink, path optimization, artificial bee colony algorithm.

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Enhancing QoS and Residual Energy by Using of Grid-Size Clustering, K-Means, and TSP Algorithms With MDC in LEACH Protocol

2022

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Some recent researches have shown that the energy consumption problem caused by data collection in a wireless sensor network (WSN) based on a static data collector is a main threat to the network lifetime. However, with the progress of the mobile terminal technology, the implementation of mobile data collectors (MDCs) has become more popular in large-scale WSNs, but it remains a big problem to improve the Quality of Service (QoS) criteria and minimize the energy consumption at the same time. However, most existing systems based on MDCs do not successfully strike a balance between routing energy consumption and QoS. In addition, most WSN protocols fail to maintain their impact when the network topology changes. Thus, for a dynamic WSN, it is important to support an intelligent MDC to continue data propagation despite the inevitable changes in the WSN topology. Considering all the above challenges, we propose a new intelligent MDC based on the traveling salesman problem (TSP) to determine the optimal path traveled by the MDC for energy efficiency and latency. Specifically, our proposed Mobile Data Collectors-Traveling Salesman Problem-Low Energy Adaptive Clustering Hierarchy-K-Means (MDC-TSP-LEACH-K) protocol uses K-Means and Grid clustering algorithm to decrease energy consumption in the cluster head (CH) election phase. Additionally, MDC is utilized as an intermediate between CH and the sink to further enhance the QoS of WSNs, to reduce delays while collecting data, and improve the transmission phase of the LEACH protocol.INDEX TERMS Energy consumption, large-scale wireless sensor networks, optimal path, QoS.

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An Efficient Mobile Data Collector Based Data Aggregation Scheme for Wireless Sensor Networks

Cited by 3 publications

References 13 publications

Steiner Zone Yaklaşımı İle Bölünmüş Kablosuz Algılayıcı Ağlarda Hareketli Veri Toplama

Steiner Zone Yaklaşımı İle Bölünmüş Kablosuz Algılayıcı Ağlarda Hareketli Veri Toplama

Mobile Sink-Based Path Optimization Strategy in Wireless Sensor Networks Using Artificial Bee Colony Algorithm

Enhancing QoS and Residual Energy by Using of Grid-Size Clustering, K-Means, and TSP Algorithms With MDC in LEACH Protocol

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