A rendezvous-based data collection algorithm with mobile sink in wireless sensor networks

Bagheri, Leila; Fooladi, Mehdi Dehghan Takht

doi:10.1109/iccke.2014.6993373

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…To illustrate the effectiveness and performance of the proposed MSPO-ABC algorithm, we test and compare its performance with a number of competing clustering design protocols, namely, Rendezvous-based Data Collection algorithm (RDCA) [35] and Indegree-based Path Design for Mobile Sink algorithm (IPDMS) [36]. The parameter values for these approaches are selected in accordance with the values specified in [15] and [31], and the following simulation parameters are used in Table 1.…”

Section: Results and Analysismentioning

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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Section: Results and Analysismentioning

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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“…The result shows that the proposed heuristic is useful in decreasing energy consumption and improve data retrieval of a mobile sink within a given deadline. In another study by Bagheri and Mehdi, the authors also utilized RPs to called data from sensor nodes [49]. Using the RP allows the MS to collect a huge volume of data with minimal energy cost and less time.…”

Section: ) Data Collection In Wsn Using Single Mobile Sinkmentioning

confidence: 99%

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

Ali¹

2020

Preprint

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<div>In recent years, Wireless Sensor Networks (WSNs) technologies are used in many important</div><div>areas, including weather forecasting, security, environmental monitoring, health care, and industry.</div><div>Nonetheless, due to the constraints of WSNs in terms of energy, processing, connectivity, computation and</div><div>data integrity. An important issue in this research field is the efficient management of the large number</div><div>of WSN’s data. Therefore, there is a need for strong and scalable high-performance computing and large</div><div>storage infrastructure for real-time processing and storage of WSN’s data. This article attempts to discuss</div><div>data collection methods in WSNs, sensor cloud, and IoT. Also, in this context, a general overview of</div><div>WSNs and sensor cloud platforms, including their definitions, architectures, and applications are given.</div><div>We investigate, highlight and report recent advances in data collection technology by categorizing and</div><div>classifying data collection methods in WSNs, sensor cloud, IoT and developing a taxonomy based on</div><div>these classifications. Furthermore, the analysis and synthesis of existing works in this research domain</div><div>is highlighted. The identification of key research challenges for future work and use case in this research</div><div>domain were also highlighted and discussed.</div>

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“…Sink moves across the network to collect data. Mobile sink based approaches are of two categories: rendezvous [3] or cluster based [4]. In rendezvous based approach, a suitable sensor nodes are selected close to the mobile sink trajectory path.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Rendezvous Clustering Model for Efficient Data Collection in Multi Sink Based Wireless Sensor Networks

Raghavendra

Mahadevaswamy

2022

Wireless Pers Commun

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Mobile sink based data collection has comparatively lower energy consumption compared to multi hop forwarding in wireless sensor networks. Energy e ciency is achieved by minimizing the hop count to sink in mobile sink based approaches. These approaches are of two types: Mobile sink collect stored data at rendezvous nodes or cluster heads forwarding data to mobile sink without storage by using its maximum transmission range. First approach provides higher energy gain at cost of delay. Second approach provides lower delay at cost of additional energy consumption. This work combines both the approaches with rendezvous node deciding to store or forward based on prediction of trajectory of multiple mobile sinks and packet latency deadlines.

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A rendezvous-based data collection algorithm with mobile sink in wireless sensor networks

Cited by 12 publications

References 10 publications

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

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

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

Hybrid Rendezvous Clustering Model for Efficient Data Collection in Multi Sink Based Wireless Sensor Networks

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