Node Scheduling and Compressed Sampling for Event Reporting in WSNs

Singh, Vishal Krishna; Kumar, Manish; Verma, Shekhar

doi:10.1109/tnse.2018.2799845

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…As discussed in Section III, in node scheduling schemes, the activation of sensor nodes follows a schedule that can either be pre-defined or dynamic. Node scheduling is the process that decides the ON-duty and OFF-duty eligibility of the sensor nodes [135], [136]. A schedule is a subset of the sensor nodes to be activated in a specific time slot.…”

Section: Node Scheduling Schemes a Definitionmentioning

confidence: 99%

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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Section: Node Scheduling Schemes a Definitionmentioning

confidence: 99%

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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“…The proposed solution selects the best node according to (9) and reconstructs the signal using (8). If the RMSE computed according to (10) is below the given threshold then the selected node becomes a set. Otherwise, the next node in the list is added in the sampling set and the process continues until the RMSE is below threshold; in which case the set is complete.…”

Section: An Iterative Graph Partitioning Strategymentioning

confidence: 99%

“…Algorithm 1 shows the whole procedure. Select first node in L and add to sampling mask m; 10 Reconstruct graph signalX using (8); 11 Compute RMSE according to (10); 12 Remove sampled node from list L; 13 end 14 Store sampling mask m in set S; 15 Add selected nodes to Sampled node list; 16 end 17 return S Fig. 1.…”

Section: An Iterative Graph Partitioning Strategymentioning

confidence: 99%

“…Traditional Compressive Sensing techniques do rely on time-domain sparsity or, at the very least, allow for a sparse representation of the signal to be sampled, where a sampled observation is compressed at the sensing node and then the aggregate samples are reconstructed at the sink or receiving node [9]. In order to develop a lifetime-preserving scheduling strategy for WSN based on CS techniques, the authors in [10] have presented a probabilistic solution which determines which sensor nodes are correlated (in space and time) and maximise network lifetime by maximising the sleep time of the nodes while keeping coverage high (enough sensors are engaged to cover the sensing area completely). All the above solutions require either sparsity in the time domain or strong time-domain correlation.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficient WSN: a Cross-layer Graph Signal Processing Solution to Information Redundancy

Chiumento

Marchetti

Macaluso

2019

2019 16th International Symposium on Wireless Communication Systems (ISWCS)

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In this work an iterative solution to build a network lifetime-preserving sampling strategy for WSNs is presented. The paper describes the necessary steps to reconstruct a graph from application data. Once the graph structure is obtained, a sampling strategy aimed at finding the smallest number of concurrent sensors needed to reconstruct the data in the unsampled nodes within a specific error bound, is presented. An iterative method then divides the sensor nodes into sets to be sampled sequentially to increase lifetime. Results on a real-life dataset show that the reconstruction RMSE can be easily traded off for a larger number of disjoint sampling sets which improve the network lifetime linearly.

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“…Succinctly, in order to perform energy-efficient data collection in UAV-IWSN, an intelligent data collection scheme integrating a node clustering algorithm, intelligent signal processing methods together with path planning policies is advantageous. With this goal in mind, in this article, the compressed sampling (CS) technique [13], [14] is employed, and we propose a hierarchical data collection scheme for an UAV-IWSN based agricultural monitoring system. Our contribution can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

Energy-Optimal Data Collection for Unmanned Aerial Vehicle-Aided Industrial Wireless Sensor Network-Based Agricultural Monitoring System: A Clustering Compressed Sampling Approach

Lin

Han

et al. 2021

IEEE Trans. Ind. Inf.

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In this paper, we propose a hierarchical data collection scheme, towards the realization of unmanned aerial vehicles (UAV)-aided industrial wireless sensor networks (IWSN). The particular application is that of agricultural monitoring. For that, we propose the use of hybrid compressed sampling (CS) through exact and greedy approaches. With the exact approach -to model the energy-optimal formulation -it is utilized an improved linear programming formulation of the minimum cost flow problem. The greedy approach is based on a proposed balance factor parameter, consisting of data sparsity and the distance from the cluster head to the normal nodes. To improve the node clustering efficiency, a hierarchical data collection scheme is implemented, by which, the nodes in different layers are adaptively clustered, and the UAV can be scheduled to perform energy-efficient data collection. Simulation results show that our method can effectively collect the data and plan the path for the UAV at a low energy cost. Index Terms-artificial intelligence, unmanned aerial vehicles (UAV), industrial wireless sensor network (IWSN), agricultural monitoring system, intelligent signal processing.

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Node Scheduling and Compressed Sampling for Event Reporting in WSNs

Cited by 11 publications

References 18 publications

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Energy Efficient WSN: a Cross-layer Graph Signal Processing Solution to Information Redundancy

Energy-Optimal Data Collection for Unmanned Aerial Vehicle-Aided Industrial Wireless Sensor Network-Based Agricultural Monitoring System: A Clustering Compressed Sampling Approach

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