Dynamic channel management for advanced, energy-efficient sensor-actor-networks

Vodel, Matthias; Lippmann, Mirko; Hardt, Wolf‐Dietrich

doi:10.1109/wict.2011.6141281

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…In our test cases, simple token-based scheduling mechanisms are feasible and efficient. In mid-and large-scale wireless network infrastruc tures, advanced data management techniques are essential to coordinate the transmission tasks in time and space [22]. …”

Section: Results S and Analysismentioning

confidence: 99%

Data aggregation and data fusion techniques in WSN/SANET topologies - a critical discussion

Vodel

Hardt

2012

TENCON 2012 IEEE Region 10 Conference

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WSN and SANET topologies generate huge amount of heterogeneous data, which has to be transmitted in a dynam ically changing network infrastructure. Especially in the domain of wireless low-power applications, the energy-efficiency and the prioritisation of communication tasks is critical. Several research areas deal with this issue. They optimising the respective hardware components as well as the protocols within the PHY, MAC or network layer. But for an optimised media transport in the topology also the data management and the task scheduling on the application layer is essential. Here, the key challenge is to minimise the data amount without decreasing the information quality. Related research work in the field of data aggregation and data fusion ofTers interesting techniques for an efficient data handling.In this paper, we discuss usual ways for data aggregation, in cluding the adapted communication process. We critically analyse the benefits in theory and compare these conceptual advantages with measured real-world results. The evaluation was done in two steps. The first one is based on simulation scenarios of typical WSN/SANET applications. In a second step, we implement a demonstrator platform for a respective real-world environment.The test bed configuration is similar to the simulation scenario and provides comparable data. Based on the results and the respective analysis, we propose feasible methods for optimising data aggregation techniques. We clarify, that these improvements are essential for an efficient usage in resource-limited, embedded sensor network environments.

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Section: Results S and Analysismentioning

confidence: 99%

Data aggregation and data fusion techniques in WSN/SANET topologies - a critical discussion

Vodel

Hardt

2012

TENCON 2012 IEEE Region 10 Conference

Self Cite

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“…where x t is location including (x t , y t ), F is transfer matrix, and ω t is noise matrix, namely Gaussian White noise [25].…”

Section: ) Analysis Of Transmission Modelmentioning

confidence: 99%

Intelligent Sensing Scheduling for Mobile Target Tracking Wireless Sensor Networks

Zhou

Leng

Chen

et al. 2021

Preprint

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Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in Mobile Target Tracking Wireless Sensor Networks (MTT-WSN). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is difficult to execute dynamic and critical tasks in the MTT-WSN network. Besides, the network cannot ensure consecutive tracking due to the limited energy. To address the problems, this paper proposes a new hierarchical target tracking structure based on Edge Intelligence (EI) technology. The structure integrates the computing resource of both mobile nodes and edge servers to provide efficient computation capability for real-time target tracking. Based on the proposed structure, we formulate an energy optimization model with the constrains of system execution latency and trajectory prediction accuracy. Moreover, we propose a long-term dynamic resource allocation algorithm to obtain the optimal resource allocation solution for the accurate and consecutive tracking. Simulation results demonstrate that our algorithm outperforms the deep Q-learning over 14.5% in terms of system energy consumption. It can also obtain a significant enhancement in tracking accuracy compared with the non-cooperative scheme.

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