The Shared Sensor Network (SSN) model has recently emerged to reduce the high deployment and management costs of application-specific WSNs. In a SSN, the underlying physical infrastructure is shared among multiple applications simultaneously. Sensor tasks are likely to have different QoS requirements, e.g. in terms of sensing rate and coverage. In this scenario, we advocate the use of a local application broker (typically deployed on an edge device) to act as a mediator between the physical sensing resources and the sensing tasks, and to support efficient resource allocation and controlled sharing of cached data between applications. To this end, we have formulated an optimisation problem to determine: (i) the set of sensing resources to use; (ii) a mapping between activated sensor resources and admitted applications; and (iii) the probing rate of the broker for the activated sensors. The objective of our model is to maximise both the number of admitted applications (i.e. the revenue for the provider of the sensing infrastructure) and the system lifetime. We have implemented a prototype of the proposed broker using native CoAP functionalities, and we have conducted an extensive evaluation in an emulation environment. Results showed that our application broker provides better performance in terms of the number of admitted applications and energy efficiency than a state-of-the-art benchmark

Edge-Assisted Resource Management for Data-Centric IoT Applications in Shared Sensor Networks

S Bolettieri;R Bruno
2020

Abstract

The Shared Sensor Network (SSN) model has recently emerged to reduce the high deployment and management costs of application-specific WSNs. In a SSN, the underlying physical infrastructure is shared among multiple applications simultaneously. Sensor tasks are likely to have different QoS requirements, e.g. in terms of sensing rate and coverage. In this scenario, we advocate the use of a local application broker (typically deployed on an edge device) to act as a mediator between the physical sensing resources and the sensing tasks, and to support efficient resource allocation and controlled sharing of cached data between applications. To this end, we have formulated an optimisation problem to determine: (i) the set of sensing resources to use; (ii) a mapping between activated sensor resources and admitted applications; and (iii) the probing rate of the broker for the activated sensors. The objective of our model is to maximise both the number of admitted applications (i.e. the revenue for the provider of the sensing infrastructure) and the system lifetime. We have implemented a prototype of the proposed broker using native CoAP functionalities, and we have conducted an extensive evaluation in an emulation environment. Results showed that our application broker provides better performance in terms of the number of admitted applications and energy efficiency than a state-of-the-art benchmark
2020
Istituto di informatica e telematica - IIT
Cloud Computing
Monitoring
Quality of service
Resource management
Sensors
Task analysis
Wireless Sensor Networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/381967
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