Energy-Optimal Data Aggregation and Dissemination for the Internet of Things

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Energy-Optimal Data Aggregation and Dissemination for the Internet of Things. / Fitzgerald, Emma; Pioro, Michal; Tomaszewski, Artur.

In: IEEE Internet of Things Journal, Vol. 5, No. 2, 2018, p. 955-969.

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Fitzgerald, Emma ; Pioro, Michal ; Tomaszewski, Artur. / Energy-Optimal Data Aggregation and Dissemination for the Internet of Things. In: IEEE Internet of Things Journal. 2018 ; Vol. 5, No. 2. pp. 955-969.

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TY - JOUR

T1 - Energy-Optimal Data Aggregation and Dissemination for the Internet of Things

AU - Fitzgerald, Emma

AU - Pioro, Michal

AU - Tomaszewski, Artur

PY - 2018

Y1 - 2018

N2 - Established approaches to data aggregation in wirelesssensor networks (WSNs) do not cover the variety of new usecases developing with the advent of the Internet of Things. In particular,the current push towards fog computing, in which control,computation, and storage are moved to nodes close to the networkedge, induces a need to collect data at multiple sinks, ratherthan the single sink typically considered in WSN aggregationalgorithms. Moreover, for machine-to-machine communicationscenarios, actuators subscribing to sensor measurements may alsobe present, in which case data should be not only aggregated andprocessed in-network, but also disseminated to actuator nodes. Inthis paper, we present mixed-integer programming formulationsand algorithms for the problem of energy-optimal routing andmultiple-sink aggregation, as well as joint aggregation anddissemination, of sensor measurement data in IoT edge networks.We consider optimisation of the network for both minimal totalenergy usage, and min-max per-node energy usage. We alsoprovide a formulation and algorithm for throughput-optimalscheduling of transmissions under the physical interference modelin the pure aggregation case. We have conducted a numericalstudy to compare the energy required for the two use cases, aswell as the time to solve them, in generated network scenarioswith varying topologies and between 10 and 40 nodes. Althoughaggregation only accounts for less than 15% of total energyusage in all cases tested, it provides substantial energy savings.Our results show more than 13 times greater energy usage for40-node networks using direct, shortest-path flows from sensorsto actuators, compared with our aggregation and disseminationsolutions.

AB - Established approaches to data aggregation in wirelesssensor networks (WSNs) do not cover the variety of new usecases developing with the advent of the Internet of Things. In particular,the current push towards fog computing, in which control,computation, and storage are moved to nodes close to the networkedge, induces a need to collect data at multiple sinks, ratherthan the single sink typically considered in WSN aggregationalgorithms. Moreover, for machine-to-machine communicationscenarios, actuators subscribing to sensor measurements may alsobe present, in which case data should be not only aggregated andprocessed in-network, but also disseminated to actuator nodes. Inthis paper, we present mixed-integer programming formulationsand algorithms for the problem of energy-optimal routing andmultiple-sink aggregation, as well as joint aggregation anddissemination, of sensor measurement data in IoT edge networks.We consider optimisation of the network for both minimal totalenergy usage, and min-max per-node energy usage. We alsoprovide a formulation and algorithm for throughput-optimalscheduling of transmissions under the physical interference modelin the pure aggregation case. We have conducted a numericalstudy to compare the energy required for the two use cases, aswell as the time to solve them, in generated network scenarioswith varying topologies and between 10 and 40 nodes. Althoughaggregation only accounts for less than 15% of total energyusage in all cases tested, it provides substantial energy savings.Our results show more than 13 times greater energy usage for40-node networks using direct, shortest-path flows from sensorsto actuators, compared with our aggregation and disseminationsolutions.

U2 - 10.1109/JIOT.2018.2803792

DO - 10.1109/JIOT.2018.2803792

M3 - Article

VL - 5

SP - 955

EP - 969

JO - IEEE Internet of Things Journal

T2 - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 2

ER -