Energy-efficient reporting mechanisms for multi-type real-time monitoring in Machine-to-Machine communications networks

Fu, Huai-Lei; Chen, Hou-Chun; Lin, Pengzhi; Fang, Yuguang

doi:10.1109/infcom.2012.6195501

Cited by 22 publications

(13 citation statements)

References 18 publications

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“…Optimal approaches with multiple sinks include [38], however here only cluster formation is optimised, not the overall routing tree, and cluster heads then transmit data to a central collection point using single-hop transmissions. In [17] multiple sinks were also used, giving an integer linear programming formulation for an optimal data collection schedule within each cluster such that at least one sensor measurement of each type is collected in each frame. Again, multihop transmission is not considered; sensor nodes transmit directly to their nearest collection point.…”

mentioning

confidence: 99%

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

Fitzgerald

Pióro

Tomaszewski

2018

IEEE Internet Things J.

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Abstract-Established approaches to data aggregation in wireless sensor networks (WSNs) do not cover the variety of new use cases 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 network edge, induces a need to collect data at multiple sinks, rather than the single sink typically considered in WSN aggregation algorithms. Moreover, for machine-to-machine communication scenarios, actuators subscribing to sensor measurements may also be present, in which case data should be not only aggregated and processed in-network, but also disseminated to actuator nodes. In this paper, we present mixed-integer programming formulations and algorithms for the problem of energy-optimal routing and multiple-sink aggregation, as well as joint aggregation and dissemination, of sensor measurement data in IoT edge networks. We consider optimisation of the network for both minimal total energy usage, and min-max per-node energy usage. We also provide a formulation and algorithm for throughput-optimal scheduling of transmissions under the physical interference model in the pure aggregation case. We have conducted a numerical study to compare the energy required for the two use cases, as well as the time to solve them, in generated network scenarios with varying topologies and between 10 and 40 nodes. Although aggregation only accounts for less than 15% of total energy usage in all cases tested, it provides substantial energy savings. Our results show more than 13 times greater energy usage for 40-node networks using direct, shortest-path flows from sensors to actuators, compared with our aggregation and dissemination solutions.

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confidence: 99%

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

Fitzgerald

Pióro

Tomaszewski

2018

IEEE Internet Things J.

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“…Thus, a node i ∈ N uses Eq. 2 to determine the number of payloads composing the output aggregated message in the communication round k. outP ayloads k i = inP ayloads(InputDatai)(1−AggLeveli) (2) As outP ayload k i is a real number, DAV-NET rounds the number of output payloads if it is less than 1.…”

Section: Dynamic Aggregation Levelmentioning

confidence: 99%

“…In this context, some important applications require Many-to-One and Periodic (MOP) communication, where the nodes sense the environment and communicate it to the sink (also called gateway) node, periodically [2]. Examples of M2M applications that perform MOP communication are smart-metering, environment monitoring, and agriculture monitoring [3].…”

Section: Introductionmentioning

confidence: 99%

Data aggregation for machine-to-machine communication with energy harvesting

Riker

Cerqueira

Curado

et al. 2015

2015 IEEE International Workshop on Measurements &Amp; Networking (M&N)

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Machine-to-Machine (M2M) communications have emerged as a new concept for the next generation of sensing and actuating systems. With the recent emergence of energy harvesting technologies, the current communication solutions have addressed the problem of controlling the data communication to regulate efficiently the energy consumption, aiming to avoid under and overuse of energy. However, these solutions do not consider data aggregation as means of controlling the network traffic. To fill this gap, this paper proposes the Data Aggregation for energy harVesting NETworks (DAV-NET), which regulates the energy consumption in accordance with the residual energy stored in the batteries, exploiting the data aggregation capabilities to control the network traffic. The performed simulations show that the proposed solution is able to regulate the energy consumption in case of abundant or scarce energy, controlling the aggregation level in a distributed fashion.978-1-4799-1860-7/15/$31.00 ©2015 IEEE This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.

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“…Taking into account of the energy supply problem in M2M network, a recently developed technology, called energy-harvesting, has attracted considerable attentions [14], [15]. By using energy harvesting, wireless networks could potentially have unlimited and green energy, supplied by renewable energy sources, such as solar, wind, thermal, acoustic, etc.…”

Section: Introductionmentioning

confidence: 99%

An Efficient MAC Protocol With Adaptive Energy Harvesting for Machine-to-Machine Networks

Liu

Yang

et al. 2015

IEEE Access

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In a machine-to-machine network, the throughput performance plays a very important role. Recently, an attractive energy harvesting technology has shown great potential to the improvement of the network throughput, as it can provide consistent energy for wireless devices to transmit data. Motivated by that, an efficient energy harvesting-based medium access control (MAC) protocol is designed in this paper. In this protocol, different devices first harvest energy adaptively and then contend the transmission opportunities with energy level related priorities. Then, a new model is proposed to obtain the optimal throughput of the network, together with the corresponding hybrid differential evolution algorithm, where the involved variables are energy-harvesting time, contending time, and contending probability. Analytical and simulation results show that the network based on the proposed MAC protocol has greater throughput than that of the traditional methods. In addition, as expected, our scheme has less transmission delay, further enhancing its superiority.INDEX TERMS Machine-to-machine network, adaptive energy harvesting, medium access control.

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Energy-efficient reporting mechanisms for multi-type real-time monitoring in Machine-to-Machine communications networks

Cited by 22 publications

References 18 publications

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

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

Data aggregation for machine-to-machine communication with energy harvesting

An Efficient MAC Protocol With Adaptive Energy Harvesting for Machine-to-Machine Networks

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