In-Network Computations of Machine-to-Machine Communications for Wireless Robotics

Tseng, Fan-Min; Lin, Chi-Heng; Chen, Kwang-Cheng

doi:10.1007/s11277-013-1119-7

Cited by 6 publications

(4 citation statements)

References 44 publications

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“…In this case, the aggregation function would be to take the maximum of the aggregated temperature measurements. With the use of in-network processing, the functions to be executed at intermediate nodes can range from simple aggregation functions, such as the maximum function for the nuclear reactor, to more complex computations like compress-andforward [1] or error correction, which may use an appreciable amount of energy in their execution.…”

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confidence: 99%

“…RELATED WORK The problem of energy-efficient data aggregation in wireless sensor networks first began to receive substantial attention in the 2000's [10]. Given the constrained computation capacity of sensor nodes and the distributed nature of such networks, much of the focus was on approximation algorithms for practical implementation, although some work has also been carried out on finding optimal solutions [1], [11]- [18]. Approximation algorithms for data aggregation can be mostly divided into tree-based [19]- [23], clustered [24]- [29], and hybrid [30] approaches [10].…”

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confidence: 99%

“…A scenario with multiple gateways motivates the work in [1], but only a single sink is used in the actual optimisation formulation. This work is however interesting from another perspective, as it uses compress-and-forward to reduce the size of a node's transmission after it has overheard correlated measurements from other nodes.…”

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confidence: 99%

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

Fitzgerald

Pióro

Tomaszewski

2018

IEEE Internet Things J.

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“…This is first due to the new challenges this causes in routing of sensor measurements, which we addressed in [1], and also because this involves multicast transmissions. Research considering multiple data sinks is limited, but includes the works in [16][17][18]. However, none of these works addresses multi-hop routing with multicast transmission, which we focus on in this paper.…”

Section: Related Workmentioning

confidence: 99%

Energy Versus Throughput Optimisation for Machine-to-Machine Communication

Fitzgerald

Pióro

Tomaszewski

2020

Sensors

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We investigate the trade-off between energy usage and (packet) throughput in wireless mesh networks performing machine-to-machine communication. For this we provide a novel mixed-integer programming formulation to maximise the throughput while maintaining minimal energy usage, together with an effective price-and-branch solution algorithm based on column generation. The resulting optimisation model is the main original contribution of the presented paper. We conducted a numerical study using network examples from 10 to 40 nodes, in which periodic sensor measurements are aggregated and disseminated to actuators. In almost all cases, we were able to achieve maximum throughput and minimum energy usage simultaneously, and in those cases where this was not possible, the costs incurred in one objective in order to achieve the other were typically low. The solution times for all network sizes were of the order of seconds, showing that our optimisation model is feasible to use in practice.

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