Optimizing link rate assignment and transmission scheduling in WMN through compatible set generation

Li, Yuan; Pióro, Michał; Yuan, Di; Su, Jinshu

doi:10.1007/s11235-015-0004-y

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Observe that the final simplex solution of the linear relaxation of (2) will contain at most |A| non-zero values in the vector T * = (T * c , c ∈ C) specifying the optimal vertex. As shown in [40], this property helps to develop a reasonable heuristic for solving the primal problem.…”

Section: Non-aggregating Sensor Nodesmentioning

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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Section: Non-aggregating Sensor Nodesmentioning

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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“…Extensions for multiple modulation and coding schemes (MCSs) and power control were given in [36] and [37]. C-sets have been applied to joint link rate assignment and transmission scheduling [38], link scheduling [39], routing and scheduling for throughput optimization [40], and multicast routing and scheduling [41]. While the above were primarily focused on throughput maximization, work using c-sets has also considered fairness [37], [42], delay minimization [43], [44] and energy efficiency [45].…”

Section: Related Workmentioning

confidence: 99%

Massive MIMO Optimization With Compatible Sets

Fitzgerald

Pióro

Tufvesson

2019

IEEE Trans. Wireless Commun.

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Massive multiple-input multiple-output (MIMO) is expected to be a vital component in future 5G systems. As such, there is a need for new modeling in order to investigate the performance of massive MIMO not only at the physical layer, but also higher up the networking stack. In this paper, we present general optimization models for massive MIMO, based on mixedinteger programming and compatible sets, with both maximum ratio combing and zero forcing precoding schemes. We then apply our models to the case of joint device scheduling and power control for heterogeneous devices and traffic demands, in contrast to existing power control schemes that consider only homogeneous users and saturated scenarios. Our results show substantial benefits in terms of energy usage can be achieved without sacrificing throughput, and that both signalling overhead and the complexity of end devices can be reduced by abrogating the need for uplink power control through efficient scheduling.

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