Heuristic Algorithms for One-Slot Link Scheduling in Wireless Sensor Networks under SINR

Deng, Hui; Yu, Jiguo; Yu, Dongxiao; Li, Guangshun; Huang, Baogui

doi:10.1155/2015/806520

Cited by 10 publications

(7 citation statements)

References 24 publications

(43 reference statements)

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“…In the context of IoT deployments, formal modelling approaches have been recently proposed to achieve connectivity and coverage optimisation [20], [21], improved resource exploitation of Wireless Sensors Networks [22], and to estimate reliability and cost of service compositions [23]. Our modelling effort aims at complementing that work, by describing the interactions among software components and IoT devices at a higher level of abstraction to achieve segmentation of applications through the Fog-that, to the best of our knowledge, was not addressed by previous work.…”

Section: Related Workmentioning

confidence: 99%

QoS-Aware Deployment of IoT Applications Through the Fog

Brogi

Forti

2017

IEEE Internet Things J.

385

262

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Abstract-Fog computing aims at extending the Cloud by bringing computational power, storage and communication capabilities to the edge of the network, in support of the IoT. Segmentation, distribution and adaptive deployment of functionalities over the continuum from Things to Cloud are challenging tasks, due to the intrinsic heterogeneity, hierarchical structure and very large scale infrastructure they will have to exploit.In this paper we propose a simple, yet general, model to support the QoS-aware deployment of multi-component IoT applications over Fog infrastructures. The model describes operational systemic qualities of the available infrastructure (latency and bandwidth), interactions among software components and Things, and business policies. Algorithms to determine eligible deployment plans for an application over a Fog infrastructure are presented. A Java tool, FogTorch, based on the proposed model has been prototyped.

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Section: Related Workmentioning

confidence: 99%

QoS-Aware Deployment of IoT Applications Through the Fog

Brogi

Forti

2017

IEEE Internet Things J.

385

262

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“…The algorithm considered both heavy requests and light requests of A for determining the link schedule. In [27], a maximum tolerance and minimum (MTMA) model based on a greedy schedule was studied for maximum link scheduling in one-slot. The experiments showed that the algorithm improved 28–64% of the current algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In Figure 4, we compare the time required (seconds) for reaching optimality with the DNN-based algorithm against the ones experienced by using respectively the conventional integer linear programming optimization scheme solved with CPLEX (C-Solver) and the maximum weighted links scheduling (MWLS) algorithm, a greedy approach for D2D link scheduling under the SINR constraints based on [27,28]. The TensorFlow framework has been used to train the DNN-based algorithm.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Deep Learning Approach for Maximum Activity Links in D2D Communications

Zhang

Palmieri

et al. 2019

Sensors

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Mobile cellular communications are experiencing an exponential growth in traffic load on Long Term Evolution (LTE) eNode B (eNB) components. Such load can be significantly contained by directly sharing content among nearby users through device-to-device (D2D) communications, so that repeated downloads of the same data can be avoided as much as possible. Accordingly, for the purpose of improving the efficiency of content sharing and decreasing the load on the eNB, it is important to maximize the number of simultaneous D2D transmissions. Specially, maximizing the number of D2D links can not only improve spectrum and energy efficiency but can also reduce transmission delay. However, enabling maximum D2D links in a cellular network poses two major challenges. First, the interference between the D2D and cellular communications could critically affect their performance. Second, the minimum quality of service (QoS) requirement of cellular and D2D communication must be guaranteed. Therefore, a selection of active links is critical to gain the maximum number of D2D links. This can be formulated as a classical integer linear programming problem (link scheduling) that is known to be NP-hard. This paper proposes to obtain a set of network features via deep learning for solving this challenging problem. The idea is to optimize the D2D link schedule problem with a deep neural network (DNN). This makes a significant time reduction for delay-sensitive operations, since the computational overhead is mainly spent in the training process of the model. The simulation performed on a randomly generated link schedule problem showed that our algorithm is capable of finding satisfactory D2D link scheduling solutions by reducing computation time up to 90% without significantly affecting their accuracy.

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“…Moscibroda and Wattenhofer [10] shifted interference models from simple graph-based models to a more realistic model, that is, the signal to interference plus noise ratio (SINR) model. Recently, much research on link scheduling problems has been conducted under the SINR model (e.g., [11], [12], [13], [14], [15], [16], [17], [18]). The SINR model considers global interference, i.e., the sum of all interfering signals plus the background noise.…”

Section: Introductionmentioning

confidence: 99%

Shortest Link Scheduling in Wireless Networks Under The Rayleigh Fading Model

Huang

et al. 2021

Preprint

Self Cite

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Many shortest link scheduling algorithms adopt non-fading SINR interference model, which assumes that the received signal power will always remain determinate as long as the transmission power of the corresponding sender is fixed. In fact, because environment always influences the propagation of radio signals, the received signal power is by no means a certain value. Rayleigh fading is a statistical model for radio signals propagation. It assumes that the strength of a signal on a receiver is a random variable, varying with the Rayleigh distribution. This paper proposes a shortest link scheduling algorithm under the Rayleigh fading model (SLSRF). The SLSRF partitions the wireless network area into hexagons and colors the hexagons with 3 different colors such that two neighboring hexagons have different colors. The senders of the links scheduled simultaneously are arranged in hexagons with the same color. The correctness of the SLSRF is proved through theoretical analysis, and the efficiency is illustrated by elaborate simulations. Our simulation results demonstrate that the schedule delay of SLSRF is less than that of some results under the non-fading SINR interference model. Furthermore, we extend the SLSRF to a distributed version, which is suitable for large wireless networks.

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Heuristic Algorithms for One-Slot Link Scheduling in Wireless Sensor Networks under SINR

Cited by 10 publications

References 24 publications

QoS-Aware Deployment of IoT Applications Through the Fog

QoS-Aware Deployment of IoT Applications Through the Fog

A Deep Learning Approach for Maximum Activity Links in D2D Communications

Shortest Link Scheduling in Wireless Networks Under The Rayleigh Fading Model

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