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arxiv: 1907.09544 · v1 · pith:5IGQB3DDnew · submitted 2019-07-22 · 📡 eess.SP · cs.NI

Networking and processing in optical wireless

Osama Zwaid Alsulami , Amal A. Alahmadi , Sarah O. M. Saeed , Sanaa Hamid Mohamed , T. E. H. El-Gorashi , Mohammed T. Alresheedi , Jaafar M. H. Elmirghani This is my paper

Pith reviewed 2026-05-24 17:39 UTC · model grok-4.3

classification 📡 eess.SP cs.NI
keywords optical wireless communicationcloud fog architecturewavelength division multiple accessmixed integer linear programmingresource allocationtask placementvisible light communicationfog computing
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The pith

A cloud and fog architecture for optical wireless communication provides both high data rate links and distributed processing.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a cloud and fog architecture for optical wireless communication to support multiple users. Visible indoor light forms high data rate connections to mobile nodes that are clustered as fog mini servers to deliver processing services through the same optical channel. Two mixed-integer linear programming models optimize resource allocation in the indoor system and the placement of processing tasks across room, building, campus, metro and remote cloud levels. A sympathetic reader would care because the approach combines communication and computation in one optical setup, potentially allowing mobile nodes to both receive data and contribute processing without separate radio infrastructure.

Core claim

The authors propose for the first time a cloud/fog-integrated architecture for OWC that uses visible indoor light to create high data rate connections with potential mobile nodes. These optical wireless nodes are clustered and used as fog mini servers to provide processing services through the optical wireless channel for other users, with additional fog processing units located in the room, the building, the campus and at the metro level and further capabilities provided by remote cloud sites. Two MILP models are developed and utilised to optimise resource allocation in the indoor OWC system and the placement of processing tasks in the different fog and cloud nodes, with analysis across a=2

What carries the argument

The cloud/fog-integrated architecture that clusters optical wireless nodes as fog mini-servers to provide processing services through the optical wireless channel while using WDMA for multiple access.

Load-bearing premise

The optical wireless nodes can reliably act as fog mini-servers while maintaining the stated data rates and processing services.

What would settle it

Solving the two MILP models for scenarios with dozens of nodes and realistic task loads to check whether feasible solutions appear in practical computation time, or measuring actual sustained data rates when nodes simultaneously handle communication and processing tasks.

Figures

Figures reproduced from arXiv: 1907.09544 by Amal A. Alahmadi, Jaafar M. H. Elmirghani, Mohammed T. Alresheedi, Osama Zwaid Alsulami, Sanaa Hamid Mohamed, Sarah O. M. Saeed, T. E. H. El-Gorashi.

Figure 1
Figure 1. Figure 1: CDF of the optical channel bandwidth at different locations in the room using the parameters in [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A room with three users. Solid lines indicate assignment of an access point to a user. Dot-dashed lines show interference between users using the same wavelength. Dotted lines indicate unmodulated wavelengths from access points causing background light shot noise. Users were distributed over this room using a 2D Poisson point process (PPP) and two 8-users scenarios with fixed user locations were considered… view at source ↗
Figure 3
Figure 3. Figure 3: Optical channel bandwidth [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: SINR for different users [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Data rates for different users. 4. Optimum placement of processing to minimise power consumption The proposed integrated cloud/fog architecture is shown in [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Cloud/fog-based architecture. The processing power consumption, 𝑃𝑛 is given as: 𝑃𝑛 = ∑ 𝑋𝑘𝑛 𝑘 𝜖 𝐾 𝐸𝑛 ∀ 𝑛 ∈ 𝑃𝑁, (19) where 𝑋𝑘𝑛 is the workload demanded by task k, in million instructions per second (MIPS), assigned to processing node n. 𝐸𝑛 is the energy in watts per MIPS of the node processor, calculated using the maximum processing capacity of the node. The networking power consumption, 𝒫𝑛 is given as: 𝒫𝑛 =… view at source ↗
Figure 5
Figure 5. Figure 5: The results in [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Processing, networking, and total power consumption vs. processing workload per demand, for different DRR values. At DRR= 0.04 and 0.06, the placement decision remains affected by the processing power consumption. This results in a small increase at DRR=0.04 compared to that at 0.06 because the workload is offloaded in a further location (MetroFog) at 0.04 while at 0.06 it is offloaded to a more networking… view at source ↗
Figure 8
Figure 8. Figure 8: shows the overall workload assignment for each processing location in relation to the networking power consumption for the case of DRR=0.6. For the assigned workload, the figure shows where demands are placed for each given data rate and how this affects the networking power consumption of this placement. Starting from low data rate demands, demands are assigned to the most efficient location in term of to… view at source ↗
Figure 10
Figure 10. Figure 10: Processing power consumption, networking power consumption, and total power consumption, for scenario 1 (S1) and scenario 2 (S2) (DRR=0.6) [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Mobile processing utilisation per assigned wavelength for scenario 1 (S1), scenario 2 (S2) and the ideal scenario (DRR=0.6) [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
read the original abstract

Optical wireless communication (OWC) is a promising technology that can provide high data rates while supporting multiple users. The Optical Wireless (OW) physical layer has been researched extensively, however less work was devoted to multiple access and how the OW front end is connected to the network. In this paper, an OWC system which employs a wavelength division multiple access (WDMA) scheme is studied, for the purpose of supporting multiple users. In addition, a cloud/fog architecture is proposed for the first time for OWC to provide processing capabilities. The cloud/fog-integrated architecture uses visible indoor light to create high data rate connections with potential mobile nodes. These optical wireless nodes are further clustered and used as fog mini servers to provide processing services through the optical wireless channel for other users. Additional fog processing units are located in the room, the building, the campus and at the metro level. Further processing capabilities are provided by remote cloud sites. A mixed-integer linear programming (MILP) model was developed and utilised to optimise resource allocation in the indoor OWC system. A second MILP model was developed to optimise the placement of processing tasks in the different fog and cloud nodes available. The optimisation of tasks placement in the cloud-/fog-integrated architecture was analysed using the MILP models. Multiple scenarios were considered where the mobile node locations were varied in the room and the amount of processing and data rate requested by each optical wireless node is varied. The results help identify the optimum colour and access point to use for communication for a given mobile node location and OWC system configuration, the optimum location to place processing and the impact of the network architecture. Areas for future work are identified.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes a cloud/fog architecture for optical wireless communication (OWC) using wavelength division multiple access (WDMA) in indoor settings. It introduces two mixed-integer linear programming (MILP) models—one for optimizing color and access-point assignments to support high data-rate connections with mobile nodes, and a second for placing processing tasks across fog mini-servers (clustered OW nodes) and higher-tier fog/cloud nodes. Multiple scenarios varying mobile-node locations and processing/data-rate demands are stated to have been optimized to identify optimum configurations and network impacts.

Significance. If the MILP formulations prove tractable at realistic scales and the OW nodes can sustain the claimed data rates while acting as fog servers, the work would represent a novel integration of cloud/fog processing with OWC, extending beyond physical-layer studies to address networking and distributed computation. The explicit use of MILP for joint resource and task optimization, together with the multi-tier architecture spanning room-to-metro levels, would provide a concrete framework for future OWC system design.

major comments (2)
  1. [Abstract] Abstract: the central claim that the two MILP models successfully optimise resource allocation and task placement across multiple scenarios with varying mobile-node locations and demands is unsupported, because the abstract (and therefore the manuscript's reported results) supplies no data on problem dimensionality (binary variables, constraints), solver runtimes, or scaling behaviour when the number of OW nodes or fog tiers increases. Since MILP is NP-hard, this omission directly undermines the practicality of the proposed architecture.
  2. [Abstract] Abstract: the assumption that clustered optical wireless nodes can reliably function as fog mini-servers while simultaneously maintaining the stated high data rates is load-bearing for the architecture claim, yet no validation, error analysis, or performance metrics against simulation or measurement are provided to support it.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'for the first time' for the cloud/fog proposal should be accompanied by a brief literature comparison to substantiate novelty.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address each major comment below, indicating planned changes to the manuscript where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the two MILP models successfully optimise resource allocation and task placement across multiple scenarios with varying mobile-node locations and demands is unsupported, because the abstract (and therefore the manuscript's reported results) supplies no data on problem dimensionality (binary variables, constraints), solver runtimes, or scaling behaviour when the number of OW nodes or fog tiers increases. Since MILP is NP-hard, this omission directly undermines the practicality of the proposed architecture.

    Authors: We agree that the abstract provides no quantitative information on problem size, number of binary variables or constraints, solver runtimes, or scaling behaviour. The full manuscript formulates the two MILP models and applies them to a set of small-scale scenarios (limited numbers of access points, mobile nodes, and fog tiers) to obtain optimum configurations, but contains no dedicated computational analysis. We will revise the abstract to qualify the optimisation claims and add a short discussion of the problem dimensions and observed runtimes for the evaluated instances, while noting that the models are intended for offline planning and that larger instances would require heuristics. This directly addresses the concern about demonstrated practicality. revision: yes

  2. Referee: [Abstract] Abstract: the assumption that clustered optical wireless nodes can reliably function as fog mini-servers while simultaneously maintaining the stated high data rates is load-bearing for the architecture claim, yet no validation, error analysis, or performance metrics against simulation or measurement are provided to support it.

    Authors: The manuscript proposes an architecture in which clustered OW nodes act as fog mini-servers; the data-rate assumptions are drawn from the WDMA OWC physical-layer model used throughout the paper. No hardware validation, error analysis, or joint communication-processing metrics are supplied because the work centres on the MILP resource-allocation and task-placement formulations rather than experimental demonstration. We will revise the text to state this modelling assumption explicitly and to list combined communication-processing validation as an item of future work. revision: partial

Circularity Check

0 steps flagged

No circularity: architecture proposal and MILP optimization are forward derivations

full rationale

The paper introduces a cloud/fog architecture for OWC and develops two MILP models to optimize colour/AP assignments and task placements across scenarios with varying mobile node locations and demands. No equations, predictions, or central claims reduce by construction to fitted inputs, self-definitions, or self-citation chains; the MILP formulations are presented as independent optimization tools applied to the proposed system without any load-bearing uniqueness theorems or ansatzes imported from prior author work. The results identify optima for given configurations, remaining self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms or invented entities; the MILP models are presumed to contain standard linear-programming variables whose concrete values are not stated.

pith-pipeline@v0.9.0 · 5877 in / 967 out tokens · 24775 ms · 2026-05-24T17:39:13.194113+00:00 · methodology

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