Spectrum Allocation and Device Association in Federated Learning-Enabled Industrial IoT via Hypergraph Matching

10.36227/techrxiv.15074076 ◽

2021 ◽

Author(s):

Suiyuan Wu ◽

Long Zhang ◽

Yao Wang ◽

Zhu Han

Keyword(s):

Search Algorithm ◽

Spectrum Allocation ◽

Superior Performance ◽

Maximization Problem ◽

Integer Linear Programming Problem ◽

Hypergraph Matching ◽

Hypergraph Model ◽

Industrial Internet ◽

Industrial Iot ◽

Utility Maximization Problem

In this paper, a joint spectrum allocation and device association problem is investigated for a federated learning aided hierarchical Industrial Internet of Things (IIoT) system for smart factory. To achieve the optimization jointly, we design a weighted learning utility maximization problem, which is a 0-1 integer linear programming problem. To solve this problem, we convert it into a weighted 3D hypergraph model by capturing the 3D mapping relation for IIoT device, subchannel, and edge server. A local search algorithm is then presented to find a 3D hypergraph matching with maximum total weights as the suboptimal solution. Simulation results demonstrate the superior performance of the proposed algorithm compared with the greedy algorithm in the system learning utility.

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Satellite-Aerial Integrated Computing in Disasters: User Association and Offloading Decision

10.36227/techrxiv.11396760 ◽

2020 ◽

Author(s):

Long Zhang ◽

Hongliang Zhang ◽

Chao Guo ◽

Haitao Xu ◽

Lingyang Song ◽

...

Keyword(s):

Low Earth Orbit ◽

User Association ◽

Integer Linear Programming Problem ◽

Hypergraph Matching ◽

Hypergraph Model ◽

High Altitude Platforms ◽

Np Complete ◽

Sum Rate ◽

Vertex Disjoint ◽

Leo Satellite

In this paper, a satellite-aerial integrated computing (SAIC) architecture in disasters is proposed, where the computation tasks from two-tier users, i.e., ground/aerial user equipments, are either locally executed at the high-altitude platforms (HAPs), or offloaded to and computed by the Low Earth Orbit (LEO) satellite. With the SAIC architecture, we study the problem of joint two-tier user association and offloading decision aiming at the maximization of the sum rate. The problem is formulated as a 0-1 integer linear programming problem which is NP-complete. A weighted 3-uniform hypergraph model is obtained to solve this problem by capturing the 3D mapping relation for two-tier users, HAPs, and the LEO satellite. Then, a 3D hypergraph matching algorithm using the local search is developed to find a maximum-weight subset of vertex-disjoint hyperedges. Simulation results show that the proposed algorithm has improved the sum rate when compared with the conventional greedy algorithm.

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Satellite-Aerial Integrated Computing in Disasters: User Association and Offloading Decision

10.36227/techrxiv.11396760.v2 ◽

2020 ◽

Author(s):

Long Zhang ◽

Hongliang Zhang ◽

Chao Guo ◽

Haitao Xu ◽

Lingyang Song ◽

...

Keyword(s):

Low Earth Orbit ◽

User Association ◽

Integer Linear Programming Problem ◽

Hypergraph Matching ◽

Hypergraph Model ◽

High Altitude Platforms ◽

Np Complete ◽

Sum Rate ◽

Vertex Disjoint ◽

Leo Satellite

In this paper, a satellite-aerial integrated computing (SAIC) architecture in disasters is proposed, where the computation tasks from two-tier users, i.e., ground/aerial user equipments, are either locally executed at the high-altitude platforms (HAPs), or offloaded to and computed by the Low Earth Orbit (LEO) satellite. With the SAIC architecture, we study the problem of joint two-tier user association and offloading decision aiming at the maximization of the sum rate. The problem is formulated as a 0-1 integer linear programming problem which is NP-complete. A weighted 3-uniform hypergraph model is obtained to solve this problem by capturing the 3D mapping relation for two-tier users, HAPs, and the LEO satellite. Then, a 3D hypergraph matching algorithm using the local search is developed to find a maximum-weight subset of vertex-disjoint hyperedges. Simulation results show that the proposed algorithm has improved the sum rate when compared with the conventional greedy algorithm.

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Satellite-Aerial Integrated Computing in Disasters: User Association and Offloading Decision

10.36227/techrxiv.11396760.v1 ◽

2019 ◽

Author(s):

Long Zhang ◽

Hongliang Zhang ◽

Chao Guo ◽

Haitao Xu ◽

Lingyang Song ◽

...

Keyword(s):

Low Earth Orbit ◽

User Association ◽

Integer Linear Programming Problem ◽

Hypergraph Matching ◽

Hypergraph Model ◽

High Altitude Platforms ◽

Np Complete ◽

Sum Rate ◽

Vertex Disjoint ◽

Leo Satellite

In this paper, a satellite-aerial integrated computing (SAIC) architecture in disasters is proposed, where the computation tasks from two-tier users, i.e., ground/aerial user equipments, are either locally executed at the high-altitude platforms (HAPs), or offloaded to and computed by the Low Earth Orbit (LEO) satellite. With the SAIC architecture, we study the problem of joint two-tier user association and offloading decision aiming at the maximization of the sum rate. The problem is formulated as a 0-1 integer linear programming problem which is NP-complete. A weighted 3-uniform hypergraph model is obtained to solve this problem by capturing the 3D mapping relation for two-tier users, HAPs, and the LEO satellite. Then, a 3D hypergraph matching algorithm using the local search is developed to find a maximum-weight subset of vertex-disjoint hyperedges. Simulation results show that the proposed algorithm has improved the sum rate when compared with the conventional greedy algorithm.

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Utility maximization problem using curve trapezoidal fuzzy number

Ifost ◽

10.1109/ifost.2013.6616992 ◽

2013 ◽

Author(s):

Ser-Od Bayaraa ◽

U. Delgersaikhan ◽

N. Dalaisaikhan

Keyword(s):

Fuzzy Number ◽

Utility Maximization ◽

Maximization Problem ◽

Trapezoidal Fuzzy Number ◽

Utility Maximization Problem

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UTILITY THEORY FRONT TO BACK — INFERRING UTILITY FROM AGENTS' CHOICES

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024914500186 ◽

2014 ◽

Vol 17 (03) ◽

pp. 1450018 ◽

Cited By ~ 7

Author(s):

ALEXANDER M. G. COX ◽

DAVID HOBSON ◽

JAN OBłÓJ

Keyword(s):

Utility Function ◽

Utility Theory ◽

Consistency Condition ◽

Maximization Problem ◽

Consumption Pattern ◽

Investment Strategies ◽

Inverse Approach ◽

To Come ◽

Utility Maximization Problem ◽

Stochastic Setting

We pursue an inverse approach to utility theory and associated consumption and investment problems. Instead of specifying a utility function and deriving the actions of an agent, we assume that we observe the actions of the agent (i.e. consumption and investment strategies) and ask if it is possible to derive a utility function for which the observed behavior is optimal. We work in continuous time both in a deterministic and stochastic setting. In the deterministic setup, we find that there are infinitely many utility functions generating a given consumption pattern. In the stochastic setting of a geometric Brownian motion market it turns out that the consumption and investment strategies have to satisfy a consistency condition (PDE) if they are to come from a classical utility maximization problem. We show further that important characteristics of the agent such as risk attitudes (e.g., DARA) can be deduced directly from the agent's consumption and investment choices.

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STABILITY OF THE UTILITY MAXIMIZATION PROBLEM WITH RANDOM ENDOWMENT IN INCOMPLETE MARKETS

Mathematical Finance ◽

10.1111/j.1467-9965.2010.00433.x ◽

2010 ◽

pp. no-no ◽

Cited By ~ 4

Author(s):

Constantinos Kardaras ◽

Gordan Žitković

Keyword(s):

Incomplete Markets ◽

Utility Maximization ◽

Maximization Problem ◽

Random Endowment ◽

Utility Maximization Problem

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Ls-II: An Improved Locust Search Algorithm for Solving Optimization Problems

Mathematical Problems in Engineering ◽

10.1155/2018/4148975 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 9

Author(s):

Octavio Camarena ◽

Erik Cuevas ◽

Marco Pérez-Cisneros ◽

Fernando Fausto ◽

Adrián González ◽

...

Keyword(s):

Optimization Problems ◽

Search Algorithm ◽

Search Space ◽

Optimization Method ◽

Superior Performance ◽

Solution Quality ◽

Original Algorithm ◽

Motion Models ◽

Search Mechanism ◽

Solitary Phase

The Locust Search (LS) algorithm is a swarm-based optimization method inspired in the natural behavior of the desert locust. LS considers the inclusion of two distinctive nature-inspired search mechanism, namely, their solitary phase and social phase operators. These interesting search schemes allow LS to overcome some of the difficulties that commonly affect other similar methods, such as premature convergence and the lack of diversity on solutions. Recently, computer vision experiments in insect tracking methods have conducted to the development of more accurate locust motion models than those produced by simple behavior observations. The most distinctive characteristic of such new models is the use of probabilities to emulate the locust decision process. In this paper, a modification to the original LS algorithm, referred to as LS-II, is proposed to better handle global optimization problems. In LS-II, the locust motion model of the original algorithm is modified incorporating the main characteristics of the new biological formulations. As a result, LS-II improves its original capacities of exploration and exploitation of the search space. In order to test its performance, the proposed LS-II method is compared against several the state-of-the-art evolutionary methods considering a set of benchmark functions and engineering problems. Experimental results demonstrate the superior performance of the proposed approach in terms of solution quality and robustness.

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