This paper concentrates on the problem of associating an intelligent reflecting surface (IRS) to multiple users in a multiple-input single-output (MISO) downlink wireless communication network. The main objective of the paper is to maximize the sum-rate of all users by solving the joint optimization problem of the IRS-user association, IRS reflection, and BS beamforming, formulated as a non-convex mixed-integer optimization problem. The variable separation and relaxation are used to transform the problem into three convex sub-problems, which are alternatively solved through the convex optimization (CO) method. The major drawback of the proposed CO-based algorithm is high computational complexity. Thus, we make use of machine learning (ML) to tackle this problem. To this end, first, we convert the optimization problem into a regression problem. Then, we solve it with feed-forward neural networks (FNNs), trained by CO-based generated data. Simulation results show that the proposed ML-based algorithm has a performance equivalent to the CO-based algorithm, but with less computation complexity due to its offline training procedure.

Online manufacturing platforms are spreading in the manufacturing industry in the shade of Cloud Manufacturing systems. The paper manipulates a matching mechanism for public Cloud Manufacturing platforms based on the Intuitionistic Fuzzy Sets Theory, VIKOR method, and Deferred Acceptance algorithm. The VIKOR method provides a rational, scientific, and systematic approach to enable providers and consumers to evaluate the attributes of each other and develop their preference lists. The method is augmented by the Intuitionistic Fuzzy Sets Theory to grasp the high level of vagueness and uncertainty in the process of preference list development. The utilization of Deferred Acceptance algorithm from matching games augmented the proposed mechanism for presenting stable pairs of manufacturing partners. Also, the paper studies the influence of proposer, matching algorithm, and ranking method on the mechanism. Finally, the paper presents some managerial insights, concludes the results, and states future research directions.

In this paper, we concentrate on the peak age of information (PAoI) in a discrete-time slotted ALOHA network comprised of M buffer-less nodes, capable of keeping just one packet at each slot. In this network, a collision happens whenever at least two nodes transmit their packets simultaneously. Thus, there is some interaction among queues, and the transmission delay of a packet might prolong more than one slot. The packets are generated at each node stochastically and independently. The nodes follow the preemptive queueing policy. In this network, we propose a trellis-based model to analyze packet system time and derive the exact average PAoI of symmetric and asymmetric slotted ALOHA networks. We confirm our analysis by simulation results in different conditions. By exploiting numerical results, we optimize the average PAoI of the symmetric slotted ALOHA network, find the PAoI-constrained regions, and compare the PAoI of slotted ALOHA networks with and without retransmission of the failed packets.

Online manufacturing markets are new emerging types of markets that are spreading in the shade of public Cloud Manufacturing. This paper introduces a public Cloud Manufacturing market and denotes its differences from a private one. Public Cloud Manufacturing enables service-oriented manufacturing models for everyone in society. The prosperity of such market is tied to appropriate matching between providers to consumers. This paper elaborates on a mechanism to prescribe an appropriate allocation between resources and demands in this public market. The mechanism exploits the VIKOR method to develop preference lists for providers and consumers, the method also is augmented by the Intuitionistic Fuzzy Sets to remove complexities and uncertainties that exist in the process of ranking; and Deferred Acceptance (DA) algorithm is utilised as the matching algorithm of the mechanism. The performance of the mechanism is analysed under influence of the proposer, resource availability and matching algorithm. The influence of proposer study indicates that the situation of the consumer as the proposer is preferred to the providers’ situation as proposer; the influence of resource availability study indicates a balanced situation is preferred to the scarcity and abundancy situations and the influence of matching algorithm indicates that DA outperforms Top Trading Cycle (TTC) algorithm as the matching algorithm of the mechanism.

In this paper, we propose an analytical approach to compute the probability of connectivity for one-dimensional ad hoc networks. The proposed analysis gives the exact probability of connectivity for an arbitrary distribution of nodes, provided that nodes are independently and identically distributed. We conduct separate analyses for two cases; in the first case, the number of nodes varies by time under a stationary distribution and in the second case, there is a fixed (known) number of nodes in the network. Using the approaches presented in this work, we are able to derive closed-form formulas for the probability of connectivity for some spatial distributions, while for more complicated distributions, our approach leads to tractable numerical algorithms. As an example, we apply our method to a special case (uniform distribution) and derive a closed-form formula for its probability of connectivity. Finally, we confirm the validity of our analytical approach by simulation for several distributions and show higher accuracy and applicability of the proposed approach compared with existing methods.

In this article, we analyze the effect of implementing variable backoff algorithms on the average peak age of information (PAoI) and the average age of information (AoI) in slotted ALOHA networks. To this end, we first analytically derive the average PAoI in exponential backoff slotted ALOHA when the number of backoff stages is finite. We also show that the reduction in average PAoI achieved by the exponential backoff algorithm can significantly increase the average AoI, which is not desirable. Then, we propose an age-based backoff algorithm and present the intuition behind it. In contrast to exponential backoff, our proposed age-based backoff algorithm reduces the average PAoI significantly, while changing the average AoI limitedly. The numerical results confirm our analytical results as well as the superiority of the proposed age-based backoff algorithm over exponential backoff.