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Recent research on reconfigurable intelligent surfaces (RIS) suggests that the RIS panel, containing passive elements, enhances channel performance for the internet of things (IoT) systems by reflecting transmitted signals to the receiving nodes. This paper investigates RIS panel assisted-wireless network to instigate minimal base station (BS) transmit power in the form of energy harvesting for batteryless IoT sensors to maximize bits transmission in the significant multi-path environment, such as urban areas. Batteryless IoT sensors harvest energy through the RIS panel from external sources, such as from nearby BS radio frequency (RF) signal in the first optimal time frame, for a given time frame. The bits transmission among IoT sensors, followed by a device-to-device (D2D) communications protocol, is maximized using harvested energy in the final optimal time frame. The bits transmission is at least equal to the number of bits sampled by the IoT sensor. We formulate a non-convex mixed-integer non-linear problem to maximize the number of communicating bits subject to energy harvesting from BS RF signals, RIS panel energy consumption, and required time. We propose a robust solution by presenting an iterative algorithm. We perform extensive simulation results based on the 3GPP Urban Micro channel model to validate our model.

5G network operators consider the dense deployment of small base-stations (SBSs) to increase network coverage and capacity. Hence, operators face the challenge of X-hauling, i.e., backhauling or fronthauling, their traffic to the core network. Also, SBSs densification will increase the possibility of failure of these X-haul links. To cope with this problem, an X-haul outage compensation scheme with the assistance of Reconfigurable Intelligent Surfaces (RIS) is proposed to mitigate or at least alleviate the effect of X-haul failure. The RIS is a newly adopted technology that is able to improve the performance of wireless networks. In this paper, we present and evaluate an X-haul outage compensation scheme based on placing a number of RIS panels in pre-planned locations to mitigate the effect of X-haul failure. This evaluation is done using frequencies below and beyond 6 GHz. Based on our analytical results, the proposed RIS scheme shows that placing a sufficient number of RIS elements in proximity to the failed SBS under certain conditions can help acquire the same X-haul rate before the occurrence of the failure. Also, we show that for high X-haul spectral density, the RIS-assisted transmission with a certain number of elements can be more energy-efficient than line-of-sight and non-line-of-sight transmissions. Finally, the system's energy efficiency is addressed with and without RIS, and the optimal number of RIS reflecting elements is derived., This paper was submitted to a conference May 2022

Due to significant blockage conditions in wireless networks, transmitted signals may considerably degrade before reaching the receiver. The reliability of the transmitted signals, therefore, may be critically problematic due to blockages between the communicating nodes. Thanks to the ability of Reconfigurable Intelligent Surfaces (RISs) to reflect the incident signals with different reflection angles, this may counter the blockage effect by optimally reflecting the transmit signals to receiving nodes, hence, improving the wireless network's performance. With this motivation, this paper formulates a RIS-aided wireless communication problem from a base station (BS) to a mobile user equipment (UE). The BS is equipped with an RGB camera. We use the RGB camera at the BS and the RIS panel to improve the system's performance while considering signal propagating through multiple paths and the Doppler spread for the mobile UE. First, the RGB camera is used to detect the presence of the UE with no blockage. When unsuccessful, the RIS-assisted gain takes over and is then used to detect if the UE is either "present but blocked" or "absent". The problem is determined as a ternary classification problem with the goal of maximizing the probability of UE communication blockage detection. We find the optimal solution for the probability of predicting the blockage status for a given RGB image and RIS-assisted data rate using a deep neural learning model. We employ the residual network 18-layer neural network model to find this optimal probability of blockage prediction. Extensive simulation results reveal that our proposed RIS panel-assisted model enhances the accuracy of maximization of the blockage prediction probability problem by over 38\% compared to the baseline scheme.

In this paper, we consider the use of UAVs to provide wireless connectivity services, for example after failures of wireless network components or to simply provide additional bandwidth on demand, and introduce the concept of UAVs as a service (UaaS). To facilitate UaaS, we introduce a novel framework, dubbed D3S, which consists of four phases: demand, decision, deployment, and service. The main objective of this framework is to develop efficient and realistic solutions to implement these four phases. The technical problems include determining the type and number of UAVs to be deployed, and also their final locations (e.g., hovering or on-ground), which is important for serving certain applications. These questions will be part of the decision phase. They also include trajectory planning of UAVs when they have to travel between charging stations and deployment locations and may have to do this several times. These questions will be part of the deployment phase. The service phase includes the implementation of the backbone communication and data routing between UAVs and between UAVs and ground control stations.

The trend is to implement intelligent agents capable of analyzing available information and utilize it efficiently. This work presents a number of reinforcement learning (RL) architectures; one of them is designed for intelligent agents. The proposed architectures are called selector-actor-critic (SAC), tuner-actor-critic (TAC), and estimator-selector-actor-critic (ESAC). These architectures are improved models of a well known architecture in RL called actor-critic (AC). In AC, an actor optimizes the used policy, while a critic estimates a value function and evaluate the optimized policy by the actor. SAC is an architecture equipped with an actor, a critic, and a selector. The selector determines the most promising action at the current state based on the last estimate from the critic. TAC consists of a tuner, a model-learner, an actor, and a critic. After receiving the approximated value of the current state-action pair from the critic and the learned model from the model-learner, the tuner uses the Bellman equation to tune the value of the current state-action pair. ESAC is proposed to implement intelligent agents based on two ideas, which are lookahead and intuition. Lookahead appears in estimating the values of the available actions at the next state, while the intuition appears in maximizing the probability of selecting the most promising action. The newly added elements are an underlying model learner, an estimator, and a selector. The model learner is used to approximate the underlying model. The estimator uses the approximated value function, the learned underlying model, and the Bellman equation to estimate the values of all actions at the next state. The selector is used to determine the most promising action at the next state, which will be used by the actor to optimize the used policy. Finally, the results show the superiority of ESAC compared with the other architectures.

Automatic resource scaling is one advantage of Cloud systems. Cloud systems are able to scale the number of physical machines depending on user requests. Therefore, accurate request prediction brings a great improvement in Cloud systems' performance. If we can make accurate requests prediction, the appropriate number of physical machines that can accommodate predicted amount of requests can be activated and Cloud systems will save more energy by preventing excessive activation of physical machines. Also, Cloud systems can implement advanced load distribution with accurate requests prediction. We propose an algorithm that predicts a probability distribution parameters of requests for each time interval. Maximum Likelihood Estimation (MLE) and Local Linear Regression (LLR) are used to implement this algorithm. An evaluation of the proposed algorithm is performed with the Google cluster-trace data. The prediction is implemented about the number of task arrivals, CPU requests, and memory requests. Then the accuracy of prediction is measured with Mean Absolute Percentage Error (MAPE)., 7 pages

Link and node failures are common two fundamental problems that affect operational networks. Hence, protection of communication networks is essential to increase their reliability, performance, and operations. Much research work has been done to protect against link and node failures, and to provide reliable solutions based on pre-defined provision or dynamic restoration of the domain. In this paper we develop network protection strategies against multiple link failures using network coding and joint capacities. In these strategies, the source nodes apply network coding for their transmitted data to provide backup copies for recovery at the receivers' nodes. Such techniques can be applied to optical, IP, and mesh networks. The encoding operations of protection codes are defined over finite fields. Furthermore, the normalized capacity of the communication network is given by $(n-t)/n$ in case of $t$ link failures. In addition, a bound on the minimum required field size is derived.

Agile recovery from link failures in autonomic communication networks is essential to increase robustness, accessibility, and reliability of data transmission. However, this must be done with the least amount of protection resources, while using simple management plane functionality. Recently, network coding has been proposed as a solution to provide agile and cost efficient network self-healing against link failures, in a manner that does not require data rerouting, packet retransmission, or failure localization, hence leading to simple control and management planes. To achieve this, separate paths have to be provisioned to carry encoded packets, hence requiring either the addition of extra links, or reserving some of the resources for this purpose. In this paper we introduce autonomic self-healing strategies for autonomic networks in order to protect against link failures. The strategies are based on network coding and reduced capacity, which is a technique that we call network protection codes (NPC). In these strategies, an autonomic network is able to provide self-healing from various network failures affecting network operation. The techniques improve service and enhance reliability of autonomic communication. Network protection codes are extended to provide self-healing from multiple link failures in autonomic networks. We provide implementation aspects of the proposed strategies. We present bounds and network protection code constructions. Finally, we study the construction of such codes over the binary field. The paper also develops an Integer Linear Program formulation to evaluate the cost of provisioning connections using the proposed strategies., 12 pages, 2 figures, and 3 tables

In this paper we develop network protection schemes against two link failures in optical networks. The motivation behind this work is the fact that the majority of all available links in an optical network suffer from single and double link failures. In the proposed network protection schemes, NPS2-I and NPS2-II, we deploy network coding and reduced capacity on the working paths to provide backup protection paths. In addition, we demonstrate the encoding and decoding aspects of the proposed schemes., 2 figures, 5 pages, private comments are welcome