Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework

Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved

Spectrum Assignment in Hardware-Constrained Cognitive Radio IoT Networks Under Varying Channel-Quality Conditions

[EN] The integration of cognitive radio (CR) technology with the future Internet-of-Things (IoT) architecture is expected to allow effective massive IoT deployment by providing huge spectrum opportunities to the IoT devices. Several communication protocols have been proposed for the CR networks while ignoring the adjacent channel interference (ACI) problem by assuming sharp filters at the transmit and receive chains of each CR device. However, in practice, such an assumption is not feasible for low-cost hardware-constrained CR-capable IoT (CR-IoT) devices. Specifically, when a large number of CR-IoT devices are operating in the same vicinity, guard-band channels (GBs) are needed to mitigate the ACI problem, introducing GB adds constraints on the efficient use of spectrum and protocol design. In this paper, we develop a channel assignment mechanism for the hardware-constrained CR-IoT networks under time-varying channel conditions with GB-awareness. The objective of our assignment is to serve the largest possible number of CR-IoT devices by assigning the least number of idle channels to each device subject to rate demand and interference constraints. The proposed channel assignment in this paper is conducted on a per-block basis for the contending CR-IoT devices while considering the time-varying channel conditions for each CRIoT transmission over each idle channel, such that spectrum efficiency is improved. Specifically, our channel assignment problem is formulated as a binary linear programming problem, which is NP-hard. Thus, we propose a polynomial-time solution using a sequential fixing algorithm that achieves a suboptimal solution. The simulation results demonstrate that our proposed assignment provides significant increase in the number of served IoT devices over existing assignment mechanisms.This work was supported in part by the QR Global Challenges Research Fund, Staffordshire University, Staffordshire, U.K.Salameh, HAB.; Al-Masri, S.; Benkhelifa, E.; Lloret, J. (2019). Spectrum Assignment in Hardware-Constrained Cognitive Radio IoT Networks Under Varying Channel-Quality Conditions. IEEE Access. 7:42816-42825. https://doi.org/10.1109/ACCESS.2019.2901902S4281642825

Virtualization-Based Cognitive Radio Networks

Abstract The emerging network virtualization technique is considered as a promising technology that enables the deployment of multiple virtual networks over a single physical network. These virtual networks are allowed to share the set of available resources in order to provide different services to their intended users. While several previous studies have focused on wired network virtualization, the field of wireless network virtualization is not well investigated. One of the promising wireless technologies is the Cognitive Radio (CR) technology that aims to handle the spectrum scarcity problem through efficient Dynamic Spectrum Access (DSA). In this paper, we propose to incorporate virtualization concepts into CR Networks (CRNs) to improve their performance. We start by explaining how the concept of multilayer hypervisors can be used within a CRN cell to manage its resources more efficiently by allowing the CR Base Station (BS) to delegate some of its management responsibilities to the CR users. By reducing the CRN users' reliance on the CRN BS, the amount of control messages can be decreased leading to reduced delay and improved throughput. Moreover, the proposed framework allows CRNs to better utilize its resources and support higher traffic loads which is in accordance with the recent technological advances that enable the Customer-Premises Equipments (CPEs) of potential CR users (such as smart phone users) to concurrently run multiple applications each generating its own traffic. We then show how our framework can be extended to handle multi-cell CRNs. Such an extension requires addressing the self-coexistence problem. To this end, we use a traffic load aware channel distribution algorithm. Through simulations, we show that our proposed framework can significantly enhance the CRN performance in terms of blocking probability and network throughput with different primary user level of activities

A Formula for Successful Transmission Probability in Opportunistic Networks Under Memory-Time Correlated Channel Availability

Dynamic Spectrum Access (DSA) technology in wireless Cognitive Radio Networks (CRNs) provides opportunistic access for unlicensed users, also known as Secondary Users (SUs), which can offer huge bandwidth to enable future wireless communication. Mainly, this technology aims to improve the end-to-end throughput by allowing SUs to exploit the licensed channels only when their licensed users, also known as Primary Users (PUs), are not using them. Most existing communication protocols designed for CRNs are based on the assumption that the channel availability time is considered based on a memory-less distribution for PUs arrivals. Unfortunately, this assumption is impractical because the PU channels’ activity and availability are memory-time correlated. Worse yet, designing communication protocols for CRNs under this assumption can result in overestimating the Probability of Success (PoS) for SU packet transmissions, resulting in severe degradation in network performance in realistic scenarios. This paper derives a closed-form formula under memory-time correlation for channel availability that quantifies the PoS for SUs’ packet transmission in CRNs. This will empower the network designers to get practical expectations about network efficiency rather than the overestimated PoS. Therefore, this work is also useful for emerging wireless networks with multi-hop routing, such as 5G, 6G, vehicular networks, etc., which incorporate DSA techniques. Our numerical and simulation results demonstrate that the PoS is overestimated in most of the literature due to adopting memoryless-based distribution in modeling channels’ availability; such overestimation can impact communication protocol decisions, resulting in severe network performance degradation

Energy-Efficient Bi-Objective Optimization Based on the Moth–Flame Algorithm for Cluster Head Selection in a Wireless Sensor Network

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth–Flame Optimization (MFO) algorithm and the Salp Swarm Algorithm (SSA), as well as the Whale Optimization Algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes’ residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency

A joint beamforming and power-splitter optimization technique for SWIPT in MISO-NOMA system

In this paper, we propose a joint beamforming and power-splitter optimization technique for simultaneous wireless power and information transfer in the downlink transmission of a multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system. Accordingly, each user employs a power splitter to decompose the received signal into two parts, namely, the information decoding and energy harvesting. The former part is used to decode the corresponding transmitted information, whereas the latter part is utilized for harvesting energy. For this system model, we solve an energy harvesting problem with a set of design constraints at the transmitter and the receiver ends. In particular, the beamforming vector and the power splitting ratio for each user are jointly designed such that the overall harvested power is maximized subject to minimum per-user rate requirements and the available power budget constraints at the base station. As the formulated problem turns out to be non-convex in terms of the design parameters, we propose a sequential convex approximation technique and demonstrate a superior performance compared to a baseline scheme