Abstract

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

Introduction

At present, wireless sensor networks have been widely applied in various fields such as environmental monitoring, agriculture, and the Internet of Things. In these applications [1,2], wireless communication between nodes in wireless sensor networks plays a crucial role. The Internet of Things has characteristics such as connectivity, intelligence, and real-time. It connects devices through wireless communication technology and endows them with perception, computing, and decision-making capabilities. This enables the Internet of Things to obtain and transmit data in real-time, automate collaboration and data sharing, improve production and operational efficiency, and reduce costs. At the same time, the Internet of Things utilizes big data analysis and predictive maintenance to provide insight into market trends and user needs, helping enterprises make more accurate decisions. In addition, the application of the Internet of Things in fields such as smart homes and health monitoring is improving people’s quality of life and promoting the process of sustainable development. Therefore, the Internet of Things has significant advantages in improving efficiency, reducing costs, enhancing insight, improving quality of life, and promoting sustainable development. The routing optimization of wireless sensor networks has become an important and challenging issue due to the limited energy of sensor nodes, wide and random distribution of nodes, and other characteristics. The goal of wireless sensor network routing optimization is to improve network performance and energy utilization efficiency by selecting appropriate paths, minimizing energy consumption, maintaining low latency, and maximizing network lifespan [3]. It involves techniques and mechanisms such as path selection and transmission scheduling to ensure efficient and accurate data transmission between nodes. The research on routing optimization in wireless sensor networks is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. By adopting effective routing optimization algorithms and mechanisms, more reliable, intelligent, and efficient wireless sensor network systems can be achieved. Wireless sensor network routing optimization plays an important role in the Internet of Things, as it can improve network performance and energy utilization efficiency, extend network lifespan, provide reliable data transmission and support real-time applications for various application fields. Therefore, routing optimization in wireless sensor networks is of great significance in promoting the development and application of animal networking [4,5].

Conclusion

Wireless sensor network routing optimization is one of the key issues in the Internet of Things, which is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. The current routing optimization methods have problems such as high energy consumption, high transmission delay, high number of dead nodes, and low network throughput. To address these issues, this article proposes a wireless sensor network routing optimization method based on improved ant colony algorithm in the Internet of Things. Through experimental testing, it has been proven that the proposed method has absolute advantages over the current method in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. This research achievement can not only improve the energy efficiency of wireless sensor networks, but also improve the quality of data transmission and provide better support for real-time applications. At the same time, this method can also optimize network capacity and meet the network performance requirements of different application scenarios. These contributions provide important support for the reliability, intelligence, and efficiency of wireless sensor networks, and promote the further development of wireless sensor network technology in various application fields. However, there are still some challenges in the research of routing optimization in wireless sensor networks. For example, when optimizing routing in a dynamic environment, it is necessary to consider issues such as changes in network topology and the balance between energy consumption and data transmission quality. These challenges require further in-depth research to propose more comprehensive and adaptable routing optimization methods for wireless sensor networks, and to promote the development and application of animal networking technology.

Abstract

Keywords

References

Abstract

In this paper, the traditional ant colony algorithm has a slow convergence rate for routing optimization of Mesh networks. A multi-path routing protocol based on improved ant colony algorithm, Fortified Ant protocol, is proposed. The protocol first adds a sorting algorithm based on the ant colony algorithm, and introduces the concept of elite ants to improve the speed of routing optimization. Secondly, this paper also studies the multipath transmission of self-organizing networks. The simulation results show that compared with ADOV, DSR and AOC routing algorithms, the algorithm can quickly find multiple paths with better quality, with fast convergence and overhead. Less advantage.

1. Introduction

Wireless Mesh network is a kind of self-organizing network, mainly composed of two parts, respectively is wireless router (Mesh routers) and wireless client (Mesh clients), Mesh router constitute the backbone network, Mesh clients through the Internet backbone network connection.Under the communication environment of wireless mesh network, the topology of mesh network is relatively stable, so it has great research value in the temporary arrangement of communication network.

As the future of wireless Mesh networks has gradually been recognized by many scholars, many experts have studied the multipath routing methods of wireless Mesh networks, and many have achieved certain results. In [1], a multi-path routing discovery method based on particle swarm optimization for wireless mesh networks is proposed. The congestion prediction degree and node forwarding goodness function are defined as the fitness function of the particle swarm algorithm model to ensure the validity of the calculated path. High efficiency. Literature [2] mainly studies the feasibility of ant colony algorithm applied to route optimization in Mesh networks. Simulation experiments show that ant colony algorithm can be used for route optimization of Mesh networks, and its optimization speed is fast, and it can achieve global optimization.

In this paper, on the basis of above research, in view of the traditional Ant colony algorithm optimization is slow and single path routing is easy to lose data grouping problem, put forward a kind of Ant colony algorithm based on sorting multipath routing protocols – Fortified Ant protocol.

1- Introduction

2- Related work

3- Flow-aware multi-topology adaptive routing

4- Implementation environment

5- Router components

6- Tests of the FAMTAR router

7- Conclusion

References

Abstract

Flow-Aware Multi-Topology Adaptive Routing (FAMTAR) is a new approach to multipath and adaptive routing in IP networks which enables automatic use of alternative paths when the primary one becomes congested. It provides more efficient network resource utilization and higher quality of transmission compared to standard IP routing. However, thus far it has only been evaluated through simulations. In this paper we share our experiences from building a real-time FAMTAR router and present results of its tests in a physical network. The results are in line with those obtained previously through simulations and they open the way to implementation of a production grade FAMTAR router.

Introduction

FAMTAR (Flow-Aware Multi-Topology Adaptive Routing) is a new approach to routing in IP networks, which provides multipath and adaptive capabilities. It is based on flows, notion currently incorporated in many architectures, including Software-Defined Networking (SDN). Unlike many SDN solutions, FAMTAR is a fully distributed mechanism and does not depend on a central entity. This is a key characteristic, as it significantly improves scalability and resilience. Our aim was to test the efficiency of FAMTAR in real network conditions and validate the results obtained earlier through network simulations. In order to do that, we needed to build a FAMTAR router. In this paper we present our experiences in building a FAMTAR router.

1- Introduction

2- GROLL Overview

3- GROLL Design

4- GROLL Routing Engine

5- Related Work

6- Conclusions

References

Abstract

Routing in low-power lossy networks (LLNs) remains an important research problem for Internet of Things (IoT), despite the recent standardization in RFC 6550 of RPL as an IPv6 Routing Protocol [1]. Recent deployment experiences [2] have emphasized the challenges faced by a complex design in real world deployments. One of the key challenges is versatile routing protocol design that can handle efficiently and seamlessly various combinations of routing primitives, namely unicast, multicast, and convergecast. Although RPL supports these traffic types, due to its design principle oriented around data collection, its unicast and multicast functionalities are limited. In this article, we present the design and system implementation of the first Geographic Routing framework for Low Power and Lossy Networks (GROLL). GROLL’s routing engine effectively integrates routing for unicast, multicast, and convergecast and takes into account Complex Network Topologies (CNT), i.e., network holes and cuts. GROLL detects CNTs and abstracts the boundary information of detected CNTs with significantly reduced memory size. The proposed unified routing framework was implemented on real hardware and it was evaluated extensively on a testbed of 42 TelosB motes.

Introduction

RPL was adopted by IETF as the standard IoT routing protocol for low power and lossy networks [1]. Recent deployment experiences [2] have emphasized the complexities faced by RPL routing in real world, and pointed out some inefficiencies, especially in terms of routing path stretch. RPL is designed to support unicast, multicast and convergecast, but its design makes limiting assumptions: the predominant traffic is from nodes to the base station, followed, on rare occasions by traffic from the base station to the nodes and the traffic among nodes, while supported, is extremely rare. These assumptions may not hold well in some deployments, such as wireless sensor deployment for disaster response. On the other hand, geographic routing is well suited for LLNs because it is simple and highly scalable; and more importantly location information of devices, sensors, and machines are often readily available in IoT applications. Furthermore, different routing primitives such as 1-1 (unicast), 1-n (multicast), and n-1 (convergecast) can be easily supported based on geographic routing. Unfortunately, to the best of our knowledge, there is no unified location-based routing framework that integrates location-based routing protocols developed for different routing primitives. However, the needs for an unified routing framework has ever increased recently. In recent IoT applications [3], nodes do not just report data to the base station. For example, peer-to-peer or multicast data communications have become essential routing traffic patterns for in-network processing, data aggregation, and feedback control [3].

This work is devoted to the study of application of new topologies in the design of networks-on-chip (NoCs). It is proposed to use two-dimensional optimal circulant topologies for NoC design, and it is developed an optimized routing algorithm with the decreased memory usage. The proposed routing algorithm was compared with Table routing, Clockwise routing, and Adaptive routing algorithms, previously developed for ring circulant topologies, and specialized routing algorithm for multiplicative circulants. The results of synthesis of routers implementing proposed routing algorithms are presented. The cost of ALM and register resources for the implementation of communication subsystems in NoCs with circulant topologies is estimated.

Introduction

Multiprocessor systems-on-chip (MPSoCs) [1] are currently one of the fastest growing areas in computing. An extensive increase in the size of the chip and the number of transistors per unit area allows developing chips with tens and hundreds of processor cores even now. At the same time, the problem of effective organization of a communication subsystem that would ensure quick data exchange between nodes of such NoCs is becoming urgent [2]. Since communication is carried out within the framework of a single chip, laws and principles that exist in telecommunication networks work differently in NoCs; therefore, the development of new solutions and methods for organizing data transfer in such networks is required. In particular, it is the topology that has a significant impact on NoC performance. The most common are classical regular topologies such as mesh [3] and torus [4], which do not always meet modern requirements for NoCs, especially with an increase in the number of nodes [5]. Different researchers suggest other topology options among which we can distinguish hypercube [6, 7, 8], chordal ring [9, 10], spidergon [11], L-networks [12]. Their feature is that they can be reduced to presentation in the form of ring-like graphs called circulant topologies in the general form. Optimal circulants have several significant advantages over mesh and torus topologies – better structural survivability, reliability, and connectivity [13, 14].

Internet of Underwater Things (IoUTs) has been proposed to autonomously monitor and collect data from aquatic environments. In these networks, the acoustic channel is currently considered as the most viable technology for underwater wireless communication among the nodes. By using acoustic modems, underwater sensor nodes are able to autonomously and collaboratively sense the area of interest and report collected data to a monitoring center through multi-hop underwater wireless communication. However, underwater wireless communication through acoustic channels is daunting, due to the limited bandwidth, multipath propagation, shadowing zones, high and variable delay, noisy environment and high energy cost. These characteristics impair data collection and shorten the IoUT lifetime. In this paper, we propose a novel power controlbased opportunistic (PCR) routing protocol for IoUTs. The proposed protocol considers the neighborhood density, link quality, distance, packet advancement, and energy waste to select the suitable transmission power level at each sensor node. Accordingly, each neighboring node eligible to continue forwarding the data packet is considered as a next-hop forwarder node if its inclusion in the next-hop nodes candidate set does not increase the energy waste in the considered hop. Numerical results showed that the proposed protocol improves data delivery rate while maintaining the energy cost at levels comparable to the related work.

Introduction

Oceans cover more than 2/3 of Earth’s surface and they play a fundamental role to support life on our planet. Oceans have absorbed more than 1/4 of carbon dioxide (CO2) produced by humans since 1800 and 2/3 of the heating produced by human activities since 1995 [1]. Moreover, they are one of the main sources of food and mineral resources and they serve as a transportation medium for people and goods. Despite all benefits, it is estimated that no more than 5% of the oceans’ volume has been seen by human eyes [2]. The critical challenge, therefore, is how we can change this worrying reality. Overall, ship missions for ocean monitoring are expensive and might jeopardize humans life, due to extreme weather conditions, for instance. Hence, there is a growing demand for the development of aquatic monitoring technologies that can autonomously collect data from oceans and events beneath them. In this regard, Internet of Underwater Things (IoUTs) [3], as an evolution of underwater wireless sensor networks (UWSNs) [4, 5], emerges as a cost-effective solution for increasing maritime awareness through the large-scale and real-time monitoring of aquatic environments. IoUTs will be composed of intelligent sensor nodes (floats) that will collaboratively sense the area and events of interest and them report collected data to a cloud computing system [6]. To do so, the underwater sensor nodes will be equipped with acoustic modems that will enable underwater wireless communication among them.

Abstract
Video transmission in Internet of Vehicles (IoVs) is an emerging technology which utilizes the multimedia inside and outside vehicles itself through Vehicle Ad Hoc Networks (VANETs). In IoVs, traditional multi-hop routing protocols are not adaptive to mobile environment, especially the high-mobility driving environment in which vehicles need to accomplish video transmission under high quality of service (QoS). In this paper, we propose a multi-hop routing protocol for video transmission in IoVs based on cellular attractor selection (MRVT-CAS). We design a packet generation method for MRVT-CAS and use Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to construct the candidate set of next-hop selection. Then we map the expression of different genes in cell to selection of different next-hop nodes, and employ the mechanics of cellular attractor selection to select next-hop node. Moreover, we present a real-time feedback process to improve self-adaptability and robustness of routing protocol. Our simulation study compares MRVT-CAS with other routing protocols to evaluate performance of video transmission. The simulation results demonstrate the performance improvement over traditional methods, in terms of reachability, delay, stability and frame loss rate.

In recent years, the utilization of licensed spectrum is very low resulting in a serious waste of spectrum resources [1]. The proposed of cognitive wireless sensor network (CWSN) effectively address the problem of spectrum scarcity [2]. CWSN combines the characteristics of sensor network and cognitive technology. Cognitive users access the vacant frequencies opportunistically to improve the spectrum utilization. In CWSN, the selection of routing and spectrum are considered to be the most important research hotspots [3]. There are two routing metrics proposed to adjust to CWSN in [4]. The main purpose of this paper is to maximize data rates and minimize delay for a set of user communication sessions. A distributed and efficient cluster-based spectrum and interference aware routing protocol is proposed in [5], the proposed algorithm can well fit into the mobile CR ad hoc network and improve the network performance. Cognitive low-energy adaptive clustering hierarchy (CogLEACH) was introduced in [6], which is a spectrum-aware extension of the LEACH routing protocol. CogLEACH is a spectrum-aware and energy efficient clustering protocol for CWSN. The clustering routing protocol LEACH is introduced to CWSN in [7], and adapting the cognitive dynamic spectrum environment. Low-energy adaptive clustering hierarchy (LEACH) has been introduced into CWSN, however, without taking into account the energy efficiency. One of the drawbacks of CWSN is that node energy is limited and cannot be added. It is necessary to use an effective routing protocol to improve energy efficiency and extend the survival time of CWSN. Reducing energy consumption in the process of data transmission is very important in CWSN. Studies have shown that the design of routing protocols in the CWSN introduces clustering mechanism which can improve network performance. LEACH is a typical cluster routing protocol and is introduced to CWSN. So this article made comparison of LEACH protocol with TEEN protocol, and TEEN protocol with advanced thresholdsensitive energy efficient sensor network (A-TEEN) protocol. Threshold-sensitive energy efficient sensor network (TEEN) routing protocol is the improvement of LEACH, which is added the hard threshold (HT) and soft threshold (ST) to control the number of data transmission. Introduce TEEN into CWSN, compared with LEACH, can save the energy consumption of nodes and extend the life cycle of network. This paper proposes that use TEEN routing protocol to set up the CWSN.

1- Introduction

2- Related work

3- Proposed IoT architecture

4- IoT applications

5- Simulation and result

6- Conclusion

References

Abstract

Converging of MANET with WSN used in ubiquitous smart environments opens new prospect in monitoring the large scale urban area and makes a new communication platform for different applications in Internet of Things (IoT) domain. Sensors used for IoT applications, sense the environment and send the data to the gateway node, which in turn send the collected data to the MANET node, especially used for data harvesting. Here we considered two IoT applications which are monitored by wireless sensor nodes. The challenging part of this work is to make a platform by converging sensor network with the MANET network because nodes have different power levels, heterogeneous protocols and have chances of co-channel interferences. We proposed a total solution which includes network protocols, spectrum distribution, node deployment, MANET routing and mobility pattern and finally implementation of the IoT applications which are simulated using Omnet++ simulator and shown their performances and feasibility.

Introduction

A recent paradigm of communication which popularly known as Internet of Things (IoT), where all the objects of different kinds of our daily life spanning from smart phones, sensors or devices are associated with network enabled objects (like RFID) can communicate with each other and makes a part of Internet. The main aim of IoT is to make Internet more and more inspiring and pervasive. By connecting wide variety of heterogeneous devices and making easy access of the devices, several applications are being used in IoT, which deals with the large number of data generated by the attached devices and make some decisions which is very important for the industry or control the attached devices based on the generated data. There is a huge use of IoT in several domains like medical aids, home automation, industrial automation, mobile health care, electricity transmission and distribution etc. Now a day smart cities are one of the emerging domain of IoT, where researchers are proposing several communication models and standards and provide some applications which facilitate the residing citizens by enhancing their everyday standard of life. With the advancement of communication technology and microcontroller technology, Wireless Sensor Network (WSN) plays an important role in IoT. Sensor nodes, which are very tiny low cost devices, can sense different parameters in the environment, it can store data in its memory temporarily and can communicate with other electronics devices by using communication technology like IEEE802.15.4 or Bluetooth Low Energy (BLE). On the other hand RFID tag is also a useful device in IoT, which is used to identify an object attached with a RFID tag. In our work, MANET plays a vital function, which is used as the backbone of the IoT network. Due to its inherent properties, the MANET nodes can build spontaneous connections with other nodes without the need of any infrastructure. MANET nodes can move around the IoT network and collect data from the sensors, RFID attached nodes, or any fixed Wireless nodes. Thereafter it can process the data and send it to the Internet gateway through some intermediate MANET nodes. The MANET nodes use the efficient path to reach one of the available Internet gateways. Like sensor nodes, MANET nodes can be used as key technologies in several applications in IoT.

I- Introduction

II- Related Work

III- Unicast Routing Protocol Based on Attractor Selection

IV- Simulation Results

V- Conclusion

References

Abstract

We present a bio-inspired unicast routing protocol for vehicular Ad Hoc Networks which uses the cellular attractor selection mechanism to select next hops. The proposed unicast routing protocol based on attractor selecting (URAS) is an opportunistic routing protocol, which is able to change itself adaptively to the complex and dynamic environment by routing feedback packets. We further employ a multi-attribute decision-making strategy, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), to reduce the number of redundant candidates for next-hop selection, so as to enhance the performance of attractor selection mechanism. Once the routing path is found, URAS maintains the current path or finds another better path adaptively based on the performance of current path, that is, it can self-evolution until the best routing path is found. Our simulation study compares the proposed solution with the state-of-the-art schemes, and shows the robustness and effectiveness of the proposed routing protocol and the significant performance improvement, in terms of packet delivery, end-toend delay, and congestion, over the conventional method.

Introduction

Over the past few decades, the Intelligent Transportation System (ITS) has been developing rapidly, owing to the rapid development of wireless network technologies and the increasing traffic demand. The Vehicular Ad Hoc Networks (VANETs) has become an emerging research focusing on the communication among vehicles without the extra support of infrastructure [1], [2]. There are many potential applications in the field of VANETs, such as traffic management, collision avoidance, cooperative driving [3], [4]. As one type of Mobile Ad Hoc Networks (MANETs), VANETs have to face many problems not only inherent in MANETs, such as competition, interference, channel weakness but also high vehicular mobility, quick topology changes, limited geographical position and direction of movement. Due to the mobility nature of vehicles, the duration time of communication link between two vehicles is quite short [5]. For example, when the signal radius is 250m and the average speed is 100 km/h, the probability of two vehicles keeping continuous communication for 15 seconds is only 57% [6]. Due to the restrictions of the urban road and the differences in time, development and function of various regions as well as distribution among traffic flows, traditional MANETs routing protocols will not be suitable in VANETs and thus we need seek new mechanisms.

Smart manufacturing describes the new manufacturing intelligence applied to modern information technologies such as the Internet of Things, cloud computing, and artificial intelligence in the manufacturing process (Tao et al. 2018a, b). Data from various sources is becoming integrated into manufacturing intelligence to improve manufacturing in various ways. A typical example is the wide deployment of sensors in manufacturing to monitor and provide real-time manufacturing data such as temperature, humidity, speed, vibration, and acidity for better decision-making and control of the manufacturing process (Li et al. 2015). Therefore, multiple sensors of different modalities are needed in distributed locations. Wired sensor networks are extensively adopted, but the cost for their installation, testing, maintenance, and shutdown are quite high. In many cases, a wireless sensor network (WSN) is a more attractive alternative because it does not require any fixed infrastructure and can be applied over distributed areas where cabling is costly (Akyildiz et al. 2002). A WSN is composed of sensor nodes and sink nodes. Sensor nodes are responsible for collecting and forwarding data. Sink nodes either analyze data locally or forward data to a base station (Magaia et al. 2015). Each sensor node consists of small devices for sensing, processing, transceiving, and power, and it is able to communicate with other sensor nodes or directly with a base station. A WSN can be regarded as a self-supporting unit, with which unattended operation can be realized (Stankovic 2008). It is mostly applicable where power supplies and cabling are difficult, or in hostile environments that people cannot enter. With the technological developments in cloud computing and Internet of Things, WSNs are becoming even more widely deployed. They have also been used for machine health monitoring (Tiwari et al. 2007), data center monitoring (Wang et al. 2013), and data logging (Saleem et al. 2014) in various industries. Moreover, a WSN can be used for environmental monitoring (Hart and Martinez 2006), remote health care monitoring (Malasinghe et al. 2017), and other monitoring scenarios. Overall, its application in cloud computing and Internet of things and has significant social and economic benefit (Tao et al. 2014, 2011). WSNs have a key difference with the wired sensor network in that the sensor nodes in a WSN rely solely on their own battery, and thus have limited power resources. Moreover, their computing power and storage resources are also limited. As a consequence, reducing the energy consumption of each sensor node is a critical issue for WSNs (Carrabs et al. 2015). Ant colony optimization (ACO) has been used in WSNs to identify shortest paths, and thus reduce the energy consumed by a network. However, the ACO is prone to falling into local optima and converges slowly. We hence propose an improved ACO (IACO) that can be used to construct the sensor node transfer function and pheromone update rule, and adaptively construct a data route using the characteristics of a dynamic network. The major contributions of this paper are listed below. 1. The IACO consumes less energy. 2. The transmission delay is reduced using the IACO. 3. Fewer transmission packets are lost when using the IACO. This paper is organized as follows. Section 2 analyzes the current WSN routing algorithms and their weakness. In Sect. 3, we describe our model and its notation. In Sect. 4, a detailed description of the proposed routing IACO algorithm is given. In Sect. 5, simulation results comparing the proposed algorithm with several other algorithms are presented to show the effectiveness of the proposed method.

Cognitive Radio Networks (CRNs) was proposed as a promising solution for the spectrum scarcity problem. This problem is increasing more and more and that is why CRNs are imminent to pervade into all fields of wireless communications. We backup this assertion with the following observations. First, with the inherent inefficiency of the static spectrum licensing policies [1] and proliferation of spectrum accessing mobile and connected devices [2], we are quickly heading towards a wireless spectrum crisis [3]. Second, the spectrum regulatory authorities are now working towards new regulations allowing for wireless spectrum reuse by unlicensed users [4]. These regulations allow the unlicensed secondary users (SUs) to access the spectrum as long as needs of the licensed primary users (PUs) are satisfied. One of the emerging use cases of Cognitive Radios is 5G. 5G networks utilize the concept of Licensed-Assisted- Access (LAA) [5], which basically says that, in order to get very high rates (and due to spectrum scarcity), the network does not just rely on the licensed part of the spectrum, but also uses part of the unlicensed and shared spectrum. However, using the shared spectrum should be done carefully to avoid interference between different parties. For example, 5G should be able to provide cellular access to IoT networks, which are mainly private networks that consist of many devices which should co-exist. Since they are supposed to use the same shared spectrum as that of WiFi, then in such a scenario the WiFi access points could act as the primary users, while the IoT devices are secondary ones. In such a network, powerful nodes (which have multiple antennas for example) should use their capabilities to avoid interfering with the less powerful ones. In order to use the Cognitive Radio (CR) in practice, many challenges need to be considered. Since one of the biggest goals is ensuring the integrity of the PU transmissions, all components of the CR cycle are developed such that this goal is attained [6]. For example, various spectrum sensing and sharing techniques have been developed, each with varying levels of complexity and efficiency[7]. In addition, different spectrum management policies have been envisioned which are better suited for different CR scenarios. For example, an overlay access policy allows a SU to access a spectrum only if a PU is not detected. In contrast, an underlay access policy would allow the coexistence of PU and SU transmissions provided that the level of interference incurred at the PU is not excessive. Developing such systems is therefore by no means an easy feat, and many practical challenges arise which makes designing such systems a challenging task [7, 8].