Machine Learning Approach for Spectrum Sharing in the Next Generation Cognitive Mesh Network

Nowadays, there is an unexpected explosion in the demand for wireless network resources. This is due to the dramatic increase in the number of the emerging web-based services. For wireless computer network, limited bandwidth along with the transmission quality requirements for users, make quality of service (QoS) provisioning a very challenging problem. To overcome spectrum scarcity problem, Federal Communications Commission (FCC) has already started working on the concept of spectrum sharing where unlicensed users (secondary users, SUs) can share the spectrum with licensed users (primary users, PUs), provided they respect PUs rights to use spectrum exclusively. Cognitive technology presents a revolutionary wireless communication where users can exploit the spectrum dynamically. The integration of cognitive technology capability into the conventional wireless networks is perhaps the significant promising architectural upgrade in the next generation of wireless network that helps to solve spectrum scarcity problem. In this work, we propose integrating cognitive technology with wireless mesh network to serve the maximum number of SUs by utilizing the limited bandwidth efficiently. The architecture for this network is selected first. In particular, we introduce the cluster-based architecture, signaling protocols, spectrum management scheme and detailed algorithms for the cognitive cycle. This new architecture is shown to be promising for the cognitive network. In order to manage the transmission power for the SUs in the cognitive wireless mesh network, a dynamic power management framework is developed based on machine learning to improve spectrum utilization while satisfying users requirements. Reinforcement learning (RL) is used to extract the optimal control policy that allocates spectrum and transmission powers for the SUs dynamically. RL is used to help users to adapt their resources to the changing network conditions. RL model considers the spectrum request arrival rate of the SUs, the interference constraint for the PUs, the physical properties of the channel that is selected for the SUs, PUs activities, and the QoS for SUs. In our work, PUs trade the unused spectrum to the SUs. For this sharing paradigm, maximizing the revenue is the key objective of the PUs, while that of the SUs is to meet their requirements and obtain service from the rented spectrum. However, PUs have to maintain their QoS when trading their spectrum. These complex conflicting objectives are embedded in our machine learning model. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. We use a machine learning to help the PUs to make a decision about the optimal size and price of the offered spectrum for trading. The trading model considers the QoS for PUs and SUs, traffic load at the PUs, the changes in the network conditions, and the revenues of the PUs. Finally, we integrate all the mechanisms described above to build a new cognitive network where users can interact intelligently with network conditions

Efficient 3D Placement of a UAV Using Particle Swarm Optimization

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. We assume that the locations of indoor users are uniformly distributed in each floor and we propose a particle swarm optimization algorithm to find an efficient 3D placement of a UAV that minimizes the total transmit power required to cover the indoor users.Comment: 6 pages, 7 figure

Lightweight edge authentication for software defined networks

OpenFlow is considered as the most known protocol for Software Defined Networking (SDN). The main drawback of OpenFlow is the lack of support of new header definitions, which is required by network operators to apply new packet encapsulations. While SDN’s logically centralized control plane could enhance network security by providing global visibility of the network state, it still has many side effects. The intelligent controllers that orchestrate the dumb switches are overloaded and become prone to failure. Delegating some level of control logic to the edge or, to be precise, the switches can offload the controllers from local state based decisions that do not require global network wide knowledge. Thus, this paper, to the best of our knowledge, is the first to propose the delegation of typical security functions from specialized middleboxes to the data plane. We leverage the opportunities offered by programming protocol-independent packet processors (P4) language to present two authentication techniques to assure that only legitimate nodes are able to access the network. The first technique is the port knocking and the second technique is the One-Time Password. Our experimental results indicate that our proposed techniques improve the network overall availability by offloading the controller as well as reducing the traffic in the network without noticeable negative impact on switches’ performance

Optimal spectrum utilisation in cognitive network using combined spectrum sharing approach: overlay, underlay and trading

Cognitive radio technology enables unlicensed users (secondary users, SUs) to access the unused spectrum. In the literature, there are three spectrum sharing paradigms that enable SUs to access the licensed spectrum. These access techniques include underlay, overlay and spectrum trading, and have their own drawbacks. To combat these drawbacks, we propose a new approach for each of them and merge them into one combined system. Our overlay scheme provides quick access to the unused spectrum. We propose a new cooperative sensing protocol to reduce the likelihood of interfering with PUs. In order to enable SUs for transmitting simultaneously with PUs, we suggest using our underlay scheme. Our trading scheme allows PUs to trade the unused spectrum for the SUs that require better quality of service. The new combined scheme increases the size of spectrum in the cognitive network. Simulation results show the ability of the new scheme to serve extra traffic

A Lightweight and Efficient Digital Image Encryption Using Hybrid Chaotic Systems for Wireless Network Applications

Due to limited processing capabilities and other constraints of most wireless networks, many existing security algorithms do not consider the network efficiency. This is because most of these security solutions exhibit intolerable overhead and consider only securing scalar data, which are not suitable for other data types such as digital images, hence affecting the provided security level and network performance. Thus, in this paper, we propose a lightweight and efficient security scheme based on chaotic algorithms to efficiently encrypt digital images. Our proposed algorithm handles digital images in two phases: Firstly, digital images are split into blocks and compressed by processing them in frequency domain instead of Red-Green-Blue (RGB) domain. The ultimate goal is to reduce their sizes to speed up the encryption process and to break the correlation among image pixel values. Secondly, 2D Logistic chaotic map is deployed in key generation, permutation, and substitution stages for image pixel shuffling and transposition. In addition, 2D Henon chaotic map is deployed to change the pixel values in the diffusion stage in order to enhance the required level of security and resist various security attacks. Security performance analysis based on standard test images shows that our proposed scheme overcomes the performance of other existing techniques

A Survey of Limitations and Enhancements of the IPv6 Routing Protocol for Low-power and Lossy Networks: A Focus on Core Operations

Driven by the special requirements of the Low-power and Lossy Networks (LLNs), the IPv6 Routing Protocol for LLNs (RPL) was standardized by the IETF some six years ago to tackle the routing issue in such networks. Since its introduction, however, numerous studies have pointed out that, in its current form, RPL suffers from issues that limit its efficiency and domain of applicability. Thus, several solutions have been proposed in the literature in an attempt to overcome these identified limitations. In this survey, we aim mainly to provide a comprehensive review of these research proposals assessing whether such proposals have succeeded in overcoming the standard reported limitations related to its core operations. Although some of RPL’s weaknesses have been addressed successfully, the study found that the proposed solutions remain deficient in overcoming several others. Hence, the study investigates where such proposals still fall short, the challenges and pitfalls to avoid, thus would help researchers formulate a clear foundation for the development of further successful extensions in future allowing the protocol to be applied more widely

A Novel Approach to Improve the Adaptive-Data-Rate Scheme for IoT LoRaWAN

The long-range wide-area network (LoRaWAN) uses the adaptive-data-rate (ADR) algorithm to control the data rate and transmission power. The LoRaWAN ADR algorithm adjusts the spreading factor (SF) to allocate the appropriate transmission rate and transmission power to reduce power consumption.The updating SF and transmission power of the standard ADR algorithm are based on the channel state, but it does not guarantee efficient energy consumption among all the nodes in complex environments with high-varying channel conditions. Therefore, this article proposes a new enhancement approach to the ADR+ algorithm at the network server, which only depends on the average signal-to-noise ratio (SNR). The enhancement ADR algorithm ADR++ introduces an energy-efficiency controller α related to the total energy consumption of all nodes, to use it for adjusting the average SNR of the last records. We implement our new enhanced ADR at the network server (NS) using the FLoRa module in OMNET++. The simulation results demonstrate that our proposed ADR++ algorithm leads to a significant improvement in terms of the network delivery ratio and energy efficiency that reduces the network energy consumption up to 17.5% and improves the packet success rate up to 31.55% over the existing ADR+ algorithm

Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications

Wireless communication using existing coding models poses several challenges for RF signals due tomultipath scattering, rapid fluctuations in signal strength and path loss effect. Unlike existing works, thisstudy presents a novel coding technique based on Analogue Network Coding (ANC) in conjunction withSpace Time Block Coding (STBC), termed as Space Time Analogue Network Coding (STANC). STANCachieves the transmitting diversity (virtual MIMO) and supports big data networks under low transmittingpower conditions. Furthermore, this study evaluates the impact of relay location on smart devices networkperformance in increasing interfering and scattering environments. The performance of STANC is analyzedfor Internet of Things (IoT) applications in terms of Symbol Error Rate (SER) and the outage probabilitythat are calculated using analytical derivation of expression for Moment Generating Function (MGF).In addition, the ergodic capacity is analyzed using mean and second moment. These expressions enableeffective evaluation of the performance and capacity under different relay location scenario. Differentfading models are used to evaluate the effect of multipath scattering and strong signal reflection. Undersuch unfavourable environments, the performance of STANC outperforms the conventional methods suchas physical layer network coding (PNC) and ANC adopted for two way transmission