Intelligent Approaches for Routing Protocols In Cognitive Ad-Hoc Networks

This dissertation describes the CogNet architecture and five cognitive routing protocols designed to function within this architecture. In this document, I first provide detailed modeling and analysis of CogNet architecture and then provide the detailed approach, mathematical analysis, and simulation results for each of the developed cognitive routing protocols. The fundamental idea for these cognitive routing protocols is that a proper and adaptive network topology should be constructed from network nodes based on predictions using cognitive functions and past experience. The nodes in the cognitive radio network employ machine learning techniques to use past experience and make wise decisions by predicting future network conditions. The cognitive protocol architecture is a cross-layer optimized construct where the lower layer knowledge of the wireless medium is shared with the network layer. This dissertation investigates several intelligent approaches for cognitive routing protocols, such as the multi-channel optimized approach, the scalability optimized cognitive approach, the multi-path optimized approach, and the mobility optimized approach. Analytical and simulation results demonstrate that network performance can be increased significantly by applying cognitive routing protocols

Optimization and Learning in Energy Efficient Cognitive Radio System

Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network

A new connectivity strategy for wireless mesh networks using dynamic spectrum access

The introduction of Dynamic Spectrum Access (DSA) marked an important juncture in the evolution of wireless networks. DSA is a spectrum assignment paradigm where devices are able to make real-time adjustment to their spectrum usage and adapt to changes in their spectral environment to meet performance objectives. DSA allows spectrum to be used more efficiently and may be considered as a viable approach to the ever increasing demand for spectrum in urban areas and the need for coverage extension to unconnected communities. While DSA can be applied to any spectrum band, the initial focus has been in the Ultra-High Frequency (UHF) band traditionally used for television broadcast because the band is lightly occupied and also happens to be ideal spectrum for sparsely populated rural areas. Wireless access in general is said to offer the most hope in extending connectivity to rural and unconnected peri-urban communities. Wireless Mesh Networks (WMN) in particular offer several attractive characteristics such as multi-hopping, ad-hoc networking, capabilities of self-organising and self-healing, hence the focus on WMNs. Motivated by the desire to leverage DSA for mesh networking, this research revisits the aspect of connectivity in WMNs with DSA. The advantages of DSA when combined with mesh networking not only build on the benefits, but also creates additional challenges. The study seeks to address the connectivity challenge across three key dimensions, namely network formation, link metric and multi-link utilisation. To start with, one of the conundrums faced in WMNs with DSA is that the current 802.11s mesh standard provides limited support for DSA, while DSA related standards such as 802.22 provide limited support for mesh networking. This gap in standardisation complicates the integration of DSA in WMNs as several issues are left outside the scope of the applicable standard. This dissertation highlights the inadequacy of the current MAC protocol in ensuring TVWS regulation compliance in multi-hop environments and proposes a logical link MAC sub-layer procedure to fill the gap. A network is considered compliant in this context if each node operates on a channel that it is allowed to use as determined for example, by the spectrum database. Using a combination of prototypical experiments, simulation and numerical analysis, it is shown that the proposed protocol ensures network formation is accomplished in a manner that is compliant with TVWS regulation. Having tackled the compliance problem at the mesh formation level, the next logical step was to explore performance improvement avenues. Considering the importance of routing in WMNs, the study evaluates link characterisation to determine suitable metric for routing purposes. Along this dimension, the research makes two main contributions. Firstly, A-link-metric (Augmented Link Metric) approach for WMN with DSA is proposed. A-link-metric reinforces existing metrics to factor in characteristics of a DSA channel, which is essential to improve the routing protocol's ranking of links for optimal path selection. Secondly, in response to the question of “which one is the suitable metric?”, the Dynamic Path Metric Selection (DPMeS) concept is introduced. The principal idea is to mechanise the routing protocol such that it assesses the network via a distributed probing mechanism and dynamically binds the routing metric. Using DPMeS, a routing metric is selected to match the network type and prevailing conditions, which is vital as each routing metric thrives or recedes in performance depending on the scenario. DPMeS is aimed at unifying the years worth of prior studies on routing metrics in WMNs. Simulation results indicate that A-link-metric achieves up to 83.4 % and 34.6 % performance improvement in terms of throughput and end-to-end delay respectively compared to the corresponding base metric (i.e. non-augmented variant). With DPMeS, the routing protocol is expected to yield better performance consistently compared to the fixed metric approach whose performance fluctuates amid changes in network setup and conditions. By and large, DSA-enabled WMN nodes will require access to some fixed spectrum to fall back on when opportunistic spectrum is unavailable. In the absence of fully functional integrated-chip cognitive radios to enable DSA, the immediate feasible solution for the interim is single hardware platforms fitted with multiple transceivers. This configuration results in multi-band multi-radio node capability that lends itself to a variety of link options in terms of transmit/receive radio functionality. The dissertation reports on the experimental performance evaluation of radios operating in the 5 GHz and UHF-TVWS bands for hybrid back-haul links. It is found that individual radios perform differently depending on the operating parameter settings, namely channel, channel-width and transmission power subject to prevailing environmental (both spectral and topographical) conditions. When aggregated, if the radios' data-rates are approximately equal, there is a throughput and round-trip time performance improvement of 44.5 - 61.8 % and 7.5 - 41.9 % respectively. For hybrid links comprising radios with significantly unequal data-rates, this study proposes an adaptive round-robin (ARR) based algorithm for efficient multilink utilisation. Numerical analysis indicate that ARR provides 75 % throughput improvement. These results indicate that network optimisation overall requires both time and frequency division duplexing. Based on the experimental test results, this dissertation presents a three-layered routing framework for multi-link utilisation. The top layer represents the nodes' logical interface to the WMN while the bottom layer corresponds to the underlying physical wireless network interface cards (WNIC). The middle layer is an abstract and reductive representation of the possible and available transmission, and reception options between node pairs, which depends on the number and type of WNICs. Drawing on the experimental results and insight gained, the study builds criteria towards a mechanism for auto selection of the optimal link option. Overall, this study is anticipated to serve as a springboard to stimulate the adoption and integration of DSA in WMNs, and further development in multi-link utilisation strategies to increase capacity. Ultimately, it is hoped that this contribution will collectively contribute effort towards attaining the global goal of extending connectivity to the unconnected

QoS based Route Management in Cognitive Radio Networks

Cognitive radio has become a revolutionary technology that enables the functionalities of dynamic spectrum access. These are the radios that can be programmed and configured dynamically and aims at enhancing the efficiency of spectrum usage by allowing unlicensed users to access/share the licensed spectrum. Cognitive radio networks, a network of cognitive radios, are smart networks that automatically sense the channel and adjust the network parameters accordingly. Therefore, cognitive radio networks raise many challenges such as power management, spectrum management, route management, environment awareness, path robustness, and security issues. As Cognitive Radio (CR) enables dynamic spectrum access which causes adverse effects on network performance because routing protocols that exists were designed considering fixed frequency band. Also, effective routing in CRNs needs local and continual knowledge of its environment. If licensed user (primary user) requests for its channel which is currently used by unlicensed user (secondary user) then unlicensed user has to return the channel to licensed user. However, unlicensed user has to search for another channel and accordingly it needs to seek for route discovery. So, all these important factors need to be accounted for while performing route management. In this thesis, QoS based route management technique is proposed. Proposed model makes use of functionalities of profile exchange mechanism and location services. The proposed QoS routing algorithm contains following elements: (a) each licensed user prepares channel property table which lists all the properties of the channel, whereas all the unlicensed users in the network due to cognitive functionality sense the environment and prepare a table which contains identification information of neighbor node and channel present between them. All unlicensed users share their table with central entity. (b) Central entity with the help of received information and location services prepares routing table for all the nodes in the network. (c) Various Quality of Service (QoS) metrics are considered to improve the performance of the network. The metrics include power transmission, probability of channel availability, probability of PU presence, and Expected Transmission Count. Central entity provides a route to destination based on the QoS level requested by unlicensed users. Proposed model provides a route with minimum end-to-end transmission power, high probability of channel availability, low probability of PU presence and low value of expected transmission count, to increase life span of users in the network, to decrease the delay, to stabilize wireless connectivity and to increase the throughput of the communication, respectively, based on the QoS level requested by a secondary user. Performance of the network is examined by simulating the network in NS2 under simulation environment with the help of end to end delay, throughput, packet delivery ratio, and % packet loss. Proposed model performs better than two other reference models mentioned in the thesis and is shown in the simulation results

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio

Benefits and limits of machine learning for the implicit coordination on SON functions

Bedingt durch die Einführung neuer Netzfunktionen in den Mobilfunknetzen der nächsten Generation, z. B. Slicing oder Mehrantennensysteme, sowie durch die Koexistenz mehrerer Funkzugangstechnologien, werden die Optimierungsaufgaben äußerst komplex und erhöhen die OPEX (OPerational EXpenditures). Um den Nutzern Dienste mit wettbewerbsfähiger Dienstgüte (QoS) zu bieten und gleichzeitig die Betriebskosten niedrig zu halten, wurde von den Standardisierungsgremien das Konzept des selbstorganisierenden Netzes (SON) eingeführt, um das Netzmanagement um eine Automatisierungsebene zu erweitern. Es wurden dafür mehrere SON-Funktionen (SFs) vorgeschlagen, um einen bestimmten Netzbereich, wie Abdeckung oder Kapazität, zu optimieren. Bei dem konventionellen Entwurf der SFs wurde jede Funktion als Regler mit geschlossenem Regelkreis konzipiert, der ein lokales Ziel durch die Einstellung bestimmter Netzwerkparameter optimiert. Die Beziehung zwischen mehreren SFs wurde dabei jedoch bis zu einem gewissen Grad vernachlässigt. Daher treten viele widersprüchliche Szenarien auf, wenn mehrere SFs in einem mobilen Netzwerk instanziiert werden. Solche widersprüchlichen Funktionen in den Netzen verschlechtern die QoS der Benutzer und beeinträchtigen die Signalisierungsressourcen im Netz. Es wird daher erwartet, dass eine existierende Koordinierungsschicht (die auch eine Entität im Netz sein könnte) die Konflikte zwischen SFs lösen kann. Da diese Funktionen jedoch eng miteinander verknüpft sind, ist es schwierig, ihre Interaktionen und Abhängigkeiten in einer abgeschlossenen Form zu modellieren. Daher wird maschinelles Lernen vorgeschlagen, um eine gemeinsame Optimierung eines globalen Leistungsindikators (Key Performance Indicator, KPI) so voranzubringen, dass die komplizierten Beziehungen zwischen den Funktionen verborgen bleiben. Wir nennen diesen Ansatz: implizite Koordination. Im ersten Teil dieser Arbeit schlagen wir eine zentralisierte, implizite und auf maschinellem Lernen basierende Koordination vor und wenden sie auf die Koordination zweier etablierter SFs an: Mobility Robustness Optimization (MRO) und Mobility Load Balancing (MLB). Anschließend gestalten wir die Lösung dateneffizienter (d. h. wir erreichen die gleiche Modellleistung mit weniger Trainingsdaten), indem wir eine geschlossene Modellierung einbetten, um einen Teil des optimalen Parametersatzes zu finden. Wir nennen dies einen "hybriden Ansatz". Mit dem hybriden Ansatz untersuchen wir den Konflikt zwischen MLB und Coverage and Capacity Optimization (CCO) Funktionen. Dann wenden wir ihn auf die Koordinierung zwischen MLB, Inter-Cell Interference Coordination (ICIC) und Energy Savings (ES) Funktionen an. Schließlich stellen wir eine Möglichkeit vor, MRO formal in den hybriden Ansatz einzubeziehen, und zeigen, wie der Rahmen erweitert werden kann, um anspruchsvolle Netzwerkszenarien wie Ultra-Reliable Low Latency Communications (URLLC) abzudecken.Due to the introduction of new network functionalities in next-generation mobile networks, e.g., slicing or multi-antenna systems, as well as the coexistence of multiple radio access technologies, the optimization tasks become extremely complex, increasing the OPEX (OPerational EXpenditures). In order to provide services to the users with competitive Quality of Service (QoS) while keeping low operational costs, the Self-Organizing Network (SON) concept was introduced by the standardization bodies to add an automation layer to the network management. Thus, multiple SON functions (SFs) were proposed to optimize a specific network domain, like coverage or capacity. The conventional design of SFs conceived each function as a closed-loop controller optimizing a local objective by tuning specific network parameters. However, the relationship among multiple SFs was neglected to some extent. Therefore, many conflicting scenarios appear when multiple SFs are instantiated in a mobile network. Having conflicting functions in the networks deteriorates the users’ QoS and affects the signaling resources in the network. Thus, it is expected to have a coordination layer (which could also be an entity in the network), conciliating the conflicts between SFs. Nevertheless, due to interleaved linkage among those functions, it is complex to model their interactions and dependencies in a closed form. Thus, machine learning is proposed to drive a joint optimization of a global Key Performance Indicator (KPI), hiding the intricate relationships between functions. We call this approach: implicit coordination. In the first part of this thesis, we propose a centralized, fully-implicit coordination approach based on machine learning (ML), and apply it to the coordination of two well-established SFs: Mobility Robustness Optimization (MRO) and Mobility Load Balancing (MLB). We find that this approach can be applied as long as the coordination problem is decomposed into three functional planes: controllable, environmental, and utility planes. However, the fully-implicit coordination comes at a high cost: it requires a large amount of data to train the ML models. To improve the data efficiency of our approach (i.e., achieving good model performance with less training data), we propose a hybrid approach, which mixes ML with closed-form models. With the hybrid approach, we study the conflict between MLB and Coverage and Capacity Optimization (CCO) functions. Then, we apply it to the coordination among MLB, Inter-Cell Interference Coordination (ICIC), and Energy Savings (ES) functions. With the hybrid approach, we find in one shot, part of the parameter set in an optimal manner, which makes it suitable for dynamic scenarios in which fast response is expected from a centralized coordinator. Finally, we present a manner to formally include MRO in the hybrid approach and show how the framework can be extended to cover challenging network scenarios like Ultra-Reliable Low Latency Communications (URLLC)

Novel Interference And Spectrum Aware Routing Techniques}{for Cognitive Radio Ad Hoc Networks

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2011Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2011Yüksek hızlı kablosuz ağlara artan rağbet nedeniyle, radyo spektrumu dünya üzerinde en çok kullanılan ve pahalı doğal kaynaklardan biri haline gelmiştir. Lisanslı spektrumu etkin şekilde kullanma ve paylaşmaya olanak sağlaması nedeniyle radyo spektrumundan yararlanma potansiyelini arttıran bilişsel radyo teknolojisi büyük ilgi toplamaktadır. Söz konusu potansiyelden faydalanmak üzere bilişsel radyo ağları tasarlanırken üzerinde önemle durulması gereken en önemli konulardan bir tanesi de yönlendirmedir. Çalışmamızda bilişsel radyo ağlarında kullanılmak üzere önerilen yönlendirme teknikleri hakkında bir bakış açısı sunulmakla beraber asıl olarak girişim ve spektruma dayalı özgün yönlendirme teknikleri önerilmektedir. Öncelikle, spektrum kullanım karakteristikleri ve ağdaki akışların yarattığı girişim göz önüne alınarak yönlendirme ölçütleri tasarlanmıştır. Ayrıca, bilişsel radyo ağları için otonom dağıtık uyarlanır menzil kontrol stratejisi önerilmiştir. Bu önerilere ek olarak dağıtık ve etkin bir kümeleme tabanlı yönlendirme tekniği geliştirilmiştir. Son olarak, bilişsel radyo ağları için otonom dağıtık uyarlanır menzil kontrol stratejisi ve spektrum erişebilirliği ve girişim maliyeti ölçütlerini bir arada kullanan özgün bir yönlendirme tekniği önerilmiştir. Önerilen yeni yönlendirme ölçütlerinin kullanımı nedeniyle önerilen teknik trafiği kullanılabilir spektrumun daha çok ve girişimin daha az olduğu rotalara yönlendirmektedir. NS2 benzetim ortamı kullanılarak gerçekleştirilen testler, önerilen yöntemlerin bilişsel radyo ağlarına uygunluğunu ve ağ başarımını arttırdığını göstermiştir. Ayrıca güncel bilişsel radyo teknolojisini kullanan diğer yöntemlerle karşılaştırıldığında önerilen tekniklerin hem uçtan uca veri aktarımını arttırdığı hem de uçtan uca gecikmeyi azalttığı ve başarımlarının daha yüksek olduğu gözlemlenmiştir.Radio spectrum has become one of the most heavily used and expensive natural resource around the world because of the growing demand for high-speed wireless networks. Cognitive radio has received great attention due to tremendous potential to improve the utilization of the radio spectrum by efficiently reusing and sharing the licensed spectrum. To design such mobile cognitive radio networks, routing is one of the key challenging issues to be addressed and requires deep investigation. This study gives some insights about the potential routing approaches that can be employed, and suggests novel interference and spectrum aware routing techniques for cognitive radio networks. First, the spectrum usage characteristics, and the interference created by existing flows in the network both from the primary and secondary users are taken into account to define routing metrics. Next, an autonomous distributed adaptive transmission range control scheme for cognitive radio networks is proposed. A distributed and efficient cluster based routing technique, which benefits from new metrics, is also introduced. The last proposed routing algorithm incorporates novel metrics and autonomous distributed adaptive transmission range control mechanism to provide self adaptivity. As a consequence, the proposed protocol routes traffic across paths with better spectrum availability and reduced interference via these new routing metrics. Extensive experimental evaluations are performed in the ns2 simulator to show that proposed protocols provide better adaptability to the environment and maximize throughput, minimize end-to-end delay in a number of realistic scenarios and outperforms recently proposed routing protocols developed for cognitive radio networks.DoktoraPh

Design and optimisation of a low cost Cognitive Mesh Network

Wireless Mesh Networks (WMNs) have been touted as the most promising wireless technology in providing high-bandwidth Internet access to rural, remote and under-served areas, with relatively lower investment cost as compared to traditional access networks. WMNs structurally comprise of mesh routers and mesh clients. Furthermore, WMNs have an envisaged ability to provide a heterogeneous network system that integrates wireless technologies such as IEEE 802.22 WRAN, IEEE 802.16 WiMAX, IEEE 802.11 Wi-Fi, Blue-tooth etc. The recent proliferation of new devices on the market such as smart phones and, tablets, and the growing number of resource hungry applications has placed a serious strain on spectrum availability which gives rise to the spectrum scarcity problem. The spectrum scarcity problem essentially results in increased spectrum prices that hamper the growth and efficient performance of WMNs as well as subsequent transformation of WMN into the envisaged next generation networks. Recent developments in TV white space communications technology and the emergence of Cognitive radio devices that facilitate Dynamic Spectrum Access (DSA) have provided an opportunity to mitigate the spectrum scarcity problem. To solve the scarcity problem, this thesis reconsiders the classical Network Engineering (NE) and Traffic Engineering (TE) problems to objectively design a low cost Cognitive Mesh network that promotes efficient resources utilization and thereby achieve better Quality of Service (QoS) levels

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

Internet of Things From Hype to Reality

The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions