Interference-constrained adaptive simultaneous spectrum sensing and data transmission scheme for unslotted cognitive radio network

Cognitive radio (CR) is widely recognized as a novel approach to improve the spectrum efficiency. However, there exists one problem needed to be resolved urgently, that is the two conflicting goals in CR network: one is to minimize the interference to primary (licensed) system; the other is to maximize the throughput of secondary (unlicensed) system. Meanwhile, the secondary user (SU) has to monitor the spectrum continuously to avoid the interference to primary user (PU), thus the throughput of the secondary system is affected by how often and how long the spectrum sensing is performed. Aiming to balance the two conflicting goals, this article proposes a novel Interference-Constrained Adaptive Simultaneous spectrum Sensing and data Transmission (ICASST) scheme for unslotted CR network, where SUs are not synchronized with PUs. In the ICASST scheme, taking advantage of the statistic information of PU's activities, the data transmission time is adaptively adjusted to avoid the interference peculiar to unslotted CR network; the operation of spectrum sensing is moved to SU receiver from SU transmitter to increase the data transmission time and hence improve the throughput of SU. Simulation results validate the efficiency of ICASST scheme, which significantly increases the throughput of secondary system and decreases the interference to PU simultaneously. © 2012 Yang et al

Cognitive MAC protocols for mobile Ad-Hoc networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The term of Cognitive Radio (CR) used to indicate that spectrum radio could be accessed dynamically and opportunistically by unlicensed users. In CR Networks, Interference between nodes, hidden terminal problem, and spectrum sensing errors are big issues to be widely discussed in the research field nowadays. To improve the performance of such kind of networks, this thesis proposes Cognitive Medium Access Control (MAC) protocols for Mobile Ad-Hoc Networks (MANETs). From the concept of CR, this thesis has been able to develop a cognitive MAC framework in which a cognitive process consisting of cognitive elements is considered, which can make efficient decisions to optimise the CR network. In this context, three different scenarios to maximize the secondary user's throughput have been proposed. We found that the throughput improvement depends on the transition probabilities. However, considering the past information state of the spectrum can dramatically increases the secondary user's throughput by up to 40%. Moreover, by increasing the number of channels, the throughput of the network can be improved about 25%. Furthermore, to study the impact of Physical (PHY) Layer errors on cognitive MAC layer in MANETs, in this thesis, a Sensing Error-Aware MAC protocols for MANETs has been proposed. The developed model has been able to improve the MAC layer performance under the challenge of sensing errors. In this context, the proposed model examined two sensing error probabilities: the false alarm probability and the missed detection probability. The simulation results have shown that both probabilities could be adapted to maintain the false alarm probability at certain values to achieve good results. Finally, in this thesis, a cooperative sensing scheme with interference mitigation for Cognitive Wireless Mesh Networks (CogMesh) has been proposed. Moreover, a prioritybased traffic scenario to analyze the problem of packet delay and a novel technique for dynamic channel allocation in CogMesh is presented. Considering each channel in the system as a sub-server, the average delay of the users' packets is reduced and the cooperative sensing scenario dramatically increases the network throughput 50% more as the number of arrival rate is increased

Innovative energy-efficient wireless sensor network applications and MAC sub-layer protocols employing RTS-CTS with packet concatenation

of energy-efficiency as well as the number of available applications. As a consequence there are challenges that need to be tackled for the future generation of WSNs. The research work from this Ph.D. thesis has involved the actual development of innovative WSN applications contributing to different research projects. In the Smart-Clothing project contributions have been given in the development of a Wireless Body Area Network (WBAN) to monitor the foetal movements of a pregnant woman in the last four weeks of pregnancy. The creation of an automatic wireless measurement system for remotely monitoring concrete structures was an contribution for the INSYSM project. This was accomplished by using an IEEE 802.15.4 network enabling for remotely monitoring the temperature and humidity within civil engineering structures. In the framework of the PROENEGY-WSN project contributions have been given in the identification the spectrum opportunities for Radio Frequency (RF) energy harvesting through power density measurements from 350 MHz to 3 GHz. The design of the circuits to harvest RF energy and the requirements needed for creating a WBAN with electromagnetic energy harvesting and Cognitive Radio (CR) capabilities have also been addressed. A performance evaluation of the state-of-the art of the hardware WSN platforms has also been addressed. This is explained by the fact that, even by using optimized Medium Access Control (MAC) protocols, if the WSNs platforms do not allow for minimizing the energy consumption in the idle and sleeping states, energy efficiency and long network lifetime will not be achieved. The research also involved the development of new innovative mechanisms that tries and solves overhead, one of the fundamental reasons for the IEEE 802.15.4 standard MAC inefficiency. In particular, this Ph.D. thesis proposes an IEEE 802.15.4 MAC layer performance enhancement by employing RTS/CTS combined with packet concatenation. The results have shown that the use of the RTS/CTS mechanism improves channel efficiency by decreasing the deferral time before transmitting a data packet. In addition, the Sensor Block Acknowledgment MAC (SBACK-MAC) protocol has been proposed that allows the aggregation of several acknowledgment responses in one special Block Acknowledgment (BACK) Response packet. Two different solutions are considered. The first one considers the SBACK-MAC protocol in the presence of BACK Request (concatenation) while the second one considers the SBACK-MAC in the absence of BACK Request (piggyback). The proposed solutions address a distributed scenario with single-destination and single-rate frame aggregation. The throughput and delay performance is mathematically derived under both ideal conditions (a channel environment with no transmission errors) and non ideal conditions (a channel environment with transmission errors). An analytical model is proposed, capable of taking into account the retransmission delays and the maximum number of backoff stages. The simulation results successfully validate our analytical model. For more than 7 TX (aggregated packets) all the MAC sub-layer protocols employing RTS/CTS with packet concatenation allows for the optimization of channel use in WSNs, v8-48 % improvement in the maximum average throughput and minimum average delay, and decrease energy consumption

On a Joint Physical Layer and Medium Access Control Sublayer Design for Efficient Wireless Sensor Networks and Applications

Wireless sensor networks (WSNs) are distributed networks comprising small sensing devices equipped with a processor, memory, power source, and often with the capability for short range wireless communication. These networks are used in various applications, and have created interest in WSN research and commercial uses, including industrial, scientific, household, military, medical and environmental domains. These initiatives have also been stimulated by the finalisation of the IEEE 802.15.4 standard, which defines the medium access control (MAC) and physical layer (PHY) for low-rate wireless personal area networks (LR-WPAN). Future applications may require large WSNs consisting of huge numbers of inexpensive wireless sensor nodes with limited resources (energy, bandwidth), operating in harsh environmental conditions. WSNs must perform reliably despite novel resource constraints including limited bandwidth, channel errors, and nodes that have limited operating energy. Improving resource utilisation and quality-of-service (QoS), in terms of reliable connectivity and energy efficiency, are major challenges in WSNs. Hence, the development of new WSN applications with severe resource constraints will require innovative solutions to overcome the above issues as well as improving the robustness of network components, and developing sustainable and cost effective implementation models. The main purpose of this research is to investigate methods for improving the performance of WSNs to maintain reliable network connectivity, scalability and energy efficiency. The study focuses on the IEEE 802.15.4 MAC/PHY layers and the carrier sense multiple access with collision avoidance (CSMA/CA) based networks. First, transmission power control (TPC) is investigated in multi and single-hop WSNs using typical hardware platform parameters via simulation and numerical analysis. A novel approach to testing TPC at the physical layer is developed, and results show that contrary to what has been reported from previous studies, in multi-hop networks TPC does not save energy. Next, the network initialization/self-configuration phase is addressed through investigation of the 802.15.4 MAC beacon interval setting and the number of associating nodes, in terms of association delay with the coordinator. The results raise doubt whether that the association energy consumption will outweigh the benefit of duty cycle power management for larger beacon intervals as the number of associating nodes increases. The third main contribution of this thesis is a new cross layer (PHY-MAC) design to improve network energy efficiency, reliability and scalability by minimising packet collisions due to hidden nodes. This is undertaken in response to findings in this thesis on the IEEE 802.15.4 MAC performance in the presence of hidden nodes. Specifically, simulation results show that it is the random backoff exponent that is of paramount importance for resolving collisions and not the number of times the channel is sensed before transmitting. However, the random backoff is ineffective in the presence of hidden nodes. The proposed design uses a new algorithm to increase the sensing coverage area, and therefore greatly reduces the chance of packet collisions due to hidden nodes. Moreover, the design uses a new dynamic transmission power control (TPC) to further reduce energy consumption and interference. The above proposed changes can smoothly coexist with the legacy 802.15.4 CSMA/CA. Finally, an improved two dimensional discrete time Markov chain model is proposed to capture the performance of the slotted 802.15.4 CSMA/CA. This model rectifies minor issues apparent in previous studies. The relationship derived for the successful transmission probability, throughput and average energy consumption, will provide better performance predictions. It will also offer greater insight into the strengths and weaknesses of the MAC operation, and possible enhancement opportunities. Overall, the work presented in this thesis provides several significant insights into WSN performance improvements with both existing protocols and newly designed protocols. Finally, some of the numerous challenges for future research are described

Data Aggregation Scheduling in Wireless Networks

Data aggregation is one of the most essential data gathering operations in wireless networks. It is an efficient strategy to alleviate energy consumption and reduce medium access contention. In this dissertation, the data aggregation scheduling problem in different wireless networks is investigated. Since Wireless Sensor Networks (WSNs) are one of the most important types of wireless networks and data aggregation plays a vital role in WSNs, the minimum latency data aggregation scheduling problem for multi-regional queries in WSNs is first studied. A scheduling algorithm is proposed with comprehensive theoretical and simulation analysis regarding time efficiency. Second, with the increasing popularity of Cognitive Radio Networks (CRNs), data aggregation scheduling in CRNs is studied. Considering the precious spectrum opportunity in CRNs, a routing hierarchy, which allows a secondary user to seek a transmission opportunity among a group of receivers, is introduced. Several scheduling algorithms are proposed for both the Unit Disk Graph (UDG) interference model and the Physical Interference Model (PhIM), followed by performance evaluation through simulations. Third, the data aggregation scheduling problem in wireless networks with cognitive radio capability is investigated. Under the defined network model, besides a default working spectrum, users can access extra available spectrum through a cognitive radio. The problem is formalized as an Integer Linear Programming (ILP) problem and solved through an optimization method in the beginning. The simulation results show that the ILP based method has a good performance. However, it is difficult to evaluate the solution theoretically. A heuristic scheduling algorithm with guaranteed latency bound is presented in our further investigation. Finally, we investigate how to make use of cognitive radio capability to accelerate data aggregation in probabilistic wireless networks with lossy links. A two-phase scheduling algorithm is proposed, and the effectiveness of the algorithm is verified through both theoretical analysis and numerical simulations

Méthodes d'Accès au Canal pour les Réseaux Dédiés à l'Internet des Objets

Dedicated networks for the Internet of Things appeared with the promise of connecting thousands of nodes, or even more, to a single base station in a star topology. This new logic represents a fundamental change in the way of thinking about networks, after decades during which research work mainly focused on multi-hop networks.Internet of Things networks are characterized by long transmission range, wide geographic coverage, low energy consumption and low set-up costs. This made it necessary to adapt the protocols at different architectural layers in order to meet the needs of these networks.Several players compete in the Internet of Things market, each trying to establish the most efficient solution. These players are mostly focused on modifying the physical layer, on the hardware part or through proposing new modulations. However, with regard to the channel access control solution (known as the MAC protocol), all the solutions proposed by these players are based on classic approaches such as Aloha and CSMA.The objective of this thesis is to propose a dynamic MAC solution for networks dedicated to the Internet of Things. The proposed solution has the ability to adapt to network conditions. This solution is based on a machine learning algorithm that learns from network history in order to establish the relationship between network conditions, MAC layer parameters and network performance in terms of reliability and energy consumption. The solution also has the originality of making possible the coexistence of nodes using different MAC configurations within the same network. The results of simulations have shown that a MAC solution based on machine learning could take advantage of the good properties of different conventional MAC protocols. The results also show that a cognitive MAC solution always offers the best compromise between reliability and energy consumption, while taking into account the fairness between the nodes of the network. The cognitive MAC solution tested for high density networks has proven better scalability compared to conventional MAC protocols, which is another important advantage of our solution.Les réseaux dédiés pour l’Internet des Objets sont apparus avec la promesse de connecter des milliers de nœuds, voire plus, à une seule station de base dans une topologie en étoile. Cette nouvelle logique représente un changement fondamental dans la façon de penser les réseaux, après des décennies pendant lesquelles les travaux de recherche se sont focalisés sur les réseaux multi-sauts.Les réseaux pour l’Internet des Objets se caractérisent par la longue portée des transmissions, la vaste couverture géographique, une faible consommation d’énergie et un bas coût de mise en place. Cela a rendu nécessaire des adaptations à tous les niveaux protocolaires afin de satisfaire les besoins de ces réseaux.Plusieurs acteurs sont en concurrence sur le marché de l’Internet des Objets, essayant chacun d’établir la solution la plus efficiente. Ces acteurs se sont concentrés sur la modification de la couche physique, soit au niveau de la partie matérielle, soit par la proposition de nouvelles techniques de modulation. Toutefois, en ce qui concerne la solution de contrôle d’accès au canal (connue sous le nom de couche MAC), toutes les solutions proposées par ces acteurs se fondent sur des approches classiques, tel que Aloha et CSMA.L'objectif de cette thèse est de proposer une solution MAC dynamique pour les réseaux dédiés à l’Internet des Objets. La solution proposée a la capacité de s'adapter aux conditions du réseau. Cette solution est basée sur un algorithme d'apprentissage automatique, qui apprend de l'historique du réseau afin d'établir la relation entre les conditions du réseau, les paramètres de la couche MAC et les performances du réseau en termes de fiabilité et de consommation d'énergie. La solution possède également l'originalité de faire coexister des nœuds utilisant de différentes configurations MAC au sein du même réseau. Les résultats de simulations ont montré qu'une solution MAC basée sur l'apprentissage automatique pourrait tirer profit des avantages des différents protocoles MAC classiques. Les résultats montrent aussi qu'une solution MAC cognitive offre toujours le meilleur compromis entre fiabilité et consommation d'énergie, tout en prenant en compte l'équité entre les nœuds du réseau. La solution MAC cognitive testée pour des réseaux à haute densité a prouvé des bonnes propriétés de passage à l’échelle par rapport aux protocoles MACs classiques, ce qui constitue un autre atout important de notre solution

A Comprehensive Survey on RF Energy Harvesting: Applications and Performance Determinants

\ua9 2022 by the authors. Licensee MDPI, Basel, Switzerland.There has been an explosion in research focused on Internet of Things (IoT) devices in recent years, with a broad range of use cases in different domains ranging from industrial automation to business analytics. Being battery-powered, these small devices are expected to last for extended periods (i.e., in some instances up to tens of years) to ensure network longevity and data streams with the required temporal and spatial granularity. It becomes even more critical when IoT devices are installed within a harsh environment where battery replacement/charging is both costly and labour intensive. Recent developments in the energy harvesting paradigm have significantly contributed towards mitigating this critical energy issue by incorporating the renewable energy potentially available within any environment in which a sensor network is deployed. Radio Frequency (RF) energy harvesting is one of the promising approaches being investigated in the research community to address this challenge, conducted by harvesting energy from the incident radio waves from both ambient and dedicated radio sources. A limited number of studies are available covering the state of the art related to specific research topics in this space, but there is a gap in the consolidation of domain knowledge associated with the factors influencing the performance of RF power harvesting systems. Moreover, a number of topics and research challenges affecting the performance of RF harvesting systems are still unreported, which deserve special attention. To this end, this article starts by providing an overview of the different application domains of RF power harvesting outlining their performance requirements and summarizing the RF power harvesting techniques with their associated power densities. It then comprehensively surveys the available literature on the horizons that affect the performance of RF energy harvesting, taking into account the evaluation metrics, power propagation models, rectenna architectures, and MAC protocols for RF energy harvesting. Finally, it summarizes the available literature associated with RF powered networks and highlights the limitations, challenges, and future research directions by synthesizing the research efforts in the field of RF energy harvesting to progress research in this area