Slotted Aloha for Networked Base Stations

We study multiple base station, multi-access systems in which the user-base station adjacency is induced by geographical proximity. At each slot, each user transmits (is active) with a certain probability, independently of other users, and is heard by all base stations within the distance $r$. Both the users and base stations are placed uniformly at random over the (unit) area. We first consider a non-cooperative decoding where base stations work in isolation, but a user is decoded as soon as one of its nearby base stations reads a clean signal from it. We find the decoding probability and quantify the gains introduced by multiple base stations. Specifically, the peak throughput increases linearly with the number of base stations $m$ and is roughly $m/4$ larger than the throughput of a single-base station that uses standard slotted Aloha. Next, we propose a cooperative decoding, where the mutually close base stations inform each other whenever they decode a user inside their coverage overlap. At each base station, the messages received from the nearby stations help resolve collisions by the interference cancellation mechanism. Building from our exact formulas for the non-cooperative case, we provide a heuristic formula for the cooperative decoding probability that reflects well the actual performance. Finally, we demonstrate by simulation significant gains of cooperation with respect to the non-cooperative decoding.Comment: conference; submitted on Dec 15, 201

A State of Art Concept in Contriving of Underwater Networks

the underwater ocean environment is widely considered as one of the most difficult communications channels. Underwater acoustic networks have recently emerged as a new area of research in wireless networking. Underwater networks are generally formed by acoustically connected ocean - bottom sensors, underwater gateways and a surfa ce station, which provides a link to an on - shore control center. In recent years, there has been substantial work on protocol design for these networks with most efforts focusing on MAC and network layer protocols. Low communication bandwidth, large propag ation delay, floating node mobility, and high error probability are the challenges of building mobile underwater wireless sensor networks (WSN) for aquatic applications. Underwater sensor networks (WSNs) are the enabling technology for wide range of appl ications like monitoring the strong influences and impact of climate regulation, nutrient production, oil retrieval and transportation, man y scientific, environmental, commercial, safety, and military applications. This paper first introduces the concept o f UWSN, operation, applications and then reviews some recent developments within this research area and proposes an adaptive push system for dissemination of data in underwater wireless sensor networks. The goal of this paper is to survey the existing net w ork technology and its applicability to underwater acoustic channels. In this paper we provide an overview of recent medium acces s control, routing, transport, and cross - layer networking protocols. It examines the main approaches and challenges in the desi gn and implementation of underwater wireless sensor networks. Finally, some suggestions and promising solutions are given for th ese issues

Distributed detection, localization, and estimation in time-critical wireless sensor networks

In this thesis the problem of distributed detection, localization, and estimation (DDLE) of a stationary target in a fusion center (FC) based wireless sensor network (WSN) is considered. The communication process is subject to time-critical operation, restricted power and bandwidth (BW) resources operating over a shared communication channel Buffering from Rayleigh fading and phase noise. A novel algorithm is proposed to solve the DDLE problem consisting of two dependent stages: distributed detection and distributed estimation. The WSN performs distributed detection first and based on the global detection decision the distributed estimation stage is performed. The communication between the SNs and the FC occurs over a shared channel via a slotted Aloha MAC protocol to conserve BW. In distributed detection, hard decision fusion is adopted, using the counting rule (CR), and sensor censoring in order to save power and BW. The effect of Rayleigh fading on distributed detection is also considered and accounted for by using distributed diversity combining techniques where the diversity combining is among the sensor nodes (SNs) in lieu of having the processing done at the FC. Two distributed techniques are proposed: the distributed maximum ratio combining (dMRC) and the distributed Equal Gain Combining (dEGC). Both techniques show superior detection performance when compared to conventional diversity combining procedures that take place at the FC. In distributed estimation, the segmented distributed localization and estimation (SDLE) framework is proposed. The SDLE enables efficient power and BW processing. The SOLE hinges on the idea of introducing intermediate parameters that are estimated locally by the SNs and transmitted to the FC instead of the actual measurements. This concept decouples the main problem into a simpler set of local estimation problems solved at the SNs and a global estimation problem solved at the FC. Two algorithms are proposed for solving the local problem: a nonlinear least squares (NLS) algorithm using the variable projection (VP) method and a simpler gird search (GS) method. Also, Four algorithms are proposed to solve the global problem: NLS, GS, hyperspherical intersection method (HSI), and robust hyperspherical intersection (RHSI) method. Thus, the SDLE can be solved through local and global algorithm combinations. Five combinations are tied: NLS2 (NLS-NLS), NLS-HSI, NLS-RHSI, GS2, and GS-N LS. It turns out that the last algorithm combination delivers the best localization and estimation performance. In fact , the target can be localized with less than one meter error. The SNs send their local estimates to the FC over a shared channel using the slotted-Aloha MAC protocol, which suits WSNs since it requires only one channel. However, Aloha is known for its relatively high medium access or contention delay given the medium access probability is poorly chosen. This fact significantly hinders the time-critical operation of the system. Hence, multi-packet reception (MPR) is used with slotted Aloha protocol, in which several channels are used for contention. The contention delay is analyzed for slotted Aloha with and without MPR. More specifically, the mean and variance have been analytically computed and the contention delay distribution is approximated. Having theoretical expressions for the contention delay statistics enables optimizing both the medium access probability and the number of MPR channels in order to strike a trade-off between delay performance and complexity

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

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

Advanced Trends in Wireless Communications

Physical limitations on wireless communication channels impose huge challenges to reliable communication. Bandwidth limitations, propagation loss, noise and interference make the wireless channel a narrow pipe that does not readily accommodate rapid flow of data. Thus, researches aim to design systems that are suitable to operate in such channels, in order to have high performance quality of service. Also, the mobility of the communication systems requires further investigations to reduce the complexity and the power consumption of the receiver. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers rather than researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics

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

Actas da 10ª Conferência sobre Redes de Computadores

Universidade do MinhoCCTCCentro AlgoritmiCisco SystemsIEEE Portugal Sectio