Resilient routing mechanism for wireless sensor networks with deep learning link reliability prediction

Wireless sensor networks play an important role in Internet of Things systems and services but are prone and vulnerable to poor communication channel quality and network attacks. In this paper we are motivated to propose resilient routing algorithms for wireless sensor networks. The main idea is to exploit the link reliability along with other traditional routing metrics for routing algorithm design. We proposed firstly a novel deep-learning based link prediction model, which jointly exploits Weisfeiler-Lehman kernel and Dual Convolutional Neural Network (WL-DCNN) for lightweight subgraph extraction and labelling. It is leveraged to enhance self-learning ability of mining topological features with strong generality. Experimental results demonstrate that WL-DCNN outperforms all the studied 9 baseline schemes over 6 open complex networks datasets. The performance of AUC (Area Under the receiver operating characteristic Curve) is improved by 16% on average. Furthermore, we apply the WL-DCNN model in the design of resilient routing for wireless sensor networks, which can adaptively capture topological features to determine the reliability of target links, especially under the situations of routing table suffering from attack with varying degrees of damage to local link community. It is observed that, compared with other classical routing baselines, the proposed routing algorithm with link reliability prediction module can effectively improve the resilience of sensor networks while reserving high-energy-efficiency

A Real-Time Communication Framework for Wireless Sensor Networks

Recent advances in miniaturization and low power design have led to a flurry of activity in wireless sensor networks. Sensor networks have different constraints than traditional wired networks. A wireless sensor network is a special network with large numbers of nodes equipped with embedded processors, sensors, and radios. These nodes collaborate to accomplish a common task such as environment monitoring or asset tracking. In many applications, sensor nodes will be deployed in an ad-hoc fashion without careful planning. They must organize themselves to form a multihop, wireless communication network. In sensor network environments, much research has been conducted in areas such as power consumption, self-organisation techniques, routing between the sensors, and the communication between the sensor and the sink. On the other hand, real-time communication with the Quality of Service (QoS) concept in wireless sensor networks is still an open research field. Most protocols either ignore real time or simply attempt to process as fast as possible and hope that this speed is sufficient to meet the deadline. However, the introduction of real-time communication has created additional challenges in this area. The sensor node spends most of its life routing packets from one node to another until the packet reaches the sink; therefore, the node functions as a small router most of the time. Since sensor networks deal with time-critical applications, it is often necessary for communication to meet real time constraints. However, research that deals with providing QoS guarantees for real-time traffic in sensor networks is still in its infancy.This thesis presents a real-time communication framework to provide quality of service in sensor networks environments. The proposed framework consists of four components: First, present an analytical model for implementing Priority Queuing (PQ) in a sensor node to calculate the queuing delay. The exact packet delay for corresponding classes is calculated. Further, the analytical results are validated through an extensive simulation study. Second, report on a novel analytical model based on a limited service polling discipline. The model is based on an M/D/1 queuing system (a special class of M/G/1 queuing systems), which takes into account two different classes of traffic in a sensor node. The proposed model implements two queues in a sensor node that are served in a round robin fashion. The exact queuing delay in a sensor node for corresponding classes is calculated. Then, the analytical results are validated through an extensive simulation study. Third, exhibit a novel packet delivery mechanism, namely the Multiple Level Stateless Protocol (MLSP), as a real-time protocol for sensor networks to guarantee the traffic in wireless sensor networks. MLSP improves the packet loss rate and the handling of holes in sensor network much better than its counterpart, MMSPEED. It also introduces the k-limited polling model for the first time. In addition, the whole sending packets dropped significantly compared to MMSPEED, which it leads to decrease the consumption power. Fourth, explain a new framework for moving data from the sink to the user, at a low cost and low power, using the Universal Mobile Telecommunication System (UMTS), which is standard for the Third Generation Mobile System (3G). The integration of sensor networks with the 3G mobile network infrastructure will reduce the cost of building new infrastructures and enable the large-scale deployment of sensor network

MIMO communication systems: receiver design and diversity-multiplexing tradeoff analysis

After a few decades\u27 evolution of wireless communication systems, to ensure reliable high-speed communication over unreliable wireless channels is still one of the major challenges facing researchers and engineers. The use of multiple antennas at transmitter and receiver, known as multiple-input multiple-output (MIMO) communications, is one promising technology delivering desired wireless services. The main goal of this thesis is to study two important issues in wireless MIMO communication systems: receiver design for coded MIMO systems, and diversity-multiplexing tradeoff analysis in general fading channels;In the first part of this thesis, we decompose the receiver design problem into two sub-problems: MIMO channel estimation and MIMO detection. For the MIMO channel estimation, we develop an expectation-maximization (EM) based semi-blind channel and noise covariance matrix estimation algorithm for space-time coding systems under spatially correlated noise. By incorporating the proposed channel estimator into the iterative receiver structure, both the channel estimation and the error-control decoding are improved significantly. We also derive the modified Cramer-Rao bounds (MCRB) for the unknown parameters as the channel estimation performance metric, and demonstrate that the proposed channel estimation algorithm can achieve the MCRB after several iterations. For the MIMO detection, we propose a novel low-complexity MIMO detection algorithm, which has only cubic order computational complexity, but with near-optimal performance. For a 4x4 turbo-coded system, we show that the proposed detector had the same performance as the maximum a posteriori (MAP) detector for BPSK modulation, and 0.1 dB advantage over the approximated MAP detector (list sphere decoding algorithm) for 16-QAM modulation at BER = 10-4;In the second part of this thesis, we derive the optimal diversity-multiplexing tradeoff for general MIMO fading channels, which include different fading types as special cases. We show that for a MIMO system with long coherence time, the optimal diversity-multiplexing tradeoff is also a piecewise linear function, and only the first segment is affected by different fading types. We proved that under certain full-rank assumptions spatial correlation has no effect on the optimal tradeoff. We also argued that non-zero channel means in general are not beneficial for multiplexing-diversity tradeoff

Energy-efficient coverage with wireless sensors

Many sensor networks are deployed for the purpose of covering and monitoring a particular region, and detecting the object of interest in the region. In these applications, coverage is one of the centric problems in sensor networks. Such problem is centered around a basic question: ``How well can the sensors observe the physical world?\u27\u27 The concept of coverage can be interpreted as a measure of quality of service provided by the sensing function in various ways depending on sensor devices and applications. On the other hand, sensor nodes are usually battery-powered and subject to limitations based on the available battery energy. It is, therefore, critical to design, deploy and operate a wireless sensor network in an energy-efficient manner, while satisfying the coverage requirement. In order to prolong the lifetime of a sensor network, we explore the notion of connected-k-coverage in sensor networks. It requires the monitored region to be k-covered by a connected component of active sensors, which is less demanding than requiring k-coverage and connectivity among all active sensors simultaneously. We investigate the theoretical foundations about connected-k-coverage and, by using the percolation theorem, we derive the critical conditions for connected-k-coverage for various relations between sensors\u27 sensing radius and communication range. In addition, we derive an effective lower bound on the probability of connected-k-coverage, and propose a simple randomized scheduling algorithm and select proper operational parameters to prolong the lifetime of a large-scale sensor network. It has been shown that sensors\u27 collaboration (information fusion) can improve object detection performance and area coverage in sensor networks. The sensor coverage problem in this situation is regarded as information coverage. Based on a probabilistic sensing model, we study the object detection problem and develop a novel on-demand framework (decision fusion-based) for collaborative object detection in wireless sensor networks, where inactive sensors can be triggered by nearby active sensors to collaboratively sense and detect the object. By using this framework, we can significantly improve the coverage performance of the sensor networks, while the network power consumption can be reduced. Then, we proceed to study the barrier information coverage problem under the similar assumption that neighboring sensors may collaborate with each other to form a virtual sensor which makes the detection decision based on combined sensed readings. We propose both centralized and distributed schemes to operate a sensor network to information-cover a barrier efficiently. At last, we propose and study a multi-round sensor deployment strategy based on line-based sensor deployment model, which can use the fewest sensors to cover a barrier. We have an interesting discovery that the optimal two-round sensor deployment strategy yields the same barrier coverage performance as other optimal strategies with more than two rounds. This result is particularly encouraging as it implies that the best barrier coverage performance can be achieved with low extra deployment cost by deploying sensors in two rounds. In addition, two practical solutions are presented to deal with realistic situations when the distribution of a sensor\u27s residence point is not fully known

An adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networks

The paradigm of wireless sensor networks (WSNs) has gained a lot of popularity in the recent years due to the proliferation of wireless devices. This is evident as WSNs find numerous application areas in fields such as agriculture, infrastructure monitoring, transport, and security surveillance. Traditionally, most deployments of WSNs operate in the unlicensed industrial scientific and medical (ISM) band and more specifically, the globally available 2.4 GHz frequency band. This band is shared with several other wireless technologies such as Bluetooth, Wi-Fi, near field communication and other proprietary technologies thus leading to overcrowding and interference problems. The concept of dynamic spectrum access alongside cognitive radio technology can mitigate the coexistence issues by allowing WSNs to dynamically access new spectrum opportunities. Furthermore, cognitive radio technology addresses some of the inherent constraints of WSNs thus introducing a myriad of benefits. This justifies the emergence of cognitive radio sensor networks (CRSNs). The selection of a spectrum sensing technique plays a vital role in the design and implementation of a CRSN. This dissertation sifts through the spectrum sensing techniques proposed in literature investigating their suitability for CRSN applications. We make amendments to the conventional energy detector particularly on the threshold selection technique. We propose an adaptive threshold energy detection model with noise variance estimation for implementation in CRSN systems. Experimental work on our adaptive threshold technique based on the recursive one-sided hypothesis test (ROHT) technique was carried out using MatLab. The results obtained indicate that our proposed technique is able to achieve adaptability of the threshold value based on the noise variance. We also employ the constant false alarm rate (CFAR) threshold to act as a defence mechanism against interference of the primary user at low signal to noise ratio (SNR). Our evaluations indicate that our adaptive threshold technique achieves double dynamicity of the threshold value based on the noise variance and the perceived SNR

Medium Access Control Facing the Dynamics of Wireless Sensor Networks

Cette dispose d'un résumé en français en annexe.A WSN consists in spatially distributed autonomous and embedded devices that cooperatively monitor physical or environmental conditions in a less intrusive fashion. The data collected by each sensor node (such as temperature, vibrations, sounds, movements etc.) are reported to a sink station in a hop-by-hop fashion using wireless transmissions. In the last decade, the challenges raised by WSN have naturally attracted the interest of the research community. Especially, significant improvements to the communication stack of the sensor node have been proposed in order to tackle the energy, computation and memory constraints induced by the use of embedded devices. A number of successful deployments already denotes the growing interest in this technology. Recent advances in embedded systems and communication protocols have stimulated the elaboration of more complex use cases. They target dense and dynamic networks with the use of mobile sensors or multiple data collection schemes. For example, mobility in WSN can be employed to extend the network coverage and connectivity, as well as improve the routing performances. However, these new scenarios raise novel challenges when designing communication protocols. The work presented in this thesis focuses on the issues raised at the MAC layer when confronted to dynamic WSN. We have first studied the impact of mobility and defined two new MAC protocols (Machiavel and X-Machiavel) which improve the medium access of mobile sensor nodes in dense networks. Our second contribution is an auto-adaptive algorithm for preamble sampling protocols. It aims at minimizing the global energy consumption in networks with antagonist traffic patterns by obtaining an optimal configuration on each node. This mechanism is especially energy-efficient during burst transmissions that could occur in such dynamic networks.     Un réseau de capteurs sans fil (Wireless Sensor Network, WSN) consiste en une distribution spatiale d'équipements embarqués autonomes, qui coopèrent de manière à surveiller l'environnement de manière non-intrusive. Les données collectées par chaque capteur (tels que la température, des vibrations, des sons, des mouvements etc.) sont remontées de proche en proche vers un puits de collecte en utilisant des technologies de communication sans fil. Voilà une décennie que les contraintes inhérentes à ces réseaux attirent l'attention de la communauté scientifique. Ainsi, de nombreuses améliorations à différents niveaux de la pile de communication ont été proposées afin de relever les défis en termes d'économie d'énergie, de capacité de calcul et de contrainte mémoire imposés par l'utilisation d'équipements embarqués. Plusieurs déploiements couronnés de succès démontrent l'intérêt grandissant pour cette technologie. Les récentes avancées en termes d'intégration d'équipements et de protocoles de communication ont permis d'élaborer de nouveaux scénarios plus complexes. Ils mettent en scène des réseaux denses et dynamiques par l'utilisation de capteurs mobiles ou de différentes méthodes de collection de données. Par exemple, l'intérêt de la mobilité dans les WSN est multiple dans la mesure où les capteurs mobiles peuvent notamment permettre d'étendre la couverture d'un réseau, d'améliorer ses performances de routage ou sa connexité globale. Toutefois, ces scénarios apportent de nouveaux défis dans la conception de protocoles de communication. Ces travaux de thèse s'intéressent donc à la problématique de la dynamique des WSN, et plus particulièrement à ce que cela implique au niveau du contrôle de l'accès au médium (Medium Access Control, MAC). Nous avons tout d'abord étudié l'impact de la mobilité et défini deux nouvelles méthodes d'accès au médium (Machiavel et X-Machiavel) qui permettent d'améliorer les conditions d'accès au canal pour les capteurs mobiles dans les réseaux denses. Notre deuxième contribution est un algorithme d'auto-adaptation destiné aux protocoles par échantillonnage. Il vise à minimiser la consommation énergétique globale dans les réseaux caractérisés par des modèles de trafic antagonistes, en obtenant une configuration optimale sur chaque capteur. Ce mécanisme est particulièrement efficace en énergie pendant les transmissions par rafales qui peuvent survenir dans de tels réseaux dynamiques