Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Smart-antenna techniques for energy-efficient wireless sensor networks used in bridge structural health monitoring

Abstract: It is well known that wireless sensor networks differ from other computing platforms in that 1- they typically require a minimal amount of computing power at the nodes; 2- it is often desirable for sensor nodes to have drastically low power consumption. The main benefit of the this work is a substantial network life before batteries need to be replaced or, alternatively, the capacity to function off of modest environmental energy sources (energy harvesting). In the context of Structural Health Monitoring (SHM), battery replacement is particularly problematic since nodes can be in difficult to access locations. Furthermore, any intervention on a bridge may disrupt normal bridge operation, e.g. traffic may need to be halted. In this regard, switchbeam smart antennas in combination with wireless sensor networks (WSNs) have shown great potential in reducing implementation and maintenance costs of SHM systems. The main goal of implementing switch-beam smart antennas in our application is to reduce power consumption, by focusing the radiated energy only where it is needed. SHM systems capture the dynamic vibration information of a bridge structure in real-time in order to assess the health of the structure and to predict failures. Current SHM systems are based on piezoelectric patch sensors. In addition, the collection of data from the plurality of sensors distributed over the span of the bridge is typically performed through an expensive and bulky set of shielded wires which routes the information to a data sink at one end of the structure. The installation, maintenance and operational costs of such systems are extremely high due to high power consumption and the need for periodic maintenance. Wireless sensor networks represent an attractive alternative, in terms of cost, ease of maintenance, and power consumption. However, network lifetime in terms of node battery life must be very long (ideally 5–10 years) given the cost and hassle of manual intervention. In this context, the focus of this project is to reduce the global power consumption of the SHM system by implementing switched-beam smart antennas jointly with an optimized MAC layer. In the first part of the thesis, a sensor network platform for bridge SHM incorporating switched-beam antennas is modelled and simulated. where the main consideration is the joint optimization of beamforming parameters, MAC layer, and energy consumption. The simulation model, built within the Omnet++ network simulation framework, incorporates the energy consumption profiles of actual selected components (microcontroller, radio interface chip). The energy consumption and packet delivery ratio (PDR) of the network with switched-beam antennas is compared with an equivalent network based on omnidirectional antennas. In the second part of the thesis, this system model is leveraged to examine two distinct but interrelated aspects: Gallium Arsenide (GaAs) based solar energy harvesting and switched-beam antenna strategies. The main consideration here is the joint optimization of solar energy harvesting and switchedbeam directional antennas, where an equivalent network based on omnidirectional antennas acts as a baseline reference for comparison purposes.Il est bien connu que les réseaux de capteurs sans fils diffèrent des autres plateformes informatiques étant donné 1- qu’ils requièrent typiquement une puissance de calcul minimale aux noeuds du réseau ; 2- qu’il est souvent désirable que les noeuds capteurs aient une consommation d’énergie dramatiquement faible. La principale retombée de ce travail réside en la durée de vie allongée du réseau avant que les piles ne doivent être remplacées ou, alternativement, la capacité de fonctionner indéfiniment à partir de modestes sources d’énergie ambiente (glânage d’énergie). Dans le contexte du contrôle de la santé structurale (CSS), le remplacement de piles est particulièrement problématique puisque les noeuds peuvent se trouver en des endroits difficiles d’accès. De plus, toute intervention sur un pont implique une perturbation de l’opération normale de la structure, par exemple un arrêt du traffic. Dans ce contexte, les antennes intelligentes à commutation de faisceau en combinaison avec les réseaux de capteurs sans fils ont démontré un grand potentiel pour réduire les coûts de réalisation et d’entretien de systèmes de CSS. L’objectif principal de l’intégration d’antennes à commutation de faisceau dans notre application réside dans la réduction de la consommation énergétique, réalisée en concentrant l’énergie radiée uniquement là où elle est nécessaire. Les systèmes de CSS capturent l’information dynamique de vibration d’une structure de pont en temps réel de manière à évaluer la santé de la structure et prédire les failles. Les systèmes courants de CSS sont basés sur des senseurs piézoélectriques planaires. De plus, la collecte de données à partir de la pluralité de senseurs distribués sur l’étendue du pont est typiquement effectuée par le biais d’un ensemble coûteux et encombrant de câbles blindés qui véhiculent l’information jusqu’à un point de collecte à une extremité de la structure. L’installation, l’entretien, et les coûts opérationnels de tels systèmes sont extrêmement élevés étant donné la consommation de puissance élevée et le besoin d’entretien régulier. Les réseaux de capteurs sans fils représentent une alternative attrayante, en termes de coût, facilité d’entretien et consommation énergétique. Toutefois, la vie de réseau en termes de la durée de vie des piles doit être très longue (idéalement de 5 à 10 ans) étant donné le coût et les problèmes liés à l’intervention manuelle. Dans ce contexte, ce projet se concentre sur la réduction de la consommation de puissance globale d’un système de CSS en y intégrant des antennes intelligentes à commutation de faisceau conjointement avec une couche d’accès au médium (couche MAC) optimisée. Dans la première partie de la thèse, une plateforme de réseau de capteurs sans fils pour le CSS d’un pont incorporant des antennes à commutation de faisceaux est modélisé et simulé, avec pour considération principale l’optimisation des paramètres de sélection de faisceau, de la couche MAC et de la consommation d’énergie. Le modèle de simulation, construit dans le logiciel de simulation de réseaux Omnet++, incorpore les profils de consommation d’énergie de composants réels sélectionnés (microcontrôleur, puce d’interface radio). La consommation d’énergie et le taux de livraison de paquets du réseau avec antennes à commutation de faisceau est comparé avec un réseau équivalent basé sur des antennes omnidirectionnelles. Dans la deuxième partie de la thèse, le modèle système proposé est mis à contribution pour examiner deux aspects distrincts mais interreliés : le glânage d’énergie à partir de cellules solaire à base d’arséniure de Gallium (GaAs) et les stratégies liées aux antennes à commutation de faisceau. La considération principale ici est l’optimisation conjointe du glânage d’énergie et des antennes à commutation de faisceau, en ayant pour base de comparaison un réseau équivalent à base d’antennes omnidirectionnelles

Narrowband delay tolerant protocols for WSN applications. Characterization and selection guide

This article focuses on delay tolerant protocols for Wireless Sensor Network (WSN) applications, considering both established and new protocols. We obtained a comparison of their characteristics by implementing all of them on an original platform for network simulation, and by testing their behavior on a common test-bench. Thereafter, matching the requirements linked to each application with the performances achieved in the test-bench, allowed us to define an application oriented protocol selection guide

Building a green connected future: smart (Internet of) Things for smart networks

The vision of Internet of Things (IoT) promises to reshape society by creating a future where we will be surrounded by a smart environment that is constantly aware of the users and has the ability to adapt to any changes. In the IoT, a huge variety of smart devices is interconnected to form a network of distributed agents that continuously share and process information. This communication paradigm has been recognized as one of the key enablers of the rapidly emerging applications that make up the fabric of the IoT. These networks, often called wireless sensor networks (WSNs), are characterized by the low cost of their components, their pervasive connectivity, and their self-organization features, which allow them to cooperate with other IoT elements to create large-scale heterogeneous information systems. However, a number of considerable challenges is arising when considering the design of large-scale WSNs. In particular, these networks are made up by embedded devices that suffer from severe power constraints and limited resources. The advent of low-power sensor nodes coupled with intelligent software and hardware technologies has led to the era of green wireless networks. From the hardware perspective, green sensor nodes are endowed with energy scavenging capabilities to overcome energy-related limitations. They are also endowed with low-power triggering techniques, i.e., wake-up radios, to eliminate idle listening-induced communication costs. Green wireless networks are considered a fundamental vehicle for enabling all those critical IoT applications where devices, for different reasons, do not carry batteries, and that therefore only harvest energy and store it for future use. These networks are considered to have the potential of infinite lifetime since they do not depend on batteries, or on any other limited power sources. Wake-up radios, coupled with energy provisioning techniques, further assist on overcoming the physical constraints of traditional WSNs. In addition, they are particularly important in green WSNs scenarios in which it is difficult to achieve energy neutrality due to limited harvesting rates. In this PhD thesis we set to investigate how different data forwarding mechanisms can make the most of these green wireless networks-enabling technologies, namely, energy harvesting and wake-up radios. Specifically, we present a number of cross-layer routing approaches with different forwarding design choices and study their consequences on network performance. Among the most promising protocol design techniques, the past decade has shown the increasingly intensive adoption of techniques based on various forms of machine learning to increase and optimize the performance of WSNs. However, learning techniques can suffer from high computational costs as nodes drain a considerable percentage of their energy budget to run sophisticated software procedures, predict accurate information and determine optimal decision. This thesis addresses also the problem of local computational requirements of learning-based data forwarding strategies by investigating their impact on the performance of the network. Results indicate that local computation can be a major source of energy consumption; it’s impact on network performance should not be neglected

Rate-distortion Balanced Data Compression for Wireless Sensor Networks

This paper presents a data compression algorithm with error bound guarantee for wireless sensor networks (WSNs) using compressing neural networks. The proposed algorithm minimizes data congestion and reduces energy consumption by exploring spatio-temporal correlations among data samples. The adaptive rate-distortion feature balances the compressed data size (data rate) with the required error bound guarantee (distortion level). This compression relieves the strain on energy and bandwidth resources while collecting WSN data within tolerable error margins, thereby increasing the scale of WSNs. The algorithm is evaluated using real-world datasets and compared with conventional methods for temporal and spatial data compression. The experimental validation reveals that the proposed algorithm outperforms several existing WSN data compression methods in terms of compression efficiency and signal reconstruction. Moreover, an energy analysis shows that compressing the data can reduce the energy expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294

A critical analysis of mobility management related issues of wireless sensor networks in cyber physical systems

Mobility management has been a long-standing issue in mobile wireless sensor networks and especially in the context of cyber physical systems; its implications are immense. This paper presents a critical analysis of the current approaches to mobility management by evaluating them against a set of criteria which are essentially inherent characteristics of such systems on which these approaches are expected to provide acceptable performance. We summarize these characteristics by using a quadruple set of metrics. Additionally, using this set we classify the various approaches to mobility management that are discussed in this paper. Finally, the paper concludes by reviewing the main findings and providing suggestions that will be helpful to guide future research efforts in the area

Optimization Based Hybrid Congestion Alleviation for 6LoWPAN Networks

The IPv6 over Low-Power Wireless Personal Area Network (6LoWPAN) protocol stack is a key part of the Internet of Things (IoT) where the 6LoWPAN motes will account for the majority of the IoT ‘things’. In 6LoWPAN networks, heavy network traffic causes congestion which significantly effects the network performance and the quality of service (QoS) metrics. Generally, two main strategies are used to control and alleviate congestion in 6LoWPAN networks: resource control and traffic control. All the existing work of congestion control in 6LoWPAN networks use one of these. In this paper, we propose a novel congestion control algorithm called optimization based hybrid congestion alleviation (OHCA) which combines both strategies into a hybrid solution. OHCA utilizes the positive aspects of each strategy and efficiently uses the network resources. The proposed algorithm uses a multi-attribute optimization methodology called grey relational analysis for resource control by combining three routing metrics (buffer occupancy, expected transmission count and queuing delay) and forwarding packets through non-congested parents. Also, OHCA uses optimization theory and Network Utility Maximization (NUM) framework to achieve traffic control when the non-congested parent is not available where the optimal nodes’ sending rate are computed by using Lagrange multipliers and KKT conditions. The proposed algorithm is aware of node priorities and application priorities to support the IoT application requirements where the applications’ sending rate allocation is modelled as a constrained optimization problem. OHCA has been tested and evaluated through simulation by using Contiki OS and compared with comparative algorithms. Simulation results show that OHCA improves performance in the presence of congestion by an overall average of 28.36%, 28.02%, 48.07%, 31.97% and 90.35% in terms of throughput, weighted fairness index, end-to-end delay, energy consumption and buffer dropped packets as compared to DCCC6 and QU-RPL