Towards Energy-Fairness in Asynchronous Duty-Cycling Sensor Networks

Abstract—In this paper, we investigate the problem of controlling node sleep intervals so as to achieve the min-max energy fairness in asynchronous duty-cycling sensor networks. We propose a mathematical model to describe the energy efficiency of such networks and observe that traditional sleep interval setting strategy, i.e., operating sensor nodes with identical sleep intervals, or intuitive control heuristics, i.e., greedily increasing sleep intervals of sensor nodes with high energy consumption rates, hardly perform well in practice. There is an urgent need to develop an efficient sleep interval control strategy for achieving fair and high energy efficiency. To this end, we theoretically formulate the Sleep Interval Control (SIC) problem and find it a convex optimization problem. By utilizing the convex property, we decompose the original problem and propose a distributed algorithm, called GDSIC. In GDSIC, sensor nodes can tune sleep intervals through a local information exchange such that the maximum energy consumption rate in the network approaches to be minimized. The algorithm is self-adjustable to the traffic load variance and is able to serve as a unified framework for a variety of asynchronous duty-cycling MAC protocols. We implement our approach in a prototype system and test its feasibility and applicability on a 50-node testbed. We further conduct extensive trace-driven simulations to examine the efficiency and scalability of our algorithm with various settings. I

Réseaux de capteurs sans fil étendus dédiés aux collectes de données environnementales

Wireless sensor networks are used in many environmental monitoring applications (e.g., to monitor forest fires or volcanoes). In such applications, sensor nodes have a limited quantity of energy, but must operate for years without having their batteries changed. The main mechanism used to allow nodes to save energy is to sequence periods of activity and inactivity. However, the design of MAC and routing protocols for applications with low duty-cycle is still a challenge. In this thesis, we proposed unsynchronized MAC and routing protocols for wireless sensor networks devoted to environmental monitoring applications. The main specificity of our protocols is that they are adapted to very low duty-cycle (less than 1 % for all nodes). Our protocols are analyzed and compared to existing protocols by simulation and experimentation on TelosB nodes. Despite this low duty-cycle for all nodes, our protocols are able to achieve good performance, unlike other protocols in the literature, which are not adapted to these extreme conditions.Les réseaux de capteurs sans fil sont utilisés dans de nombreuses applications de surveillance de l’environnement (par exemple, pour surveiller les volcans ou pour détecter les incendies de forêts). Dans de telles applications, les nœuds capteurs disposent d’une quantité limitée d’énergie, mais doivent fonctionner pendant des années sans avoir leurs batteries changées. La principale méthode utilisée pour permettre aux nœuds d’économiser leur énergie est de séquencer les périodes d’activité et d’inactivité. Cependant, la conception de protocoles MAC et de routage pour les applications avec des taux d’activité faibles est un défi. Dans cette thèse nous proposons des protocoles MAC avec de très faibles taux d’activité (moins de 1% d’activité) et des protocoles de routages adaptés pour des réseaux de capteurs sans fil dédiés aux applications de surveillance environnementale. Nos protocoles sont analysés et comparés aux protocoles existants par simulation et par expérimentation sur des nœuds TelosB. Malgré un taux d’activité très faible pour tous les nœuds, nos protocoles sont capables d’obtenir de bonnes performances, contrairement aux autres protocoles de la littérature, qui ne sont pas adaptés à opérer avec de faibles taux d’activité

An approach to understand network challenges of wireless sensor network in real-world environments

The demand for large-scale sensing capabilities and scalable communication networks to monitor and control entities within smart buildings have fuelled the exponential growth in Wireless Sensor Network (WSN). WSN proves to be an attractive enabler because of its accurate sensing, low installation cost and flexibility in sensor placement. While WSN offers numerous benefits, it has yet to realise its full potential due to its susceptibility to network challenges in the environment that it is deployed. Particularly, spatial challenges in the indoor environment are known to degrade WSN communication reliability and have led to poor estimations of link quality. Existing WSN solutions often generalise all link failures and tackle them as a single entity. However, under the persistent influence of spatial challenges, failing to provide precise solutions may cause further link failures and higher energy consumption of battery-powered devices. Therefore, it is crucial to identify the causes of spatial- related link failures in order to improve WSN communication reliability. This thesis investigates WSN link failures under the influence of spatial challenges in real-world indoor environments. Novel and effective strategies are developed to evaluate the WSN communication reliability. By distinguishing between spatial challenges such as a poorly deployed environment and human movements, solutions are devised to reduce link failures and improve the lifespans of energy constraint WSN nodes. In this thesis, WSN test beds using proprietary wireless sensor nodes are developed and deployed in both controlled and uncontrolled office environments. These test beds provide diverse platforms for investigation into WSN link quality. In addition, a new data extraction feature called Network Instrumentation (NI) is developed and implemented onto the communication stacks of wireless sensor nodes to collect ZigBee PRO parameters that are under the influence of environmental dynamics. To understand the relationships between WSN and Wi-Fi devices communications, an investigation on frequency spectrum sharing is conducted between IEEE 802.15.4 and IEEE 802.11 bgn standards. It is discovered that the transmission failure of WSN nodes under persistent Wi-Fi interference is largely due to channel access failure rather than corrupted packets. The findings conclude that both technologies can co- exist as long as there is sufficient frequency spacing between Wi-Fi and WSN communication and adequate operating distance between the WSN nodes, and between the WSN nodes and the Wi-Fi interference source. Adaptive Network-based Fuzzy Inference System (ANFIS) models are developed to predict spatial challenges in an indoor environment. These challenges are namely, “no failure”, “failure due to poorly deployed environment” and “failure due to human movement”. A comparison of models has found that the best-produced model represents the properties of signal strength, channel fluctuations, and communication success rates. It is recognised that the interpretability of ANFIS models have reduced due to the “curse of dimensionality”. Hence, Non-Dominated Sorting Genetic Algorithm (NSGA-II) technique is implemented to reduce the complexity of these ANFIS models. This is followed by a Fuzzy rule sensitivity analysis, where the impacts of Fuzzy rules on model accuracy are found to be dependent on factors such as communication range and controlled or uncontrolled environment. Long-term WSN routing stability is measured, taking into account the adaptability and robustness of routing paths in the real-world environments. It is found that routing stability is subjected to the implemented routing protocol, deployed environment and routing options available. More importantly, the probability of link failures can be as high as 29.9% when a next hop’s usage rate falls less than 10%. This suggests that a less dominant next hop is subjected to more link failures and is short-lived. Overall, this thesis brings together diverse WSN test beds in real-world indoor environments and a new data extraction platform to extract link quality parameters from ZigBee PRO stack for a representative assessment of WSN link quality. This produces realistic perspectives of the interactions between WSN communication reliability and the environmental dynamics, particularly spatial challenges. The outcomes of this work include an in-depth system level understanding of real-world deployed applications and an insightful measure of large-scale WSN communication performance. These findings can be used as building blocks for a reliable and sustainable network architecture built on top of resource–constrained WSN