Lifetime Maximization of Wireless Sensor Networks with a Mobile Source Node

We study the problem of routing in sensor networks where the goal is to maximize the network's lifetime. Previous work has considered this problem for fixed-topology networks. Here, we add mobility to the source node, which requires a new definition of the network lifetime. In particular, we redefine lifetime to be the time until the source node depletes its energy. When the mobile node's trajectory is unknown in advance, we formulate three versions of an optimal control problem aiming at this lifetime maximization. We show that in all cases, the solution can be reduced to a sequence of Non- Linear Programming (NLP) problems solved on line as the source node trajectory evolves.Comment: A shorter version of this work will be published in Proceedings of 2016 IEEE Conference on Decision and Contro

A survey of network lifetime maximization techniques in wireless sensor networks

Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteri

Joint Optimization of Energy Efficiency and Data Compression in TDMA-Based Medium Access Control for the IoT - Extended Version

Energy efficiency is a key requirement for the Internet of Things, as many sensors are expected to be completely stand-alone and able to run for years without battery replacement. Data compression aims at saving some energy by reducing the volume of data sent over the network, but also affects the quality of the received information. In this work, we formulate an optimization problem to jointly design the source coding and transmission strategies for time-varying channels and sources, with the twofold goal of extending the network lifetime and granting low distortion levels. We propose a scalable offline optimal policy that allocates both energy and transmission parameters (i.e., times and powers) in a network with a dynamic Time Division Multiple Access (TDMA)-based access scheme.Comment: 8 pages, 4 figures, revised and extended version of a paper that was accepted for presentation at IEEE Int. Workshop on Low-Layer Implementation and Protocol Design for IoT Applications (IoT-LINK), GLOBECOM 201

Wireless Power Transfer and Data Collection in Wireless Sensor Networks

In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using Wireless Power Transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, while the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aiming to minimize data packet loss from a network of sensor nodes in terms of the nodes' energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semi-decentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular Technolog

Energy management in harvesting enabled sensing nodes: prediction and control

Energy efficient transmission rate regulation of wireless sensing nodes, is a critical issue when operating in an energy harvesting (EH) enabled environment. In this work, we view the energy management problem as a queue control problem where the objective is to regulate transmission such that the energy level converges to a reference value. We employ a validated non-linear queuing model to derive two non-linear robust throughput controllers. A notable feature of the proposed scheme is its capability of predicting harvest-able energy. The predictions are generated using the proposed Accurate Solar Irradiance prediction Model (ASIM) whose effectiveness in generating accurate both long and short term predictions is demonstrated using real world data. The stability of the proposed controllers is established analytically and the effectiveness of the proposed strategies is demonstrated using simulations conducted on the Network Simulator (NS-3). The proposed policy is successful in guiding the energy level to the reference value, and outperforms the Throughput Optimal (TO) policy in terms of the throughput achieved

A Novel Approach to Transmission Power, Lifetime and Connectivity Optimization in Asymmetric Networks

This thesis deals with the problem of proper power management over asymmetric networks represented by weighted directed graphs (digraphs) in the presence of various constraints. Three different problems are investigated in this study. First, the problem of total transmission power optimization and connectivity control over the network is examined. The notion of generalized algebraic connectivity (GAC), used as a network connectivity measure, is formulated as an implicit function of the nodes' transmission powers. An optimization problem is then presented to minimize the total transmission power of the network while considering constraints on the values of the GAC and the individual transmission power levels. The problem of network lifetime maximization and connectivity control is investigated afterwards. Each node is assumed to deplete its battery linearly with respect to the transmission powers used for communication, and the network lifetime is defined as the minimum lifetime over all nodes. Finally, it is desired to maximize the connectivity level of the network with constraints on the total transmission power of the network and the individual transmission powers. The interior point and the mixed interior point-exterior point methods are utilized to transform these constrained optimization problems into sequential optimization problems. Given the new formulation, each subproblem is then solved numerically via the subgradient method with backtracking line search. A distributed version of the algorithm, taking into account the estimation of global quantities, is provided. The asymptotic convergence of the proposed centralized and distributed algorithms is demonstrated analytically, and their effectiveness is verified by simulations

Pile de protocoles pour des réseaux des capteurs avec récupération d'énergie

This thesis concerns energy efficient protocols for harvested wireless sensor networks. It is a part of an industrial Internet of Things project. STMicroelectronics started the GreenNet project with the objective to develop and design a new generation of harvesting smart objects to be integrated in the Internet of Things. The GreenNet platform is novel with respect to the existing solutions due to its small size that implies a small energy buffer and small harvesting capabilities. This aspect makes the standard protocols and precedent solutions not directly applicable on this extremely low power platform. In this dissertation, we analyse standard protocols and existing solutions to identify their issues in the gn platform. Then, we provide protocol and algorithm adaptations to make feasible the concept of auto configurable and sustainable networks of GreenNet nodes. We proposed MCBT, an energy efficient protocol for the bootstrap procedure. It enables low power nodes to be enrolled in mh mc wireless sensor networks thanks to the network support for enrolling new nodes. It represents an energy efficient solution that extends the standard protocol. We proposed STADA, a sustainable algorithm to adapt the node activity according to the available energy and traffic conditions. STADA is based on a weighted function that takes into account the energy present in the battery, the energy harvesting rate, and network traffic. In this way, the algorithm takes into account all main parameters to adapt the energy consumption and improve the node performance. To make the harvested network more efficient according to light variations, we proposed a novel metric that makes the path choice a simple process. With the Expected Delay, we synthesize all network parameters in a single monotonic variable that facilitates the path choice in mh harvesting wireless sensor networks. All proposed solutions are designed to work with standard beacon-enabled IEEE 802.15.4 protocols and are easily portable on the future version of IEEE 802.15.4e. We validated the proposed protocols with emulations and simulations. The evaluation results shown better performance in terms of energy consumption and quality of service.Cette thèse vise à améliorer la pile de protocoles pour réseaux de capteurs sans fil à récupération d'énergie afin de les rendre autonomes dans un contexte multi-saut. Elle s'inscrit dans le projet GreenNet de STMicroelectronics qui a pour objectif de concevoir et développer une nouvelle génération d'objets intelligent basés sur la récupération d'énergie ambiente en vue de l'intégration dans l'Internet des Objets. L'originalité de la plateforme GreenNet repose sur sa petite taille qui implique une faible capacité de stockage d'énergie ainsi qu'une faible capacité de récupération d'énergie. Avec un si faible budget d'énergie, les protocoles standards ou les solutions proposées par les communautés académique/industrielle ne permettant pas d'assurer un fonctionnement autonome de ces réseaux. Dans cette thèse, nous analysons les protocoles standards et les solutions existantes pour identifier leurs limites avec la plateforme GreenNet. Ensuite, nous proposons 3 contributions afin de permettre cette autonomie. La première contribution est MCBT, un protocole permettant d'accélérer la découverte et le rattachement de nouveaux noeuds à un réseau multi saut et multi-canaux en formation ou existent. Ce protocole réduit efficacement l'énergie dépensée dans cette phase fortement consommatrice. La deuxième contribution est STADA, un algorithme adaptant l'activité des capteurs en fonction des conditions locales de trafic et d'énergie disponible. STADA est basé sur une fonction de pondération qui tient compte de l'énergie présente dans la batterie, du taux de récupération d'énergie et du trafic local. Enfin, notre troisième contribution propose une nouvelle métrique de routage basée sur Expected Delay synthétisant en une seule variable monotone des facteurs tels que l'éloignement au puits, les chemins bénéficiant d'un ordonnancement de relayage de paquet privilégié et de périodes cumulées d'activité des radios sur le chemin favorable. Toutes les solutions proposées sont conçues pour fonctionner avec la norme IEEE 802.15.4 slotté et sont facilement transposables à son évolution définie par la norme IEEE 802.15.4e. Nous avons validé les protocoles proposés grâce à un simulateur émulant des noeuds réels (Cooja) et au simulateur WSNet. Les résultats ont montré de meilleures performances en termes de consommation d'énergie et de qualité de service par rapport à l'existant

Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts