Data Dissemination Performance in Large-Scale Sensor Networks

As the use of wireless sensor networks increases, the need for (energy-)efficient and reliable broadcasting algorithms grows. Ideally, a broadcasting algorithm should have the ability to quickly disseminate data, while keeping the number of transmissions low. In this paper we develop a model describing the message count in large-scale wireless sensor networks. We focus our attention on the popular Trickle algorithm, which has been proposed as a suitable communication protocol for code maintenance and propagation in wireless sensor networks. Besides providing a mathematical analysis of the algorithm, we propose a generalized version of Trickle, with an additional parameter defining the length of a listen-only period. This generalization proves to be useful for optimizing the design and usage of the algorithm. For single-cell networks we show how the message count increases with the size of the network and how this depends on the Trickle parameters. Furthermore, we derive distributions of inter-broadcasting times and investigate their asymptotic behavior. Our results prove conjectures made in the literature concerning the effect of a listen-only period. Additionally, we develop an approximation for the expected number of transmissions in multi-cell networks. All results are validated by simulations

Multiple Redundancy Constants with Trickle

International audienceWireless sensor network protocols very often use the Trickle algorithm to govern information dissemination. For example, the widely used IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) uses Trickle to emit control packets. We derive an analytical model of Trickle to take into account multiple redundancy constants and the common lack of synchronization among nodes. Moreover, we demonstrate message count unfairness when Trickle uses a unique global redundancy constant because nodes with less neighbors transmit more often. Consequently, we propose a heuristic algorithm that calculates a redundancy constant for each node as a function of its number of neighbors. Our calculated redundancy constants reduce unfairness among nodes by distributing more equally the number of transmitted messages in the network. Our analytical model is validated by emulations of constrained devices running the Contiki Operating System and its IPv6 networking stack. Furthermore, results very well corroborate the heuristic algorithm improvements

Multiple Redundancy Constants with Trickle

International audienceWireless sensor network protocols very often use the Trickle algorithm to govern information dissemination. For example, the widely used IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) uses Trickle to emit control packets. We derive an analytical model of Trickle to take into account multiple redundancy constants and the common lack of synchronization among nodes. Moreover, we demonstrate message count unfairness when Trickle uses a unique global redundancy constant because nodes with less neighbors transmit more often. Consequently, we propose a heuristic algorithm that calculates a redundancy constant for each node as a function of its number of neighbors. Our calculated redundancy constants reduce unfairness among nodes by distributing more equally the number of transmitted messages in the network. Our analytical model is validated by emulations of constrained devices running the Contiki Operating System and its IPv6 networking stack. Furthermore, results very well corroborate the heuristic algorithm improvements

Contributions to the performance evaluation and improvement of the IPv6 routing protocol for low-power and lossy networks

Wireless Sensor Networks (WSNs) have become increasingly important. These networks comprise sensor and actuator nodes that enable intelligent monitoring and control applications in a wide spectrum of environments including smart cities, home automation, remote health and precision agriculture to mention a few. In certain IETF circles, networks of these characteristics are called Low Power and Lossy Networks (LLNs). Whereas most LLN protocol architectures were born without native IP support, there exists a tendency in the market towards IP convergence, since IP-based LLNs offer an open and tandardized way of connecting LLNs to the Internet, thus nabling the Internet of Things (IoT). Since most LLN configurations are multihop, and thus a routing protocol is required, the IETF created the Routing Over Low power and Lossy networks (ROLL) working group, which decided to develop a new routing protocol called IPv6 Routing Protocol for LLNs (RPL). RPL was specifically designed to meet the requirements of LLNs and is a central component of the IETF protocol suite for the IoT. Since RPL has already been deployed in millions of nodes, it is fundamental to characterize its properties, evaluate the influence of its main parameters and options on network performance, and analyze performance improvement possibilities. This PhD thesis presents the following original contributions in this field: 1. Evaluation of the influence of the main RPL parameters on the network convergence process over IEEE 802.15.4 multihop networks, in terms of network characteristics such as size and density. In addition, a mechanism that leverages an option available in RPL for accelerating network convergence has been proposed and evaluated. This study provides a guideline for configuring and selecting adequately crucial RPL parameters and mechanisms for achieving high network convergence performance, as well as a characterization of the related performance trade-offs. 2. Development of an analytical model for estimating the network convergence time of RPL in a static chain topology network of IEEE 802.15.4 nodes, in the presence of bit errors. Results show the scenarii in terms of BER and chain topology length that may dramatically degrade performance experienced by a user. The model provides a lower bound on the network convergence time for a random topology network. 3. Development of an analytical tool to estimate the number of control messages transmitted in a random topology static network which uses the Trickle algorithm (a transmission scheduling algorithm used in RPL) under steady state conditions. Results show the accuracy of the model, which can be used for both synchronous and asynchronous networks. The slight difference in performance between these two network configurations is discussed and illustrated. 4. Theoretical evaluation of the route change latency incurred by RPL when 6LoWPAN Neighbor Discovery (ND) is used. On this basis, a study on the impact of the relevant 6LoWPAN ND and RPL parameters on path availability and the trade-off between path availability and message overhead, has been carried out. 5. Development of a RPL simulator for OMNeT++ using the MiXiM framework.La importància de les Wireless Sensor Networks (WSNs) ha estat creixent significativament en els darrers anys. Aquestes xarxes comprenen node sensors i actuadors que possibiliten aplicacions de control i monitorització en un ampli ventall d'entorns, incloent les ciutats intel·ligents, automatització residencial, etc. En alguns cercles de l'IETF, aquestes xarxes són anomenades Low Power and Lossy Networks (LLNs). La majoria d'arquitectures de protocols van néixer sense suport natiu per a IP, per ha existit recentment una tendència en el mercat envers la convergència IP, ja que les LLN basades en IP ofereixen una manera oberta i estandaritzada de connectar LLNs a Internet, tot creant la Internet de les Coses. Atès que moltes configuracions de LLNsn multisalt, i per tant es requereix un protocol d'encaminament, l'IETF va crear el Routing Over Low power and Lossy networks (ROLL) working group, que va decidir dissenyar un nou protocol anomenat IPv6 Routing Protocol for LLNs (RPL). RPL va ser específicament dissenyat per complir amb els requeriments de les LLNs i és un component central de la pila de protocols de l'IETF per a la Internet de les Coses. Atès que RPL ha estat desplegat en milions de nodes, és fonamental caracteritzar les seves propietats, avaluar la influència dels seus paràmetres principals i opcions en el rendiment, i analitzar les possibilitats de millora del protocol. Aquesta tesi presenta les següents contribucions originals en aquest camp: 1. Avaluació de la influència dels principals paràmetres de RPL en el procés de convergència de la xarxa en xarxes IEEE 802.15.4, en termes de característiques com la mida i la densitat de la xarxa. A més a més, s'ha proposat i avaluat un mecanisme que utilitza una opci disponible en el RPL per a accelerar la convergència de xarxa. Aquest estudi proporciona una guia per configurar i escollir adequadament paràmetres crucials del protocol RPL per tal d'assolir una ràpida convergència de xarxa, això com una caracteritzaci dels compromisos relacionats. 2. Desenvolupament d'un model analátic per estimar el temps de convergència de xarxa de RPL en una topologia de xarxaestàtica en cadena de nodes IEEE 802.15.4, en presència d'errors. Els resultats mostren els escenaris en termes de BER i mida de la cadena que poden degradar les prestacions percebudes per l'usuari. El model proporciona una fita inferior del temps de convergència de xarxa per a una topologia aleatòria. 3. Desenvolupament d'una eina analítica per estimar el nombre de missatges de control transmesos en una xarxa de topologia aleat ria i estàtica on s'usa l'algoritme Trickle (algoritme de planificació i control de les transmissions emprat en RPL), en condicions de règim permanent. Els resultats mostren la precisió del model, que pot ser emprat en xarxes síncrones i asíncrones. La lleugera diferència de prestacions entre un escenari i l'altre és discutida i il·lustrada. 4. Avaluació teòrica del temps de canvi de ruta de RPL quan s'usa 6LoWPAN Neighbor Discovery (ND). En base a aixó , es realitza un estudi de l'impacte dels paràmetres rellevants de RPL i 6LoWPAN ND en la disponibilitat de cambi el compromís amb l'overhead de missatges. 5. Desenvolupament d'un simulador de RPL per a OMNeT++ usant el MiXiM framewor

Trickle-D: High Fairness and Low Transmission Load with Dynamic Redundancy

International audienceEmbedded devices of the Internet of Things form the so-called low-power and lossy networks. In these networks, nodes are constrained in terms of energy, memory and processing. Links are lossy and exhibit a transient behavior. From the point of view of energy expenditure, governing control overhead emission is crucial and is the role of the Trickle algorithm. We address Trickle's fairness problem to evenly distribute the transmission load across the network, while keeping the total message count low. First, we analytically analyze two underlying causes of unfairness in Trickle networks: desynchronization among nodes and non-uniform topologies. Based on our analysis, we propose a first algorithm whose performance and parameters we study in an emulated environment. From this feedback, we design a second algorithm TrickleD that adapts the redundancy parameter to achieve high fairness while keeping the transmission load low. We validate TrickleD in real-life conditions using a large scale experimental testbed. TrickleD requires minimal changes to Trickle, zero user input, emits 17.7% less messages than state-of-the-art and 37.2% less messages than state-of-practice, while guaranteeing high fairness across the network

Improving the performance of trickle-based data dissemination in low-power networks

Trickle is a polite gossip algorithm for managing communication traffic. It is of particular interest in low-power wireless networks for reducing the amount of control traffic, as in routing protocols (RPL), or reducing network congestion, as in multicast protocols (MPL). Trickle is used at the network or application level, and relies on up-to-date information on the activity of neighbors. This makes it vulnerable to interference from the media access control layer, which we explore in this paper. We present several scenarios how the MAC layer in low-power radios violates Trickle timing. As a case study, we analyze the impact of CSMA/CA with ContikiMAC on Trickle's performance. Additionally, we propose a solution called Cleansing that resolves these issues