Impact of mobility and deployment in confined spaces on low power and lossy network. (Impact de la mobilité et du déploiement dans des espaces confinés sur un réseau à faible consommation et à perte)

Wireless Sensor Networks (WSNs) technology is one of the building blocks ofthe Internet of Things (IoT). Due to their features of easy deployment and flexibility,they are used in many application domains. Low-Power and Lossy Networks(LLNs) are a special type of WSNs in which nodes are largely resources constrained.For LLNs, convergecast is one of the basic traffic modes, where all traffic in the networkis destined to a predefined destination called the sink. While considering theIoT application domains, convergecast is not the only traffic mode in the network.The sink needs to send commands to certain sensors to perform actions. In this application,anycast is another basic traffic mode. In anycast, the traffic from the sinkis destined to any member of a group of potential receivers in the network.Traditionally LLNs are formed by static sensor nodes and rarely change positions.Due to the strict resource constraints in computation, energy and memory ofLLNs, most routing protocols only support static network. However, mobility hasbecome an important requirement for many emerging applications. In these applications,certain nodes are free to move and organize themselves into a connectednetwork. The topology would continuously change due to the movement of nodesand radio links instability. This is a hard task for most routing protocols of LLNs toadapt rapidly to the movement and to reconstruct topology in a timely manner.The goal of this thesis is to propose an efficient mobility support for routingprotocols in LLNs. We focus on convergecast and anycast, which are the most usedtraffic modes in LLNs, in mobile network scenarios.We propose an enhancement mechanism, named RL (RSSI and Level), to supportrouting protocols in convergecast LLNs in mobility. This mechanism helps routingprotocol make faster decisions for detecting mobility and updating next-hop neighborsbut suffers from high overhead. We propose a dynamic control message managementto enhance the overhead performance of RL and implement it on top ofRouting Protocol for Low-power and Lossy network (RPL) and we named it RRD(RSSI, Rank and Dynamic). After taking into account hysteresis of the coveragezone of the transmission range of nodes, we optimized RRD. This enhanced versionis called RRD+. Based on RRD+, we proposed MRRD+ (Multiple, RSSI, Rankand Dynamic) to support multiple sinks in convergecast LLNs in mobility. ADUP(Adaptive Downward/Upward Protocol) is a routing solution that supports bothconvergecast and anycast in LLNs concurrently.We evaluated the performance of our contributions in both simulation usingCooja simulator and experiment (only for ADUP) on TelosB motes. The resultsobtained in both simulation and experiment confirm the efficiency of our routingprotocols.

[1]  Julien Montavont,et al.  Improving the medium access in highly mobile Wireless Sensor Networks , 2013, Telecommun. Syst..

[2]  Leïla Azouz Saïdane,et al.  A modified RPL for Wireless Sensor Networks with Bayesian inference mobility prediction , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[3]  Carles Gomez,et al.  Adapting AODV for IEEE 802.15.4 mesh sensor networks: theoretical discussion and performance evaluation in a real environment , 2006, 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06).

[4]  Lothar Thiele,et al.  Low-power wireless bus , 2012, SenSys '12.

[5]  Hamadoun Tall,et al.  CoLBA: A Collaborative Load Balancing Algorithm to Avoid Queue Overflow in WSNs , 2015, 2015 IEEE International Conference on Data Science and Data Intensive Systems.

[6]  Julien Montavont,et al.  Analysis and performance evaluation of RPL under mobility , 2014, 2014 IEEE Symposium on Computers and Communications (ISCC).

[7]  Philip Levis,et al.  Collection tree protocol , 2009, SenSys '09.

[8]  Stephen Dawson-Haggerty,et al.  Hydro: A Hybrid Routing Protocol for Low-Power and Lossy Networks , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[9]  Eduardo Tovar,et al.  Co-RPL: RPL routing for mobile low power wireless sensor networks using Corona mechanism , 2014, Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems (SIES 2014).

[10]  Marco Conti,et al.  Reliable Data Delivery With the IETF Routing Protocol for Low-Power and Lossy Networks , 2014, IEEE Transactions on Industrial Informatics.

[11]  A. M. Kurien,et al.  RSSI based indoor and outdoor distance estimation for localization in WSN , 2013, 2013 IEEE International Conference on Industrial Technology (ICIT).

[12]  Marcus Chang,et al.  MoMoRo: Providing Mobility Support for Low-Power Wireless Applications , 2015, IEEE Systems Journal.

[13]  Kenneth N. Brown,et al.  RPL-based routing protocols for multi-sink wireless sensor networks , 2015, 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[14]  Daniel G. Costa,et al.  Wireless visual sensor networks for smart city applications: A relevance-based approach for multiple sinks mobility , 2017, Future Gener. Comput. Syst..

[15]  Yao Liang,et al.  Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation , 2018, 2018 IEEE 43rd Conference on Local Computer Networks (LCN).

[16]  Elyes Ben Hamida,et al.  An adaptive timer for RPL to handle mobility in wireless sensor networks , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[17]  Charles E. Perkins,et al.  Ad hoc On-Demand Distance Vector (AODV) Routing , 2001, RFC.

[18]  Julien Montavont,et al.  Integrating Mobility in RPL , 2015, EWSN.

[19]  Ms. S. Sudha,et al.  A Hospital Healthcare Monitoring System Using Wireless Sensor Networks , 2017 .

[20]  Nabanita Das,et al.  Multiple sink deployment in multi-hop wireless sensor networks to enhance lifetime , 2015, 2015 Applications and Innovations in Mobile Computing (AIMoC).

[21]  Anis Koubaa,et al.  Smart-HOP: A Reliable Handoff Mechanism for Mobile Wireless Sensor Networks , 2012, EWSN.

[22]  Hamadoun Tall,et al.  DFTR: Dynamic Fault-Tolerant Routing Protocol for Convergecast WSNs , 2017, 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall).

[23]  Licai Yang,et al.  Improved AODV Routing Protocol Based on Restricted Broadcasting by Communication Zones in Large-Scale VANET , 2015 .

[24]  Jean-Marie Bonnin,et al.  Wireless sensor networks: a survey on recent developments and potential synergies , 2013, The Journal of Supercomputing.

[25]  I Chih-Lin,et al.  Wireless Communications and Networks , 2004 .

[26]  Adam Dunkels,et al.  Contiki - a lightweight and flexible operating system for tiny networked sensors , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[27]  Tayyab Mehmood COOJA Network Simulator: Exploring the Infinite Possible Ways to Compute the Performance Metrics of IOT Based Smart Devices to Understand the Working of IOT Based Compression & Routing Protocols , 2017, ArXiv.

[28]  Ingrid Moerman,et al.  RPL Mobility Support for Point-to-Point Traffic Flows towards Mobile Nodes , 2015, Int. J. Distributed Sens. Networks.

[29]  P. R. Deshmukh,et al.  Energy balancing multiple sink optimal deployment in multi-hop Wireless Sensor Networks , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[30]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[31]  Emmanuel Nataf,et al.  Survey on RPL enhancements: A focus on topology, security and mobility , 2018, Comput. Commun..

[32]  A. Erman-Tüysüz Multi-sink mobile wireless sensor networks: dissemination protocols, design and evaluation , 2011 .

[33]  A. F. Adams,et al.  The Survey , 2021, Dyslexia in Higher Education.

[34]  Qutaiba Ibrahem Ali,et al.  Simulation & performance study of wireless sensor network (WSN) using MATLAB , 2010, 2010 1st International Conference on Energy, Power and Control (EPC-IQ).

[35]  Mário Alves,et al.  mRPL: Boosting mobility in the Internet of Things , 2015, Ad Hoc Networks.

[36]  Xavier Vilajosana,et al.  Addressing Mobility in RPL With Position Assisted Metrics , 2016, IEEE Sensors Journal.

[37]  Olaf Landsiedel,et al.  Let the tree Bloom: scalable opportunistic routing with ORPL , 2013, SenSys '13.

[38]  Ahmed Helmy,et al.  A survey of mobility modeling and analysis in wireless adhoc networks , 2004 .

[39]  Leila Azouz Saidane,et al.  A Bayesian model for mobility prediction in wireless sensor networks , 2016, 2016 International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN).

[40]  Hassan Artail,et al.  An energy efficient Genetic Algorithm based approach for sensor-to-sink binding in multi-sink wireless sensor networks , 2014, Wirel. Networks.

[41]  B. G. Prasad,et al.  An efficient hand-off optimization based RPL routing protocol for optimal route selection in mobility enabled LLNs , 2016, 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC).

[42]  Lothar Thiele,et al.  Efficient network flooding and time synchronization with Glossy , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[43]  Al-Sakib Khan Pathan,et al.  Prolonging the lifetime of wireless sensor networks using secondary sink nodes , 2016, Telecommun. Syst..

[44]  Davinder S. Saini,et al.  Lifetime Optimization of a Multiple Sink Wireless Sensor Network through Energy Balancing , 2015, J. Sensors.

[45]  Ingrid Moerman,et al.  Support of multiple sinks via a virtual root for the RPL routing protocol , 2014, EURASIP J. Wirel. Commun. Netw..

[46]  Renke Sun,et al.  Energy Optimized Routing Algorithm in Multi-sink Wireless Sensor Networks , 2014 .

[47]  Mohsin Ur Rahman,et al.  Investigating the impacts of entity and group mobility models in MANETs , 2016, 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube).

[48]  David A. Maltz,et al.  DSR: the dynamic source routing protocol for multihop wireless ad hoc networks , 2001 .

[49]  Gabriel Montenegro,et al.  AODV for IEEE 802.15.4 Networks , 2005 .

[50]  Elfed Lewis,et al.  A comparative review of wireless sensor network mote technologies , 2009, 2009 IEEE Sensors.

[51]  Mário Alves,et al.  mRPL+: A mobility management framework in RPL/6LoWPAN , 2017, Comput. Commun..

[52]  Elyes Ben Hamida,et al.  On the complexity of an accurate and precise performance evaluation of wireless networks using simulations , 2008, MSWiM '08.

[53]  JeongGil Ko,et al.  The Trickle Algorithm , 2011, RFC.

[54]  Eric Anderson,et al.  X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks , 2006, SenSys '06.