Dynamic Gallager-Humblet-Spira Algorithm for Wireless Sensor Networks

Diaz, Sergio; Méndez, Diego; Scholzel, Mario

doi:10.1109/colcomcon.2018.8466704

Cited by 5 publications

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“…When compared to our previous work [6], this paper presents: (i) a detailed description of the stages of the DGHS algorithm (neighbor discovery, tree construction and data collection), (ii) an in-depth analysis of extensive results that validates our proposal, and (iii) an exhaustive description of the limitations of GHS for WSNs. This paper is organized as follows: Section 2 presents recent work about tree construction mechanisms; Section 3 briefly describes GHS and its limitations; Section 4 explains in detail the DGHS algorithm; Section 5 shows the evaluation setting and the performance metrics; Section 6 evaluates the performance of DGHS and LIBP; and, Section 7 presents our conclusions and future work.…”

Section: Introductionmentioning

confidence: 99%

Dynamic minimum spanning tree construction and maintenance for Wireless Sensor Networks

Diaz

Méndez

2019

Rev.Fac.Ing.Univ.Antioquia

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In a wireless sensor network (WSN), finding the optimal route from each node to the sink is not a straightforward task because of the distributed and dynamic characteristics of the network. For instance, the network suffers frequent changes because the channel quality varies over time and the nodes can leave or join the network at any moment. In order to deal with this variability, we propose the Dynamic Gallager-Humblet-Spira (DGHS) algorithm that builds and maintains a minimum spanning tree for distributed and dynamic networks, and we have found that DGHS reduces the number of control messages and the energy consumption, at the cost of a slight increase in the memory size and convergence time. This paper presents a detailed description of the different stages of the DGHS algorithm (neighbor discovery, tree construction and data collection), an in-depth analysis of extensive results that validates our proposal, and an exhaustive description of the GHS limitations.

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Section: Introductionmentioning

confidence: 99%

Dynamic minimum spanning tree construction and maintenance for Wireless Sensor Networks

Diaz

Méndez

2019

Rev.Fac.Ing.Univ.Antioquia

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“…In the topology construction stage, a tree or cluster topology is built. 2 In the topology management stage, several mechanisms are implemented to transport data with high-quality standards, such as sleep schedule, transmission power control, and load balancing. 3 Once the topology has been constructed, WSNs cope with two additional problems.…”

Section: Research Problem and Solution Strategymentioning

confidence: 99%

“…In the fault classification stage, the nodes classify the failure into four types: data failure, node failure, link failure, and node reboot. 7 In the fault recovery stage, the nodes employ hardware and software redundancies to 1 For more information on self-organization, please see Section 3.2 2 For more information on topology construction, please see Section 3.2.1 3 For more information on topology management, please see Section 3.2.2 4 For more information on self-healing, please see Section 3.3 5 For more information on information collection, please see Section 3.3.1 6 For more information on fault detection, please see Section 3.3.2 7 For more information on fault classification, please see Section 3.3.3 keep transporting packets even in the presence of failures. 8 On the basis of self-organizing and self-healing mechanisms, we propose a selfhealing framework to cope with unstable wireless links and faulty nodes.…”

Section: Research Problem and Solution Strategymentioning

confidence: 99%

A self-healing framework for WSNs : detection and recovery of faulty sensor nodes and unreliable wireless links

Salas¹

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Proponemos un marco conceptual para acoplar técnicas de auto-organización y técnicas de autocuración. A este marco se le llama de auto-curación y es capaz de hacer frente a enlaces inalámbricos inestables y nodos defectuosos. Dividimos el marco en dos componentes principales: la auto-organización y auto-curación. En el componente de auto-organización, nosotros construimos una topología de árbol que determine las rutas hacia el sumidero. En el componente de auto-curación, la topología del árbol se adapta a ambos tipos de fallas siguiendo tres pasos: recopilación de información, detección de fallas, y la recuperación de fallos. En el paso de recopilación de información, los nodos determinan el estado actual de la red mediante la recopilación de información de la capa MAC. En el paso de detección de fallas, los nodos analizan la información recopilada y detectan nodos/enlaces defectuosos. En el paso de recuperación de fallos, los nodos recuperan la topología del árbol mediante la sustitución de componentes defectuosos con redundantes (es decir, componentes de respaldo). Este marco permite una red con resiliencia que se recupera sin agotar los recursos de la red.

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“…2. Each adjacent grid constructs and maintains a minimum spanning tree [105], having a common sink node as the root of the tree (Figure 5.1).…”

Section: M2-dagnos Designmentioning

confidence: 99%

“…The rest of the nodes send packets towards the sink through the collect tree built during the neighbor discovery process. 2 Non-adjacent grids work similar to the adjacent grids, but they transmit data packets towards the sink through the adjacent grids using a power-aware multi-hop routing mechanism [105]. Inter-grid communications are done via the same RF channel, but under a different logical connection channel for message exchanging.…”

Section: M2-dagnos Designmentioning

confidence: 99%

A framework for multimodal wireless sensor networks

King¹

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Wireless Sensor Networks (WSNs) are a widely used solution for monitoring oriented applications (e.g., water quality on watersheds, pollution monitoring in cities). These kinds of applications are characterized by the necessity of two data-reporting modes: time-driven and event-driven. The former is used mainly for continually supervising an area and the latter for event detection and tracking. By switching between both modes, a WSN can improve its energy-efficiency and event reporting latency, compared to single data-reporting schemes. We refer to those WSNs, where both data-reporting modes are required simultaneously, as MultiModal Wireless Sensor Networks (M2WSNs).M2WSNs arise as a solution for the trade-off between energy savings and event reporting latency in those monitoring oriented applications where regular and emergency reporting are required simultaneously. The multimodality in these M2WSNs allows sensor nodes to perform data-reporting in two possible schemes, time-driven and event-driven, according to the circumstances, providing higher energy savings and better reporting results when compared to traditional schemes. Traditionally, sophisticated power-aware wake-up schemes have been employed to achieve energy efficiency in WSNs, such as low-duty cycling protocols using a single radio architecture. These protocols achieve good results regarding energy savings, but they suffer from idle-listening and overhearing issues, that make them not reliable for most ultra-low-power demanding applications, especially, those deployed in hostile and unattended environments. Currently, Wake-up Radio Receivers (WuRx) based protocols, under a dual-radio architecture and always-on operation, are emerging as a solution to overcome these issues, promising higher energy consumption reduction and reliability in terms of latency and packet-delivery-ratio compared to classic wake-up protocols. By combining different transceivers and reporting protocols regarding energy efficiency and reliability, multimodality in M2WSNs is achieved.This dissertation proposes a conceptual framework for M2WSNs that integrates the goodness of both data-reporting schemes and the Wake-up Radio (WuR) paradigm-data periodicity, responsiveness, and energy-efficiency-, that might be suitable for monitoring oriented applications with low bandwidth requirements, that operates under normal circumstances and emergencies. The framework follows a layered approach, where each layer aims to v fulfill specific tasks based on its information, the functions provided by its adjacent layers, and the information resulted from the cross-layer interactions. The main contributions of this dissertation are:• The concept of M2WSNs is introduced from the data-reporting perspective and a taxonomy of energy-efficient and responsive techniques for M2WSNs is proposed.• An energy consumption estimation model for M2WSNs is proposed that considers the behavior and performance of wake-up protocols based on WuRx in multi-hop communications. The model is compared to traditional lo...

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Dynamic Gallager-Humblet-Spira Algorithm for Wireless Sensor Networks

Cited by 5 publications

References 0 publications

Dynamic minimum spanning tree construction and maintenance for Wireless Sensor Networks

Dynamic minimum spanning tree construction and maintenance for Wireless Sensor Networks

A self-healing framework for WSNs : detection and recovery of faulty sensor nodes and unreliable wireless links

A framework for multimodal wireless sensor networks

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