Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

Pietrabissa, Antonio; Liberati, Francesco

doi:10.1080/00207179.2017.1378441

“…For each cluster, a cluster-head (𝐶𝐻 ) plays significant roles such as scheduling tasks and aggregating and relaying data generated by its cluster members to limit inter-clusters communications to 𝐶𝐻𝑠 only thus reducing communication load [3]. Many clustering algorithms have been proposed for 𝑊 𝑆𝑁 [8,41,46,63] but they are not suitable to modular robots due to their specific constraints which make them inapplicable on modular robots: wireless communication, existence of a base station, pre-election of cluster heads....…”

Section: Workmentioning

confidence: 99%

Distributed Size-constrained Clustering Algorithm for Modular Robot-based Programmable Matter

Bassil

¹

,

Makhoul

²

,

Piranda

³

et al. 2023

ACM Trans. Auton. Adapt. Syst.

3

0

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Modular robots are defined as autonomous kinematic machines with variable morphology. They are composed of several thousands or even millions of modules which are able to coordinate in order to behave intelligently. Clustering the modules in modular robots has many benefits, including scalability, energy-efficiency, reducing communication delay and improving the self-reconfiguration process that focuses on finding a sequence of reconfiguration actions to convert robots from an initial shape to a goal one. The main idea of clustering is to divide the modules in an initial shape into a number of groups based on the final goal shape in order to enhance the self-reconfiguration process by allowing clusters to reconfigure in parallel. In this work, we prove that the size-constrained clustering problem is NP-complete and we propose a new tree-based size-constrained clustering algorithm called ”SC-Clust”. The idea is to divide a network into a predefined number of clusters constrained by a given number of modules in each cluster based on the final goal shape. The result is an efficient algorithm that scales to large modular robot systems. To show the efficiency of our approach, we implement and demonstrate our algorithm in simulation on networks of up to 30,000 modules and on the Blinky Blocks hardware with up to 144 modules.

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“…In fact, Voronoi-based techniques have been presented as effective solutions for disseminating and querying data in decentralized, distributed systems. This kind of techniques have been successfully applied in the IoT [20], wireless sensor networks [21], [22], underwater networks [23], [24], embedded computing systems [25], vehicular networks [26], and even distributed virtual environments [27], [28].…”

Section: A Contributionmentioning

confidence: 99%

AoI-Based Multicast Routing Over Voronoi Overlays With Minimal Overhead

Albano

¹

,

Mordacchini

²

,

Ricci

³

2020

IEEE Access

0

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The increasing pervasive and ubiquitous presence of devices at the edge of the Internet is creating new scenarios for the emergence of novel services and applications. This is particularly true for location and context-aware services. These services call for new decentralized, self-organizing communication schemes that are able to face issues related to demanding resource consumption constraints, while ensuring efficient locality-based information dissemination and querying. Voronoi-based communication techniques are among the most widely used solutions in this field. However, when used for forwarding messages inside closed areas of the network (called Areas of Interest, AoIs), these solutions generally require a significant overhead in terms of redundant and/or unnecessary communications. This fact negatively impacts both the devices' resource consumption levels, as well as the network bandwidth usage. In order to eliminate all unnecessary communications, in this paper we present the MABRAVO (Multicast Algorithm for Broadcast and Routing over AoIs in Voronoi Overlays) protocol suite. MABRAVO allows to forward information within an AoI in a Voronoi network using only local information, reaching all the devices in the area, and using the lowest possible number of messages, i.e., just one message for each node included in the AoI. The paper presents the mathematical and algorithmic descriptions of MABRAVO, as well as experimental findings of its performance, showing its ability to reduce communication costs to the strictly minimum required. INDEX TERMS Area of Interest, multicast, Voronoi networks 1 I. INTRODUCTION We are witnessing a fast and vast expansion of the Internet at its edges [1]. This is mainly due to the pervasive diffusion in the environment of smart objects, such as sensors, Internet of Things (IoT) devices, user personal devices, etc. This scenario allows the emergence of novel services and applications [2]-[8], supported by potentially large networks of highly distributed and autonomous devices. Traditional centralized control and communication techniques do not suit the needs and requirements of such an environment. In particular, these devices are usually equipped with computing and communication capabilities, that allow them to create and exchange information both among themselves and with other remote services. One of the most challenging problems is related to the fact that this kind of systems typically requires frequent exchanges of information among a large number of geographically dispersed devices. The communication complexity is further increased by the fact that devices cannot always count on the support of central communication infrastructures, posing the need to apply autonomous, self-organizing forms of communication and interaction among devices [9]-[13]. Location and context-aware services [14] in this scenario are faced with additional issues. In fact, these services are characterized by the fact that most of the messages are directed (and of interest) only to limited/specific are...

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“…In fact, Voronoi-based techniques have been presented as effective solutions for disseminating and querying data in decentralized, distributed systems. This kind of techniques have been successfully applied in the IoT [19], wireless sensor networks [20], [21], underwater networks [22], [23], embedded computing systems [24], vehicular networks [25], and even distributed virtual environments [26], [27].…”

Section: A Contributionmentioning

confidence: 99%

AoI-based Multicast Routing over Voronoi Overlays with Minimal Overhead

Albano,

Mordacchini,

Ricci

2020

Preprint

0

View full text Add to dashboard Cite

The increasing pervasive and ubiquitous presence of devices at the edge of the Internet is creating new scenarios for the emergence of novel services and applications. This is particularly true for location-and context-aware services. These services call for new decentralized, self-organizing communication schemes that are able to face issues related to demanding resource consumption constraints, while ensuring efficient locality-based information dissemination and querying. Voronoi-based communication techniques are among the most widely used solutions in this field. However, when used for forwarding messages inside closed areas of the network (called Areas of Interest, AoIs), these solutions generally require a significant overhead in terms of redundant and/or unnecessary communications. This fact negatively impacts both the devices' resource consumption levels, as well as the network bandwidth usage. In order to eliminate all unnecessary communications, in this paper we present the MABRAVO (Multicast Algorithm for Broadcast and Routing over AoIs in Voronoi Overlays) protocol suite. MABRAVO allows to forward information within an AoI in a Voronoi network using only local information, reaching all the devices in the area, and using the lowest possible number of messages, i.e., just one message for each node included in the AoI. The paper presents the mathematical and algorithmic descriptions of MABRAVO, as well as experimental findings of its performance, showing its ability to reduce communication costs to the strictly minimum required.

show abstract

Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

Cited by 6 publications

References 32 publications

Distributed Size-constrained Clustering Algorithm for Modular Robot-based Programmable Matter

Distributed Size-constrained Clustering Algorithm for Modular Robot-based Programmable Matter

AoI-Based Multicast Routing Over Voronoi Overlays With Minimal Overhead

AoI-based Multicast Routing over Voronoi Overlays with Minimal Overhead

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