Geographical Cluster Based Routing in Sensing-Covered Networks

Abstract

The relationship between coverage and connectivity in sensor networks has been investigated in recent research treating both network parameters in a unified framework. It is known that networks covering a convex area are connected if the communication range of each node is at least twice a unique sensing range used by each node. Furthermore, geographic greedy routing is a viable and effective approach providing guaranteed delivery for this special network class. In this work we will show that the result about network connectivity does not suffer from generalizing the concept of sensing coverage to arbitrary network deployment regions. However, dropping the assumption that the monitored area is convex requires the application of greedy recovery strategies like traversing a locally extracted planar subgraph. A recently proposed variant performs message forwarding along edges of a virtual overlay graph instead of using wireless links for planar graph construction directly. However, there exist connected network configurations where this routing variant may fail. In this work we will proof a theoretical bound which is a sufficient condition for guaranteed delivery of this routing strategy applied in sensing covered networks. By simulation results we show that this bound may also be relaxed from a practical point of view and that geographical cluster based routing achieves a comparable performance compared to other planar graph routing variants based on two-hop neighbor information.

Target Tracking And Energy Optimization Based On Dynamic Clusters Mechanism In WSN

“…Static centralized routing tracking information is sent to the fixed cluster head to process. This approach often lead to results that the fixed cluster head energy consumption is too large, the network lifetime can not be guaranteed [2]. Transmission tree-based tracking algorithm is a distributed algorithm, the cluster head is dynamic, so you can effectively reduce energy consumption, ensure the whole network life [3,4].…”

Geographical Area Network—Structural Health Monitoring Utility Computing Model

“…UC is the application of cloud computing that encompasses algorithms and theorems in a way that consumer is getting direct applications and benefits like in cases of Uber, Careem, AliExpress, Food Panda and Google Maps [2][3][4]. UC services react with distributed Geographical Information Systems (GIS) platforms like Google Maps to enable applications like Navisworks and Building Information Modeling (BIM) resulting in heterogeneous Geographical Area Networks (GAN) [5][6][7][8][9]. On the other hand, Structural Health Monitoring (SHM) is a systematic framework that shows the fitness of a structure as a front-end tool-less focused on Mechanical Electrical and Plumbing (MEP) that are defined in BIM.…”

A Wireless Sensor Network for RF-Based Indoor Localization

“…At application level, activities such as location and asset tracking, monitoring, context aware applications [2], and personal positioning [3] are made possible. Enabled protocol-level functions include location-based routing [4,5], and geographic addressing [6].…”