A power-efficient wireless sensor network for continuously monitoring seismic vibrations

Weiss, Beat; Truong, Hong-Linh; Schott, Wolfgang; Munari, Andrea; Lombriser, Clemens; Hunkeler, Urs; Chevillat, P.R.

doi:10.1109/sahcn.2011.5984921

“…In recent years sensor networks have been deployed to tackle some of the most fundamental problems facing human beings such as disaster warnings, climate change, and renewable energy. These emerging scientific applications include underwater or ocean sensor networks [9,22,28,35,43,60], wind and solar harvesting [34,40], seismic sensor networks [42,55], and monitoring of volcano eruption and glacial melting [18,47]. One common characteristic of these applications is that they are all deployed in challenging environments such as in remote or inhospitable regions, or under extreme weather, to continuously collect large volumes of data for a long period of time.…”

Section: Introductionmentioning

confidence: 99%

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

Tang¹

2022

Preprint

0

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Many emerging sensor network applications operate in challenging environments wherein the base station is not available. Data generated from such intermittently connected sensor networks (ICSNs) therefore must be stored inside the network for some unpredictable period of time before uploading opportunities become available. Consequently, sensory data could overflow the limited storage capacity available in the entire network, making discarding valuable data inevitable. To overcome such overall storage overflow in ICSNs, we propose and study a new algorithmic framework called data aggregation for overall storage overflow (DAO2). Utilizing spatial data correlation that commonly exists among sensory data, DAO2 employs data aggregation techniques to reduce the overflow data size while minimizing the total energy consumption in data aggregation. At the core of our framework are two new graph theoretical problems that have not been studied. We refer to them as traveling salesmen placement problem (TSP2) and quota traveling salesmen placement problem (Q-TSP2). Different from the well-known multiple traveling salesman problem (mTSP) and its variants, which mainly focus on the routing of multiple salesmen initially located at fixed locations, TSP2 and Q-TSP2 must decide the placement as well as the routing of the traveling salesmen. We prove that both problems are NP-hard and design approximation, heuristic, and distributed algorithms. Our algorithms outperform the state-of-the-art data aggregation work with base stations by up to 71.8% in terms of energy consumption. The building blocks of our framework are two novel graph structures called aggregation network and minimum q-edge forest that accurately capture the information needed for energy-efficient data aggregation in ICSNs.

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“…In recent years sensor networks have been deployed to tackle some of the most fundamental problems facing human beings such as disaster warnings, climate change, and renewable energy. These emerging scientific applications include underwater or ocean sensor networks [9,22,28,35,43,60], wind and solar harvesting [34,40], seismic sensor networks [42,55], and monitoring of volcano eruption and glacial melting [18,47]. One common characteristic of these applications is that they are all deployed in challenging environments such as in remote or inhospitable regions, or under extreme weather, to continuously collect large volumes of data for a long period of time.…”

Section: Introductionmentioning

confidence: 99%

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

Tang¹

2022

Preprint

0

View full text Add to dashboard Buy / Rent full text

Many emerging sensor network applications operate in challenging environments wherein the base station is not available. Data generated from such intermittently connected sensor networks (ICSNs) therefore must be stored inside the network for some unpredictable period of time before uploading opportunities become available. Consequently, sensory data could overflow the limited storage capacity available in the entire network, making discarding valuable data inevitable. To overcome such overall storage overflow in ICSNs, we propose and study a new algorithmic framework called data aggregation for overall storage overflow (DAO2). Utilizing spatial data correlation that commonly exists among sensory data, DAO2 employs data aggregation techniques to reduce the overflow data size while minimizing the total energy consumption in data aggregation. At the core of our framework are two new graph theoretical problems that have not been studied. We refer to them as traveling salesmen placement problem (TSP2) and quota traveling salesmen placement problem (Q-TSP2). Different from the well-known multiple traveling salesman problem (mTSP) and its variants, which mainly focus on the routing of multiple salesmen initially located at fixed locations, TSP2 and Q-TSP2 must decide the placement as well as the routing of the traveling salesmen. We prove that both problems are NP-hard and design approximation, heuristic, and distributed algorithms. Our algorithms outperform the state-of-the-art data aggregation work with base stations by up to 71.8% in terms of energy consumption. The building blocks of our framework are two novel graph structures called aggregation network and minimum q-edge forest that accurately capture the information needed for energy-efficient data aggregation in ICSNs.

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“…Wireless sensor networks (WSN) play an important role in many industrial [1], health [2] and body monitoring systems [3,4], seismic vibration sensing [5], ad-hoc systems [6] and spectrum sensing [7] applications. Almost all modern applications and services require some form of sensors.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Distribution Algorithm for Resource Allocation in Green Cooperative Cognitive Radio Sensor Networks

Naeem¹,

Pareek²,

Lee³

et al. 2021

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0

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Due to the rapid increase in the usage and demand of wireless sensor networks (WSN), the limited frequency spectrum available for WSN applications will be extremely crowded in the near future. More sensor devices also mean more recharging/replacement of batteries, which will cause significant impact on the global carbon footprint. In this paper, we propose a relay-assisted cognitive radio sensor network (CRSN) that allocates communication resources in an environmentally friendly manner. We use shared band amplify and forward relaying for cooperative communication in the proposed CRSN. We present a multi-objective optimization architecture for resource allocation in a green cooperative cognitive radio sensor network (GC-CRSN). The proposed multi-objective framework jointly performs relay assignment and power allocation in GC-CRSN, while optimizing two conflicting objectives. The first objective is to maximize the total throughput, and the second objective is to minimize the total transmission power of CRSN. The proposed relay assignment and power allocation problem is a non-convex mixed-integer non-linear optimization problem (NC-MINLP), which is generally non-deterministic polynomial-time (NP)-hard. We introduce a hybrid heuristic algorithm for this problem. The hybrid heuristic includes an estimation-of-distribution algorithm (EDA) for performing power allocation and iterative greedy schemes for constraint satisfaction and relay assignment. We analyze the throughput and power consumption tradeoff in GC-CRSN. A detailed analysis of the performance of the proposed algorithm is presented with the simulation results.

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A power-efficient wireless sensor network for continuously monitoring seismic vibrations

Cited by 20 publications

References 11 publications

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

DAO2: Overcoming Overall Storage Overflow inIntermittently Connected Sensor Networks

Estimation of Distribution Algorithm for Resource Allocation in Green Cooperative Cognitive Radio Sensor Networks

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