Data Redundancy Implications in Wireless Sensor Networks

Verma, Neetu; Singh, Dinesh

doi:10.1016/j.procs.2018.05.036

Cited by 27 publications

(10 citation statements)

References 22 publications

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“…5) as in algorithm 2. Therefore, in our fault recovery mechanism, the number of redundant routing paths to send data from the source node to the destination node is N path as formula (6), where N path is a 2-combination of a set D(η, β). Therefore, during the routing process, if the next CH fault on the real axis (β 1 is faulty), it will be replaced by the next CH node in the imaginary axis (β 2 ), similarly, if the CH fault on the imaginary axis (β 2 is faulty) will be replaced by β 1 as shown in formula (7).…”

Section: B Fault Recovery Phasementioning

confidence: 99%

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A hybrid fault-tolerant routing based on Gaussian network for wireless sensor network

2022

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Section: B Fault Recovery Phasementioning

confidence: 99%

“…-Redundancy based mechanisms: The fault tolerance is improved by redundancy factors such as redundant routing paths, redundancy time, redundant data [6]. In this mechanism, the main fault recovery approach includes [7] active replication and passive replication.…”

mentioning

confidence: 99%

A hybrid fault-tolerant routing based on Gaussian network for wireless sensor network

2022

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“…This is a generalised swarm algorithm. Next, the Generalised Particle Model Algorithm (GPMA) [125] assists in confronting the energy consumption problem. Specifically, it reduces the complexity by optimising the process of cluster formation with the goal of allocating optimal paths (concerning the reduction of energy consumption) to the base station.…”

Section: Fusion Solutions For Various Iot Environmentsmentioning

confidence: 99%

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Tsanousa

Bektsis

Kyriakopoulos

et al. 2022

Sensors

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Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

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“…Current approaches seek to minimize data redundancy when sending CPMs. However, such redundancy can also have positive effects, for example, to improve accuracy or security [33][34][35]. In addition, approaches for channel load control do not take in account message content to prioritize the transmitted packets and few works considered channel load as a metric for CPM content selection.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware Content Selection and Message Generation for Collective Perception Services

2021

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Collective perception messages (CPMs) inform neighbors about detected objects and their current status. Although CPMs can greatly contribute to improving vehicle perception, their performance will degrade if the wireless channel is saturated and/or vehicles must process excessively high amount of data to build such a perception. Channel saturation and processing overhead can be induced by transmissions of CPMs if vehicles and roadside infrastructure transmit CPMs too often with too much redundant information. Indeed, according to the current draft standard, the CPM generation frequency at individual vehicles and infrastructure can be as high as 10 Hz and depending on the perception capability, individual CPMs can be quite heavy (around 1500 Bytes), containing redundant information. It is hence important to integrate techniques that reduce information redundancy while providing sufficient perception with optimal resource use. In this paper, we propose context-aware communication schemes that control CPM content selection and transmission. Two types of contexts, particularly radio resource use (channel busy ratio: CBR) and infrastructure availability, have been considered in the proposed schemes. Using a network simulator, we evaluated the performances of the proposed schemes in terms of CBR, packet delivery ratio and awareness ratio. The simulation results show that the scheme that takes into account both resource use and infrastructure availability offers the best performance on all the above metrics.

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Data Redundancy Implications in Wireless Sensor Networks

Cited by 27 publications

References 22 publications

A hybrid fault-tolerant routing based on Gaussian network for wireless sensor network

A hybrid fault-tolerant routing based on Gaussian network for wireless sensor network

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Context-Aware Content Selection and Message Generation for Collective Perception Services

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