An Effective Delay Reduction Approach through a Portion of Nodes with a Larger Duty Cycle for Industrial WSNs

Xie

et al. 2019

J Wireless Com Network

Self Cite

Sensor nodes equipped with various sensory devices can sense a wide range of information regarding human or things, thereby providing a foundation for Internet of Thing (IoT). Fast and energy-efficient data collection to the control center (CC) is of significance yet very challenging. To deal with this challenge, a low redundancy data collection (LRDC) scheme is proposed to reduce delay as well as energy consumption for monitoring network by using matrix completion technique. Due to the correlation of the location-dependent sensing data, some data without being collected can still be recovered by the matrix completion technology, thereby reducing the data amount for data collection and transmission, reducing the network energy consumption, and accelerating the process of data acquisition. Based on matrix completion technique, LRDC scheme can select only part of the nodes to sense data and transmit less data to CC. By doing so, the data collected by the network can be greatly reduced, which can effectively improve the network lifetime. In addition, LRDC scheme also proposes a method for quickly compensate sample data in cases of packet loss, whereby part of redundant data is sent in advance to the area closer to CC. If the data required for matrix completion is lost, these redundant data can be quickly obtained by CC, so the LRDC scheme has low delay characteristics. Simulation results demonstrate that LRDC scheme can achieve better performance than the traditional strategy, and it can reduce the maximum energy consumption of the network by 27.6-57.9% and reduce the delay by 0.7-17.9%.

“…NðNT þmÞ . Obviously, N(NT + m) must be greater than 0, so only need to prove (NT) 2 − mN − NT + m, and let g(T) = (NT) 2…”

Section: Lrdc Scheme In Grid Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A low redundancy data collection scheme to maximize lifetime using matrix completion technique

Tan

Xie

et al. 2019

J Wireless Com Network

Self Cite

“…Delay is another factor that requires our attention in addition to energy consumption [61]. Real-time performance is a very important feature for WSNs.…”

Section: Background and Related Workmentioning

confidence: 99%

Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic IoT

Xiang

Wang

et al. 2019

J Wireless Com Network

Self Cite

Data collection is the basic functions of the Internet of Things (IoT), in which the sensed data are concentrations from sensor nodes to the sink, with a timely style, so the smart response can be done for emergency. The goal of multi-modal sensor data fusion is to obtain simple and accurate data to enhance system reliability and fault tolerance. Energy efficiency and small delay are the most important indicators which govern the performance of IoT. Convergecast is a low-latency data collection strategy based on effective time division multiple access (TDMA), in which each sensor node generates a packet, and m packets can aggregate to a packet. However, in most practical networks, sensor nodes do not necessarily generate packets during each data collection cycle, but instead generate packets from time to time. In the previous convergecast strategy, each node was fixedly allocated a slot, which increased the delay and wasted energy. A delay and energy-efficient data collection (DEEDC) scheme-based matrix filling theory is proposed to collect data in a randomly generated WSNs with minimum delay and energy consumption. The DEEDC scheme uses a clustering approach. For each cluster, the number of slots required for transmission is calculated by matrix filling theory, not the number of nodes that actually generate data. This ensures that data can be collected in a network with randomly generated data (number of slots ≤ number of nodes), thereby avoiding the allocation of slots for each node and the acquisition of redundant data to lead to the wastage of time and energy. Based on the above, a mixed slot scheduling strategy is proposed to construct energy and delay-efficient, collision-free schedule scheme. After extensive theoretical analysis, by using the DEEDC scheme, the delay is reduced by about 50~80%, and the energy consumed is reduced by about 40~57%.

“…In recent years, unstoppable data explosion [Wu, Wu, Liu et al (2018); Sun, Cai, Li et al (2018)] generated by wireless sensors [Yu, Liu, Liu et al (2018)] and terminals ; Guo, Liu, Cai et al (2018)] keeps driving up the demand for larger space in various big data storage systems. Due to the ever-growing data volume and ensuing space overhead concern, erasure coding, blessed with its capability to provide higher levels of reliability at a much lower storage cost, are gaining popularity [Wang, Pei, Ma et al (2017)].…”

Section: Introductionmentioning

confidence: 99%

GFCache: A Greedy Failure Cache Considering Failure Recency and Failure Frequency for an Erasure-coded Storage System

Deng

Computers, Materials &Amp; Continua

Zhao

et al. 2019

In the big data era, data unavailability, either temporary or permanent, becomes a normal occurrence on a daily basis. Unlike the permanent data failure, which is fixed through a background job, temporarily unavailable data is recovered on-the-fly to serve the ongoing read request. However, those newly revived data is discarded after serving the request, due to the assumption that data experiencing temporary failures could come back alive later. Such disposal of failure data prevents the sharing of failure information among clients, and leads to many unnecessary data recovery processes, (e.g. caused by either recurring unavailability of a data or multiple data failures in one stripe), thereby straining system performance. To this end, this paper proposes GFCache to cache corrupted data for the dual purposes of failure information sharing and eliminating unnecessary data recovery processes. GFCache employs a greedy caching approach of opportunism to promote not only the failed data, but also sequential failure-likely data in the same stripe. Additionally, GFCache includes a FARC (Failure ARC) catch replacement algorithm, which features a balanced consideration of failure recency, frequency to accommodate data corruption with good hit ratio. The stored data in GFCache is able to support fast read of the normal data access. Furthermore, since GFCache is a generic failure cache, it can be used anywhere erasure coding is deployed with any specific coding schemes and parameters. Evaluations show that GFCache achieves good hit ratio with our sophisticated caching algorithm and manages to significantly boost system performance by reducing unnecessary data recoveries with vulnerable data in the cache.