Minimizing Convergecast Time and Energy Consumption in Green Internet of Things

Li, Zhetao; Liu, Yuxin; Liu, Anfeng; Wang, Shiguo; Liu, Haolin

doi:10.1109/tetc.2018.2844282

Cited by 106 publications

(149 citation statements)

References 27 publications

Supporting

Mentioning

146

Contrasting

Unclassified

Order By: Relevance

“…However, due to the large amount of data forwarded by the nodes close to the CC, it also causes the problem of energy holes. A lot of research has been done on the issue of energy holes [45][46][47][48]. There are node deployment aspects [49,50] to avoid this problem, Chen at el.…”

Section: Background and Related Workmentioning

confidence: 99%

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

Tan

Liu

Xie

et al. 2019

J Wireless Com Network

Self Cite

View full text Add to dashboard 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%.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

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

Tan

Liu

Xie

et al. 2019

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

show abstract

“…Different kinds of sensing devices deployed in smart city, such as water pollution sensor, temperature sensor, humidity sensor, PM2.5 sensor, intelligent garbage can, intelligent street light. The sensing devices are the basis of the Internet of Things (IoT) [10,11]. The sensing devices can be applied into a large number of application scenarios, such as the environmental pollution monitoring and traffic monitoring.…”

Section: Introductionmentioning

confidence: 99%

Computation offloading through mobile vehicles in IoT-edge-cloud network

Long

Luo

Zhu

et al. 2020

Preprint

View full text Add to dashboard Cite

With the developing of Wireless Sensor Networks (WSNs), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

show abstract

“…The Internet of Things (IoT) is the latest Internet evolution, with billions of Internet devices. Such as cameras, RFIDs, in addition to wearable, vehicles, to smart meters, medication pills, industrial, and signs machines [1][2][3][4][5]. An important part of the IoT is wireless sensor networks [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…To realize the enormous potential of wireless sensor networks (WSNs), it is important for sink to integrate the sensed data by many sensor nodes, and distill the high value information for each application needs [46][47][48]. Based on economic considerations, sensor nodes are simple in construction, small in size, and powered by batteries, which can be used with limited energy [3,5,7,43,49,50]. Therefore, energy efficiency has become an important indicator [51,52].…”

Section: Introductionmentioning

confidence: 99%

“…For example, in the monitoring of critical facilities, important buildings, industrial sites, geological hazards, and fires, delayed data routing can lead to the destruction of important facilities, and people and objects in geological disaster areas cannot be transferred, resulting in serious losses. Energy-efficient and fast data collection has become an important research content of WSNs [4,5,21,[53][54][55].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Xiang

Liu

Wang

et al. 2019

J Wireless Com Network

Self Cite

View full text Add to dashboard 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%.

show abstract

Minimizing Convergecast Time and Energy Consumption in Green Internet of Things

Cited by 106 publications

References 27 publications

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

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

Computation offloading through mobile vehicles in IoT-edge-cloud network

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

Contact Info

Product

Resources

About