A trust-based minimum cost and quality aware data collection scheme in P2P network

Ren, Yingying; Zeng, Zhiwen; Wang, Tian; Zhang, Shaobo; Zhi, Guoming

doi:10.1007/s12083-020-00898-2

Cited by 56 publications

(44 citation statements)

References 58 publications

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“…Therefore, most researches revolved around WBSN merely concentrated on the reduction of the energy consumed in addition to extending the lifespan of batteries of biosensors devices. 10 The authors in previous studies [11][12][13][14] have proposed different data collection approaches for various applications. These approaches have reduced the collected data, saved the energy, and improved the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks

Jaber

Idrees

2020

Int J Communication

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SummaryOne of the best and cheapest solutions for continuous and remote monitoring and evaluation of patient health is to use the WBSNs (wireless body sensor networks) due to its great role in decreasing the expenses of the health care system. In this type of network, the sensed vital signs are gathered by biosensor devices then transmitted to the coordinator for further processing and fusion. Since the limited resources for biosensor devices (energy, memory, and processing) in addition to the periodic transmission for the large volume of data, it is essential to optimize the data transmission to save energy while keeping the data accuracy at the coordinator. This work suggests an AREDaCoT (adaptive rate energy‐saving data collecting technique) which aims to energy‐efficient patient health monitoring in periodic WBSNs. The AREDaCoT works in terms of so‐called periods. Each period has two stages: removing redundancy and adapting the sampling rate. The first stage uses improved LED to remove the redundancy in measurement of vital signs, whereas the second stage applies two techniques, assesses the risk score according to the risk level of the patient and the CSr (calibrate sampling rate) fittingly with different ranges of risk level for the sensor sampling ratio to be adapted. The performance of AREDaCoT has been evaluated in light of multiple series of simulations on real health datasets being compared to an existing approach. The acquired results illustrate how AREDaCoT decreases the volume of gathered data; thus, a significant energy saving has been made whilst preserving data accuracy and integrity. Moreover, the percentage results of data reduction over the original data set and the score differences between them are both acceptable.

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Section: Related Workmentioning

confidence: 99%

Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks

Jaber

Idrees

2020

Int J Communication

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“…These huge changes have led to extremely huge computing and storage capacity of the edge network, so the current network computing center is transferred from the network center to the edge of the network, forming an emerging computing model such as Edge Computing (EC) or Fog Computing [1,[10][11][12]. Because most applications are based on the data sensed and acquired by IoT devices [13,14], many emerging applications sensitive to latency and bandwidth, such as virtual reality, augmented reality and infrastructure for smart cities, benefit from edge computing. In the past, data needs to be sent to the cloud over a long distance, and the results of the cloud center are returned to the user through a long path.…”

Section: Introductionmentioning

confidence: 99%

“…The other type is a generally applicable method of defending against attacks. The most important method of defending against attacks is a strategy based on trust [5,6,11,14,30,34,36]. This type of strategy is not designed for a specific attack behavior, but adopts the corresponding data routing strategy to obtain the trust status of the node.…”

Section: Introductionmentioning

confidence: 99%

An active and verifiable trust evaluation approach for edge computing

Wang

Zhang

et al. 2020

J Cloud Comp

Self Cite

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Billions of Internet of Thing (IoT) devices are deployed in edge network. They are used to monitor specific event, process and to collect huge data to control center with smart decision based on the collected data. However, some malicious IoT devices may interrupt and interfere with normal nodes in data collection, causing damage to edge network. Due to the open character of the edge network, how to identify the credibility of these nodes, thereby identifying malicious IoT devices, and ensure reliable data collection in the edge network is a great challenge. In this paper, an Active and Verifiable Trust Evaluation (AVTE) approach is proposed to identify the credibility of IoT devices, so to ensure reliable data collection for Edge Computing with low cost. The main innovations of the AVTE approach compared with the existing work are as follows: (1) In AVTE approach, the trust of the device is obtained by an actively initiated trusted detection routing method. It is fast, accurate and targeted. (2) The acquisition of trust in the AVTE approach is based on a verifiable method and it ensures that the trust degree has higher reliability. (3) The trust acquisition method proposed in this paper is low-cost. An encoding returned verification method is applied to obtain verification messages at a very low cost. This paper proposes an encoding returned verification method, which can obtain verification messages at a very low cost. In addition, the strategy of this paper adopts initiation and verification of adaptive active trust detection according to the different energy consumption of IoT devices, so as to reliably obtain the trust of device under the premise of ensuring network lifetime. Theoretical analysis shows that AVTE approach can improve the data collection rate by 0.5 ~ 23.16% while ensuring long network lifetime compared with the existing scheme.

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“…There exists a great amount of sensing devices in the urban environment, the sensing devices in WSNs can sense the environmental data [1][2][3][4]. The collected sensing data can be processed and sent to the remote server to make decisions, which can help improve the city management [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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

Long

Luo

Zhu

et al. 2020

Preprint

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With the developing of Internet of Things (IoT) and mobile edge computing (MEC), 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.

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A trust-based minimum cost and quality aware data collection scheme in P2P network

Cited by 56 publications

References 58 publications

Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks

Adaptive rate energy‐saving data collecting technique for health monitoring in wireless body sensor networks

An active and verifiable trust evaluation approach for edge computing

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

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