A novel trust mechanism based on Fog Computing in Sensor–Cloud System

Wang, Tian; Zhang, Guangxue; Bhuiyan, Zakirul Alam; Liu, Anfeng; Jia, Weijia; Xie, Mei

doi:10.1016/j.future.2018.05.049

Cited by 122 publications

(75 citation statements)

References 24 publications

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“…It allows us to determine the degree to which data can be trusted and whether nodes can be trusted. Similarly, Wang et al [40] proposed a Fog-based hierarchical trust mechanism framework for detecting hidden data attacks and ensure communication only with credible edge nodes. In this study, the Fog computing layer acts as a trust buffer between the Cloud computing layer and the sensor network layer.…”

Section: Trust Management Approaches and Positioning Of The Present Workmentioning

confidence: 99%

Trust management in a blockchain based fog computing platform with trustless smart oracles

Kochovski

Gec

Stankovski

et al. 2019

Future Generation Computer Systems

155

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Trust is a crucial aspect when cyber-physical systems have to rely on resources and services under ownership of various entities, such as in the case of Edge, Fog and Cloud computing. The DECENTER's Fog Computing Platform is developed to support Big Data pipelines, which start from the Internet of Things (IoT), such as cameras that provide video-streams for subsequent analysis. It is used to implement Artificial Intelligence (AI) algorithms across the Edge-Fog-Cloud computing continuum which provide benefits to applications, including high Quality of Service (QoS), improved privacy and security, lower operational costs and similar. In this article, we present a trust management architecture for DECENTER that relies on the use of blockchain-based Smart Contracts (SCs) and specifically designed trustless Smart Oracles. The architecture is implemented on Ethereum ledger (testnet) and three trust management scenarios are used for illustration. The scenarios (trust management for cameras, trusted data flow and QoS based computing node selection) are used to present the benefits of establishing trust relationships among entities, services and stakeholders of the platform.

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Section: Trust Management Approaches and Positioning Of The Present Workmentioning

confidence: 99%

Trust management in a blockchain based fog computing platform with trustless smart oracles

Kochovski

Gec

Stankovski

et al. 2019

Future Generation Computer Systems

155

View full text Add to dashboard Cite

show abstract

“…Mobile cloud and remote cloud provide data storage and computing services. Compared to the traditional client-server model [30][31][32], the cloudlet architecture with mobile cloud can effectively reduce the access delay. Because the mobile device from the cloudlets are relatively close, getting connected to the cloudlet through the AP so as to obtain the cloud storage and computing performance is very fast.…”

Section: Cloudlet Placement and Movement Analysis For Mobile Devicesmentioning

confidence: 99%

ECPM: an energy-efficient cloudlet placement method in mobile cloud environment

Shen

Xue

2019

J Wireless Com Network

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The development of mobile cloud computing has greatly improved the computing and storage performance of mobile devices. And mobile cloud computing is undoubtedly the necessary way to solve the performance of the process for mobile applications with high performance requirements. However, migrating the mobile applications to the cloud brings about a migration delay, which is intolerable for high real-time demanding applications. This can be technically achieved by expanding mobile cloudlets, co-located with access points (AP). Then, how to deploy the cloudlet to reduce the energy consumption has become a part of major challenges in the current study. In this paper, we propose an energy-efficient cloudlet placement method, named ECPM, to effectively reduce the number of cloudlets, so as to achieve the energy savings. Specifically, the clustering of mobile devices at each time is obtained, and then the cloudlet will move to the clustering to achieve the best use of energy. Finally, the experimental results demonstrate that the proposed method is energy saving.

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“…This has made the application range of wireless sensor networks more widely expanded, including smart home, intelligent agriculture, and other fields [21][22][23][24][25]. With the development, sensing device has also developed rapidly; the current Internet is experiencing a trend from centralization to marginalization, Cloud computing [26,27], Edge computing [28,29], and Fog computer [30][31][32] which correspond to the new computational model proposed for such development [26,28,[33][34][35]. With the rapid rise of artificial intelligence technology [36,37], the combination of artificial intelligence and Internet of Things (IoT) has made it a longer development [38][39][40], which has become the focus of researchers.…”

Section: Introductionmentioning

confidence: 99%

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

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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%.

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A novel trust mechanism based on Fog Computing in Sensor–Cloud System

Cited by 122 publications

References 24 publications

Trust management in a blockchain based fog computing platform with trustless smart oracles

Trust management in a blockchain based fog computing platform with trustless smart oracles

ECPM: an energy-efficient cloudlet placement method in mobile cloud environment

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

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