Access control and authorization in smart homes: A survey

Mohammad, Ziarmal Nazar; Farha, Fadi; Abuassba, Adnan Omer; Yang, Shunkun; Zhou, Fang

doi:10.26599/tst.2021.9010001

Cited by 44 publications

(21 citation statements)

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“…We use this arbitration result as a reliable prior condition for iterative calculation. Therefore, for each iteration, both M and P conditions need to be combined to make the various nodes converge quickly, and singularity and periodicity are also avoided [25] . The calculation formula is given in Algorithm 5.…”

Section: Trust Updatementioning

confidence: 99%

“…We improve the Eigen trust model [24] to evaluate trust in two dimensions: communication and storage. The communication and storage data are sent to the leader node regularly through P2P network [25] , and the trust result is calculated by the improved model. Then, we select a group of trusted nodes by the trust ranking as the leader candidate nodes to speed up the transition process and reduce the network split [26] .…”

Section: Introductionmentioning

confidence: 99%

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A Trust-Based Hierarchical Consensus Mechanism for Consortium Blockchain in Smart Grid

Jiang

Sun

et al. 2023

Tsinghua Sci. Technol.

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As the smart grid develops rapidly, abundant connected devices offer various trading data. This raises higher requirements for secure and effective data storage. Traditional centralized data management does not meet the above requirements. Currently, smart grid with conventional consortium blockchain can solve the above issues. However, in the face of a large number of nodes, existing consensus algorithms often perform poorly in terms of efficiency and throughput. In this paper, we propose a trust-based hierarchical consensus mechanism (THCM) to solve this problem. Firstly, we design a hierarchical mechanism to improve the efficiency and throughput.Then, intra-layer nodes use an improved Raft consensus algorithm and inter-layer nodes use the Byzantine Fault Tolerance algorithm. Thirdly, we propose a trust evaluation method to improve the election process of Raft. Finally, we implement a prototype system to evaluate the performance of THCM. The results demonstrate that the consensus efficiency is improved by 19.8%, the throughput is improved by 12.34% , and the storage is reduced by 37.9%.

show abstract

Section: Trust Updatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Trust-Based Hierarchical Consensus Mechanism for Consortium Blockchain in Smart Grid

Jiang

Sun

et al. 2023

Tsinghua Sci. Technol.

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show abstract

“…6 For privacy protection, users often do not want to expose their personal information. 7,8 For example, some users do not want to reveal that they have been to the hospital, or some users deliberately hide their experience of having been to the bar. Users act in this way to protect their privacy and avoid check-in, which leads to the sparse check-in.…”

Section: Introductionmentioning

confidence: 99%

“…Privacy protection 6 . For privacy protection, users often do not want to expose their personal information 7,8 . For example, some users do not want to reveal that they have been to the hospital, or some users deliberately hide their experience of having been to the bar.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional GRU networks‐based next POI category prediction for healthcare

Liu

Song

et al. 2021

Int J of Intelligent Sys

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The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point‐of‐interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check‐in. Faced with this challenge, we propose an attention‐based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check‐ins. Furthermore, since the neural network is similar to a “black box” in feature learning, the decision‐making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check‐in records, which can improve the interpretability of the model. Considering the time impact on users' check‐in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check‐in data.

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“…APPs in the general world enter the monitor mode using a subset of Secure Monitor Call instructions or HW exception mechanisms. TrustZone technology is widely used for its high efficiency, lightweightness, and security, which can be deployed in heterogeneous mobile systems [4,5] and smart homes [6] , such as Qualcomm SEE architecture, Kinibi microkernel, Trusty, and open-source (OP)-TEE. Among them, OP-TEE is an open-source implementation of TEE developed by STMicroelectronics and Linaro.…”

mentioning

confidence: 99%

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

Wang

Cai

et al. 2023

Tsinghua Sci. Technol.

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A trusted execution environment (TEE) is a system-on-chip and CPU system with a wide security solution available on today's Arm application (APP) processors, which dominate the smartphone market. Generally, mobile APPs create a trusted application (TA) in the TEE to process sensitive information, such as payment or message encryption, which is transparent to the APPs running in the rich execution environments (REEs). In detail, the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA.Such an operation definitely increases the overhead of mobile APPs. In this paper, we first present a comprehensive analysis of the performance of open-source TEE encrypted text. We then propose a high energy-efficient task scheduling strategy (ETS-TEE). By leveraging the deep learning algorithm, our policy considers the complexity of TA tasks, which are dynamically scheduled between modeling on the local device and offloading to an edge server.We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server. The results show that compared with the default scheduling strategy on the local device, our approach achieves an average of 38.0% energy reduction and 1.6×speedup. This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications, so that the trusted execution environment has both security and high performance.

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Access control and authorization in smart homes: A survey

Cited by 44 publications

References 0 publications

A Trust-Based Hierarchical Consensus Mechanism for Consortium Blockchain in Smart Grid

A Trust-Based Hierarchical Consensus Mechanism for Consortium Blockchain in Smart Grid

Bidirectional GRU networks‐based next POI category prediction for healthcare

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

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