14 research outputs found
An Intelligent Trust Cloud Management Method for Secure Clustering in 5G enabled Internet of Medical Things
5G edge computing enabled Internet of Medical Things (IoMT) is an efficient
technology to provide decentralized medical services while Device-to-device
(D2D) communication is a promising paradigm for future 5G networks. To assure
secure and reliable communication in 5G edge computing and D2D enabled IoMT
systems, this paper presents an intelligent trust cloud management method.
Firstly, an active training mechanism is proposed to construct the standard
trust clouds. Secondly, individual trust clouds of the IoMT devices can be
established through fuzzy trust inferring and recommending. Thirdly, a trust
classification scheme is proposed to determine whether an IoMT device is
malicious. Finally, a trust cloud update mechanism is presented to make the
proposed trust management method adaptive and intelligent under an open
wireless medium. Simulation results demonstrate that the proposed method can
effectively address the trust uncertainty issue and improve the detection
accuracy of malicious devices
Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks
Emerging six generation (6G) is the integration of heterogeneous wireless
networks, which can seamlessly support anywhere and anytime networking. But
high Quality-of-Trust should be offered by 6G to meet mobile user expectations.
Artificial intelligence (AI) is considered as one of the most important
components in 6G. Then AI-based trust management is a promising paradigm to
provide trusted and reliable services. In this article, a generative
adversarial learning-enabled trust management method is presented for 6G
wireless networks. Some typical AI-based trust management schemes are first
reviewed, and then a potential heterogeneous and intelligent 6G architecture is
introduced. Next, the integration of AI and trust management is developed to
optimize the intelligence and security. Finally, the presented AI-based trust
management method is applied to secure clustering to achieve reliable and
real-time communications. Simulation results have demonstrated its excellent
performance in guaranteeing network security and service quality
A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks
Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols
A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
Clustering is an effective topology control method in wireless sensor networks (WSNs), since it can enhance the network lifetime and scalability. To prolong the network lifetime in clustered WSNs, an efficient cluster head (CH) optimization policy is essential to distribute the energy among sensor nodes. Recently, game theory has been introduced to model clustering. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy. Then it decides whether to act as the CH according to this strategy for a tradeoff between providing required services and energy conservation. However, how to get the equilibrium strategy while maximizing the payoff of sensor nodes has rarely been addressed to date. In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs. With our novel payoff function, realistic sensor behaviors can be captured well. The energy heterogeneity of nodes is considered by incorporating a penalty mechanism in the payoff function, so the nodes with more energy will compete for CHs more actively. We have obtained the Nash equilibrium (NE) strategy of the clustering game through convex optimization. Specifically, each sensor node can achieve its own maximal payoff when it makes the decision according to this strategy. Through plenty of simulations, our proposed game theoretic clustering is proved to have a good energy balancing performance and consequently the network lifetime is greatly enhanced
Electrochemical Detection of Peanut Allergen Ara h 1 Using a Sensitive DNA Biosensor Based on Stem–Loop Probe
A novel electrochemical DNA sensor was developed by using
a stem–loop
probe for peanut allergen Ara h 1 detection. The probe was modified
with a thiol at its 5′ end and a biotin at its 3′ end.
The biotin-tagged “molecular beacon”-like probe was
attached to the surface of a gold electrode to form a stem–loop
structure by self-assembly through facile gold–thiol affinity.
6-Mercaptohexanol (MCH) was used to cover the remnant bare region.
The stem–-loop probe was “closed” when the target
was absent, and then the hybridization of the target induced the conformational
change to “open”, along with the biotin at its 3′
end moved away from the electrode surface. The probe conformational
change process was verified by circular dichroism (CD); meanwhile,
electron-transfer efficiency changes between probe and electrode were
proved by electrochemical impedance spectroscopy (EIS). The detection
limit of this method was 0.35 fM with the linear response ranging
from 10<sup>–15</sup> to 10<sup>–10</sup> M. Moreover,
a complementary target could be discriminated from one-base mismatch
and noncomplementarity. The proposed strategy has been successfully
applied to detect Ara h 1 in the peanut DNA extracts of peanut milk
beverage, and the concentration of it was 3.2 × 10<sup>–13</sup> mol/L