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

Development of Fly Ash Derived Sorbents to Capture CO2 from Flue Gas of Power Plants

This research program focused on the development of fly ash derived sorbents to capture CO{sub 2} from power plant flue gas emissions. The fly ash derived sorbents developed represent an affordable alternative to existing methods using specialized activated carbons and molecular sieves, that tend to be very expensive and hinder the viability of the CO{sub 2} sorption process due to economic constraints. Under Task 1 'Procurement and characterization of a suite of fly ashes', 10 fly ash samples, named FAS-1 to -10, were collected from different combustors with different feedstocks, including bituminous coal, PRB coal and biomass. These samples presented a wide range of LOI value from 0.66-84.0%, and different burn-off profiles. The samples also spanned a wide range of total specific surface area and pore volume. These variations reflect the difference in the feedstock, types of combustors, collection hopper, and the beneficiation technologies the different fly ashes underwent. Under Task 2 'Preparation of fly ash derived sorbents', the fly ash samples were activated by steam. Nitrogen adsorption isotherms were used to characterize the resultant activated samples. The cost-saving one-step activation process applied was successfully used to increase the surface area and pore volume of all the fly ash samples. The activated samples present very different surface areas and pore volumes due to the range in physical and chemical properties of their precursors. Furthermore, one activated fly ash sample, FAS-4, was loaded with amine-containing chemicals (MEA, DEA, AMP, and MDEA). The impregnation significantly decreased the surface area and pore volume of the parent activated fly ash sample. Under Task 3 'Capture of CO{sub 2} by fly ash derived sorbents', sample FAS-10 and its deashed counterpart before and after impregnation of chemical PEI were used for the CO{sub 2} adsorption at different temperatures. The sample FAS-10 exhibited a CO{sub 2} adsorption capacity of 17.5mg/g at 30 C, and decreases to 10.25mg/g at 75 C, while those for de-ashed counterpart are 43.5mg/g and 22.0 mg/g at 30 C and 75 C, respectively. After loading PEI, the CO{sub 2} adsorption capacity increased to 93.6 mg/g at 75 C for de-ashed sample and 62.1 mg/g at 75 C for raw fly ash sample. The activated fly ash, FAS-4, and its chemical loaded counterparts were tested for CO{sub 2} capture capacity. The activated carbon exhibited a CO{sub 2} adsorption capacity of 40.3mg/g at 30 C that decreased to 18.5mg/g at 70 C and 7.7mg/g at 120 C. The CO{sub 2} adsorption capacity profiles changed significantly after impregnation. For the MEA loaded sample the capacity increased to 68.6mg/g at 30 C. The loading of MDEA and DEA initially decreased the CO{sub 2} adsorption capacity at 30 C compared to the parent sample but increased to 40.6 and 37.1mg/g, respectively, when the temperature increased to 70 C. The loading of AMP decrease the CO{sub 2} adsorption capacity compared to the parent sample under all the studied temperatures. Under Task 4 'Comparison of the CO{sub 2} capture by fly ash derived sorbents with commercial sorbents', the CO{sub 2} adsorption capacities of selected activated fly ash carbons were compared to commercial activated carbons. The CO{sub 2} adsorption capacity of fly ash derived activated carbon, FAS-4, and its chemical loaded counterpart presented CO{sub 2} capture capacities close to 7 wt%, which are comparable to, and even better than, the published values of 3-4%

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^–15 to 10^–10 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^–13 mol/L