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In this paper, we consider a downlink non-orthogonal multiple access (NOMA) system over Nakagami-m channels. The single-antenna base station serves two single-antenna NOMA users based on statistical channel state information (CSI). We derive the closed-form expression of the exact outage probability under a given decoding order, and we also deduce the asymptotic outage probability and diversity order in a high-SNR regime. Then, we analyze all the possible power allocation ranges and theoretically prove the optimal power allocation range under the corresponding decoding order. The demarcation points of the optimal power allocation ranges are affected by target data rates and total power, without an effect from the CSI. In particular, the values of the demarcation points are proportional to the total power. Furthermore, we formulate a joint decoding order and power allocation optimization problem to maximize the sum throughput, which is solved by efficiently searching in our obtained optimal power allocation ranges. Finally, Monte Carlo simulations are conducted to confirm the accuracy of our derived exact outage probability. Numerical results show the accuracy of our deduced demarcation points of the optimal power allocation ranges. And the optimal decoding order is not constant at different total transmit power levels. [ABSTRACT FROM AUTHOR]

In this paper, we consider a computation offloading problem in a three-tier network consisting of multiple mobile users (MUs), multiple edge clouds, and a central cloud. On this basis, we formulate a system Energy Time Cost ($ETC$) minimization problem by jointly optimizing the resource allocation, mobile-edge matching decision, and offloading decision. Due to the difficulty of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Gale-Shapley based Minimum/Sequential Offloading Algorithm (GS-MOA/GS-SOA), to optimize offloading decisions by minimizing the system $ETC$ , in which the mobile-edge matching strategies based on the proposed Gale-Shapley based Mobile-Edge mAtching aLgorithm (GS-MEAL) and resource allocations are optimized iteratively. The simulations show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

In this article, we investigate the integration of NOMA into cooperative relaying. We first introduce several fundamental cooperative NOMA network structures, and then classify them into two categories: user relaying and dedicated relaying. After that, we discuss the combination of cooperative NOMA with various advanced transmission technologies, such as full-duplex, cognitive radio, energy harvesting and multiple-input multiple-output. Following this, we focus on system evaluation and resource allocation of cooperative NOMA. Finally, major challenges and open issues are highlighted. [ABSTRACT FROM AUTHOR]

Nowadays, advanced smart mobile devices equipped with multi-core central processing units for handling multithreaded (MT) applications. However, existing research mainly uses single-thread (ST) computing to deal with applications, which limits the performance of mobile computing. To make full use of multi-core resources, this study proposes a fine-grained MT offloading strategy to solve the offloading problem of MT application. The strategy jointly schedules cloud computing resources, as well as local multi-core computing and communication resources. Precisely, the authors first formulate the minimum energy consumption problem for ST offloading. Then, they prove that the problem is convex and solve it by standard convex optimisation technique. Thirdly, they extend the optimisation goals from ST applications to MT applications, and design calculation rules for MT applications to reduce computing costs. Finally, based on these calculation rules and the optimal solution for ST offloading, they develop a MT offloading strategy to solve the computation offloading problem of MT applications. Simulation results show that the proposed fine-grained MT offloading strategy effectively reduces the minimum delay requirement of mobile computing. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has been envisioned as a promising technology for enhancing the computational capacities of mobile devices, by enabling computational task offloading. In this article, we employ massive multiple-input multiple-output methods to facilitate offloading in MEC. Our objective is to minimize the maximum delay for offloading and computing among the users, which requires a joint allocation of wireless and computational resources. Both perfect and imperfect channel state information (CSI) are considered. Under perfect CSI, we derive a semi-closed-form solution for the formulated problem. Under imperfect CSI, since the formulated problem is non-convex, we transform it into a convex one using a successive convex approximation technique and propose an iterative algorithm to solve it. Presented numerical results show the benefits of having a large number of antennas at the base station, and the necessity of performing joint radio and computational resource allocation. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the downlink secure beamforming (BF) design problem of cloud radio access networks (C-RANs) relying on multicast fronthaul, where millimeter-wave and microwave carriers are used for the access links and fronthaul links, respectively. The base stations (BSs) jointly serve users through cooperating hybrid analog/digital BF. We first develop an analog BF for cooperating BSs. On this basis, we formulate a secrecy rate maximization (SRM) problem subject both to a realistic limited fronthaul capacity and to the total BS transmit power constraint. Due to the intractability of the non-convex problem formulated, advanced convex approximated techniques, constrained concave convex procedures and semi-definite programming (SDP) relaxation are applied to transform it into a convex one. Subsequently, an iterative algorithm of jointly optimizing multicast BF, cooperative digital BF and the artificial noise (AN) covariance is proposed. Next, we construct the solution of the original problem by exploiting both the primal and the dual optimal solution of the SDP-relaxed problem. Furthermore, a per-BS transmit power constraint is considered, necessitating the reformulation of the SRM problem, which can be solved by an efficient iterative algorithm. We then eliminate the idealized simplifying assumption of having perfect channel state information (CSI) for the eavesdropper links and invoke realistic imperfect CSI. Furthermore, a worst-case SRM problem is investigated. Finally, by combining the so-called $\mathcal {S}$ -Procedure and convex approximated techniques, we design an efficient iterative algorithm to solve it. Simulation results are presented to evaluate the secrecy rate and demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. We adopt two sparse radio frequency chain antenna structures at the base station (BS), i.e., fully-connected and subarray structures, and design the codebook-based analog precoding according to the different structures. Then, we formulate a joint digital multicast, unicast precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at the BS and minimum harvested energy at receivers are considered. Due to its difficulty to directly solve the formulated problem, we equivalently transform the fractional objective function into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques and propose an iterative algorithm to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop a zero forcing (ZF) technique-based low complexity iterative algorithm. Specifically, the ZF technique is applied to cancel the inter-unicast interference and the first order Taylor approximation is used for the convexification of the non-convex constraints in the original problem. Finally, simulation results are provided to compare the performance of the proposed algorithms under different schemes. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the dynamic small cell (SC) clustering strategy and their precoding design problem for interference coordination in two-tier heterogeneous networks (HetNets) with massive MIMO (mMIMO). To reduce interference among different SCs, an interference graph-based dynamic SC clustering scheme is proposed. Based on this, we formulate an optimization problem as design precoding weights at macro base station (MBS) and clustered SCs for maximizing the downlink sum rate of SC users (SUs) subject to the power constraint of each SC BS (SBS), while mitigating inter-cluster, eliminating inter-tier, intra-cluster and multi-macro user (MU) interference. To eliminate the inter-tier and multi-MU interference simultaneously, we propose a clustered SC block diagonalization precoding scheme for the MBS. Next, each SU’s precoding vector at clustered SCs is designed as the product of the following two parts. The first part is designed with singular value decomposition to remove the intra-cluster interference. The second part is designed to coordinate the inter-cluster interference for maximizing the downlink sum rate of SUs, which is a non-convex optimization problem and difficult to solve directly. A non-cooperative game-based distributed algorithm is proposed to obtain a suboptimal solution. Meanwhile, we prove the existence and uniqueness of Nash equilibrium for the formed game. Finally, simulation results verify the effectiveness of our proposed schemes. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the resource allocation problem in two-tier heterogenous networks (HetNets) with massive MIMO, where two frequency bands, cellular frequency band and millimeter wave frequency band, are used for wireless backhaul links. To coordinate the interference among small cells (SCs), SCs are grouped into multiple SC clusters (SCCs), where SC users are jointly served by SC base stations (BSs) (SBSs) belonging to the same SCC. To eliminate the multi-user and inter-tier interference, we propose a block diagonalization based precoding scheme at macro-BS and each SCC, where the transmit precoding matrix for users is projected onto the null space of interference channels. Following this, we formulate a power allocation optimization problem to maximize the downlink sum rate of the system subject to per-SCC power and users’ quality of service requirements. Since the formulated problem is nonconvex, we transform it into a convex one by appropriate approximation, and a constrained concave convex procedure based iterative algorithm is proposed to obtain the solution. Furthermore, we prove that the obtained solution converges to the local optimum. Meanwhile, according to the structure of the original formulated problem, we divide it into multiple subproblems and propose an optimal iterative water-filling algorithm. Next, we extend the original problem with per-SBS power constraint, which results in that the precoding of each SCC has to be redesigned. On this basis, we propose an iterative water-filling algorithm to obtain the optimal power allocation and precoding. Numerical results are presented to verify the performance of our proposed algorithms. [ABSTRACT FROM PUBLISHER]

Mucus hypersecretion and plugging of lower respiratory tract airways due to mucus plugs have long been recognized as the leading cause of the morbidity and mortality in asthma. MUC5AC protein is a major component of airway mucus. Here, we showed that interleukin (IL)-13 induced MUC5AC production and secretion, and leptin expression in the human bronchial epithelial cell line-16 (HBE16) cells in a concentration-dependent manner. Leptin knockdown suppressed MUC5AC production and secretion induced by IL-13. We further investigated the molecular mechanism by which leptin functioned, and found that leptin regulated IL-13-induced MUC5AC production and secretion via the JAK2-STAT3 pathway. Subsequently, Munc18b, a limiting component of the exocytic machinery of airway epithelial and mast cells, was found that when knockdown, MUC5AC secretion was significantly inhibited. SABiosciences ChIP search tool identified three STAT3 binding sites with Munc18b promoter. Chromatin immunoprecipitation analysis further confirmed that Stat3 upregulated Munc18b expression by directly binding to its promoter. These data suggested that leptin promotes MUC5AC secretion via JAK2-STAT3-MUNC18b regulatory network. Taken together, our data highlight a positive feedback role and molecular mechanism for leptin in the control of MUC5AC production and secretion from airway epithelial cells stimulated by IL-13, which encourage further exploration of the therapeutic potentials of manipulating leptin in the treatment of mucus hypersecretion in chronic inflammation lung diseases. [ABSTRACT FROM AUTHOR]

Beam selection aims to select beams with large power and little inter‐user interference (IUI). In wireless communication scenarios, the levels of IUI are usually different, but, most existing beam selection methods apply the same beam selection criteria for all users, which is detrimental to maintain an attractive trade‐off between computational complexity and system performance. Considering the discrepancy of IUI comprehensively, this paper proposes a beam selection method based on interfering user grouping (IUG) for wideband millimetre‐wave massive multiple‐input multiple‐output (MIMO) systems. Firstly, a candidate beam set (CBS) is constructed in light of the sparsity and power distribution of the beamspace channel. Based on the CBS, all users are classified into non‐interfering users (NIUs), low‐interference users (LIUs) and high‐interference users (HIUs). For NIUs, the beams with large power are selected from CBS under the magnitude maximization (MM) criterion; for LIUs, a novel MM criterion with a tabu list is designed, which can effectively tackle the drawback of assigning shared beams in traditional MM; for HIUs, the incremental algorithm based on the sum‐rate maximization criterion is developed to find the optimal beams from CBS. Simulation results validate that the proposed IUG‐based method can obtain a good performance with lower computational complexity compared with the existing methods. [ABSTRACT FROM AUTHOR]

Background: Heart development protein with EGF‐like domains 1 (HEG1), generally related to angiogenesis and embryonic development, was reported to participate in the occurrence and progression of some tumors recently. However, the role of HEG1 in lung adenocarcinoma (LUAD) is unclear. Patients and Methods: To explore the effect of HEG1 on LUAD, GEPIA platform and UALCAN database, as well as Kaplan–Meier plotter were adopted to analyze the association of HEG1 with clinicopathological characteristics and survival outcomes for LUAD firstly. And then the HEG1 in LUAD tissues, blood and cell lines were detected by qRT‐PCR, western blot, immunofluorescence, immunohistochemistry, and ELISA. Gene set enrichment analysis (GSEA) was conducted to identify pathways that might be affected by HEG1 in LUAD. Results: In this study, HEG1 in lung tissues and cell lines of LUAD were significantly downregulated compared to benign pulmonary disease tissues and alveolar epithelial cells (p < 0.05). Moreover, compared with other groups, patients with advanced tumor stage had lower HEG1 mRNA expression levels (p = 0.025), which were negatively correlated with Ki67 index in tumor tissues (r = −0.427, p = 0.033). On the other hand, the LUAD patients with lower HEG1 had shorter overall survival (OS) (HR = 0.51, 95% CI: 0.40–0.65, p < 0.001) according to Kaplan–Meier plotter. In addition, HEG1 in serum of LUAD patients was negatively associated with CEA (r = −0.636, p < 0.001). GSEA showed that HEG1 was enriched in various metabolic‐related pathways, including glucose metabolism, lipid metabolism, and nucleotide metabolism signaling. Conclusions: HEG1 was downregulated in LUAD patients and associated with poor prognosis, which indicating HEG1 may serve as a potential biomarker for diagnosis and prognosis of LUAD. [ABSTRACT FROM AUTHOR]

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

Considering a reconfigurable intelligent surface (RIS) aided wireless powered Internet of Things (WP IoT) network. To address the energy-limitation issue, IoT devices in such a network can be wirelessly powered by a power station (PS) first and then connect with an access point (AP) using their own harvested energy. The RIS helps enhance energy and information receptions in the downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), respectively. This work unveils the impact of phase shift error (PSE) and transceiver hardware impairment (THI) on the considered network. Our investigation starts with a scenario where only the impact of the PSE on system under study is considered, then moves toward a scenario with the compound effect of both PSE and THI. A maximization problem of the system sum throughput is formulated to evaluate the overall performance for these two scenarios, subject to the constraints of the adjustable RIS phase shifts, the statistical PSE and the transmission time scheduling. To handle the non-convexity of the formulated problem due to those coupled variables, we first adopt the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling in closed-form. Next, we recast the stochastic PSE into the deterministic counterpart for its tractability. Then, we adopt a successive convex approximation (SCA) to iteratively derive the optimal WIT’s phase shifts, and element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) methods to iteratively derive the optimal WET’s phase shifts. Finally, we complete our solution approach for the scenario with both PSE and THI. Simulation results highlight the performance of the proposed scheme and the benefits induced by the RIS in comparison to benchmark schemes. [ABSTRACT FROM AUTHOR]

This article discusses the self-sustainability of reconfigurable intelligent surface (RIS) in wireless powered Internet of Things (IoT) networks. Our vision is that RIS helps improve energy harvesting and data transmission capabilities simultaneously, without the extra utilization of RF spectrum and energy consumption. The inherent properties of RIS are first discussed to unveil its distinctive features, followed by a broader range of use cases motivated by RIS as their enabling technology. The focus is on the application of RIS in the wireless powered IoT networks, and its potential to interconnect and support these practical use cases. Such an application is then thoroughly evaluated in a case study of an RIS-assisted wireless powered sensor network, with system throughput, energy transmission time consumption, and energy harvesting as the key performance metrics. The comprehensive performance evaluation showcases the self-sustainable property of RIS being unlocked in the considered scenario, identifying a clear pathway toward the future wireless powered IoT networks. We further pave the way by exploring research challenges and open issues related to emerging technological development. [ABSTRACT FROM AUTHOR]

Considering orthogonal frequency division multiplexing (OFDM) system, we propose a joint channel estimation, noise precision estimation, symbol detection and bit decoding algorithm. In this algorithm, we incorporate the recently proposed vector approximate message passing (VAMP) with the belief propagation and mean field (BP-MF) framework, where BP is applied within subgraph of demodulation and decoding, and MF is used to handle nonlinear observations for estimating unknown noise precision, and VAMP is employed to estimate channels in densely connected factor graph. Furthermore, we use expectation propagation (EP) to demonstrate that the proposed approach converges after only one iteration. Compared to the state-of-the-art algorithm which exploits generalized approximate message passing (GAMP), the proposed scheme achieves the same bit error ratio (BER) performance, but lower per-iteration complexity and faster convergence speed. [ABSTRACT FROM AUTHOR]

Comparing with the cloud computing, mobile edge computing (MEC) can further decrease the latency and improve the stability of the networks. However, it is challenging for the edge servers to deal with the large computation task due to the limited computing capacity. In this study, we design a novel three‐layer network architecture consisting of mobile devices, edge cloudlets, and helper cloudlets, where the computing data can be partially processed at the edge cloudlet and helper cloudlet. Based on this, a joint communication, offloading, and computation resource allocation problem is formulated to minimise the computation cost and energy consumption. Due to its difficulty to directly solve the formulated problem, we first propose an offloading scheme to obtain the closed‐form solutions for the optimal offloading data size. Next, we decompose the optimisation problem into two subproblems: (i) for the cloud execution, we dynamically adjust the data transmission rate according to the stochastic channel condition, (ii) for the mobile execution, the energy consumption can be further reduced by applying the dynamic voltage and frequency scaling technique. Finally, the numerical results demonstrate the efficiency of the proposed scheme, and show the performance gains in terms of delay, computation cost and energy consumption. [ABSTRACT FROM AUTHOR]

Background: Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) induces uncontrolled and self-amplified pulmonary inflammation, and has high morbidity and mortality rates in critically ill patients. In recent years, many bioactive ingredients extracted from herbs have been reported to effectively ameliorate ALI/ARDS via different mechanisms. Ferroptosis, categorized as regulated necrosis, is more immunogenic than apoptosis and contributes to the progression of ALI. In this study, we examined the impact of panaxydol (PX), isolated from the roots of Panax ginseng, on lipopolysaccharide (LPS)-induced ALI in mice.Methods: In vivo, the role of PX on LPS-induced ALI in mice was tested by determination of LPS-induced pulmonary inflammation, pulmonary edema and ferroptosis. In vitro, BEAS-2B cells were used to investigate the molecular mechanisms by which PX functions via determination of inflammation, ferroptosis and their relationship.Results: Administration of PX protected mice against LPS-induced ALI, including significantly ameliorated lung pathological changes, and decreased the extent of lung edema, inflammation, and ferroptosis. In vitro, PX inhibited LPS-induced ferroptosis and inflammation in bronchial epithelial cell line BEAS-2B cells. The relationship between ferroptosis and inflammation was investigated. The results showed that ferroptosis mediated inflammation in LPS-treated BEAS-2B cells, and PX might ameliorate LPS-induced inflammation via inhibiting ferroptosis. Meanwhile, PX could upregulate Keap1-Nrf2/HO-1 pathway, and selective inhibition of Keap1-Nrf2/HO-1 pathway significantly abolished the anti-ferroptotic and anti-inflammatory functions of PX in LPS-treated cells.Conclusion: PX attenuates ferroptosis against LPS-induced ALI via Keap1-Nrf2/HO-1 pathway, and is a promising novel therapeutic candidate for ALI. [ABSTRACT FROM AUTHOR]

Comparing with the cloud computing, mobile edge computing (MEC) can further decrease the latency and improve the stability of the networks. However, it is challenging for the edge servers to deal with the large computation task due to the limited computing capacity. In this study, we design a novel three‐layer network architecture consisting of mobile devices, edge cloudlets, and helper cloudlets, where the computing data can be partially processed at the edge cloudlet and helper cloudlet. Based on this, a joint communication, offloading, and computation resource allocation problem is formulated to minimise the computation cost and energy consumption. Due to its difficulty to directly solve the formulated problem, we first propose an offloading scheme to obtain the closed‐form solutions for the optimal offloading data size. Next, we decompose the optimisation problem into two subproblems: (i) for the cloud execution, we dynamically adjust the data transmission rate according to the stochastic channel condition, (ii) for the mobile execution, the energy consumption can be further reduced by applying the dynamic voltage and frequency scaling technique. Finally, the numerical results demonstrate the efficiency of the proposed scheme, and show the performance gains in terms of delay, computation cost and energy consumption. [ABSTRACT FROM AUTHOR]

This paper investigates the power allocation problem for secure secondary transmission in a multiple-input single-output cognitive wiretap system with artificial noise (AN). The secondary transmitter exchanges confidential information with the secondary destination in the presence of an unknown eavesdropper. Since the eavesdropper's channel state information (CSI) is unavailable, we propose an optimal adaptive power allocation scheme, and derive a closed-form expression of the optimal power allocation factor that minimizes the secrecy outage probability (SOP). A suboptimal fixed power allocation scheme is also proposed to reduce the system complexity. Moreover, exact closed-form expressions of SOP under both schemes are derived. Numerical results are provided to corroborate the accuracy of our analytical results and the superiority of the proposed schemes to the benchmarks. [ABSTRACT FROM AUTHOR]

This paper presents an approach to the design of an optimal control strategy for plug-in hybrid electric vehicles (PHEVs) incorporating Internet of Vehicles (IoVs). The optimal strategy is designed and implemented by employing a mobile edge computing (MEC) based framework for IoVs. The thresholds in the optimal strategy can be instantaneously optimized by chaotic particle swarm optimization with sequential quadratic programming (CPSO-SQP) in the mobile edge computing units (MECUs). The vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are adopted in IoV to collect traffic information for a CPSO-SQP based optimization and transmit the optimized control commands to vehicle from MECUs. To guarantee real-time optimal performance, the communication delay in V2V and V2I is decreased via an alternative iterative optimization algorithm (AIOA) approach. The simulation results demonstrate the superior performance of the novel optimal control strategy for PHEV with 9% improvement, compared with the original strategy. • An optimal control strategy is proposed based on Internet of Vehicles. • A mobile computing framework is devised to support the power management. • Control thresholds in strategy are optimized via evolutionary computational method. • Communication delay in the strategy is mitigated by iterative optimization. [ABSTRACT FROM AUTHOR]

This paper proposes a novel control framework and the corresponding strategy for power sources management in connected plug-in hybrid electric vehicles (cPHEVs). A mobile edge computation (MEC) based control framework is developed first, evolving the conventional on-board vehicle control unit (VCU) into the hierarchically asynchronous controller that is partly located in cloud. Elaborately contrastive analysis on the performance of processing capacity, communication frequency and communication delay manifests dramatic potential of the proposed framework in sustaining development of the cooperative control strategy for cPHEVs. On the basis of MEC based control framework, a specific cooperative strategy is constructed. The novel strategy accomplishes energy flow management between different power sources with incorporation of the active energy consumption plan and adaptive energy consumption management. The method to generate the reference battery state-of-charge (SOC) trajectories in energy consumption plan stage is emphatically investigated, fast outputting reference trajectories that are tightly close to results by global optimization methods. The estimation of distribution algorithm (EDA) is employed to output reference control policies under the specific terminal conditions assigned via the machine learning based method. Finally, simulation results highlight that the novel strategy attains superior performance in real-time application that is close to the offline global optimization solutions. • A novel power sources management strategy is proposed for cPHEVs. • The mobile edge computing framework is built for the developing the strategy. • Energy consumption is properly planed by EDA in each route segment. • BP-NN and IDP are employed to actively adjust manners of energy consumption plan. [ABSTRACT FROM AUTHOR]