Environment preservation has become a prominent issue around the world. As traditional internal combustion engine (ICE) vehicles have been major contributors of air pollution, electric vehicles (EVs) are gaining popularity. However, due to the limited electricity supply of battery pack, EVs need to be charged frequently and each charge takes long time. This may degrade travel efficiency and driver comfort. To address this issue, this paper aims to minimize charging waiting time through intelligently scheduling charging activities spatially and temporally. A theoretical study has been conducted to formulate the waiting time minimized charging scheduling problem and derive a performance upper bound (i.e., the theoretical lower bound of charging waiting time). Based on the insights discovered from the theoretical analysis, a practical distributed scheme has been proposed. Extensive simulation results verify that the proposed design can achieve a waiting time near the theoretical lower bound.

Abstract-WiFi transmission can consume much energy on energy-constrained mobile devices. To improve energy efficiency, the Power Saving Management (PSM) has been standardized and applied. The standard PSM, however, may not deliver satisfactory energy efficiency in many cases as the wakeup strategy adopted by it cannot adapt dynamically to traffic pattern changes. Motivated by the fact that it has been more and more popular for a mobile device to have both WiFi and other low-power wireless interfaces such as Bluetooth and ZigBee, we propose a ZigBee-assisted Power Saving Management (ZPSM) scheme, leveraging the ZigBee interface to wake up WiFi interface on demand to improve energy efficiency without violating delay requirements. The simulation results have shown that ZPSM can save energy significantly without violating delay requirements in various scenarios.Index Terms-WiFi, ZigBee, power saving management, energy efficiency, delay bound.

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High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein–protein interaction (PPI) networks. In this study, based on penalized matrix decomposition (PMD), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMDpc) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMDpc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR).

The price-based demand response has been considered one of the most effective ways to reduce the peak demand of power grids. However, it is possible to form a new rebound critical peak to threaten the grid stability and economic operation when large-scale loads respond to the price signal simultaneously. Based on the Internet of Things (IoT), this paper proposes a cloud-edge coordination (CEC) automatic control strategy to enable interaction and cooperation among the power grid and massive individual air conditioners (ACs) and eliminate the grid rebound critical peak of the synchro-response. Edge computing actively provides optional cooperation electricity plans, migrates computationally intensive tasks from the cloud and guarantees the privacy of users. Considering the unreliability of network transmission, data packet dropouts (/or delays and even downtime) are inevitable and usually random in the transmission, and a dual-feedback closed-loop control is first proposed in this paper. Finally, the effectiveness of the optimized closed-loop control strategy is verified by simulation cases.