Highly luminescent metal–organic frameworks (LMOFs) have received great attention for their potential use in energy-efficient general lighting devices such as white-light-emitting diodes (WLEDs); however, achieving strong emission with controllable color, especially high-quality white light, remains a considerable challenge. Herein, we present a new strategy to encapsulate in situ multiple dyes into nanocrystalline ZIF-8 pores to form an efficient dyes@MOF system. Using this strategy, we build three models, namely, multiphase single-shell dye@ZIF-8, single-phase single-shell dyes@ZIF-8, and single-phase multishell dyes@ZIF-8, to systematically and fine-tune the white emission color by varying the components and concentration of encapsulated dyes. The study of these three models demonstrates the importance of the multishell structure, which can effectively reduce the interactions such as Förster resonance energy transfer (FRET) between encapsulated dyes. This energy transfer would otherwise be unavoidable in a single-shell setting, which often reduces the efficiency of white-light emission in the dyes@MOF system. This approach offers a new perspective not only for fine-tuning the emission color within nanoporous dyes@MOFs but also for fabricating MOF nanocrystals that are easily solution-processable. The strategy may also facilitate the development of other types of MOF–guest nanocomposite systems.

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatiotemporal dependency even among distant cell towers, which is largely overlooked in previous works. To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction. Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference.

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Motivated by the observation that channel assignment for multiradio multi-channel mesh networks should support both unicast and local broadcast 1 , should be interference-aware, and should result in low overall switching delay, high throughput, and low overhead, we propose two flexible localized channel assignment algorithms based on s-disjunct superimposed codes. These algorithms support the local broadcast and unicast effectively, and achieve interference-free channel assignment under certain conditions. In addition, under the primary interference constraints 2 , the channel assignment algorithm for unicast can achieve 100% throughput with a simple scheduling algorithm such as the maximal weight independent set scheduling, and can completely avoid hidden/exposed terminal problems under certain conditions. Our algorithms make no assumptions on the underlying network and therefore are applicable to a wide range of MR-MC mesh network settings. We conduct extensive theoretical performance analysis to verify our design.

A central problem in sensor network security is that sensors are susceptible to physical capture attacks. Once a sensor is compromised, the adversary can easily launch clone attacks by replicating the compromised node, distributing the clones throughout the network, and starting a variety of insider attacks. Previous works against clone attacks suffer from either a high communication/storage overhead or a poor detection accuracy. In this paper, we propose a novel scheme for detecting clone attacks in sensor networks, which computes for each sensor a social fingerprint by extracting the neighborhood characteristics, and verifies the legitimacy of the originator for each message by checking the enclosed fingerprint. The fingerprint generation is based on the superimposed s-disjunct code, which incurs a very light communication and computation overhead. The fingerprint verification is conducted at both the base station and the neighboring sensors, which ensures a high detection probability. The security and performance analysis indicate that our algorithm can identify clone attacks with a high detection probability at the cost of a low computation/communication/storage overhead. To our best knowledge, our scheme is the first to provide realtime detection of clone attacks in an effective and efficient way.