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We design a joint radio and computational resource allocation policy for a multi-user mobile edge computing system, such that the expected power consumption is minimized while satisfying long-term delay constraints. The problem is formulated as a constrained Markov decision process (CMDP) that is efficiently solved by the proposed constrained reinforcement learning (CRL) algorithm, called successive convex programming based policy optimization (SCPPO). SCPPO solves a convex objective/feasibility surrogate problem at each update and it can provably converge to a Karush-Kuhn-Tucker (KKT) point of the original CMDP problem almost surely under some mild conditions. Moreover, SCPPO adopts an application-specific policy architecture and employs a data-efficient estimation strategy that can reuse old experiences, such that SCPPO can realize fast learning with low computational complexity.

Batch manufacturing has attracted extensive attention as a crucial way in process industry. It has typical wide-range nonstationary characteristics subject to time variant conditions, not only within each batch but also for different batches. The presence of closed-loop control complicates the task of batch process monitoring which shows that closed-loop control can significantly alter the process characteristics with both static deviations and varying temporal correlations. It is of a considerable challenge for batch process analysis and monitoring under closed-loop control which may work quite unlike that under open-loop control. In this article, a fine-scale process modeling and monitoring method are developed with dynamics analytics for wide-range nonstationary batch manufacturing with duration uncertainty. Both static and dynamic monitoring statistics are designed with clear physical interpretation which can work together to achieve fine-scale batch process status identification and monitoring. On one hand, it can identify frequent normal switching of operation status at different time by simultaneously checking both static and dynamic deviations regulated by closed-loop control. On the other hand, it can detect small disturbances in early stage where the impact may be overshadowed by the regulation of closed-loop controllers, making faults difficult to detect in time by conventional methods. Experiments have shown the validity of the proposed fine-scale monitoring method for wide-range batch process.

Owing to the accuracy and flexibility, mobile advertising has become a very attractive marketing method based on smart mobile terminals. The more common mobile advertisement distribution methods are based on location and content. But most of them donart take into account the limited duration time of mobile users and the timeliness of advertising, which makes mobile advertisement distribution suffer from low propagation efficiency. To make up for this deficiency, we propose the advertisement platformars expected income maximization problem (EIMP), and prove its NP-hardness. As far as we know, EIMP is a combination of the 0-1 multi-dimensional and multiple knapsack problem, which is even harder. In dealing with this difficulty, we fortunately find that this problem could be transformed into a maximizing monotone submodular set function, which is subject to partition matroid constraints. Then we propose a simple but effective greedy algorithm TIMAO (Time-sensitive Mobile Advertisement Offloading) which has a constant approximation ratio of 1/3 to provide a performance guarantee for this issue. Finally, extensive evaluations are conducted. The results show that compared with the random selection method, TIMAO increases the expected income of the platform by about two times. And it achieves 99.2% of the near optimal results obtained by the CPLEX tool-box. Moreover, we propose an important metric, the duty cycle, to evaluate system performance in time domain. The results show that our scheme could increase the time duty cycle by about average 10% compared with the random selection.

The benefits of network coding on multicast in traditional multihop wireless networks have already been extensively demonstrated in previous works. However, most existing approaches cannot be directly applied to multihop cognitive radio networks (CRNs), given the unpredictable primary user occupancy on licensed channels. Specifically, due to the unpredictable occupancy, the channel’s available bandwidth is time-varying and uncertain. Accordingly, the capacity of the link using that channel is also uncertain, which can significantly affect the network coding subgraph optimization and may result in severe throughput loss if not properly handled. In this paper, we study the problem of network coding-based multicast in multihop CRNs while considering the uncertain spectrum availability. To capture the uncertainty of spectrum availability, we first formulate our problem as a chance-constrained program. Given the computational intractability of the above-mentioned program, we then transform the original problem into a tractable convex optimization problem, through appropriate Bernstein approximation with relaxation on link scheduling. We further leverage Lagrangian relaxation-based optimization techniques to propose an efficient distributed algorithm for the original problem. Extensive simulation results show that the proposed algorithm achieves higher multicast rates, compared with a state-of-the-art non-network coding algorithm in multihop CRNs, and a conservative robust network coding algorithm that treats the link capacity as a constant value in the optimization.

Secondary users (SUs) can flexibly access licensed channels to improve the spectrum utilization in cognitive radio networks (CRNs). In mobile cognitive radio ad hoc networks (MCRAHNs) where nodes can move, SUs are intermittently connected because of node mobility, as well as the uncertain channel availability. Providing multicast services in MCRAHNs is urgently needed with the compelling demand on service qualities and varieties, and the proliferation of cognitive technology. The benefits of network coding over multicast in traditional wireless networks or CRNs have been well demonstrated in existing works. However, the existing studies merely address the two crucial issues (i.e., uncertain channel availability and node mobility) together and, thereby, cannot be applied to network coding-based multicast in MCRAHNs. In this paper, we study the problem of network coding-based multicast in MCRAHNs considering both channel uncertainty and node mobility. We utilize discrete-time Markov chains to model the channel availability and node mobility in MCRAHNs, and we then formulate a spectrum-aware network coded multicast problem as an optimization problem, which minimizes the total transmission cost, subject to the timely successful delivery constraint and link transmission constraint. Since the formulated problem is hard to tackle according to mixed integer programing, we further design a distributed spectrum-aware cost-based (SACB) scheme based on the metric of forwarding benefit. With extensive simulations based on both synthetic and realistic traces, we show that, compared with existing schemes, SACB can achieve almost the minimum transmission cost while maintaining high multicast success probability.

Received Signal Strength (RSS) maps provide fundamental information for mobile users, aiding the development of conflict graph and improving communication quality to cope with the complex and unstable wireless channels. In this paper, we present CARM: a scheme that exploits crowd-sensing to construct outdoor RSS maps using smartphone measurements. An alternative yet impractical approach in literature is to appeal to professionals with customized devices. Our work distinguishes itself from previous studies by supporting off-the-shelf smartphone devices, and more importantly, by mitigating the error-prone nature and inaccuracies of these devices to build RSS maps through crowd-sensing. The main challenges are that, we need to calibrate error-prone smartphone measurements with “inaccurate” and “incomplete” data. To address these challenges, we build the measurement error model of smartphone based on the experimental observations and analyses. Moreover, we propose an iterative method based on Davidon-Fletcher-Powell (DFP) algorithm, to estimate the parameters for the error models of each smartphone and the signal propagation models of each AP simultaneously. The key intuition is that, the calibrated measurements based on the error model are constrained by the physics of the signal propagation model. Finally, a model-driven RSS map construction scheme is built upon these two models with these estimated parameters. The theoretical analyses prove the optimality and convergence of this iterative method. Also, the crowd-sensing experiments show that, CARM can achieve an accurate RSS map, decreasing the average error from 19.8 to 8.5 dBm.

Mobile crowdsensing has shown elegant capacity in data collection and has given rise to numerous applications. In the sense of coverage quality, marginal works have considered the efficient (less cost) and effective (considerable coverage) design for mobile crowdsensing networks. We investigate the optimal quality-aware coverage in mobile crowdsensing networks. The difference between ours and the conventional coverage problem is that we only select a subset of mobile users so that the coverage quality is maximized with constrained budget. To address this new problem, which is proved to be NP-hard, we first prove that the set function of coverage quality is nondecreasing submodular. By leveraging the favorable property in submodular optimization, we then propose an $(\text{1}-(\text{1}/e))$ approximation algorithm with $O(n^{k+2})$ time complexity, where $k$ is an integer that is greater than or equal to 3. Finally, we conduct extensive simulations for the proposed scheme, and the results demonstrate that ours outperforms the random selection scheme and one of the state of the art in terms of total coverage quality by, at most, 2.4× and 1.5× and by, on average, 1.4× and 1.3×, respectively. Additionally, ours achieves a near-optimal solution, compared with the brute-force search results.

Mobile crowdsensing networks have emerged to show elegant data collection capability in loosely cooperative network. However, in the sense of coverage quality, marginal works have considered the efficient (less participants) and effective (more coverage) designs for mobile crowdsensing network. We investigate the optimal coverage problem in distributed crowdsensing networks. In that, the sensing quality and the information delivery are jointly considered. Different from the conventional coverage problem, ours only select a subset of mobile users, so as to maximize the crowdsensing coverage with limited budget. We formulate our concerns as an optimal crowdsensing coverage problem, and prove its NP-completeness. In tackling this difficulty, we also prove the submodular property in our problem. Leveraging the favorable property in submodular optimization, we present the greedy algorithm with approximation ratio $O(\sqrt {k})$ , where $k$ is the number of selected users. Such that the information delivery and sensing coverage ratio could be guaranteed. Finally, we make extensive evaluations for the proposed scheme, with trace-driven tests. Evaluation results show that the proposed scheme could outperform the random selection by $2 \times $ with a random walk model, and over $3 \times $ with real trace data, in terms of crowdsensing coverage. Besides, the proposed scheme achieves near optimal solution comparing with the brute-force search results.

Feature and metric designing are two vital aspects in person re-identification. In this letter, we firstly propose a novel spatiogram based person descriptor. Such spatiograms of different image regions from several color channels are calculated and accumulated to create a histogram vector and two distinctive spatial statistical vectors. Secondly, through further investigating the multi-shot set-based metric based on the recent collaborative representation model, we propose an effective and efficient multi-shot metric, which fuses the residual and coding coefficients after collaboratively coding samples on all person classes. Finally, we evaluate the proposed descriptor and metric with other published methods on benchmark datasets. Our methods not only achieve state-of-the-art results but also are novel, straightforward and computationally efficient, which will facilitate the real-time surveillance applications such as pedestrian tracking.

We have designed, fabricated, and characterized a self-closed parylene cuff electrode for peripheral nerve recording, whose curliness and closeness is realized by the difference in self-stress of different parylene layers due to the heat treatments. Compared with the conventional cuff electrodes, the self-closed structure without any additional mechanical locking structure minimizes the mechanical structure of the cuff electrode. Moreover, the self-closed structure made of thin and flexible parylene film minimizes mechanical damage to the nerve and the surrounding tissues. The zero insertion force interface facilitates the connection of 16 channels with external circuits through the integrated parylene bonding pads. To reduce the impedance and increase the charge storage capacity of the electrode, the electrodeposited iridium oxide film is electrodeposited on the electrode sites. Using the fabricated self-closed parylene cuff electrode, acute neural recoding of 16 channels was performed on the rat sciatic nerve to verify the capability of recording the neural activities. The present self-closed parylene cuff electrode is promising to be used as an neural interface for peripheral nerve recording. [2014-0100]

With widespread usages of smart phones, participatory sensing becomes mainstream, especially for applications requiring pervasive deployments with massive sensors. However, the sensors on smart phones are prone to the unknown measurement errors, requiring automatic calibration among uncooperative participants. Current methods need either collaboration or explicit calibration process. However, due to the uncooperative and uncontrollable nature of the participants, these methods fail to calibrate sensor nodes effectively. We investigate sensor calibration in monitoring pollution sources, without explicit calibration process in uncooperative environment. We leverage the opportunity in sensing diversity, where a participant will sense multiple pollution sources when roaming in the area. Further, inspired by expectation maximization (EM) method, we propose a two-level iterative algorithm to estimate the source presences, source parameters and sensor noise iteratively. The key insight is that, only based on the participatory observations, we can “calibrate sensors without explicit or cooperative calibrating process”. Theoretical analysis proves that, our method can converge to the optimal estimation of sensor noise, where the likelihood of observations is maximized. Also, extensive simulations show that, ours improves the estimation accuracy of sensor bias up to 20 percent and that of sensor noise deviation up to 30 percent, compared with three baseline methods.

Biodegradable junctionless transistors with extremely simple structure are fabricated at room temperature. The free-standing sodium alginate membrane is used as both a substrate and a dielectric layer for the transistors. The source/drain electrodes and the channel region are made of one single patterned Al:ZnO (AZO) thin film. The proposed transistors can be operated at a low voltage of 1 V. Dissolution tests of those transistors in deionized water demonstrate their completely physical transience within 60 min. The operation mode of such transistors can effectively be tuned from the depletion mode to the enhancement mode by varying the thickness of the AZO film. Moreover, a resistor-loaded invertor was demonstrated by connecting those transistors in series with a 3 $\text{M}\Omega $ resistor.

In this letter, we report a novel transient/ biodegradable transistor. The Al:ZnO (AZO) source, drain, and gate electrodes are directly self-assembled on the free-standing sodium alginate (SA) membrane by magnetron sputtering, and the SA membrane is used simultaneously as the substrates and dielectrics for the transistor. Due to the strong lateral electrostatic coupling effects of SA membrane, the transistor can operate at a low voltage of 1 V. Dissolution tests of the transistor in deionized water suggest its completely physical transience within 1 h.

With the increasing ubiquitous usage of the smart phone, participatory sensing networks become applicable for urban sensing applications. Notably, in pollution monitoring applications, untrained participants and heterogeneously unreliable sensors bring much noise into the reported data. Status quo researches fail to identify truthful pollution sources when the parameters are not known. In this paper, we cluster the reported data and make parameter estimations of the pollution sources. Inspired by the expectation maximization method, we estimate the existence of the pollution sources and leverage these results for noise estimations iteratively. The key insight is that, we can identify the truthful pollution sources with the noisy data mitigation iteratively. Further, theoretical analysis also shows that, we can achieve optimal estimation in the sense of maximum likelihood (ML). Simulation results show that, compared with basic ML algorithm, we improve the false positive and false negative of identification by 99% and 82%, respectively, at the same time, the mean error and the deviation error of noise estimation by 49% and 70%, respectively.