Crowdsensing systems collect large-scale sensor data from mobile devices to provide a wide-area view of phenomena including traffic, noise and air pollution. Because such data often exhibits sparse structure, it is natural to apply compressive sensing (CS) for data sampling and recovery. However in practice, crowd participants are often distributed highly unevenly across the sensing area, and thus the numbers of observations collected over different areas may vary wildly - an issue we call density disparity. Density disparity leads to inaccuracy in low density areas, and potentially undermines the recovery performance if conventional compressive sensing is applied directly, which equally treats data from areas of different density.

Mobile crowdsourcing is a powerful tool for collecting data of various types. The primary bottleneck in such systems is the high burden placed on the user who must manually collect sensor data or respond in-situ to simple queries (e.g., experience sampling studies). In this work, we present Compressive CrowdSensing (CCS) -- a framework that enables compressive sensing techniques to be applied to mobile crowdsourcing scenarios. CCS enables each user to provide significantly reduced amounts of manually collected data, while still maintaining acceptable levels of overall accuracy for the target crowd-based system. Naïve applications of compressive sensing do not work well for common types of crowdsourcing data (e.g., user survey responses) because the necessary correlations that are exploited by a sparsifying base are hidden and non-trivial to identify. CCS comprises a series of novel techniques that enable such challenges to be overcome. We evaluate CCS with four representative large-scale datasets and find that it is able to outperform standard uses of compressive sensing, as well as conventional approaches to lowering the quantity of user data needed by crowd systems.

Compressive Sensing is a technique that can help reduce the sampling rate of sensing tasks. In mobile crowdsensing applications or wireless sensor networks, the resource burden of collecting samples is often a major concern. Therefore, compressive sensing is a promising approach in such scenarios. An implicit assumption underlying compressive sensing -- both in theory and its applications -- is that every sample has the same cost: its goal is to simply reduce the number of samples while achieving a good recovery accuracy. In many networked sensing systems, however, the cost of obtaining a specific sample may depend highly on the location, time, condition of the device, and many other factors of the sample.

In this paper, we investigate the power of online learning in stochastic network optimization with unknown system statistics a priori. We are interested in understanding how information and learning can be efficiently incorporated into system control techniques, and what are the fundamental benefits of doing so. We propose two Online Learning-Aided Control techniques, OLAC and OLAC2, that explicitly utilize the past system information in current system control via a learning procedure called dual learning. We prove strong performance guarantees of the proposed algorithms: OLAC and OLAC2 achieve the near-optimal [O(ε), O([log(1/ε)]2)] utility-delay tradeoff and OLAC2 possesses an O(ε-2/3) convergence time. Simulation results also confirm the superior performance of the proposed algorithms in practice. To the best of our knowledge, OLAC and OLAC2 are the first algorithms that simultaneously possess explicit near-optimal delay guarantee and sub-linear convergence time, and our attempt is the first to explicitly incorporate online learning into stochastic network optimization and to demonstrate its power in both theory and practice.

This article presents a framework for deploying a minimal number of smart meters to accurately track the ON/OFF states of a massive number of electrical appliances which exploits the sparseness feature of simultaneous ON/OFF switching events of the massive appliances. A theoretical bound on the least number of required smart meters is studied by an entropy-based approach, which qualifies the impact of meter deployment strategies to the state tracking accuracy. It motivates a meter deployment optimization algorithm (MDOP) to minimize the number of meters while satisfying given requirements to state tracking accuracy. To accurately decode the real-time ON/OFF states of appliances by the readings of meters, a fast state decoding (FSD) algorithm based on the hidden Markov model (HMM) is presented to track the state sequence of each appliance for better accuracy. Although traditional HMM needs O ( t 2 2 N ) time complexity to conduct online sequence decoding, FSD improves the complexity to O ( tn U+1 ), where n < N and U is an upper bound of the simultaneous switching events. Both MDOP and FSD are verified extensively using simulations and real PowerNet data. The results show that the meter deployment cost can be saved by more than 80% while still getting over 90% state tracking accuracy.

With our hope of having one world standard for fieldbus being dashed to the ground, new technology of OPC data exchange (DX) and industrial Ethernet emerge, as the times require. The advantages of Ethernet, such as great bandwidth, resourceful hardware and software, potential of durative development and especially based on open IEEE Std 802.3, are porting the traditional fieldbus architectures to industrial Ethernet. Finding industrial Ethernet's proper place among the fieldbus and its limitation using Ethernet to the manufactory floor is analyzed in detail. OPC DX, defining a set of interfaces that can be used to remotely configure and manage the connections maintained by each OPC DX server, provides application interoperability between disparate fieldbuses. By analyzing an application example in process control using both industrial Ethernet and OPC DX in detail, this paper presents that OPC DX and industrial Ethernet would complement each other rather than replace, and they would exit in parallel for a long time. With the development of OPC DX and industrial Ethernet, they must bring an end to the dispute among fieldbus.