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Mobile crowdsensing (MCS) has now become an effective paradigm to collect massive data for various sensing applications. However, the interactions between mobile users and the platform, and the data release to third parties, pose severe challenges of privacy leakage for MCS systems, such as the leakage of users' identities and locations. Although several works on MCS have explored the privacy issues in task allocation, incentive, and data reporting, there is still a lack of a comprehensive privacy preserving framework for MCS to protect the privacy of users throughout users' involvement in crowdsensing tasks. In this article, we divide the life cycle of each crowdsensing task in MCS into four phases: task allocation, incentive, data collection, and data publishing, and design a privacy-preserving framework for MCS to protect users' privacy in the whole life cycle of MCS. [ABSTRACT FROM AUTHOR]

Advances in software defined radio (SDR) technology allow unprecedented control on the entire processing chain, allowing modification of each functional block as well as sampling the changes in the input waveform. This article describes a method for uniquely identifying a specific radio among nominally similar devices using a combination of SDR sensing capability and machine learning (ML) techniques. The key benefit of this approach is that ML operates on raw I/Q samples and distinguishes devices using only the transmitter hardware-induced signal modifications that serve as a unique signature for a particular device. No higher-level decoding, feature engineering, or protocol knowledge is needed, further mitigating challenges of ID spoofing and coexistence of multiple protocols in a shared spectrum. The contributions of the article are as follows: (i) The operational blocks in a typical wireless communications processing chain are modified in a simulation study to demonstrate RF impairments, which we exploit. (ii) Using an overthe- air dataset compiled from an experimental testbed of SDRs, an optimized deep convolutional neural network architecture is proposed, and results are quantitatively compared with alternate techniques such as support vector machines and logistic regression. (iii) Research challenges for increasing the robustness of the approach, as well as the parallel processing needs for efficient training, are described. Our work demonstrates up to 90-99 percent experimental accuracy at transmitter- receiver distances varying between 2-50 ft over a noisy, multi-path wireless channel. [ABSTRACT FROM AUTHOR]

With the development of mCRAHNs, more emerging mobile applications and services could be foreseen and applied in our human society. In this article, we propose an emotion-aware cognitive system (ECS), which aims to provide a systematic approach that can facilitate deployment of different emotion-aware mobile applications in mCRAHNs. ECS is constructed to provide multidimensional emotional data collection and processing approaches for mobile applications working in mCRAHNs. Also, we present a flow of emotion-aware solution designed on ECS, and highlight the implementation and orchestration of several key technologies and schemes we apply in this system for different emotion-aware mobile applications in runtime. In addition, we demonstrate the feasibility of the proposed ECS by presenting a novel emotion-aware mobile application, iSmile, and evaluate the system performance based on this application. [ABSTRACT FROM AUTHOR]

The article focuses on the use of distributed communication architecture in implementing a cost-effective smart grid applications. Topics discussed include the importance of data collection in smart grids, the role of distributed operation centers in delivering and utilizing meter data management system (MDMS), and benefits of distributed architecture in preventing power outages.

As one of the fastest growing technologies, machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity. M2M devices can be used for a wide range of emerging applications that have various communications requirements. While M2M communications have been developed for many years, major challenges still remain with their efficient implementation from the perspective of low energy consumption and wide coverage. To address these challenges, low power wide area (LPWA) technology is investigated as one of the potential candidate solutions. In this article, we first introduce some typical LPWA M2M application scenarios. Given their requirements, we highlight key techniques and standards that are explicitly designed for LPWA M2M communications. Finally, we present an LPWA prototype system to evaluate its performance and demonstrate its potential in bridging a technological gap for future Internet-of-Things (IoT) applications. [ABSTRACT FROM PUBLISHER]

Mobile crowd sensing (MCS) presents a new sensing paradigm that empowers ordinary citizens to contribute data sensed or generated from their mobile devices (e.g., smartphones, wearable devices, smart vehicles), and aggregates and fuses the data in the cloud for crowd intelligence extraction and human-centric service delivery. MCS benefits a number of application areas regarding urban/community dynamics monitoring, environment monitoring, traffic planning, public safety, and beyond. At the same time, numerous and unique research challenges, such as participatory data collection, optimal sensing node selection, proper incentive mechanisms, transient network communication, data quality/trust maintenance, cross-space data processing, and so on, arise from the MCS paradigm. [ABSTRACT FROM AUTHOR]