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The evolution of Big Data in large-scale Internet-of-Vehicles has brought forward unprecedented opportunities for a unified management of the transportation sector, and for devising smart Intelligent Transportation Systems. Nevertheless, such form of frequent heterogeneous data collection between the vehicles and numerous applications platforms via diverse radio access technologies has led to a number of security and privacy attacks, and accordingly demands for a secure data collection in such architectures. In this respect, this chapter is primarily an effort to highlight the said challenge to the readers, and to subsequently propose some security requirements and a basic system model for secure Big Data collection in Internet-of-Vehicles. Open research challenges and future directions have also been deliberated.

Over the past decade, most factories across developed parts of the world employ a varying amount of the manufacturing technologies including autonomous robots, RFID (radio frequency identification) technology, NCs (numerically controlled machines), wireless sensor networks embedded with specialized computerized softwares for sophisticated product designs, engineering analysis, and remote control of machinery, etc. The ultimate aim of these all dramatic developments in manufacturing sector is thus to achieve aspects such as shorter innovation / product life cycles and raising overall productivity via efficiently handling complex interactions among the various stages (functions, departments) of a production line. The notion, Factory of the Future, is an unpredictable heaven of efficaciousness, wherein, issues such as the flaws and downtime would be issues of the long forgotten age. This technical note thus provides an overview of this awesome revolution waiting to be soon realized in the manufacturing sector.

Wireless communication technology has seen a remarkably fast evolution due to its capability to provide a quality, reliable and high-speed data transmission amongst the users. However, transmission of information in wireless channels is primarily impaired by deleterious multipath fading, which affects the quality and reliability of the system. In order to overcome the detrimental effects of fading, Multiple-Input Multiple-Output (MIMO) technology is an attractive scheme that employs multiple transceiver antennas to carry the data over the same frequency band over a variety of signal paths. This technology has shown great solutions due to its ability to provide better spectral efficiency, capacity, throughput and robustness of the data transmission. But in practice, it is impractical to install multiple antennas on small-sized devices. Hence, to overcome the limitations of MIMO gain in the future wireless networks, cooperative diversity has recently draw in attention due to its ability to circumvent the difficulties of implementing actual antenna arrays in Multiple-Input and Multiple-Output (MIMO). By exploiting the broadcast feature of the wireless medium, cooperation among multiple nearby nodes is formed for data transmission. At the receiver, the signals are either coherently or differentially detected. Coherent detection requires exact channel estimation, which is difficult to apply in a time-varying channel. Hence, when the nodes are mobile, or when the channel is inaccurately estimated, the differential detection techniques that omit channel estimation become an alternative as compared to coherent detection. This article presents a review of the differential transmission techniques for cooperative diversity networks.