Wireless industrial monitoring and control networks: the journey so far and the road ahead

While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks

DMT Optimal Cooperative Protocols with Destination-Based Selection of the Best Relay

We design a cooperative protocol in the context of wireless mesh networks in order to increase the reliability of wireless links. Destination terminals ask for cooperation when they fail in decoding data frames transmitted by source terminals. In that case, each destination terminal D calls a specific relay terminal B with a signaling frame to help its transmission with source terminal S. To select appropriate relays, destination terminals maintain tables of relay terminals, one for each possible source address. These tables are constituted by passively overhearing ongoing transmissions. Hence, when cooperation is needed between S and D, and when a relay B is found by terminal D in the relay table associated with terminal S, the destination terminal sends a negative acknowledgment frame that contains the address of B. When the best relay B has successfully decoded the source message, it sends a copy of the data frame to D using a selective decode-andforward transmission scheme. The on-demand approach allows maximization of the spatial multiplexing gain and the cooperation of the best relay allows maximization of the spatial diversity order. Hence, the proposed protocol achieves optimal diversitymultiplexing trade-off performance. Moreover, this performance is achieved through a collision-free selection process

A Survey of Low Duty Cycle MAC Protocols in Wireless Sensor Networks

22 page(s

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial