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.

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Wireless sensor networks (WSNs) are increasingly being used to monitor various parameters in a wide range of environmental monitoring applications. In many instances, environmental scientists are interested in collecting raw data using long-running queries injected into a WSN for analyzing at a later stage, rather than injecting snap-shot queries containing data-reducing operators (e.g., MIN, MAX, AVG) that aggregate data. Collection of raw data poses a challenge to WSNs as very large amounts of data need to be transported through the network. This not only leads to high levels of energy consumption and thus diminished network lifetime but also results in poor data quality as much of the data may be lost due to the limited bandwidth of present-day sensor nodes. We alleviate this problem by allowing certain nodes in the network to aggregate data by taking advantage of spatial and temporal correlations of various physical parameters and thus eliminating the transmission of redundant data. In this article we present a distributed scheduling algorithm that decides when a particular node should perform this novel type of aggregation. The scheduling algorithm autonomously reassigns schedules when changes in network topology, due to failing or newly added nodes, are detected. Such changes in topology are detected using cross-layer information from the underlying MAC layer. We first present the theoretical performance bounds of our algorithm. We then present simulation results, which indicate a reduction in message transmissions of up to 85% and an increase in network lifetime of up to 92% when compared to collecting raw data. Our algorithm is also capable of completely eliminating dropped messages caused by buffer overflow.

In this paper we present a novel TDMA-based medium access control (MAC) protocol for wireless sensor networks. Unlike conventional MAC protocols which function independently of the application, we introduce an Adaptive, Informationcentric and Lightweight MAC(AI-LMAC) protocol that adapts its operation depending on the requirements of the application. We also present a completely localised data management framework that helps capture information about traffic patterns in the network. This information is subsequently used by AI-LMAC to modify its operation accordingly. The data management framework can additionally be used for efficient query dissemination and query optimisation. We present preliminary results showing how the MAC protocol efficiently manages the issues of fairness and latency.

The surface marker profile of mesenchymal stromal cells (MSCs) suggests that they can escape detection by the immune system of an allogeneic host. This could be an optimal strategy for bone regeneration applications, where off-the-shelf cells could be implanted to heal bone defects. However, it is unknown how pre-differentiation of MSCs to an osteogenic lineage, a means of improving bone formation, affects their immunogenicity. Using immunohistological techniques in a rat ectopic implantation model, we demonstrate that allogeneic osteoprogenitors mount a T cell- and B cell-mediated immune response resulting in an absence of in vivo bone formation. Suppression of the host immune response with daily administration of an immunosuppressant, FK506, is effective in preventing the immune attack on the allogeneic osteoprogenitors. In the immunosuppressed environment, the allogeneic osteoprogenitors are capable of generating bone in amounts similar to those of syngeneic cells. However, using osteoprogenitors from one of the allogeneic donors led to newly deposited bone that was attacked by the host immune system, despite the continued administration of the immunosuppressant. This suggests that, although using an immunosuppressant can potentially suppress the immune attack on the allogeneic cells, optimizing the dose of the immunosuppressant may be crucial to ensure bone formation within the allogeneic environment. Overall, allografts comprising osteoprogenitors derived from allogeneic MSCs have the potential to be used in bone regeneration applications.