Supriyo Chatterjea

AI-LMAC: an adaptive, information-centric and lightweight MAC protocol for wireless sensor networks

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, information-centric 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. 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.

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.

A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks

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.

Suppression of the immune system as a critical step for bone formation from allogeneic osteoprogenitors implanted in rats

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.

A distributed scheduling algorithm for real-time (D-SAR) industrial wireless sensor and actuator networks

Current wireless standards and protocols for industrial applications, such as WirelessHART and ISA100.11a, typically use centralized network management for communication scheduling and route establishment. However, due to their centralized nature, these protocols have difficulty coping with dynamic large-scale networks. To address this problem, we propose D-SAR, a distributed resource reservation algorithm that allows source nodes to meet the Quality-of-Service requirements for peer-to-peer communication. D-SAR uses concepts derived from circuit switching and Asynchronous Transfer Mode (ATM) networks and applies them to wireless sensor and actuator networks. Simulations show that latency in connection setup is 93% less in D-SAR compared to WirelessHART and that 89% fewer messages are sent during connection setup in case the distance from source to destination is 12 hops.

Energy-efficient data acquisition using a distributed and self-organizing scheduling algorithm for wireless sensor networks

Wireless sensor networks are often densely deployed for environmental monitoring applications. Collecting raw data from these networks can lead to excessive energy consumption. Thus using the spatial and temporal correlations that exist between adjacent nodes we appoint a few as representative nodes that perform in-network aggregation. This reduces the total number of transmissions. Our distributed scheduling algorithm autonomously assigns a particular node to perform aggregation and reassigns schedules when network topology changes. These topology changes are detected using cross-layer information from the underlying MAC layer. We also present theoretical performance estimates and upper bounds of our algorithm and evaluate it by implementing the algorithm on actual sensor nodes, demonstrating an energy-saving of up to 80% compared to raw data collection.

A Taxonomy of Distributed Query Management Techniques for Wireless Sensor Networks

the not too distant future, wireless sensor networks are envisioned to proliferate through the entire spectrum of the environmental monitoring market allowing users to monitor a multitude of environments. Thousands or even millions of sensor nodes may span vast geographical areas enabling various environmental parameters to be monitored with significantly higher spatial and temporal resolutions than what is achievable using existing monitoring technologies. In order to manage the large amount of data that will be generated by these numerous sensor nodes, novel querying methods are needed to extract the required information in an energy-efficient manner. This paper studies the techniques used to manage the queries in a distributed manner and classifies the current state-of-the-art in this field into four main categories: in-network processing, acquisitional query processing, cross-layer optimization and data-centric data/query dissemination. This taxonomy not only illustrates how query management techniques have advanced over the recent past, but also allows researchers to identify the relevant features when designing sensor networks for different applications.

Improving Temporal Coverage of an Energy-Efficient Data Extraction Algorithm for Environmental Monitoring Using Wireless Sensor Networks

Collecting raw data from a wireless sensor network for environmental monitoring applications can be a difficult task due to the high energy consumption involved. This is especially difficult when the application requires specialized sensors that have very high energy consumption, e.g. hydrological sensors for monitoring marine environments. This paper introduces a technique for reducing energy consumption by minimizing sensor sampling operations. In addition, we illustrate how a randomized algorithm can be used to improve temporal coverage such that the time between the occurrence of an event and its detection can be minimized. We evaluate our approach using real data collected from a sensor network deployment on the Great Barrier Reef.

Experiences with Implementing a Distributed and Self-Organizing Scheduling Algorithm for Energy-Efficient Data Gathering on a Real-Life Sensor Network Platform

We report our experiences with implementing a distributed and self-organizing scheduling algorithm designed for energy-efficient data gathering on a 25-node multihop wireless sensor network (WSN). The algorithm takes advantage of spatial correlations that exist in readings of adjacent sensor nodes and utilizes cross-layer information from the underlying MAC layer to minimize message transmissions. We describe how we modify our experiments in order to meet the assumptions made in the earlier theoretical analysis of the algorithm. The implementation results which are virtually identical to the preliminary simulation results, show that the algorithm achieves up to 80% energy savings when compared to conventional raw data collection.

An adaptive directed query dissemination scheme for wireless sensor networks

This paper describes a directed query dissemination scheme, DirQ that routes queries to the appropriate source nodes based on both constant and dynamic-valued attributes such as sensor types and sensor values. Unlike certain other query dissemination schemes, location information is not essential for the operation of DirQ. DirQ uses only locally available information in order to route queries accurately. Nodes running DirQ are able to adapt autonomously to changes in network topology due to certain cross-layer features that allow it to exchange information with the underlying MAC protocol. DirQ allows nodes to autonomously control the rate of sending update messages in order to keep the routing information updated. The rate of sending updates is dependent on both the number of queries injected into the network and the rate of variation of the measured physical parameter. Our results show that DirQ spends between 45% and 55% the cost of flooding