A Distributed Query Processing Engine

Wireless sensor networks (WSNs) are formed of tiny, highly energy-constrained sensor nodes that are equipped with wireless transceivers. They may be mobile and are usually deployed in large numbers in unfamiliar environments. The nodes communicate with one another by autonomously creating ad-hoc networks which are subsequently used to gather sensor data. WSNs also process the data within the network itself and only forward the result to the requesting node. This is referred to as in-network data aggregation and results in the substantial reduction of the amount of data that needs to be transmitted by any single node in the network. In this paper we present a framework for a distributed query processing engine (DQPE) which would allow sensor nodes to examine incoming queries and autonomously perform query optimisation using information available locally. Such qualities make a WSN the perfect tool to carryout environmental\ud monitoring in future planetary exploration missions in a reliable and cost effective manner

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

A Low-Latency, Information-Centric Medium Access Protocol for Wireless Sensor Networks

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, 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 traf��?c patterns in the network. This information is subsequently used by AI-LMAC to modify its operation accordingly. We present preliminary results showing how the MAC protocol ef��?ciently manages the issues of fairness, latency and message buffer management

D-SAR: A Distributed Scheduling Algorithm for Real-time, Closed-Loop Control in 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 techniques for communication scheduling and route establishment. However, large-scale centralized systems can have several drawbacks. They have difficulty in coping with disturbances or changes within the network in real-time. Large-scale centralized systems can also have highly variable latencies thus making them unsuitable for closed-loop control applications. To address these problems, this paper describes D-SAR, a distributed resource reservation algorithm which would allow source nodes to meet the Quality-of-Service (QoS) requirements of the application in real-time, when carrying out peer-to-peer communication. The presented solution uses concepts derived from relevant networking-related domains such as circuit switching and Asynchronous Transfer Mode (ATM) networks and applies them to wireless sensor and actuator 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