On-Demand Information Retrieval in Sensor Networks with Localised Query and Energy-Balanced Data Collection

On-demand information retrieval enables users to query and collect up-to-date sensing information from sensor nodes. Since high energy efficiency is required in a sensor network, it is desirable to disseminate query messages with small traffic overhead and to collect sensing data with low energy consumption. However, on-demand query messages are generally forwarded to sensor nodes in network-wide broadcasts, which create large traffic overhead. In addition, since on-demand information retrieval may introduce intermittent and spatial data collections, the construction and maintenance of conventional aggregation structures such as clusters and chains will be at high cost. In this paper, we propose an on-demand information retrieval approach that exploits the name resolution of data queries according to the attribute and location of each sensor node. The proposed approach localises each query dissemination and enable localised data collection with maximised aggregation. To illustrate the effectiveness of the proposed approach, an analytical model that describes the criteria of sink proxy selection is provided. The evaluation results reveal that the proposed scheme significantly reduces energy consumption and improves the balance of energy consumption among sensor nodes by alleviating heavy traffic near the sink

Real-time Query Scheduling for Wireless Sensor Networks

Recent years have seen the emergence of wireless sensor network (WSN) systems that require high data rate real-time communication. This paper proposes Real-Time Query Scheduling (RTQS), a novel approach to conflict-free transmission scheduling for real-time queries in WSNs. We show that there is an inherent trade-off between prioritization and throughput in conflict-free query scheduling. RTQS provides three new real-time scheduling algorithms. The non-preemptive query scheduling algorithm achieves high throughput while introducing priority inversions. The preemptive query scheduling algorithm eliminates priority inversion at the cost of reduced throughput. The slack stealing query scheduling algorithm combines the benefits of preemptive and non-preemptive scheduling by improving the throughput while meeting query deadlines. We provide schedulability analysis for each scheduling algorithm. The analysis and advantages of our scheduling algorithms are validated through NS2 simulations

Quality of Information in Wireless Sensor Networks: A Survey 1 (Completed paper)

Abstract: In Wireless Sensor Networks (WSNs) the operating conditions and/or user requirements are often desired to be evolvable, whether driven by changes of the monitored parameters or WSN properties of configuration, structure, communication capacities, node density, and energy among many others. While considering evolvability, delivering the required information with the specified quality (accuracy, timeliness, reliability etc) defined by the user constitutes a key objective of WSNs. Most existing research efforts handle fluctuations of operation conditions in order to deliver information with the highest possible specified quality. In this paper, we take these aspects into consideration and survey existing work on Quality of Information (QoI). As a contribution, we categorize WSN information into a set of abstract classes for generality across varied application types. Our survey shows that currently QoI is usually addressed in isolation by focusing on discrete data processing operations/building blocks such as raw data collection, in-network processing (compression, aggregation), information transport and sink operations for decision making. This survey comprehensively explains the different views of QoI on attributes, metrics and WSN functional operations mapped with existing approaches. The survey also forms the basis for specifying needed QoI research issues

Data gathering tours in sensor networks

A basic task in sensor networks is to interactively gather data from a subset of the sensor nodes. When data needs to be gathered from a selected set of nodes in the network, existing communication schemes often behave poorly. In this paper, we study the algorithmic challenges in efficiently routing a fixed-size packet through a small number of nodes in a sensor network, picking up data as the query is routed. We show that computing the optimal routing scheme to visit a specific set of nodes is NP-complete, but we develop approximation algorithms that produce plans with costs within a constant factor of the optimum. We enhance the robustness of our initial approach to accommodate the practical issues of limited-sized packets as well as network link and node failures, and examine how different approaches behave with dynamic changes in the network topology. Our theoretical results are validated via an implementation of our algorithms on the TinyOS platform and a controlled simulation study using Matlab and TOSSIM

Data gathering tours in sensor networks

Copyright c ○ 2005 b

ABSTRACT Data Gathering Tours in Sensor Networks

A basic task in sensor networks is to interactively gather data from a subset of the sensor nodes. When data needs to be gathered from a selected set of nodes in the network, existing communication schemes often behave poorly. In this paper, we study the algorithmic challenges in efficiently routing a fixed-size packet through a small number of nodes in a sensor network, picking up data as the query is routed. We show that computing the optimal routing scheme to visit a specific set of nodes is NP-complete, but we develop approximation algorithms that produce plans with costs within a constant factor of the optimum. We then enhance the robustness of our initial approach to accommodate the practical issues of limited-sized packets as well as network link and node failures, and examine how different approaches behave with dynamic changes in the network topology. Our theoretical results are validated via an implementation of our algorithms on the TinyOS platform and a controlled simulation study using Matlab and TOSSIM