Diffuse: A topology building engine for wireless sensor networks☆
Introduction
Wireless sensor networks (WSNs) [2] comprise a large number of nodes with sensing capability. The applicability of WSNs includes environmental, medical, industrial, and military applications. Commonly, WSNs are strongly computational and power limited. In addition, these networks demand self-organizing features to autonomously adapt themselves to eventual changes resulted from external interventions such as topological changes due to failures or node addition. An important task in WSNs consists in delivering the data gathered from the environment to a sink node for further processing and evaluation. Consequently, data routing is a fundamental task, which is commonly performed in a multihop fashion due to the radio range limitation and energy consumption constraints of a sensor node.
Tree topologies are frequently used to route data in a continuous flat sensor network [3], [4], [5], [6], [7]. Even the pioneer Directed Diffusion algorithm [8] provides a tree-like variant called One-Phase Pull Diffusion [7]. However, these solutions do not autonomously detect when the routing topology needs to be rebuilt. Although periodic [5] and user-triggered rebuilding are natural approaches to accommodate network changes, these strategies may lead to unnecessary reconstructions.
This work proposes the use of information fusion mechanisms to autonomously detect when the routing topology needs to be rebuilt. Briefly, information fusion deals with the combination of multiple sources to obtain improved information (e.g., cheaper, greater quality, or greater relevance). Information fusion is commonly used in detection and classification tasks in robotics and military applications [9]. Lately, it has been used in other applications such as intrusion [10] and Denial of Service (DoS) detection [11]. Within the WSNs domain, simple aggregation techniques (e.g., maximum, minimum, and average) have been used to reduce the overall data traffic to save energy [4], [8], [12].
The major contribution of this work is the improvement of routing algorithms by reducing unnecessary topology constructions, which is achieved by means of an inference engine called Diffuse. Diffuse is an ample solution that may be used to detect the need for topology reconstructions to pursue failure recovery, energy balance, and data aggregation improvements. As a proof of concept, we show how to implement Diffuse to allow the network to recover from routing failures avoiding unnecessary topology reconstructions. We also sketch how to extend Diffuse to other application domains. For the failure recovery evaluation, the simulation results show that, compared to the periodical rebuilding strategy, in some cases, our solution can reduce the control traffic by a scale factor of 5.
The rest of this paper is organized as follows. Section 2 presents the related work about tree topologies for multihop routing and failure detection for WSNs. Section 3 provides an overview of Diffuse, an inference engine, which is the building block we propose to detect the need for topology reconstructions. In Section 4, we show how Diffuse can be used to detect routing failures in WSNs, and present some theoretical results related to this scenario. Section 5 evaluates the rebuilding approaches through a set of simulations. In Section 6, we revisit the Diffuse applicability discussing how it can be extended to fulfill other applications. Finally, in Section 7, we present our conclusions and future work.
Section snippets
Related work
Among the current multihop routing strategies for flat sensor networks [3], [4], [5], [6], [7], [8], routing trees [3], [4], [5], [6] distinguish themselves from other approaches due to their simplicity and efficiency. Even the pioneer Directed Diffusion algorithm [8] provides a variant, called One-Phase Pull Diffusion [7], that implicitly builds a tree to route data towards the sink node.
Sohrabi et al. [3] present the Sequential Assignment Routing (SAR) algorithm to create multiple trees. Each
Diffuse: an inference engine
This section presents Diffuse, an inference engine that uses information fusion mechanisms to combine data and features to detect when the routing topology needs to be rebuilt. Diffuse may be schematically represented by the elements depicted in Fig. 1, as discussed below.
Diffuse for failure recovery
As discussed in Section 3.2, Diffuse can be used in different contexts. For the sake of exemplification, this section considers only the aspects that allow Diffuse to detect routing failures and rebuild the routing tree to reach disconnected nodes. In Section 6.2, we discuss again the applicability of Diffuse.
Simulation results
The simulation experiments performed in this section evaluate the use of Diffuse to detect routing failures and trigger topology reconstructions. The evaluation methodology and results are presented as follows.
Why Diffuse?
Since we use Diffuse to detect node failure, one might ask why use Dempster–Shafer if similar results could be obtained using a heartbeat approach. In this section, we try to elucidate when Diffuse is preferable to a failure detector based on heartbeats.
Conclusions and future work
This work presents an inference engine, called Diffuse, designed to detect when the routing topology needs to be rebuilt based on different goals, such as to recover from routing failures, improve data aggregation, and balance the energy consumption. For the sake of exemplification, we evaluate Diffuse aiming at recovering from routing failures. We provide theoretical results that show some properties of the periodic rebuilding, sink-centered, and source-centered approaches. These results show
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This work extends a preliminary research [1]. The current version includes several new contributions: a generic framework for topology rebuilding; a more energy-efficient (in-network) algorithm; a new empirical evaluation, and several theoretical bounds. In addition, we show in this paper how our framework could be used to extend the current solution and provide a broader topology rebuilding system.