Performance evaluation of network lifetime spatial-temporal distribution for WSN routing protocols
Introduction
A Wireless Sensor Network (WSN) is comprised of small sensors with limited computational and communication power. One of the fundamental services provided by a WSN is the monitoring of a specified region of observation, where the main duty is the environment sensing and communicating the information to the Base Station (BS).
WSN for monitoring applications must measure environmental conditions, such as temperature over extended periods (Cardell-Oliver et al., 2005; E-Musaloiu et al., 2006; Werner-Allen et al., 2006). For example, the WSN “macroscope" (Tolle et al., 2005) offers a dense temporal and spatial monitoring of the redwood trees in Sonoma, California. In such applications, the knowledge of how the network lifetime is distributed over time and space is required (Cardell-Oliver et al., 2005). Moreover, sensor nodes must be left unattended. For example, in Tolle et al. (2005), sensor nodes are placed at different heights of the trees to measure air temperature, relative humidity, and photo-synthetically-active solar radiation, which makes it difficult or impossible to re-charge or replace sensors’ batteries (solar energy is not always an option). It requires, therefore, a long system lifetime. In this case, extending the lifetime is extremely important. This requires devising novel energy-efficient solutions to some of the conventional wireless networking problems, such as medium access control, routing, self-organization, bandwidth sharing, and security. Exploiting the tradeoffs among energy, accuracy, latency and using hierarchical (tiered) architectures are important techniques for prolonging network lifetime (Estrin et al., 2001).
The network lifetime strongly depends on the routing protocol used and can be defined in several ways. For example, the lifetime can be defined as the time elapsed until the first node (or the last node) in the network depletes its energy (dies) (Younis and Fahmy, 2004). In some scenarios, such as intrusion or fire detection, it is necessary that all nodes stay alive as long as possible, since network quality decreases considerably as soon as one node dies. In these scenarios, it is important to know when the first node dies. The FND metric (First Node Dies) gives an estimated value for this event. In some applications, the loss of a single or few nodes does not automatically reduce the Quality of Service (QoS) of the network. In this case, the HNA metric (Half of the Nodes Alive) denotes an estimated value for the half-life period of a WSN whereas the LND metric (Last Node Dies) gives an estimated value for the overall lifetime of a WSN. Using these three metrics (FND, HNA, and LND), the network lifetime distribution over time can be analyzed. Moreover, sensor nodes are placed at different distances from the BS so the network lifetime distribution over space can be also analyzed.
In this study, the two aspects are considered: the network lifetime distribution over time and over space, or, in other words, the spatial-temporal (ST) distribution of the network lifetime.
The purpose of this study is two-fold:
- 1.
The analysis of the ST distribution of WSN lifetime using some representative routing protocols, namely:
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One-hop networks: DIRECT
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Multi-hop networks:
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Flat routing: FLOODING and GOSSIPING.
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Hierarchical routing: LEACH and HEED.
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Routing protocols are implemented following a standard TinyOS routing architecture (Levis et al., 2003a). Metrics are observed in various real scenarios using realistic assumptions (see Section 5), while avoiding several weak points noticed in some recent works (Bellaachia and Weerasinghe, 2008), which give a clear view about the studied routing protocols.
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- 2.
Based on this study, it was noticed that even state of the art routing protocols, such as Younis and Fahmy (2004) do not provide a good ST distribution of the network lifetime. To overcome this lack, a new technique aimed at providing a good ST distribution of lifetime is proposed. The proposed technique is integrated in HEED and the resulting protocol EHEED (Extended HEED) is compared to other protocols.
The remainder of this paper is organized as follows. Section 2 consists of background notes presenting in a synthetic manner the considered protocols. Section 3 discusses related research. Section 4 elaborates the proposed protocol while a comparative empirical study is described in Section 5. Section 6 concludes the paper.
Section snippets
Background
In this section, the studied protocols are summarized and their pseudo-codes under TinyOS (Hill et al., 2000) are presented.
Related work
Navin et al. (2009) define network lifetime as the time for half nodes death. They compare their proposed Dynamic clustering and Distance Aware Routing protocol (DDAR) with conventional routing protocols such as MTE (Minimum Transmitted Energy (Heinzelman et al., 2000)), LEACH, and LEACH-C (Heinzelman et al., 2002). Simulation results show that the 50th node dies at 258, 448, and 531 s in MTE, LEACH, and LEACH-C, respectively. On the other hand, it dies after 681.65 s in DDAR protocol. The
Our proposal: EHEED protocol
Our proposal starts from the work done in Ivan and Xu (2001) in which authors demonstrate that energy consumption is proportional to the square of the distance between the sender S and the destination D. In this latter, authors proved also an interesting result: if an intermediate node A, that forwards the messages between S and D, can be found (Fig. 6), some energy should be saved as follows.
Let assume that the power needed for the transmission and reception of a signal is u(d)=a×dα+c (the
Comparative empirical study
In terms of network lifetime analysis, the recent work done in (Bellaachia and Weerasinghe, 2008) is the closest to ours. However, there are some weak points, listed below, that the current work tries to avoid.
- 1.
The rate of CH was fixed, which prevent them from observing its influence on protocols;
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All simulation tests were run for a fixed period. In such a scenario, they cannot observe the different lifetimes if the period is not enough to reach the last node death, which limits the obtained
Conclusion
The importance of this study resides in the fact that a real monitoring scenario is considered, where all nodes have data to send and the BS is in the sensor field. Compared to others studies, some representative flat and hierarchical WSN routing protocols are considered. Several definitions of network lifetime and a common evaluation framework are used.
All simulations were run until the last node death, with an observation of different lifetime levels. The number of nodes was varied to assess
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