1. Introduction
Large-scale economic wireless sensor networks (WSNs) become increasing attractive to environmental monitoring, control and interaction applications. Object tracking and localization is one of the key challenges for these applications [1]. Various solutions have been proposed based on two ranging techniques: 1) time of arrival (TOA) [2], such as GPS, 2) the path loss model based on radio RSSI signal strength [3] or acoustic signal strength [4] attenuation in relation to the signal propagation distance. Sometimes, range-free techniques are also applied to estimation locations, such as hop count or centroid methods [5].
However, most of these localization methods require generic signal propagation and network formation assumptions. In this paper, we place our localization method in coal mine environments for monitoring and tracking human and vehicle locations using multiple reference points installed in the WSNs. This approach is especially valid given the practical value of the localization system in helping people in the frequent emergency situations and reducing the high costs of coal mine operations.
Coal mine environments present extremely harsh conditions for wireless communications. First, the power of the transmitter underground must be reduced to the lowest level to avoid sparkling gas explosions. Secondly, signal propagations are especially prone to multipath effects. Third, wireless networks are more dynamic than surface networks due to signal attenuation, movements etc. Last but not the least, wireless sensor network in coal mines is a multiple users system, and the MUI (multiple users’ interference) has dramatic impacts on the precision of a localization system. Coding is an important method to depress MUI.
UWB (ultra-wide band) transmission and coding technologies provide an ideal solution to the coal mine environment. On one hand, UWB systems can provide high bandwidth data transmissions; on the other hand, UWB exhibits excellent characteristics to reduce co-channel interference. IEEE 802.15.4a is the de facto standards to provide low power long distant low data rate service for real-time communication and precise ranging and localization applications [6,7].
There are many UWB localization algorithms proposed in the past [8-11]. Wang et al. demonstrated the use of UWB in coal mines to realize short-distance high-rate applications such as video monitoring, as well as localization and monitoring [12].
Of the different UWB transmission techniques, Impulse Radio Ultra-wideband (IR-UWB) provides a desirable platform to enable efficient and precise localization solutions in coal mines environments [13]. Different coding algorithms for IR-UWB communication systems have been proposed so far, such as DS-UWB (Direct Sequence UWB) and TH-UWB (Time Hopping UWB) [14]. However, none was shown to guarantee high quality localization. The simple DS-UWB cannot even meet the localization requirements when the multipath and multi-user interference exist. In this paper, we apply the Orthogonal Variable Spread Factor (OVSF) coding algorithm in IR-UWB networks to solve the multi-user interference problem.
Other than TOA (time-of-arrival), TDOA (time difference of arrival) and AOA (angle of arrival) based ranging techniques, ranging based on the path loss model is an intuitive method, especially in low-cost WSNs. The path loss model defines the signal propagation characteristics, and determines the received signal strength. Therefore, given the received signal strength (RSSI), we can estimate the distance between the receiving node and other reference points using computational methods, rather than expensive hardware [15]. Several channel models were proposed to evaluate UWB systems in different propagation environments in the IEEE 802.15.4a. However, these models relied on insufficient measurements and fixed parameters, and cannot reflect the real channel characteristics. In [16], a statistical path loss model was established for channels in the residential areas based on over 300,000 frequency response measurements. This approach shows good agreement with measured data, but requires a highly complex modeling and simulation procedures, as mentioned in IEEE 802.15.4a. Li et al. analyzed the propagation mode of UWB signal in coal mine, and proposed a free-space propagation alike model based on the existing residential indoor model [17]. So far, channel path loss modeling in coal mine environment remains a complex task. In this paper, we will provide a more practical and accurate coal mine model using UWB medium based on IEEE 802.15.4a.
Once the range or distance information is available between the mobile target and the reference points in the WSNs, the location of the mobile target is fairly easy to derive. Trilateration is a common approach. Savarese et al. presented a trilateration algorithm based on least squares (LS) method in large-scale WSNs [18]. We apply similar method, but because the number of reference points involved in the localization algorithm could be limited, the LS method is adapted to run multiple iterations in order to reduce the power consumption of the reference points, and to provide accurate location coordinates.
The rest of the paper is organized as follows. Section 2 describes the basic assumptions of the localization system, and some of the symbols used in this paper. Section 3 presents a new IR-UWB coding method, called U-BOTH (UWB based on Orthogonal Variable Spreading Factor and Time Hopping), and provides the signal processing model of UWB in coal mine environments. Section 4 specified a WSN communication protocol in order to collect reference point location information. According to the path loss model and the RSSI information gathered by the mobile target, Section 5 and Section 6 present the ranging and localization algorithms using the maximum likelihood estimation (MLE) and the least squares methods, respectively. Section 7 evaluates the system using simulations. Section 8 concludes the paper.
2. Assumptions and Notation
Although we focus on coal mine wireless sensor network (WSN) deployment, the results can be easily adapted to other deployment scenarios. The key difference between various WSN deployments is the signal propagation characteristics, reflected in the path loss model used for range calculations. In each of these deployment scenarios, we assume that a number of reference nodes are deployed the network, and have already acquired their exact location coordinate through other means, such as initial location calibrations. The task in our localization computation is to derive the position coordinate of a mobile target object by running the localization algorithms on the target. We do not elaborate on the application of the coordinate information in this paper.
Figure 1 illustrate such a WSN in which a target node, denoted by triangle, moves across the network. The tar-