An environment adaptive ZigBee-based indoor positioning algorithm
ABSTRACT Lately, there has been significant progress in the field of wireless communications and networking. Furthermore, the number of applications that require context information as the user's location will increase in the coming years. However, this issue still has not been solved indoors due to the RF (Radio Frequency) signals' behaviour in this kind of scenarios. In this paper, we present a robust, easy to deploy and flexible indoor localization system based on ZigBee Wireless Sensor Networks. It is important to mention that our localization system is based on RSSI (Received Signal Strength Indicator) level measurements since this information can be obtained directly from the messages exchanged between nodes, so no extra hardware is required. Our localization system consists of two phases: calibration and localization. Anytime a blind node needs to be located, our system performs calibration using a matrices system, so that the environment can be characterized, taking into account possible changes on it since the last request. Then, in the localization phase, the central server processes all the information and calculates the blind node's position with the new iterative algorithm we present. With this indoor positioning algorithm we can estimate the blind node's position with a good resolution (3 m average error), so we can say that this ZigBee localization algorithm provides very promising results.
Conference Paper: New fuzzy-based indoor positioning scheme using ZigBee wireless protocol[Show abstract] [Hide abstract]
ABSTRACT: This paper proposes new fuzzy-based scheme providing position information vital for smart home applications. The scheme runs efficiently on the economical wireless ZigBee nodes and uses the link quality indicator (LQI) measured without the need for any additional hardware. It uses fuzzy logic to represent measure noise and surrounding environmental impacts. Position estimation is based upon the fuzzy information provided by all available ZigBee nodes and the surrounding environment is modeled by assembling a set of representative fuzzy vectors. Fuzzy levels are determined by applying the K-means algorithm given the LQI distribution as input. The scheme performance is compared to two popular schemes, the I3BM and the Environment Adaptive. The three are implemented using the Jennic JN5148 ZigBee PRO kit. It is demonstrated that for regular motion the proposed scheme significantly outperforms the other two without high computational cost, slow response, or large memory requirement.Computer Engineering & Systems (ICCES), 2012 Seventh International Conference on; 01/2012
- [Show abstract] [Hide abstract]
ABSTRACT: The fingerprint-based techniques are commonly used for indoor location estimation, in which a radio map is built by calibrating signal-strength values at several training locations in the offline phase. However, the signal-strength values change as the environment changes, and hence the radio map built may be out of date. Further, the recalibration of signal-strength values for each environment change is laborious and time consuming. Therefore, in this paper, an efficient radio map management method is proposed in response to the environment changes. We first demonstrate that different features of signal propagation are obtained in different regions of the indoor environment. According to the different features of signal propagation, the environment is divided into several regions and the signal-strength values collected at the training locations in different regions can be updated accurately and individually using proper propagation features. The experimental results show the usefulness of the proposed method and the accuracy of the localization can be improved.01/2012;
- [Show abstract] [Hide abstract]
ABSTRACT: The ability of a sensor node to determine its position is a fundamental requirement for many applications in wireless sensor networks (WSNs). In this article, we address a scenario where a subset of sensors, called anchor nodes, knows its own position and helps other nodes determine theirs through range-based positioning techniques. Such techniques benefit from a high degree of connectivity, since range measurements from at least four anchor nodes are necessary (three-dimensional scenario). On the other hand, WSN topologies, most notably the cluster-tree topology, tend to limit connectivity between nodes to save energy. This results in very poor performance of the network in terms of localization. In this article, we propose LACFA, a network formation algorithm that increases the probability of localization of sensors in a cluster-tree topology. It does so by properly allocating anchor nodes to different clusters during the network formation phase. Our algorithm achieves very high localization probability when compared with existing cluster formation algorithms, at no additional cost. Moreover, a distributed cluster formation algorithm, with no need for any centralized information exchange mechanisms, is defined.EURASIP Journal on Wireless Communications and Networking 10/2011; 2011(121):1. · 0.54 Impact Factor