Kumar Vikram, Arablouei Reza
IIIT Una, Una, India.
Data61, CSIRO, Pullenvale, QLD 4069 Australia.
Wirel Pers Commun. 2022;124(2):1623-1644. doi: 10.1007/s11277-021-09423-x. Epub 2021 Dec 2.
Location-enabled Internet of things (IoT) has attracted much attention from the scientific and industrial communities given its high relevance in application domains such as agriculture, wildlife management, and infectious disease control. The frequency and accuracy of location information plays an important role in the success of these applications. However, frequent and accurate self-localization of IoT devices is challenging due to their resource-constrained nature. In this paper, we propose a new algorithm for self-localization of IoT devices using noisy received signal strength indicator (RSSI) measurements and perturbed anchor node position estimates. In the proposed algorithm, we minimize a weighted sum-square-distance-error cost function in an iterative fashion utilizing the gradient-descent method. We calculate the weights using the statistical properties of the perturbations in the measurements. We assume log-normal distribution for the RSSI-induced distance estimates due to considering the log-distance path-loss model with normally-distributed perturbations for the RSSI measurements in the logarithmic scale. We also assume normally-distributed perturbation in the anchor position estimates. We compare the performance of the proposed algorithm with that of an existing algorithm that takes a similar approach but only accounts for the perturbations in the RSSI measurements. Our simulation results show that by taking into account the error in the anchor positions, a significant improvement in the localization accuracy can be achieved. The proposed algorithm uses only a single measurement of RSSI and one estimate of each anchor position. This makes the proposed algorithm suitable for frequent and accurate localization of IoT devices.
基于位置的物联网（IoT）因其在农业、野生动物管理和传染病控制等应用领域的高度相关性，已引起科学界和工业界的广泛关注。位置信息的频率和准确性在这些应用的成功中起着重要作用。然而，由于物联网设备资源受限的特性，其频繁且准确的自我定位具有挑战性。在本文中，我们提出了一种新的物联网设备自我定位算法，该算法使用有噪声的接收信号强度指示（RSSI）测量值和受干扰的锚节点位置估计值。在所提出的算法中，我们利用梯度下降法以迭代方式最小化加权和平方距离误差成本函数。我们根据测量中扰动的统计特性来计算权重。由于考虑到对数距离路径损耗模型，在对数尺度下RSSI测量值具有正态分布的扰动，因此我们假设RSSI引起的距离估计值服从对数正态分布。我们还假设锚位置估计值存在正态分布的扰动。我们将所提出算法的性能与一种现有算法进行比较，该现有算法采用类似方法，但仅考虑了RSSI测量中的扰动。我们的仿真结果表明，通过考虑锚位置的误差，可以显著提高定位精度。所提出的算法仅使用一次RSSI测量值和每个锚位置的一个估计值。这使得所提出的算法适用于物联网设备的频繁且准确的定位。
