Compressed RSS Measurement for Communication and Sensing in the Internet of Things

Abstract

The receiving signal strength (RSS) is crucial for the Internet of Things (IoT), as it is the key foundation for communication resource allocation, localization, interference management, sensing, and so on. Aside from its significance, the measurement process could be tedious, time consuming, inaccurate, and involving human operations. The state-of-the-art works usually applied the fashion of "measure a few, predict many," which use measurement calibrated models to generate the RSS for the whole networks. However, this kind of methods still cannot provide accurate results in a short duration with low measurement cost. In addition, they also require careful scheduling of the measurement which is vulnerable to measurement conflict. In this paper, we propose a compressive sensing-(CS-) based RSS measurement solution, which is conflict-tolerant, time-efficient, and accuracyguaranteed without any model-calibrate operation. The CS-based solution takes advantage of compressive sensing theory to enable simultaneous measurement in the same channel, which reduces the time cost to the level of O(log ) (where is the network size) and works well for sparse networks. Extensive experiments based on real data trace are conducted to show the efficiency of the proposed solutions.

Indoor WLAN localization via adaptive Lasso Bayesian inference and convex optimization

“…Then the dictionary learning was established by concatenating each cell and the unknown cell was projected to the dictionary-learning algorithm to form the vector of measurement. Several studies (Zhao et al, 2017) have proposed a novel approach by leveraging the sparse signal of the RSS measurement. These techniques open a new ear for deterministic approaches by dividing the RSS measurement into a subset of small vectors containing indices of each nonzero measurement.…”

Spectrum Gap Analysis With Practical Solutions for Future Mobile Data Traffic Growth in Malaysia

“…Since the wireless network consists of several types of technologies and each technology has a various number of sites, so the site's number per technology need to be considered for calculating the average spectrum efficiency per site. Thus, the average spectrum efficiency per site overall technologies can be represented by Eq.27: (27) Thus, from Eq.26 and Eq.27, the average spectrum efficiency per site overall technologies can be evaluated by Eq.28:…”

“…The IoT was titled as the upcoming industrial revolution in mobile telecommunication systems. It has begun to significantly grow and will influence the way businesses and customers interact with the physical world [2], [27], [31]- [36]. Similarly, M2M is also noticeably growing [16], [37]; Machina Research predicted that worldwide M2M connections will expand from five to 27 billion between the years 2014 and 2024, with the Compound Annual Growth Rate (CAGR) of 18% [38].…”