Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.The associate editor coordinating the review of this manuscript and approving it for publication was Ahmed Farouk .

Because of the increasing relevance of the Internet of Things and location-based services, researchers are evaluating wireless positioning techniques, such as fingerprinting, on Low Power Wide Area Network (LPWAN) communication. In order to evaluate fingerprinting in large outdoor environments, extensive, time-consuming measurement campaigns need to be conducted to create useful datasets. This paper presents three LPWAN datasets which are collected in large-scale urban and rural areas. The goal is to provide the research community with a tool to evaluate fingerprinting algorithms in large outdoor environments. During a period of three months, numerous mobile devices periodically obtained location data via a GPS receiver which was transmitted via a Sigfox or LoRaWAN message. Together with network information, this location data is stored in the appropriate LPWAN dataset. The first results of our basic fingerprinting implementation, which is also clarified in this paper, indicate a mean location estimation error of 214.58 m for the rural Sigfox dataset, 688.97 m for the urban Sigfox dataset and 398.40 m for the urban LoRaWAN dataset. In the future, we will enlarge our current datasets and use them to evaluate and optimize our fingerprinting methods. Also, we intend to collect additional datasets for Sigfox, LoRaWAN and NB-IoT.

The spectrum of Internet of Things (IoT) applications is exponentially growing, driving the demand for better energy performance metrics. In conjunction, Low Power Wide Area Networks (LPWAN) have evolved as long-range connectivity enabler with low management cost. The integration of LPWAN communication assists in reliable IoT operation with extended lifetime. Notable LPWAN technologies that contend for many of the IoT applications are LoRaWAN, DASH7, Sigfox, and NB-IoT. Most of the end-devices such as sensors and actuators are battery powered, therefore investigating energy consumption becomes crucial. To estimate the consumed power, it is important to analyze the energy consumption in wireless communication. This paper describes an empirical evaluation of energy consumption for LPWAN wireless technologies. We measure the current consumption of LoRaWAN, DASH7, Sigfox, and NB-IoT and derive the respective battery lifetime. These measurements help to quantify the energy performance of different protocols. We observe that LoRaWAN and DASH7 are more energy efficient when compared to Sigfox and NB-IoT. Finally, a case study on energy consumption is done on precision agriculture in the greenhouse, showing that battery lifetime in real applications can drop significantly from the ideal case. These results can be used for increasing the effectiveness of the IoT application by selecting the right technology and battery capacity.

No abstract

Practical location estimation is never ideal, and each location estimate is burdened with a certain level of error. In many use-case scenarios, knowing the magnitude of these errors can significantly improve the usability of the location estimates. The localization errors for different localization approaches are currently assessed using static performance benchmarks. These benchmarks typically provide aggregate metrics that statistically characterize the localization errors across the entire deployment environment. Due to the potentially dynamic nature and spatial heterogeneity of the environment, this characterization can be too generic to be really useful from the point of view of an individual location estimate. To address this issue for fingerprinting-based localization, we propose a regression-based procedure for estimating the individual (i.e., per-location estimate) localization errors. We use the received signal strength (RSS) values from various locations in an environment, as well as the observed localization errors in case the location estimates are generated using these RSS values, for training a number of contemporary regression models. Using the trained models, we are able to estimate the localization error of a location estimate at a new location using only RSS values collected at that location. Both by simulation and experimentally, we demonstrate the feasibility of the proposed procedure for Wi-Fi-based indoor and LoRa-and SigFoxbased outdoor fingerprinting approaches. We do that by showing that the proposed procedure can, in the best-case scenario, yield more than 50% more accurate estimation than the reference procedure based on the average localization errors derived from the static performance benchmarks.