Underwater Wireless Sensor Network (UWSN) communication at high frequencies is extremely challenging. The intricacies presented by the underwater environment are far more compared to the terrestrial environment. The prime reason for such intricacies are the physical characteristics of the underwater environment that have a big impact on electromagnetic (EM) signals. Acoustics signals are by far the most preferred choice for underwater wireless communication. Because high frequency signals have the luxury of large bandwidth (BW) at shorter distances, high frequency EM signals cannot penetrate and propagate deep in underwater environments. The EM properties of water tend to resist their propagation and cause severe attenuation. Accordingly, there are two questions that need to be addressed for underwater environment, first what happens when high frequency EM signals operating at 2.4 GHz are used for communication, and second which factors affect the most to high frequency EM signals. To answer these questions, we present real-time experiments conducted at 2.4 GHz in terrestrial and underwater (fresh water) environments. The obtained results helped in studying the physical characteristics (i.e., EM properties, propagation and absorption loss) of underwater environments. It is observed that high frequency EM signals can propagate in fresh water at a shallow depth only and can be considered for a specific class of applications such as water sports. Furthermore, path loss, velocity of propagation, absorption loss and the rate of signal loss in different underwater environments are also calculated and presented in order to understand why EM signals cannot propagate in sea water and oceanic water environments. An optimal solk6ution for underwater communication in terms of coverage distance, bandwidth and nature of communication is presented, along with possible underwater applications of UWSNs at 2.4 GHz.

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

Bluetooth Low Energy (BLE) is widely considered for wireless Indoor Localization Systems (ILS) in which BLE Received Signal Strength (RSS) is used to derive the location of the target. The efficacy of a BLE based ILS depends on the localization accuracy. Whereas, localization accuracy depends on the stability of the BLE RSS. A number of researchers have focused on studying the impact of different parameters (such as different type of devices, environments and deployments) that cause the variations in BLE RSS. Today, BLE devices are capable of operating at multiple transmission power levels. Unlike previous studies, this paper focuses on evaluating the performance of BLE based ILS at multiple transmission power levels. Furthermore, the impact of different parameters such as multiple advertising interval, scanning interval, device orientation, distance, the effect of Line of Sight (LOS), Non-Line of Sight (NLOS) and device density over BLE RSS is also studied with multiple transmission power levels. Our main findings show that multiple transmission power level can attain localization accuracy with multiple precisions (i.e. from 2m to 5m) in an indoor classroom environment.

In this paper, we exploit the wireless signals of the two most common wireless standard used in indoor environments i.e. Wireless Fidelity (WiFi) and Bluetooth Low Energy (BLE) for localization application. We demonstrate that by inferring wireless signals, we can keep a track of localization that can provide key performance indicators in maintaining a healthy and active life of elderlies. As WiFi and BLE operate at multiple transmission power levels, the localization accuracy can vary. In this regard, we investigate the feasibility of the WiFi and BLE wireless signals in tracking the location of an elderly in a cluttered home environment. We use fingerprinting localization algorithm for location estimation. We first demonstrate that both wireless standards can be used for indoor localization. We further present the results for localization estimated from multiple transmission power levels for WiFi and BLE. The results show WiFi signals to be more stable compared to BLE signals. However, despite operating at much low transmission power levels BLE provides comparable results that are sufficient for locating elderlies in an indoor environment.

Fingerprinting technique is one of the most widely used localization techniques in an indoor environment. This technique maps the most likely locations of the target, also referred to as Reference Points (RPs), with fingerprints of the Access Points (APs) collected at those RPs. A wireless fingerprint is a feature (such as RSS, Path-Loss or Data Rate) that is derived from the collection of wireless signal samples of the APs. The most important aspect of the fingerprinting technique is the distinct wireless fingerprints that represent different RPs. However, different factors such as equal distance of multiple RPs from the same AP, may lead to fingerprint duplication, i.e. multiple RPs may have similar fingerprints, which can result in false localization. This paper highlights the problem of fingerprint duplication in fingerprinting based indoor localization. The paper further discusses the fingerprint duplication with three different fingerprint metrics i.e. RSS fingerprint, Path-loss fingerprint and Data Rate fingerprint, used in fingerprinting based indoor localization. To address this problem, RSS spread is proposed to be used in conjunction with different fingerprint metrics (i.e. RSS, Path-Loss or Data Rate) to differentiate among RPs. The results of the experiment verifies the occurrence of similar fingerprints of AP that operates with multiple transmission power levels at different RPs with all 3 fingerprint metrics. The results also shows that RSS spread fingerprints of AP with multiple transmission levels are all different and distinct. Additionally, it is observed that the significance of RSS spread tends to increase at low transmission power level which motivates and leverage the use of low transmission power levels in fingerprinting based indoor localization.