The advances in localization based technologies and the increasing importance of ubiquitous computing and context-dependent information have led to a growing business interest in location-based applications and services. Today, most application requirements are locating or real-time tracking of physical belongings inside buildings accurately; thus, the demand for indoor localization services has become a key prerequisite in some markets. Moreover, indoor localization technologies address the inadequacy of global positioning system inside a closed environment, like buildings. Based on this, though, this paper aims to provide the reader with a review of the recent advances in wireless indoor localization techniques and system to deliver a better understanding of state-of-the-art technologies and motivate new research efforts in this promising field. For this purpose, existing wireless localization position system and location estimation schemes are reviewed, as we also compare the related techniques and systems along with a conclusion and future trends.

In indoor environments, WiFi (RSS) based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies) in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs). This model exploits machine learning, in particular Artificial Natural Network (ANN) techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA) based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.

In indoor environments, accurate location or positioning becomes an essential requirement, driven by the need for autonomous moving devices, or to identify the position of people in large spaces. Single technology schemes which use WiFi and Bluetooth are affected by fading effects as well as by signal noise, providing inaccuracies in location estimation. Hybrid locating or positioning schemes have been used in indoor situations and scenarios in order to improve the location accuracy. Hence, this paper proposes a hybrid scheme (technique) to implement fingerprint-based indoor positioning or localization, which uses the Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points as well as Wireless Sensor Networks (WSNs) technologies. Our approach consists of performing a virtual tessellation of the indoor surface, with a set of square tiles encompassing the whole area. The model uses an Artificial Neural Network (ANN) approach for position estimate, in which related RSS is associated to a 1 m × 1 m tile. The ANN was trained to match the RSS signal strength to the corresponding tile. Experimental results indicate that the average distance error, based on tile identification accuracy, is 0.625 m from tile-to-tile, showing a remarkable improvement compared to previous approaches.

Background: Anaesthetists frequently tailor the subarachnoid local anaesthetic dosage according to parturient height to achieve sensory blockade up to the T4 dermatome for lower segment Caesarean sections (LSCSs). Studies that have been conducted have demonstrated that height does not affect the spread of subarachnoid hyperbaric bupivacaine. This study aimed to nd the correlation between the spinal column length of term parturients and the highest level of sensory blockade after spinal anaesthesia. Methods: The authors studied 60 singleton term parturients of American Society of Anesthesiologists (ASA) physical status I or II scheduled for elective LSCSs. The length of the spinal column was taken as an average of three measurements from the C7 spinous process to the sacral hiatus in a sitting upright and facing forward position. Spinal anaesthesia was given by administering 1.8 ml of 0.5% hyperbaric bupivacaine and 25 μg fentanyl through the L3/L4 or L4/L5 intervertebral space. The level of sensory blockade was assessed using pin-prick testing for pain sensation. Linear regression analysis was used to analyse the correlation; R < 0.25 indicates no correlation with the level of signi cance being < 0.05. Results: The spinal column lengths measured were between 42.2 cm and 85.8 cm (median: 58.5 cm). Spinal anaesthesia given was adequate for all patients, with the highest levels of anaesthesia ranging from T8 to T2 with sensory levels between T6 and T4. The parturients' spinal column length showed no correlation with the highest level of sensory blockade achieved, namely R = 0.11. Conclusions:The study found no correlation between the parturients' spinal column length and the highest level of sensory blockade achieved.

In the recent past, a significant increase has been observed in the use of underwater wireless sensor networks for aquatic applications. However, underwater wireless sensor networks face several challenges including large propagation delays, high mobility, limited bandwidth, three-dimensional deployments, expensive manufacturing, and energy constraints. It is crucial for underwater wireless sensor networks to mitigate all these limitations primarily caused by the harsh underwater environment. To address some of the pertinent challenges, adaptive hop-by-hop cone vector-based forwarding routing protocol is proposed in this article which is based on the adaptive hop-by-hop vector-based forwarding. The novelty of adaptive hop-by-hop cone vector-based forwarding includes increasing the transmission reliability in sparse sensor regions by changing the base angle of the cone according to the network structure. The number of duplicate packets and end-to-end delay is also reduced because of the reduced base angle and a smart selection criterion for the potential forwarder node. The proposed routing protocol adaptively tunes the height and opening of the cone based on the network structure to effectively improve the performance of the network. Conclusively, this approach significantly reduces energy tax, end-to-end delay, and packet delivery ratio.