High-definition (HD) maps have recently become a key piece of technology in autonomous driving. Over the past few years, various methods and sensors, such as those based on inertial navigation system (INS), global navigation satellite system (GNSS), cameras, and light detection and ranging (LiDAR), have been used to develop HD maps. In this study, we developed novel techniques for enhancing the creation and verification of HD point cloud maps. First, a tightly coupled (TC) INS/GNSS-assisted 3-D normal distribution transform (NDT)-LiDAR mapping system has been developed. Utilizing an integrated INS/GNSS, the system provides a reliable initial pose, thereby mitigating the issue of divergence in NDT scan matching, particularly when the vehicle operates at high speeds in challenging LiDAR environments. This approach enhances both navigation accuracy and the precision of the point cloud map. Second, alternative ground control points (GCPs) have been established as substitutes for conventional techniques, addressing freeway regulations and managing safety concerns. Third, to ensure the desired accuracy for “where-in-lane” positioning in autonomous vehicle applications, the created point cloud map was validated against the criteria outlined by standardized procedures. Overall, our preliminary results indicate that our HD point cloud map meets the positioning accuracy criteria outlined by the Taiwan Association of Information and Communication Standards. Our point density results also indicate that our generated point cloud map can achieve a high degree of accuracy in in-lane positioning for autonomous vehicle navigation.

Ultrasonic rangefinders are low-cost sensors that have been utilized in the past to perform mapping, localization and Simultaneous Localization and Mapping (SLAM). Ultrasonic has a low angular resolution, and it is challenging to identify and correspond features in ultrasonic scans. In the past, occupancy mapping has been proposed to address this issue. However, for the localization of the sensor with respect to such occupancy grids, post-processing for landmark identification is required (which increases the computational cost). In contrast to the occupancy grid approach, direct feature-based mapping methods are also proposed in the literature, where landmarks are extracted using many consecutive ultrasonic sensor scans instead of using a single sparser scan. In this article, a novel direct feature-based mapping method using ultrasonic sensors in the indoor manmade environment is proposed. The proposed method utilizes the prior knowledge of the structure of indoor environments (such as most houses or warehouses), where the walls are either parallel or perpendicular (Manhattan World Constraint (MWC)). This prior knowledge is used with multiple consecutive ultrasonic scans to provide accurate line-based maps. Compared to the occupancy grid mapping, the proposed method avoids the discretization of the point cloud coordinates (which can be a source of inaccuracy). Further, it does not require a second step for landmark detection. Compared to the direct feature-based methods, which are general-purpose, our approach is the first method- to the best of the authors' knowledge- that imposes MWC to map using panoramic ultrasonic scans. The accuracy of the proposed mapping method is demonstrated in a cluttered indoor environment. The results indicate that the proposed method accurately detects and distinguishes most edges and corners.

The inertial navigation system (INS) has been widely used to provide self-contained and continuous motion estimation in intelligent transportation systems. Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data. Meanwhile, benefit from the emergence of big data and the improvement of algorithms and computing power, machine learning (ML) has become a consensus tool that has been successfully applied in various fields. This article reviews the research on using ML technology to enhance inertial sensing from various aspects, including sensor design and selection, calibration and error modeling, navigation and motion-sensing algorithms, multi-sensor information fusion, system evaluation, and practical application. It summarizes the state of the art, advantages, and challenges on each aspect, and points out future research directions.

Localization techniques are becoming key to add location context to the Internet-of-Things (IoT) data without human perception and intervention. Meanwhile, the newly emerged low-power wide-area network (LPWAN) and 5G technologies have become strong candidates for mass-market localization applications. However, various error sources have limited localization performance by using such IoT signals. This article reviews the IoT localization system through the following sequence: IoT localization system review, localization data sources, localization algorithms, localization error sources and mitigation, and localization performance evaluation. Compared to the related surveys, this article has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.

Automated vehicles (AVs) have gained increasing interest over the past few years. A crucial feature of these vehicles is an accurate and robust positioning system. Global navigation satellite system (GNSS) precise point positioning (PPP) can achieve decimeter-level accuracy without the need for local reference stations. Nevertheless, the solution availability is affected by GNSS signal outages, which frequently occur in AVs driving scenarios. The integration with an inertial navigation system (INS) provides a continuous positioning solution; however, high-end inertial sensors are bulky and expensive. The recent improvements to the low-cost micro-electro-mechanical (MEMS) sensors opened the way to utilize these sensors in high-precision applications. The objective of this work is to investigate the performance of integrating dual-frequency PPP with low-cost MEMS sensors for land vehicles on highways and suburban areas. Furthermore, the Reduced Inertial Sensor System (RISS) is used instead of the traditional INS system. RISS uses two horizontal accelerometers and one vertical gyroscope in addition to the vehicle odometer, eliminating two gyroscopes and one accelerometer compared to the full IMU system. The lower number of sensors contributes to reducing the error growth over time and reducing the system cost and complexity. A road test was performed that included suburban areas and highway driving with multiple overpasses. The result showed that the developed PPP/RISS system was able to achieve decimeter-level rms positioning errors and a maximum of one meter horizontal positioning error.

Recently, Autonomous land vehicle navigation became an important research topic. Most of the land vehicle navigation systems are based on Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated system. However, this system doesn't efficiently work in some environments due to the GNSS signal outages and the deterioration of the navigation solution due to the large INS errors. Therefore, INS should be aided to limit its drift during GNSS signal blockage. This research proposes a multi low-cost INS configuration in land vehicle where two low-cost IMU sensors are mounted on the center of rear wheels of the land vehicle to estimate the vehicle's forward velocity through the gyroscopes located in the perpendicular direction of the wheel. A differential wheel odometry based on the Inertial Measurement Unit (IMU) mounted on the rear wheels is proposed to estimate the vehicle's change of heading. The proposed IMU wheel odometers are calibrated by providing GNSS/INS integrated forward velocity and heading change during GNSS signal availability. On the other hand, during GNSS signal outages, the IMU wheel based aiding system provides both velocity and heading change updates to the navigation filter to mitigate the large drift of the on-board IMU.Experimental tests have been implemented and the results show that the Root Mean Square Error (RMSE) of the IMU-based wheel odometer velocity is 0.08 m/sec while the RMSE of the typical odometer velocity obtained from On-Board Diagnostics II (OBD-II) is 0.26 m/sec. On the other hand, the RMSE of the estimated vehicle's heading change by the proposed differential wheel odometry reached 2 degrees/second for 360 second simulated GNSS signal outage. The navigation solution is enhanced when the IMU-based odometer velocity updates the navigation filter Extended Kalman Filter (EKF) and the average position RMSE reaches 4.96 meters, instead of 88.83 meters, for the INS standalone navigation solution during 60 seconds GNSS signal outage while the RMSE reaches 6.10 meters when OBD-II typical odometer is used as update. On the other hand, the RMSE reaches to 3.81 meters when both heading change and velocity updates estimated from the IMU-based odometer are used to aid the INS during GNSS signal outages.

Self-driving vehicles have attained a huge research interest as it offers new opportunities for more secure and convenient transportation. Vision is considered one of the significant inputs to self-driving systems in terms of obstacle avoidance and navigation purposes. Recently, Consumer Portable Devices (CPDs) such as smartphones and tablets are continuously equipped with enhanced sensors and cameras that can be used in the navigation or obstacle avoidance applications. This paper presents an approach for enhancing the land vehicle navigation in GNSS denied environment by integrating the Visual Odometry (VO), On-Board Diagnostics information, and inertial sensors. The visual odometry is collected through a CPD camera (smartphone) which is mounted on the vehicle dashboard where the vehicle change of heading is estimated. On the other hand, the vehicle forward velocity information is acquired through On-Board Diagnostics II (OBD-II). In this paper, the proposed approach is a Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) loosely coupled integration scheme where the VO heading change and the forward OBD-II velocity are used to aid the low-cost INS during GNSS signal outage to limit its drift through Extended Kalman Filter (EKF). The employed visual odometry estimates the orientation change between successive frames which requires a feature extraction step to extract the features in every frame followed by primary matching to match the features and estimate the rotation between frames. Experimental road test is implemented, and the results show significant position accuracy enhancement using the proposed approach compared with typical Dead Reckoning (DR) solution where the average position Root Mean Square Error (RMSE) of the DR is around 14 meters for 60 s GNSS signal outage while the average position RMSE for the proposed navigation approach is 4.40 meters during the same GNSS signal outage.

As an advanced and efficient high-precision positioning technology, Precise Point Positioning (PPP) is capable of providing precise navigation solutions globally with the Global Navigation Satellite System (GNSS) observations from a single receiver. Benefiting from maturation of the communication infrastructure, the precise products such as satellite orbit and satellite clock corrections are currently widely available with low latency, which opens doors for its applications to various emerging applications, such as self-driving cars. The rapid rise of the low-cost GNSS receivers and chipsets is pushing on the development of low-cost and robust navigation systems with strong industry demands. To meet this market demand, the Instant PPP (IP3) featuring multi-constellations, global availability, fast convergence and continuous navigation solutions (even with GNSS correction outages) is developed. IP3 can provide navigation solution with half meter (50 cm) (Circular Error Probability (CEP) 50%) accuracy in real-time with orbits, clocks and ionosphere and code bias corrections and using observations from a single frequency (SF) GNSS receiver. To mitigate the effects of GNSS signal blockages and multipath in challenging environments, IP3/DR integration is proposed in this research by combining the IP3 with low-cost inertial navigation system (INS) to provide continuous and robust navigation solutions. We will first introduce the scheme of IP3/DR system as well as the key features. Afterwards, the performance of the IP3/DR system will be verified in three different situations, namely the normal situation with open-sky and sub-urban environment, the field test with GNSS corrections outage and the field test with GNSS observations outage. The results of the above three experiments indicate the IP3/DR can provide high precision navigation solution better than 50 cm (CEP 50%) even with 5 minutes GNSS corrections outage and short-term GNSS observations outage.

Location is key to spatialize Internet of Things (IoT) data. However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i.e., localization without training data that have known location labels). Thus, this article proposes a deep-reinforcement-learning (DRL)-based unsupervised wireless-localization method. The main contributions are as follows: 1) this article proposes an approach to model a continuous wireless-localization process as a Markov decision process and process it within a DRL framework; 2) to alleviate the challenge of obtaining rewards when using unlabeled data (e.g., daily life crowdsourced data), this article presents a reward-setting mechanism, which extracts robust landmark data from unlabeled wireless received signal strengths (RSS); and 3) to ease requirements for model retraining when using DRL for localization, this article uses RSS measurements together with agent location to construct DRL inputs. The proposed method is tested by using field testing data from multiple Bluetooth 5 smart ear tags in a pasture. Meanwhile, the experimental verification process reflects the advantages and challenges for using DRL in wireless localization.

The next-generation Internet of Things (IoT) systems have an increasingly demand on intelligent localization which can scale with big data without human perception. Thus, traditional localization solutions without accuracy metric will greatly limit vast applications. Crowd sourcing-based localization has been proven to be effective for mass-market location-based IoT applications. This paper proposes an enhanced crowd sourcing-based localization method by integrating inertial, wireless, and magnetic sensors. Both wireless and magnetic fingerprinting accuracy are predicted in real time through the introduction of fingerprinting accuracy indicators (FAIs) from three levels (i.e., signal, geometry, and database). The advantages and limitations of these FAI factors and their performances on predicting location errors and outliers are investigated. Furthermore, the FAI-enhanced extended Kalman filter (EKF) is proposed, which improved the dead-reckoning (DR)/WiFi, DR/Magnetic, and DR/WiFi/Magnetic integrated localization accuracy by 30.2%, 19.4%, and 29.0%, and reduced the maximum location errors by 41.2%, 28.4%, and 44.2%, respectively. These outcomes confirm the effectiveness of the FAI-enhanced EKF on improving both accuracy and reliability of multisensor integrated localization using crowd sourced data.

In the past decade, technological advancements have facilitated the manufacturing of compact, inexpensive, and low-power consuming receivers and sensors for smart devices (e.g., GPS, WiFi, MEMS sensors, RFID, UWB, BLE, etc.). This led to the fast development of positioning, navigation, and location-based services (PNLBS), and much broader new applications than just providing a location or navigation.

Location service is fundamental to many Internet of Things applications such as smart home, wearables, smart city, and connected health. With existing infrastructures, wireless positioning is widely used to provide the location service. However, wireless positioning has the limitations such as highly depending on the distribution of access points (APs); providing a low sample-rate and noisy solution; requiring extensive labor costs to build databases; and having unstable RSS values in indoor environments. To reduce these limitations, this paper proposes an innovative integrated platform for indoor localization by integrating low-cost microelectromechanical systems (MEMS) sensors and wireless signals. This proposed platform consists of wireless AP localization engine and sensor fusion engine, which is suitable for both dense and sparse deployments of wireless APs. The proposed platform can automatically generate wireless databases for positioning, and provide a positioning solution even in the area with only one observed wireless AP, where the traditional trilateration method cannot work. This integration platform can integrate different kinds of wireless APs together for indoor localization (e.g., WiFi, Bluetooth low energy, and radio frequency identification). The platform fuses all of these wireless distances with low-cost MEMS sensors to provide a robust localization solution. A multilevel quality control mechanism is utilized to remove noisy RSS measurements from wireless APs and to further improve the localization accuracy. Preliminary experiments show the proposed integration platform can achieve the average accuracy of 3.30 m with the sparse deployment of wireless APs (1 AP per 800 m2).

This objective of this paper is to propose and evaluate a new algorithm to increase the computation and storage efficiency and to reduce the bandwidth requirements of the Wi-Fi received signal strength indicator (RSSI) maps based on Gaussian Process (GP) models. GP models are non-parametric models that estimate the likelihood function of the target variable, in this case the Wi-Fi RSSI values, conditioned on a set of training data. This paper introduces the Parametric Grid Sparse GP (PGSGP) algorithm, to improve the efficiency of using GP maps. The PGSGP reduces the complexity of evaluating the likelihood function, by reducing the number of points in the training dataset, without significant loss of the mapping or positioning accuracy. This is achieved by finding a set of pseudo-inputs arranged over a parametric grid, then optimizing the corresponding target values and the GP model hyperparameters.

In disaster management, reconstructing the environment and quickly collecting the geospatial data of the impacted areas in a short time are crucial. In this letter, a light detection and ranging (LiDAR)-based unmanned aerial vehicle (UAV) is proposed to complete the reconstruction task. The UAV integrate an inertial navigation system (INS), a global navigation satellite system (GNSS) receiver, and a low-cost LiDAR. An unmanned helicopter is introduced and the multisensor payload architecture for direct georeferencing is designed to improve the capabilities of the vehicle. In addition, a new strategy of iterative closest point algorithm is proposed to solve the registration problems in the sparse and inhomogeneous derived point cloud. The proposed registration algorithm addresses the local minima problem by the use of direct-georeferenced points and the novel hierarchical structure as well as taking the feedback bias into INS/GNSS. The generated point cloud is compared with a more accurate one derived from a high-grade terrestrial LiDAR which uses real flight data. Results indicate that the proposed UAV system achieves meter-level accuracy and reconstructs the environment with dense point cloud.

Current WiFi positioning systems (WPSs) require databases - such as locations of WiFi access points and propagation parameters, or a radio map - to assist with positioning. Typically, procedures for building such databases are time-consuming and labour-intensive. In this paper, two autonomous crowdsourcing systems are proposed to build the databases on handheld devices by using our designed algorithms and an inertial navigation solution from a Trusted Portable Navigator (T-PN). The proposed systems, running on smartphones, build and update the database autonomously and adaptively to account for the dynamic environment. To evaluate the performance of automatically generated databases, two improved WiFi positioning schemes (fingerprinting and trilateration) corresponding to these two database building systems, are also discussed. The main contribution of the paper is the proposal of two crowdsourcing-based WPSs that eliminate the various limitations of current crowdsourcing-based systems which (a) require a floor plan or GPS, (b) are suitable only for specific indoor environments, and (c) implement a simple MEMS-based sensors' solution. In addition, these two WPSs are evaluated and compared through field tests. Results in different test scenarios show that average positioning errors of both proposed systems are all less than 5.75 m.

This paper introduces a new sensor fusion approach for using multiple MEMS sensor-based pedestrian navigation systems (PNSs) to enhance the performance of each individual navigation system. First, we propose a novel single IMU-based PNS which integrates both the inertial navigation system (INS) mechanization and the pedestrian dead reckoning (PDR) mechanization. When two identical PNSs are used by a user at the same time, the output of each PNS is then shared within a Kalman filter (KF) with the state-constrained approach, which, in turn, feeds the state error correction information back to each PNS. Several real experiments are done to assess the proposed methodology for the integration of multiple PNSs. The experimental studies clearly indicate that through applying the proposed state-constrained approach, using motion sensor data from multiple mobile/wearable devices could provide more accurate navigation information for a pedestrian in all indoor and outdoor environments.

This paper provides a real-time attitude determination algorithm using gyros, accelerometers, and magnetometers on consumer portable devices. The main advantage of this algorithm is that uses a Kalman filter algorithm and utilizes multi-level constraints, including pseudo-observation updates, measurements from accelerometers and magnetometers, and the quasi-static attitude updates. Walking tests with different brands of smartphones showed that the algorithm provided promising absolute heading results outdoors, and provided smooth relative heading results indoors with different phone places such as handheld, at an ear, dangling with hand, and in a pants pocket.

This paper outlines an indoor navigation algorithm that uses multiple kinds of sensors and technologies, such as 9-axis sensors (i.e., 3D gyros, accelerometers, and magnetometers), WiFi, and magnetic matching. The corresponding real-time software on smartphones includes modules such attitude determination and gyro bias estimation, pedestrian dead-reckoning (PDR), WiFi positioning, and magnetic matching. The heading from the attitude-determination module is fed into the PDR-based position-tracking module. Then, PDR is used for providing continuous position solutions and for the blunder detection of both WiFi fingerprinting and magnetic matching. Meanwhile, WiFi fingerprinting utilizes a point-by-point matching technology, while magnetic matching is based on profile-matching. Finally, WiFi and magnetic matching results are passed into the position-tracking module as updates. This algorithm was tested with two smartphones in two indoor environments. The results indicates the proposed navigation algorithm provided better navigation results than those of PDR, WiFi, or magnetic matching by itself, and better than the results of PDR/WiFi and PDR/magnetic matching (MM) in challenging indoor environment. The proposed using off-the-shelf sensors available in consumer portable devices and existing WiFi infrastructures, and have been realized on smartphones.

This paper presents an indoor navigation algorithm that uses multiple kinds of sensors and technologies, such as MEMS sensors (i.e., gyros, accelerometers, magnetometers, and a barometer), WiFi, and magnetic matching. The corresponding real-time software on smartphones includes modules such dead-reckoning, WiFi positioning, and magnetic matching. DR is used for providing continuous position solutions and for the blunder detection of both WiFi fingerprinting and magnetic matching. Finally, WiFi and magnetic matching results are passed into the position-tracking module as updates. Meanwhile, a barometer is used to detect floor changes, so as to switch floors and the WiFi and magnetic databases. This algorithm was tested during the 5th EvAAL indoor navigation competition. Position errors on three quarters (75 %) of test points (totally 62 test points were selected to evaluate the algorithm) were under 6.6 m.

A novel calibration architecture that calculates the gyro biases without any external equipment and without any user intervention is proposed. This architecture uses a Kalman filter algorithm and utilizes multi-level constraints, such as pseudo-observation updates and the accelerometer and magnetometer measurements. Walking tests with smartphones show that the proposed architecture is effective and accurate when being used under various scenarios with different phone contexts.

For vehicle navigation, Global Positioning System (GPS) provides long term accurate positions, but only when direct lines of sight to four or more satellites exist. Inertial Navigation Systems (INS), on the other hand, are self contained sensors that can provide short term accurate navigation information. The integration of the two systems can effectively provide continuous navigation data even during GPS signal outages. Traditional inertial systems were heavy, bulky and costly. In the past two decades, the use and development of light weight, compact and cost effective Micro-Electro-Mechanical Systems (MEMS) based inertial sensors has made the civilian integrated vehicle navigation systems more affordable. However, these sensors still have to make their way in the field due to their significant error sources such as turn-on biases or scale factors variations. Moreover, the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can degrade the system performance.

Vehicle navigation poses difficulties as it requires the uninterrupted availability of accurate positioning information, even in circumstances without an ideal condition. Global Positioning System (GPS) provides consistently accurate positioning solutions if four or more GPS satellites can be observed. Unfortunately, this condition is usually not satisfied if a vehicle is going through urban canyon, tunnel or forest canopy. Even with High Sensitivity and Assisted GPS receivers, reliable positioning using GPS alone in difficult urban situations is still a challenge. Inertial Navigation System (INS), consists of self- contained sensors that can continuously provide accurate short term positioning solutions. The integration of GPS and INS can overcome the GPS drawback and provide continuous navigation solutions even during GPS signal outages. Though newly developed MEMS-based INS sensors have relatively low accuracy, they are compact and inexpensive, which is very suitable for vehicle navigation. Hence, there is a growing interest in exploring the capabilities of these sensors in the field of vehicle navigation. This paper presents the integration of GPS with MEMS-based INS in a tightly coupled scheme. Tightly coupled integration can make use of GPS signals even if less than four GPS satellites are observed. Thus it offers better integration options. To further improve the GPS/INS integration results, non- holonomic constraints and heading observations were used in this study to improve the online positioning accuracies. The results showed the drift errors could be significantly reduced when non- holonomic constraints and/or heading information were used, during periods with GPS signal outage. In addition, a backward smoother called Rauch-Tung-Striebel (RTS) was also implemented for offline processing needs purpose. The integration results showed that the RTS smoother can significantly reduce the drift errors even if neither non-holonomic constraints nor heading information were used.

Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position.

The Kalman filter is often used to integrate satellite navigation systems with inertial navigation systems. Such integrated systems are especially useful for navigation of vehicles in urban environments where satellite signals are frequently blocked by tall buildings. The filter weights the measurements of both navigation systems to provide an overall optimal solution. Unfortunately, an optimal solution is only achieved when the filter has been supplied with ideal a priori information such as proper measurement noise characteristics and system dynamics. If such parameters are not perfect they can be detected and compensated for using an intelligent navigation scheme which is adaptable to different sensors. As dynamics are encountered, satellite signal blockages are simulated to test the optimality of the filter. A neural network is then trained to learn any residual deterministic errors which are then removed from future system drifts during signal blockages.

For vehicle navigation, Global Positioning System (GPS) provides long term accurate measurements, but only when a direct line of sight to four or more satellites exists. Inertial navigation systems (INS), on the other hand, are self contained sensors that can provide short term measurements. The integration of the two systems can effectively provide continuous navigation data even during GPS signal outages. Traditional INSs are bulky and expensive, and therefore, can not be used for daily civilian applications. With the evolution of MEMS technology, MEMS-based INS sensors are evolving into more accurate, compact and inexpensive units. Hence, there is a growing interest in exploring the capabilities of these sensors in the field of vehicle navigation. Most of the research is targeted towards finding the best error models and integration techniques that can reduce the high drift and errors associated with these sensors. One of the important aspects of this integration is the optimal configuration for sampling frequency, number of bits and time delay during recording of the various sensor outputs. The very low cost of the MEMS sensors makes the cost of the signal sampling, i.e. analog to digital conversion (ADC), an issue. These parameters will reduce the on-board memory requirement, speed up the computation and hence, significantly reduce the final cost to the consumers.

The evolving structural health monitoring (SHM) targets monitoring the in-situ behavior of a structure such as a bridge, assessing its performance, detecting damage or deterioration, and determining the structure condition or health. For SHM systems to gain further effectiveness and reliability, the state-of-the-art of SHM systems needs to make use of the quantum jump achieved in the field of information technology and wireless communications. This paper presents a proposed system through which wireless and information technologies (IT) based techniques and methods are deployed, with the goal that these could lead to the development of commercial, cost-effective, yet reliable SHM systems. This incorporates the use of innovative sensing technologies, the deployment of communications and the application of artificial intelligence techniques to establish damage detection methods that use deductive reasoning technologies to effectively monitor and intelligently detect structural performance under various loads.