Site Survey Process Research Articles

DISAIN BANTARAN YANG ESTETIS, EKOLOGIS DAN FUNGSIONAL DI SITU PENGARENGAN DEPOK

Situ Pengarengan Depok has banks with the potential for the beauty of the landscape that has not been optimally utilized as a city green open space, if it is managed properly and correctly, it can be one of the alternative tourism in Depok. The problems felt by the surrounding community are (1) The occurrence of flooding in the rainy season due to silting, garbage, and plants that spread throughout the banks; (2) The lack of green open space that can be used by the community as a passive or active recreation area, (3) The lake bank has not been used optimally, and (4) There is no clarity on the authority to manage the lake bank.This Community Service (PkM) aims to (1) socialize the importance of public awareness to participate in conserving the lake and its banks, (2) making a "sustainable lake-banks landscape arrangement" by considering ecological aspects, aesthetic aspects and functional aspects according to community needs. The method used starts from the result of preliminary research, site survey and design process. The design process begins with conducting a physical and non-physical analysis, user analysis, mapping of the needs programs, then followed by generating concept, design schematic, and site plan along Situ Pengarengan. The site plan images are presented to the surrounding community to get feedback through the Forum Group Discussion (FGD), then the result from FGD is used to finalize the product image. The results of the FGD are increasing awareness of community behavior to participate in conserving the lake and its banks, published articles in the PkM journal, copyright for the site plan (Site Plan) and copyright for a simulation video for the design of the Situ Pengarengan riverbank. To conclude, the FGD activity and the site plan design are very functionally, aesthetically and ecologically beneficial for the sustainability of Situ Pengarengan banks.

Sensor-Aided Learning for Wi-Fi Positioning With Beacon Channel State Information

Because each indoor site has its own radio propagation characteristics, a site survey process is essential to optimize a Wi-Fi ranging strategy for range-based positioning solutions. This study examines an unsupervised learning technique that autonomously learns an optimal ranging strategy for each site using Wi-Fi and sensor data accumulated while users access a positioning application. Using the collected sensor data, the device trajectory is regenerated, and a Wi-Fi ranging module is optimized to generate the shape of the estimated trajectory using Wi-Fi, similar to that obtained from sensors. In this process, the ranging module learns the way to identify the channel conditions from each Wi-Fi access point (AP) and produces ranging results accordingly. Furthermore, we collect the channel state information (CSI) from beacon frames to investigate the benefit of using CSI in addition to received signal strength measurements. With the CSI, the ranging module can identify diverse channel conditions from each AP and more accurately generate the reliability of each distance estimate to achieve accurate positioning results. The effectiveness of the proposed learning technique is verified using a real-time positioning application implemented on a PC platform.

Practical Evaluation of a Crowdsourcing Indoor Localization System Using Hidden Markov Models

The extensive deployment of wireless infrastructure provides a low-cost approach to tracking of mobile phone users in indoor environments using received signal strength (RSS). Crowdsourcing has been promoted as an efficient way to reduce the labor-intensive site survey process in conventional fingerprint-based localization systems. Despite its stated advantages, use of crowdwourcing for localization has issues of accuracy and reliability in indoor applications, in large part because of multipath propagation. This paper discusses and evaluates a Bayesian approach to localization of mobile users based on a crowdsourced fingerprint, in which environmental constraints as well as dynamic property of the mobile are incorporated as priors. Both a Markov chain and a semi-Markov chain approach are applied for modeling the transition and duration statistics of the mobile user across different location cells. Field test results demonstrate the effectiveness of introducing this additional information for localization in a real-world wireless network.

FineLoc: A Fine-Grained Self-Calibrating Wireless Indoor Localization System

Self-calibrating wireless indoor localization systems construct the radio map even the indoor floor plan automatically, which avoids the labor-intensive site survey process; however, existing systems utilizing the feature of Wi-Fi signals can only provide coarse-grained indoor maps, which hinders improvement of localization accuracy. In this paper, we present FineLoc, a fine-grained self-calibrating localization system based on the freely-deployed Bluetooth low energy (BLE) nodes and crowdsourced data, which can profile more detailed layout information of the indoor space. We first reveal that existing systems can only generate inaccurate floor plans owning to the coarse-grained Wi-Fi reference information. Then, we utilize the increasingly popular BLE beacon nodes as the source of reference information, with which a series of dead-reckoning optimization and new schemes particularly for finer-grained indoor map construction are presented. We implement a prototype FineLoc system, which is deployed in around $11,000\;\mathrm{m}^2$11,000m2 areas. Our experimental results with the prototype show that FineLoc can achieve 80 percent localization errors within $1.6\;\mathrm{m}$1.6m, $1.4\;\mathrm{m}$1.4m, and $1.1\;\mathrm{m}$1.1m in the library, classroom building, and office building, respectively, with an average density of deployed BLE nodes less than $2.6/100\;\mathrm{m}^2$2.6/100m2.

A Probabilistic Indoor Localization System for Heterogeneous Devices

The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environments. Crowdsourcing has been promoted as an efficient way to reduce the labour-intensive site survey process in conventional fingerprint-based localization systems. Alongside its promising advantages, crowdsourcing produces a number of new challenges, including the heterogeneity of devices resulting in signal diversity and varying sensitivities to different access points. These are caused by different sensor specifications and antenna attenuation. These challenges are exacerbated by differences in the device populations between the survey and client phases. This paper presents a prototype model of a multiple-surveyor-multiple-client system to localize mobile users based on a crowdsourced fingerprint. A linear regression model is applied to calibrate across participating training devices. The conditional likelihood of a client observing an access point not visible in the training phase is obtained using a geometric distribution. The proposed system is able to achieve comparable localization performance. Field test results demonstrate the efficiency and benefit that the previously built radio map can be adapted to localize a new device that has not participated in the training phase with an average matching accuracy of 94% in a real world wireless network.

Crowdsourcing and Sensing for Indoor Localization in IoT: A Review

Among the emerging concepts in the context of IoT, crowdsourcing, and crowdsensing are known as two critical building blocks on the intersection point of things and human-based techniques. Although contribution and involvement of people is a key factor in both crowdsensing and crowdsourcing systems which provides reliability and data quality, level of human contribution is what makes crowd-powered systems different. To scrutinize this difference it is considered that user intervention in crowd-based schemes can be either implicit or explicit. Since one of the most important applications of crowdsourcing and crowdsensing systems is providing location services in indoor environments, in this paper, we study the level of user contribution in available crowd-powered techniques and propose a classification for crowd-powered indoor localization solutions to clarify which crowds-based approach is utilized in each indoor localization solution because in many cases the distinction is not clear and often leads to misconception. Dependency on site survey process is considered as another distinction point in our proposed classification. Hence, we consider indoor localization solutions with implicit user participation and latent mobile utilization as crowdsensing indoor localization systems. Respectively, indoor localization solutions with explicit user participation and manifest mobile utilization are classified as crowdsourcing indoor localization systems.

WinIPS: WiFi-Based Non-Intrusive Indoor Positioning System With Online Radio Map Construction and Adaptation

WiFi fingerprinting-based indoor positioning system (IPS) has become the most promising solution for indoor localization. However, there are two major drawbacks that hamper its large-scale implementation. First, an offline site survey process is required which is extremely time-consuming and labor-intensive. Second, the RSS fingerprint database built offline is vulnerable to environmental dynamics. To address these issues comprehensively, in this paper, we propose WinIPS, a WiFi-based non-intrusive IPS that enables automatic online radio map construction and adaptation, aiming for calibration-free indoor localization. WinIPS can capture data packets transmitted in existing WiFi traffic and extract the RSS and MAC addresses of both WiFi access points (APs) and mobile devices in a non-intrusive manner. APs can be used as online reference points for radio map construction. A novel Gaussian process regression model is proposed to approximate the non-uniform RSS distribution of an indoor environment. Extensive experiments were conducted, which demonstrated that WinIPS outperforms existing solutions in terms of both RSS estimation accuracy and localization accuracy.

Practical passive localization system based on wireless signals for fast deployment of occupancy services

Occupancy is relevant information about key aspects, such as energy consumption or comfort management. Energy-saving and environmental quality strategies can be carried out in response to real-time facility occupancy. Some relevant solutions to measure and monitor occupancy information leverage radio-based indoor localization systems and employ Received Signal Strength (RSS) as the main source of data for location determination. However, those approaches usually require a previous training and calibration stage that involves a time-consuming and labor intensive site survey process, and which is also readily affected by environmental dynamics. In this paper, we propose a practical passive localization system for fast deployment of occupancy services able to track unmodified and heterogeneous devices after a quick and straightforward training phase. We present an experimental validation of the system that was conducted for 9 months in a lecture building of 6000 square meters with 20 classrooms and 4000 frequent users, where the existing teaching computers themselves were used as monitors to capture 802.11 traffic. In this environment, we test different representations and metrics to process the RSSI information and perform a thorough analysis of some important design parameters, which have a direct impact on both accuracy and time granularity of the localization system.

Adaptive Sampling of RF Fingerprints for Fine-Grained Indoor Localization

Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promis- ing approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a fingerprint database a priori for localization. To address this problem, we visualize the 3-D RF fingerprint data as a function of locations (x-y) and indices of access points (fingerprint), as a tensor and use tensor algebraic methods for an adaptive tubal-sampling of this fingerprint space. In particular using a recently proposed tensor algebraic framework in [1] we capture the complexity of the fingerprint space as a low-dimensional tensor-column space. In this formulation the proposed scheme exploits adaptivity to identify reference points which are highly informative for learning this low-dimensional space. Further, under certain incoherency conditions we prove that the proposed scheme achieves bounded recovery error and near-optimal sampling complexity. In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy. The approach is validated on both data generated by the ray-tracing indoor model which accounts for the floor plan and the impact of walls and the real world data. Simulation results show that, while maintaining the same localization accuracy of existing approaches, the amount of samples can be cut down by 71% for the high SNR case and 55% for the low SNR case.

Smartphones Based Crowdsourcing for Indoor Localization

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. Considering user movements in a building, originally separated RSS fingerprints are geographically connected by user moving paths of locations where they are recorded, and they consequently form a high dimension fingerprint space, in which the distances among fingerprints are preserved. The fingerprint space is then automatically mapped to the floor plan in a stress-free form, which results in fingerprints labeled with physical locations. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1,600 m $^2$ , and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.

Optimization of Sampling Cell Size for Fingerprint Positioning

The fingerprint positioning based on received signal strength has a widespread development in many applications since the technology does not depend on additional hardware deployment. However, the site survey process which is executed before fingerprint positioning is time consuming and labor intensive. Sampling cell size directly affects the localization precision and the survey workload. We propose a sampling cell size optimization method to release the survey workload while holding the precision. We adopted the Gaussian process model to estimate the received signal strength based on some samples and used the inherent maximum Euclidean error of fingerprint vectors to compute the optimal cell size. We conduct some experiments to validate the efficiency of the mechanism. And the results show that our method can largely reduce the workload of the site survey process.

Calibration-less indoor location systems based on wireless sensors

In case of a crisis event, it is the responsibility of public and government authorities to manage the response operations. Positioning is a crucial task when managing emergency, which aims at estimating the positions of the first responders that act on the crisis site. On the other hand, the radio-based positioning solutions require a process of site survey, in which radio signatures have to be collected and stored in a radio map for further comparison and matching. Site survey involves intensive manual effort and time, which is no feasible during the crisis event. This paper proposes an approach for rapid site survey of the considered area. A specific tool has been developed to draw the site topography and to define the radio map generated by the wireless sensors located in the considered area, by using an accurate signal attenuation model. Experiment results show that the proposed solution can achieve a position accuracy that can be considered acceptable in the context of the rescuers localization even without a site survey activity.

WILL: Wireless Indoor Localization without Site Survey

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Site survey involves intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and leverage user motions to construct radio floor plan that is previously obtained by site survey. On this basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones. WILL is deployed in a real building covering over 1600 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and its deployment is easy and rapid since site survey is no longer needed. The experiment results show that WILL achieves competitive performance comparing with traditional approaches.

New Standards Proposed

The Commission on Accreditation of Medical Transport Systems (CAMTS) Accreditation Standards are revised every 2 to 3 years to keep current with the dynamic change in medical transport. The current 7th edition standards were published in 2006 and will not be fully revised until late 2009. The Standards Committee will be working on very broad changes in format for the eighth edition to focus on outcomes and streamline the pre-survey questionnaire and site survey process. A risk analysis tool is also in the testing process by site surveyors and will be introduced to programs by Spring 2009. Since the 7th edition accreditation standards were published, a lot has happened. From February 5 to June 29, 2008, there were seven rotor-wing accidents; four occurred at night, with a total of 17 fatalities. The Federal Aviation Administration held a meeting with the aviation operators on July 11, and the air medical associations held a Safety Summit on July 25 to discuss the accidents and the need to refocus our efforts to reduce accidents. Safety culture was a large part of the discussions at the summit. Safety Culture has been discussed at CAMTS board meetings since 2006, when we started to develop a publication we are calling Creating a Culture of Quality and Safety. The goal of this book is to give readers the multidisciplinary insight to culture as the foundation of safety and quality. Guest authors who are experts in human factors, safety culture, risk analysis, Just Culture, and so forth, will contribute chapters to aid readers in managing multidisciplinary teams and strengthening the culture in various business models. At the July 2008 CAMTS board of directors meeting, the board discussed each accident in 2008 and drafted some proposed changes and additions to the 7th edition standards. The following proposed standards have been posted and announced on the web site, in newsletters, and in public forums for open discussion. We welcome all comments and questions about the proposed changes, which will become effective in the first quarter of 2009 after the extensive comment period. These proposed changes will be published as an addendum to the 7th edition standards.

Hydroacoustic monitoring of the oceans

In order to verify compliance with this Treaty, an international Monitoring System based on four technologies, seismology, infrasounds, hydroacoustic, and radionuclides, shall be implemented. Therefore, a network of hydrophones and seismic T-wave stations has been designed and evaluated in terms of detectability and location capability. Hydroacoustic waves generated by an undersea explosion or by oceanic earthquakes are propagating over several thousands of kilometers within an oceanic waveguide called So.F.A.R., channel (sound fixing and ranging) as shown by Ewing and Tolstoy (1950). When they are reaching any continental slope or island they are converted in seismic T-waves which are recorded by seismographs. By using both numerical simulations and experimental analysis, a methodology has developed which allows to better understand and quantify the effects of the source parameters and of the propagation characteristics (specifically on the So.F.A.R. channel and of the continental slope) on the recorded waveforms. The results obtained should be useful to improve the evaluation of detectability and location capability of the future hydroacoustic network and should provide some effective guidance to the site survey process. For that purpose and also to have a better understanding of the source parameters and of the acoustic wave propagation, a methodology has developed using both a numerical simulation and an experimental analysis.