Fig 1 - uploaded by Moustafa Youssef
Content may be subject to copyright.
The hardware setup for the power profiling experiment. The HTC Dream’s battery is removed (right) and replaced by a fake battery connected to the Monsoon device.
Source publication
Adding location to the available information enables a new category of applications. With the constrained battery on cell phones, energy-efficient localization becomes an important challenge. In this paper we introduce a low-energy calibration-free localization scheme based on the available internal sensors in many of today's phones. We start by en...
Context in source publication
Similar publications
As new and more powerful mobile devices arrive, such as smartphones, tablets and PDAs, so do new ways of interaction emerge for the users and developers to explore. These devices allow for the creation of never-before-seen applications, using the broad range of inputs and sensors these devices integrate (such as accelerometers, light sensors, elect...
Citations
... It is based on this that the velocity and position cand be obtained using Position-Velocity-Acceleration (PVA) model which Obeys Newton's law of physical object in motion and that of Hooke's law [31]. the principle underlining PVA is the first and second integral of acceleration model to produce velocity and position respectively [42]. Therefore, for any measurements of acceleration should provide a proportional outputs of velocity and position of a body in motion with an accelerometer [40], [43], [44]. ...
This paper presents a novel model of a closed
loop capacitive accelerometer. Capacitive accelerometer is able
to detect the displacement/position of a vehicle by means of
Position-Velocity-Acceleration (PVA) model. The closed loop
design is design to overcome the steady state error obtained in
the equivalent open loop design. Therefore, in this paper, a
parallel Proportional Integral Derivative (PID) controller with
a derivative filter is designed to establish a unity feedback
accelerometer for the purpose of reducing or eliminating the
steady state error. The accelerometer gave an exact linear
dependence of displacement on a step-like function of
acceleration signal input. Matlab/Simulink software was used
to design the control system and construct the overall proposed
mathematical model. Test show that, for acceleration value
of ±1 gravity (g) produces displacement of ±1 meter (m). The
system produces an initial spike with a transient time of less
than 0.1 secs. This is same for an acceleration of ±10 g. The use
of PID controller with a derivative filter in this paper provide a
significant improvement in predicting the linear dependence
relationship between acceleration and displacement than the
traditional approach of continuous tuning of PID for
accelerometer system stability.
... Recently, approaches that take advantage of the mobile phone sensors have been proposed in [26] and [4]. The user steps are counted and their direction is determined using the accelerometer and compass. ...
One of the key requirements of context based systems and intelligent environments is a user’s location. Numerous indoor localization solutions have been proposed. In this paper, we propose an enhancement to an already implemented indoor localization algorithm that utilizes the JUDOCA operator to linearly find a match to an input image within a geo-tagged dataset of pre-stored images. The proposed approach is based on k-medoids cluster analysis, which is used to compare distances calculated with the same JUDOCA operator used in the original algorithm in an attempt to enhance its execution time. The results showed that the proposed approach introduced an enhancement in the execution speed of around 10 times compared to the original approach.
... Some systems can reduce the works [14][15][16], but most need to reduce positioning accuracy. The dead reckoning approach based on sensors of smartphones has begun to receive attention [17,18] in recent years, which uses an accelerometer to calculate the displacement of the user and a compass to learn the orientation of the user. Ref. [19] proposed a method of indoor navigation using a microelectromechanical systems (MEMS)-based strapdown inertial navigation system (INS) aided by Wi-Fi signal strength measurements. ...
Mobile sensors are widely used in indoor positioning in recent years, but most methods require cumbersome calibration for precise positioning results, thus the paper proposes a new unsupervised indoor positioning (UIP) without cumbersome calibration. UIP takes advantage of environment features in indoor environments, as some indoor locations have their signatures. UIP considers these signatures as the landmarks, and combines dead reckoning with them in a simultaneous localization and mapping (SLAM) frame to reduce positioning errors and convergence time. The test results prove that the system can achieve accurate indoor positioning, which highlights its prospect as an unconventional method of indoor positioning.
... Therefore, other techniques of outdoor localization need to be developed to cover scenarios such as energy-efficient localization or emergency response tracking (E911), where all users regardless of their devices' capabilities need to be tracked. To address that, a number of approaches have been recently proposed, including fingerprinting-based [3], [4], sensor-based [2], [5]- [15], and cellular network-based [3], [16]- [23]. In addition, due to the availability of both computation resources as well as huge datasets, neural networks and deep learning have been recently leveraged for localization [24]- [28]. ...
... However, systems such as [3], [4] require an onerous and time-consuming wardriving phase either to collect a cellular signal fingerprint or to build a model based on the road network and typical driver behavior. Sensor-based systems use sensors available on high-end phones such as WiFi [6], [7] and inertial sensors [2], [14], [15]. These techniques do not work for low-end phones. ...
A ubiquitous outdoor localization system that is easy to deploy and works equally well for all mobile devices is highly-desirable. The GPS, despite its high accuracy, cannot be reliably used for this purpose since it is not available on low-end phones nor in areas with low satellite coverage. The application of classical fingerprinting approaches, on the other hand, is prohibited by excessive maintenance and deployment costs. In this paper, we propose Crescendo, a cellular network-based outdoor localization system that does not require calibration or infrastructure support. Crescendo builds on techniques borrowed from computational geometry to estimate the user's location. Specifically, given the network cells heard by the mobile device it leverages the Voronoi diagram of the network sites to provide an initial ambiguity area and incrementally reduces this area by leveraging pairwise site comparisons and visible cell information. Evaluation of Crescendo in both an urban and a rural area using real data shows median accuracies of 152m and 224m, respectively. This is an improvement over classical techniques by at least 18% and 15%, respectively.
... In addition, GPS is power-hungry, which can drain the battery of the energy-limited mobile devices quickly [22]. To mitigate the highenergy requirement of the GPS, several techniques are proposed [12,30,38,55] that use mobile sensors with duty-cycling of the GPS to trade-off localization accuracy with energy-efficiency. To address the shortcomings of GPS-based techniques, WiFi-based outdoor localization systems have been proposed, e.g. ...
... To reduce the high energy consumption of GPS, a number of techniques have been proposed that combine other techniques, e.g. inertial sensors or map matching [6,35], with a duty cycled GPS [14,55]. However, those techniques trade the accuracy with low power consumption. ...
Recent years have witnessed fast growth in outdoor location-based services. While GPS is considered a ubiquitous localization system, it is not supported by low-end phones, requires direct line of sight to the satellites, and can drain the phone battery quickly.
In this paper, we propose DeepLoc: a deep learning-based outdoor localization system that obtains GPS-like localization accuracy without its limitations. In particular, DeepLoc leverages the ubiquitous cellular signals received from the different cell towers heard by the mobile device as hints to localize it. To do that, crowd-sensed geo-tagged received signal strength information coming from different cell towers is used to train a deep model that is used to infer the user's position. As part of DeepLoc design, we introduce modules to address a number of practical challenges including scaling the data collection to large areas, handling the inherent noise in the cellular signal and geo-tagged data, as well as providing enough data that is required for deep learning models with low-overhead.
We implemented DeepLoc on different Android devices. Evaluation results in realistic urban and rural environments show that DeepLoc can achieve a median localization accuracy within 18.8m in urban areas and within 15.7m in rural areas. This accuracy outperforms the state-of-the-art cellular-based systems by more than 470% and comes with 330% savings in power compared to the GPS. This highlights the promise of DeepLoc as a ubiquitous accurate and low-overhead localization system.
... This is an open ample room for enhancing and extending indoor/outdoor localization and navigation applica- tions. Localization-based and tracking applications are often preferred built-in GPS sensor because it is known to be more accurate in outdoor environments [1], [2], [3]. Unfor- tunately, these applications suffer from that the GPS source is power-hunger device and doesnot accurately infer the user location in indoors, urban canyons and semi-outdoors. ...
Indoor/outdoor localization, tracking and positioning applications are developed using the GPS receivers, ultrasound, infrared and RF (Wi-Fi and cellular) signals. The key point of such upper layer applications is to detect precisely whether a user is indoor or outdoor. This detection is crucial to improve the performance drastically through making a clever decision whether it is suitable to turn ON/OFF the sensors. Due to this, an unrealistic assumption is posed by the applications that the testbed environment type (indoor or outdoor) must be pre-known. In this paper, we present a realistic and ubiquitous (SenseIO) system which provides not only binary indoor/outdoor, but also a fine-grained detection (i.e., Rural, Urban, Indoor and Complex places). Without any prior knowledge, SenseIO leverages the measurements of sensor-rich smartphones (e.g., cellular, Wi-Fi, accelerometer, proximity, light and time-clock) to infer automatically the ambient environment type. A novel SenseIO multi-model system consists of four modules: (1) single serving cell tower, (2)Wi-Fi based, (3) activity recognition, and (4) light intensity. In addition, to achieve realism and ubiquity goals, we develop a SenseIO framework which includes three scenarios (A, B, C). We implement SenseIO on android-based smartphones and test it through multi-path tracing in real I/O environments. Our experiments for each individual module and all framework scenarios show that the SenseIO provides promising detection accuracy (above 92%) and outperforms existing indoor-outdoor techniques in terms of both accuracy and fine-grained detection.
... MapCraft [181] Step counts,Heading direction. Medium Low Rely on extensive training sets; Complex training GAC [182] Compass. Accelerometer Within intracity Low Less use of GPS CompAcc [183] Compass Accelerometer Less than 11 m Low For fallback mechanism AGPS used. ...
... To overcome this difficulty several resolutions have been recommended by supplementing GPS module and synchronization by M. Youssef et. al. [182] of data from motion sensors. The main idea of reducing error is to turn on GPS module in some interval and synchronize GPS data with the motion sensor data. ...
... This process require more power consumption. Experiment shows that the proposed method [182] is able to save energy of the system exponentially. An infrastructureindependent localization system [183] is built by CompAcc using DR algorithm and Assisted-GPS (AGPS). ...
Internet of Things (IoT) is a novel design paradigm, intended as a network of billions to trillions of tiny sensors communicating with each other to offer innovative solutions to real time problems. These sensors form a network named as Wireless Sensor Networks (WSN) to monitor physical environment and disseminate collected data back to the base station through multiple hops. WSN has the capability to collect and report data for a specific application. The location information plays an important role for various wireless sensor network applications. A majority of the applications are related to location based services (LBS). The development of sensor technology, processing techniques and communication systems give rise to a development of the smart sensor for the adaptive and innovative application. So a single localization technique is not adequate for all application. In this paper, a recent extensive analysis of localization techniques and hierarchical taxonomy and their applications in the different context is presented. This taxonomy of the localization technique is classified based on presence of offline training in localization namely self determining and training dependent approaches. Finally, various open research issue related to localization schemes for IoT are compared and proposed various directions for future research.
... Different approach to infrastructure-less localisation was introduced in [50,51] where authors proposed a solution based on internal inertial sensors of mobile devices. Dead-reckoning accuracy in most cases suffers from the accumulated error from accelerometer [53]. To improve its performance modifications similar to [49] were tested [53,54]. ...
... Dead-reckoning accuracy in most cases suffers from the accumulated error from accelerometer [53]. To improve its performance modifications similar to [49] were tested [53,54]. Slightly better performance was achieved by UnLoc [31] and other solutions [30,55] that combine dead-reckoning approach with signature detection. ...
We present a novel method of fast and reliable data gathering for the purpose of location services based on radio signal strength services such as WiFi location/navigation. Our method combines the acquisition of location and mapping based on computer vision methods with WiFi signal strength stochastic data gathering. The output of the method is threefold: 3D metric space model, 2D floor plan map and metric map of stochastic radio signal strength. The binding of location data with radio data is done completely automatically, without any human intervention. The advantage of our solution lies also in a significant speed-up and density increase of Radio Map generation which opens new markets for WiFi navigation services. We have proved that presented solution produces a map allowing location in office space of accuracy 1.06 m. © 2017, Industrial Research Institute for Automation and Measurements. All rights reserved.
... For example, in the popular pedestrian and in-vehicle dead-reckoning localization techniques, e.g. [14,43], the user displacement obtained from the inertial sensors is combined with the movement direction to estimate the next user location. Erroneous direction estimation, based on the returned phone orientation from the OS API, will result in a large error in localization that accumulates quickly with time. ...
... Smartphone-based applications such as vehicular/pedestrian dead-reckoning based localization [1,14,43] require knowledge of the user's actual heading direction. Yet, for simplicity, they assume a fixed-position for the phone and that it is oriented with the user. ...
... On the other hand, the Android API is the default heading estimation technique available on Android phones and has been used by a number of systems, e.g. [14,43], to determine the phone orientation, rather than the human orientation (assuming both are the same). However, it is considered the widely deployed state-of-theart. ...
Recently, there have been a wide range of mobile computing and crowd-sourcing applications that leverage the proliferating sensing capabilities of smartphones. Many of these place a paramount importance on accurate user heading estimation. Such applications include dead-reckoning-based localization and many crowd-sensing applications where the user typically carry her phone in arbitrary positions and orientations relative to her body and her transportation mode. However, there is no general solution available to estimate the user’s heading as current state-of-the-art focus on improving the phone orientation estimation, require the phone to be placed in a particular position, require a fixed transportation mode, require user intervention, and/or do not work accurately indoors. In this paper we present Humaine, a novel ubiquitous system that reliably and accurately estimates the user orientation relative to the Earth coordinate system. Humaine works accurately whether the user is riding a vehicle or walking indoors/outdoors for arbitrary cell phone positions and orientations relative to the user body. Moreover, it requires no prior-configuration nor user intervention. The system intelligently fuses the different inertial sensors widely available in off-the-shelf smartphones and employs statistical analysis techniques to their measurements to estimate the user orientation. Implementation of the system on different Android devices with 300 experiments performed at different indoor and outdoor testbeds shows that Humaine significantly outperforms the state-of-the-art in diverse scenarios, achieving a median accuracy of 14∘ and 16∘ for indoor and outdoor pedestrian users and 20∘ for in-vehicle users over a wide variety of phone positions. This is better than the-state-of-the-art by 523% and 594% for indoor and outdoor pedestrian users and 750% for in-vehicle users. This accuracy highlights the ubiquity of Humaine and its robustness against the various noise sources.
... Additionally, nowadays users often keep Bluetooth interface switched off. Similarly to the previous case, the approach of Youssef et al. [11] cannot be be applied in our case due to Android sleep mode restrictions. The authors used accelerometer and magnetometer readings. ...
