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Special Issue "Advanced Algorithms for Mobile Sensing: from Wireless Sensor Networks to Mobile Crowd Sensing"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 August 2016).

Special Issue Editor

Guest Editor
Prof. Yu Wang

Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Website | E-Mail
Interests: wireless networks; sensor networks; mobile crowd sensing; mobile computing

Special Issue Information

Dear Colleagues,

In recent years we have witnessed tremendous development in wireless sensor networks, which enables ubiquitous monitoring of the physical environment. Furthermore, with the rapid development of mobile devices and computing capabilities, mobile sensing has increasingly emerged as one of the most important technologies to develop large-scale sensing solutions. Smartphones equipped with multiple sensors are becoming the central personal sensing device in people’s everyday life. Mobile crowd sensing leverages the power of ordinary people to contribute data from their mobile devices and aggregates and fuses the data for intelligence sensing tasks. The combination of traditional wireless sensor networks and emerging mobile crowd sensing can further enhance novel large-scale sensing applications and systems into every sector of our economy.

The unique characteristics of mobile sensing also pose considerable challenges on the design of large-scale sensing systems. Some research efforts on mobile sensing are in progress, including mobile sensor networks, mobile crowd sensing, mobile social networks, participatory sensing, urban sensing, vehicular sensor networks, etc. In many of these areas or scenarios, different analytical models as well as advanced algorithms are desired for performance optimization. This Special Issue intends to bring together researchers to report their latest progress in mobile sensing or large-scale sensor networking research from the algorithmic perspective and exchange experiences in the development of advanced algorithms for large-scale sensing systems. Research articles with new contributions as well as comprehensive reviews are solicited.

Prof. Dr. Yu Wang
Guest Editor

Manuscript Submission Information

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Keywords

  • Algorithms
  • Optimizations
  • Mobile sensing
  • Mobile crowd sensing
  • Participatory sensing
  • Urban sensing
  • Wireless sensor networks
  • Mobile sensor networks
  • Mobile social networks
  • Vehicular sensor networks

Published Papers (23 papers)

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Research

Open AccessArticle
A Truthful Incentive Mechanism for Online Recruitment in Mobile Crowd Sensing System
Sensors 2017, 17(1), 79; https://doi.org/10.3390/s17010079
Received: 29 August 2016 / Accepted: 9 December 2016 / Published: 1 January 2017
Cited by 7 | PDF Full-text (898 KB) | HTML Full-text | XML Full-text
Abstract
We investigate emerging mobile crowd sensing (MCS) systems, in which new cloud-based platforms sequentially allocate homogenous sensing jobs to dynamically-arriving users with uncertain service qualities. Given that human beings are selfish in nature, it is crucial yet challenging to design an efficient and [...] Read more.
We investigate emerging mobile crowd sensing (MCS) systems, in which new cloud-based platforms sequentially allocate homogenous sensing jobs to dynamically-arriving users with uncertain service qualities. Given that human beings are selfish in nature, it is crucial yet challenging to design an efficient and truthful incentive mechanism to encourage users to participate. To address the challenge, we propose a novel truthful online auction mechanism that can efficiently learn to make irreversible online decisions on winner selections for new MCS systems without requiring previous knowledge of users. Moreover, we theoretically prove that our incentive possesses truthfulness, individual rationality and computational efficiency. Extensive simulation results under both real and synthetic traces demonstrate that our incentive mechanism can reduce the payment of the platform, increase the utility of the platform and social welfare. Full article
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Open AccessArticle
Local Coverage Optimization Strategy Based on Voronoi for Directional Sensor Networks
Sensors 2016, 16(12), 2183; https://doi.org/10.3390/s16122183
Received: 31 August 2016 / Revised: 26 November 2016 / Accepted: 13 December 2016 / Published: 18 December 2016
Cited by 9 | PDF Full-text (1210 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we study the area coverage of directional sensor networks (DSNs) with random node distribution. The coverage of DSNs depends on the sensor’s locations, the sensing radiuses, and the working directions, as well as the angle of view (AoV), which is [...] Read more.
In this paper, we study the area coverage of directional sensor networks (DSNs) with random node distribution. The coverage of DSNs depends on the sensor’s locations, the sensing radiuses, and the working directions, as well as the angle of view (AoV), which is challenging to analyze. We transform the network area coverage problem into cell coverage problems by exploiting the Voronoi diagram, which only needs to optimize local coverage for each cell in a decentralized way. To address the cell coverage problem, we propose three local coverage optimization algorithms to improve the cell coverage, namely Move Inside Cell Algorithm (MIC), Rotate Working Direction Algorithm (RWD) and Rotation based on boundary (RB), respectively. Extensive simulations are performed to prove the effectiveness of our proposed algorithms in terms of the coverage ratio. Full article
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Open AccessArticle
GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors
Sensors 2016, 16(12), 2137; https://doi.org/10.3390/s16122137
Received: 30 August 2016 / Revised: 7 December 2016 / Accepted: 9 December 2016 / Published: 15 December 2016
Cited by 4 | PDF Full-text (11323 KB) | HTML Full-text | XML Full-text
Abstract
Although map filtering-aided Pedestrian Dead Reckoning (PDR) is capable of largely improving indoor localization accuracy, it becomes less efficient when coping with highly complex indoor spaces. For instance, indoor spaces with a few close corners or neighboring passages can lead to particles entering [...] Read more.
Although map filtering-aided Pedestrian Dead Reckoning (PDR) is capable of largely improving indoor localization accuracy, it becomes less efficient when coping with highly complex indoor spaces. For instance, indoor spaces with a few close corners or neighboring passages can lead to particles entering erroneous passages, which can further cause the failure of subsequent tracking. To address this problem, we propose GridiLoc, a reliable and accurate pedestrian indoor localization method through the fusion of smartphone sensors and a grid model. The key novelty of GridiLoc is the utilization of a backtracking grid filter for improving localization accuracy and for handling dead ending issues. In order to reduce the time consumption of backtracking, a topological graph is introduced for representing candidate backtracking points, which are the expected locations at the starting time of the dead ending. Furthermore, when the dead ending is caused by the erroneous step length model of PDR, our solution can automatically calibrate the model by using the historical tracking data. Our experimental results show that GridiLoc achieves a higher localization accuracy and reliability compared with the commonly-used map filtering approach. Meanwhile, it maintains an acceptable computational complexity. Full article
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Open AccessArticle
An Indoor Positioning Method for Smartphones Using Landmarks and PDR
Sensors 2016, 16(12), 2135; https://doi.org/10.3390/s16122135
Received: 31 August 2016 / Revised: 30 November 2016 / Accepted: 6 December 2016 / Published: 15 December 2016
Cited by 8 | PDF Full-text (877 KB) | HTML Full-text | XML Full-text
Abstract
Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs [...] Read more.
Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limitations, such as heavy computation and inconvenience for real-time use. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weight and suitable for real-time implementation by running an Android application designed for the experiment. We compared LaP with other PDR-based fusion approaches. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 2.17 m. Full article
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Open AccessArticle
Geometric Distribution-Based Readers Scheduling Optimization Algorithm Using Artificial Immune System
Sensors 2016, 16(11), 1924; https://doi.org/10.3390/s16111924
Received: 31 August 2016 / Revised: 8 November 2016 / Accepted: 12 November 2016 / Published: 16 November 2016
PDF Full-text (2664 KB) | HTML Full-text | XML Full-text
Abstract
In the multiple-reader environment (MRE) of radio frequency identification (RFID) system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling [...] Read more.
In the multiple-reader environment (MRE) of radio frequency identification (RFID) system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS) is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression) quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range. Full article
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Open AccessArticle
An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
Sensors 2016, 16(11), 1811; https://doi.org/10.3390/s16111811
Received: 25 August 2016 / Revised: 23 October 2016 / Accepted: 25 October 2016 / Published: 29 October 2016
Cited by 1 | PDF Full-text (1994 KB) | HTML Full-text | XML Full-text
Abstract
With the development of localization technologies, researchers solve the indoor localization problems using diverse methods and equipment. Most localization techniques require either specialized devices or fingerprints, which are inconvenient for daily use. Therefore, we propose and implement an accurate, efficient and lightweight system [...] Read more.
With the development of localization technologies, researchers solve the indoor localization problems using diverse methods and equipment. Most localization techniques require either specialized devices or fingerprints, which are inconvenient for daily use. Therefore, we propose and implement an accurate, efficient and lightweight system for indoor direction finding using common smartphones and loudspeakers. Our method is derived from a key insight: By moving a smartphone in regular patterns, we can effectively emulate the sensitivity and functionality of a Uniform Antenna Array to estimate the angle of arrival of the target signal. Specifically, a user only needs to hold his smartphone still in front of him, and then rotate his body around 360 duration with the smartphone at an approximate constant velocity. Then, our system can provide accurate directional guidance and lead the user to their destinations (normal loudspeakers we preset in the indoor environment transmitting high frequency acoustic signals) after a few measurements. Major challenges in implementing our system are not only imitating a virtual antenna array by ordinary smartphones but also overcoming the detection difficulties caused by the complex indoor environment. In addition, we leverage the gyroscope of the smartphone to reduce the impact of a user’s motion pattern change to the accuracy of our system. In order to get rid of the multipath effect, we leverage multiple signal classification to calculate the direction of the target signal, and then design and deploy our system in various indoor scenes. Extensive comparative experiments show that our system is reliable under various circumstances. Full article
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Open AccessArticle
Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications
Sensors 2016, 16(10), 1693; https://doi.org/10.3390/s16101693
Received: 5 September 2016 / Revised: 24 September 2016 / Accepted: 1 October 2016 / Published: 13 October 2016
Cited by 2 | PDF Full-text (2879 KB) | HTML Full-text | XML Full-text
Abstract
The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the [...] Read more.
The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution. Full article
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Open AccessArticle
Mapping Urban Environmental Noise Using Smartphones
Sensors 2016, 16(10), 1692; https://doi.org/10.3390/s16101692
Received: 27 June 2016 / Revised: 27 September 2016 / Accepted: 8 October 2016 / Published: 13 October 2016
Cited by 14 | PDF Full-text (4120 KB) | HTML Full-text | XML Full-text
Abstract
Noise mapping is an effective method of visualizing and accessing noise pollution. In this paper, a noise-mapping method based on smartphones to effectively and easily measure environmental noise is proposed. By using this method, a noise map of an entire area can be [...] Read more.
Noise mapping is an effective method of visualizing and accessing noise pollution. In this paper, a noise-mapping method based on smartphones to effectively and easily measure environmental noise is proposed. By using this method, a noise map of an entire area can be created using limited measurement data. To achieve the measurement with certain precision, a set of methods was designed to calibrate the smartphones. Measuring noise with mobile phones is different from the traditional static observations. The users may be moving at any time. Therefore, a method of attaching an additional microphone with a windscreen is proposed to reduce the wind effect. However, covering an entire area is impossible. Therefore, an interpolation method is needed to achieve full coverage of the area. To reduce the influence of spatial heterogeneity and improve the precision of noise mapping, a region-based noise-mapping method is proposed in this paper, which is based on the distribution of noise in different region types tagged by volunteers, to interpolate and combine them to create a noise map. To validate the effect of the method, a comparison of the interpolation results was made to analyse our method and the ordinary Kriging method. The result shows that our method is more accurate in reflecting the local distribution of noise and has better interpolation precision. We believe that the proposed noise-mapping method is a feasible and low-cost noise-mapping solution. Full article
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Open AccessArticle
PILA: Sub-Meter Localization Using CSI from Commodity Wi-Fi Devices
Sensors 2016, 16(10), 1664; https://doi.org/10.3390/s16101664
Received: 28 August 2016 / Revised: 27 September 2016 / Accepted: 4 October 2016 / Published: 10 October 2016
Cited by 8 | PDF Full-text (1042 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this paper is to present a new indoor localization approach by employing the Angle-of-arrival (AOA) and Received Signal Strength (RSS) measurements in Wi-Fi network. To achieve this goal, we first collect the Channel State Information (CSI) by using the commodity [...] Read more.
The aim of this paper is to present a new indoor localization approach by employing the Angle-of-arrival (AOA) and Received Signal Strength (RSS) measurements in Wi-Fi network. To achieve this goal, we first collect the Channel State Information (CSI) by using the commodity Wi-Fi devices with our designed three antennas to estimate the AOA of Wi-Fi signal. Second, we propose a direct path identification algorithm to obtain the direct signal path for the sake of reducing the interference of multipath effect on the AOA estimation. Third, we construct a new objective function to solve the localization problem by integrating the AOA and RSS information. Although the localization problem is non-convex, we use the Second-order Cone Programming (SOCP) relaxation approach to transform it into a convex problem. Finally, the effectiveness of our approach is verified based on the prototype implementation by using the commodity Wi-Fi devices. The experimental results show that our approach can achieve the median error 0.7 m in the actual indoor environment. Full article
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Open AccessArticle
A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
Sensors 2016, 16(10), 1630; https://doi.org/10.3390/s16101630
Received: 1 August 2016 / Revised: 17 September 2016 / Accepted: 26 September 2016 / Published: 1 October 2016
Cited by 3 | PDF Full-text (17721 KB) | HTML Full-text | XML Full-text
Abstract
In wireless sensor networks, in order to satisfy the requirement of long working time of energy-limited nodes, we need to design an energy-efficient and lifetime-extended medium access control (MAC) protocol. In this paper, a node cooperation mechanism that one or multiple nodes with [...] Read more.
In wireless sensor networks, in order to satisfy the requirement of long working time of energy-limited nodes, we need to design an energy-efficient and lifetime-extended medium access control (MAC) protocol. In this paper, a node cooperation mechanism that one or multiple nodes with higher channel gain and sufficient residual energy help a sender relay its data packets to its recipient is employed to achieve this objective. We first propose a transmission power optimization algorithm to prolong network lifetime by optimizing the transmission powers of the sender and its cooperative nodes to maximize their minimum residual energy after their data packet transmissions. Based on it, we propose a corresponding power-optimized cooperative MAC protocol. A cooperative node contention mechanism is designed to ensure that the sender can effectively select a group of cooperative nodes with the lowest energy consumption and the best channel quality for cooperative transmissions, thus further improving the energy efficiency. Simulation results show that compared to typical MAC protocol with direct transmissions and energy-efficient cooperative MAC protocol, the proposed cooperative MAC protocol can efficiently improve the energy efficiency and extend the network lifetime. Full article
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Open AccessArticle
QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms
Sensors 2016, 16(10), 1629; https://doi.org/10.3390/s16101629
Received: 13 July 2016 / Revised: 13 September 2016 / Accepted: 26 September 2016 / Published: 1 October 2016
PDF Full-text (2474 KB) | HTML Full-text | XML Full-text
Abstract
In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high [...] Read more.
In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution. Full article
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Open AccessArticle
A Geographical Heuristic Routing Protocol for VANETs
Sensors 2016, 16(10), 1567; https://doi.org/10.3390/s16101567
Received: 9 June 2016 / Revised: 14 September 2016 / Accepted: 18 September 2016 / Published: 23 September 2016
Cited by 3 | PDF Full-text (3163 KB) | HTML Full-text | XML Full-text
Abstract
Vehicular ad hoc networks (VANETs) leverage the communication system of Intelligent Transportation Systems (ITS). Recently, Delay-Tolerant Network (DTN) routing protocols have increased their popularity among the research community for being used in non-safety VANET applications and services like traffic reporting. Vehicular DTN protocols [...] Read more.
Vehicular ad hoc networks (VANETs) leverage the communication system of Intelligent Transportation Systems (ITS). Recently, Delay-Tolerant Network (DTN) routing protocols have increased their popularity among the research community for being used in non-safety VANET applications and services like traffic reporting. Vehicular DTN protocols use geographical and local information to make forwarding decisions. However, current proposals only consider the selection of the best candidate based on a local-search. In this paper, we propose a generic Geographical Heuristic Routing (GHR) protocol that can be applied to any DTN geographical routing protocol that makes forwarding decisions hop by hop. GHR includes in its operation adaptations simulated annealing and Tabu-search meta-heuristics, which have largely been used to improve local-search results in discrete optimization. We include a complete performance evaluation of GHR in a multi-hop VANET simulation scenario for a reporting service. Our study analyzes all of the meaningful configurations of GHR and offers a statistical analysis of our findings by means of MANOVA tests. Our results indicate that the use of a Tabu list contributes to improving the packet delivery ratio by around 5% to 10%. Moreover, if Tabu is used, then the simulated annealing routing strategy gets a better performance than the selection of the best node used with carry and forwarding (default operation). Full article
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Open AccessArticle
Using Crowdsourced Trajectories for Automated OSM Data Entry Approach
Sensors 2016, 16(9), 1510; https://doi.org/10.3390/s16091510
Received: 13 July 2016 / Revised: 11 August 2016 / Accepted: 23 August 2016 / Published: 15 September 2016
Cited by 8 | PDF Full-text (4240 KB) | HTML Full-text | XML Full-text
Abstract
The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and the generation of information by large groups of users/contributors. OpenStreetMap (OSM) is a very successful example of a crowd-sourced geospatial data project. Unfortunately, it is often the [...] Read more.
The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and the generation of information by large groups of users/contributors. OpenStreetMap (OSM) is a very successful example of a crowd-sourced geospatial data project. Unfortunately, it is often the case that OSM contributor inputs (including geometry and attribute data inserts, deletions and updates) have been found to be inaccurate, incomplete, inconsistent or vague. This is due to several reasons which include: (1) many contributors with little experience or training in mapping and Geographic Information Systems (GIS); (2) not enough contributors familiar with the areas being mapped; (3) contributors having different interpretations of the attributes (tags) for specific features; (4) different levels of enthusiasm between mappers resulting in different number of tags for similar features and (5) the user-friendliness of the online user-interface where the underlying map can be viewed and edited. This paper suggests an automatic mechanism, which uses raw spatial data (trajectories of movements contributed by contributors to OSM) to minimise the uncertainty and impact of the above-mentioned issues. This approach takes the raw trajectory datasets as input and analyses them using data mining techniques. In addition, we extract some patterns and rules about the geometry and attributes of the recognised features for the purpose of insertion or editing of features in the OSM database. The underlying idea is that certain characteristics of user trajectories are directly linked to the geometry and the attributes of geographic features. Using these rules successfully results in the generation of new features with higher spatial quality which are subsequently automatically inserted into the OSM database. Full article
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Open AccessArticle
A Movement-Assisted Deployment of Collaborating Autonomous Sensors for Indoor and Outdoor Environment Monitoring
Sensors 2016, 16(9), 1497; https://doi.org/10.3390/s16091497
Received: 22 June 2016 / Revised: 30 August 2016 / Accepted: 1 September 2016 / Published: 14 September 2016
Cited by 5 | PDF Full-text (2387 KB) | HTML Full-text | XML Full-text
Abstract
Using mobile robots or unmanned vehicles to assist optimal wireless sensors deployment in a working space can significantly enhance the capability to investigate unknown environments. This paper addresses the issues of the application of numerical optimization and computer simulation techniques to on-line calculation [...] Read more.
Using mobile robots or unmanned vehicles to assist optimal wireless sensors deployment in a working space can significantly enhance the capability to investigate unknown environments. This paper addresses the issues of the application of numerical optimization and computer simulation techniques to on-line calculation of a wireless sensor network topology for monitoring and tracking purposes. We focus on the design of a self-organizing and collaborative mobile network that enables a continuous data transmission to the data sink (base station) and automatically adapts its behavior to changes in the environment to achieve a common goal. The pre-defined and self-configuring approaches to the mobile-based deployment of sensors are compared and discussed. A family of novel algorithms for the optimal placement of mobile wireless devices for permanent monitoring of indoor and outdoor dynamic environments is described. They employ a network connectivity-maintaining mobility model utilizing the concept of the virtual potential function for calculating the motion trajectories of platforms carrying sensors. Their quality and utility have been justified through simulation experiments and are discussed in the final part of the paper. Full article
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Open AccessArticle
RSS-Based Method for Sensor Localization with Unknown Transmit Power and Uncertainty in Path Loss Exponent
Sensors 2016, 16(9), 1452; https://doi.org/10.3390/s16091452
Received: 7 July 2016 / Revised: 23 August 2016 / Accepted: 30 August 2016 / Published: 8 September 2016
Cited by 3 | PDF Full-text (1205 KB) | HTML Full-text | XML Full-text
Abstract
The localization of a sensor in wireless sensor networks (WSNs) has now gained considerable attention. Since the transmit power and path loss exponent (PLE) are two critical parameters in the received signal strength (RSS) localization technique, many RSS-based location methods, considering the case [...] Read more.
The localization of a sensor in wireless sensor networks (WSNs) has now gained considerable attention. Since the transmit power and path loss exponent (PLE) are two critical parameters in the received signal strength (RSS) localization technique, many RSS-based location methods, considering the case that both the transmit power and PLE are unknown, have been proposed in the literature. However, these methods require a search process, and cannot give a closed-form solution to sensor localization. In this paper, a novel RSS localization method with a closed-form solution based on a two-step weighted least squares estimator is proposed for the case with the unknown transmit power and uncertainty in PLE. Furthermore, the complete performance analysis of the proposed method is given in the paper. Both the theoretical variance and Cramer-Rao lower bound (CRLB) are derived. The relationships between the deterministic CRLB and the proposed stochastic CRLB are presented. The paper also proves that the proposed method can reach the stochastic CRLB. Full article
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Open AccessArticle
Clustering and Beamforming for Efficient Communication in Wireless Sensor Networks
Sensors 2016, 16(8), 1334; https://doi.org/10.3390/s16081334
Received: 31 May 2016 / Revised: 12 August 2016 / Accepted: 12 August 2016 / Published: 20 August 2016
Cited by 5 | PDF Full-text (24767 KB) | HTML Full-text | XML Full-text
Abstract
Energy efficiency is a critical issue for wireless sensor networks (WSNs) as sensor nodes have limited power availability. In order to address this issue, this paper tries to maximize the power efficiency in WSNs by means of the evaluation of WSN node networks [...] Read more.
Energy efficiency is a critical issue for wireless sensor networks (WSNs) as sensor nodes have limited power availability. In order to address this issue, this paper tries to maximize the power efficiency in WSNs by means of the evaluation of WSN node networks and their performance when both clustering and antenna beamforming techniques are applied. In this work, four different scenarios are defined, each one considering different numbers of sensors: 50, 20, 10, five, and two nodes per scenario, and each scenario is randomly generated thirty times in order to statistically validate the results. For each experiment, two different target directions for transmission are taken into consideration in the optimization process (φ = 0° and θ = 45°; φ = 45°, and θ = 45°). Each scenario is evaluated for two different types of antennas, an ideal isotropic antenna and a conventional dipole one. In this set of experiments two types of WSN are evaluated: in the first one, all of the sensors have the same amount of power for communications purposes; in the second one, each sensor has a different amount of power for its communications purposes. The analyzed cases in this document are focused on 2D surface and 3D space for the node location. To the authors’ knowledge, this is the first time that beamforming and clustering are simultaneously applied to increase the network lifetime in WSNs. Full article
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Open AccessArticle
Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones
Sensors 2016, 16(8), 1314; https://doi.org/10.3390/s16081314
Received: 7 July 2016 / Revised: 5 August 2016 / Accepted: 10 August 2016 / Published: 20 August 2016
Cited by 3 | PDF Full-text (5614 KB) | HTML Full-text | XML Full-text
Abstract
As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current [...] Read more.
As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user’s daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy. Full article
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Open AccessArticle
Incentives for Delay-Constrained Data Query and Feedback in Mobile Opportunistic Crowdsensing
Sensors 2016, 16(7), 1138; https://doi.org/10.3390/s16071138
Received: 19 April 2016 / Revised: 26 June 2016 / Accepted: 12 July 2016 / Published: 21 July 2016
Cited by 13 | PDF Full-text (419 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we propose effective data collection schemes that stimulate cooperation between selfish users in mobile opportunistic crowdsensing. A query issuer generates a query and requests replies within a given delay budget. When a data provider receives the query for the first [...] Read more.
In this paper, we propose effective data collection schemes that stimulate cooperation between selfish users in mobile opportunistic crowdsensing. A query issuer generates a query and requests replies within a given delay budget. When a data provider receives the query for the first time from an intermediate user, the former replies to it and authorizes the latter as the owner of the reply. Different data providers can reply to the same query. When a user that owns a reply meets the query issuer that generates the query, it requests the query issuer to pay credits. The query issuer pays credits and provides feedback to the data provider, which gives the reply. When a user that carries a feedback meets the data provider, the data provider pays credits to the user in order to adjust its claimed expertise. Queries, replies and feedbacks can be traded between mobile users. We propose an effective mechanism to define rewards for queries, replies and feedbacks. We formulate the bargain process as a two-person cooperative game, whose solution is found by using the Nash theorem. To improve the credit circulation, we design an online auction process, in which the wealthy user can buy replies and feedbacks from the starving one using credits. We have carried out extensive simulations based on real-world traces to evaluate the proposed schemes. Full article
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Open AccessArticle
Energy-Efficient Collaborative Outdoor Localization for Participatory Sensing
Sensors 2016, 16(6), 762; https://doi.org/10.3390/s16060762
Received: 10 March 2016 / Revised: 19 May 2016 / Accepted: 20 May 2016 / Published: 25 May 2016
Cited by 5 | PDF Full-text (1568 KB) | HTML Full-text | XML Full-text
Abstract
Location information is a key element of participatory sensing. Many mobile and sensing applications require location information to provide better recommendations, object search and trip planning. However, continuous GPS positioning consumes much energy, which may drain the battery of mobile devices quickly. Although [...] Read more.
Location information is a key element of participatory sensing. Many mobile and sensing applications require location information to provide better recommendations, object search and trip planning. However, continuous GPS positioning consumes much energy, which may drain the battery of mobile devices quickly. Although WiFi and cell tower positioning are alternatives, they provide lower accuracy compared to GPS. This paper solves the above problem by proposing a novel localization scheme through the collaboration of multiple mobile devices to reduce energy consumption and provide accurate positioning. Under our scheme, the mobile devices are divided into three groups, namely the broadcaster group, the location information receiver group and the normal participant group. Only the broadcaster group and the normal participant group use their GPS. The location information receiver group, on the other hand, makes use of the locations broadcast by the broadcaster group to estimate their locations. We formulate the broadcaster set selection problem and propose two novel algorithms to minimize the energy consumption in collaborative localization. Simulations with real traces show that our proposed solution can save up to 68% of the energy of all of the participants and provide more accurate locations than WiFi and cellular network positioning. Full article
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Open AccessArticle
An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
Sensors 2016, 16(4), 487; https://doi.org/10.3390/s16040487
Received: 9 February 2016 / Revised: 21 March 2016 / Accepted: 30 March 2016 / Published: 7 April 2016
Cited by 20 | PDF Full-text (798 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents two new energy balanced routing protocols for Underwater Acoustic Sensor Networks (UASNs); Efficient and Balanced Energy consumption Technique (EBET) and Enhanced EBET (EEBET). The first proposed protocol avoids direct transmission over long distance to save sufficient amount of energy consumed [...] Read more.
This paper presents two new energy balanced routing protocols for Underwater Acoustic Sensor Networks (UASNs); Efficient and Balanced Energy consumption Technique (EBET) and Enhanced EBET (EEBET). The first proposed protocol avoids direct transmission over long distance to save sufficient amount of energy consumed in the routing process. The second protocol overcomes the deficiencies in both Balanced Transmission Mechanism (BTM) and EBET techniques. EBET selects relay node on the basis of optimal distance threshold which leads to network lifetime prolongation. The initial energy of each sensor node is divided into energy levels for balanced energy consumption. Selection of high energy level node within transmission range avoids long distance direct data transmission. The EEBET incorporates depth threshold to minimize the number of hops between source node and sink while eradicating backward data transmissions. The EBET technique balances energy consumption within successive ring sectors, while, EEBET balances energy consumption of the entire network. In EEBET, optimum number of energy levels are also calculated to further enhance the network lifetime. Effectiveness of the proposed schemes is validated through simulations where these are compared with two existing routing protocols in terms of network lifetime, transmission loss, and throughput. The simulations are conducted under different network radii and varied number of nodes. Full article
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Open AccessArticle
Truthful Incentive Mechanisms for Social Cost Minimization in Mobile Crowdsourcing Systems
Sensors 2016, 16(4), 481; https://doi.org/10.3390/s16040481
Received: 10 February 2016 / Revised: 22 March 2016 / Accepted: 30 March 2016 / Published: 6 April 2016
Cited by 30 | PDF Full-text (889 KB) | HTML Full-text | XML Full-text
Abstract
With the emergence of new technologies, mobile devices are capable of undertaking computational and sensing tasks. A large number of users with these mobile devices promote the formation of the Mobile Crowdsourcing Systems (MCSs). Within a MCS, each mobile device can contribute to [...] Read more.
With the emergence of new technologies, mobile devices are capable of undertaking computational and sensing tasks. A large number of users with these mobile devices promote the formation of the Mobile Crowdsourcing Systems (MCSs). Within a MCS, each mobile device can contribute to the crowdsourcing platform and get rewards from it. In order to achieve better performance, it is important to design a mechanism that can attract enough participants with mobile devices and then allocate the tasks among participants efficiently. In this paper, we are interested in the investigation of tasks allocation and price determination in MCSs. Two truthful auction mechanisms are proposed for different working patterns. A Vickrey–Clarke–Groves (VCG)-based auction mechanism is proposed to the continuous working pattern, and a suboptimal auction mechanism is introduced for the discontinuous working pattern. Further analysis shows that the proposed mechanisms have the properties of individual rationality and computational efficiencies. Experimental results suggest that both mechanisms guarantee all the mobile users bidding with their truthful values and the optimal maximal social cost can be achieved in the VCG-based auction mechanism. Full article
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Open AccessArticle
The Study of Cross-layer Optimization for Wireless Rechargeable Sensor Networks Implemented in Coal Mines
Sensors 2016, 16(2), 171; https://doi.org/10.3390/s16020171
Received: 28 November 2015 / Revised: 17 January 2016 / Accepted: 22 January 2016 / Published: 28 January 2016
Cited by 6 | PDF Full-text (793 KB) | HTML Full-text | XML Full-text
Abstract
Wireless sensor networks deployed in coal mines could help companies provide workers working in coal mines with more qualified working conditions. With the underground information collected by sensor nodes at hand, the underground working conditions could be evaluated more precisely. However, sensor nodes [...] Read more.
Wireless sensor networks deployed in coal mines could help companies provide workers working in coal mines with more qualified working conditions. With the underground information collected by sensor nodes at hand, the underground working conditions could be evaluated more precisely. However, sensor nodes may tend to malfunction due to their limited energy supply. In this paper, we study the cross-layer optimization problem for wireless rechargeable sensor networks implemented in coal mines, of which the energy could be replenished through the newly-brewed wireless energy transfer technique. The main results of this article are two-fold: firstly, we obtain the optimal relay nodes’ placement according to the minimum overall energy consumption criterion through the Lagrange dual problem and KKT conditions; secondly, the optimal strategies for recharging locomotives and wireless sensor networks are acquired by solving a cross-layer optimization problem. The cyclic nature of these strategies is also manifested through simulations in this paper. Full article
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Open AccessArticle
Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device
Sensors 2015, 15(12), 31464-31481; https://doi.org/10.3390/s151229867
Received: 16 September 2015 / Revised: 8 December 2015 / Accepted: 9 December 2015 / Published: 14 December 2015
Cited by 12 | PDF Full-text (3922 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this [...] Read more.
Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design. Full article
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