Showing papers in "IEEE Transactions on Mobile Computing in 2015"

Smartphones Based Crowdsourcing for Indoor Localization

Abstract: 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.

Offloading in Mobile Cloudlet Systems with Intermittent Connectivity

Abstract: The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve performance. However, due to mobility and cloudlet capacity, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected cloudlet system, considering the users’ local load and availability of cloudlets. We examine users’ mobility patterns and cloudlets’ admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.

Friendbook: A Semantic-Based Friend Recommendation System for Social Networks

Abstract: Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. In this paper, we present Friendbook, a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by text mining, we model a user’s daily life as life documents , from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate users’ impact in terms of life styles with a friend-matching graph . Upon receiving a request, Friendbook returns a list of people with highest recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on both small-scale experiments and large-scale simulations. The results show that the recommendations accurately reflect the preferences of users in choosing friends.

Interference-Based Topology Control Algorithm for Delay-Constrained Mobile Ad Hoc Networks

Abstract: As the foundation of routing, topology control should minimize the interference among nodes, and increase the network capacity. With the development of mobile ad hoc networks (MANETs), there is a growing requirement of quality of service (QoS) in terms of delay. In order to meet the delay requirement, it is important to consider topology control in delay constrained environment, which is contradictory to the objective of minimizing interference. In this paper, we focus on the delay-constrained topology control problem, and take into account delay and interference jointly. We propose a cross-layer distributed algorithm called interference-based topology control algorithm for delay-constrained (ITCD) MANETs with considering both the interference constraint and the delay constraint, which is different from the previous work. The transmission delay, contention delay and the queuing delay are taken into account in the proposed algorithm. Moreover, the impact of node mobility on the interference-based topology control algorithm is investigated and the unstable links are removed from the topology. The simulation results show that ITCD can reduce the delay and improve the performance effectively in delay-constrained mobile ad hoc networks.

Ring Routing: An Energy-Efficient Routing Protocol for Wireless Sensor Networks with a Mobile Sink

Abstract: In a typical wireless sensor network, the batteries of the nodes near the sink deplete quicker than other nodes due to the data traffic concentrating towards the sink, leaving it stranded and disrupting the sensor data reporting. To mitigate this problem, mobile sinks are proposed. They implicitly provide load-balanced data delivery and achieve uniform-energy consumption across the network. On the other hand, advertising the position of the mobile sink to the network introduces an overhead in terms of energy consumption and packet delays. In this paper, we propose Ring Routing, a novel, distributed, energy-efficient mobile sink routing protocol, suitable for time-sensitive applications, which aims to minimize this overhead while preserving the advantages of mobile sinks. Furthermore, we evaluate the performance of Ring Routing via extensive simulations.

Evaluating the On-Demand Mobile Charging in Wireless Sensor Networks

Abstract: Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. Different from energy harvesting systems, the utilization of mobile energy chargers is able to provide more reliable energy supply than the dynamic energy harvested from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging (DMC) problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work, we analyze the on-demand mobile charging problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present two examples on determining the essential system parameters such as the optimal remaining energy level for individual sensor nodes to send out their recharging requests and the minimal energy capacity required for the mobile charger. Through extensive simulation with real-world system settings, we verify that our analytical results match the simulation results well and the system designs based on our analysis are effective.

Distortion-Aware Concurrent Multipath Transfer for Mobile Video Streaming in Heterogeneous Wireless Networks

Abstract: The massive proliferation of wireless infrastructures with complementary characteristics prompts the bandwidth aggregation for Concurrent Multipath Transfer (CMT) over heterogeneous access networks. Stream Control Transmission Protocol (SCTP) is the standard transport-layer solution to enable CMT in multihomed communication environments. However, delivering high-quality streaming video with the existing CMT solutions still remains problematic due to the stringent quality of service (QoS) requirements and path asymmetry in heterogeneous wireless networks. In this paper, we advance the state of the art by introducing video distortion into the decision process of multipath data transfer. The proposed distortion-aware concurrent multipath transfer (CMT-DA) solution includes three phases: 1) per-path status estimation and congestion control; 2) quality-optimal video flow rate allocation; 3) delay and loss controlled data retransmission. The term ‘flow rate allocation’ indicates dynamically picking appropriate access networks and assigning the transmission rates. We analytically formulate the data distribution over multiple communication paths to minimize the end-to-end video distortion and derive the solution based on the utility maximization theory. The performance of the proposed CMT-DA is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that CMT-DA outperforms the reference schemes in terms of video peak signal-to-noise ratio (PSNR), goodput, and inter-packet delay.

FlierMeet: A Mobile Crowdsensing System for Cross-Space Public Information Reposting, Tagging, and Sharing

Abstract: Community bulletin boards serve an important function for public information sharing in modern society. Posted fliers advertise services, events, and other announcements. However, fliers posted offline suffer from problems such as limited spatial-temporal coverage and inefficient search support. In recent years, with the development of sensor-enhanced mobile devices, mobile crowd sensing (MCS) has been used in a variety of application areas. This paper presents FlierMeet, a crowd- powered sensing system for cross-space public information reposting, tagging, and sharing. The tags learned are useful for flier sharing and preferred information retrieval and suggestion. Specifically, we utilize various contexts (e.g., spatio-temporal info, flier publishing/reposting behaviors, etc.) and textual features to group similar reposts and classify them into categories. We further identify a novel set of crowd-object interaction hints to predict the semantic tags of reposts. To evaluate our system, 38 participants were recruited and 2,035 reposts were captured during an eight-week period. Experiments on this dataset showed that our approach to flier grouping is effective and the proposed features are useful for flier category/semantic tagging.

Mobile Data Gathering with Load Balanced Clustering and Dual Data Uploading in Wireless Sensor Networks

Abstract: In this paper, a three-layer framework is proposed for mobile data collection in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector (called SenCar) layer. The framework employs distributed load balanced clustering and dual data uploading, which is referred to as LBC-DDU. The objective is to achieve good scalability, long network lifetime and low data collection latency. At the sensor layer, a distributed load balanced clustering (LBC) algorithm is proposed for sensors to self-organize themselves into clusters. In contrast to existing clustering methods, our scheme generates multiple cluster heads in each cluster to balance the work load and facilitate dual data uploading. At the cluster head layer, the inter-cluster transmission range is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving inter-cluster communications. Through inter-cluster transmissions, cluster head information is forwarded to SenCar for its moving trajectory planning. At the mobile collector layer, SenCar is equipped with two antennas, which enables two cluster heads to simultaneously upload data to SenCar in each time by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. The trajectory planning for SenCar is optimized to fully utilize dual data uploading capability by properly selecting polling points in each cluster. By visiting each selected polling point, SenCar can efficiently gather data from cluster heads and transport the data to the static data sink. Extensive simulations are conducted to evaluate the effectiveness of the proposed LBC-DDU scheme. The results show that when each cluster has at most two cluster heads, LBC-DDU achieves over 50 percent energy saving per node and 60 percent energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sink, and 20 percent shorter data collection time compared to traditional mobile data gathering.

An Evolutionary Game for Distributed Resource Allocation in Self-Organizing Small Cells

Abstract: We propose an evolutionary game theory (EGT)-based distributed resource allocation scheme for small cells underlaying a macro cellular network. EGT is a suitable tool to address the problem of resource allocation in self-organizing small cells since it allows the players with bounded-rationality to learn from the environment and take individual decisions for attaining the equilibrium with minimum information exchange. EGT-based resource allocation can also provide fairness among users. We show how EGT can be used for distributed subcarrier and power allocation in orthogonal frequency-division multiple access (OFDMA)-based small cell networks while limiting interference to the macrocell users below given thresholds. Two game models are considered, where the utility of each small cell depends on average achievable signal-to-interference-plus-noise ratio (SINR) and data rate, respectively. For the proposed distributed resource allocation method, the average SINR and data rate are obtained based on a stochastic geometry analysis. Replicator dynamics is used to model the strategy adaptation process of the small cell base stations and an evolutionary equilibrium is obtained as the solution. Based on the results obtained using stochastic geometry, the stability of the equilibrium is analyzed. We also extend the formulation by considering information exchange delay and investigate its impact on the convergence of the algorithm. Numerical results are presented to validate our theoretical findings and to show the effectiveness of the proposed scheme in comparison to a centralized resource allocation scheme.

Recommendation Based Trust Model with an Effective Defence Scheme for MANETs

Abstract: The reliability of delivering packets through multi-hop intermediate nodes is a significant issue in the mobile ad hoc networks (MANETs). The distributed mobile nodes establish connections to form the MANET, which may include selfish and misbehaving nodes. Recommendation based trust management has been proposed in the literature as a mechanism to filter out the misbehaving nodes while searching for a packet delivery route. However, building a trust model that adopts recommendations by other nodes in the network is a challenging problem due to the risk of dishonest recommendations like bad-mouthing, ballot-stuffing, and collusion. This paper investigates the problems related to attacks posed by misbehaving nodes while propagating recommendations in the existing trust models. We propose a recommendation based trust model with a defence scheme, which utilises clustering technique to dynamically filter out attacks related to dishonest recommendations between certain time based on number of interactions, compatibility of information and closeness between the nodes. The model is empirically tested under several mobile and disconnected topologies in which nodes experience changes in their neighbourhood leading to frequent route changes. The empirical analysis demonstrates robustness and accuracy of the trust model in a dynamic MANET environment.

An Exponential-Rayleigh Model for RSS-Based Device-Free Localization and Tracking

Abstract: A common technical difficulty in device-free localization and tracking (DFLT) with a wireless sensor network is that the change of the received signal strength (RSS) of the link often becomes more unpredictable due to the multipath interferences. This challenge can lead to unsatisfactory or even unstable DFLT performance. This work focuses on developing a new RSS model, called Exponential-Rayleigh (ER) model, for addressing this challenge. Based on data from our extensive experiments, we first develop the ER model of the received signal strength. This model consists of two parts: the large-scale exponential attenuation part and the small-scale Rayleigh enhancement part. The new consideration on using the Rayleigh model is to depict the target-induced multipath components. We then explore the use of the ER model with a particle filter in the context of multi-target localization and tracking. Finally, we experimentally demonstrate that our ER model outperforms the existing models. The experimental results highlight the advantages of using the Rayleigh model in mitigating the multipath interferences thus improving the DFLT performance.

GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation

Abstract: Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.

Context-Aware QoE Modelling, Measurement, and Prediction in Mobile Computing Systems

Abstract: Quality of Experience (QoE) as an aggregate of Quality of Service (QoS) and human user-related metrics will be the key success factor for current and future mobile computing systems. QoE measurement and prediction are complex tasks as they may involve a large parameter space such as location, delay, jitter, packet loss, and user satisfaction just to name a few. These tasks necessitate the development of practical context-aware QoE models that efficiently determine relationships between user context and QoE parameters. In this paper, we propose, develop, and validate a novel decision-theoretic approach called CaQoEM for QoE modelling, measurement, and prediction. We address the challenge of QoE measurement and prediction where each QoE parameter can be measured on a different scale and may involve different units of measurement. CaQoEM is context-aware and uses Bayesian networks and utility theory to measure and predict users’ QoE under uncertainty. We validate CaQoEM using extensive experimentation, user studies and simulations. The results soundly demonstrate that CaQoEM correctly measures range-defined QoE using a bipolar scale. For QoE prediction, an overall accuracy of 98.93% was achieved using 10-fold cross validation in multiple diverse network conditions such as vertical handoffs, wireless signal fading and wireless network congestion.

Joint Indoor Localization and Radio Map Construction with Limited Deployment Load

Abstract: One major bottleneck in the practical implementation of received signal strength (RSS) based indoor localization systems is the extensive deployment efforts required to construct the radio maps through fingerprinting. In this paper, we aim to design an indoor localization scheme that can be directly employed without building a full fingerprinted radio map of the indoor environment. By accumulating the information of localized RSSs, this scheme can also simultaneously construct the radio map with limited calibration. To design this scheme, we employ a source data set that possesses the same spatial correlation of the RSSs in the indoor environment of interest. The knowledge of this data set is then transferred to a limited number of calibration fingerprints and one or several RSS observations with unknown locations, in order to perform direct localization of these observations using manifold alignment. We test two different source data sets, namely a simulated radio propagation map and the environment’s plan coordinates. For moving users, we exploit the correlation of their observations to improve their localization accuracy. The online testing in two indoor environments shows that the plan coordinates achieve better results than the simulated radio maps, and a negligible degradation with 70-85 percent reduction in the calibration load.

$\boldsymbol{EMC^3}$ : Energy-Efficient Data Transfer in Mobile Crowdsensing under Full Coverage Constraint

Abstract: This paper proposes a novel mobile crowdsensing (MCS) framework called EMC $^3$ , which intends to reduce energyconsumption of individual user as well as all participants in data transfer caused by task assignment and data collection of MCS tasks, considering the user privacy issue, minimal number of task assignment requirement and sensing area coverage constraint.Specifically, EMC $^3$ incorporates novel pace control and decision making mechanisms for task assignment, leveraging participants’current call, historical call records as well as predicted future calls and mobility, in order to ensure the expected number of participants to return sensed results and fully cover the target area, with the objective of assigning a minimal number of tasks. Extensive evaluation with a large-scale real-world dataset shows that EMC $^3$ assigns much less sensing tasks compared to baseline approaches, it can save 43%-68% energy in data transfer compared to the traditional 3G-based scheme.

An Attack-Resistant Trust Model Based on Multidimensional Trust Metrics in Underwater Acoustic Sensor Network

Abstract: Underwater acoustic sensor networks (UASNs) have been widely used in many applications where a variable number of sensor nodes collaborate with each other to perform monitoring tasks. A trust model plays an important role in realizing collaborations of sensor nodes. Although many trust models have been proposed for terrestrial wireless sensor networks (TWSNs) in recent years, it is not feasible to directly use these trust models in UASNs due to unreliable underwater communication channel and mobile network environment. To achieve accurate and energy efficient trust evaluation in UASNs, an attack-resistant trust model based on multidimensional trust metrics (ARTMM) is proposed in this paper. The ARTMM mainly consists of three types of trust metrics, which are link trust, data trust, and node trust. During the process of trust calculation, unreliability of communication channel and mobility of underwater environment are carefully analyzed. Simulation results demonstrate that the proposed trust model is quite suitable for mobile underwater environment. In addition, the performance of the ARTMM is clearly better than that of conventional trust models in terms of both evaluation accuracy and energy consumption.

User Verification Leveraging Gait Recognition for Smartphone Enabled Mobile Healthcare Systems

Abstract: The rapid deployment of sensing technology in smartphones and the explosion of their usage in people’s daily lives provide users with the ability to collectively sense the world. This leads to a growing trend of mobile healthcare systems utilizing sensing data collected from smartphones with/without additional external sensors to analyze and understand people’s physical and mental states. However, such healthcare systems are vulnerable to user spoofing, in which an adversary distributes his registered device to other users such that data collected from these users can be claimed as his own to obtain more healthcare benefits and undermine the successful operation of mobile healthcare systems. Existing mitigation approaches either only rely on a secret PIN number (which can not deal with colluded attacks) or require an explicit user action for verification. In this paper, we propose a user verification system leveraging unique gait patterns derived from acceleration readings to detect possible user spoofing in mobile healthcare systems. Our framework exploits the readily available accelerometers embedded within smartphones for user verification. Specifically, our user spoofing mitigation framework (which consists of three components, namely Step Cycle Identification, Step Cycle Interpolation, and Similarity Comparison) is used to extract gait patterns from run-time accelerometer measurements to perform robust user verification under various walking speeds. We show that our framework can be implemented in two ways: user-centric and server-centric, and it is robust to not only random but also mimic attacks. Our extensive experiments using over 3,000 smartphone-based traces with mobile phones placed on different body positions confirm the effectiveness of the proposed framework with users walking at various speeds. This strongly indicates the feasibility of using smartphone based low grade accelerometer to conduct gait recognition and facilitate effective user verification without active user cooperation.

Attack-Resilient Mix-zones over Road Networks: Architecture and Algorithms

Abstract: Continuous exposure of location information, even with spatially cloaked resolution, may lead to breaches of location privacy due to statistics-based inference attacks. An alternative and complementary approach to spatial cloaking based location anonymization is to break the continuity of location exposure by introducing techniques, such as mix-zones, where no application can trace user movements. Several factors impact on the effectiveness of mix-zone approach, such as user population, mix-zone geometry, location sensing rate and spatial resolution, as well as spatial and temporal constraints on user movement patterns. However, most of the existing mix-zone proposals fail to provide effective mix-zone construction and placement algorithms that are resilient to timing and transition attacks. This paper presents MobiMix, a road network based mix-zone framework to protect location privacy of mobile users traveling on road networks. It makes three original contributions. First, we provide the formal analysis on the vulnerabilities of directly applying theoretical rectangle mix-zones to road networks in terms of anonymization effectiveness and resilience to timing and transition attacks. Second, we develop a suite of road network mix-zone construction methods that effectively consider the above mentioned factors to provide higher level of resilience to timing and transition attacks, and yield a specified lower-bound on the level of anonymity. Third, we present a set of mix-zone placement algorithms that identify the best set of road intersections for mix-zone placement considering the road network topology, user mobility patterns and road characteristics. We evaluate the MobiMix approach through extensive experiments conducted on traces produced by GTMobiSim on different scales of geographic maps. Our experiments show that MobiMix offers high level of anonymity and high level of resilience to timing and transition attacks, compared to existing mix-zone approaches.

How Shadowing Hurts Vehicular Communications and How Dynamic Beaconing Can Help

Abstract: We study the effect of radio signal shadowing dynamics, caused by vehicles and by buildings, on the performance of beaconing protocols in Inter-Vehicular Communication (IVC). Recent research indicates that beaconing, i.e., one hop message broadcast, shows excellent characteristics and can outperform other communication approaches for both safety and efficiency applications, which require low latency and wide area information dissemination, respectively. To mitigate the broadcast storm problem, adaptive beaconing solutions have been proposed and designed. We show how shadowing dynamics of moving obstacles hurt IVC, reducing the performance of beaconing protocols. To the best of our knowledge, this is one of the first studies on identifying the problem and the underlying challenges and proposing the opportunities presented by such challenges. Shadowing also limits the risk of overloading the wireless channel. We demonstrate how these challenges and opportunities can be taken into account and outline a novel approach to dynamic beaconing. It provides low-latency communication (i.e., very short beaconing intervals), while ensuring not to overload the wireless channel. The presented simulation results substantiate our theoretical considerations.

User-Defined Privacy Grid System for Continuous Location-Based Services

Abstract: Location-based services (LBS) require users to continuously report their location to a potentially untrusted server to obtain services based on their location, which can expose them to privacy risks. Unfortunately, existing privacy-preserving techniques for LBS have several limitations, such as requiring a fully-trusted third party, offering limited privacy guarantees and incurring high communication overhead. In this paper, we propose a user-defined privacy grid system called dynamic grid system (DGS); the first holistic system that fulfills four essential requirements for privacy-preserving snapshot and continuous LBS. (1) The system only requires a semi-trusted third party, responsible for carrying out simple matching operations correctly. This semi-trusted third party does not have any information about a user’s location. (2) Secure snapshot and continuous location privacy is guaranteed under our defined adversary models. (3) The communication cost for the user does not depend on the user’s desired privacy level, it only depends on the number of relevant points of interest in the vicinity of the user. (4) Although we only focus on range and $k$ -nearest-neighbor queries in this work, our system can be easily extended to support other spatial queries without changing the algorithms run by the semi-trusted third party and the database server, provided the required search area of a spatial query can be abstracted into spatial regions. Experimental results show that our DGS is more efficient than the state-of-the-art privacy-preserving technique for continuous LBS.

Power-Aware Computing in Wearable Sensor Networks: An Optimal Feature Selection

Abstract: Wearable sensory devices are becoming the enabling technology for many applications in healthcare and well-being, where computational elements are tightly coupled with the human body to monitor specific events about their subjects. Classification algorithms are the most commonly used machine learning modules that detect events of interest in these systems. The use of accurate and resource-efficient classification algorithms is of key importance because wearable nodes operate on limited resources on one hand and intend to recognize critical events (e.g., falls) on the other hand. These algorithms are used to map statistical features extracted from physiological signals onto different states such as health status of a patient or type of activity performed by a subject. Conventionally selected features may lead to rapid battery depletion, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power-aware feature selection, which aims at minimizing energy consumption of the data processing for classification applications such as action recognition. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. A graph model is introduced to represent correlation and computing complexity of the features. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data collected from real subjects demonstrate that our approach can significantly reduce energy consumption of the computing module, resulting in more than $30$ percent energy savings while achieving $96.7$ percent classification accuracy.

ACE: An Accurate and Efficient Multi-Entity Device-Free WLAN Localization System

Abstract: Device-free (DF) localization in WLANs has been introduced as a value-added service that allows tracking of indoor entities that do not carry any devices. Previous work in DF WLAN localization focused on the tracking of a single entity due to the intractability of the multi-entity tracking problem whose complexity grows exponentially with the number of humans being tracked. In this paper, we introduce ACE : a system that uses a probabilistic energy-minimization framework that combines a conditional random field with a Markov model to capture the temporal and spatial relations between the entities’ poses. A novel cross-calibration technique is introduced to reduce the calibration overhead of multiple entities to linear, regardless of the number of humans being tracked. We design an efficient energy-minimization function that can be mapped to a binary graph-cut problem whose solution has a linear complexity on average and a third order polynomial in the worst case. We further employ clustering on the estimated location candidates to reduce outliers and obtain more accurate tracking in the continuous space. Experimental evaluation in two typical testbeds, with a side-by-side comparison with the state-of-the-art, shows that ACE can achieve a multi-entity tracking accuracy of less than 1.3 m. This corresponds to at least 11.8 percent, and up to 33 percent, enhancement in median distance error over the state-of-the-art DF localization systems. In addition, ACE can estimate the number of entities correctly to within one difference error for 100 percent of the time. This highlights that ACE achieves its goals of having an accurate and efficient multi-entity indoors localization.

Accurate and Efficient Object Tracking Based on Passive RFID

Abstract: RFID technology has been widely used for object tracking in indoor environment due to their low cost and convenience for deployment. In this paper, we consider RFID reader tracking which refers to continuously locating a mobile object by attaching it with a RFID reader that communicates with passive RFID tags deployed in the environment. One difficulty is that the RFID readings gathered from the environment are often noisy. Existing approaches for tracking with noisy RFID readings are mostly based on using Particle Filter (PF). However, continuous execution of PF has extremely high computational cost, and may be difficult to be done on mostly resource constrained mobile RFID devices. In this paper, we propose a hybrid method which combines PF with Weighted Centroid Localization (WCL) to achieve high accuracy and low computational cost. Our observation is that WCL has the same accuracy with PF with much lower cost if the object’s velocity is low. Our method has two critical features. The first feature is adaptive switching between using WCL and PF based on the estimated velocity of the mobile object. The second feature is the further reduction of computational cost by offloading costly PF algorithm onto nearby servers. We evaluate the performance of our method through extensive simulations and experiments in two real world applications, namely, indoor wheelchair navigation and in-station Light Rail Vehicle (LRV) tracking at one of Hong Kong MTR depots. The result shows that our proposed approach has significantly less computational cost than existing PF based methods, while being as accurate as them.

CoCoWa: A Collaborative Contact-Based Watchdog for Detecting Selfish Nodes

Abstract: Mobile ad-hoc networks (MANETs) assume that mobile nodes voluntary cooperate in order to work properly. This cooperation is a cost-intensive activity and some nodes can refuse to cooperate, leading to a selfish node behaviour. Thus, the overall network performance could be seriously affected. The use of watchdogs is a well-known mechanism to detect selfish nodes. However, the detection process performed by watchdogs can fail, generating false positives and false negatives that can induce to wrong operations. Moreover, relying on local watchdogs alone can lead to poor performance when detecting selfish nodes, in term of precision and speed. This is specially important on networks with sporadic contacts, such as delay tolerant networks (DTNs), where sometimes watchdogs lack of enough time or information to detect the selfish nodes. Thus, we propose collaborative contact-based watchdog (CoCoWa) as a collaborative approach based on the diffusion of local selfish nodes awareness when a contact occurs, so that information about selfish nodes is quickly propagated. As shown in the paper, this collaborative approach reduces the time and increases the precision when detecting selfish nodes.

Robust Estimators for Variance-Based Device-Free Localization and Tracking

Abstract: Device-free localization systems, such as variance-based radio tomographic imaging (VRTI), use received signal strength (RSS) variations caused by human motion in a static wireless network to locate and track people in the area of the network, even through walls. However, intrinsic motion, such as branches moving in the wind or rotating or vibrating machinery, also causes RSS variations which degrade the performance of a localization system. In this paper, we propose a new estimator, least squares variance-based radio tomography (LSVRT), which reduces the impact of the variations caused by intrinsic motion. We compare the novel method to subspace variance-based radio tomography (SubVRT) and VRTI. SubVRT also reduces intrinsic noise compared to VRTI, but LSVRT achieves better localization accuracy and does not require manually tuning additional parameters compared to VRTI. We also propose and test an online calibration method so that LSVRT and SubVRT do not require “empty-area” calibration and thus can be used in emergency situations. Experimental results from five data sets collected during three experimental deployments show that both estimators, using online calibration, can reduce localization root mean squared error by more than 40 percent compared to VRTI. In addition, the Kalman filter tracking results from both estimators have 97th percentile error of 1.3 m, a 60 percent reduction compared to VRTI.

Privacy and Quality Preserving Multimedia Data Aggregation for Participatory Sensing Systems

Abstract: With the popularity of mobile wireless devices equipped with various kinds of sensing abilities, a new service paradigm named participatory sensing has emerged to provide users with brand new life experience. However, the wide application of participatory sensing has its own challenges, among which privacy and multimedia data quality preservations are two critical problems. Unfortunately, none of the existing work has fully solved the problem of privacy and quality preserving participatory sensing with multimedia data. In this paper, we propose SLICER , which is the first $k$ -anonymous privacy preserving scheme for participatory sensing with multimedia data. SLICER integrates a data coding technique and message transfer strategies, to achieve strong protection of participants’ privacy, while maintaining high data quality. Specifically, we study two kinds of data transfer strategies, namely transfer on meet up (TMU) and minimal cost transfer (MCT). For MCT, we propose two different but complimentary algorithms, including an approximation algorithm and a heuristic algorithm, subject to different strengths of the requirement. Furthermore, we have implemented SLICER and evaluated its performance using publicly released taxi traces. Our evaluation results show that SLICER achieves high data quality, with low computation and communication overhead.

A Distributed Multi-Channel MAC Protocol for Ad Hoc Wireless Networks

Abstract: This paper proposes a novel distributed multi-channel medium access control (MAC) protocol using fast and slow hopping sequences with dual radio interfaces. Specifically, one interface follows fast hopping and is primarily for transmission, and the other interface follows slow hopping and is generally for reception. The proposed protocol, which is in line with the IEEE 802.11 MAC strategies, is based on the multiple rendezvous approach and able to enhance the network performance and resolve the congestion. An analytical model is developed to evaluate the network performance in terms of the aggregate throughput. Furthermore, the maximum saturation throughput and the upper achievable throughput are computed. Simulations using network simulator-2 (ns-2) are conducted to validate the analytical model and demonstrate the significant enhancement of the proposed protocol in single- and multi-hop networks.

Game-Theoretic Topology Controlfor Opportunistic Localizationin Sparse Underwater Sensor Networks

Abstract: In this paper, we propose a localization scheme named Opportunistic Localization by Topology Control (OLTC), specifically for sparse Underwater Sensor Networks (UWSNs). In a UWSN, an unlocalized sensor node finds its location by utilizing the spatio-temporal relation with the reference nodes. Generally, UWSNs are sparsely deployed because of the high implementation cost, and unfortunately, the network topology experiences partitioning due to the effect of passive node mobility. Consequently, most of the underwater sensor nodes lack the required number of reference nodes for localization in underwater environments. The existing literature is deficient in addressing the problem of node localization in the above mentioned scenario. Antagonistically, however, we promote that even in such sparse UWSN context, it is possible to localize the nodes by exploiting their available opportunities. We formulate a game-theoretic model based on the Single-Leader-Multi-Follower Stackelberg game for topology control of the unlocalized and localized nodes. We also prove that both the players choose strategies to reach a socially optimal Stackelberg-Nash-Cournot Equilibrium . NS-3 based simulation results indicate that the localization coverage of the network increases upto 1.5 times compared to the existing state-of-the-art. The energy-efficiency of OLTC has also been established.

Unlocking Smart Phone through Handwaving Biometrics

Abstract: Screen locking/unlocking is important for modern smart phones to avoid the unintentional operations and secure the personal stuff. Once the phone is locked, the user should take a specific action or provide some secret information to unlock the phone. The existing unlocking approaches can be categorized into four groups: motion, password, pattern, and fingerprint. Existing approaches do not support smart phones well due to the deficiency of security, high cost, and poor usability. We collect 200 users’ handwaving actions with their smart phones and discover an appealing observation: the waving pattern of a person is kind of unique, stable and distinguishable. In this paper, we propose OpenSesame, which employs the users’ waving patterns for locking/unlocking. The key feature of our system lies in using four fine-grained and statistic features of handwaving to verify users. Moreover, we utilize support vector machine (SVM) for accurate and fast classification. Our technique is robust compatible across different brands of smart phones, without the need of any specialized hardware. Results from comprehensive experiments show that the mean false positive rate of OpenSesame is around 15 percent, while the false negative rate is lower than 8 percent.