Sensors (e.g., light, gyroscope, and accelerometer) and sensing-enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor Application Programming Interface (API). In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users’ sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT). We implemented 6thSense on several sensor-rich Android-based smart devices (i.e., smart watch and smartphone) and collected data from typical daily activities of 100 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor, (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96 percent without compromising the normal functionality of the device. Moreover, our framework reveals minimal overhead.

We propose practical sets of rules for adapting Clear Channel Assessment (CCA) and Transmit Power (TP) parameters by simulating ensembles of randomly generated wireless networks and collecting throughput statistics. The rules’ performances depend strongly on network topology, with increases in throughput in many cases. However, networks with a high clustering coefficient are often adversely effected by the adaptations. But simulations of small-scale networks show that apparently adverse adaptations may still yield benefits for uneven demand combinations. Finally, we have found that throughput is not usually correlated with the number of hidden or exposed nodes in any non-trivial network set-up.

This paper considers a device-based Fog Radio Access Network (F-RAN), where users’ smart devices (denoted by F-UEs) can be exploited to cache popular files so as to communicate them when requested by other F-UEs among them. This architecture restricts the involvement of the central baseband processing unit (BBU) to serving those F-UEs not immediately served by their peers, thus offloading the BBU's fronthaul spectrum and increasing the overall capacity of the network. This paper aims to further maximize fronthaul offloading in this architecture using network coding (NC). The latter exploits both the cached and previously received files by different F-UEs as side information to serve more requesting F-UEs in each transmission from their peers or the BBU so as to maximize fronthaul offloading. Being half-duplex devices (they can only either send or receive in any given time), the problem of BBU fronthaul offloading in this setting is formulated over two transmission phases on an NC graph. After showing that this problem is NP-hard, the paper proposes two novel heuristic algorithms to solve it in real-time. In addition, a lower-bound on the offloading gain of these algorithms is derived for a special file placement case, their asymptotic optimality is proven, and their complexities are analyzed. Simulation results both show that these proposed algorithms perform closely to the optimal solution and quantify the significant offloading gains achieved by them.

Caching popular videos at mobile edge servers (MESs) has been confirmed as a promising method to improve mobile users (MUs) perceived quality of experience (QoE) and to alleviate the server load. However, with the multiple bitrate encoding techniques prevalently employed in modern streaming services, caching deployment is challenging for the following three facts: (1) cooperative caching should be explored for MUs located at overlapped coverage areas of MESs; (2) there exists tradeoff consideration for caching either high bitrate videos or high diversity videos; and (3) the relationship between MU perceived QoE and MU received bitrate, known as QoE function, varies in different services. Aiming to maximize the MU perceived QoE, we formulate the multiple bitrate video caching problem, and prove this problem is NP-hard for any given positive and strictly increasing QoE function. We then propose a polynomial complexity algorithm based on a general QoE function, which can achieve an approximate ratio arbitrarily close to 1/2. Specifically, for a linear QoE function, we explore useful property of optimal solutions, based on which more efficient algorithms are proposed. We demonstrate the effectiveness of our solutions via both theoretical analysis and extensive simulations.

Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) is still considered an important module in autonomous vehicles and Driver Assistance Systems (DAS). To overcome low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a lightweight and real-time traffic light detector for the autonomous vehicle platform. Our model consists of a heuristic candidate region selection module to identify all possible traffic lights, and a lightweight Convolution Neural Network (CNN) classifier to classify the results obtained. Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption. By integrating our detector module on NVidia Jetson TX1/TX2, we conduct on-road tests on two full-scale self-driving vehicle platforms (a car and a bus) in normal traffic conditions. Our model can achieve an average detection accuracy of 99.3 percent (mRttld) and 99.7 percent (Rttld) at 10Hz on TX1 and TX2, respectively. The on-road tests also show that our traffic light detection module can achieve $<\pm\; 1.5m$<±1.5m errors at stop lines when working with other self-driving modules.

Due to their on-body and ubiquitous nature, wearables can generate a wide range of unique sensor data creating countless opportunities for deep learning tasks. We propose DeepWear, a deep learning (DL) framework for wearable devices to improve the performance and reduce the energy footprint. DeepWear strategically offloads DL tasks from a wearable device to its paired handheld device through local network connectivity such as Bluetooth. Compared to the remote-cloud-based offloading, DeepWear requires no Internet connectivity, consumes less energy, and is robust to privacy breach. DeepWear provides various novel techniques such as context-aware offloading, strategic model partition, and pipelining support to efficiently utilize the processing capacity from nearby paired handhelds. Deployed as a user-space library, DeepWear offers developer-friendly APIs that are as simple as those in traditional DL libraries such as TensorFlow. We have implemented DeepWear on the Android OS and evaluated it on COTS smartphones and smartwatches with real DL models. DeepWear brings up to 5.08X and 23.0X execution speedup, as well as 53.5 and 85.5 percent energy saving compared to wearable-only and handheld-only strategies, respectively.

Recently, wireless edge caching has emerged as a promising technology for future wireless networks to cope with exponentially increasing demands for high data rate and low latency multimedia services by proactively storing contents at the network edge. Here, we aim to design efficient cache placement and delivery strategies for an orthogonal frequency division multiple access (OFDMA)-based cache-enabled heterogeneous cellular network (C-HetNet) which operates in two separated phases: caching phase (CP) and delivery phase (DP). Since guaranteeing fairness among mobile users (MUs) is not well investigated in cache-assisted wireless networks, we first propose two delay-based fairness schemes called proportional fairness (PF) and min-max fairness (MMF). The PF scheme deals with minimizing the total weighted latency of MUs while MMF aims at minimizing the maximum latency among them. In the CP, we propose a novel proactive fairness and transmission-aware cache placement strategy (CPS) corresponding to each target fairness scheme by exploiting the flexible wireless access and backhaul transmission opportunities. Specifically, we jointly perform the allocation of physical resources as storage and radio, and user association to improve the flexibility of the CPSs. Moreover, in the DP of each fairness scheme, an efficient delivery policy is proposed based on the arrival requests of MUs, CSI, and caching status. Numerical assessments demonstrate that our proposed CPSs outperform the total latency of MUs up to 27 percent compared to the conventional baseline popular CPSs.

The in-band full-duplexing is a promising technique to boost wireless network throughput by allowing a node to transmit and receive simultaneously. This paper provides a comprehensive investigation on the hidden-node problem that arises in the full-duplex (FD) enabled carrier-sensing multiple-access (CSMA) networks. In particular, we first provide the fundamental conditions that guarantee successful receptions for all the FD transmission cases, and propose an ellipse interference model and an ellipse carrier-sensing model to capture the interference relations and the carrier-sensing mechanism in FD CSMA networks, respectively. We further establish the hidden-node-free design in FD CSMA networks. Specifically, we show the sufficient conditions on the carrier-sensing power threshold that can eliminate hidden-node collisions. We show that compared with half-duplex CSMA networks, the FD CSMA network needs a much smaller carrier-sensing power threshold to prevent hidden-node collisions, which leads to poor network spatial reuse. This motivates us to further propose a new medium access control (MAC) protocol with Full-duplex Enhanced Carrier-Sensing (FECS) mechanism. The FECS-MAC enables the secondary carrier-sensing before starting the secondary transmission. We show that with the secondary carrier-sensing design, the required carrier-sensing power threshold can be increased while keeping the network hidden-node free. Therefore, the network spatial reuse and throughput can be significantly improved. Simulation results demonstrate that the FECS-MAC can improve the throughput of dense three-node FD networks by more than 30 percent, compared with relay full-duplex (RFD) MAC protocol proposed in [1] .

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issues of matching workers to their assigned tasks and processing the collected data. In this paper, we reexamine these issues by considering the spatio-temporal worker mobility and task arrivals, which more fit the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the non-linear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby, we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Finally, we propose an optimization framework to aggregate the sensing data which can estimate worker expertise and task truth simultaneously. Performance evaluations on real and synthetic data set validate the proposed algorithm, where 80 percent gain of fairness is achieved at the expense of 12 percent loss of sensing value on average.

In an offload-capable Long Term Evolution (LTE)- Wireless Fidelity (WiFi) Heterogeneous Network (HetNet), we consider the problem of maximization of the total system throughput under voice user blocking probability constraint. The optimal policy is threshold in nature. However, computation of optimal policy requires the knowledge of the statistics of system dynamics, viz., arrival processes of voice and data users, which may be difficult to obtain in reality. Motivated by the Post-Decision State (PDS) framework to learn the optimal policy under unknown statistics of system dynamics, we propose, in this paper, an online Radio Access Technology (RAT) selection algorithm using Relative Value Iteration Algorithm (RVIA). However, the convergence speed of this algorithm can be further improved if the underlying threshold structure of the optimal policy can be exploited. To this end, we propose a novel structure-aware online RAT selection algorithm which reduces the feasible policy space, thereby offering lesser storage and computational complexity and faster convergence. This algorithm provides a novel framework for designing online learning algorithms for other problems and hence is of independent interest. We prove that both the algorithms converge to the optimal policy. Simulation results demonstrate that the proposed algorithms converge faster than a traditional scheme. Also, the proposed schemes perform better than other benchmark algorithms under realistic network scenarios.

This paper presents a novel network architecture for an integrated nanosatellite (nSAT)-5G system operating in the millimeter-wave (mmWave) domain. The architecture is realized adopting a delay/disruption tolerant networking (DTN) approach allowing end users to adopt standard devices. A buffer aware contact graph routing algorithm is designed to account for the buffer occupancy of the nSATs and for the connection planning derived from their visibility periods. At the terrestrial uplink, a coded random access is employed to realize a high-capacity interface for the typically irregular traffic of 5G users, while, at the space uplink, the DTN architecture is combined with the contention resolution diversity slotted Aloha protocol to match the recent update of the DVB-RCS2 standard. To achieve a reliable testing of the introduced functionalities, an accurate analysis of the statistic of the signal to interference-plus-noise ratio and of the capture probability at each mmWave link is developed by including interference, shadowing, fading, and noise. The application of the designed architecture to data transfer services in conjunction with possible delay reduction strategies, and an extension to inter-satellite communication, are finally presented by estimating the resulting loss/delay performance through a discrete-time discrete-event platform based on the integration of Matlab with Network Simulator 3.

Deploying Autonomous Underwater Vehicles (AUVs) is a necessity to enable a range of civilian/military underwater applications; yet, achieving a reliable coordination among the vehicles is a challenging issue due to the time- and space-varying characteristics of the acoustic communication channel. The design of a Medium Access Control (MAC) based on a probabilistic Space Division Multiple Access (SDMA) method for short/medium distances (less than $2\; \mathrm {km}$2 km ) is presented. This method considers the inherent vehicle position uncertainty due to the inaccuracies in models and the drift of the vehicles. It minimizes the acoustic interference statistically by considering the angular position of neighboring vehicles via a two-step estimation and by keeping the transmitter antenna's beamwidth of each vehicle at an optimal value. Such value is chosen considering three contrasting goals, i.e.: $(i)$(i) spreading the signal beam towards the vehicle to combat position uncertainty using a coarse estimation; $(ii)$(ii) focusing the beam to reduce acoustic energy dispersion through a fine estimation; and $(iii)$(iii) minimizing interference to other vehicles. Simulation results in a sparse underwater network show that this approach mitigates interference, reduces the probability of retransmission, and achieves higher data rates over conventional underwater MAC techniques.

Due to ubiquitous Internet connectivity, widely available cloud services, and popular mobile devices, mobile networks have become service delivery and consumption platforms for many industries worldwide. To recommend optimal mobile Web services with trustworthy Quality-of-Service (QoS) and dynamic user preferences, this paper proposes a novel service recommendation model based on Fuzzy Analytic Hierarchy Process (FAHP) and ordinal utility function. First, a Multi-QoS vector is defined, and to take into account the trustworthiness of QoS, the fidelity of QoS is modeled as one component of the Multi-QoS vector. Then, a fuzzy hierarchy including dual attributes of QoS (objective attribute and subjective evaluation) is established to fully consider the objective and subjective attributes’ impact on optimal service recommendation. Furthermore, a FAHP-based weighting mode is developed, in which the resolution ratio of weight can be adjusted dynamically by decision-maker according to user preferences. Finally, the optimal service is obtained through the calculation of ordinal utility function of candidate service. Experimental results and method comparison illuminate the feasibility and efficiency of the proposed model.

In this study, we present WindTalker, a novel and practical keystroke inference framework that can be used to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from an observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). An attacker can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's password input. Compared with the previous keystroke inference approaches, WindTalker neither deploys external equipment physically close to the target device nor compromises the target device. Instead, it employs a more practical setting by deploying a free public WiFi hotspot and collects the CSI data from the target device as long as the device is connected to the hotspot. In addition, to improve inference accuracy and efficiency, it analyzes the WiFi traffic to selectively collect CSI only for the sensitive period where password entering occurs. WindTalker can be implemented without the requirement of visually seeing the target device, or installing any malware on the device. We tested Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. Furthermore, we proposed a novel CSI obfuscation countermeasure to thwart the inference attack. The evaluation results show that the performance of WindTalker can be dramatically reduced by adopting the proposed countermeasures.

Cellular network positioning is a mandatory requirement for localizing emergency callers, such as E911 in North America. Although smartphones are normally equipped with GPS modules, there are still a large number of users with cell phones only as basic devices, and GPS could be ineffective in urban canyon environments. To this end, the RF fingerprints based positioning mechanism is incorporated into LTE architecture by 3GPP, where the major challenge is to collect geo-tagged RF fingerprints in vast areas. This paper proposes to utilize the subspace identification approach for large-scale RF fingerprints prediction. We formulate the problem into the problem of finding the optimal subspace over Stiefel manifold, and redesign the Stiefel-manifold optimization method with fast convergence rate. Moreover, we propose a sliding window mechanism for the practical large-scale fingerprints prediction scenario, where recorded fingerprints are unevenly distributed in the vast area. Combining the two proposed mechanisms enables an efficient method of large-scale fingerprints prediction in the city level. Further, we validate our theoretical analysis and proposed mechanisms by conducting experiments with real mobile data, which shows that the resulted localization accuracy and reliability with our predicted fingerprints exceed the requirement of E911.

Recent hardware advances have led to the development and consumerization of mobile devices, which mainly include smartphones and various wearable devices. To protect the privacy of users, various user authentication mechanisms have been proposed. In particular, biometrics has been widely used for multi-factor authentication. However, biometrics-based authentication mechanisms usually require costly sensors deployed on devices, and rely on explicit user input and Internet connection for performing user authentication. In this article, we propose a system, called RiskCog , which can authenticate the ownership of mobile devices unobtrusively and in a real-time manner by adopting a learning-based approach. Unlike previous studies on user authentication, for cross-platform deployment, maximum user privacy protection, and unobtrusive authentication, RiskCog only relies on those widely available and privacy-insensitive motion sensors to capture the data related to the users’ daily device usage. It requires no users’ explicit input and has no requirement on the users’ motion state or the device placement. RiskCog is also usable in the environment without Internet access by performing offline user identity verification. We conduct comprehensive experiments on smartphones and smartwatches, which show that RiskCog can authenticate device users rapidly and with high accuracy.

To fully realize the potential of vehicular networks, several obstacles and challenges need to be addressed. Chief among the obstacles are strict QoS requirements of applications and differentiated service requirements in different situations. Although DSRC and WAVE have been adopted as the de facto standards, they do not address all the problems and there is room for improvements. In this study, we propose a generic prioritization and resource management algorithm that can be used to prioritize processing of received packets in vehicular networks. We formulate the generic severity-based prioritized packet processing problem as Penalized Multiple Knapsack Problem (PMKP) and prove that it is an NP-Hard problem. We thus develop a real-time heuristic that utilizes a relaxed version of the formulation. The relaxed formulation executes in polynomial time and guarantees a minimum delay per severity-level while respecting the processing rate constraint. To measure the performance of the proposed heuristic, real traffic data is used in a small-scale experiment. The proposed heuristic is tested against the PMKP solution and results show a small degradation of up to 4 percent in profit for the heuristic compared to the PMKP solution. Also, the proposed heuristic is tested against a non-prioritized processing algorithm that works using first come first served policy. Results show that the proposed heuristic gains 9 to 67 percent more profit than the non-prioritized processing algorithm in moderate and high congestion scenarios.

5G is the next telecommunications standards that will enable the sharing of physical infrastructures to provision ultra short-latency applications, mobile broadband services, Internet of Things, etc. Network slicing is the virtualization technique that is expected to achieve that, as it can allow logical networks to run on top of a common physical infrastructure and ensure service level agreement requirements for different services and applications. In this vein, our paper proposes a novel and complete solution for planning network slices of the LTE EPC, tailored for the enhanced Mobile BroadBand use case. The solution defines a framework which consists of: i) an abstraction of the LTE workload generation process, ii) a compound traffic model, iii) performance models of the whole LTE network, and iv) an algorithm to jointly perform the resource dimensioning and network embedding. Our results show that the aggregated signaling generation is a Poisson process and the data traffic exhibits self-similarity and long-range-dependence features. The proposed performance models for the LTE network rely on these results. We formulate the joint optimization problem of resources dimensioning and embedding of a virtualized EPC and propose a heuristic to solve it. By using simulation tools, we validate the proper operation of our solution.

With the exponentially increasing number of mobile devices, crowdsensing has been a hot topic to use the available resource of neighbor mobile devices to perform sensing tasks cooperatively. However, there still remain three main obstacles to be solved in the practical system. First, since mobile devices are selfish and rational, it is natural to provide cooperation for sensing with a reasonable payment. Meanwhile, due to the arrival and departure of sensing tasks, resource should be allocated and released dynamically when sensing task comes or leaves. To this end, this paper designs a game theoretic approach based incentive mechanism to encourage the “best” neighbor mobile devices to share their own resource for sensing. Next, in order to adjust resource among mobile devices for the better crowdsensing response, an auction based task migration algorithm is proposed, which can guarantee the truthfulness of announced price of auctioneer, individual rationality, profitability, and computational efficiency. Moreover, taking into account the random movement of mobile devices resulting in the stochastic connection, we also use multi-stage stochastic decision to take posterior resource allocation to compensate for inaccurate prediction. The numerical results show the effectiveness and improvement of the proposed multi-stage stochastic programming based distributed game theoretic methodology ( SPG ) for crowdsensing.

In this paper, a mobile edge computing framework with multi-user computation offloading and transmission scheduling for delay-sensitive applications is studied. In the considered model, computation tasks are generated randomly at mobile users along the time. For each task, the mobile user can choose to either process it locally or offload it via the uplink transmission to the edge for cloud computing. To efficiently manage the system, the network regulator is required to employ a network-wide optimal scheme for computation offloading and transmission scheduling while guaranteeing that all mobile users would like to follow (as they may naturally behave strategically for benefiting themselves). By considering tradeoffs between local and edge computing, wireless features and noncooperative game interactions among mobile users, we formulate a mechanism design problem to jointly determine a computation offloading scheme , a transmission scheduling discipline , and a pricing rule . A queueing model is built to analytically describe the packet-level network dynamics. Based on this, we propose a novel mechanism, which can maximize the network social welfare (i.e., the network-wide performance), while achieving a game equilibrium among strategic mobile users. Theoretical and simulation results examine the performance of our proposed mechanism, and demonstrate its superiority over the counterparts.

In this paper, we consider the scenario in which a mobile charger (MC) periodically travels within a sensor network to recharge the sensors wirelessly. We design joint charging and scheduling schemes to maximize the Quality of Monitoring (QoM) for stochastic events, which arrive and depart according to known probability distributions of time. Information is considered captured if it is sensed by at least one sensor. We focus on two closely related research issues, i.e., how to choose the sensors for charging and decide the charging time for each of them, and how to schedule the sensors’ activation schedules according to their received energy. We formulate our problem as the maximum QoM CHA rging and S ch E duling problem (CHASE). We first ignore the MC's travel time and study the resulting relaxed version of the problem, which we call CHASE-R. We show that both CHASE and CHASE-R are NP-hard. For CHASE-R, we prove that it can be formulated as a submodular function maximization problem, which allows two algorithms to achieve $1/6$1/6 - and $1/(4 + \epsilon)$1/(4+ε) -approximation ratios. Then, for CHASE, we propose approximation algorithms to solve it by extending the CHASE-R results. We conduct simulations to validate our algorithm design.

As the number of mobile devices that natively support ad hoc communication protocols increase, large ad hoc networks can be created not only to facilitate communication among the mobile devices, but also to assist devices that are executing computationally intensive applications. Prior work has developed computation offloading systems for mobile devices, but this work has focused exclusively on offloading to single hop neighbors, due in part to the practical challenges of setting up multi-hop networks using existing ad hoc communication protocols. However, limiting the offloading of computation to one-hop neighbors inherently restricts the number of devices that can participate in the distributed computation. By presenting a heuristic, aimed at avoiding partitioning the network, as well as an iterative task assignment algorithm that can optimize the assignment of computational tasks to devices in a multi-hop cooperative network, we are able to evaluate the effect of computational offloading in multi-hop networks. Experimental results, obtained from an implementation on Android devices, are integrated with an analytical model that enables the evaluation of system performance under a variety of conditions. These experimental and analytic results demonstrate the benefit of enabling computation offloading to all devices in a multi-hop cooperative network.

Passive Radio Frequency Identification (RFID) tags have been widely applied in many applications, such as logistics, retailing, and warehousing. In many situations, the order of objects is more important than their absolute locations. However, state-of-art ordering methods need a continuing movement of tags and readers, which limit the application domain and scalability. In this paper, we propose a 2-dimension ordering approach for passive tags that requires no device movement. Instead, our method utilizes signal changes caused by arbitrary movement of human beings around tags, who carry no device for horizontal dimension ordering. Hence, our method is called Human Movement based Ordering (HMO). The basic idea of HMO is that when people pass between the reader antenna and tags, the received signal strength will change. By observing the time-series RSS changes of tags, HMO can obtain the order of tags along with a specific horizontal direction. For vertical dimension, we employ a linear programming method that is tolerant of tiny errors in practice. We implement HMO with commodity off-the-shelf RFID devices. The experimental results show that HMO can achieve up to 88.71 and 90.86 percent average accuracies in the signal- and multi-person cases, respectively.

Malware collusion is a technique utilized by attackers to evade standard detection. It is a new threat where two or more applications, appearing benign, communicate to perform a malicious task. Most proposed approaches aim at detecting stand-alone malicious applications. We point out the need for analyzing data flows across multiple Android apps, a problem referred to as end-to-end flow analysis . In this work, we present a flow analysis for app pairs that computes the risk level associated with their potential communications. Our approach statically analyzes the sensitivity and context of each inter-app flow based on inter-component communication (ICC) between communicating apps, and defines fine-grained security policies for inter-app ICC risk classification. We perform an empirical study on 7,251 apps from the Google Play store to identify the apps that communicate with each other via ICC channels. Our results report four times fewer warnings on our dataset of 197 real app pairs communicating via explicit external ICCs than the state-of-the-art permission-based collusion detection.

To accommodate the explosively growing demands for mobile traffic service, wireless network virtualization is proposed as the main evolution towards 5G. In this work, a novel contract theoretic incentive mechanism is proposed to study how to manage the resources and provide services to the users in the wireless virtualized networks. We consider that the infrastructure providers (InPs) own the physical networks and the mobile virtual network operator (MVNO) has the service information of the users and needs to lease the physical radio resources for providing services. In particular, we utilize the contract theoretic approach to model the resource trading process between the MVNO and multiple InPs. Two scenarios are considered according to whether the information (such as the radio resource they can provide) of the InPs are globally known. Subsequently, the corresponding optimal contracts regarding the user association and transmit power allocation are derived to maximize the payoff of the MVNOs while maintaining the requirements of the InPs in the trading process. To evaluate the proposed scheme, extensive simulation studies are conducted. It can be observed that the proposed contract theoretic approach can effectively stimulate InPs’ participation, improve the payoff of the MVNO, and outperform other schemes.

OFDMA femtocell is a promising technology to improve indoor cellular network coverage cost-effectively. Large-scale deployment of femtocells in the urban area is expected to be realized in the near future. However, inter-femtocell interference significantly limits the achievable throughput of an OFDMA femtocell system, which calls for interference management tailored for femtocell networks. A typical approach to mitigate inter-femtocell interference is known as resource isolation, which aims at assigning non-overlapping resources to interfering femtocells. One of the main challenges for interference mitigation in femtocell networks is that end consumers often install the femtocells. Very limited information about the femtocells is available, making it hard to decipher the inter-femtocell interference. Previous studies either take time to resolve collisions online or adopt a conservative approach to identify interferers. Although the latter approach avoids wasting time on resolving collisions, it may result in resource underutilization. In this paper, we propose an efficient method to identify inter-femtocell interference by analyzing the received patterns observed by mobile stations. We conducted experiments on GNU Radio/USRP to demonstrate that the proposed interference identification method can successfully identify real interferers while excluding non-interfering femtocells from suspect femtocells. Based on the proposed interference identification, we propose a weighted vertex-coloring based resource assignment algorithm to allocate resources with better fairness and higher throughput.

With rapid development of radio frequency identification (RFID) technology, ever-increasing research effort has been dedicated to devising various RFID-enabled services. The missing tag identification, which is to identify all missing tags, is one of the most important services in many Internet-of-Things applications such as inventory management. Prior work on missing tag detection all rely on hash functions implemented at individual tags. However, in reality hash functions are not supported by commercial off-the-shelf (COTS) RFID tags. To bridge this gap between theory and practice, this paper is devoted to detecting missing tags with COTS Gen2 devices. We first introduce a point-to-multipoint protocol, named P2M that works in an analog frame slotted Aloha paradigm to interrogate tags and collect their electronic product codes (EPCs). A missing tag will be found if its EPC is not present in the collected ones. To reduce time cost of P2M resulted from tag response collisions, we further present a collision-free point-to-point protocol, named P2P that selectively specifies a tag to reply with its EPC in each slot. If the EPC is not received, this tag is regarded to be missing. We develop two bitmask selection methods to enable the selective query while reducing communication overhead. We implement P2M and P2P with COTS RFID devices and evaluate their performance under diverse settings.

Multipath TCP (MPTCP) enables a client to exploit multiple wireless paths simultaneously for improved throughput and mobility resilience. However, MPTCP clients in WiFi networks may easily lower the achievable throughput of the network due to excessively created connections over access points (APs), particularly those that own a weak link quality. This is caused by the fact that the maximal achievable throughput of a WiFi AP can be easily affected by the number of connections accessing it and the link qualities of those connections. In this paper, we first verify such effects through extensive experiments and analysis. We then propose a novel scheme, denoted MPWiFi, to solve this issue while keeping the benefits of MPTCP based multipath access in WiFi networks. Our scheme allows a MPTCP client to obtain resources on its best WiFi path freely and suppresses its subflows in additional paths when congestion happens. As a result, the degrading to the WiFi AP's achievable throughput is greatly prevented with acceptable influences on the benefits of MPTCP clients (i.e., their multipath access is intervened only on congested non-best APs). The fairness to MPTCP clients is also guaranteed through their best WiFi connection. The proposed solution is implemented along with the Linux Kernel MPTCP implementation. Extensive real-world deployment based experiments and NS3 simulation show that the proposed scheme can effectively alleviate the adverse impact of MPTCP based multipath access in WiFi networks while keeping its benefits.

Most client hosts are equipped with multiple network interfaces (e.g., WiFi and cellular networks). Simultaneous access of multiple interfaces can significantly improve the users’ quality of experience (QoE) in video streaming. An intuitive approach to achieve it is to use Multi-path TCP (MPTCP). However, the deployment of MPTCP, especially with link preference, requires OS kernel update at both the client and server side, and a vast amount of commercial content providers do not support MPTCP. Thus, in this paper, we realize a multi-path video streaming algorithm in the application layer instead, by considering Scalable Video Coding (SVC), where each layer of every chunk can be fetched from only one of the orthogonal paths. We formulate the quality decisions of video chunks subject to the available bandwidth of the different paths, chunk deadlines, and link preferences as an optimization problem. The objective is to to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among chunks. The proposed metric prefers to use bandwidth of the links to optimize a concave utility function of the chunk quality. Even though the formulation is a non-convex discrete optimization, we provide a quadratic complexity algorithm which is shown to be optimal in some special cases. We further propose an online algorithm where several challenges including bandwidth prediction errors, are addressed. Extensive emulated experiments in a real testbed with real traces of public dataset reveal the robustness of our scheme and demonstrate its significant performance improvement compared to other multi-path algorithms.

In wireless ad hoc networks, due to the interference between concurrent transmissions, the per-node capacity generally decreases with the increasing number of nodes in the network. Caching can help improve the network capacity, as it shortens the content transmission distance and reduces the communication interference. However, current researches on the capacity of wireless ad hoc networks with caching generally assume that content popularity follows a uniform distribution. They ignore the fact that contents in reality have skewed popularity, which may lead to totally different capacity results. In this paper, we evaluate how the distribution of the content popularity affects the per-node capacity, and derive different capacity scaling laws based on the skewness of the content popularity. Our results suggest that for wireless networks with caching, when contents have skewed popularity, increasing the number of nodes monotonically increases the per-node capacity.

Crowdsensing has been well recognized as a promising approach to enable large scale urban data collection. In a typical crowdsensing system, the task owner usually needs to provide incentives to the users (say participants) to encourage their participation. Among existing incentive mechanisms, posted pricing has been widely adopted because it is easy to implement while ensuring truthfulness and fairness. One critical challenge to the task owner is to set the right posted price to recruit a crowd with small total payment and reasonable sensing quality, i.e., posted pricing problem for robust crowdsensing. However, this fundamental problem remains largely open so far. In this paper, we model the robustness requirement over sensing data quality as chance constraints in an elegant manner, and study a series of chance constrained posted pricing problems in crowdsensing systems. Although some chance-constrained optimization techniques have been applied in the literature, they cannot provide any performance guarantees for their solutions. In this work, we propose a binary search based algorithm, and show that using this algorithm allows us to establish theoretical guarantees on its performance. Extensive numerical simulations demonstrate the effectiveness of our proposed algorithm.

A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these problems, we propose an event-trust and user-reputation model, called $QnQ$QnQ , to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both ‘quality’ (accuracy of contribution) and ‘quantity’ (degree of participation) of their contributions. Specifically, $QnQ$QnQ exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which in turn is used to judiciously disburse user incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT) , we propose a risk tolerance and reputation aware trustworthy decision making scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate $QnQ$QnQ experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Jøsang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory. Experimental results demonstrate that $QnQ$QnQ is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses, and significantly improves operational accuracy in presence of rogue contributions.

The lack of digital floor plans is a huge obstacle to pervasive indoor location based services (LBS). Recent floor plan construction work crowdsources mobile sensing data from smartphone users for scalability. However, they incur long time (e.g., weeks or months) and tremendous efforts in data collection. In this paper, we propose BatMapper , which explores a previously untapped sensing modality–acoustics–for fast, fine grained, and low cost floor plan construction. We design sound signals suitable for heterogeneous microphones on commodity smartphones, and acoustic signal processing techniques to produce accurate distance measurements to nearby objects. We further develop robust probabilistic echo-object association, recursive outlier removal, and probabilistic resampling algorithms to identify the correspondence between distances and objects, thus the geometry of corridors and rooms. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically. Experiments in real buildings show BatMapper achieves $1-2$1-2 $cm$cm distance accuracy in ranges up around 4 $m$m ; a $2\sim 3$2∼3 minute walk generates fine grained corridor shapes, detects doors at 92 percent precision and $1\sim 2$1∼2 $m$m location error at 90-percentile; and tens of seconds of measurement gestures produce room geometry with errors $<0.3$<0.3 $m$m at 80-percentile, at $1-2$1-2 orders of magnitude less data amounts and user efforts.

Control of conventional transportation networks aims at bringing the state of the network (e.g., the traffic flows in the network) to the system optimal (SO) state. This optimum is characterized by the minimality of the social cost function, i.e., the total cost of travel (e.g., travel time) of all drivers. On the other hand, drivers are assumed to be rational and selfish, and make their travel decisions (e.g., route choices) to optimize their own travel costs, bringing the state of the network to a user equilibrium (UE). A classic approach to influence users’ route choice is using congestion tolls. In this paper, we study the SO and UE of future connected vehicular transportation networks, where users consider both the travel cost and the utility from data communication, when making their travel decisions. We leverage the data communication aspect of the decision making to influence the user route choices, driving the UE state to the SO state. We assume the cache-enabled vehicles can communicate with other vehicles via vehicle-to-vehicle (V2V) connections. We propose an algorithm for calculating the values of the data communication utility that drive the UE to the SO. This result provides a guideline on how the system operator can adjust the parameters of the communication network (e.g., data pricing and bandwidth) to achieve the optimal social cost. We discuss the insights that the results shed on a secondary optimization that the operator can conduct to maximize its own utility without deviating the transportation network state from the SO. We validate the proposed communication model via Veins simulation. The simulation results also show that the system cost can be lowered even if the bandwidth allocation does not exactly match the optimal allocation policy under 802.11p protocol.

Presents the 2019 subject/author index for this publication.

This paper presents a new holistic vision-based mobile assistive navigation system to help blind and visually impaired people with indoor independent travel. The system detects dynamic obstacles and adjusts path planning in real-time to improve navigation safety. First, we develop an indoor map editor to parse geometric information from architectural models and generate a semantic map consisting of a global 2D traversable grid map layer and context-aware layers. By leveraging the visual positioning service (VPS) within the Google Tango device, we design a map alignment algorithm to bridge the visual area description file (ADF) and semantic map to achieve semantic localization. Using the on-board RGB-D camera, we develop an efficient obstacle detection and avoidance approach based on a time-stamped map Kalman filter (TSM-KF) algorithm. A multi-modal human-machine interface (HMI) is designed with speech-audio interaction and robust haptic interaction through an electronic SmartCane. Finally, field experiments by blindfolded and blind subjects demonstrate that the proposed system provides an effective tool to help blind individuals with indoor navigation and wayfinding.