A Mobile App Search Engine

With the popularity of mobile apps on mobile devices based on iOS, Android, Blackberry and Windows Phone operating systems, the numbers of mobile apps in each of the respective native app stores are increasing in leaps and bounds. Currently there are close to one million mobile apps across these four major native app stores. Due to the enormous number of apps, both the constituents in the app ecosytem, consumers and app developers, face problems in ‘app discovery’. For consumers, it is a daunting task to discover the apps they like and need among the huge number of available apps. Likewise, for developers, enabling their apps to be discovered is a challenge. To address these issues, Mobilewalla (MW) an app search engine provides an independent unbiased search for mobile apps with semantic search capabilities. It has also developed an objective scoring mechanism based on user and developer involvement with an app. The scoring mechanism enables MW to provide a number of other ways to discover apps—such as dynamically maintained ‘hot’ lists and ‘fast rising’ lists. In this paper, we describe the challenges of developing the MW platform and how these challenges have been mitigated. Lastly, we demonstrate some of the key functionalities of MW.     

Download or swipe left: The role of complexity,future-oriented emotions and feature overload

With the intense competition in the mobile applications (apps) market, it is imperative for app providers to understand how visual stimuli from their apps can create a positive first impression and enhance the app download rates. In the present study, the mechanism through which visual complexity influences mobile app download intention is examined. Using a combination of convenience and snowball sampling, 218 participants were recruited to take part in a single-group post-test only quasi-experimental design. The findings of the study showed that visual complexity influences mobile app download intentions through the mediating role of two future-oriented emotions (i.e. hope and anticipated regret). Additionally, the study showed that the indirect effect of visual complexity on download intentions was moderated by feature overload with the importance of visual complexity significantly reducing as perceptions of feature overload increases. The proposed model explained 46% variance in the intentions to download a mobile app. The findings not only provide practical insights for mobile app developers and publishers but also theoretical insights on consumer decision making in the pre-use context of mobile apps.     

Systematic evaluation of mobile fitness apps: Apps as the Tutor,Recorder, Game Companion,and Cheerleader

Mobile fitness applications are innovating the ways in which smartphone users self-manage their health. Prior research found that app functions may impact app efficacy. However, research to date has not sought to systematically investigate how different combinations of app functions impact user response to apps, especially adoption intent. This article describes two studies on mobile fitness app characteristics and user attitudes. Study One used content analysis and hierarchical cluster analysis on 98 iPhone fitness apps and identified four app clusters: “Tutor”, “Recorder”, “Game Companion”, and “Cheerleader.” Tracking was the predominant function in current market, but tracking-focused Recorder apps received lowest user ratings among all app clusters. Users favored Tutor apps that combine exercise education and tracking, and Game Companion apps that combine gamification, tracking, and social functions. Function combinations, rather than standalone functions, impact app success. Following a Reasoned Action Approach, Study Two found various effects of individual differences (age, gender, BMI, eHealth literacy, smartphone experience, function preference) on user attitude toward different fitness app types. A comparison between two studies demonstrated a mismatch between market offerings and user needs regarding app functions. Implications of results for mobile fitness app design to improve consumer health and for theories are discussed.     

Predicting numeric ratings for Google apps using text features and ensemble learning

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.     

Examining the antecedents and consequences of mobile app engagement

Understanding how and why consumers engage with mobile apps is critical to the success of ubiquitous mobile marketing. This study proposed and tested a structural model to investigate the antecedents and consequences of mobile app engagement. Results show that time convenience, interactivity, and compatibility positively influenced mobile app engagement, in turn leading to strong relationship commitment and self-brand connections. Furthermore, informational and experiential mobile apps moderated the effects of time convenience, interactivity, and compatibility on mobile app engagement. Theoretical and practical implications for effective app engagement strategies are discussed.     

Network traffic identification of several open source secure proxy protocols

Secure proxy technique has a wide range of applications especially in the area of VPN to protect the private communication from being surveilled. However, the adversaries may also take advantage of the VPN services for other purposes. Thus, knowing the characteristics of the corresponding security protocols will benefit the administration to narrow down the targets. Also, given the various VPN services on the market, we also want to know whether there exists some security flaws from the cryptographic protocol's point of view, so that can be used to improve analyzing the protocol. In this paper, we first target several widely used open source secure proxy protocols and provide their weaknesses through the static analysis. Then we apply the deep learning technique to train all the related network traffic, and our results showed that all the secure proxy traffics can be successfully distinguished with high accuracy.     

Efficient on-demand routing for mobile ad hoc wireless access networks

In this paper, we consider a mobile ad hoc wireless access network in which mobile nodes can access the Internet via one or more stationary gateway nodes. Mobile nodes outside the transmission range of the gateway can continue to communicate with the gateway via their neighboring nodes over multihop paths. On-demand routing schemes are appealing because of their low routing overhead in bandwidth restricted mobile ad hoc networks, however, their routing control overhead increases exponentially with node density in a given geographic area. To control the overhead of on-demand routing without sacrificing performance, we present a novel extension of the ad hoc on-demand distance vector (AODV) routing protocol, called LB-AODV, which incorporates the concept of load-balancing (LB). Simulation results show that as traffic increases, our proposed LB-AODV routing protocol has a significantly higher packet delivery fraction, a lower end-to-end delay and a reduced routing overhead when compared with both AODV and gossip-based routing protocols.     

Location estimation and trajectory prediction for cellular networks with mobile base stations

This paper provides mobility estimation and prediction for a variant of the GSM network that resembles an ad hoc wireless mobile network in which base stations and users are both mobile. We propose using a Robust Extended Kalman Filter (REKF) to derive an estimate of the mobile user's next mobile base station from the user's location, heading, and altitude, to improve connection reliability and bandwidth efficiency of the underlying system. Our analysis demonstrates that our algorithm can successfully track the mobile users with less system complexity, as it requires measurements from only one or two closest mobile base stations. Further, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.     

Secure routing in MANETs using local times

We present a new approach to secure routing in mobile ad-hoc networks based solely on the relative transmission times of overhead packets. Unlike most previous works aimed at securing route computation, we eliminate a key vulnerability (explicitly stated routing metrics) altogether. We introduce the Secure Time-Ordered routing Protocol (STOP), which uses time-based orderings to ensure the establishment of multiple loop-free paths between a source and a destination. STOP is the first routing protocol to use performance-based path selection without source routing, path vectors, or complete topology information, making it far more efficient that similar approaches. We prove that adversaries cannot take any action to manipulate the time-based ordering so as to unfairly gain control of the forwarding topology and, by design, nodes which drop data packets will be avoided. Furthermore, at convergence, traffic load is evenly distributed over the well-performing paths, so adversaries cannot gain complete control over the data flow through temporary good behavior. Simulation results show that the countermeasures in STOP are effective against a variety of attacks from independent and colluding adversaries, and that this improved security does not come at the expense of routing performance.     

Anonymous Communication in Ubiquitous Computing Environments

Wireless networks such as WLANs which have already been commonplace will play an important role in providing the last mile access for ubiquitous computing environments. However, the wireless access technologies are accompanied with some security vulnerabilities that stem from the broadcasting medium. Although most of the vulnerabilities can be solved by the existing security countermeasures, there still exists the vulnerability of a message header. In most wireless access networks, the header part of each message, including the source and destination addresses, is transmitted in a plain-text format. This can be a security hole with adversaries collecting the revealed header information for a traffic analysis attack that can breach the privacy of the transmitter and receiver. In this paper, we focus on describing a solution to this problem, namely, the undesirable loss of privacy. Our main idea is to integrate address information with a conventional Message Authentication Code (MAC) and to replace both fields of the address and the MAC by an integrated code called the Address-embedded MAC (AMAC). Through detailed performance and security analysis of our scheme, we show that our AMAC scheme can guarantee privacy of a network while providing a provable security level with less overhead.     

Mobile apps usage and dynamic capabilities: A structural equation model of SMEs in Lagos,Nigeria

Significant knowledge exists regarding the application of dynamic capability (DC) frameworks in large firms, but their impact on smaller organisations is yet to be fully researched. This study surveyed 1162 small and medium sized enterprises (SMEs) in Lagos in an effort to understand how SMEs in developing country contexts use mobile apps to enhance their businesses through DCs. Through the use of the covariance-based structural equation modelling (SEM) technique, the study explored the fitness of a conceptual formative model for SMEs. The model assembled 7 latent variables namely: mobile app usage, adaptive capability, absorptive capability, innovative capability, opportunity sensing ability, opportunity shaping ability and opportunity seizing ability. Subsequently, 15 hypotheses aimed at testing the relationships between the latent variables were developed and tested. The findings revealed that mobile app usage increases the adaptive, absorptive and innovative capabilities of SMEs. Absorptive capabilities help SMEs to maximise opportunities, while innovative capabilities negatively influence SMEs’ tendency to maximise opportunities. The results failed to establish a direct relationship between mobile app usage and SMEs’ ability to maximise opportunities. The research outcomes indicate that SMEs in Lagos respond to opportunities innovatively but they seldom exhibit innovation in order to create opportunities. The heterogeneous nature of SMEs complicates any clear-cut narrative as to how SMEs in Lagos should employ mobile apps to create and maximise opportunities. However, mobile apps could induce innovation and, as such, impact significantly when developed and applied to the contextual requirements of SMEs. The research revealed the untapped potential of SMEs’ mobile app usage in Lagos.     

基于数字证书的自有APP签名保护方案

为应对移动应用(APP)盗版、仿冒、篡改风险,保证自有APP来源可信、内容完 整,本文提出了一套基于数字证书的APP签名保护与安全控制技术方案,实现了对 自有应用进行签名认证和全流程安全管控。该方案规范了自有APP的签名证书,明 确了正版标识;采用可信副署签名技术,加强代码保护,实现被篡改应用可识别可 追溯;通过中央监控平台,及时发现并限制异常应用,实现了APP的全流程实时安全管控。     

A data reduction scheme for active authentication of legitimate smartphone owner using informative apps ranking

Smartphones are vulnerable to fraudulent use despite having strong authentication mechanisms. Active authentication based on behavioral biometrics is a solution to protect the privacy of data in smart devices. Machine-learning-based frameworks are effective for active authentication. However, the success of any machine-learning-based techniques depends highly on the relevancy of the data in hand for training. In addition, the training time should be very efficient. Keeping in view both issues, we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate users. We hypothesized that every user has a unique pattern hidden in his/her usage of apps. Motivated by this observation, we’ve designed a way to obtain training data, which can be used by any machine learning model for effective authentication. To achieve better accuracy with reduced training time, we removed data instances related to any specific user from the training samples which did not contain any apps from the user-specific priority list. An information theoretic app ranking scheme was used to prepare a user-targeted apps priority list. Predictability of each instance related to a candidate app was calculated by using a knockout approach. Finally, a weighted rank was calculated for each app specific to every user. Instances with low ranked apps were removed to derive the reduced training set. Two datasets as well as seven classifiers for experimentation revealed that our reduced training data significantly lowered the prediction error rates in the context of classifying the legitimate user of a smartphone.     

基于Jaccard的移动终端自动识别并行算法及其MapReduce实现

The ability of accurate and scalable mobile device recognition is critically important for mobile network operators and ISPs to understand their customers’ behaviours and enhance their user experience. In this paper, we propose a novel method for mobile device model recognition by using statistical infor-mation derived from large amounts of mobile network traffic data. Specifically, we create a Jaccard-based coefficient measure method to identify a proper keyword representing each mobile device model from massive unstruc-tured textual HTTP access logs. To handle the large amount of traffic data generated from large mobile networks, this method is designed as a set of parallel algorithms, and is imple-mented through the MapReduce framework which is a distributed parallel programming model with proven low-cost and high-efficiency features. Evaluations using real data sets show that our method can accurately recognise mobile client models while meeting the scalability and pro-ducer-independency requirements of large mobile network operators. Results show that a 91.5% accuracy rate is achieved for rec-ognising mobile client models from 2 billion records, which is dramatically higher than existing solutions.     

A hybrid multiagent routing approach for wireless ad hoc networks

Wireless ad-hoc networks are infrastructureless networks that comprise wireless mobile nodes able to communicate each other outside wireless transmission range. Due to frequent network topology changes in one hand and the limited underlying bandwidth in the other hand, routing becomes a challenging task. In this paper we present a novel routing algorithm devoted for mobile ad hoc networks. It entails both reactive and proactive components. More precisely, the algorithm is based on ant general behavior, but differs from the classic ant methods inspired from Ant-Colony-Optimization algorithm [1]. We do not use, during the reactive phase, a broadcasting technique that exponentially increases the routing overhead, but we introduce a new reactive route discovery technique that considerably reduces the communication overhead. In the simulation results, we show that our protocol can outperform both Ad hoc On-demand Distance Vector (AODV) protocol [2], one of the most important current state-of-the-art algorithms, and AntHocNet protocol [5], one of the most important ant-based routing algorithms, in terms of end-to-end delay, packet delivery ratio and the communication overhead.     

Facilitation of consumer loyalty toward branded applications: The dual-route perspective

Although many brands develop mobile applications (apps) to build relationships with consumers, most branded apps fail to retain consumers’ loyalty. This study examines the facilitation of consumer loyalty toward branded apps (continuance intention, in-app purchase intention, and word-of-mouth intention) from the dual-route perspective. One route is the affective (relationship) route, where brand benefits (functional benefits, experiential benefits, symbolic benefits, and monetary benefits) drive parasocial interactions between consumers and the brand, which, in turn, influences branded app loyalty. The other route is the utility route, where system characteristics (system quality and information quality) affect perceived usefulness, which, in turn, facilitates branded app loyalty. An online survey was conducted, and the research model was empirically tested using partial least squares structural equation modeling. The findings support the dual-route perspective according to which both affective and utilitarian paths facilitate branded app loyalty. The key theoretical contribution of this study is that it moves beyond the utilitarian path and finds the affective (relationship) path to give a more complete picture of the facilitation of consumer loyalty in the branded app context. A strategy is provided to suggest to practitioners how to design branded apps to facilitate consumer loyalty.     

Automatic labeling of mobile apps by the type of psychological needs they satisfy

App usage is now a ubiquitous phenomenon, but little is known about what types of psychological needs are met by which apps. We proposed a method to label automatically mobile apps in terms of whether and to what extent they can satisfy users’ particular psychological needs. First, using the grounded theory approach, we conducted semi-structured in-depth interviews to identify types of needs associated with app usage. Substantive and theoretical coding of the data from the interviews as well as data from samples of app reviews yielded eight types of psychological needs app users had: utilitarian, low-cost, security, health, hedonic, social, cognitive, and self-actualization needs. Second, using the needs corpus (words and phrases) generated above, a classifier was trained using latent Dirichlet allocation (LDA) and support vector machine (SVM) algorithms to filter reviews in terms of whether they included needs-related comments. The classifier showed good performance. Finally, Labeled-LDA was used to automatically provide each review with multiple labels of the types of needs mentioned and the apps were analyzed for the different types of needs they satisfied.     

Symptotics: a framework for estimating the scalability of real-world wireless networks

We present a framework for non-asymptotic analysis of real-world multi-hop wireless networks that captures protocol overhead, congestion bottlenecks, traffic heterogeneity and other real-world concerns. The framework introduces the concept of symptotic scalability to determine the number of nodes to which a network scales, and a metric called change impact value for comparing the impact of underlying system parameters on network scalability. A key idea is to divide analysis into generic and specific parts connected via a signature—a set of governing parameters of a network scenario—such that analyzing a new network scenario reduces mainly to identifying its signature. Using this framework, we present the first closed-form symptotic scalability expressions for line, grid, clique, randomized grid and mobile topologies. We model both TDMA and 802.11, as well as unicast and broadcast traffic. We compare the analysis with discrete event simulations and show that the model provides sufficiently accurate estimates of scalability. We show how our impact analysis methodology can be used to progressively tune network features to meet a scaling requirement. We uncover several new insights, for instance, on the limited impact of reducing routing overhead, the differential nature of flooding traffic, and the effect real-world mobility on scalability. Our work is applicable to the design and deployment of real-world multi-hop wireless networks including community mesh networks, military networks, disaster relief networks and sensor networks.     

Mobile flow-aware networks for mobility and QoS support in the IP-based wireless networks

As the volume of mobile traffic consisting of video, voice, and data is rapidly expanding, a challenge remains with the mobile transport network, which must deliver data traffic to mobile devices without degrading the service quality. Since every Internet service holds its own service quality requirements, the flow-aware traffic management in fine granularity has been widely investigated to guarantee Quality of Service (QoS) in the IP networks. However, the mobile flow-aware management has not been sufficiently developed yet because of the inherent constraints of flow routing in the mobile networks regarding flow-aware mobility and QoS support. In this paper, we propose a flow-aware mobility and QoS support scheme called mobile flow-aware network (MFAN) for IP-based wireless mobile networks. The proposed scheme consists of dynamic handoff mechanisms based on QoS requirements per flow to reduce the processing overhead of the flow router while ensuring QoS guarantee to mobile flows. The performance analyses of the proposed scheme demonstrate that MFAN successfully supports the mobile flow traffic delivery while satisfying the QoS requirement of flows in the wireless mobile IP networks.     

ARMR: Anonymous routing protocol with multiple routes for communications in mobile ad hoc networks

A mobile ad hoc network consists of mobile nodes that communicate in an open wireless medium. Adversaries can launch analysis against the routing information embedded in the routing message and data packets to detect the traffic pattern of the communications, thereby obtaining sensitive information of the system, such as the identity of a critical node. In order to thwart such attacks, anonymous routing protocols are developed. For the purposes of security and robustness, an ideal anonymous routing protocol should hide the identities of the nodes in the route, in particular, those of the source and the destination. Multiple routes should be established to increase the difficulty of traffic analysis and to avoid broken paths due to node mobility. Existing schemes either make the unrealistic and undesired assumption that certain topological information about the network is known to the nodes, or cannot achieve all the properties described in the above. In this paper, we propose an anonymous routing protocol with multiple routes called ARMR, which can satisfy all the required properties. In addition, the protocol has the flexibility of creating fake routes to confuse the adversaries, thus increasing the level of anonymity. In terms of communication efficiency, extensive simulation is carried out. Compared with AODV and MASK, our ARMR protocol gives a higher route request success rate under all situations and the delay of our protocol is comparable to the best of these two protocols.