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Systematically and quantitatively determining patterns in consumer flow is an important problem in marketing research. Identifying these patterns can facilitate an understanding of where and when consumers purchase products and services at physical retail shops. Collecting data on real consumers who shop at retail stores is one of the most challenging and expensive aspects of these studies. This article introduces ConvenienceProbe, a phone-based data collection system for retail trade-area analysis. The proposed method targets local residents shopping at neighborhood convenience stores. This study deploys and tests the system by collecting real customer flow data in neighborhood convenience stores. Results show that the consumer flow data collected from the ConvenienceProbe system is comparable to that from a traditional face-to-face interview method.

In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in an MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: Its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7× and 2.3×, for 2-antenna and 3-antenna APs, respectively.

Social network applications are becoming increasingly popular on mobile devices. A mobile presence service is an essential component of a social network application because it maintains each mobile user's presence information, such as the current status (online/offline), GPS location and network address, and also updates the user's online friends with the information continually. If presence updates occur frequently, the enormous number of messages distributed by presence servers may lead to a scalability problem in a large-scale mobile presence service. To address the problem, we propose an efficient and scalable server architecture, called PresenceCloud, which enables mobile presence services to support large-scale social network applications. When a mobile user joins a network, PresenceCloud searches for the presence of his/her friends and notifies them of his/her arrival. PresenceCloud organizes presence servers into a quorum-based server-to-server architecture for efficient presence searching. It also leverages a directed search algorithm and a one-hop caching strategy to achieve small constant search latency. We analyze the performance of PresenceCloud in terms of the search cost and search satisfaction level. The search cost is defined as the total number of messages generated by the presence server when a user arrives; and search satisfaction level is defined as the time it takes to search for the arriving user's friend list. The results of simulations demonstrate that PresenceCloud achieves performance gains in the search cost without compromising search satisfaction.

With the advances of communications, computing, and positioning technologies, mobile devices have been regarded as mobile computing platforms for various kinds of location-based and human-computation services. However, most existing applications regard each device as a sensor or focus on services with the computation on a single device. In contrast, this paper leverages a group of mobile devices as a collaborative and distributed search platform. Specifically, we propose a search system with mobile devices for rescue and patrol operations. The system utilizes mobile devices to find and assign the search route to each searcher in a collaborative and distributed manner. Given the roads to be searched in an area and the candidate start locations, our system minimizes the time required to search the whole area and guarantees that each road will be searched at least once. We first formulate the k-Person Search Problem for k mobile devices and prove that the problem is NP-Hard. To find the optimal solutions, we propose a centralized algorithm for a special case and an Integer Linear Programming formulation for general cases. We also devise an approximation algorithm. The algorithms can be used to dispatch the searchers before the operation starts. Moreover, to support online adaptation, we formulate the Path Refinement Problem for path exchange among searchers and propose a distributed algorithm to adaptively adjust the paths after the search starts. We also implement the proposed algorithms in mobile devices as a collaborative and distributed search system and demonstrate the efficiency of our algorithms with computation simulations and field trials.

With the ubiquity of cellular phones, mobile applications with 2D barcodes have drawn a lot of attentions in recent years. When a user takes a barcode image with the camera in a mobile device, the captured image tends to be blurred due to camera shaking when the user presses the shutter. In addition, the captured image includes part of the complex background of the page with the barcode. In this paper, we point out that the above two issues, which have not been identified in previous works, deteriorate the accuracy of barcode recognition in the mobile computing. We then propose an efficient and effective algorithm to restore and extract 2D barcode from a complex background in a camera-shaken image. Compared with previous approaches, our algorithm outperforms in not only smaller running time but also higher accuracy of the barcode recognition in the mobile computing.

Data broadcast is an advanced technique to realize large scalability and bandwidth utilization in a mobile computing environment. In this environment, the channel bandwidth of each channel is variant with time in real cases. However, traditional schemes do not consider time-variant bandwidth of each channel to schedule data items. Therefore, the above drawback degrades the performance in generating broadcast programs. In this paper, we address the problem of generating a broadcast program to disseminate data via multiple channels of time-variant bandwidth. In view of the characteristics of time-variant bandwidth, we propose an algorithm using adaptive allocation on time-variant bandwidth to generate the broadcast program to avoid the above drawback to minimize average waiting time. Experimental results show that our approach is able to generate the broadcast programs with high quality and is very efficient in a data broadcasting environment with the time-variant bandwidth.

We explore in this paper the problem of generating broadcast programs in a heterogeneous data broadcasting environment, in which disseminated data items can be of different sizes. Given the broadcast database and the number of channels, we first derive the analytical model of the heterogeneous data broadcasting to obtain the average waiting time of mobile users, and prove the allocation problem as an NP-complete problem. In order to solve such problem, we propose a two-phase architecture to perform channel allocation. Algorithm DRP (dimension reduction partitioning) is employed to perform rough allocation to derive the satisfactory solutions, whereas mechanism CDMS (cost-diminishing movement selection) is used for fine allocation to achieve local optimum solutions. In addition, we also propose algorithm GA-CDMS according to the concept of hybrid genetic algorithm for comparison purposes. GA-CDMS can perform global search more accurately and efficiently than conventional genetic algorithm GA and the suboptimum that GA-CDMS achieves will be very close to the optimal solution. In the experiments, we consider the important issues such as accuracy, scalability, diversity and the efficiency. From the experimental results, we show that the proposed two-phase channel allocation is very practical in performing an effective channel allocation with high efficiency in a heterogeneous broadcasting environment.

The scalability of data broadcasting has been manifested by prior studies on the base of the traditional data management systems where data objects, mapped to a pair of state and value in the database, are independent, persistent, and static against simple queries. However, many modern information applications spread dynamic data objects and process complex queries for retrieving multiple data objects. Particularly, the information servers dynamically generate data objects that are dependent and can be associated into a complete response against complex queries. Accordingly, the study in this paper considers the problem of scheduling dynamic broadcast data objects in a clients-providers-servers system from the standpoint of data association, dependency, and dynamics. Since the data broadcast problem is NP-hard, we derive the lower and the upper bounds of the mean service access time. In light of the theoretical analyses, we further devise a deterministic algorithm with several gain measure functions for the approximation of schedule optimization. The experimental results show that the proposed algorithm is able to generate a dynamic broadcast schedule and also minimize the mean service access time to the extent of being very close to the theoretical optimum.

In this paper, we propose a new data broadcast mechanism with network coding in heterogeneous wireless networks. Our mechanism adaptively clusters the mobile hosts in fewer cells to minimize the bandwidth consumption. In addition, we adaptively code the data according to the data temporarily stored in each mobile host with a distributed manner. Our mechanism allows each delivered message to be coded from only a subset of data to further reduce the number of required messages. We formulate the cell selection and broadcast coding problem with integer programming and prove that the problem is NP-hard. We design a distributed algorithm based on Lagrangean relaxation. Our algorithm needs no server to record the location, queried, and stored information of receivers. Moreover, our algorithm is adaptive to the dynamic group membership, mobility, queried, and stored data of receivers.

Recent technology advances in multimedia communication have ushered in a new era of personal communication. Users can ubiquitously access the Internet via various mobile devices. For the mobile devices featured with lower bandwidth network connectivity, transcoding can be used to reduce the object size by lowering the quality of a multimedia object. In view of the monolithic transcoders which only provide transcoding services and have limited performances due to the unknown data types and protocols in the prior research, we propose the architecture of versatile transcoding proxy (VTP). Based on the concept of the agent system, the VTP architecture can accept and execute the transcoding preference script provided by the client or the server to transform the corresponding data or protocol according to the user's specification. In order to enhance the effectiveness of the VTP architecture, we adopt the concept of dynamic cache categories and propose the scheme maximum profit replacement with dynamic cache categories (DCC-MPR). Based on the weighted transcoding graph, scheme DCC-MPR performs cache replacement according to the content in the caching candidate set, which is generated by the concept of dynamic programming. The experimental results show that the proposed architecture VTP and the corresponding scheme DCC-MPR have better performances in many aspects compared to the conventional transcoding proxy systems.

In recent years, data broadcasting becomes a promising technique to design a mobile information system with power conservation, high scalability and high bandwidth utilization. In many applications, the query issued by a mobile client corresponds to multiple items which should be accessed in a sequential order. In this paper, we study the scheduling approach in such a sequential data broadcasting environment. Explicitly, we propose a general framework referred to as MULS (standing for MUlti-Level Service) for an information system. There are two primary stages in MULS: on-line scheduling and optimization procedure. In the first stage, we propose an On- Line Scheduling algorithm (denoted by OLS) to allocate the data items into multiple channels. As for the second stage, we devise an optimization procedure SCI, standing for Sampling with Controlled Iteration, to enhance the quality of broadcast programs generated by algorithm OLS. Procedure SCI is able to strike a compromise between effectiveness and efficiency by tuning the control parameters. According to the experimental results, we show that algorithm OLS with procedure SCI outperforms the approaches in prior works prominently in both effectiveness (i.e., the average access time of mobile users) and efficiency (i.e., the complexity of the scheduling algorithm). Therefore, by cooperating algorithm OLS with procedure SCI, the proposed MULS framework is able to generate broadcast programs with flexibility of providing different service qualities under different requirements of effectiveness and efficiency: in the dynamic environment in which the access patterns and information contents change rapidly, the parameters used in SCI will perform online scheduling with satisfactory service quality. As for the static environment in which the query profile and the database are updated infrequently, larger values of parameters are helpful to generate an optimized broadcast program, indicating the advantageous feature of MULS.

Most research works in transcoding proxies in mobile computing environments are on the basis of the traditional client-server architecture and do not employ the data broadcast technique. In addition, the issues of QoS provision and energy conservation are also not addressed in the prior studies. In view of this, we design in this paper a QoS-aware and energy-conserving transcoding proxy by utilizing the on-demand broadcasting technique. We first propose a QoS-aware and energy-conserving transcoding proxy architecture, abbreviated as QETP, and model it as a queuing network consisting of three queues. By analyzing the queuing network, three lemmas are derived to estimate the load of these queues. We then propose a version decision policy and a service admission control scheme to provide QoS in QETP. The derived lemmas are used to guide the execution of the proposed version decision policy and service admission control scheme to achieve the given QoS requirement. In addition, we also propose a data indexing method to reduce the power consumption of clients. To measure the performance of the proposed architecture, three experiments are conducted. Experimental results show that the average access time reduction of the proposed scheme over the traditional client-server architecture ranges from 45 percent to 75 percent. Experimental results also show that the proposed scheme is more scalable than the traditional client-server architecture and is able to effectively control the system load to attain the given QoS requirements. In addition, the proposed scheme is able to greatly reduce the average tuning time of clients at the cost of a slight increase (around 5 percent in our experiments) in average access time.

In this paper, we explore a new data mining capability for a mobile commerce environment. To better reflect the customer usage patterns in the mobile commerce environment, we propose an innovative mining model, called mining mobile sequential patterns, which takes both the moving patterns and purchase patterns of customers into consideration. How to strike a compromise among the use of various knowledge to solve the mining on mobile sequential patterns is a challenging issue. We devise three algorithms (algorithm TJLS, algorithm TJPT, and algorithm TJPF) for determining the frequent sequential patterns, which are termed large sequential patterns in this paper, from the mobile transaction sequences. Algorithm TJLS is devised in light of the concept of association rules and is used as the basic scheme. Algorithm TJPT is devised by taking both the concepts of association rules and path traversal patterns into consideration and gains performance improvement by path trimming. Algorithm TJPF is devised by utilizing the pattern family technique which is developed to exploit the relationship between moving and purchase behaviors, and thus is able to generate the large sequential patterns very efficiently. A simulation model for the mobile commerce environment is developed, and a synthetic workload is generated for performance studies. In mining mobile sequential patterns, it is shown by our experimental results that algorithm TJPF significantly outperforms others in both execution efficiency and memory saving, indicating the usefulness of the pattern family technique devised in this paper. It is shown by our results that by taking both moving and purchase patterns into consideration, one can have a better model for a mobile commerce system and is thus able to exploit the intrinsic relationship between these two important factors for the efficient mining of mobile sequential patterns

In a mobile computing environment, the combined use of broadcast and on-demand channels can utilize the bandwidth effectively for data dissemination. We explore in this paper the problem of dynamic data and channel allocation with the number of communication channels and the number of data items given. We first derive the analytical models of the average access time when the data items are requested through the broadcast and on-demand channels. Then, we transform this problem into a guided search problem. In light of the theoretical properties derived, we devise algorithm SOM to obtain the optimal allocation of data and channels. Algorithm SOM is a composite algorithm which will cooperate with 1) a search strategy and 2) a broadcast program generation algorithm. According to the analytical mode, we devise scheme BIS-incremental on the basis of algorithm SOM, which is able to obtain solutions of high quality efficiently by employing binary interpolation search. In essence, scheme BIS-incremental is guided to explore the search space with higher likelihood to be the optimal first, thereby leading to an efficient and effective search. It is shown by our simulation results that the solution obtained by scheme BIS-incremental is of very high quality and is in fact very close to the optimal one. A sensitivity study on several parameters, including the number of data items and the number of communication channels, is conducted. The experimental results show that scheme BIS-incremental is of very good scalability, which is particularly important for its practical use in a mobile computing environment

The growth in wireless communication technologies attracts a considerable amount of attention in mobile ad hoc networks. Since mobile hosts in an ad hoc network usually move freely, the topology of the network changes dynamically and disconnection occurs frequently. These characteristics make it likely for a mobile ad hoc network to be separated into several disconnected partitions, and the data accessibility is hence reduced. Several schemes are proposed to alleviate the reduction of data accessibility by replicating data items. However, little research effort was elaborated upon exploiting the group mobility where the group mobility refers to the phenomenon that several mobile nodes tend to move together. In this paper, we address the problem of replica allocation in a mobile ad hoc network by exploring group mobility. We first analyze the group mobility model and derive several theoretical results. In light of these results, we propose a replica allocation scheme to improve the data accessibility. Several experiments are conducted to evaluate the performance of the proposed scheme. The experimental results show that the proposed scheme is able to not only obtain higher data accessibility, but also produce lower network traffic than prior schemes.

With the cutting edge technology advance in wireless and mobile computers, the query processing in a mobile environment involves join processing among different sites, which include static servers and mobile computers. Because of the need for energy saving and also the presence of asymmetric features in a mobile computing environment, the conventional query processing for a distributed database cannot be directly applied to a mobile computing system. In this paper, we first explore three asymmetric features of a mobile environment. Then, in light of these features, we devise query processing methods for both join and query processing. Intuitively, employing semijoin operations in a mobile computing environment is able to further reduce both the amount of data transmission and energy consumption. A semijoin which is initiated by a mobile computer (respectively, the server) and is beneficial to reduce the cost of a join operation is termed a mobile-initiated or MI (respectively, server-initiated or SI) profitable semijoin. According to those asymmetric features of a mobile computing system, we examine three different join methods and devise some specific criteria to identify MI/SI profitable semijoins. For query processing, which refers to the processing of multijoin queries, we develop three query processing schemes. In particular, we formulate the query processing in a mobile computing system as a two-phase query processing procedure that can determine a join sequence and interleave that join sequence with SI profitable semijoins to reduce both the amount of data transmission and energy consumption. Performance of these join and query methods is comparatively analyzed and sensitivity analysis on several parameters: is conducted. Furthermore, we develop a systematic procedure to derive the characteristic functions of MI and SI profitable semijoins. It is noted that, given some system parameters, those characteristic functions are very important in determining which join method is the most appropriate one to employ in that configuration. It is shown by our simulation results that, by exploiting the three asymmetric features, these characteristic functions are very powerful in reducing both the amounts of energy consumption and data transmission incurred and can lead to the design of an efficient and effective query processing procedure for a mobile computing environment.

In this paper, we devise data allocation algorithms that can utilize the knowledge of user moving patterns for proper allocation of shared data in a mobile computing system. By employing the data allocation algorithms devised, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. The data allocation algorithms for shared data, which are able to achieve local optimization and global optimization, are developed. Local optimization refers to the optimization that the likelihood of local data access by an individual mobile user is maximized whereas global optimization refers to the optimization that the likelihood of local data access by all mobile users is maximized. Specifically, by exploring the features of local optimization and global optimization, we devise algorithm SD-local and algorithm SD-global to achieve local optimization and global optimization, respectively. In general, the mobile users are divided into two types, namely, frequently moving users and infrequently moving users. A measurement, called closeness measure which corresponds to the amount of the intersection between the set of frequently moving user patterns and that of infrequently moving user patterns, is derived to assess the quality of solutions provided by SD-local and SD-global. Performance of these data allocation algorithms is comparatively analyzed. From the analysis of SD-local and SD-global, it is shown that SD-local favors infrequently moving users whereas SD-global is good for frequently moving users. The simulation results show that the knowledge obtained from the user moving patterns is very important in devising effective data allocation algorithms which can lead to prominent performance improvement in a mobile computing system.

In a mobile computing system, as users move to a new service area, the new server is usually considered to take over the execution of running programs for mobile users from the previous server so as to reduce the communication overhead of a mobile system. This procedure is referred to as service handoff. Note that when service handoff occurs, the new server will lose its advantage for cache access. To remedy this, we explore in this paper several cache retrieval schemes to improve the efficiency of cache retrieval. In particular, we analyze the impact of using a coordinator buffer to improve the overall performance of cache retrieval. Moreover, in light of the properties of transactions (i.e., temporal locality of data access among transactions), we devise a Dynamic and Adaptive cache Retrieval scheme (DAR) that can utilize proper cache methods according to some specific criteria to deal with the service handoff situation in a mobile computing environment. Performance of these cache retrieval schemes is analyzed and a system simulator is developed to validate our results. We devise a systematic procedure for determining the optimal operating points of DAR. Our experimental results show that by adaptively adopting the advantages of different cache retrieval methods, DAR significantly outperforms other schemes and is particularly effective for a mobile computing environment.

The dissemination of numerous information broadcast services, gives rise to the scalability issue in wireless networks. Previous researchers have shown that the push-based data broadcast mechanism is efficient in reducing message traffic. However, most research efforts are dedicated to the dissemination of static information contents. In practice, information broadcast services can produce and deliver dynamic information contents. To efficiently convey the dynamic data, we device, in this paper, an adaptive information dissemination mechanism by exploiting the functionality of data broadcasting, to support the dissemination of static and dynamic information services simultaneously. In our design, both static and dynamic information services are subsumed as service groups, i.e. the building blocks with the uniform representation of structure and group popularity and, thus, the conventional scenario becomes a special case of our framework. Furthermore, in order to tolerate the broadcast traffic dynamics, we design an online load-based slot allocation and feedback control technique to deal with the adaptation of the service group classification, bandwidth allocation, and broadcast schedule so as to avoid performance degradation. It is shown by the experimental study that the proposed adaptive information dissemination mechanism associated with the online loan-based feedback control is able to achieve a substantial reduction of message traffic for dynamic dissemination in wireless networks.

Recent technology advances in mobile networking have ushered in a new era of personal communication. Users can ubiquitously access the Internet via many emerging mobile appliances, such as portable notebooks, personal digital assistants (PDAs), and WAP-enabled cellular phones. While the transcoding proxy is attracting an increasing amount of attention in this environment, it is noted that new caching strategies are required for these transcoding proxies. We propose an efficient cache replacement algorithm for transcoding proxies. Specifically, we formulate a generalized profit function to evaluate the profit from caching each version of an object. This generalized profit function explicitly considers several new emerging factors in the transcoding proxy and the aggregate effect of caching multiple versions of the same object. It is noted that the aggregate effect is not simply the sum of the costs of caching individual versions of an object, but rather, depends on the transcoding relationship among these versions. The notion of a weighted transcoding graph is devised to evaluate the corresponding aggregate effect efficiently. Utilizing the generalized profit function and the weighted transcoding graph, we propose, in this paper, an innovative cache replacement algorithm for transcoding proxies. In addition, an effective data structure is designed to facilitate the management of the multiple versions of different objects cached in the transcoding proxy. Using an event-driven simulation, it is shown that the algorithm proposed consistently outperforms companion schemes in terms of the delay saving ratios and cache hit ratios.

Energy saving is one of the most important issues in wireless mobile computing. Among others, one viable approach to achieving energy saving is to use an indexed data organization to broadcast data over wireless channels to mobile units. Using indexed broadcasting, mobile units can be guided to the data of interest efficiently and only need to be actively listening to the broadcasting channel when the relevant information is present. We explore the issue of indexing data with skewed access for sequential broadcasting in wireless mobile computing. We first propose methods to build index trees based on access frequencies of data records. To minimize the average cost of index probes, we consider two cases: one for fixed index fanouts and the other for variant index fanouts, and devise algorithms to construct index trees for both cases. We show that the cost of index probes can be minimized not only by employing an imbalanced index tree that is designed in accordance with data access skew, but also by exploiting variant fanouts for index nodes. Note that, even for the same index tree, different broadcasting orders of data records will lead to different average data access times. To address this issue, we develop an algorithm to determine the optimal order for sequential data broadcasting to minimize the average data access time. Performance evaluation on the algorithms proposed is conducted. Examples and remarks are given to illustrate our results.

The research issue of broadcasting has attracted a considerable amount of attention in a mobile computing system. By utilizing broadcast channels, a server continuously and repeatedly broadcasts data to mobile users. These broadcast channels are also known as "broadcast disks" from which mobile users can retrieve data. Using broadcasting, mobile users can obtain the data of interest efficiently and only need to wait for the required data to present on the broadcast channel. The issue of designing proper data allocation in the broadcast disks is to reduce the average expected delay of all data items. We explore in this paper the problem of generating hierarchical broadcast programs with the data access frequencies and the number of broadcast disks in a broadcast disk array given. Specifically, we first transform the problem of generating hierarchical broadcast programs into the one of constructing a channel allocation tree with variant-fanout. By exploiting the feature of tree generation with variant-fanout, we develop a heuristic algorithm VFK to minimize the expected delay of data items in the broadcast program. In order to evaluate the solution quality obtained by algorithm VFK and compare its resulting broadcast program with the optimal one, we devise an algorithm OPT based on a guided search to obtain the optimal solution. Performance of these algorithms is comparatively analyzed. Sensitivity analysis on several parameters, including the number of data items and the number of broadcast disks, is conducted. It is shown by our simulation results that by exploiting the feature of variant-fanout in constructing the channel allocation tree, the solution obtained by algorithm VFK. is of very high quality and is in fact very close to the optimal one resulted by algorithm OPT. Moreover, algorithm VFK is of very good scalability which is important for algorithm VFK to be of practical use to generate hierarchical broadcast programs dynamically in a mobile computing environment.

In this paper, we present a new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality of results obtained. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two schemes are devised: one utilizes the set level of moving patterns and the other utilizes their path level. Schemes for shared data are also developed. Performance of these schemes is comparatively analyzed.

The research issue of broadcasting has attracted a considerable amount of attention in a mobile computing system. By utilizing broadcast channels, a server is able to continuously and repeatedly broadcast data to mobile users. From these broadcast channels, mobile users obtain the data of interest efficiently and only need to wait for the required data to be present on the broadcast channel. Given the access frequencies of data items, one can design proper data allocation in the broadcast channels to reduce the average expected delay of data items. In practice, the data access frequencies may vary with time. We explore in this paper the problem of adjusting broadcast programs to effectively respond to the changes of data access frequencies, and develop an efficient algorithm DL to address this problem. Performance of algorithm DL is analyzed and a system simulator is developed to validate our results. Sensitivity analysis on several parameters, including the number of data items, the number of broadcast disks, and the variation of access frequencies, is conducted. It is shown by our results that the broadcast programs adjusted by algorithm DL are of very high quality and are in fact very close to the optimal ones.

The query processing in a mobile computing environment involves join processing among different sites which include static servers and mobile computers. Because of the presence of asymmetric features in a mobile computing environment, the conventional query processing for a distributed database cannot be directly applied to a mobile computing system. In this paper, we first explore some unique features of a mobile environment and then, in light of these features, devise query processing methods for both join and query processing. Remote mobile joins are said to be effectual if they are, when being interleaved into a join sequence, able to reduce the amount of data transmission cost required for distributed mobile query processing. Since mobile relations are employed as reducers in our proposed query processing cost model, more mobile joins in the query processing lead to less data transmitted through the network. With proper scheduling, interleaving effectual remote mobile joins into a query scheduling can significantly reduce the total amount of data transmission among different sites. A simulator is developed to evaluate the performance of algorithms devised. Our results show that the approach of interleaving the processing of distributed mobile queries with effectual remote mobile joins is not only efficient, but also effective in reducing the total amount of data transmission cost required to process distributed mobile queries.

Mobile devices, especially smart phones, have been popular in recent years. With users spending much time on mobile devices, service providers deliver advertising messages to mobile device users and look forward to increasing their revenue. However, delivery of proper advertising messages is challenging since strategies of advertising in TV, SMS, or website may not be applied to the banner-based advertising on mobile devices. In this work, we study how to properly recommend advertising messages for mobile device users. We propose a novel approach which simultaneously considers several important factors: user profile, apps used, and clicking history. We apply experiments on real-world mobile log data, and the results demonstrate the effectiveness of the proposed approach.

Caching data in a wireless mobile computer can significantly reduce the bandwidth requirement. However, due to battery power limitation, a wireless mobile computer may often be forced to operate in a doze or even totally disconnected mode. As a result, the mobile computer may miss some cache invalidation reports. In this paper, we present an energy-efficient cache invalidation method for a wireless mobile computer. The new cache invalidation scheme is called grouping with cold update-set retention (GCORE). Upon waking up, a mobile computer checks its cache validity with the server. To reduce the bandwidth requirement for validity checking, data objects are partitioned into groups. However, instead of simply invalidating a group if any of the objects in the group has been updated, GCORE retains the cold update set of objects in a group if possible. We present an efficient implementation of GCORE and conduct simulations to evaluate its caching effectiveness. The results show that GCORE can substantially improve mobile caching by reducing the communication bandwidth (thus energy consumption) for query processing.

The Quorum-based Power Saving (QPS) protocols have been proposed to increase the energy efficiency of wireless communication. However, it remains challenging to apply existing QPS protocols to the Mobile Ad Hoc Networks (MANETs) as the timers of nodes are usually asynchronous, the incurred delay are expected to be adaptive, and the network topology is asymmetric. In this paper, we propose an Asynchronous, Adaptive, and Asymmetric (AAA) power management protocol that fulfills the unique requirements of MANETs. We present the asymmetric grid quorum system, a generalization of traditional grid-based quorum systems, to ensure the network connectivity. Theoretical analysis is conducted to demonstrate the benefits of AAA over previous arts.

On-demand data broadcast is widely deployed to achieve high scalability in a mobile computing environment. However, traditional on-demand data broadcasting assumes that each time slot includes only one data item. Therefore, the above constraint requires the mobile users to wait until the next broadcast cycle to retrieve a data item if they miss the item in this cycle. The above constraint also limits the number of users that can be served in each time slot. In this paper, we propose a new on-demand data broadcast model with modified network coding. Our approach enables a server to encode multiple data items in each time slot, while each mobile user retrieves the data items by decoding the encoded data items with the locally stored data items. Our approach is different from the traditional network coding because each time slot encodes only a subset of data items, which are decided according to identities of the requested and stored data items of users. The simulation results show that our algorithm can reduce the access time by 66% as compared to the traditional schemes.

Data broadcast is widely deployed to realize bandwidth efficiency in a mobile computing environment. However, traditional data broadcasting assumes that mobile users can only retrieve one data item in each time slot. Therefore, the above constraint leads to access conflicts and requires the mobile users to wait until the next broadcast cycle to retrieve the requested information. In this paper, we propose a novel data broadcasting framework that adopts network coding with the stored data items in users. In our approach, a server encodes multiple data items in each time slot, while each mobile user is able to retrieve the data item by decoding the encoded data items with the locally stored data items. We propose an algorithm to generate the broadcast program to minimize the average access time. In addition, our algorithm can avoid coding redundant data in each time slot to reduce access delay. Our simulation results show that our algorithm can reduce the access time by 56% as compared to no coding scheme.

Data broadcast is a technique to realize energy saving and bandwidth utilization in a mobile computing environment. However, traditional schemes schedule data items without considering channel bandwidth. Therefore, the above drawback leads to the unfair broadcasting rate of each item of the different access frequency. In this paper, we address the problem of generating a broadcast program to disseminate data via multiple channels with variant bandwidth. In view of the characteristics of variant bandwidth, we propose an algorithm using adaptive partition on bandwidth to generate broadcast program to avoid the above drawback so as to minimize the average waiting time. The empirical results show that our approach is able to produce broadcast programs of high quality and is very efficient in a data broadcasting environment with variant bandwidth.

Current approaches to k nearest neighbor (KNN) search in mobile sensor networks require certain kind of indexing support. This index could be either a centralized spatial index or an in-network data structure that is distributed over the sensor nodes. Creation and maintenance of these index structures, to reflect the network dynamics due to sensor node mobility, may result in long query response time and low battery efficiency, thus limiting their practical use. In this paper, we propose a maintenance-free, itinerary-based approach called density-aware itinerary KNN query processing (DIKNN). The DIKNN divides the search area into multiple cone-shape areas centered at the query point. It then performs a query dissemination and response collection itinerary in each of the cone-shape areas in parallel. The design of the DIKNN scheme also takes into account challenging issues such as the the dynamic adjustment of the search radius (in terms of number of hops) according to spatial irregularity or mobility of sensor nodes. The simulation results show that DIKNN yields substantially better performance and scalability over previous work, both as k increases and as the sensor node mobility increases. It outperforms the second runner with up to 50% saving in energy consumption and up to 40% reduction in query response time, while rendering the same level of query result accuracy.

Most of the prior research works in data broadcasting are based on the assumption that the disseminated items are independent of one another. Since in many applications, a mobile user will be interested in more than one item simultaneously, we discuss in this paper the issue of dependency in generating a broadcast program. Algorithm PBA, standing for Placement-Based Allocation, is proposed to generate a broadcast program with high quality and low complexity in the dependent data broadcasting environment. The experimental results show that the proposed placement-based allocation for scheduling dependent items leads to better execution efficiency and solution quality than those by prior works.

Digital TV already rises. Unlike the traditional TV system, the digital TV architecture allows users to execute application programs in their set-top-boxes. One of the benefits is that users can communicate interactively with others by executing certain applications via some channels, e.g., the Internet. However, the only control device in recent years is the remote controller, which is limited to the transmission range. In this paper, we devise a system that allows mobile appliances, e.g., mobile phones and laptops, to control our set-top-boxes. Due to the issue of compatibilities, we adopt MHP (multimedia home platform) as the middleware. The proposed system model is also compatible with the EPG (electronic program guide) architecture. Two services are provided: (1) viewing DTV contents online; (2) remotely commanding the set-top-box to record a DTV service. Assuming that in the future when every house will be equipped with a "personal multimedia server", this server will even process complex tasks such as transcoding and broadcasting. We believe that our system model is very feasible for future DTV systems and is applicable to advanced DTV applications.

Hybrid data dissemination, which combines the push-based (i.e., broadcast) and pull-based (i.e., on-demand) data delivery, is the most common technique to deliver information in a mobile computing system. Most of the prior works in hibrid dissemination are based on the assumption that each delivered data item is of the same size. However, in the modern communication environment in which various information is delivered, the conventional dissemination schemes suffer from the efficiency issues. In this paper, we consider a general model of hybrid data dissemination, in which each data item is allowed to have an arbitrary size. The analytical model MGBC (Model of General Broadcast Channels) and MGOD (Model of General On-demand Channels) are first proposed to describe the broadcast and on-demand channels, respectively. In addition, the scheme GDS (General Dissemination Scheme) is adopted to perform the channel allocation and the data classification. Experimental results show that the proposed approach gives a near-optimal solution in achieving the minimun access time in the general dissemination environment.

The high diversity in the capabilities of various mobile devices such as display capabilities and computation power makes the design of mobile information systems more challenging. A transcoding proxy is placed between a client and an information server to coordinate the mismatch between what the server provides and what the client prefers. However, most research works in transcoding proxies in mobile computing environments are under the traditional client-server architecture and do not employ the data broadcast technique which is has been deemed a promising technique to design a power conservation, high scalable and high band-width utilization. In addition, the issue of QoS provision is also not addressed. In view of this, we design in this paper a QoS-aware transcoding proxy by utilizing the on-demand broadcasting technique. We first propose a QoS-aware transcoding proxy architecture, abbreviated as QTP, and model it as a queueing network. By analyzing the queueing network, several theoretical results are derived. We then propose a version decision policy and a service admission control scheme to provide QoS in QTP. The derived results are used to guide the execution of the proposed version decision policy and service admission control scheme to achieve the given QoS requirement. To measure the performance of QTP, several experiments are conducted. Experimental results show that the proposed scheme is more scalable than traditional client-server systems. In addition, the proposed scheme is able to effectively control the system load to attain the desired QoS

In several mobile applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data mainly consider single broadcast channel environments. In view of this, we explore the problem of broadcasting dependent data in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs.

Data broadcast is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. In many applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data are restricted to a single broadcast channel environment, and as a consequence, the results are of limited applicability to the upcoming mobile environments. In view of this, we relax this restriction and explore the problem of broadcasting dependent data in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs. Our experimental results show that the theoretical results derived are able to guide the search of the genetic algorithm very effectively, thus leading to broadcast programs of very high quality.

In this paper we devise data allocation algorithms that can utilize the knowledge of user moving patterns for proper allocation of shared data in a mobile computing system. By employing the data allocation algorithms devised, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved The data allocation algorithms for shared data, which are able to achieve local optimization and global optimization, are developed. Local optimization refers to the optimization that the likelihood of local data access by an individual mobile user is maximized whereas global optimization refers to the optimization that the likelihood of local data access by all mobile users is maximized By exploring the corresponding features, we devise algorithm SD-local and algorithm SD-global to achieve local optimization and global optimization, respectively. The simulation results show that the knowledge obtained from the user moving patterns is very important in devising effective data allocation algorithms which can lead to prominent performance improvement in a mobile computing system.

With the proliferation of wireless information applications and services, data broadcasting known from radio networks has become an important mechanism for mobile data access in wireless communication networks. Two data delivery techniques, push and pull, and their hybrid combination bad been introduced to satisfy mobile user demands. However, most previous research efforts were elaborated upon the premise of a single channel with static workload and access pattern. The emergence of a multi-channel broadcast paradigm has attracted much research attention in the data broadcast community. We design an adaptive balanced scheme (ABS) which performs a heuristic search in pursuit of a fair balance of access time for a hybrid data delivery in a multi-channel data broadcast environment. The experimental results show that at the balanced point exploited by ABS, the result of channel partition and data item classification is very close to the optimal balance, and consequently, we are able to obtain the lowest mean access time in both push and pull channels simultaneously.

Data dissemination has significantly served as a scalable data delivery mechanism in wireless networks. However, even though the broadcast traffic has the nature of dynamic changes, most previous research efforts were elaborated upon the premise of static workloads and access patterns without having proper traffic awareness. In this paper, we address the existence of client impatience and accordingly devise an online traffic awareness mechanism based on a novel selective deferment and reflection (SDR) technique to estimate the dynamic workloads and access patterns in a granularity of a broadcast cycle. In comparison with prior probing and feedback approaches, our design is of practical usefulness in that it has low complexity and is light-weight without performance degradation. With various dynamic traffic scenarios, the experimental results show that with an increasing/decreasing workload, the real access frequency distribution is bounded by two specific estimated distributions. This fact in turn suggests us to employ a trigonometric tuning method to further enhance the estimation. In addition, we examine that the mean difference between the estimated access frequency distribution and the real one is very small, consequently indicating the feasibility and reliability of our proposed data broadcast mechanism with traffic awareness.

We study in this paper the problem of broadcasting dependent data for unordered queries. However, most prior studies on dependent data broadcasting are limited to the premise of no data replication. Different from other prior studies, we investigate the effect of data replication in this paper. Specifically, we first derive several theoretical properties for the average access time by analyzing the model of dependent data broadcasting. On the basis of the theoretical results, we develop a genetic algorithm to generate broadcast programs with replication. In order to compare the performance of the proposed algorithm and the prior studies, several experiments are conducted. Our experimental results show that with the analytical results derived, the theoretical results derived are able to guide the search of the genetic algorithm very effectively, and lead to solution broadcast programs of higher quality than those of the prior studies.

Storage Management.- Storing and Accessing User Context.- Cooperative Caching in Ad Hoc Networks.- Investigation of Cache Maintenance Strategies for Multi-cell Environments.- Resilient Data-Centric Storage in Wireless Ad-Hoc Sensor Networks.- Location Tracking.- Shape-Based Similarity Query for Trajectory of Mobile Objects.- An Efficient Spatiotemporal Indexing Method for Moving Objects in Mobile Communication Environments.- DynaMark: A Benchmark for Dynamic Spatial Indexing.- Information Management.- Using Separate Processing for Read-Only Transactions in Mobile Environment.- Publish/Subscribe Tree Construction in Wireless Ad-Hoc Networks.- Personal Workflows: Modeling and Management.- Location-Aware Services.- Architectural Support for Global Smart Spaces.- FATES: Finding A Time dEpendent Shortest path.- Search K Nearest Neighbors on Air.- Adaptive Location Management in Mobile Environments.- Context-Aware Services.- Policy-Driven Binding to Information Resources in Mobility-Enabled Scenarios.- Constructing Environment-Aware Mobile Applications Adaptive to Small, Networked Appliances in Ubiquitous Computing Environment.- Experiences in Using CC/PP in Context-Aware Systems.- Document Visualization on Small Displays.- Resource Discovery.- Towards Autonomous Services for Smart Mobile Devices.- Nomad: Application Participation in a Global Location Service.- Mobiscope: A Scalable Spatial Discovery Service for Mobile Network Resources.- Location Management.- Presence, Location, and Instant Messaging in a Context-Aware Application Framework.- CAMEL: A Moving Object Database Approach for Intelligent Location Aware Services.- Using Hashing and Caching for Location Management in Wireless Mobile Systems.- SEB-tree: An Approach to Index Continuously Moving Objects.- Best Movement of Mobile Agent in Mobile Computing Systems.- Storage Management and Query Processing.- Clique: A Transparent, Peer-to-Peer Replicated File System.- Transactional Peer-to-Peer Information Processing: The AMOR Approach.- Performance Evaluation of Transcoding-Enabled Streaming Media Caching System.- Adaptive File Cache Management for Mobile Computing.- Adaptive Power-Aware Prefetching Schemes for Mobile Broadcast Environments.- A Multi-layered Database Model for Mobile Environment.- Context-Aware Information Services.- A Task Oriented Approach to Delivery in Mobile Environments.- Picturing the Future Personal Navigation Products and Services by Means of Scenarios.- Personal Digest System for Professional Baseball Programs in Mobile Environment.- Handling Client Mobility and Intermittent Connectivity in Mobile Web Accesses.- Enabling Web-Based Location-Dependent Information Services in Mobile Environments.

In this paper, we devise a new data mining algorithm which involves mining for user moving patterns in a mobile computing environment, and utilize the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we devise an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes for proper allocation of personal data. Two personal data allocation schemes, which explore different levels of mining results, are devised: one utilizes the set level of moving patterns and the other utilizes the path level of moving patterns. Performance of these data allocation schemes is comparatively analyzed. It is shown by our simulation results that the user moving patterns is very important in devising effective data allocation schemes which can lead to significant performance improvement in a mobile computing system.

We explore in this paper the problem of dynamic data and channel allocations with the number of communication channels and the number of data items given. It is noted that the combined use of broadcast and on-demand channels can utilize the bandwidth effectively for data dissemination in a mobile computing environment. We first derive the an-alytical models of the expected delays when the data are requested through the broadcast and on-demand channels. Then, we transform this problem into to a guided search problem. In light of the theoretical properties derived, we devise an algorithm based on binary interpolation search, referred to as algorithm BIS, to obtain solutions of high quality efficiently. In essence, algorithm BIS is guided to explore the solution space with higher likelihood to be the optimal first, thereby leading to an efficient and effective search. It is shown by our simulation results that the solution obtained by algorithm BIS is of very high quality and is in fact very close to the optimal one. Sensitivity analysis on several parameters, including the number of data items and the number of communication channels, is conducted.

The query processing in a mobile computing environment involves join processing among different sites which include static servers and mobile computers. In this paper, we first present some unique features of a mobile environment, and then, in light of these features, devise query processing methods for both join and query processing. Remote mobile joins are said to be effectual if they are, when interleaved into a join sequence, able to reduce the data transmission cost required for distributed mobile query processing. It can be verified that the total data transmission cost of the processing in a distributed mobile query can be reduced by algorithms designed by using effectual remote joins. A simulator is developed to evaluate the performance of the devised algorithms. Our results show that the approach of interleaving the processing of distributed mobile queries with effectual remote mobile joins is not only efficient but also effective in reducing the total data transmission cost required to process distributed mobile queries.

With the cutting edge technology advance in wireless and mobile computers, the query processing in a mobile environment involves join processing among different sites which include static servers and mobile computers. Because of the need of energy saving and also the presence of asymmetric features in a mobile computing environment, the conventional query processing for a distributed database cannot be directly applied to a mobile computing system. In this paper, we first explore three asymmetric features of a mobile environment. Then, in light of these features, we devise query processing schemes. Performance of these query processing schemes is comparatively analyzed and sensitivity analysis on several parameters is conducted.

Recent advances in wireless technologies have made the mobile computing a reality. In order to provide services of good quality to mobile users and improve the system performance, the mobile computing system is usually of a distributed server architecture. As users move to a new service area, the new server is expected to take over the execution of running programs for mobile users so as to reduce the communication overhead of the mobile system. This procedure is referred to as service handoff. Note that when service handoff occurs, the cache of the new sewer does not contain any data entry that was accessed by prior transactions and the new server will thus lose its advantages for cache access. To remedy this, the authors examine several cache retrieval schemes to improve the efficiency of cache retrieval. In particular they analyze the impact of using a coordinator buffer to improve the overall performance of cache retrieval. Moreover, in light of the properties of transactions (i.e., temporal locality of data access among transactions), they devise a dynamic and adaptive cache retrieval scheme (DAR) that can adopt proper cache methods based on some specific criteria devised to deal with the service handoff situation in a mobile computing environment. The performance of these cache retrieval schemes is analyzed and a system simulator is developed to validate the results.