Digital maps have become a part of our daily lives with a growing number of commercial and free map services. However, these services still have a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages commodity off-the-shelf smartphones in a crowd-sensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that the smartphones sensors, whether with a user riding a vehicle or walking, get affected by the different road features which can be mined to extend the features of both free and commercial mapping services. Map++ leverages these detected features and employs a probabilistic framework that can handle the heterogeneity and uncertainty in the crowd-sensed data to update the digital maps. We present the design and implementation of Map++ and evaluate it in four cities. Our evaluation shows that we can detect different map features accurately with 4 percent false positive and 8 percent false negative rates for in-vehicle traces, and 3 percent false positive and 4 percent false negative rates for pedestrian traces. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.

Existing Location-based social networks (LBSNs), e.g., Foursquare, depend mainly on GPS or cellular-based localization to infer users' locations. However, GPS is unavailable indoors and cellular-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89 percent of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small screens of mobile devices, misses business opportunities, and leads to reduced venues coverage. In this paper, we present CheckInside: a system that can provide a fine-grained indoor location-based social network. CheckInside leverages the crowd-sensed data collected from users' mobile devices during the check-in operation and knowledge extracted from current LBSNs to associate a place with a logical name and a semantic fingerprint. This semantic fingerprint is used to obtain a more accurate list of nearby places as well as to automatically detect new places with similar signature. A novel algorithm for detecting fake check-ins and inferring a semantically-enriched floorplan is proposed as well as an algorithm for enhancing the system performance based on the user implicit feedback. Furthermore, CheckInside encompasses a coverage extender module to automatically predict names of new venues increasing the coverage of current LBSNs. Experimental evaluation of CheckInside in four malls over the course of six weeks with 20 participants shows that it can infer the actual user place within the top five venues 99 percent of the time. This is compared to 17 percent only in the case of current LBSNs. In addition, it increases the coverage of existing LBSNs by more than 37 percent.

Recently, many ride sharing systems have been commercially introduced (e.g., Uber, Flinc, and Lyft) forming a multi-billion dollars industry. The main idea is to match people requesting a certain ride to other people who are acting as drivers on their own spare time. The matching algorithm run by these services is very simple and ignores a wide sector of users who can be exploited to maximize the benefits of these services. In this demo, we demonstrate SHAREK; a driver-rider matching algorithm that can be embedded inside existing ride sharing services to enhance the quality of their matching. SHAREK has the potential to boost the performance and widen the user base and applicability of existing ride sharing services. This is mainly because within its matching technique, SHAREK takes into account user preferences in terms of maximum waiting time the rider is willing to have before being picked up as well as the maximum cost that the rider is willing to pay. Then, within its course of execution, SHAREK applies a set of smart filters that enable it to do the matching so efficiently without the need to many expensive shortest path computations.

Recent pioneer work has shown that packet receptions on adjacent links are correlated, which contradicts the long held assumption that wireless links are statistically independent. Since wireless link correlation affects a wide range of protocol designs, it is essential to quantify the impact generically. In particular, this paper focuses on a unified transmission cost metric for diversity-based routing schemes, including opportunistic routing, network coding, and hybrid routing. This paper covers both unicast and broadcast. Compared with the legacy metrics, our metric provides a direct and accurate estimation of the transmission cost in the presence of link correlation. The new metric helps a wide range of routing algorithms determine when they can benefit from reception diversity, and how to maximize the benefit, at negligible costs. We evaluate the metric on one 802.11 test bed and three 802.15.4 test beds running TelosB, MICAz, and GreenOrbs nodes. The experimental results show that our metric 1) reduces 92% and 94% of the estimation error of the transmission cost in unicast and broadcast and 2) outperforms the link independent metric in both unicast and broadcast applications.

Mobile devices including smart phones are becoming an essential component of our daily life. These devices have powerful capabilities and useful applications to help us performing several tasks in less time and effort especially within cloud services. On the other hand, mobile devices have serious limitations such as battery life time, computation power, and storage capacity. Mobile Cloud Computing (MCC) is an emerging technology that helps in avoiding such limitations in mobile devices, mainly saving energy. In this paper, we will address the energy efficiency in Mobile Cloud Computing since it is the most important design requirement for mobile devices. We will present techniques and prototypes in MCC to reduce power consumption and increase resources efficiency and utilization.

Understanding crowd dynamics of large-scale events is crucial to deliver a pleasant experience for the participants. In this paper, we propose a novel hybrid participatory sensing approach to capture large group dynamics. Specifically, our approach is based on distributing a large number of tiny, wearable Bluetooth Low Energy (BLE) tags and few smartphones among the group members. We start by identifying the best configuration; in terms of transmit power and beaconing interval; for detecting BLE tags in indoor and outdoor environments. Then, as a case study, we deploy the system during the six main days of the Hajj pilgrimage in 2014, which is the world's largest annual religious gathering. We used the proposed hybrid participatory approach to collect the mobility data of pilgrim groups based on GPS location and co-occurring BLE tag detections. Our system provided up to 80% group-wise detectability in a single scan event. Moreover, 98% cumulative unique tags detectability is achieved in the whole event, leading to 0.74 million records. Analysis of the group dynamics revealed unexpected behavior and interesting findings. In particular, regional variations are found among different groups in the entry or exit times, duration of stay, and group cohesion; which are attributed to congestion, improper arrangements, or the nature of activities. Furthermore, we show sub-community clusters could be identified revealing clear biases based on the demographic characteristics. Based on this case study, we also give suggestions on using the proposed system for other large-scale events.

Due to its significant economic and environmental impact, sharing the ride among a number of drivers (i.e., Car pooling) has recently gained significant interest from industry and academia. Hence, a number of ride sharing services have appeared along with various algorithms on how to match a rider request to a driver who can provide the ride sharing service. However, existing techniques have several limitations that affect the quality of the ride sharing service, and hence hinder its wide applicability. This paper proposes SHAREK, a scalable and efficient ride sharing service that overcomes the limitations of existing approaches. SHAREK allows riders requesting the ride sharing service to indicate the maximum price they are willing to pay for the service and the maximum waiting time before being picked up. In the mean time, SHAREK computes the price of the service based on the distance of the rider trip and the detour that the driver will make to offer the service. Then, SHAREK returns a set of drivers that can make it to the rider within its price and temporal constraints. Since there could be many of such drivers, SHAREK internally prunes those drivers that are dominated by others, i.e., They provide higher price and higher waiting time than other drivers. To realize its efficiency and scalability, SHAREK employs a set of early pruning techniques that minimize the need for any actual shortest path computations.

Road-network data compression reduces the size of the network to occupy lesser storage with the aim to fit small form-factor routing devices, mobile devices, or embedded systems. Compression (1) reduces the storage cost of memory and disks, and (2) reduces the I/O and communication overhead. There are several road network compression techniques proposed in literature. These techniques are evaluated by their compression ratios. However, none of these techniques takes into consideration the possibility that the generated compressed data can be used directly in map-matching. Map-matching is an essential component of routing services that matches a measured latitude and longitude of an object to an edge in the road network graph. In this paper, we propose a novel compression technique, named COMA, that significantly reduces the size of a given road network data. Another advantage of the proposed technique is that it enables the generated compressed road network graph to be used directly in map-matching without a need to decompress it beforehand. COMA smartly deletes those nodes and edges that will not affect neither the graph connectivity nor the accuracy of map-matching objects' location. COMA is equipped with an adjustable parameter, termed conflict factor C, by which location-based services can achieve a trade-off between the compression gain and map-matching accuracy. Extensive experimental evaluation on real road network data demonstrates competitive performance on compression-ratio and the high mapmatching accuracy achieved by the proposed technique.

We demonstrate a novel location aware services framework termed COMA for efficient compression and map-matching of road-network graph data. Key innovations include working demonstration of a new compression algorithm to eliminate nodes and edges that do not affect graph connectivity while ensuring object location map-matching accuracy. The demonstration features: (1) Algorithm to leverage compressed versions of road-network graphs for map-matching of objects locations to correct road edges without decompression, (2) Upwards of 75% compression ratio implying significant savings for road network data transmission costs, a key constraint for internet of things (IOT) devices, and (3) Use of a new controllable parameter, termed conflict factor C, whereby location aware services can trade the compression efficiency with map-matching accuracy at varying granularity. In addition to above features the demonstration features an extensible framework that enables experimentation and comparison between various compression and map-matching algorithms in a rich interactive interface. In this paper we outline data management challenges for location aware services using various scenarios for compression of a real road-network map of a large region of United States, along with both real and synthetic moving object trajectories distributed over this map. We describe the COMA framework through its map-based Graphical User Interface, ability to select the area of interest from the road-network map, and submit a compression request. COMA can export and save the compact versions of the selected area in different formats and plot the compressed graph over the original map for visual inspection to study the differences between compressed and uncompressed versions and various related statistics.

Current outdoor localization techniques fail to provide the required accuracy for estimating the car's lane. In this paper, we present LaneQuest: a system that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the car's current lane. LaneQuest leverages hints from the phone sensors about the surrounding environment to detect the car's lane. For example, a car making a right turn most probably will be in the right-most lane, a car passing by a pothole will be in a specific lane, and the car's angular velocity when driving through a curve reflects its lane. Our investigation shows that there are amble opportunities in the environment, i.e. lane “anchors”, that provide cues about the car's lane. To handle the ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane-span distribution of the different lane-level anchors. Our evaluation results from implementation on different android devices and 260Km driving traces by 13 drivers in different cities shows that LaneQuest can detect the different lane-level anchors with an average precision and recall of more than 90%. This leads to an accurate detection of the exact car's lane position 80% of the time, increasing to 89% of the time to within one lane. This comes with a low-energy footprint, allowing LaneQuest to be implemented on the energy-constrained mobile devices.

Recent empirical studies have shown clear evidence that wireless links are not independent and that the packet receptions on adjacent wireless links are correlated. This finding contradicts the widely held link-independence assumption in the calculation of the core metric, i.e., the expected number of transmissions to the candidate forwarder set, in opportunistic routing (OR). The inappropriate assumption may cause serious estimation errors in the forwarder set selection, which further leads to underutilized diversity benefits or extra scheduling costs. We thus advocate that OR should be made aware of link correlation. In this paper, we propose a novel link-correlation-aware OR scheme, which significantly improves the performance by exploiting the diverse low correlated forwarding links. We evaluate the design in a real-world setting with 24 MICAz nodes. Testbed evaluation and extensive simulation show that higher link correlation leads to fewer diversity benefits and that, with our link-correlation-aware design, the number of transmissions is reduced by 38%.

Designing a comprehensive curriculum for information system engineering (ISE) that accounts for the needs and expectations of the students, the current trends and advances in the field, and the skills required by the job market is an extremely demanding task. The curriculum must balance the highest academic standards and integrity against the emerging expectations of the market. Here, we identify the specific requirements of a newly commissioned ISE curriculum, and we explain the framework we adopted and developed in the process. We based the framework on a commonly recognized information system model namely service-oriented architecture (SOA). Thus, we derived the different knowledge areas to be covered in the curriculum from the layers of the SOA style.

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.

Digital maps have become a part of our everyday lives as they are integrated into a wide range of map-based services like traffic estimation, navigation systems, and many more. These services still have a huge opportunity for enhancements with semantically richer maps to support a large class of new services. In this demo, we demonstrate the MAP++ crowd-sensing system for map semantics identification. Map++ leverages standard smart-phone sensors to automatically enrich digital maps with various road semantics such as bridges, crossroads, roundabouts, underpasses, among others. The goal of this demo is to showcase Map++ in action where it takes crowd-sensed motion traces and process them automatically in real-time to identify the map semantics. The demo also allows attendees to analyze the effect of the Map++ different parameters on system performance and road semantics.

By exploiting reception diversity of wireless network links, researchers have shown that opportunistic routing can improve network performance significantly over traditional routing schemes. However, recently empirical studies indicate that we are too optimistic, i.e. diversity gain can be overestimated if we continue to assume that packet receptions of wireless links are independent events. For the first time, this paper formally analyzes the opportunistic routing gain under the presence of link correlation considering the loss of DATA and ACK packets. Based on the model, we introduce a new link-correlation-aware opportunistic routing scheme, which improves the performance by exploiting the diverse uncorrelated forwarding links. Our design is evaluated using simulation where we show (i) link correlation leads to less diversity gain, (ii) and with our link-correlation-aware design; improvement can be gained. We also provide a unique model to generate strings of randomly correlated receptions.

For wireless multicast applications like multimedia conferencing, voice over IP and video/audio streaming which require reliable transmission of packets within short delivery delay, adding forward error correction (FEC) can enhance packet error rate (PER) performance under low channel conditions, but cannot guarantee a reliable transmission. However, for applications like file transfer, distributed computing, chat and whiteboard sharing, reliability is crucial to the performance of the application. Reliable FEC or hybrid automatic repeat request (H-ARQ) can provide reliability, and can be deployed at different levels of the protocol stack. In this paper, we compare the performance of a reliable packet-level FEC protocol and a reliable byte-level FEC protocol through simulation and analytical analysis. Our result show that byte-level FEC can deliver a higher reliability performance in terms of throughput and delivery ratios.

For end-to-end wireless multicast applications like multimedia conferencing, voice over IP and video/audio streaming applications which require reliable transmission of frames within short delivery delay, adding MAC layer H-ARQ (hybrid automatic repeat request) can enhance reliability and delivery delay. However, IEEE802.11 does not support reliable multicast due to its inability to exchange ACKs with multiple recipients. In this paper, we propose a protocol that enhances WLAN reliability performances by using adaptive FEC (forward error correction) while maintaining an optimal throughput performance.

IEEE 802.11b standard provides 1, 2, 5.5, 11 Mbps data rates. These data rates can be made possible by using different modulation techniques: DBPSK, DQPSK, CCK5.5 and CCK11 respectively. Rate adaptation is the process of dynamically selecting a proper modulation scheme depending on channel conditions in order to improve total throughput. Current rate adaptation protocols deal with unicast links rather than multicast. Channel conditions can be estimated by measuring the received signal strength (RSS) of a feedback message (CTS, ACK). However, IEEE 802.11 standard does not provide feedback messages for MAC layer recovery on multicast frames. This is due to collisions which may occur to simultaneous feedback messages from multicast group members. Therefore, in order to rate adapt multicast, a reliable multicast MAC protocol has to be introduced. In this paper, we propose a protocol which provides multicast reliability to WLANs and enhances its throughput by using rate adaptation. Further, we evaluate our protocol by throughput analysis and computer simulation. Simulation results suggest that our protocol performs better than a related/existing protocol in both throughput as well as reliability performances