Search

The recent release of the third-generation partnership project, Release 17, calls for sub-meter cellular positioning accuracy with reduced latency in the calculation. To provide such high accuracy on a worldwide scale, leveraging the received signal strength (RSS) for positioning promises ubiquitous availability in the current and future equipment. RSS Fingerprint-based techniques have shown great potential for providing high accuracy in both indoor and outdoor environments. However, fingerprint-based positioning faces the challenge of providing a fast matching algorithm that can scale worldwide. In this paper, we propose a cosine similarity-based quantum algorithm for enabling fingerprint-based high accuracy and worldwide positioning that can be integrated with the next generation of 5G and 6G networks and beyond. By entangling the test RSS vector with the fingerprint RSS vectors, the proposed quantum algorithm has a complexity that is exponentially better than its classical version as well as the state-of-the-art quantum fingerprint positioning systems, both in the storage space and the running time. We implement the proposed quantum algorithm and evaluate it in a cellular testbed on a real IBM quantum machine. Results show the exponential saving in both time and space for the proposed quantum algorithm while keeping the same positioning accuracy compared to the traditional classical fingerprinting techniques and the state-of-the-art quantum algorithms.

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its discernible signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments demonstrate that SolarGest achieves 99% for six gestures with a single cell and 95% for fifteen gesture with a $2\times 2$2×2 solar cell array. The power measuement study suggests that SolarGest consume 44% less power compared to light sensor based systems.

Marine pollution is a growing worldwide concern, affecting the health of marine ecosystems, human health, and weather patterns. To reduce underwater pollution, it is critical to have access to accurate information about the extent of marine pollutants, as otherwise appropriate countermeasures and cleaning measures cannot be chosen. Currently, such information is difficult to acquire as existing monitoring solutions are highly laborious or costly, limited to specific pollutants, and have limited spatial and temporal resolution. In this article, we present a research vision of large-scale autonomous marine pollution monitoring that uses coordinated groups of autonomous underwater vehicles to monitor the extent and characteristics of marine pollutants. We highlight key requirements and reference technologies to establish a research roadmap for realizing this vision. We also address the feasibility of our vision, carrying out controlled experiments that address classification of pollutants and collaborative underwater processing, two key research challenges for our vision.

In this paper, we introduce the novel concept of cost-effective mobile health care which leverages the multiple wireless interfaces onboard most mobile phones today. First, we study the problem of uploading medical data using the “least cost” radio interface. Toward this objective, we propose the wireless interface selection algorithm (WISA) which decides the wireless interface yielding the least cost, depending on the data size, modality, and quality of service (QoS). Second, we study using modeling and simulations, the problem of cost-effective medical advisory message dissemination (on the downlink) which gives rise to an interesting cost-delay trade-off when leveraging free short range phone-to-phone (P2P) communication. Finally, we build a proof-of-concept testbed, coined CellChek, which showcases the proposed WISA algorithm and demonstrates its operation using sample wireless-enabled medical devices, namely pulse oximeter and blood pressure monitor. The experimental results gathered using the prototype provide key insights into the system and the involved tradeoffs. The concepts explored in this paper along with the proposed schemes hold great promise for this emerging area of research that is of equal importance to developing and developed countries promising considerable cost savings.

Location-based services have become an important part of our daily lives. However, such services require continuous user tracking while preserving the scarce cell-phone battery resource. In this article, we present Dejavu, a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization. Dejavu is capable of localizing and navigating both pedestrian and in-vehicle users in real time. Our analysis shows that, whether walking or in-vehicle, when the user encounters a road landmark such as going inside a tunnel, ascending a staircase, or even moving over a bump, all these different landmarks affect the inertial sensors on the phone in a unique pattern. Dejavu employs a dead-reckoning localization approach and leverages these road landmarks, among other automatically discovered virtual landmarks, to reset the dead-reckoning accumulated error and achieve accurate localization. To maintain a low energy profile, Dejavu uses only energy-efficient sensors or sensors that are already running for other purposes. Moreover, Dejavu provides a localization confidence measure along with its predicted location. This improves the usability of the predicted location from end users’ perspective. We present the design of Dejavu and how it leverages crowd-sourcing to automatically learn virtual landmarks and their locations. Our evaluation results from implementation on different Android devices using different testbeds showing that Dejavu can localize cell-phones in vehicles with a median error of 8.4m in city roads and 16.6m on highways and can localize cell-phones carried by pedestrians with a median error of 3.0m. Moreover, compared to the global position system (GPS) and other state-of-the-art systems, Dejavu can extend the battery lifetime by up to 347%, while achieving even better localization results than GPS in the more challenging in-city areas. In addition, Dejavu estimates the localization confidence measure accurately with a median error of 2.3m and 31cm for in-vehicle and pedestrian users, respectively.

WiFi-based localization became one of the main indoor localization techniques due to the ubiquity of WiFi connectivity. However, indoor environments exhibit complex wireless propagation characteristics. Typically, these characteristics are captured by constructing a fingerprint map for the different locations in the area of interest. This finger print requires significant overhead in manual construction, and thus has been one of the major drawbacks of WiFi-based localization. In this paper, the authors present an automated tool for finger print constructions and leverage it to study novel scenarios for device-based and device-free WiFi-based localization that are difficult to evaluate in a real environment. In a particular, the authors examine the effect of changing the access points (AP) mounting location, AP technology upgrade, crowd effect on calibration and operation, among others; on the accuracy of the localization system. The authors present the analysis for the two classes of WiFi-based localization: device-based and device-free. The authors analysis highlights factors affecting the localization system accuracy, how to tune it for better localization, and provides insights for both researchers and practitioners.

Wireless Intelligent Networks Center, Nile University, Smart Village, Egypt Many of today's mobile devices are equipped with multiple network interfaces that can be used to connect to the Internet, including Ethernet, WiFi, 3G, and Bluetooth. However, current operating systems, such as Windows and Linux, typically choose only one of the available network interfaces and assign all the traffic to it, even if more than one is connected to the Internet. This results in an obvious under utilization of the available bandwidth. Different bandwidth aggregation techniques suggested altering different layers of the TCP/IP stack which requires applying modifications on the client's stack and/or the cloud, which cannot be widely deployed easily. In this work, we present DNIS, a networking middleware that achieves bandwidth aggregation using per-TCP connection scheduling on different interfaces in a way that is transparent to both the user and the applications. DNIS is composed of two main components: (1) a parameter estimator that estimates the applications' characteristics and requirements as well as interfaces' properties; (2) a scheduler that uses the estimated parameters to assign different TCP connections to network interfaces. We present an implementation for DNIS for the Windows OS and show its performance for different scheduling algorithms. Our initial results show significant enhancement of the overall device's throughput, up to 54%, increasing resource utilization and enhancing the user's experience.

In this work we consider the design principles of the Instance-Based Network (IBN), an extended version of a generic Content-Based Network (CBN). IBN acts as an overlay communication platform over which end-point entities, called contents, communicate independently from their physical locations while providing the flexibility of having different instances of the same content. The semantics of different instances are assigned by the application using the IBN. Routing in the IBN is instance-based; the IBN can route a message to a specific content instance or to the closest instance, if no exact match is found for the destination content instance.

Purpose: Transthoracic 3D echocardiography (3DE) allows an unparalleled opportunity for quantifying the dynamic changes of the tricuspid annulus (TA). Accordingly, our aims were: (I) to assess the determinants of TA size during cardiac cycle in healthy subjects; (II) to propose an approach and timing for TA sizing using 3DE. Methods: In 50 healthy volunteers (45±14 yrs, range 18-74, 27 males, with no risk factors, symptoms, signs or history of cardiovascular disease and on no medication), a full-volume dataset of the right ventricle (RV) containing the tricuspid valve (TV) was acquired (Vivid E9, GE Healthcare). TA diameters (septo-lateral, SL; antero-posterior, AP) and areas were measured on multiplanar images (Flexi-slice, EchoPac BT12, GE Healthcare) at 5 time points during the cardiac cycle: OS (onset of systole, at TV closure); MS (mid-systole); ES (end-systole); ED (onset of diastole); LD (late diastole, after the P wave). RV volumes and ejection fraction (EF) were analyzed with commercial software (4D RV analysis, TomTec, D). Results: Temporal resolution of the 3D datasets was 32±4 vps (range 24-53). TA areas were more closely correlated with RV volumes and body surface area (BSA) than with either SL or AP diameters. TA areas increased during systole from OS (3.9±0.6 cm2/m2) to ES (4.9±0.8 cm2/m2) and reached its largest area in LD (6.7±1.0 cm2/m2). All 5 TA areas were correlated with BSA (r range 0.57-0.62) and RV volumes (r ranges 0.53-0.60 for end-diastolic volume and 0.43-0.50 for end-systolic volume, p<0.0001 for all). Indexed TA areas were not related to either age or gender. With multivariable analysis, both RV end-diastolic volume and BSA determined TA areas during systole and early diastole, while TA area at LD and at OS were independently related with BSA only. Conclusions: In healthy subjects, the main determinants of TA size are RV volume and BSA. The largest TA area occurs at LD and is independently related with BSA only. Therefore, normative values should be based on TA areas measured at LD and indexed for BSA. However, the rapid change in TA areas occurring from LD to OS underscores the importance of adequate temporal resolution of 3DE data sets for reliable TA measurements.

In hybrid wireless sensor networks, where trusted and un-trusted nodes coexist, it becomes important to allow trusted nodes to share information, especially, location information and prevent un-trusted nodes from gaining access to this information. We focus on anchor-based localization algorithms in WSNs, where a small set of specialized nodes, that is, anchor nodes, broadcast their location to the network and other nodes can use the broadcast information to estimate their own location. The main challenge is that both trusted and un-trusted nodes can measure the physical signal transmitted from anchor nodes and use it to estimate their locations. In this paper, we propose Hidden Anchor, an algorithm that provides anchor physical layer location privacy for different classes of localization algorithms. The Hidden Anchor algorithm exploits the inherently noisy wireless channel and uses identity cloning of neighboring trusted nodes to make anchors unobservable to un-trusted nodes while providing complete information to trusted nodes. Evaluation of the Hidden Anchor algorithm through analysis and simulation shows that it can hide the identity, and hence the location, of anchor nodes with very low overhead. In addition, the results show that by adding artificial noise, we can achieve significant improvement in anchor's location privacy.