Empowered by deep learning, data-driven smartphone inertial tracking approaches have attracted much attention due to high accuracy and robustness. However, training deep models requires significant amounts of IMU data with high-precision labels which incurs high annotation costs. In this work, we propose a practical data-driven IMU tracking solution without tedious annotations in a systematic way. We firstly design a simple but efficient method to annotate IMU data with phone labels based on the location service provided by smartphones. However, the phone labels are too noisy to train an accurate deep model. In-depth experimental studies reveal the noises are dependent along an IMU sequence; the noisy phone labels have knowledge useful for learning and make sense. The unique characteristics of the phone labels make existing learning-with-noisy-label models (LNLs) fail to work. We then propose EasyTrack, a noise-resistant data-driven IMU tracking system, which adopts a dual-model framework to enable LNLs. To handle the noisy labels, we design a series of effectively noise-resistant techniques. Extensive experiment results demonstrate without tedious annotations, EasyTrack achieves high accuracy by learning with noisy phone labels, outperforming existing LNLs and data-driven IMU tracking approaches.

Current multicast protocols assume prior knowledge of destination locations, leading to excessive message transmissions, especially in sensor networks with mobile sinks. Quorum-based systems, known for efficient information dissemination, have not been explored in the context of multicast. To address this gap, we propose ORM (Orthogonal Rendezvous Multicast), a quorum-based multicast protocol that solves location dissemination and data multicasting as a whole. The protocol utilizes a simple geometric principle to disseminate the information from sinks and sources. By exploiting the footprint of disseminated information, ORM transforms the underlying infrastructure to reveal the hidden multicasting structure. Furthermore, two efficient algorithms are proposed to fetch the optimal efficient multicasting paths based on the built infrastructure. ORM was implemented in the NS-2 simulator. Our detailed simulations demonstrate that ORM achieves significantly enhanced scalability in terms of message overhead and low cost without the burden of complex state maintenance.

In resource-constrained mobile ad hoc networks(MANETs), routing protocols are usually implemented inside mobile nodes for resource saving and fast response to topology changes. However, existing routing protocols are implemented in either user space or inside the kernel of operating systems, which suffers from sophisticated system-level programming and inefficient routing management. In this paper, we design a unified routing framework integrating implementation and management for both proactive and reactive routing protocols in MANETs. Under the framework, we provide a series of application interfaces in user space for implementation of routing protocols, hiding complicated programming details in the kernel. In this way, both types of routing protocols are able to be implemented just in the user space thus significantly reducing implementation efforts. Moreover, through interactive terminal multiple routing protocol routines can be managed and setup in a unified pattern. To demonstrate the feasibility of the unifed routing framework, we implement its components on Linux 5.4 kernel. Furthermore, a well-known routing protocol AODV is implemented under the framework as an example. The source codes are publicly available for further research in the community.

Vehicle positioning using raw global navigation satellite system (GNSS) observations from smartphones is crucial for intelligent transportation and smart driving. However, it is challenging to achieve high accuracy due to GNSS signal blockage and reflection in urban areas. In this work, we propose a data-driven smartphone positioning system PhoeNix-RTK empowered by deep transformer model. PhoeNix-RTK corrects positions estimated by the real-time kinematic (RTK) approach by learning from GNSS features with PhoeNix model. To make latent features independent of satellites, PhoeNix encodes the GNSS features based on set transformer. To exploit the temporal dependence in the GNSS features, PhoeNix predicts correction vectors based on transformer in a sequence-to-sequence manner. Extensive experimental results on a public vehicle dataset demonstrate PhoeNix-RTK achieves high accuracy in urban areas, outperforming its counterparts.

Wi-Fi signals vary over time due to multipath fading and dynamic indoor environment. Hence in the long-run deployment of Wi-Fi fingerprinting localization, to retain high accuracy the fingerprint database has to be updated regularly, which is usually labor-intensive and time-consuming. In this paper, we propose a novel unsupervised domain adaptation model TransLoc for Wi-Fi fingerprint update, to keep high accuracy yet at a low cost. TransLoc consists of a feature extractor, a generator, a discriminator, and a location predictor. The feature extractor learns domain-invariant features by cooperating with other components. To further guarantee localization accuracy, the location predictor is designed as a semi-supervised regressor with three parallel sub-modules. We carry out extensive experiments in two typical real-world indoor environments with a total area of over 8,200$m^{2}$across three months. Experimental results show that with only an initial fingerprint database and current unlabeled fingerprints, TransLoc maintains high localization accuracy at a low cost in the long run.

In Wi-Fi fingerprinting indoor localization, since signals of Wi-Fi APs vary over time, to retain high localization accuracy, the fingerprint database has to be updated periodically, which is labor-intensive and time-consuming. In this paper, we consider how to detect Wi-Fi fingerprint alteration accurately on line so as to update fingerprint database efficiently. To this end, we propose a deep learning model, AReAE (Alteration Reducing AutoEncoder), to reconstruct fingerprints from altered ones by learning alteration distribution. Based on AReAE, we further develop a FADet (Fingerprint Alteration Detection) system, which constructs a fingerprint ASmap (Alteration Strength map) with crowd-sourced Wi-Fi RSS (Received Signal Strength) measurements. The ASmap indicates how strong fingerprints change anywhere in the area of interest. FADet is verified through extensive experiments in real-world indoor scenarios. Results show that FADet yields accurate ASmaps for all scenarios tested, which can facilitate fingerprint database updating in time.

Geomagnetic field and WiFi networks have attracted much attention in indoor localization, since they are pervasive, and their signals are easy to collect by off-the-shelf smartphones. However, there are still challenges in practice: 1) GFI (Geomagnetic Field intensity) read by magnetometer in smartphones is hard to be used directly, since it is in phone coordinate system and varies with phone attitude. On the other hand, the magnitude of GFI lacks location diversity; 2) WiFi RSS (Received Signal Strength) suffers from fluctuations due to multipath fading effect indoors. To address the above issues, we first propose Mag2D, a high quality feature of GFI. Mag2D is robust to phone attitude and has better spatial discrimination than the magnitude. We then study the complementary properties of Mag2D and RSS, and design MagWi, a practical fingerprinting indoor localization system fusing Mag2D and RSS. MagWi adjusts fusion weights of Mag2D and RSS according to their capabilities of discriminating locations. To do so, we propose LCD (Local Clustering Degree) to quantify the location-discriminating capability and build a LCD-Error model. The fusion weights are thus assigned dynamically based on the LCD-Error model during localization. We have implemented MagWi to provide location service on an Android phone in three typical indoor environments, covering a total area size over 600m2. Our experiment results show that MagWi achieves high accuracy and is easy to deploy in practice.

Many real-world wireless networks for industrial internet of things applications have diverse connectivity characteristics, which makes routing protocol design challenging. Although adaptive routing protocols have been emerging to deal with connectivity diversity, there is still lack of a unified routing framework in well-connected and intermittently connected networks. In this paper, we present a unified routing metric for wireless networks with diverse connectivity, and formulate the adaptive routing problem as an integer linear programming optimization one. We then propose a heuristic routing protocol, caching-optimized adaptive routing protocol (COARP). Extensive simulation results show that COARP is near optimal in simple static network scenarios, and performs well across all the range of connectivity spectrum in dynamic network scenarios.

A whole extensible authentication protocol (EAP)-based authentication scheme is time-consuming due to the operations of public key cryptography and validation of certificates which is unacceptable for the wireless regional area networks (WRANs) handover. In this paper, an EAP-based pre-authentication scheme for WRANs handover (EPH) over TV white space is proposed. By applying the proposed pre-authentication scheme, the customer premises equipment and the target secondary user base station can accomplish a seamless handover using a symmetric key. Approved by the logic derivation by Burrows, Abadi, and Needham (BAN) logic, we conclude that the proposed EPH scheme can obtain mutual authentication and hold key secrecy. Additionally, the performance of the EPH scheme has been investigated by simulation experiments with the results showing that the EPH scheme is much more efficient in terms of lower computation delay required than the security scheme regulated in IEEE 802.22 standard.

Thanks to the highly-dense lighting infrastructure in public areas, visible light emerges as a promising means to indoor localization and navigation. State-of-the-art techniques generally require customized hardware (sensing boards), and mainly work with one single light source (e.g., customized LEDs). This greatly limits their application scope. In this paper, we propose NaviLight, a generic indoor localization and navigation framework based on existing lighting infrastructure with any unmodified light sources (e.g., LED, fluorescent, and incandescent lights). NaviLight simply adopts commercial off-the-shelf mobile phones as receivers, and light intensity values as location signatures. Unlike existing WiFi systems, a single light intensity value is not discriminative enough over space though the light intensity field does vary, which makes our design more challenging. We thus propose a LightPrint as a location signature using a vector of multiple light intensity values obtained during user's walks. Such LightPrints are created by leveraging any user movement (of varying distance and direction) in order to minimize user efforts. A set of techniques are proposed to achieve quick LightPrint matching, which includes a coarse-grained classification and a fine-grained matching over dynamic time warping. We have implemented NaviLight to provide real-time service on Android phones in three typical indoor environments, covering a total area size over 1000m2. Our experiments show that NaviLight can achieve sub-meter localization accuracy to meet practical engineering requirements.

Cognitive radio technique has been recognized as an effective solution to the dilemma introduced by the rapid growth of wireless communications and the scarcity of spectrum resources. One example of the cognitive radio systems is the wireless regional area network operating in television white space TVWS spectrum, which has been specified by IEEE 802.22 standard. The wireless regional area networks in TVWS face not only the traditional security threats but also cognitive security challenges. Especially, the authentication between the base station and the customer premises equipment is vital for secure communication. We discovered that the authentication protocol specified in IEEE 802.22 is vulnerable to the interleaving attacks and cannot achieve mutual key confirmation. In this paper, an Enhanced Certificate-based Authentication scheme ECA has been proposed to overcome the vulnerability of the authentication scheme in IEEE 802.22 standard with much less requirements on the computation and communication resources. The drawbacks of the authentication protocol in IEEE 802.22 standard has been explored followed by the presentation of the proposed enhanced authentication protocol, ECA. The ECA protocol has been evaluated in terms of security functionality and the performance. The correctness of the Enhanced Certificate-based Authentication scheme is analyzed by the Burrows, Abadi, and Needham logic. Copyright © 2015 John Wiley & Sons, Ltd.

For mobile devices, communication via cellular networks consumes more energy than via WiFi networks, and suffers an expensive limited data plan. On the other hand, as the coverage and the density of WiFI networks are smaller than those of the cellular networks, users cannot purely rely on WiFi to access the Internet. In this work we present a behavior-aware and preference-based approach to prefetch news webpages for the user to visit in the near future, by exploiting the WiFi network connections to reduce the energy and monetary cost. We first design an efficient preference learning algorithm to keep track of the user's changing interests, and then by predicting the appearance and durations of the WiFi network connections, our prefetch approach optimizes when to prefetch to maximize the user experience while lowing the prefetch cost. Our prefetch approach also exploits the idle period of WiFi connections to reduce the tail-energy consumption. We implement our approach in iPhone and our extensive evaluations show that our system achieves about 60% hit ratio, saves about 50% cellular data usage, and reduces the energy cost by 7%.

Wireless coexistence is crucial with the explosive development of wireless technologies in recent years. The coexistence issues of IEEE 802.11 b/g and IEEE 802.15.4 have been well studied, however few work focused on 802.11n new features including MIMO, channel bonding and frame aggregation. In this paper, we conducted extensive experiments to understand how 802.11n impact on 802.15.4 and vice versa in a systematic way. We consider primary features of 802.11n both in symmetric and asymmetric scenarios. The goal of our work is to gain more insights into the coexistence issues of 802.11n and 802.15.4 and thus to help protocol design and co-located network deployments.

Legacy 802.11b/g protocols have been successfully applied in wireless long-distance (WiLD) networks to provide low-cost network connectivity for rural areas and developing countries, by designing TDMA MAC protocols. In contrast, we investigate the performance, and enhancement of using 802.11n for WiLD networks still using CSMA/CA. Our extensive experiments from our testbed show that, although 802.11n with CSMA/CA can provide network connectivity in WiLD environment, it suffers from poor performance. We thus propose RainbowRate, an efficient rate adaptation mechanism specified for WiLD links. RainbowRate leverages unique features found in WiLD links. We implement RainbowRate in off-the-shelf platforms and evaluate various aspects of our design using a testbed consisting of 6 mesh routers and 7 long-distance links, with the longest link length 20 km. Our experiment results show that RainbowRate out-performs state-of-the-art RA algorithms (MiRA, Ath9k and Minstrel) significantly in terms of throughput.

GasNet is a sensor network for real-timely monitoring the gas leak in residential buildings, in order to efficiently protect the buildings from gas leak explosion. GasNet provides low-latency and reliable gas leak ALARM message delivery in complicated RF environments. It does so through ROAP, a novel opportunistic forwarding mechanism considering link correlation. ROAP leverages a hop tree in opportunistic forwarder list selection to ensure the shortest hops to the sink. Furthermore, link correlation is used to obtain more accurate forwarding probability and to achieve fast retransmission. We implement the prototype of GasNet in campus testbed and evaluate its performance via extensive experiments. The experiment results show that ROAP achieves 100% gas leak ALARM message delivery in low latency.

In this paper, we consider the double-blindness problem in large-scale wireless sensor networks (WSNs) with mobile sinks, where the mobile sink(s) and data do not know the locations of each other a priori. We first propose a random line walk mechanism for message forwarding and based on this forwarding mechanism we further propose an efficient data discovery scheme called Double Cross to address the double-blindness problem. Double Cross exploits a simple geometric property of a planar, i.e., for a couple of pairs of orthogonal lines in a planar, the probability that they intersect within the planar is larger than 99%. However, it does not depend on the geographic location or directional information of a node, which is difficult to obtain in such networks. Instead, each sensor only needs to know the distances between neighbor nodes within its transmission range. Analytical and simulation results show that Double Cross can achieve a high successful discovery rate with low energy consumption.

It is important to protect power supply grid system against various damages for national economics. A high-voltage transmission line monitoring system is an effective way to protect the power supply system. This paper presents an energy-efficient data gathering mechanism for such a system. Our contributions are two-fold: (1) a detailed measurement of the energy consumption for wireless nodes, (2) the collaboration between backbone network nodes and subnets to implement the sleep-wakeup mechanism. To validate the proposed mechanism, we have established an indoor test bed consisting of 8 backbone network nodes. The energy consumption of each node with our mechanism is evaluated and compared to that without sleep. The results show that the energy consumption has decreased by 24% by our scheme.

Admission and rate control are key components for providing QoS (Quality of Service) flows in multi- rate wireless ad hoc networks where transmission rate at the physical layer is adapted dynamically according to the channel condition. Key to these controls is the formulation of a rate constraint that can regulate the flow rate passing through each node. This paper formulates a cross-layer rate constraint to serve this purpose. Although node- based, the new rate constraint captures the interference level at the receiver side as well as the contention at the sender side, which is more accurate than that of existed work. Simulation results on DSR incorporated with such constraint show that the QoS routing algorithm can admit feasible flows and make full use of the bandwidth resource based on the rate constraint, moreover it outperforms the other algorithms in the scenarios we tested.

Long-distance wireless networks are expected to provide multimedia traffic service in addition to basic Internet access, as more and more such networks have been emerged in real life. However, few work has been done on QoS provisioning in this area. 2P MAC has been successfully deployed in many long-distance networks for its efficiency. In this paper, we propose a QoS routing - Maximum Allocation with Reservation-based (MAR) QoS Dynamic Source Routing (MQDSR) on 2P MAC. MQDSR integrates bandwidth reservation and admission control in route discovery mechanism, according to MAR bandwidth constraints model. Simulation results in NS2 show that MQDSR can provide QoS support in terms of end-to-end delay and throughput for traffic with high and normal priority, while avoiding the starvation of the best effort traffic.

Intra- and Inter-flow interferences are the principal causes of network utilization degradation in MR-MC (Multi-Radio Multi-Channel) wireless mesh networks. To minimize these interferences, we propose in this paper a channel assignment algorithm and incorporate it in an on-demand routing protocol called the HCARP (Hybrid Channel Assignment Routing Protocol). In addition, HCARP uses load balancing in route discovery to avoid "hot-spots" with severe interference and to find a route satisfying QoS requirements of the incoming flows. Simulation results and test bed experiments demonstrate that intra- and interflow interferences can be mitigated significantly and the overall network throughput is improved considerably. In addition, our algorithm outperforms the one based on superimposed code if the strength of the superimposed code is less than the interferer number of a node. Our simulation results show that HCARP has the capability of circumventing congested areas in the network and enhancing the QoS support for bandwidth-intensive traffic.

Traditional medium access control (MAC) protocols utilize temporal mechanism to do contention resolution. This paper explores an alternative approach by adapting the space occupied by a transmission to resolve contention. Each successful transmission in a wireless network needs to occupy a certain part of space in which there are no other simultaneous transmissions. The size of the space occupied depends on the transmission rate and it consequently determines the level of spatial reuse. To optimize the aggregate network throughput needs to balance the transmission rate and the level of spatial reuse. This paper firstly derives the optimal transmission rate by analyzing the relationship between the transmission rate and the occupied space size, then a distributed algorithm is proposed to achieve the optimal spatial allocation approximately through tuning the carrier sense threshold and the transmission rate. Simulation results show that, the distributed algorithm works well in balancing the transmission rate and the level of spatial reuse, and the aggregate network throughput can approximate the theoretical one and is higher than that can be gained by other similar algorithms.

This paper proposes a set of node-based rate constraints called NABE (node-based available bandwidth estimation) to model the interference relationship among nodes in a wireless ad hoc network and to provide rate constraints for its QoS flows. We evaluate the performance of NABE from three different perspectives: its capacity, its complexity to implement the MAC layer protocol and its efficiency. Our simulation results demonstrate that, NABE can always admit the feasible flows as well as make full use of the bandwidth resource when serving as an admission control scheme in the presence of both intra-flow and inter-flow interference.

The packet-level traffic characteristics in the Internet are so complex that it is natural to do traffic engineering (TE) and control at the flow level. The emerging multi-protocol label switching (MPLS) has introduced an attractive solution to TE in IP networks. The main objective of this paper is to balance traffic at the flow level among the parallel label switched paths (LSPs) in MPLS networks. We first introduce a multipath load-balancing model at the flow level. In this model, each LSP is modeled as an M/G/1 processor-sharing queue. Based on the analysis of the model, we propose a heuristic but efficient mechanism that is implemented only at the ingress label switched routers (LSRs) and egress LSRs in the network. Extensive simulations using NS2 are performed with MPLS modules. The simulation results show that the load through the network is well balanced so that the network throughput is improved and the delay is decreased significantly.

A load-balancing scheme has a significant effect on the performance of the multipath routing protocol, especially in an ad hoc network environment. In order to analyze the effect on the distribution of input traffic among multiple paths in MSR (multipath source routing), we first established a network queuing model that would incorporate the cross-traffic among these paths. We then considered the load balancing as an optimization problem. The solution to the optimization problem is interestingly in accordance with the heuristic equation proposed by Wang ICC'2001 (2001). Our simulation results show that MSR with load balancing is so effective that the end-to-end delay is decreased significantly while the network resource can be utilized more efficiently than that in DSR (dynamic source routing).