This paper proposes MAGIC that uses magnetic resonant (MR) coupling for intra-body communication between implants and wearables. MAGIC includes not only the hardware-software design of the coupled coils and methods of manipulating the magnetic field for relaying information, but also the ability to raise immediate emergency-related alerts. MR coupling makes the design of the transmission link robust to channel-related parameters, as the magnetic permeability of skin and muscle is close to that of air. Thus, changes in tissue moisture content and thickness does not impact the design, which is a persistent problem in other approaches for implant communications like RF, ultrasound and galvanic coupling (GC). The paper makes three main contributions: It develops the theory leading to the design of the information relaying coils in MAGIC. It proposes a systems-level design of a communication link that extends up to 50cm with a low expected BER of 10^-4. Finally, the paper includes an experimental setup demonstrating how MAGIC operates in air and muscle tissue, as well as a comparison with alternative implant communication technologies, such as classical RF and GC. Results reveal that MAGIC offers instantaneous alerts with up to 5 times lower power consumption compared to other forms of communication.

It is desirable goal to fuse satellite and ground integrated networks (SGINs) to improve the resource utilization efficiency. However, existing work did not consider how to integrate them as a whole network because they lack of function-configurable network management and efficient cooperation among satellite and ground networks. In this paper, we firstly propose a SDN based network architecture that manages and schedules SGIN resources in the layered and on-demand way. Then, we formulate the dynamical cooperation transmission in SGINs as an optimization problem and prove its NP hardness. Finally, we propose a Satellite-Ground Cooperative Transmission (SGCT) algorithm based on dynamical cooperation among satellite and ground networks, which is network-aware and workload-driven. Comprehensive experiment results demonstrate that our approach outperforms related schemes in terms of network throughput, end-to-end delay, transmission quality and load balancing.

Due to the harsh environment and energy limitation, maintaining efficient communication is crucial to the lifetime of Underwater Sensor Networks (UWSN). Named Data Networking (NDN), one of future network architectures, begins to be applied to UWSN. Although Underwater Named Data Networking (UNDN) performs well in data transmission, it still faces some security threats, such as the Denial-of-Service (DoS) attacks caused by Interest Flooding Attacks (IFAs). In this paper, we present a new type of DoS, named as Synergetic Denial-of-Service (SDoS). Attackers synergize with each other, taking turns to reply to malicious Interests as late as possible. SDoS attacks will damage the Pending Interest Table (PIT), Content Store (CS), and Forwarding Information Base (FIB) in routers with high concealment. Simulation results demonstrate that the SDoS attacks quadruple the increased network traffic compared with normal IFAs and the only currently existing IFA detection algorithm in UNDN is completely invalid to SDoS attacks. In addition, we analyze the infection problem in UNDN and propose Trident: a defense method with adaptive threshold, burst traffic judgment and attacker identification. Simulation experiments illustrate that Trident can effectively detect and resist both SDoS attacks and normal IFAs. Meanwhile, Trident can robustly undertake burst traffic and congestion.

This paper concerns the data transmission strategy based on underwater multimodal communication for marine applications in underwater wireless sensor networks (UWSNs). Underwater data required by various applications have different values of information (VoI) depending on event type and event timeliness. These data should be transmitted in different time latency according to their VoI for accommodating both application requirements and network performance. Our objective is to design a multi-level transmission strategy by using underwater multimodal communication system so that multiple paths with transmission delay and energy consumption are provided for underwater data in UWSNs. For this purpose, we first define a minimum cost flow (MCF) model for the design of transmission strategy that considers time latency, energy efficiency, and transfer load. Then a distributed multi-level transmission strategy EMTS is given based on time backoff method for large-scale UWSNs. Finally we compared transmission latency, energy efficiency and network lifetime obtained by our EMTS and the optimum solution of the MCF model, a transmission algorithm based on greedy strategy. Although the latency of EMTS is slightly higher than that of other algorithms, our average network lifetime can reach 88.7% of that of the optimum solution of the MCF model.

Localization and navigation play a key role in many location-based services and have attracted numerous research efforts from both academic and industrial community. In recent years, visual SLAM has been prevailing for robots and autonomous driving cars. However, the ever-growing computation resource demanded by SLAM impedes its application to resource-constrained mobile devices. In this paper, we present the design, implementation, and evaluation of edgeSLAM, an edge assisted real-time semantic visual SLAM service running on mobile devices. edgeSLAM leverages the state-of-the-art semantic segmentation algorithm to enhance localization and mapping accuracy and speeds up the computation-intensive SLAM and semantic segmentation algorithms by computation offloading. The key innovations of edgeSLAM include an efficient computation offloading strategy, an opportunistic data sharing mechanism, and an adaptive task scheduling algorithm. We fully implement edgeSLAM on an edge server and different types of mobile devices. Extensive experiments are conducted under 3 data sets, and the results show that edgeSLAM is able to run on mobile devices at 35fps frame rate and achieves a 5cm localization accuracy, outperforming existing solutions by more than 15%. To the best of our knowledge, edgeSLAM is the first real-time semantic visual SLAM for mobile devices.

Millimeter-wave systems not only provide high data rates and low latency, but the very large bandwidth also allows for highly accurate environment sensing. Such properties are extremely useful for smart factory scenarios. At the same time, reusing existing communication links for passive object localization is significantly more challenging than radar-based approaches due to the sparsity of the millimeter-wave multi-path environment and the weakness of the reflected paths compared to the line-of-sight path.

In this paper we explore the localization accuracy that can be achieved with IEEE 802.11ad devices. We use commercial APs while for the stations we design a full-bandwidth 802.11ad compatible FPGA-based platform with phased antenna array. The stations exploit the preamble of the beam training packets of the APs to obtain CIR measurements for all antenna patterns. With this, we determine distance and angle information for the different multi-path components in the environment to passively localize a mobile object. We evaluate our system with multiple APs and a moving robot with metallic surface. Despite the strong limitations of the hardware, our system operates in real-time and achieves 30 cm mean error accuracy and sub-meter accuracy in 98% of the cases.

Acoustic based tracking has been shown promising in many applications like Virtual Reality, smart home, video gaming, etc. Its real life deployments, however, face fundamental limitations.Existing approaches generally need three sound sources, while most COTS devices (e.g., TVs) and speakers have only two sound sources.Most tracking approaches require periodical localization to bootstrap and alleviate accumulated tracking error.

We present AcouRadar, an acoustic-based localization system with single sound source. In the heart of AcouRadar we adopt a general new model which quantifies signal properties to different frequencies, distances and angles to the source. We verify the model and show that signal from a single source can provide features for localization.To address practical challenges, (1) we design an online model adaption method to address model deviation from real signal, (2) we design pulse modulated signals to alleviate the impact of environment such as multipath effect, and (3) to address signal dynamics over time, we derive relatively stable amplitude ratio between different frequencies. We implement AcouRadar on Android and evaluate its performance for different COTS speakers in different environments. The results show that AcouRadar achieves single source localization with average error less than 5 cm.

The recent standardization by IEEE of Fine Time Measurement (FTM), a time-of-flight based approach for ranging has the potential to be a turning point in bridging the gap between the rich literature on indoor localization and the so-far tepid market adoption. However, experiments with the first WiFi cards supporting FTM show that while it offers meter-level ranging in clear line-of-sight settings (LOS), its accuracy can collapse in non-line-of-sight (NLOS) scenarios.

We present FUSIC, the first approach that extends FTM's LOS accuracy to NLOS settings, without requiring any changes to the standard. To accomplish this, FUSIC leverages the results from FTM and MUSIC -- both erroneous in NLOS -- into solving the double challenge of 1) detecting when FTM returns an inaccurate value and 2) correcting the errors as necessary. Experiments in 4 different physical locations reveal that a) FUSIC extends FTM's LOS ranging accuracy to NLOS settings -- hence, achieving its stated goal; b) it significantly improves FTM's capability to offer room-level indoor positioning.

Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology for increased resistance against malicious attacks is an NP-hard problem. Heuristic algorithms can effectively handle such problems, particularly genetic algorithms. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the evolution of the population. Therefore, we combine the population state with the evolutionary process and propose an adaptive robustness evolution algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity index to ensure global search ability. Moreover, a self-competition operation is used to ensure convergence. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods.

This paper presents a stochastic geometry-based model for bandwidth part (BWP) in device-to-device wireless networks. BWP allows one to adapt the bandwidth allocated to users depending on their data rate needs. Specifically, in BWP, a wide bandwidth is divided into chunks of smaller bandwidths and the number of bandwidth chunks allocated to a user depends on its needs or type. The BWP model studied here is probabilistic in that the user locations are assumed to form a realization of a Poisson point process and each user decides independently to be of a certain type with some probability. This model allows one to quantify spectrum sharing and service differentiation in this context, namely to predict what performance a user gets depending on its type and the overall performance. This is based on exact representations of key performance metrics for each user type, namely its success probability, the meta distribution of its signal-to-interference ratio, and its Shannon throughput. We also show that, surprisingly, the higher traffic variability stemming from BWP is beneficial: when comparing two networks using BWP and having the same mean signal and the same mean interference powers, the network with higher traffic variability outperforms for all these performance metrics.

In this paper, we study low-overhead uplink multi-access algorithms for massive Internet-of-Things (IoT) that can exploit the MIMO performance gain. Although MIMO improves system capacity, it usually requires high overhead due to Channel State Information (CSI) feedback, which is unsuitable for IoT. Recently, a Pseudo-Random Beam-Forming (PRBF) scheme was proposed to exploit the MIMO performance gain for uplink IoT access with uniform channel and load, without collecting CSI at BS. For non-uniform channel and load, new adaptive beam-selection and random-access algorithms are needed to efficiently utilize the system capacity with low overhead. While most existing algorithms for a related multi-channel scheduling problem require each node to at least know some information of the queue length of all contending nodes, we propose a new Low-overhead Multi-Channel Joint Channel-Assignment and Random-Access (L-MC-JCARA) algorithm that reduces the overhead to be independent of the number of interfering nodes. A key novelty is to let the BS estimate the total backlog in each contention group by only observing the random-access events, so that no queue-length feedback is needed from IoT devices. We prove that L-MC-JCARA can achieve at least 0.24 of the capacity region of the optimal centralized scheduler for the corresponding multi-channel system.

Internet of Things (IoT) is the ongoing paradigm that offers a truly connected society by integrating several heterogeneous service and applications. The major transformation lies in the fact that the use cases of connected devices would not only become at par with people oriented connections but would ultimately exceed it and by volumes. Moreover, due to diversity in applications and requirements, the connected devices would manifest different priorities. With the variety of requirements, in terms of latency, payload size, number of connections, update frequency, reliability, excreta, the Random access process (RAP) would also require modification in cellular IoT. RAP is the first step to establish connection between devices and the base station. In order to prioritize the IoT devices in RAP, we propose a novel online algorithm with dynamic preamble distribution over multiple priorities. In the algorithm, we estimate the number of activated devices in each priority based on Bayesian rule to online control the number of preambles in each priority. Subsequently, we extend our proposal to incorporate access class baring (ACB) to optimize the algorithm. Extensive simulations show the effectiveness of proposed algorithm over multiple priorities.

High-accuracy localization technology has gained increasing attention in the diverse applications gesture and motion control, among others. Due to the shadowing, multi-path fading, blockage effects in indoor propagation, 0.1m-level precise localization is still challenging. Promising for 6G wireless communications, the Terahertz (THz) spectrum provides ultra-broad bandwidth for indoor applications. Applying to indoor localization, the channel state information (CSI) of THz wireless signals, including angle of arrival (AoA), received power, and delay, has high resolution, which can be explored for positioning. In this paper, a Structured Bidirectional Long Short-term Memory (SBi-LSTM) recurrent neural network (RNN) architecture is proposed to solve the CSI-based three-dimensional (3D) THz indoor localization problem with significantly improved accuracy. First, the features of individual multi-path ray are analyzed in the Bi-LSTM network at the base level. Furthermore, the upper level residual network (ResNet) of the constructed SBi-LSTM network extracts for the geometric information for localization. Simulation results validate the convergence of our SBi-LSTM method and the robustness against indoor non-line-of-sight (NLoS) blockage. Specifically, the localization accuracy in the metric of mean distance error is within 0.27m under the NLoS environment, which demonstrates over 60% enhancement over the state-of-the-art techniques.

Interest in fine-grained indoor localization remains high and various approaches including those based on Radio Frequency (RF), ultrasound, acoustic, magnetic field and light have been proposed. However, while the achieved accuracy may be high, many of these approaches do not work well in environments with lots of obstructions.In this paper, we present MagB, a decimeter-level localization scheme that uses the magnetometer commonly available on existing IoT devices. MagB estimates the bearing of beacons by detecting changes in the magnetic field strength. Localization is then performed based on Angle-of-Arrival (AoA) information. We have built a prototype of MagB using low cost, off-the-shelf components. Our evaluation shows that MagB is able to achieve a median accuracy of about 13cm and can localize devices even when they are placed in steel filing cabinet or inside the casing of a running PC.

Passive human localization and tracking using RF signals has been studied for over a decade. Most of existing solutions, however, can only track a single moving subject due to the coarse multipath resolvability limited by bandwidth and antenna number. In this paper, we breakdown the limitations by leveraging the emerging 60GHz 802.11ad radios. We present mmTrack, the first system that passively localizes and tracks multiple users simultaneously using a single commodity 802.11ad radio. The design of mmTrack consists of three key components. First, we significantly improve the spatial resolution, limited by the small aperture of the compact 60GHz array, by performing digital beamforming over all receive antennas. Second, we propose a novel multi-target detection approach that tackles the near-far-effect and measurement noises. Finally, we devise a robust clustering technique to accurately recognize multiple targets and estimate the respective locations, from which their individual trajectories are further derived by a continuous tracking algorithm. We implement mmTrack on commodity 802.11ad devices and evaluate it in indoor environments. Experiments demonstrate that mmTrack counts multiple users precisely with an error <1 person for 97.8% of the time and achieves a respective median location error of 9.9cm and 19.7cm for dynamic and static targets.

We address the problem of localizing an (illegal) transmitter using a distributed set of sensors. Our focus is on developing techniques that perform the transmitter localization in an efficient manner. Localization of illegal transmitters is an important problem which arises in many important applications. Localization of transmitters is generally done based on observations from a deployed set of sensors with limited resources, thus it is imperative to design techniques that minimize the sensors' energy resources.

In this paper, we design greedy approximation algorithms for the optimization problem of selecting a given number of sensors in order to maximize an appropriately defined objective function of localization accuracy. The obvious greedy algorithm delivers a constant-factor approximation only for the special case of two hypotheses (potential locations). For the general case of multiple hypotheses, we design a greedy algorithm based on an appropriate auxiliary objective function---and show that it delivers a provably approximate solution for the general case. We evaluate our techniques over multiple simulation platforms, including an indoor as well as an outdoor testbed, and demonstrate the effectiveness of our designed techniques---our techniques easily outperform prior and other approaches by up to 50-60% in large-scale simulations.