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We present a resource management framework for load-coupled downlink cellular OFDMA networks considering the load factor of an individual base station (BS) per resource block (RB), i.e., the number of adjacent sub-carriers (SCs), as the variable of interest in the resource management problem. The load factor of a BS per RB, which corresponds to the fraction of active SCs in the BS per RB, is an indicator of the level of resource consumption, and it affects the interference caused to that RB reused in other BSs, and thereby, results in a load-coupled OFDMA system. We first propose two distributed schemes to minimize: (i) the total load factor of the BSs (which would in turn increase the number of supportable users in the system), and (ii) the total downlink transmit power level of the BSs. Then, we derive the necessary and sufficient conditions for checking the feasibility of given target-rate requirements (also referred to as demand vector ) for users. Accordingly, an iterative and distributed scheme is proposed to check the feasibility of a given demand vector. Next, for a priority-based load-coupled network, we propose a priority-based gradual removal algorithm to support the maximal number of low-priority users while satisfying the demands of the high-priority users. To evaluate the performance of our proposed schemes for resource management and admission control in load-coupled OFDMA networks, the theoretical investigations are complemented with Monte Carlo simulations.

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

Indoor localization is important for a variety of applications, such as location-based services, mobile social networks, and emergency response. Fusing spatial information is an effective way to achieve accurate indoor localization with little or with no need for extra hardware. However, the existing indoor localization methods that make use of spatial information are either computationally expensive or sensitive to the completeness of landmarks. In this article, we propose a novel, low-cost, high-accuracy indoor localization method based on a landmark graph. The experimental results show that the proposed method outperforms the state-of-the-art methods.

In this paper, amplify-and-forward beamforming (AFB) is considered to establish a communication, through wireless sensor networks (WSNs) of $K$ sensor nodes, from a source to a receiver in the presence of both scattering and interference. All sources send their data to the WSN during the first time slot, while the nodes forward a properly weighted version of their received signals during the second slot. These weights are properly selected to maximize the desired power while completely canceling the interference signals. We show, however, that they depend on information locally unavailable at each node, making the zero-forcing beamformer (ZFB) unsuitable for WSNs, due to the prohibitive data exchange overhead and the power depletion it would require. To address this issue, we exploit the asymptotic expression at large $K$ of the ZFB weights that is locally computable at every node and, further, well-approximates their original counterparts. The performance of the resulting new distributed ZFB (DZFB) version is analyzed and compared with the conventional ZFB and two other distributed AFB benchmarks: the monochromatic (i.e., single-ray) AFB whose design neglects the presence of scattering and the bichromatic AFB which relies on an efficient two-ray channel approximation valid only for low angular spread (AS). We show that the proposed DZFB outperforms its monochromatic and bichromate counterparts while incurring much less overhead and power depletion than ZFB. We show also that it is able to provide optimal performance even in highly scattered environments as in the latter.

Cellular V2X, the “Vehicle to Everything” standard, defines a framework for information exchange among vehicles and other network entities. In one of the main modes, LTE V2X relies on a central scheduler to minimize the consumed resources in a conflict-free manner. This NP-hard problem leads to a scheduling strategy that assigns separate resources to the conflicting links and can enjoy from opportunistic resource reuse. In this paper, a novel polynomial-time heuristic, MUCS, is introduced, which models this scheduling as a Vehicle Routing Problem. The existing conflict-free schedulers face major incompetence in satisfying the LTE and 5G V2X requirements mainly due to their reliance on simplifications that are unnatural to the V2X environment. On the contrary, MUCS can flexibly accommodate general Device-to-Device topologies as the basis of V2X networks without imposing any packet segmentation. This way, MUCS minimizes the control information overhead in the cellular V2X standard. Due to scalability and a high quality resource utilization (near-optimal in certain conditions), compared to the existing literature, MUCS is desirable for LTE V2X and the 5G networks.

Indoor localization has been an active research area in the last decade. Various localization systems have been proposed based on different types of signals, including but not limited to WiFi, ultrawideband, inertial measurements, and visual signals. Fingerprinting-based methods are among the most popular methods due to their accuracy and ease of deployment. However, a disadvantage to fingerprinting-based methods is the training phase in which fingerprints have to be collected at known locations and stored for future localization inquiries. Recently a few methods have been proposed to alleviate this problem. In this paper, we focus on the possibility of reducing the burden of the training phase for visual (image-based) indoor localization systems by proposing a system that automatically generates the image-location database. The proposed system will be referred to as automatic visual fingerprinting that can be paired with any indoor image-based localization method. We will also validate the proposed system through extensive experiments.

Indoor localization is essential for healthcare, security, augmented reality gaming, and many other location-based services. There is currently a wealth of relevant literature on indoor localization. This article focuses on recent advances in indoor localization methods that use spatial context to improve the location estimation. Spatial context in the form of maps and spatial models have been used to improve the localization by constraining location estimates in the navigable parts of indoor environments. Landmarks such as doors and corners, which are also one form of spatial context, have proved useful in assisting indoor localization by correcting the localization error. This survey gives a comprehensive review of state-of-the-art indoor localization methods and localization improvement methods using maps, spatial models, and landmarks.

Mobile sensor networks (MSNs) with semi-flocking control protocols have demonstrated promising performances in both area coverage and target tracking. However, they may not operate at their highest efficiencies due to poor utilization of local information and deficient motion coordinations among mobile nodes. In this brief, a distributed semi-flocking control protocol based on local information exchanges is proposed to address the above issues in MSNs. Most existing semi-flocking control protocols are designed for patrolling in flat terrains and maneuvering nodes using shortest paths between two points on the given terrains. Such assumptions and the corresponding decisions do not apply well on real-world rough terrains and they often impose extra energy expenditure to mobile nodes. To address this problem, a terrain adaptation force and a navigation goal selection method are integrated into the proposed control protocol. Our study on rough terrains illustrates that the proposed control protocol is capable of achieving better performances in both area coverage and target tracking with lower energy expenditure when compared to the state-of-the-art flocking-based control protocols.

In this paper, we present a novel cooperative positioning (CP) technique to improve localization accuracy in vehicular environments. The proposed method uses position information obtained from the Global Positioning System (GPS) and Angle-of-Arrivals (AoAs) between the nodes estimated using Millimeter wave (mmWave) V2X communication and MUSIC algorithm. We propose two methods to improve the positioning accuracy: pairwise cooperation and angular Multi-dimensional scaling (AMDS). Simulation results show that the proposed methods outperform other CP methods in the literature.

Macro base stations are densely overlaid by small cells to satisfy the demands of user equipment in heterogeneous networks. Due to their dense deployment, some small cells are not directly connected to macro base stations and thus backhaul connections are required to connect small cells to macro base stations. Millimeter wave backhauls which have high bandwidths are preferred for small cell backhaul communication, since they can increase the capacity of network considerably. In this context, association of user equipment to base stations becomes challenging due to the backhaul architecture. Considering environmental concerns, energy efficiency is a vital criterion in designing user association algorithms. In this paper, we study the user association problem aiming at the maximization of energy efficiency. We develop centralized and distributed user association algorithms based on sequentially minimizing the power consumption. We evaluate the performance of the proposed algorithms under two scenarios and show that they achieve higher energy efficiency compared to the existing algorithms in the literature, while maintaining high spectral efficiency and backhaul load balancing.

This paper presents a l 0 -norm minimization based angleof-arrival (AoA) estimation approach using an Ising model (AIM). The proposed method takes advantage of the fact that incoming signals are spatially sparse and that the angular domain can be discretized. To solve the NP-hard problem, we formulate the l 0 norm minimization as an Ising energy minimization problem. The minimization of the Ising energy results in the maximization of the Gibbs distribution. The performance of the proposed method in identifying and resolving the angle of arrival of incoming signals is studied for different scenarios, in all of which the proposed method outperforms the state-of-the-art methods.

Macro base stations are densely overlaid by small cells to satisfy the demands of user equipments in heterogeneous networks. Due to their dense deployment, some small cells are not directly connected to macro base stations and thus backhaul connections are required to connect small cells to macro base stations. Millimeter wave backhauls which have high bandwidths are preferred for small cell backhaul communication, since they can increase the capacity of network considerably. In this context, association of user equipments to base stations becomes challenging due to the backhaul architecture. Considering environmental concerns, energy efficiency is a vital criterion in designing the user association algorithms. In this paper, we study the user association problem aiming at the maximization of energy efficiency given a specific spectral efficiency target. Firstly, we derive a quadratic upper bound on the total power consumption in heterogeneous network (access network and backhaul). Then we develop an energy and spectrum efficient user association method based on minimizing the derived quadratic upper bound, which indeed turns our target problem into a generalized quadratic assignment problem. Since this problem is NP-hard in general, we propose a heuristic algorithm for solving it. Extensive simulations show that our approach provides an enhanced level of energy efficiency compared to other well-known alternatives in the literature.

Intelligent Transportation System's main objective is to provide sustainable and optimized means of transportation for citizens of urban centers. Identifying the travel modes used by citizens, in real- time, allows these systems to better adapt transportation infrastructure and services according to user needs and possibilities of interaction. Many works have explored the use of machine learning algorithms and ensemble methods, combined with general and domain specific feature extraction techniques, for this task. More recently, several works evaluated the use of deep learning algorithms while none of them has explored the use of Recurrent Neural Networks (RNNs) in conjunction with domain feature extraction to enable the development of flexible and lightweight travel mode detection solutions based on multiple smartphone sensor readings. In this paper, we propose a deep RNN architecture for building online travel mode detection models using Long-Short Term Memory (LSTM) cells and evaluate its performance using real mobility data collected with a wide variety of sensors. The experiments show that the proposed architecture allows the generation of models with high accuracy and lower memory consumption and computation cost than state-of-the-art supervised machine learning (ML) approaches.

The knowledge of the floor-level location of a user in a multi-storey building is important for many applications, especially for emergency response. Existing floor identification systems suffer from a variety of limitations such as low accuracy, the need for a time-consuming site survey, assumption of user encounters, knowledge of the initial floor, and/or poor applicability. In this paper, we propose a novel, zero-effort, deep learning-based floor identification system, called \textit{ZeeFi}. The proposed system uses the widely-available smartphone sensing to identify on which floor a user is located. By recognizing the ground floor automatically, the proposed system does not require site survey, initial floor knowledge, and other assumptions. To achieve accurate floor identification performance, we have developed a deep learning-based method. Experimental results show that the proposed system outperforms the state-of-the-art systems, and is very promising for large-scale deployment.

Locomotion activity recognition (LAR) is important for a number of applications, such as indoor localization, fitness tracking, and aged care. Existing methods usually use handcrafted features, which requires expert knowledge and is laborious, and the achieved result might still be suboptimal. To relieve the burden of designing and selecting features, we propose a deep learning method for LAR by using data from multiple sensors available on most smart devices. Experimental results show that the proposed method, which learns useful features automatically, outperforms conventional classifiers that require the hand-engineering of features. We also show that the combination of sensor data from four sensors (accelerometer, gyroscope, magnetometer, and barometer) achieves a higher accuracy than other combinations or individual sensors.

In this paper, we study the problem of sensing targets in the context of vehicular networks. First, we define targets to be the vehicles moving on the road and sensors to be the roadside cameras. Then, we study the effect of predicted mobility on reducing the number of times each camera is activated in order to guarantee the coverage of targets. We formulate the sensing problem as an integer linear program using an opportunistic scheduler. Afterward, we extend the formulation and propose a novel strategic scheduler for coverage, which utilizes the predicted mobility information. We then extend this method to a fully distributed version and propose an approximation algorithm by exchanging messages among the sensors. Using a Markovian and a car-following availability models, we show by simulations that the number of activated sensors is significantly reduced by utilizing predicted mobility information. After that, we analyze both schedulers to quantify the gain of utilizing mobility information in sensing. We adopt an independent node mobility model due to its tractability. The analysis is composed of two main components; calculation of mobility gain in terms of sensing cost and probability of feasibility. Our analysis and simulations demonstrate the gain of mobility in sensing targets in terms of higher probability of feasibility and lower sensing cost.

In this article, we present a survey of recent advances in passive human behavior recognition in indoor areas using the channel state information (CSI) of commercial WiFi systems. The movement of the human body parts cause changes in the wireless signal reflections, which result in variations in the CSI. By analyzing the data streams of CSIs for different activities and comparing them against stored models, human behavior can be recognized. This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers. The techniques from the literature that are presented herein have great performance; however, instead of the machine learning techniques employed in these works, we propose to use deep learning techniques such as long-short term memory (LSTM) recurrent neural networking (RNN) and show the improved performance. We also discuss different challenges such as environment change, frame rate selection, and the multi-user scenario; and finally suggest possible directions for future work.

In this paper, we study the problem of velocity estimation in vehicular networks by exploiting the sparsity of vehicle velocity trajectory. First, we exploit the sparsity of vehicular velocity trajectories to reduce the beaconing load on the channel. To this end, we propose a sublayer that reduces the number of transmitted packets through a superframe. At each superframe, each vehicle transmits a few samples of its velocity as well as an encoded measurement of its past velocities. Sparse recovery of each velocity vector is performed at the receiver. We propose the use of repetitions at the medium access control (MAC) layer. Moreover, we extend our scheme into a streaming estimation system without the superframe concept. The streaming system uses a sliding window concept. We thoroughly study the estimation error of the streaming system, and we propose an algorithm to find the best solution over a streaming sliding window. The proposed superframe and streaming schemes are tested with real velocity traces collected in the city of Toronto to capture the performance in the city and highway conditions. Experiment results show that the proposed schemes significantly reduce the number of exchanged packets while preserving the velocity information with an excellent accuracy at the receiver. We also demonstrate how past velocity can be used to enhance vehicle localization accuracy.

One of the most challenging issues in Mobile Social Networks (MSNs) is to design a messages forwarding method that achieves high delivery and low communication overhead. Single-copy forwarding methods boasting minimum communication overhead are well-known in MSNs. However, attaining a reasonable delivery ratio by using a single-copy method is an open problem. A common way to resolve the problem is social-aware forwarding, but this faces two main weaknesses: one is that they either are unaware of community detection, or use supervised learning strategies, other is that they generally use the relay-destination contact probability for predicting future contacts without considering the contact time. In this paper, we propose community-aware forwarding (CAF) as a new single-copy forwarding method using Hidden Semi-Markov Model (HSMM) to find communities by utilizing the similarity of node contact patterns in different sojourn cycles. In this study, we train HSMM as an unsupervised algorithm to compute the node community transition and then propose a novel forwarding method that utilizes message expiration in order to make relay selection. Evaluation results confirm our superior CAF performance over the popular solutions investigated in terms of message delivery and latency.

Vehicular safety applications require reliable and up-to-date knowledge of the local neighborhood. Under IEEE 802.11p, this is attained through single-hop broadcasts of safety beacons in the control channel. However, high transmission power and node mobility can cause regions of node density to form rapidly. In such situations, excessive load on the control channel must be avoided to prevent performance degradation for safety applications. Existing congestion control schemes aim to reach a fair distribution of available channel resources, but fail to account for the differing quality of service (QoS) requirements of vehicles in different driving contexts. This context depends on many factors, including the relative position and velocity of its neighbors. The problem of adapting each vehicle's transmission probability under a slotted p-persistent vehicular broadcast medium access control (MAC) protocol is formulated as a network utility maximization (NUM) problem which takes the driving context into account. A distributed algorithm is proposed to solve this problem in a decentralized manner, its convergence is analyzed, and its performance is evaluated through simulations.

Multipath interference causes the antenna array of an anchor to estimate several angles of arrival (AoA) for a single user. The resulting ambiguity regarding the line of sight (LoS) component can lead to severe errors in location estimation. This work formulates the problem within an outlier rejection framework: a set of candidate locations are computed by considering all AoAs at all anchors; with the observation that LoS AoA vary less than non-LoS AoA, over several time instances an inlier set can be found that clusters around the true user location. This work then derives an Ising model representation for finding the inlier set and solves the NP-hard problem using the Digital Annealer (DA). Simulations show that this approach improves median localization accuracy by 48.5% when compared to the state-of-the-art in localization methods.

Cyber attacks on the power systems can mislead the control center to produce incorrect state and topology estimate. State and topology attacks can have harmful impacts on the operation of a power system. The problem of placing secure phasor measurement units (PMUs) to detect these attacks has been studied in the literature. Specifically, it has been shown that placing secure PMUs to disable undetectable state and topology attacks can enhance the security of the system against cyber attacks. Placing secure PMUs is indeed the minimum vertex cover problem. Since the cost of deploying PMUs is high, it is important to place the secure PMUs efficiently in order to maximize the ability of detecting cyber attacks while reducing the costs. In this paper, we use Digital Annealer to solve the vertex cover problem. Digital Annealer is a hardware architecture for solving combinatorial optimization problems. We have performed numerous numerical experiments and noticed that our approach has an enhanced level of optimality compared to other well-known alternatives in the literature.

We propose a new collaborative beamforming (CB) solution robust (i.e., RCB) against major channel estimation impairments over dual-hop transmissions through a wireless sensor network (WSN) of K nodes. The source first sends its signal to the WSN. Then, each node forwards its received signal after multiplying it by a properly selected beamforming weight. The latter aims to minimize the received noise power while maintaining the desired power equal to unity. These weights depend on some channel state information (CSI) parameters. Hence, they have to be estimated locally at each node, thereby, resulting in channel estimation errors that could severely hinder CB performance. Exploiting an efficient asymptotic approximation at large K, we develop alternative RCB solutions that adapt to different implementation scenarios and wireless propagation environments ranging from monochromatic (i.e., scattering-free) to polychromatic (i.e., scattered) ones. Besides, in contrast to existing techniques, our new RCB solutions are distributed (i.e., DCB) in that they do not require any information exchange among nodes, thereby dramatically improving both WSN spectral and power efficiencies. Simulation results confirm that the proposed robust DCB (RDCB) techniques are much more robust in terms of achieved signal-to-noise ratio (SNR) against channel estimation errors than best representative CB benchmarks.

This paper aims at the problem of time-of-flight (ToF) estimation using channel state information (CSI) obtainable from commercialized multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless local area network (WLAN) receivers. It was often claimed that the CSI phase is contaminated with errors of known and unknown natures rendering the ToF-based positioning difficulty. To search for an answer, we take a bottom-up approach by first understanding CSI, its constituent building blocks, and the sources of error that contaminate it. We then model these effects mathematically. The correctness of these models is corroborated based on the CSI collected in extensive measurement campaign, including radiated, conducted, and chamber tests. Knowing the nature of contaminations in the CSI phase and amplitude, we proceed with introducing pre-processing methods to clean CSI from those errors and make it usable for range estimation. To check the validity of the proposed algorithms, the MUSIC super-resolution algorithm is applied to post-processed CSI to perform range estimates. The results substantiate that a median accuracy of 0.7, 0.8, and 0.9 m is achievable in a highly multipath line-of-sight environment where the transmitter and the receiver are 5, 10, and 15 m apart.

Dropout and similar stochastic neural network regularization methods are often interpreted as implicitly averaging over a large ensemble of models. We propose STE (stochastically trained ensemble) layers, which enhance the averaging properties of such methods by training an ensemble of weight matrices with stochastic regularization while explicitly averaging outputs. This provides stronger regularization with no additional computational cost at test time. We show consistent improvement on various image classification tasks using standard network topologies.

The next generation of cellular networks promises to be the platform for ubiquitous, precise, and accurate location-awareness. Although positioning using radio-frequency waves has many advantages in terms of cost and performance, propagation phenomena such as multipath propagation and shadowing can immensely deteriorate this performance leading to incorrect estimation of a user's location. This paper introduces LocHunt, an algorithm that addresses such a problem. Concretely, at each time-instance, angle-of-arrivals (AoAs) of multipath waves from several anchors are estimated and all the candidate locations of the user are derived. Repeating this operation for a span of time, the algorithm exploits the heuristics that those candidate locations obtained from non-line-of-sight (NLoS) paths exhibit large spatial variations, whereas the true location of the user is manifested as a denser cluster. Subsequently, the peak of the underlying empirical probability density function corresponds to an estimate of the user's location, which can be found by the mean-shift algorithm followed by the connected-components algorithm. The proposed data-driven approach is compared to the state-of-the-art, showing improved localization accuracy with up to one order of magnitude reduction in computational complexity. Channel state information (CSI), similar to that exposed by 802.11n MIMO-OFDM systems, is used for testing the algorithms and is generated by a modified Winner 2 (WIM2) channel simulator.

We propose a new collaborative beamforming (CB) solution robust against major implementation impairments over dual-hop transmissions from a source to a destination communicating through a wireless sensor network (WSN) of K nodes. In the first time slot, the source sends its signal to the WSN while, in the second, each node forwards its received signal after multiplying it by a properly selected beamforming weight. The latter aims to minimize the received noise power while maintaining the desired power equal to unity. These weights depend a priori on some channel state information (CSI) parameters. Hence, the latter have to be estimated locally at each node thereby resulting in implementation errors that could severely hinder CB performance. Exploiting an efficient asymptotic approximation at large K, we develop alternative CB solutions that not only account for estimation errors, but also adapt to different implementation scenarios and wireless propagation environments ranging from monochromatic (i.e., scattering-free) to polychromatic (i.e., P-DCB) ones. Besides, in contrast to existing techniques, our new CB solutions are distributed (i.e., DCB) in that they do not require any information exchange among nodes, thereby dramatically improving both WSN spectral and power efficiencies. Simulation results confirm that the proposed DCB techniques are much more robust against implementations errors than their benchmarks at much lower complexity.

As a dramatic advancement in mobile communications, 5G has put together several state of the art technologies including Cognitive Radio (CR) and Quality-of-Experience (QoE). While CR is to overcome frequency scarcity, how to maintain QoE for all the connected users is one of the paramount issues in such integration, especially for multimedia communications. As a key technology for 5G, Non-Orthogonal-Multiple Access (NOMA) is combined with Orthogonal Frequency Division Multiplexing (OFDM) to improve the spectral efficiency. In this paper, the QoE requirements for typical applications in a CR platform are characterized, based on which a method for user grouping and power management in an OFDM–NOMA system is proposed. The performance of the proposed method is analyzed and evaluated by simulating a typical network. The evaluations show noticeable improvement in transmit power reduction while the requested users’ perception levels are maintained.

The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15 percent of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1 percent of the fingerprinting load, some crowd sourced readings, and plan coordinates of the indoor area. The 1 percent fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5 m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50 percent performance improvement by using this information as compared to using only fingerprints.

Employment of ground-based positioning systems has been consistently growing over the past decades due to the growing number of applications that require location information where the conventional satellite-based systems have limitations. Such systems have been successfully adopted in the context of wireless emergency services, tactical military operations, and various other applications offering location-based services. In current and previous generation of cellular systems, i.e., 3G, 4G, and LTE, the base stations, which have known locations, have been assumed to be stationary and fixed. However, with the possibility of having mobile relays in 5G networks, there is a demand for novel algorithms that address the challenges that did not exist in the previous generations of localization systems. This paper includes a review of various fundamental techniques, current trends, and state-of-the-art systems and algorithms employed in wireless position estimation using moving receivers. Subsequently, performance criteria comparisons are given for the aforementioned techniques and systems. Moreover, a discussion addressing potential research directions when dealing with moving receivers, e.g., receiver's movement pattern for efficient and accurate localization, non-line-of-sight problem, sensor fusion, and cooperative localization, is briefly given.

The popularity of online social networks (OSNs) makes them attractive platforms to advertise products. Previous work on marketing in OSNs utilized older diffusion models that do not capture the interactions of modern OSNs and hence there is a need to develop a model that accounts for the interactions that occur in current OSNs. In this paper, we introduce a new model for information flow in online social networks that captures the sharing behavior exercised by users when they pass information from one online social network to their social circles in another network. We, then, formulate a problem of maximizing the marketing reach where the diversity of users’ other social networks is taken as a constraint. We also propose a greedy algorithm to solve the aforementioned optimization problem. Numerical results show that the proposed algorithm achieves better results than algorithms that are based on classical degree centrality metric and with comparable running time.

In this paper, we propose a novel technique for sampling sequential images using a cylindrical transform in a cylindrical coordinate system for kidney semantic segmentation in abdominal computed tomography (CT). The images generated from a cylindrical transform augment a limited annotated set of images in three dimensions. This approach enables us to train contemporary classification deep convolutional neural networks (DCNNs) instead of fully convolutional networks (FCNs) for semantic segmentation. Typical semantic segmentation models segment a sequential set of images (e.g. CT or video) by segmenting each image independently. However, the proposed method not only considers the spatial dependency in the x-y plane, but also the spatial sequential dependency along the z-axis. The results show that classification DCNNs, trained on cylindrical transformed images, can achieve a higher segmentation performance value than FCNs using a limited number of annotated images., Comment: This paper is accepted for presentation at IEEE Global Conference on Signal and Information Processing (IEEE GlobalSIP), California, USA, 2018

V2X, “Vehicle-to-Everything” sidelink in 5G, facilitates communication between vehicles, pedestrians and cyclists while relying on a central scheduler in the main mode. In many applications, sidelinks in highways are formed based on the location of vehicles on the road. This defines an inherent ordered-tree structure to be used for delay-aware scheduling in LTE frame. Having these in mind, we propose the first two-dimensional expansion of a popular leading delay-aware scheduler in the literature. Our designed scheduler matches the V2X LTE multichannel environment and benefits from resource reuse while being capable of handling delay in sensitive vehicular safety applications. Compared to the existing competitors, delay control is the main feature of our scheduler. Having less or similar complexity order, this feature comes at the cost of less efficient resource reuse.

Many popular fingerprinting-based indoor localization methods, such as WiFi-based localization systems, rely on a dataset of fingerprints labelled by known locations (fingerprint-location dataset) to be able to localize a user by comparing the queried fingerprint with the fingerprints in the dataset. Generating and updating such a dataset is a burden and requires significant amount of time, human effort and expertise. In this work, we propose a system to build such a dataset from scratch using crowdsourced data. Consequently we reduce the required time and effort, by leveraging the information shared by the crowd. The proposed system is based on localization of user motion, hence called LocaMotion. LocaMotion takes a supervised learning perspective towards localization of user-sent motion data. In other words, LocaMotion is formalised as a classifier that extracts and assigns features from a user motion data to a sequence of points in the building. Training and testing procedures for LocaMotion will be discussed in details followed by extensive experiments to demonstrate its validity and success to localize motion patterns and generate useful fingerprint-location datasets.

V2X, the "Vehicle to Everything" communication, facilitates communication among vehicles and other network entities, which is verified to bring in substantial economic and ecological benefits. In the main mode, LTE V2X relies on a central scheduler. As an important problem from the resource management viewpoint, minimizing the consumed resources in a conflict-free manner is NP-hard. In this paper, a novel point of departure is proposed for this NP-hard problem and a polynomial-time heuristic, MUCS, is introduced that models scheduling as a Vehicle Routing Problem. The existing multichannel methods seem to be incapable of satisfying LTE and 5G V2X communication demands and requirements due to their reliance on inapplicable simplifications. Our technique enables contiguous link scheduling, as opposed to most of the existing multichannel methods, which also remarkably performs better. The proposed heuristic MUCS eliminates the need for such simplifying constraints and can be employed in general V2X network topologies. Due to its scalability and a close-to-optimal resource utilization compared to the existing literature, MUCS is desirable for the cellular V2X, i.e., LTE V2X and the upcoming 5G systems.

Conflict-free link scheduling assigns separate resources to conflicting links while taking advantage of possible reuse opportunities. Minimizing the consumed resources is NP-hard. In this paper, a polynomial-time heuristic, GenS, is introduced that models scheduling as a travelling salesman problem. GenS approximates the solution to the scheduling problem with significant reduction of complexity compared to the state of the art algorithms. GenS can be employed in general network topologies and due to its scalability and a close-to-optimal resource utilization compared to the existing literature, it is desirable for the next generations of wireless systems.

In this paper, amplify-and-forward beamforming (AFB) is considered to establish a reliable communication between devices in networked smart cities. All sources send their data during the first time slot while the cooperative terminals forward a properly weighted version of their received signals during the second. These zero-forcing AFB (ZFB) weights are properly selected to maximize the desired power while completely canceling the interference signals. We show, however, that their implementation requires a huge terminals' information exchange, making ZFB unsuitable for smart cities where the overhead and power restrictions are very stringent. To address this issue, we exploit the asymptotic expression at large K of the ZFB weights whose computation requires much less information exchange and, further, well-approximate their original counterparts. The performance of the proposed beamforming is analyzed and compared to ZFB and monochromatic (i.e., single-ray) AFB (MB) whose design neglects the presence of scattering.

The problem of joint load management and admission control (JLAC) in underlay OFDMA-based cognitive radio networks (CRNs) is studied. The fraction of the active sub-carriers in a base station (BS) in each resource block (RB) is defined as the load factor of the BS per RB. In the JLAC problem, we simultaneously minimize the secondary users' outage ratio and the total load factors of the BSs via the RBs, subject to the constraint that the primary users are protected. This problem is a NP-hard problem. We first relax it into a convex optimization problem. Then, by employing the optimal solution to the relaxed JLAC problem, we derive a heuristic algorithm to gradually remove the most adversative secondary users imposing the most interference to the primary users. The performance of the proposed algorithm is studied in terms of the outage ratio of secondary users and the total load factors of the BSs via the RBs through extensive simulations.

A fast and robust algorithm for motion estimation based on stereo vision is proposed. The algorithm consists of two consecutive stages. First, interest points are found and tracked between four images from two consecutive time steps. A novel robust method for tracking features is proposed for this step to address shortcomings of the traditional methods for indoor hand-held/wearable scenarios. Next, motion of the camera in time is estimated via optimizing the total reprojection error associated with the robust features found using the first stage. A key assumption is that motion between the two time steps is sufficiently small or equivalently, the frames are captured close enough in time. The proposed Visual Ego-motion Estimation (VEE), is robust due to robust feature tracking in the first step, and fast due to efficient reprojection error minimization used to estimate the six-degree-of-freedom (6DoF) motion of the camera. Experiments prove comparable/superior results compared to the state-of-the-art methods in the literature especially for indoor handheld scenarios.

Conflict-free TDMA link scheduling assigns separate resources to conflicting links while taking advantage of possible resource reuse opportunities. This important resource management problem is shown to be NP-hard. In this paper, a novel semi-distributed algorithm, called SD-MUCS, is proposed for the multichannel conflict-free TDMA link scheduling. SD-MUCS is designed based on the semi-distributed architecture as one of the most promising proposed network structures for 5G. Thus, it takes advantage of parallel processing while imposing a tolerable overhead on the network. Compared to the existing link schedulers, SD-MUCS offers a more spectrally efficient solution which well suits massive expected demand in 5G.

Human activity recognition is important for a large number of applications including indoor localization. Existing methods usually involve manually-designed features, which require expert knowledge and are laborious. Also, previous works use only the accelerometer for activity recognition, which may fail to recognize some complex activities. In this paper, we propose a deep learning-based method for locomotion activity recognition by using the combination of data from multiple smartphone built-in sensors. Eight types of locomotion activities are identified including the new ‘False Motion’ activity introduced for the first time in this work. Experimental results show that the proposed method, which learns useful features automatically, outperforms conventional classifiers that require hand-engineering of features. Also, using data from multiple sensors helps to improve recognition accuracy by about 10% compared to that using accelerometer data only.

Deep learning models have a large number of free parameters that must be estimated by efficient training of the models on a large number of training data samples to increase their generalization performance. In real-world applications, the data available to train these networks is often limited or imbalanced. We propose a sampling method based on the radial transform in a polar coordinate system for image augmentation to facilitate the training of deep learning models from limited source data. This pixel-wise transform provides representations of the original image in the polar coordinate system by generating a new image from each pixel. This technique can generate radial transformed images up to the number of pixels in the original image to increase the diversity of poorly represented image classes. Our experiments show improved generalization performance in training deep convolutional neural networks with radial transformed images., This paper is accepted for presentation at IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE ICASSP), 2018

Pathfinding in hospitals is challenging for patients, visitors, and even employees. Many people have experienced getting lost due to lack of clear guidance, large footprint of hospitals, and confusing array of hospital wings. In this paper, we propose Halo; An indoor navigation application based on voice-user interaction to help provide directions for users without assistance of a localization system. The main challenge is accurate detection of origin and destination search terms. A custom convolutional neural network (CNN) is proposed to detect origin and destination search terms from transcription of a submitted speech query. The CNN is trained based on a set of queries tailored specifically for hospital and clinic environments. Performance of the proposed model is studied and compared with Levenshtein distance-based word matching., This paper is accepted for presentation at 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications

Summary Mobile social networks are presenting new opportunities for content dissemination. User location, mobility, and social communities can be used to deliver delay-tolerant content. Most existing dissemination methods either fail to consider the user's interest in their protocol design or just allow exchange of contents between nodes that have a similar interest. If nodes with similar interest never encounter each other, then contents might not be exchanged with them. In this paper, we propose a method in which user location, mobility, and interest as well as certain limiting factors such as relay buffer size and communication overhead are used to select a set of relays for targeted advertisement distribution. Besides, our method does not depend on nodes with similar interest encountering one another. In the proposed method, a distribution agent exploits user location, mobility, and social networks as well as the interest of destinations to select a group of relays to carry content to the targeted destinations. Users move among social communities and carry advertisements to their peers in other communities. We have also developed an optimization problem for advertisement selection and scheduling. Since the problem is NP-hard, we propose a heuristic solution. Evaluation of the proposed approach shows that the algorithm is not sensitive to network resource variation and readily outperforms popular methods reported in the literature in terms of delay, delivery ratio, and interest compatibility.

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.