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In this paper, an unmanned aerial vehicle (UAV)-aided multi-antenna configured downlink mmWave cooperative generalized frequency division multiplexing (GFDM) system is proposed. To provide physical layer security (PLS), a 3D controlled Lorenz mapping system is introduced. Further-more, the combination of T-transformation spreading codes, walsh Hadamard transform, and discrete Fourier transform (DFT) techniques are integrated with a novel linear multi-user multiple-input multiple-output (MU-MIMO) gyre precoding (GP) for multi-user interference reduction. Furthermore, concatenated channel-coding with multi-user beamforming weighting-aided maximum-likelihood and zero forcing (ZF) signal detection schemes for an improved bit error rate (BER) are also used. The system is then simulated with a single base station (BS), eight massive machine-type communications (mMTC) users, and two UAV relay stations (RSs). Numerical results reveal the robustness of the proposed system in terms of PLS and an achievable ergodic rate with signal-to-interference-plus-noise ratio (SINR) under the implementation of T-transformation scheme. By incorporating the 3D mobility model, brownian perturbations of the UAVs are also analyzed. An out-of-band (OOB) reduction of 320 dB with an improved BER of 1 × 10−4 in 16-QAM for a signal-to-noise ratio, Eb /N0, of 20 dB is achieved.

In this paper, a coordinated multipoint joint transmission (CoMP-JT) framework at mmWave for a cyclic prefix (CP)-free multiuser OFDM wireless communication system is developed and analyzed. The aim is to provide high-quality service to cell-edge users; otherwise, the cell-users would suffer from significant signal degradation due to undesired interference. The impact of complex Hadamard transform with block diagonalization channel precoding for multiuser interference reduction and designed subcarrier mapping for out-of-band (OOB) reduction are investigated. In addition, the paper studied the input back-off-aided high-power amplifier for peak-to-average power ratio (PAPR) reduction and forward error correction channel coding for improved bit error rate (BER) for cell-edge users at mmWave frequencies. Moreover, signal-to-interference-noise ratio and ergodic achievable rate are estimated both in the presence and absence of CoMP-JT-based transmission technique to verify their significance in terms of transmitted power. Numerical investigations showed an OOB reduction of 312 dB, PAPR reduction from 17.50 dB to 7.66 dB, and improved BER of 1×10−3 in 16-QAM for a signal-to-noise ratio of −6 dB. Hence, the simulation results demonstrated the effectiveness of the developed system.

Multi-transmit-receive capability is fast becoming a significant feature of next-generation wireless mesh networks. It enables routers to transmit or receive distinct packets from multiple neighbors simultaneously. A key problem, however, is designing a distributed link-scheduling algorithm that ensures high network capacity. In this paper, we propose dMaxQ, which is a novel queue-length-aware distributed link scheduler that requires only one-hop neighbors' queue information and uses the celebrated max-weight policy in a distributed manner. We have evaluated the performance of dMaxQ in different network topologies for both single-hop and multihop traffic models and compared it against other approaches, including two queue-length-aware centralized algorithms and state-of-the-art distributed approaches: JazzyMAC and receive-oriented multiple access. The results show that for single-hop and multihop traffic scenarios, dMaxQ obtains, respectively, 100% and 90% of the throughput achieved by the theoretical centralized policy. Other distributed algorithms, such as JazzyMAC, only managed 25% of the theoretical throughput.

A key advance in enabling higher wireless mesh network capacity is allowing routers to transmit or receive (MTR) from multiple neighbors simultaneously over the same frequency. Achieving this capacity, however, is predicated on a link scheduler that is able to capitalize on the MTR capability of nodes to activate the maximum number of active links, and also to derive the shortest schedule that ensures all links are activated at least once. To date, existing schedulers do not consider the transmission or air-time of packet(s). Henceforth, this paper fills this gap and propose to derive the shortest superframe length, defined as the end time of the last transmitting link. Our scheduler, called A-TxRx, greedily adds links whenever a link finishes its transmission. As a result, unlike previous schedulers, links can start transmitting/receiving as soon as there is no conflict. We have evaluated the performance of A-TxRx in various network configurations, and compared it against two state-of-the-art approaches: 2P and JazzyMAC. The results show A-TxRx outperforming these algorithms significantly, especially when the network becomes denser. Specifically, the superframe length of A-TxRx is typically less than half of 2P and JazzyMAC, with 60 % more concurrently transmitting links.

© 2015 Elsevier B.V. Abstract Mobile augmented reality applications rely on automatically matching a captured visual scene to an image in a database. This is typically achieved by deriving a set of features from the captured image, transmitting them through a network and then matching with features derived from a database of reference images. A fundamental problem is to select as few and robust features as possible such that the matching accuracy is invariant to distortions caused by camera capture whilst minimizing the bit rate required for their transmission. In this paper, novel feature selection methods are proposed, based on the entropy of the image content, entropy of extracted features and the Discrete Cosine Transformation (DCT) coefficients. The methods proposed in the descriptor domain and DCT domain achieve better retrieval accuracy under low bit rate transmission than state-of-the-art peak based feature selection used within the MPEG-7 Compact Descriptor for Visual Search (CDVS). The robustness of the proposed methods is evaluated under controlled single distortion and the retrieval performance is verified from image retrieval experiments and results for a realistic dataset with complex real world capturing distortion. Results show that the proposed method can improve the matching accuracy for various detectors. On the one side, feature selection can achieve low bit rate transmission, on the other side, when coping with distorted images, it can result in a higher matching accuracy than using all features. Hence, even if all the features can be transmitted in high transmission bandwidth scenarios, feature selection should still be applied to the distorted query image to ensure high matching accuracy.

Multiple transmit (Tx) or receive (Rx) capability is a significant advance in wireless communications. This so called MTR capability allows the creation of wireless mesh networks (WMNs) that are ideal for use as a high-speed wireless backbone that spans vast geographical areas. A fundamental problem, however, is deriving a minimal transmission schedule or superframe that yields low end-to-end delays, with the primary constraint that routers are not allowed to Tx and Rx simultaneously. In this paper, we consider a joint routing and link scheduling approach that addresses two fundamental issues that influence end-to-end delays: superframe length and transmission slot order. Shortening the superframe length, in terms of slots, is expected to minimize the inter-link activation time while reordering transmission slots increases the likelihood that links on a path are activated consecutively. We propose two algorithms. The first called JRS-Multi-DEC uses a novel metric to minimize the load of each link while the second, called JRS-BIP, uses a Binary Integer Program approach. Both algorithms aim to minimize the overall delay and use slot re-ordering on the resulting schedule to further reduce delay. Numerical results show both algorithms are able to reduce the average end-to-end delay by approximately 50% as compared to a non joint routing algorithm.

The capacity of Wireless Mesh Networks (WMNs) has significantly increased with the recent addition of multiple transmit (Tx) and receive (Rx) (MTR) capability or smart antennas. This increase however is predicated on an effective link scheduler. The aim of any scheduler is to derive a superframe comprising the smallest number of slots that affords each link one or more transmission opportunities. In particular, the scheduler is required to solve an instance of the NP-complete, MAX-CUT problem, in each time slot. To this end, there are a number of centralised schedulers, but only a handful of distributed schedulers. However, each of these distributed schedulers has its own drawbacks;either they do not guarantee maximal activated links or do not guarantee all links are activated. Henceforth, in this paper, we add to the state-of-the-art by proposing a novel distributed scheduler,called Algo-d, which approximates the MAX-CUT problem in a distributed manner using only local information. In fact, this is the first distributed solution for MAX-CUT problem. Through theoretical analysis and simulation, we show that Algo-d achieves the following performance: 1) Algo-d schedules on average 12% fewer and 46.5% more links in each time slot than two centralised algorithms, and 2) Algo-d schedules 28% more links than ROMA and 270% more links than JazzyMAC; both state-of-the-art distributed schedulers for MTR WMNs.

© 2015, Ruiz-Rivera et al. To date, significant effort has gone into designing green traffic engineering (TE) techniques that consolidate traffic onto the minimal number of links/switches/routers during off-peak periods. However, little works exist that aim to green Multi-Protocol Label Switching (MPLS) capable networks. Critically, no work has studied the performance of green label switched paths (LSPs) establishment methods in terms of energy savings and acceptance rates. Henceforth, we add to the current state-of-the-art by studying green online and offline (LSP) establishment methods. Online methods rely only on past and current LSP requests while offline ones act as a theoretical benchmark whereby they also have available to them future LSP requests. We introduce a novel metric that takes into account both energy savings and acceptance rates. We also identify a new simpler heuristic that minimizes energy use by routing source–destination demands over paths that contain established links and require the fewest number of new links. Our evaluation of two offline and four online LSP establishment methods over the Abilene and AT&T topologies with random LSP setup requests show that energy savings beyond 20 % are achievable with LSP acceptance rates above 90 %.

© 2014 IEEE. Mobile augmented reality applications rely on automatically recognising a visual scene through matching of derived image features. To ensure the Quality of Experience (QoE) perceived by users, such applications should achieve high matching accuracy meanwhile minimizing the waiting time to meet real-time requirement. An efficient solution is to develop an effective feature selection method to select the most robust features against distortions caused by camera capture to achieve high matching accuracy whilst transmission and matching process of the features are significant reduced. Feature selection is also beneficial to reducing the computational complexities of the matching system so that waiting time is minimized and hence user QoE is maximised. In this paper, a QoE estimation for state-of-the-art feature selection in MPEG-7 CDVS based on waiting time and matching accuracy as judged by retrieval experiments on a realistic image dataset with real-world distortions caused by image capture is analysed. The predicted QoE results suggest that feature selection can provide good QoE to users.

© 2014 IEEE. Retrieval-by-capture applications allow a user to scan a printed picture in a newspaper or magazine with a mobile camera to then automatically trigger the display of predetermined related content. To ensure a high user Quality of Experience (QoE), this paper proposes a fast and accurate low bit rate solution for retrieval-by-capture applications based on extracting Scale Invariant Feature Transform (SIFT) features from images reconstructed from the low spatial frequency components. The system applies a two-dimensional block-based Discrete Cosine Transform (DCT), as typically used in image coders, and only encodes and transmits the resulting DC components. Received images are decoded using these components and SIFT features are derived and then used to find a matching image within a database of candidate images. Results show that the proposed system achieves more than 97% matching accuracy when evaluated for a wide range of typical image distortions including scaling, rotation, additive noise, image blurring and illumination. Transmission data rates are comparable to existing compressed domain image features whilst significantly reducing system latency.

© 2014 IEEE. Mobile augmented reality applications rely on automatically matching a captured visual scene to an image in a database. This is typically achieved by deriving a set of features for the captured image, transmitting them through a network and then matching with features derived for a database of reference images. A fundamental problem is to select as few and robust features as possible such that the matching accuracy is invariant to distortions caused by camera capture whilst minimising the bit rate required for their transmission. In this paper, novel feature selection methods are proposed, based on the entropy of the image content, entropy of extracted features and the Discrete Cosine Transformation (DCT) coefficients. The methods proposed in the descriptor domain and DCT domain achieve better matching accuracy under low bit rate transmission than start-of-the-art peak based feature selection used within the MPEG-7 Compact Descriptor for Visual Search (CDVS). This is verified from image retrieval experiments and results for a realistic dataset with complex real world capturing distortion. Results show that the proposed method can improve the matching accuracy for various detectors and also indicate that the feature selection can not only achieves low bit rate transmission but also results in a higher matching accuracy than using all features when applied to distorted images. Hence, even if all the features can be transmitted in high transmission bandwidth scenarios, feature selection should still be applied to the distorted query image to ensure high matching accuracy.

Recently, researchers have created Wireless Mesh Networks (WMNs) where routers have multiple transmit (Tx) or receive (Rx) capability. A fundamental problem in such WMNs is deriving a transmission schedule that yields minimal end-to-end delays. In this paper, we approach this problem via joint routing and link scheduling. Specifically, we consider twofundamental issues that influence end-to-end delays: super frame length and transmission slot order. We propose two algorithms: JRS-Multi-DEC and JRS-BIP, where the former uses a novel metric to minimize the load of each link whilst the latter uses a binary integer program solver. Both algorithms have the similar aim of minimizing overall delay and to re-order slots such that packets are forwarded quickly along their path. Numerical results show that our algorithms can reduce average delay by approximately 50% as compared to a non joint routing and scheduling algorithm.

Next generation Wireless Mesh Networks (WMNs) will require a link scheduler that exploits the full advantage of Multi-Transmit-Receive (MTR) commuication. To this end, we design a distributed link scheduler called Voting-ALGO that is aware of queue lengths and uses the celebrated max weight policy to achieve 100% throughput.

Recently, researchers have developed Wireless Mesh Networks (WMNs) where each router is capable of performing multiple transmissions or receptions concurrently; aka Multi Tx-Rx (MTR) WMNs. Consequently, each node is able to transmit (Tx) or receive (Rx) to/from its neighbors simultaneously. A fundamental problem in such WMNs is to derive a transmission schedule with minimal superframe length to maximize network capacity and minimize end-to-end delays. Unfortunately, deriving a minimal superframe length is equivalent to solving the NP-complete, MAX-CUT problem. To this end, there are a number of centralized schedulers, but only but only one distributed scheduler, called JazzyMAC. Henceforth, in this paper, we add to the state-of-the-art by proposing Algo-d, a novel distributed scheduler that solves the MAX-CUT problem using only local information. Experiment results show Algo-d generates superframes that are 37.5% shorter and it activates 264% more links as compared to JazzyMAC. Lastly, as compared to centralized schedulers, Algo-d schedules 50% more links than Algo-1 and at most 7% fewer links than Algo-2.

Green networking techniques aim to shut down the least utilized links and/or routers during off-peaks hours. In this paper, we show that such techniques negatively impact the operation of the Border Gateway Protocol (BGP). We quantify the impacts of two representative green approaches: (i) GAES, a green technique that modifies link weights, and (ii) ESOL, a green technique that does not involve link weights adjustments. Experiments over the Abilene, AT&T, GEANT and SURFnet topologies show that when using GAES, routing changes and the proportion of rerouted traffic, both of which affect BGP, are in the order of 108% and 141% greater than ESOL. Therefore, we propose Hot Potato Low UtiliZation (HotPLUZ), a greenapproach that takes hot-potato routing into account. HotPLUZ reroutes traffic from lowly utilized links and aggregate said traffic onto highly utilized links, whilst minimizing any changes to the corresponding egress router of a given destination. In addition, HotPLUZ considers link utilization in order to avoid packet loss and high latencies. Our experimental results indicate an overall saving of up to 21% under low network load.

Mobile Visual Search (MVS) is an emerging area of research given the explosion of smart and powerful mobile devices. Typically, the performance of MVS applications is influenced by the implemented image processing algorithms as well as various distortions that occur when capturing images by the mobile camera. This paper examines the joint effect of two common distortions, namely the illumination changes and image blurring on image matching accuracy for print media when using four state-of-the art local feature algorithms. Results obtained for a database of real camera images captured by two different camera models show that the illumination changes have a more negative effect on matching accuracy compared to image blurring and this influence is camera-dependent. © 2013 IEEE.

Practical use of image content-based mobile visual search (MVS) applied to print media is becoming increasingly popular. Typically, such applications process an image on a printed page taken from a digital camera to generate query information that is used to find relevant media content in a database, such as a related video or webpage. Such applications change the interaction between the users and media content from a click-based-application to a capture-based-application. The primary perception of the end user is the waiting time before receiving the desired content. Firstly, this paper studies the influence of linking two different media types, video and webpage, to the printed picture. Secondly, the results are compared to traditional click-based-applications. Thirdly, the influence of two different progress bars is examined to study the impact on users' Quality of Experience (QoE) associated with waiting time. Results show that the linked content has little influence on the resulting QoE, which is found to have a logarithmic function of waiting time, similar to other applications previously studied. In addition, the results illustrate that different progress bars have an effect on users' perception in term of waiting time and consequently arouse different QoE. © 2013 IEEE.

Mobile Visual Search (MVS) is an emerging area of research given the explosion of smart and computationally powerful mobile devices. Typically, MVS involves the computation of local image features which are then used within a matching process. Such applications pose certain unique challenges due to computation, power and bandwidth constraints of the mobile device. This paper examines the trade-off between two general frameworks for implementing MVS: 1. sending compressed images and performing feature extraction and matching on a server; and 2. performing feature extraction on the mobile device and sending these to a server for matching. A number of local image feature algorithms are studied using various image compression schemes from the point view of matching accuracy and processing time. Results show that the matching accuracy of sending compressed images is comparable to sending compact image features when using a high quality image coder, in this case JPEG2000 and HDPhoto. © 2012 IEEE.

This paper investigates the energy efficiency of RFID anti-collision protocols and their suitability for use in RFID-enhanced wireless sensor networks (WSNs). We present a detailed analytical methodology and an in-depth qualitative and Aloha anti-collision protocols and their variants. We find that Slotted Aloha variants that employ muting with early-end are the most energy efficient, but are computationally expensive. Overall, for all Aloha variants we investigated, if the offered load is very high, tag responses cause a bottleneck at the reader. Thereby, resulting in no tags being identified and incur significant identification delays - thus severely impacting a sensor node's battery life.

This paper studies the energy consumption of frame slotted Aloha (FSA) based anti-collision protocols. Specifically, we investigate twelve FSA variants using a detailed qualitative and quantitative methodology to evaluate efficiency with varying tag population. Our results show that the variant that adjusts its frame size in accordance with tag population and incorporates the muting and early-end feature has the lowest energy consumption, hence most suited for RFID-enhanced WSNs.

Dynamic Framed Slotted Aloha (DFSA) based tag reading protocols rely on a tag estimation function to calculate the best frame size to use for a given tag set. An inaccurate estimate results in high identification delays and unnecessary energy wastage. This is particularly serious when DFSA based tag reading protocols are used in RFID-enhanced wireless sensor networks (WSNs), where nodes are battery constrained. To this end, this paper presents qualitative and quantitative analysis of five tag estimation functions using Monte Carlo simulations. We iteratively estimate a given set of tags and evaluate the mean error, variability, skew and Kurtosis of each function’s error distribution. Lastly, we compare and identify the most efficient tag estimation function that is suitable for RFID-enhanced WSNs.

Previously a new matrix called the Rotation spreading matrix was proposed for Block Spread OFDM which had advantages over other well known spreading matrices such as the Hadamard in frequency selective channels such the UWB channels. Then a new paper discussed a method to expand this Rotation spreading matrix into higher order allowing the system BSOFDM to achieve higher order matrices which still showed excellent orthogonal properties which can be used in these frequency selective channels. This work is continued on the Rotation spreading matrix and this paper presents another method to expand the Rotation spreading matrix based on Complete Complementary Sets of Sequences which the authors have shown to have excellent properties in higher order matrices. This paper discusses the new method and presents a comparison between the two mentioned methods.