A real-time and calibration-free lane departure warning system algorithm is proposed. The pre-processing stage of the lane departure algorithm is carried out using Gaussian pyramid to smooth the image and reduce its dimensions, which decreases the unnecessary details in the image. A lane detection stage is then developed based on the Edge Drawing Lines (EDLines) algorithm that is a real-time line segment detector, which has false detection control. The reference-counting technique is used to track the lane boundaries and predict the missing ones. Experimental results show that the proposed algorithm has accuracy of 99.25% and average processing time of 80 fps (frame per second). The proposed algorithm is efficient to be used in the self-driving systems in the Original Equipment Manufacturers (OEMs) cars.

Cognitive radio vehicular ad‐hoc networks (CR‐VANETs) allow the vehicles to exploit the unused channels in their vicinity to fulfil the bandwidth demands of infotainment applications. Existing CR‐VANET approaches aim at incorporating spectrum sensing and reporting to the widely used IEEE 802.11p standard developed for VANETs. However, all such approaches were developed for CR‐VANETs with low vehicular traffic densities. Applying such approaches at high traffic densities results in long sensing periods, long contention periods with frequent collisions and unnecessary transmissions of sensing information, and variable delays that depend on the vehicular density. In this study, the authors present the single‐channel slotted contention ( ) protocol designed for highways with highly dense vehicular traffic. The protocol counters the deficiencies of typical CR‐VANETs in high‐density networks using three components: random single‐channel sensing, slotted contention, and implicit‐OR aggregation. The proposed protocol can be easily incorporated with the IEEE 802.11p. Analytic and simulation results show that the proposed protocol increases the CR‐VANET throughput and the overall spectrum utilisation while significantly decreasing the radio environment map time in high densities when compared to the conventional IEEE 802.11p protocols.

Scalability and the highly dynamic topology of Vehicular Ad Hoc Networks (VANETs) are the biggest challenges that slow the roll-out of such a promising technology. Adopting an effective VANET clustering algorithm can tackle these issues in addition to benefiting routing, security and media access management. In this paper, we propose a general-purpose resilient double-head clustering (DHC) algorithm for VANET. Our proposed approach is a mobility-based clustering algorithm that exploits the most relevant mobility metrics such as vehicle speed, position, and direction, in addition to other metrics related to the communication link quality such as the link expiration time (LET) and the signal-to-noise ratio (SNR). The proposed algorithm has enhanced performance and stability features, especially during the cluster maintenance phase, through a set of procedures developed to achieve these objectives. An extensive evaluation methodology is followed to validate DHC and compare its performance with another algorithm using different existing and newly proposed evaluation metrics. These metrics are analyzed under various mobility scenarios, vehicle densities, and radio channel models such as log-normal shadowing and two-ray ground loss with and without Nakagami-m fading model. The proposed algorithm DHC has proven its ability to be more stable and efficient under different simulation scenarios.

In this paper, we present the overhead-free (OHF) spectrum sensing and reporting protocol which counters the deficiencies in typical cognitive radio vehicular ad-hoc networks (CR-VANETs) in high density networks. The OHF approach allows each vehicle to randomly sense only one channel to reduce the amount of sensing performed in existing approaches in which each vehicle senses all or a subset of the channels. More importantly, the OHF protocol significantly reduces the time needed to collect the sensing information, and hence, the effort of producing the radio environment map (REM), by having only one vehicle out of those inferred the occupancy of the channel by its primary owner reporting such an information. When OR fusion is applied, it is sufficient to have a single vehicle with a positive decision to report such information. Such a simplified and implicit aggregation is implemented via slotted contention that can be easily incorporated with the IEEE 802.11p standard. By properly choosing its contention parameters, the proposed overhead-free protocol achieves constant and low REM time, and higher CR-VANET throughput with lower outages to the primary networks compared to IEEE 802.11p based CR-VANETs.

Vehicle-to-Everything (V2X) technology plays a critical role in maintaining road safety, avoiding accidents and controlling traffic flow. As self driving cars are expected to take over the roads, this paper discusses the intermediate phase in which manned and unmanned cars coexist. A heterogeneous network architecture that simultaneously serves manned and unmanned cars' different requirements in a suburban area is proposed and simulated using Riverbed Modeler. The feasibility of this architecture is examined in three different scenarios: Normal operation, congestion in both directions and Road Side Units (RSU) failure. In normal operation mode, traffic data is sent through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure/Infrastructure-to-Vehicle (V2I/I2V or to RSU) using IEEE 802.11p and infotainment information is communicated as V2I/I2V using Long Term Evolution (LTE). A special case is highlighted and tested, in which congestion is in both directions. In such situation, data needs to be relayed to the nearest RSU using multi-hop communication. A fault-tolerant model is also proposed and analyzed in case of failure of RSU. The performance metrics are end-to-end delay, LTE response time, handover delay and packet loss ratio. The architecture proves its suitability by satisfying traffic control real time application requirements.

Even though face recognition is one of the most studied pattern recognition problems, most existing solutions still lack efficiency and high speed. Here, the authors present a new framework for face recognition which is efficient, fast, and robust against variations of illumination, expression, and pose. For feature extraction, the authors propose extracting Gabor features in order to be resilient to variations in illumination, facial expressions, and pose. In contrast to the related literature, the authors then apply supervised locality‐preserving projections (SLPP) with heat kernel weights. The authors’ feature extraction approach achieves a higher recognition rate compared to both traditional unsupervised LPP and SLPP with constant weights. For classification, the authors use the recently proposed sparse representation‐based classification (SRC). However, instead of performing SRC using the computationally expensive minimisation, the authors propose performing SRC using fast matching pursuit, which considerably improves the classification performance. The authors’ proposed framework achieves ∼99% recognition rate using four benchmark face databases, significantly faster than related frameworks.

Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Various greedy recovery algorithms have been proposed to achieve a lower computational complexity compared to the optimal ℓ1 minimization, while maintaining a good reconstruction accuracy. We propose a new greedy recovery algorithm for compressed sensing, called the Adaptive Reduced-set Matching Pursuit (ARMP). Our algorithm achieves higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to ℓ1 minimization in terms of the normalized time-error product, a metric that we introduced to measure the trade-off between the reconstruction time and error.

RPL is the routing protocol for low-power and lossy networks and has recently been considered as the standard routing protocol for IPv6 based wireless sensor networks using the IEEE 802.15.4 protocol. RPL is oriented towards supporting multi-point to point communications in which multiple points typically communicate with one sink node that considered the root of the RPL tree. RPL aims to minimize the cost to reach the sink node from any node in the network using an objective function. However, RPL lacks a load balancing mechanism to maximize the lifetime of the battery-powered wireless sensor networks by preventing overloaded nodes from being drained quickly. In this paper, we propose the Minimum Degree RPL (MD-RPL) which builds a minimum degree spanning tree to enable load balancing in RPL. MD-RPL modifies the original tree formed by RPL to decrease its degree. Extensive simulations were carried out to evaluate the proposed scheme using the COOJA simulator. Our MD-RPL results in up to 15.6% improvement in the maximum power consumed which implies an improvement in the lifetime of the network.

In this study, the authors tackle the dynamic spectrum access (DSA) problem for primary operators (POs) for long‐term evolution‐advanced (LTE‐A) systems. They propose an auction‐based DSA framework that exploits the capabilities of carrier aggregation to efficiently utilise the under‐utilised spectrum which varies with time and space. Their proposed dynamic access framework defines the LTE‐A resource allocation problem as a bounded knapsack auction (BKA). They solve such a BKA by converting it to a 0–1 knapsack problem that is solved using dynamic programming. Unlike prior work, they are the first to introduce an accurate model for estimating the bandwidth required to satisfy the traffic demands of an LTE‐A operator’s subscribers for a projected demand model. Using extensive simulations of such a realistic network model, the results of their framework show more than 20% reduction in the required spectrum and up to 80% reduction in the average under‐utilised spectrum compared with traditional static spectrum access widely adopted by spectrum regulation authorities. The reduction in the average under‐utilised spectrum becomes 93.3% with 250% higher revenue compared with existing dynamic allocation strategies that are directly formulated as 0–1 knapsack auctions.

The Cognitive Radio (CR) technology is envisioned as a primary solution for the spectrum scarcity problem in wireless communication networks. Cognitive radio networking allows the exploitation of the unutilized spectrum bands in an opportunistic manner, and thereby, improves the efficiency of the spectrum utilization. Cognitive radio Networks (CRNs) provide wireless connectivity via heterogeneous wireless architectures and dynamic spectrum access techniques. In this paper, we present an overview of the state-of-the-art of the IEEE standardization efforts for CR and CRNs.

We propose and evaluate the performance of an integrated quality-of-service (QoS) aware radio resource management (RRM) framework for LTE uplink access. Unlike related work, our integrated RRM framework jointly takes into account 1) the class of service requirements of the different connections, 2) the constraints on the contiguity in resource block allocation due to the single-carrier frequency division multiple access (SC-FDMA) mechanism adopted for the LTE uplink, and 3) the selection of the appropriate modulation and coding scheme with full integration with the LTE uplink closed-loop fractional power control for proper power allocation. The proposed RRM framework uses closed loop power control and interference limit per cell to provide an autonomous inter-cell interference coordination scheme that is applied locally without the need of exchanging interference related information between neighboring cells. Simulation results show the ability of the proposed framework to meet the QoS of a highly loaded system with four classes of service. The interference-aware scheme also manages to limit interference and provides fair resource sharing between cell-edge and cell-center users.

Opportunistic Spectrum Access (OSA) in distributed cognitive radio networks (CRNs) has been well studied in the literature from a theoretical perspective. However, such theoretically-optimized distributed OSA approaches are challenged by several practical implementation issues. In this paper, we design a custom cross-layer framework that enables the: (i) clean-slate implementation of a wide variety of OSA mechanisms; (ii) experimental evaluation of the individual practical OSA components of the Rate-Adaptive Probabilistic (RAP) framework; and (iii) detailed comparison of the performance of such a practical OSA approach against theoretical OSA approaches developed for fully-capable CRNs. Our evaluation reveals the multi-fold goodput improvement and remarkable fairness characteristics of the practical RAP OSA approach compared to the OSA approaches that overlook the OSA and CR practical limitations. However, the superior performance of practical OSA comes at the expense of more outages to the primary licensed networks but within the permissible bounds. Another key finding is that the wide family of existing theoretically-optimized OSA protocols can benefit from the gains available to the individual components of the practical RAP approach, namely, the random spectrum sensing and the probabilistic non-greedy access.

Opportunistic Spectrum Access (OSA) is foreseen as the future of wireless communications. However, today's cognitive radio technologies lag far behind the OSA goals and do not allow for the applicability of exiting theoretical distributed OSA techniques. In this paper, we experimentally demonstrate the ability of realizing OSA despite the practical limitations of existing transceiver technologies. We use the general purpose Wireless open-Access Research Platform (WARP) to instrument the implementation of fundamental OSA functionalities. Then we implement a suite of OSA schemes using this implementation framework. Our experiments show that suboptimal but practical OSA approaches such as random spectrum sensing and non-greedy access achieve superior performance given cognitive radios with limited capabilities compared to OSA approaches that are optimized for fully-capable cognitive radio networks (e.g., with wide-band sensing capability and adopt winner-takes-all access relying on network-wide coordination mechanisms).

Existing distributed opportunistic spectrum management schemes do not consider the inability of today's cognitive transceivers to measure interference at primary receivers. Consequently, optimizing the constrained cognitive radio network performance based only on local interference measurements at cognitive senders does not lead to truly optimal performance due to hidden and exposed primary senders. In this paper, we present the rate-adaptive probabilistic medium access protocol (RAP-MAC) for opportunistic spectrum management. In contrast to existing techniques, our approach addresses the unavoidable inaccuracy in spectrum sensing via a probabilistic spectrum access strategy. Furthermore, RAP-MAC allows multiple cognitive flows to fairly share the available bandwidth without explicit coordination by adopting a non-greedy transmission approach that prevents a single cognitive transmitter from monopolizing an available spectral opportunity. We analytically formulate the cognitive user performance optimization problem as a mixed integer non-linear programming to derive the optimal parameter values. Packet-level simulations show that RAP-MAC achieves between 65% to 119.5% higher goodput with significantly better fairness characteristics compared to existing greedy approaches that overlook the spectrum sensing limitations.

In this paper, we demonstrate that multiple concurrent asynchronous and uncoordinated Single-Input Multiple-Output (SIMO) transmissions can successfully take place even though the respective receivers do not explicitly null out interfering signals. Thus motivated, we propose simple modifications to the widely deployed IEEE 802.11 MAC to enable multiple non-spatially-isolated SIMO sender-receiver pairs to share the medium. Namely, we propose to increase the physical carrier sensing threshold, disable virtual carrier sensing, and enable message in message packet detection. We use experiments to show that while increasing the peak transmission rate, spatial multiplexing schemes such as those employed by the IEEE 802.11n are highly non-robust to asynchronous and uncoordinated interferers. In contrast, we show that the proposed multi-flow SIMO MAC scheme alleviates the severe unfairness resulting from uncoordinated transmissions in 802.11 multi-hop networks.

IEEE 802.11-based mesh networks can yield a throughput distribution among nodes that is spatially biased, with traffic originating from nodes that directly communicate with the gateway obtaining higher throughput than all other upstream traffic. In particular, if single-hop nodes fully utilize the gateway's resources, all other nodes communicating with the same gateway will attain very little (if any) throughput. In this paper, we show that it is sufficient to rate limit the single-hop nodes in order to give transmission opportunities to all other nodes. Based on this observation, we develop a new rate limiting scheme for 802.11 mesh networks, which counters the spatial bias effect and does not require, in principle, any control overhead. Our rate control mechanism is based on three key techniques. First, we exploit the system's inherent priority nature and control the throughput of the spatially disadvantaged nodes by only controlling the transmission rate of the spatially advantaged nodes. Namely, the single-hop nodes collectively behave as a proxy controller for multi-hop nodes in order to achieve the desired bandwidth distribution. Second, we devise a rate limiting scheme that enforces a utilization threshold for advantaged single-hop traffic and guarantees a small portion of the gateway resources for the disadvantaged multi-hop traffic. We infer demand for multi-hop flow bandwidth whenever gateway resource usage exceeds this threshold, and subsequently reduce the rates of the spatially advantaged single-hop nodes. Third, since the more bandwidth the spatially disadvantaged nodes attain, the easier they can \emph{signal} their demands, we allow the bandwidth unavailable for the advantaged nodes to be elastic, i.e., the more the disadvantaged flows use the gateway resources, the higher the utilization threshold is. We develop an analytical model to study a system characterized by such priority, dynamic utilization thresholds, and control by proxy. Moreover, we use simulations to evaluate the proposed elastic rate limiting technique.

Previous work has shown that CSMA-based protocols such as the IEEE 802.11 can yield flow starvation in multi-hop wireless networks. While prior protocol designs alleviated such starvation by utilizing MIMO, they require global synchronization to temporally align transmissions and require per-packet distribution of the complete channel state information. In this paper, we experimentally show that MIMO networks based on pre-802.11 n medium access, the state-of-the-art for asynchronous MIMO CSMA, worsen starvation as compared to SISO networks. Consequently, we design an asynchronous MIMO MAC protocol that counters starvation. We show that randomized and non-greedy antenna allocation coupled with local residual capacity estimation results in previously-starving nodes capturing a fair share of system resources while simultaneously exploiting throughput gains available to multi-antenna systems.

OFDMA is an attractive multiple access technique for packet-based mobile broadband wireless access for beyond 3G and 4G systems. Radio resource allocation in OFDMA can exploit multiuser diversity to increase system capacity by implementing opportunistic scheduling techniques. This paper presents a new opportunistic scheduling scheme for OFDMA-based wireless multimedia networks. We focus the scheduling algorithm on the class of delay-sensitive packets that belong to interactive applications such as telephony and video streaming. We divide the scheduling decision into two sub-problems: the OFDMA subcarrier allocation and subsequently the subcarrier assignment. Both the sub-carrier allocation and assignment algorithms exploit multiuser diversity and are designed to provide fairness with respect to the realizable throughput per user, packet dropping ratios and packet delay distributions. We investigate various performance aspects of the proposed scheduling algorithm using actual MPEG-4 traffic traces under different system loading and requested deadline values. The results show the superiority of the proposed scheduling scheme and its excellent performance with respect to throughput, packet dropping, and delay distributions

Providing delay guarantees to time-sensitive traffic in wireless multimedia networks is a challenging issue. This is due to the time-varying link capacities and the variety of real-time applications expected to be handled by such networks. We propose and evaluate the performance of two channel-aware scheduling schemes that are capable of providing such delay guarantees in wireless networks. In the first proposed scheme, the Channel-Dependent Earliest-Due-Date (CD-EDD) discipline, the expiration time of the head of line packets of users' queues is taken into consideration in conjunction with the current channel states of users in the scheduling decision. This policy attempts to guarantee the targeted delay bounds in addition to exploiting multiuser diversity to make best utilization of the variable capacity of the channel. In the second scheme we attempt to ensure that the number of packets dropped due to deadline violation is fairly disturbed among users. This provides fairness in the quality of service (QoS) delivered to different users. A unique feature of our work is explicit provisioning of statistical QoS as well as ensuring fairness in data rates, delay bound, and delay bound violation. We provide extensive simulation results to show the different performance aspects of the proposed schemes.