Free space optics (FSO) communication links are impaired by the fading due to turbulence and misalignment. In this paper, we theoretically analyze the pointing error effects on performance of FSO systems using multi-pulse pulse position modulation (MP-PPM) over the Gamma-Gamma turbulence channel. We consider the moderate and strong atmospheric turbulence regimes with the combined effect of the pointing errors (PEs), and study the link average symbol error rate (ASER) and the outage probability. The numerical results are presented to show the impact of the pointing error on the ASER and the system outage. However, to overcome the channel degradation resulting from the turbulence effects and the PE, the single-input multiple-output (SIMO) system with maximal ratio combining (MRC) diversity is used. Exact close-form expressions for ASER and outage probability are derived and verified by using Monte Carlo simulation. We concluded that the optimal values of the system parameters are, the transmitted power is 20 dBm, jitter variance should be less than 0.5 m and laser beam width equals 0.2 cm. These values are significantly reduced the impacts of misalignment and fading whereas outage probability is less than at maximum jitter variance.

The 5G vision is not restricted solely to the wireless domain and its challenging requirements cannot be fulfilled without the efficient integration of cutting-edge technologies in all portions of the telecommunications infrastructure. The promoted architectures for next generation telecommunications systems involve high capacity network domains, which operate flexibly and seamlessly to offer full quality of experience to all types of subscribers. The proliferation of highly demanding multimedia services and the features of modern communication devices necessitate the development of end-to-end schemes which can efficiently distribute large amount of network resources anywhere and whenever needed. This study introduces a new resource allocation scheme for cutting-edge fibre-wireless networks is introduced that can be applied in the fronthaul portion of 5G-enabled architectures. The adopted technologies are the forthcoming 25G-Ethernet Passive Optical Network (EPON) for the optical domain and the 5G-ready long-term evolution -Advanced Pro for the wireless domain. The proposed scheme performs allocation decisions based on the outcome of an adjustable multi-stage optimisation problem. The optimisation factors are directly related to the major considerations in bandwidth distribution, namely priority-based traffic differentiation, power awareness, and fairness provision. The conducted evaluations prove that this approach is able to ensure high efficiency in network operations.

This study presents eM-SON, an adaptive framework for providing efficient multimedia services over self-organising, hybrid Wi-Fi networks. In this reconfigurable architecture, multimedia services can be received via a Wi-Fi router, Wi-Fi hotspot, or Wi-Fi direct (i.e. device-to-device) mode. Experimental studies are performed to obtain user equipment (UE) battery discharge and QoS/QoE parameters for adaptive multimedia service (transmission, reception, and playback) over each of these Wi-Fi configurations. These experiments are conducted on different types of UEs (smartphone, tablets) with multimedia content encoded at several quality and scalability levels, and transmitted on each of the Wi-Fi configurations over varying load conditions and dynamic channel allocation. A centrally controlled framework is developed to optimise the multimedia content (adaptive transcoding) and network resource allocation (dynamic Wi-Fi configurations and channel allocation) based on the dynamic UE and network resource constraints. The proposed solution combinedly improves the QoS (throughput, number of retransmissions, and delay), QoE, and energy-efficiency performance of the UEs, while effectively serving an increased number of users in the system.

Internet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75 MHz spectrum in the 5.9 GHz band to support vehicular communication. The authors studied the application of cognitive radio (CR) technology to IoVs to increase the spectra opportunities for vehicular communication, especially when the allocated 75 MHz is not enough due to high demands because of the increasing number of connected vehicles as already foreseen in the near era of IoVs. They proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum on the highways. They also developed a novel cooperative three-state spectrum sensing and allocation model which makes CR vehicular secondary units aware of additional spectrum resources opportunities on their current and future positions, and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing, and provide vehicles with additional spectrum opportunities.

In an unreliable cluster-based, broadcast vehicular network setting, the authors investigate the transmission reliability and throughput performance of random network coding (RNC) as a function of the percentage of packet generation rate and transmit power-to-noise ratio. In this study, a novel scheme called reliable and efficient cooperative cross-layer medium access control (RECMAC) is proposed. The proposed scheme consists of a source vehicle broadcasting packets to a set of receivers (i.e. one-to-many) over independent broadcast erasure channels. The source vehicle performs RNC on N packets and broadcasts the encoded message to a set of receivers. In each hop, several vehicles form a cluster and cooperatively transmit the encoded or re-encoded packet. The combination of the RNC, cluster based, and cooperative communications enables RECMAC to optimally minimise data redundancy, which means less overhead, and improve reliability as opposed to coding-based solutions. Theoretical analyses and simulation results show that under the same conditions RECMAC scheme can achieve improved performance in terms of transmission reliability and throughput.

Vehicle-to-anything (V2X) is a promising communication technology, which expected to revolutionise the ground transportation system by improving traffic safety and efficiency for people on roads. The future deployment of V2X requires interworking between different access technologies, i.e. dedicated short-range communications (DSRCs) and cellular networks. However, to achieve an efficient V2X interworking, the authors need to resolve the multi-hop issue, mainly originating from the V2X hybrid architecture. To resolve this issue and consequently to enhance the interconnected system, characterising the output process of IEEE 802.11p-based DSRC medium access control protocol is of a fundamental importance. This study proposes regenerative model to provide a complete description of IEEE 802.11p output process. The accuracy of the model is verified through extensive simulations. As a case study, the proposed model is compared with Poisson model in the performance evaluation of V2X interworking. Numerical and simulation results verify the ability of the regenerative model to capture the deviations of the actual output process of IEEE 802.11p under different traffic intensities as compared with the Poisson model.

The authors consider the pilot-assisted power allocation of the wireless backhaul link presented in some recent literatures in the vehicular heterogeneous small cell networks under the imperfect measurement. To implement aforementioned algorithm, it is essential to measure the received power on the backhaul link. However, due to the dynamic feature of the wireless backhaul in the vehicular small cells, the perfect measurement is approximately impossible in practice. Moreover, in spite of the signal-to-interference plus noise ratio (SINR) balancing property, the pilot-assisted algorithm is significantly sensitive to the measurement error. Accordingly, in this study at first the authors formulate the measurement error of the uplink received power in the macro base stations and then propose a preprocessing-based algorithm to reduce the effects of the imperfect measurement on the SINR balancing of the pilot-based algorithm. Specifically, in two-layer small cell network considered in this work, the authors derive a modification coefficient proportional to the maximum percentage of the measurement error in a layer by solving an optimisation problem and utilise it in another layer before transmission. Simulation results demonstrate the effectiveness of the proposed scheme in comparison with the existing algorithm in terms of the SINR balancing especially in high measurement error.

Nowadays, it is important to evaluate the use of IPv6 wireless networks. Indeed, the implementation of IPv6 mechanisms and features must be examined. IPv6 requires many objects exchanges like IPv6 routing information which could be communicated by different messages multicast. These messages are too frequent; they could impact energy consumption related to battery-powered objects. Therefore, the energy consumption is nowadays, one of the most important issues in small object networks with IPv6. Thus, the aim of this study is to suggest a new algorithm called minimising energy consumption under connectivity constraint. The performance evaluation is based on several parameters that would help in maintaining connectivity between the mobile objects and their default router. The results showed that energy consumption could be minimised by optimising IPv6 multicast messages. Overall, this method results a significant effectiveness and might be used as a future wireless networks improvements.

The authors propose two multirate teletraffic loss models in a two-link system that accommodates Poisson arriving calls from different service-classes with different bandwidth-per-call requirements. Each link has two thresholds which refer to the number of in-service calls in the link. The lowest threshold named support threshold, defines up to which point the link can support calls offloaded from the other link. The highest threshold, named offloading threshold, defines the point where the link starts offloading calls to the other link. Two different bandwidth sharing policies are considered: (i) the complete sharing policy, in which a call can be accepted in a link if there exist enough available bandwidth units and (ii) the bandwidth reservation policy, in which an integer number of bandwidth units is reserved to benefit calls of high bandwidth requirements. The two models do not have a product form solution for the steady state probabilities. However, they propose approximate formulas for the calculation of call blocking probabilities. The accuracy of the formulas is verified through simulation and found to be quite satisfactory.

The authors consider a link accommodating batched Poisson arriving calls of different service classes. A batch of generally distributed number of calls arrive to the link at exponentially distributed time-points. Each call is treated separately from the rest, and its acceptance is decided, according to the available link bandwidth (partial batch blocking discipline). For congestion control, if the number of in-service calls of a service class exceeds a threshold (dedicated to the service class), a new call of the same service class is accepted with a probability, dependent on the system state (probabilistic threshold policy). The link is analysed as a multirate loss system, via a reversible Markov chain. The latter leads to a product form solution (PFS) for the steady-state distribution. Based on the PFS, the authors propose models for the accurate determination of time and call congestion probabilities and link utilisation. Comparison against other existing models under the complete sharing or the bandwidth reservation policy reveals the necessity and consistency of the proposed models.

Data centre Ethernet (DCE) is a budding research area that has received considerable attention from the ICT sector. The traditional DCEs are considered unreliable despite being widely used in modern day data centres. In Ethernet intermediate layer 2 switching devices, the outgoing traffic is faster than the incoming traffic and therefore results in packet drops. Ethernet reliability is provided by the upper layer protocols, which is outlaw to the initial concept of the network. As such, various congestion notification (CN) techniques for hop-by-hop-based flow control have been proposed for layer 2 devices to address the issue of silent packet drops. However, a simulation-based evaluation of IEEE standards that solely focus on CN techniques remains lacking. This study investigates CN techniques for layer 2 devices that employ a hop-by-hop-based flow control. It also highlights the challenges confronting CN techniques in determining the optimal buffer threshold. In addition, FCoE protocol and its relation to CN are emphasized. A simulation-based evaluation of IEEE standards (IEEE 802.3x and IEEE 802.1Qbb) is performed on a hop-by-hop-based flow control with the traditional IEEE 802.3 Ethernet under different traffic loads. The parameters, such as throughput, end-to-end delay, and buffer space utilization, are evaluated through a simulation-based comparison.

The Internet of Things (IoT) is a ubiquitous network that interconnects and integrates the devices and cyberspace to enable the smart objects. It lays a platform to collect, process, and to analyse the data for monitoring and controlling the cyber–physical world by using IoT sensor devices. These sensor devices can be wired or wireless that connects to IoT. The wireless devices are battery-operated devices, unlike wired devices. The energy reduction is critical for battery-operated devices. The smart devices need an intelligent transmission that increases the life of the devices. There are difficulties in sensor management with regard to energy reduction by applying the energy-efficient communication energy saved over IoT devices communication. Finally, the low energy aware communication process can enhance device life time in IoT. Least energy aware communication technique is a promising paradigm for IoT is reduced 30% communication overhead.

Wireless communication is preferred in numerous sensing applications due to its convenience, cost-effectiveness, and flexibility. Modern sensors are versatile to sense the environmental factors and send them wirelessly. The information collection centres prefer to collect confined clustered information from a group of sensors rather than collecting them from individual sensors. Good connectivity, speedy communication, and effective data gathering can be ensured in the network when a good clustering algorithm is utilized. In this paper, a simple and effective clustering algorithm called energy efficient structured clustering algorithm (EESCA) is proposed for the environmental monitoring fields. Cluster heads (CHs) are elected based on average communication distance and lingering energy. Further, a new parameter called cluster head to normal ratio (CTNR) is introduced to rotate the cluster head role among the nodes. The performance evaluation is carried out in terms of first node die (FND), simulation time, scalability, load balancing, and a new parameter called complete useful data percentage (CUDP). Simulations are conducted for three different network scenarios. Results are compared with the renowned existing algorithms low energy adaptive clustering hierarchy (LEACH) and scalable energy efficient clustering hierarchy (SEECH) and it is proved that the proposed technique is beneficial for WSNs.

Network traffic classification has many applications including network management and security monitoring. Deep-packet-inspection is a commonly used method for identifying applications. However, the methods found in the literature only use these keywords or bytes in payload disregarding their position. The authors propose RDClass a content-based traffic classifier for accurately classifying network flows. RDClass uses a set of keywords extracted from the payload and the relative distance between keywords to identify applications. The idea of using the relative distance between keywords is motivated by the fact that for many applications the set of keywords appear within specific portions of payload. These sets of keywords and their relative distances are encoded in the form of a state transition machine. The authors design a new state transition machine called relative distance constrained counting automata (RDCCA) which can check both ordering of keywords and their relative distance within the payload to classify flows. RDClass can automatically generate a set of keywords and find their relative ordering to generate RDCCA when presented with unknown application payloads. The authors experiment with a range of applications and show that RDClass has better classification performance than previous methods which use only ordering of keywords.