Showing papers in "Eurasip Journal on Wireless Communications and Networking in 2014"

LTE and IEEE 802.11p for vehicular networking: a performance evaluation

Abstract: Various wireless communication systems exist, which enable a wide range of applications and use cases in the vehicular environment. These applications can be grouped into three types, namely, road safety, traffic efficiency, and infotainment, each with its own set of functional and performance requirements. In pursuance of assisting drivers to travel safely and comfortably, several of these requirements have to be met simultaneously. While the coexistence of multiple radio access technologies brings immense opportunities towards meeting most of the vehicular networking application requirements, it is equally important and challenging to identify the strength and weaknesses of each technology and understand which technology is more suitable for the given networking scenario. In this paper, we evaluate two of the most viable communication standards, Institute of Electrical and Electronics Engineers (IEEE) 802.11p and long-term evolution (LTE) by 3rd Generation Partnership Project for vehicular networking. A detailed performance evaluation study of the standards is given for a variety of parameter settings such as beacon transmission frequency, vehicle density, and vehicle average speed. Both standards are compared in terms of delay, reliability, scalability, and mobility support in the context of various application requirements. Furthermore, through extensive simulation-based study, we validated the effectiveness of both standards to handle different application requirements and share insight for further research directions. The results indicate that IEEE 802.11p offers acceptable performance for sparse network topologies with limited mobility support. On the other hand, LTE meets most of the application requirements in terms of reliability, scalability, and mobility support; however, it is challenging to obtain stringent delay requirements in the presence of higher cellular network traffic load.

Distributed resource allocation for MISO downlink systems via the alternating direction method of multipliers

Abstract: We provide distributed algorithms for the radio resource allocation problem in multicell downlink multi-input single-output systems. Specifically, the problems of (1) minimizing total transmit power subject to signal-to-interference-plus-noise ratio (SINR) constraints of each user and (2) SINR balancing subject to total transmit power constraints are considered. We propose a consensus-based distributed algorithm and the fast solution method via alternating the direction method of multipliers. First, we derive a distributed algorithm for minimization of total transmit power. Then, in conjunction with the bracketing method, a distributed algorithm for SINR balancing is derived. Numerical results show that the proposed distributed algorithms achieve the optimal centralized solution.

Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges

Abstract: With growing interest in using cognitive radio (CR) technology in wireless communication systems for vehicles, it is envisioned that future vehicles will be CR-enabled. This paper discusses CR technologies for vehicular networks aimed at improving vehicular communication efficiency. CR for vehicular networks has the potential of becoming a killer CR application in the future due to a huge consumer market for vehicular communications. This paper surveys novel approaches and discusses research challenges related to the use of cognitive radio technology in vehicular ad hoc networks. We review how CR technologies such as dynamic spectrum access, adaptive software-defined radios, and cooperative communications will enhance vehicular communications and, hence, present the potential of transforming vehicle communication in terms of efficiency and safety. Our work is different from existing works in that we provide recent advances and open research directions on applying cognitive radio in vehicular ad hoc networks (CR-VANETs) focusing on architecture, machine learning, cooperation, reprogrammability, and spectrum management as well as QoE optimization for infotainment applications. A taxonomy of recent advances in cognitive radio for vehicular networks is also provided. In addition, several challenges and requirements have been identified. The research on applying CR in vehicular networks is still in its early stage, and there are not many experimental platforms due to their complex setup and requirements. Some related testbeds and research projects are provided at the end.

Exploiting programmable architectures for WiFi/ZigBee inter-technology cooperation

Abstract: The increasing complexity of wireless standards has shown that protocols cannot be designed once for all possible deployments, especially when unpredictable and mutating interference situations are present due to the coexistence of heterogeneous technologies. As such, flexibility and (re)programmability of wireless devices is crucial in the emerging scenarios of technology proliferation and unpredictable interference conditions. In this paper, we focus on the possibility to improve coexistence performance of WiFi and ZigBee networks by exploiting novel programmable architectures of wireless devices able to support run-time modifications of medium access operations. Differently from software-defined radio (SDR) platforms, in which every function is programmed from scratch, our programmable architectures are based on a clear decoupling between elementary commands (hard-coded into the devices) and programmable protocol logic (injected into the devices) according to which the commands execution is scheduled. Our contribution is two-fold: first, we designed and implemented a cross-technology time division multiple access (TDMA) scheme devised to provide a global synchronization signal and allocate alternating channel intervals to WiFi and ZigBee programmable nodes; second, we used the OMF control framework to define an interference detection and adaptation strategy that in principle could work in independent and autonomous networks. Experimental results prove the benefits of the envisioned solution.

A survey on communication technologies and requirements for internet of electric vehicles

Abstract: Electric vehicles (EVs) are becoming a more attractive transportation option, as they offer great cost savings, decrease foreign oil dependency, and reduce carbon emissions. However, varying temporal and spatial demand patterns of EVs threatens power grid operations and its physical components. Thus, the ability of the power grid to handle the potential extra load has become a major factor in the mainstream success. In order for this integration to occur seamlessly, the power grid and the consumers need to be coordinated in harmony. In this paper, we address the critical challenges introduced by the penetration of EVs, systematically categorize the proposed frameworks for demand management, and the role of information and communication technologies in the solution process. We provide a comprehensive survey on the communication requirements, the standards and the candidate technologies towards the Internet of electric vehicles (IoEV). This survey summarizes the current state of research efforts in electric vehicle demand management and aims to shed light on the continued studies.

A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing

Abstract: Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.

Self-optimization of coverage and capacity based on a fuzzy neural network with cooperative reinforcement learning

Abstract: Self-organization is a key concept in long-term evolution (LTE) systems to reduce capital and operational expenditures (CAPEX and OPEX). Self-optimization of coverage and capacity, which allows the system to periodically and automatically adjust the key radio frequency (RF) parameters through intelligent algorithms, is one of the most important tasks in the context of self-organizing networks (SON). In this paper, we propose self-optimization of antenna tilt and power using a fuzzy neural network optimization based on reinforcement learning (RL-FNN). In our approach, a central control mechanism enables cooperation-based learning by allowing distributed SON entities to share their optimization experience, represented as the parameters of learning method. Specifically, SON entities use cooperative Q-learning and reinforced back-propagation method to acquire and adjust their optimization experience. To evaluate the coverage and capacity performance of RL-FNN, we analyze cell-edge performance and cell-center performance indicators jointly across neighboring cells and specifically consider the difference in load distribution in a given region. The simulation results show that RL-FNN performs significantly better than the best fixed configuration proposed in the literature. Furthermore, this is achieved with significantly lower energy consumption. Finally, since each self-optimization round completes in less than a minute, RL-FNN can meet the need of practical applications of self-optimization in a dynamic environment.

LSGO: Link State aware Geographic Opportunistic routing protocol for VANETs

Abstract: Robust and efficient data delivery in vehicular ad hoc networks (VANETs) with high mobility is a challenging issue due to dynamic topology changes and unstable wireless links. The opportunistic routing protocols can improve the reliability of routing by making full use of the broadcast characteristics and assist in data transmission through additional backup links. In this paper, we propose a Link State aware Geographic Opportunistic routing protocol (LSGO) which exploits a combination of geographic location and the link state information as the routing metric. The LSGO aims to improve the reliability of data transmission in a highly dynamic environment, which selects the forwarders and prioritizes them based on the vehicle’s geographic location and the link’s quality. We compare the performance of LSGO with GpsrJ + which removes the unnecessary stop at a junction and greedy traffic aware routing protocol (GyTAR) using network simulator ns-2. The simulation results show that it opens more nodes to participate in the opportunistic data forwarding and increases a connection’s throughput while using no more network capacity than traditional routing. In the simulation, compared with other two protocols, when the number of vehicles and the average vehicle velocity increase, LSGO’s packet dropping rate is reduced and the network throughput is improved.

A connectivity-aware intersection-based routing in VANETs

Abstract: Vehicular ad hoc networks (VANETs) are going to be an important communication infrastructure in our moving life. The design of routing protocols in VANETs is a significant and necessary issue for supporting VANET-based applications. However, due to high mobility, frequent link disconnection, and uneven distribution of vehicles, it becomes quite challenging to establish a robust route for delivering packets. This paper presents a connectivity-aware intersection-based routing (CAIR) protocol to address these problems by selecting an optimal route with higher probability of connectivity and lower experienced delay; then, geographical forwarding based on position prediction is used to transfer packets between any two intersections along the route. Simulation results show that the proposed protocol outperforms existing routing protocols in terms of data delivery ratio and average transmission delay in typical urban scenarios.

A study on the use of smartphones under realistic settings to estimate road roughness condition

Abstract: Almost every today's smartphone is integrated with many useful sensors. The sensors are originally designed to make the smartphones' user interface and applications more convenient and appealing. These sensors, moreover, are potentially useful for many other applications in different fields. Using smartphone sensors to estimate road roughness condition has a great potential, since many similar sensors are already in use in many sophisticated road roughness profilers. This study explores the use of data, collected by sensors from smartphones under realistic settings, in which the smartphones are placed at more realistic locations and under realistic manner inside a moving vehicle, to evaluate its relationship with the actual road pavement roughness. An experiment has been conducted to collect data from smartphone acceleration and Global Positioning System (GPS) sensors; frequency domain analysis is also carried out. It has been revealed that the data from smartphone accelerometers has a linear relationship with road roughness condition, whereas the strength of the relationship varies at different frequency ranges. The results of this paper also confirm that smartphone sensors have a great potential to be used for estimating the current status of the road pavement condition.

The impact of video-quality-level switching on user quality of experience in dynamic adaptive streaming over HTTP

Abstract: Dynamic adaptive streaming over HTTP (DASH) has become a promising solution for video delivery services over the Internet in the last few years. Currently, several video content providers use the DASH solution to improve the users’ quality of experience (QoE) by automatically switching video quality levels (VQLs) according to the network status. However, the frequency of switching events between different VQLs during a video streaming session may disturb the user’s visual attention and therefore affect the user’s QoE. As one of the first attempts to characterize the impact of VQL switching on the user’s QoE, we carried out a series of subjective tests, which show that there is a correlation between the user QoE and the frequency, type, and temporal location of the switching events. We propose a novel parameter named switching degradation factor (SDF) to capture such correlation. A DASH algorithm with SDF parameter is compared with the same algorithm without SDF. The results demonstrate that the SDF parameter significantly improves the user’s QoE, especially when network conditions vary frequently.

A distributed framework for detecting selfish nodes in MANET using Record- and Trust-Based Detection (RTBD) technique

Abstract: A mobile ad hoc network (MANET) is a self-organized system comprised by multiple mobile wireless nodes. The node misbehavior due to selfish reasons can significantly diminish the performance of MANET. A selfish node attempts to use the resources only for its own purpose and it hesitates to share the resources with their neighbors. So, it is very important to detect the selfish nodes to improve the performance of MANET. Initially, an architectural model of a MANET is constructed and the communication between the mobile is originated. The packet drop can happen in MANET due to the selfish node or network congestion. In this paper, a Record- and Trust-Based Detection (RTBD) technique is proposed to detect the selfish nodes efficiently in MANET. The main reason for using trust in this analysis is to accelerate the detection of misbehaving nodes. This study has been carried out in order to analyze the detection of selfish nodes on essential network functions such as routing and packet dropping. The results show that the proposed selfish node detection method is very efficient, since the detection time of selfish nodes is diminished and the overall overhead is very low. The simulation study demonstrates that the proposed RTBD method enhances the selfish node detection ratio, packet delivery ratio (PDR), and average packet drop ratio.

Real-time urban traffic amount prediction models for dynamic route guidance systems

Abstract: The route guidance system (RGS) has been considered an important technology to mitigate urban traffic congestion. However, existing RGSs provide only route guidance after congestion happens. This reactive strategy imposes a strong limitation on the potential contribution of current RGS to the performance improvement of a traffic network. Thus, a proactive RGS based on congestion prediction is considered essential to improve the effectiveness of RGS. The problem of congestion prediction is translated into traffic amount (i.e. the number of vehicles on the individual roads) prediction, as the latter is a straightforward indicator of the former. We thereby propose two urban traffic prediction models using different modeling approaches. Model-1 is based on the traffic flow propagation in the network, while Model-2 is based on the time-varied spare flow capacity on the concerned road links. These two models are then applied to construct a centralized proactive RGS. Evaluation results show that (1) both of the proposed models reduce the prediction error up to 52% and 30% in the best cases compared to the existing Shift Model, (2) providing proactive route guidance helps reduce average travel time by up to 70% compared to providing reactive one and (3) non-rerouted vehicles could benefit more from route guidance than rerouted vehicles do.

Building programmable wireless networks: an architectural survey

Abstract: In recent times, there is increasing consensus that the traditional Internet architecture needs to be evolved for it to sustain unstinted growth and innovation. A major reason for the perceived architectural ossification is the lack of the ability to program the network as a system. This situation has resulted partly from historical decisions in the original Internet design which emphasized decentralized network operations through colocated data and control planes on each network device. The situation for wireless networks is no different resulting in a lot of complexity and a plethora of largely incompatible wireless technologies. With traditional architectures providing limited support for programmability, there is a broad realization in the wireless community that future programmable wireless networks would require significant architectural innovations. In this paper, we will present an unified overview of the programmability solutions that have been proposed at the device and the network level. In particular, we will discuss software-defined radio (SDR), cognitive radio (CR), programmable MAC processor, and programmable routers as device-level programmability solutions, and software-defined networking (SDN), cognitive wireless networking (CWN), virtualizable wireless networking (VWN) and cloud-based wireless networking (CbWN) as network-level programmability solutions. We provide both a self-contained exposition of these topics as well as a broad survey of the application of these trends in modern wireless networks.

Characterization of path loss and absorption for a wireless radio frequency link between an in-body endoscopy capsule and a receiver outside the body

Abstract: Physical-layer characterization is important for design of in-to-out body communication for wireless body area networks (WBANs). This paper numerically investigates the path loss and absorption of an in-to-out body radio frequency (RF) wireless link between an endoscopy capsule and a receiver outside the body using a 3D electromagnetic solver. A spiral antenna in the endoscopy capsule is tuned to operate in the Medical Implant Communication Service (MICS) band at 402 MHz, accounting for the properties of the human body. The influence of misalignment, rotation of the capsule, and three different human models are investigated. Semi-empirical path loss models for various homogeneous tissues and 3D realistic human body models are provided for manufacturers to evaluate the performance of in-body to out-body WBAN systems. The specific absorption rate (SAR) in homogeneous and heterogeneous body models is characterized and compliance is investigated.

Capacity analysis of LTE-Advanced HetNets with reduced power subframes and range expansion

Abstract: The use of reduced power subframes in LTE Rel. 11 can improve the capacity of heterogeneous networks (HetNets) while also providing interference coordination to the picocell-edge users. However, in order to obtain maximum benefits from the reduced power subframes, setting the key system parameters, such as the amount of power reduction, carries critical importance. Using stochastic geometry, this paper lays down a theoretical foundation for the performance evaluation of HetNets with reduced power subframes and range expansion bias. The analytic expressions for average capacity and 5th percentile throughput are derived as a function of transmit powers, node densities, and interference coordination parameters in a two-tier HetNet scenario and are validated through Monte Carlo simulations. Joint optimization of range expansion bias, power reduction factor, scheduling thresholds, and duty cycle of reduced power subframes is performed to study the trade-offs between aggregate capacity of a cell and fairness among the users. To validate our analysis, we also compare the stochastic geometry-based theoretical results with the real macro base station (MBS) deployment (in the city of London) and the hexagonal grid model. Our analysis shows that with optimum parameter settings, the LTE Rel. 11 with reduced power subframes can provide substantially better performance than the LTE Rel. 10 with almost blank subframes, in terms of both aggregate capacity and fairness.

A traffic flow-oriented routing protocol for VANETs

Abstract: This paper presents a novel position-based routing protocol for vehicular ad hoc networks (VANETs) to enhance traffic safety and traffic organization and facilitate driving through a smart transportation system. The protocol is referred to as the traffic flow-oriented routing (TFOR) protocol for VANETs. It considers a real-time urban scenario with multi-lane and bi-directional roads. It chooses junction optimally considering vehicular traffic flows to accomplish robust routing paths and thereby forwarding the data packets. The new junction selection mechanism and routing between the junctions is based on two-hop neighbor information, which increases packet-delivery ratio and decreases end-to-end delay. We designed, implemented, and compared TFOR against existing routing protocols of VANETs (greedy-perimeter stateless routing (GPSR), geographic source routing (GSR), and enhanced greedy traffic-aware routing (E-GyTAR)). Simulation outcomes in urban scenarios show that TFOR minimizes average end-to-end delay and routing overhead by on average 15.3% and 19.5%, respectively, compared to GPSR. It reduces routing overhead up to 17% compared to GSR. TFOR maximizes packet-delivery ratio on an average of 17.5%, 10.7%, and 7.2% compared to GPSR, GSR, and E-GyTAR, respectively.

Multihop range-free localization with approximate shortest path in anisotropic wireless sensor networks

Abstract: This paper presents a multihop range-free localization algorithm that tolerates network anisotropy with a small number of anchors. A detoured path detection is proposed to detect if the shortest path between nodes is detoured from their direct path by measuring the deviation in the hop count between the direct and shortest paths. A novel distance estimation method is introduced to approximate the shortest path based on the path deviation and to estimate their distance by taking into account the extent of the detour of the approximate shortest path. Compared to other range-free algorithms, the proposed algorithm requires fewer anchors while achieving higher localization accuracy in anisotropic networks. We demonstrated its superiority over existing range-free localization algorithms through extensive computer simulations.

Particle swarm optimization (PSO)-based node and link lifetime prediction algorithm for route recovery in MANET

Abstract: In the conventional mobile ad hoc network (MANET) systems' route rediscovery methods, there exists route failure in all route discovery methods resulting in data loss and communication overheads. Hence, the routing has to be done in accordance with mobility character of the network. In this manuscript, a particle swarm optimization (PSO)-based lifetime prediction algorithm for route recovery in MANET has been proposed. This technique predicts the lifetime of link and node in the available bandwidth based on the parameters like relative mobility of nodes and energy drain rate, etc. Using predictions, the parameters are fuzzified and fuzzy rules have been formed to decide on the node status. This information is made to exchange among all the nodes. Thus, the status of every node is verified before data transmission. Even for a weak node, the performance of a route recovery mechanism is made in such a way that corresponding routes are diverted to the strong nodes. With the aid of the simulated results, the minimization of data loss and communication overhead using PSO prediction has been discussed in detail.

A machine learning framework for TCP round-trip time estimation

Abstract: In this paper, we explore a novel approach to end-to-end round-trip time (RTT) estimation using a machine-learning technique known as the experts framework. In our proposal, each of several ‘experts’ guesses a fixed value. The weighted average of these guesses estimates the RTT, with the weights updated after every RTT measurement based on the difference between the estimated and actual RTT. Through extensive simulations, we show that the proposed machine-learning algorithm adapts very quickly to changes in the RTT. Our results show a considerable reduction in the number of retransmitted packets and an increase in goodput, especially in more heavily congested scenarios. We corroborate our results through ‘live’ experiments using an implementation of the proposed algorithm in the Linux kernel. These experiments confirm the higher RTT estimation accuracy of the machine learning approach which yields over 40% improvement when compared against both standard transmission control protocol (TCP) as well as the well known Eifel RTT estimator. To the best of our knowledge, our work is the first attempt to use on-line learning algorithms to predict network performance and, given the promising results reported here, creates the opportunity of applying on-line learning to estimate other important network variables.

Outage performance of relay-assisted primary and secondary transmissions in cognitive relay networks

Abstract: In this paper, a two-phase protocol for relay-assisted spectrum sharing in cognitive relay network is proposed. The primary system comprises a transmitter-receiver pair (PT-PR) and a decode-and-forward relay (Relay), while the secondary system comprises a transmitter-receiver pair (ST-SR). In the proposed protocol, the Relay assists the transmissions of the primary users as well as the secondary users where there is no direct link between the ST and the SR. Outage probabilities of the primary system and the secondary system are derived and verified through simulations. Compared with the protocol without cooperation, better outage performance of the primary system is achieved. Meanwhile, though the ST causes interference to the PR, the interference is compensated by the cooperation of the Relay. Moreover, the proposed protocol realizes the communications of the secondary users on the condition that the secondary transmission link is not ideal.

A multipath energy-conserving routing protocol for wireless ad hoc networks lifetime improvement

Abstract: Ad hoc networks are wireless mobile networks that can operate without infrastructure and without centralized network management. Traditional techniques of routing are not well adapted. Indeed, their lack of reactivity with respect to the variability of network changes makes them difficult to use. Moreover, conserving energy is a critical concern in the design of routing protocols for ad hoc networks because most mobile nodes operate with limited battery capacity, and the energy depletion of a node affects not only the node itself but also the overall network lifetime. In all proposed single-path routing schemes, a new path-discovery process is required once a path failure is detected, and this process causes delay and wastage of node resources. A multipath routing scheme is an alternative to maximize the network lifetime. In this paper, we propose an energy-efficient multipath routing protocol, called ad hoc on-demand multipath routing with lifetime maximization (AOMR-LM), which preserves the residual energy of nodes and balances the consumed energy to increase the network lifetime. To achieve this goal, we used the residual energy of nodes for calculating the node energy level. The multipath selection mechanism uses this energy level to classify the paths. Two parameters are analyzed: the energy threshold β and the coefficient α. These parameters are required to classify the nodes and to ensure the preservation of node energy. Our protocol improves the performance of mobile ad hoc networks by prolonging the lifetime of the network. This novel protocol has been compared with other protocols: ad hoc on-demand multipath distance vector (AOMDV) and ZD-AOMDV. The protocol performance has been evaluated in terms of network lifetime, energy consumption, and end-to-end delay.

A dynamic clustering algorithm for downlink CoMP systems with multiple antenna UEs

Abstract: Coordinated multi-point (CoMP) schemes have been widely studied in the recent years to tackle inter-cell interference. In practice, latency and throughput constraints on the backhaul allow the organization of only small clusters of base stations (BSs) where joint processing (JP) can be implemented. In this work, we focus on downlink CoMP-JP with multiple antenna user equipments (UEs) where the additional degrees of freedom are used to suppress the residual interference by using an interference rejection combiner (IRC) and allow a multi-stream transmission. The main contribution of this paper is the development of a novel dynamic BS clustering algorithm with corresponding UE scheduling. In particular, we first define a set of candidate BS clusters depending on long-term channel conditions. Then, in each time block, we develop a resource allocation scheme where: (a) for each candidate BS cluster, with corresponding scheduled UEs, a weighted sum rate is estimated and then (b) we select the set of non-overlapping BS clusters that maximizes the downlink system weighted sum rate. Numerical results show that much higher rates are achieved when UEs are equipped with multiple antennas and dynamic BS clustering is used.

Intensity feedback-based beam wandering mitigation in free-space optical communication using neural control technique

Abstract: Over the last two decades, free-space optical communication (FSOC) has become more and more interesting as an adjacent and/or alternative to radio frequency (RF) and optical fiber communications. The optical wave propagation in the FSOC channel is severely affected by atmospheric parameters, and it leads to the degradation of the data transmission quality and reliability. Among the various atmospheric effects, the beam wandering is the main cause of major power loss and cannot be solved without the incorporation of the beam centroid stabilization system. Therefore, designing a suitable opto-electronic assembly with a beam wandering mitigation system becomes significant to improve the performance of the FSOC system. A FSOC experimental setup is developed for the link range of 0.5 km in the college campus. A neural controller is designed for beam wandering mitigation. The neural controller processed the beam pointing (beam location) information obtained from an opto-electronic position detector (OPD) and then generated the necessary control outputs to the fast steering mirror (FSM) to steer the beam in the counter-direction to mitigate the beam wander at the receiver station. New design approach and architecture development for the implementation of the designed neural controller in the field-programmable gate array (FPGA) to mitigate the beam wandering are presented. The investigations on the performance of the neural controller in aiming a laser beam to be at a particular point on the detector plane are tested under dynamic disturbances generated at the transmitter in addition to the atmospheric effects through which the maximum correction capability of the developed neural controller is examined. Evidence of the suitability and the effectiveness of the proposed neural controller is evaluated in terms of beam centroid displacement, power spectral density (PSD), radial displacement on a 2D plane, effective scintillation index (ESI), Q-factor, and bit error rate (BER). The beam wandering range of −0.13 to +0.16 mm, maximum of 55 dB disturbance band attenuation with a frequency range of 0 Hz through 2 kHz, ESI of 0 to 0.15, Q-factor of 6, and BER of 6.45 × 10−9 are achieved due to the incorporation of the developed neural controller in different real-world environmental conditions.

Fuzzy-based congestion control for wireless multimedia sensor networks

Abstract: Congestion is a challenging problem for sensor networks because it causes the waste of communication and reduces energy efficiency Compared to traditional wireless sensor networks, the probability of congestion occurrence in wireless multimedia sensor networks is higher due to the high volume of data arising from multimedia streaming In this article, problems for multimedia transmission over wireless multimedia sensor networks are examined and sensor fuzzy-based image transmission (SUIT); a new progressive image transport protocol is proposed as a solution SUIT provides fuzzy logic-based congestion estimation and an efficient congestion mitigation technique which decreases the image quality on-the-fly to an acceptable level In case of congestion, SUIT drops some packets of the frames in a smart way and thus transmits frames to the sink with lower, but acceptable quality In this way, SUIT improves the continuity of the video streaming We evaluate the performance of SUIT by comparing it with two different competitors The first one is an example transport protocol, namely Fuzzy Logic-Based Congestion Estimation The second one is a buffer occupancy-based congestion control mechanism which is commonly used in previous studies According to the simulation results, SUIT provides better energy consumption, frame delivery, frame loss and frame latency performance than its competitors

Anti-traffic analysis attack for location privacy in WSNs

Abstract: Traditional encryption and authentication methods are not effective in preserving a sink's location privacy from a global adversary that is monitoring the network traffic. In this paper, we first propose a novel anti-traffic analysis (ATA) method to preserve the sink's location privacy. In order to confuse a local or global adversary, each node generates dummy messages, the number of which is dependent on the number of the node's children. Hence, ATA is able to prevent the adversary from acquiring valuable information on the sink's location through the traffic analysis attack. However, a larger number of dummy messages lead to consumption of extra energy. Then, we design our improved ATA (IATA) in such a way that we select some sensors to act as fake sinks, to ensure that sensors around fake sinks generate dummy messages and discard received dummy messages. Since the problem of the optimal fake sinks' placement is nondeterministic polynomial time (NP)-hard, we employ local search heuristics based on network traffic and security entropy. Performance analysis of the ATA scheme can protect the sink's location privacy, and IATA scheme can reduce energy consumption.

YouTube QoE evaluation tool for Android wireless terminals

Abstract: In this paper, we present an Android application which is able to evaluate and analyze the perceived quality of experience (QoE) for YouTube service in wireless terminals. To achieve this goal, the application carries out measurements of objective quality of service (QoS) parameters, which are then mapped onto subjective QoE (in terms of mean opinion score, MOS) by means of a utility function. Our application also informs the user about potential causes that lead to a low MOS as well as provides some hints to improve it. After each YouTube session, the users may optionally qualify the session through an online opinion survey. This information has been used in a pilot experience to correlate the theoretical QoE model with real user feedback. Results from such an experience have shown that the theoretical model (taken from the literature) provides slightly more pessimistic results compared to user feedback. Users seem to be more indulgent with wireless connections, increasing the MOS from the opinion survey in about 20% compared to the theoretical model, which was obtained from wired scenarios.

QoS priority-based dynamic frequency band allocation algorithm for load balancing and interference avoidance in 3GPP LTE HetNet

Abstract: A 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) system uses the concept of two-tier heterogeneous networks (HetNets), where low-power and short-range femtocells are laid under macrocells to fulfill the quality of service (QoS) requirements of users and to boost overall network capacity. However, co-channel interference is one of the major issues that need to be resolved for the successful deployment of HetNets. To overcome this problem, fractional frequency reuse (FFR) schemes have been proposed that can efficiently utilize the available spectrum. Nevertheless, these schemes waste limited frequency resources owing to their static allocation and lack of following QoS requirements, network loading conditions, and service priority of users. In this paper, a QoS-based dynamic FFR (QoS-DFFR) scheme is proposed that efficiently allocates the non-occupied center-zone frequency bands, i.e., bonus bandwidth (BBW), to cell-edge users by considering their QoS requirements. Consequently, the proposed QoS-DFFR scheme can optimize cell-edge user throughput and sector throughput and reduce co-channel interference by dynamically allocating the BBW to the most demanding cell-edge users. The proposed QoS-DFFR scheme improves performance because of its ability to dynamically allocate the limited portion of the frequency bands based on the service priorities of users. The system-level simulation results show that the proposed QoS-DFFR scheme performs remarkably well in a HetNet environment. Compared with the usual FFR schemes, the proposed scheme almost doubles the cell-edge user's throughput and reduces the user's packet loss rate.

A quantitative study of Cooperative Awareness Messages in production VANETs

Abstract: In Vehicular Ad-hoc Networks (VANETs), vehicles gather Cooperative Awareness Messages (CAMs) sent from other vehicles via wireless broadcast. Each received message has to be processed by an in-vehicle system. In series implementations, such an in-vehicle system needs to cope with limited resources whose capacity is not yet defined. Therefore, information about the received CAM rate is a crucial input for the development of series VANET products. CAMs from distant vehicles are less likely to be received than those of nearby vehicles. Designers of applications leveraging CAM information are interested in the frequency of received CAMs originating from vehicles depending on their distance. In this paper, we study future CAM rates depending on various parameters. We set up a road traffic simulation for selected highway scenarios. We estimate the rates of generated CAMs and introduce the notion of relative channel load. We present a new approximative channel model to determine a vehicle’s message reception probability. That model is used to simulate the rates of received CAMs for each vehicle. Moreover, we investigate the origin of received CAMs and times between consecutive CAMs received from the same sender (inter-reception times) depending on distance. Most results depend on the penetration rate of VANET technology that will increase in the near future. We derive approximative formulae and use them to validate our simulation results. They are quite accurate, and so they may also serve for simple forecasts. The results from our analysis show that the rates of generated and received CAMs lead to several challenges for the design of an efficient and robust VANET implementation.

Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series

Abstract: Network traffic modeling significantly affects various considerations in networking, including network resource allocation, quality of service provisioning, network traffic management, congestion control, and bandwidth efficiency. These are very important issues in network protocol design, too. In this paper, a comprehensive comparison of modeling approaches of adaptive neuro fuzzy inference system (ANFIS) and autoregressive integrated moving average (ARIMA) for modeling of wireless network traffic in terms of typical statistical indicator and computational complexity has been attempted. ARIMA has been widely used in this area for past many years. On the other hand, ANFIS is comparatively new, and no network traffic modeling using ANFIS was attempted until recently to the best of our knowledge. At the same time, a detailed comparative performance evaluation of ANFIS with other modeling approaches in traffic modeling could not be found in existing literature. Reportedly, ANFIS provides a good precision in prediction in terms of statistical indicators and also gives effective description of network conditions at different times. However, the computational complexity of ANFIS for traffic modeling is a major concern and deserves a closer inspection. In our case of wireless network traffic, as a final result, we find that ANFIS model performs better than the best ARIMA model in three different scenarios.