Video Streaming over Vehicular Ad Hoc Networks: A Comparative Study and Future Perspectives

Vehicular  Ad Hoc Network  (VANET) is emerged as an important research area that provides ubiquitous short-range connectivity among moving vehicles.  This network enables efficient traffic safety and infotainment applications. One of the promising applications is video transmission in vehicle-to-vehicle or vehicle-to-infrastructure environments.  But, video streaming over vehicular environment is a daunting task due to high movement of vehicles. This paper presents a survey on state-of-arts of video streaming over VANET. Furthermore, taxonomy of vehicular video transmission is highlighted in this paper with special focus on significant applications and their requirements with challenges, video content sharing, multi-source video streaming and video broadcast services. The comparative study of the paper compares the video streaming schemes based on type of error resilient technique, objective of study, summary of their study, the utilized simulator and the type of video sharing.  Lastly, we discussed the open issues and research directions related to video communication over VANET

Machine Learning Algorithms for Detecting and Analyzing Social Bots Using a Novel Dataset

Social media is internet-based technology and an electronic form of communication that facilitates sharing of ideas, documents, and personal information. Twitter is a microblogging platform and is the most effective social service for posting microblogs and likings, commenting, sharing, and communicating with others. The problem we are shedding light on in this paper is the misuse of bots on Twitter. The purpose of bots is to automate specific repetitive tasks instead of human interaction. However, bots are misused to influence people’s minds by spreading rumors and conspiracy related to controversial topics. In this paper, we initiate a new benchmark created on a 1.5M Twitter profile. We train different supervised machine learning on our benchmark to detect bots on Twitter. In addition to increasing benchmark scalability, various autofeature selections are utilized to identify the most influential features and remove the less influential ones. Furthermore, over-under-sampling is applied to reduce the imbalance effect on the benchmark. Finally, our benchmark compared with other stateof-the-art benchmarks and achieved a 6% higher area under the curve than other datasets in the case of generalization, improving the model performance by at least 2% by applying over-/undersampling

Plant Disease Diagnosing Based on Deep Learning Techniques: A Survey and Research Challenges

Agriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community

Advances on Network Protocols and Algorithms for Vehicular Ad Hoc Networks

Vehicular Ad Hoc Network (VANET) is an emerging area of wireless ad hoc networks that facilitates ubiquitous connectivity between smart vehicles through Vehicle-to-Vehicle (V2V) or Vehicle-to-Roadside (V2R) and Roadside-to- Vehicle (R2V) communications. This emerging field of technology aims to improve safety of passengers and traffic flow, reduces pollution to the environment and enables in-vehicle entertainment applications. The safety-related applications could reduce accidents by providing drivers with traffic information such as collision avoidances, traffic flow alarms and road surface conditions. Moreover, the passengers could exploit an available infrastructure in order to connect to the internet for infomobility and entertainment applications.Lloret, J.; Ghafoor, KZ.; Rawat, DB.; Xia, F. (2013). Advances on Network Protocols and Algorithms for Vehicular Ad Hoc Networks. Mobile Networks and Applications. 18(6):749-754. doi:10.1007/s11036-013-0490-7S749754186Lloret J, Canovas A, Catalá A, Garcia M (2013) Group-based protocol and mobility model for VANETs to offer internet access. J Netw Comput Appl 36(3):1027–1038. doi: 10.1016/j.jnca.2012.02.009Khokhar RH, Zia T, Ghafoor KZ, Lloret J, Shiraz M (2013) Realistic and efficient radio propagation model for V2X communications. KSII Trans Internet Inform Syst 7(8):1933–1953. doi: 10.3837/tiis.2013.08.011Ghafoor KZ (2013) Routing protocols in vehicular ad hoc networks: survey and research challenges, Netw Protocol Algorithm 5(4). doi: 10.5296/npa.v5i4.4134Ghafoor KZ, Bakar KA, Lloret J, Ke C-H, Lee KC (2013) Intelligent beaconless geographical routing for urban vehicular environments. Wirel Netw 19(3):345–362. doi: 10.1007/s11276-012-0470-zGhafoor KZ, Bakar KA, Lee K, AL-Hashimi H (2010) A novel delay- and reliability- aware inter-vehicle routing protocol. Netw Protocol Algorithms 2(2):66–88. doi: 10.5296/npa.v2i2.427Dias JAFF, Rodrigues JJPC, Isento JN, Pereira PRBA, Lloret J (2011) Performance assessment of fragmentation mechanisms for vehicular delay-tolerant networks. EURASIP J Wirel Commun Netw 2011(195):1–14. doi: 10.1186/1687-1499-2011-195Zhang D, Yang Z, Raychoudhury V, Chen Z, Lloret J (2013) An energy-efficient routing protocol using movement trend in vehicular Ad-hoc networks. Comput J 58(8):938–946. doi: 10.1093/comjnl/bxt028Ghafoor KZ, Lloret J, Bakar KA, Sadiq AS, Mussa SAB (2013) Beaconing approaches in vehicular Ad Hoc networks: a survey. Wirel Pers Commun. doi: 10.1007/s11277-013-1222-9Sadiq AS, Bakar KA, Ghafoor KZ, Lloret J (2013) An intelligent vertical handover scheme for audio and video streaming in heterogeneous vehicular networks. Mobile Netw Appl. doi: 10.1007/s11036-013-0465-8Khamayseh YM (2013) Network size estimation in VANETs. Netw Protocol Algorithm 5(3):136–152. doi: 10.5296/npa.v5i6.3838Rawat DB, Popescu DC, Yan G, Olariu S (2011) Enhancing VANET performance by joint adaptation of transmission power and contention window size. IEEE Trans Parallel Distrib Syst 22(9):1528–1535Yan G, Rawat DB, Bista BB. Provisioning vehicular ad hoc networks with quality of services. Int J Space-Based Situated Comput 2(2):104–111Rawat DB, Bista BB, Yan G, Weigle MC (2011) Securing vehicular ad-hoc networks against malicious drivers: a probabilistic approach, International Conference on Complex, Intelligent, and Software Intensive Systems Pp. 146–151. June 30, 2011Sun W, Xia F, Ma J, Fu T, Sun Y. An optimal ODAM-based broadcast algorithm for vehicular Ad-Hoc Networks. KSII Trans Internet Inform Syst 6(12): 3257–3274Vinel AV, Dudin AN, Andreev SD, Xia F (2010) Performance modeling methodology of emergency dissemination algorithms for vehicular ad-hoc networks, 6th Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010), Pp. 397–400AL-Hashimi HN, Bakar KA, Ghafoor KZ (2010) Inter-domain proxy mobile IPv6 based vehicular network. Netw Protocol Algorithm 2(4):1–15. doi: 10.5296/npa.v2i4.488Ghafoor KZ, Bakar KA, Mohammed MA, Lloret J (2013) Vehicular cloud computing: trends and challenges, in the book “mobile computing over cloud: technologies, services, and applications”. IGI GlobalYan G, Rawat DB, Bista BB (2012) Towards secure vehicular clouds, Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2012), Pp. 370–375Fernández H, Rubio L, Reig J, Rodrigo-Peñarrocha VM, Valero A (2013) Path loss modeling for vehicular system performance and communication protocols evaluation. Mobile Netw Appl. doi: 10.1007/s11036-013-0463-xAllouche Y, Segal M (2013) A cluster-based beaconing approach in VANETs: near optimal topology via proximity information. Mobile Netw Appl. doi: 10.1007/s11036-013-0468-5Merah AF, Samarah S, Boukerche A, Mammeri A (2013) A sequential patterns data mining approach towards vehicular route prediction in VANETs. Mobile Netw Appl. doi: 10.1007/s11036-013-0459-6Zhang D, Huang H, Zhou J, Xia F, Chen Z (2013) Detecting hot road mobility of vehicular Ad Hoc Networks. Mobile Netw Appl. doi: 10.1007/s11036-013-0467-6El Ajaltouni H, Boukerche A, Mammeri A (2013) A multichannel QoS MAC with dynamic transmit opportunity for. Mobile Netw Appl. doi: 10.1007/s11036-013-0475-6Reñé S, Esparza O, Alins J, Mata-Díaz J, Muñoz JL (2013) VSPLIT: a cross-layer architecture for V2I TCP services over. 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Mobile Netw Appl. doi: 10.1007/s11036-013-0461-zLin C, Wu G, Xia F, Yao L (2013) Enhance the attacking efficiency of the node compromise attack in vehicular Ad-hoc network using connected dominating set. Mobile Netw Appl. doi: 10.1007/s11036-013-0469-

An Intelligent System for Cucumber Leaf Disease Diagnosis Based on the Tuned Convolutional Neural Network Algorithm

In agriculture farming, pests and other plant diseases are the most imperative factor that causes significant hindrance to cucumber production and its quality. Farmers around the globe are currently facing difficulty in recognizing various cucumber leaf diseases, which is imperative to preventing leaf diseases effectively. Manual techniques to diagnose cucumber diseases are often time-consuming, subjective, and laborious. To address this issue, this paper proposes a tuned convolutional neural network (CNN) algorithm to recognise five cucumber diseases and healthy leaves that comprises image enhancement, feature extraction, and classification. Data augmentation methods were utilized as a preprocessing step to enlarge the datasets, and it was also to decrease the chance of overfitting. Automatically features are extracted by using CNN layers. Finally, five cucumber leaf diseases and one healthy leaf are classified. Furthermore, to overcome the lack of a public dataset, a new dataset of cucumber leaf diseases has been constructed that includes spider, leaf miner, downy mildew, powdery mildew, one viral disease, and healthy class leaves. The dataset has a total of 4868 cucumber leaf images. In order to prove the authenticity of the proposed CNN, comparative experiments were conducted using pretrained models (AlexNet, Inception-V3, and ResNet-50). The proposed CNN achieves a recognition accuracy of 98.19% with the augmented dataset and 100% with the publicly plant disease dataset. The experimental results confirm that the proposed CNN algorithm was efficient for recognizing the cucumber leaf diseases compared with other algorithms

Indoor human tracking mechanism using integrated onboard smartphones Wi-Fi device and inertial sensors

In indoor/outdoor environments, special cares need to be given to locate smartphones which are used by most of the people. Locating or tracking is valuable for those people who are in dangerous falling-situations or they are used for shopping and billing services, inside the buildings. This tracking system needs a new positioning mechanism to offer very accurate services to the special needy people. To this end, this paper presents a hybrid mechanism to locate indoors smartphones; specifically Wi-Fi access-points signals are available. The proposed mechanism incorporates onboard Wi-Fi and sensor devices including gyroscopes and accelerometers to provide accurate indoor smart phone positioning. This paper proposes an integrated approach to offer indoor smartphone positioning. The purpose of the integrated approach is to fuse multi-technologies measurements on smartphones. The mechanism uses proximity-level (based on received-signal-strength ‘RSS’ measurement) technique between the smartphone and Wi-Fi access-points which they are exist in the vicinity. Then it combines this proximity measurement with uncertainty calculations from onboard dead-reckoning measurements using Extended-Kalman filter, which can provide seamless, low cost, and improve location accuracy significantly, especially when deep indoor. This means, in deep indoor, the approach can utilize only a single Wi-Fi access-points signals as well as using prior-estimate positions based on artificial conditions. The results from different trial experiments (using Android-based smartphones) show that around 2.5-m positioning accuracy can be obtained