Dissimilarity metric based on local neighboring information and genetic programming for data dissemination in vehicular ad hoc networks (VANETs)

This paper presents a novel dissimilarity metric based on local neighboring information and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks (VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves significant improvements in terms of reachability in comparison with the classical dissimilarity metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios

A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network likemobilemultihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic

Broadcasting Protocol for Effective Data Dissemination in Vehicular Ad Hoc Networks

VANET topology is very dynamic due to frequent movements of the nodes. Using beacon information connected dominated set are formed and nodes further enhanced with neighbor elimination scheme. With acknowledgement the inter section issues are solve. A modified Broadcast Conquest and Delay De-synchronization mechanism address the broadcasting storm issues. Although data dissemination is possible in all direction, the performance of data dissemination in the opposite direction is investigated and compared against the existing protocols

Optimasi Rute Trafik Data Dan Destinasi Pada Jaringan Bergerak Maritim Menggunakan Algoritma Blind Search Dan Swarm Intelligence

Maraknya illegal fishing di perairan Indonesia sangat berpengaruh pada keamanan negara dan sumber daya laut kita. Mengacu pada data dari Kementerian Kelautan dan Perikanan tahun 2012 bahwa jumlah kapal di bawah 30 GT mendominasi 98% dari keseluruhan jumlah kapal tangkap ikan di Indonesia. Kapal-kapal tangkap ini tidak memiliki kewajiban melengkapi dengan peralatan sistem pemantauan berbasis satelit. Sedangkan kapal asing dengan bobot kecil 20- 30 GT sudah dilengkapi sistem ini, sehingga mereka dengan mudah mendapatkan informasi tentang lokasi penangkapan ikan (rumpon, fish aggreagting device (FAD)). Oleh karena itu, khusus kapal tangkap ikan < 30 GT, perlu mendapatkan perhatian, khususnya untuk sharing informasi antar kapal dan destinasi menuju FAD. Optimasi rute trafik data dan destinasi FAD perlu dilakukan untuk memudahkan kapal menuju FAD dengan mempertimbangkan jarak dan kondisi cuaca dan tinggi gelombang di FAD. Metode pendekatan optimasi metode Swarm Intelligence (SI) banyak ditawarkan untuk menyelesaikan permasalahan tersebut. Metode optimasi seperti Gossip dan Genetic algorithm (GA) telah banyak digunakan untuk mendapatkan solusi terbaik. Usulan optimasi rute trafik data Breadth fixed gossip (BFG) dan PSO untuk jaringan dinamis ditujukan untuk menentukan rute terpilih berdasarkan pertimbangan jarak dan konektifitas dengan kapal lainnya. Algoritma optimasi rute trafik data BFG merupakan hybrid algoritma breadth first search, model fixed radius dan Gossip. Sedangkan optimasi rute destinasi FAD diusulkan menggunakan algoritma firefly dan GA. Dengan menggabungkan kedua algoritma optimasi rute, maka dibangun optimasi rute trafik data dan destinasi lokasi tangkap ikan sekaligus yaitu: BFG-G dan PSO-G. Pengujian berupa simulasi dilakukan untuk mengetahui tingkat keberhasilan menentukan rute trafik data dan lokasi FAD. Sedangkan pengujian komputasi didasarkan pada kompleksitas waktu, keakurasian, kecepatan konvergen dan jumlah relai yang diperlukan untuk mencapai kapal tujuan. ================================================================================================================== The rise of illegal fishing in Indonesian ocean is very influential on the country security and marine resources. Referring to data from the Ministry of Marine Affairs and Fisheries in 2012 that the number of ships under 30 GT dominates 98% of the total number of fishing vessels in Indonesia. These fishing vessels have no obligation to equip with the satellite-based monitoring system. While foreign ships with a small weight of 20-30 GT already equipped this system, hence they easily get information about the location of fishing (rumpon, fish aggregating device (FAD)). Therefore, the fishing vessels <30 GT, need to get attention, especially for sharing information between ships and destinations to FAD. Optimization of data traffic routes and FAD destinations needs to be done to facilitate the ship to FAD by considering the distance and weather conditions and wave height in FAD. An approach optimization method of Swarm Intelligence (SI) is widely offered to solve the problem. Optimization methods such as Gossip and Genetic algorithm (GA) have been widely used to get the best solution. The proposed optimization of Breadth fixed gossip (BFG) data traffic route and PSO for a dynamic network are intended to determine the selected route based on consideration of distance and connectivity with other vessels. BFG traffic route optimization algorithm is a hybrid algorithm of breadth first search, fixed radius model, and Gossip. While FAD route destination optimization is proposed using firefly and GA algorithm. By combining the two route optimization algorithms, the optimization of data traffic and FAD routes are BFGG and PSO-G. The simulations are performed to determine the success rate determine the route of data traffic and FAD location. While computational testing is based on the complexity of time, accuracy, convergent speed and the number of relays required to reach the destination ship in determining data traffic

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Towards music perception by redundancy reduction and unsupervised learning in probabilistic models

PhDThe study of music perception lies at the intersection of several disciplines: perceptual psychology and cognitive science, musicology, psychoacoustics, and acoustical signal processing amongst others. Developments in perceptual theory over the last fifty years have emphasised an approach based on Shannon’s information theory and its basis in probabilistic systems, and in particular, the idea that perceptual systems in animals develop through a process of unsupervised learning in response to natural sensory stimulation, whereby the emerging computational structures are well adapted to the statistical structure of natural scenes. In turn, these ideas are being applied to problems in music perception. This thesis is an investigation of the principle of redundancy reduction through unsupervised learning, as applied to representations of sound and music. In the first part, previous work is reviewed, drawing on literature from some of the fields mentioned above, and an argument presented in support of the idea that perception in general and music perception in particular can indeed be accommodated within a framework of unsupervised learning in probabilistic models. In the second part, two related methods are applied to two different low-level representations. Firstly, linear redundancy reduction (Independent Component Analysis) is applied to acoustic waveforms of speech and music. Secondly, the related method of sparse coding is applied to a spectral representation of polyphonic music, which proves to be enough both to recognise that the individual notes are the important structural elements, and to recover a rough transcription of the music. Finally, the concepts of distance and similarity are considered, drawing in ideas about noise, phase invariance, and topological maps. Some ecologically and information theoretically motivated distance measures are suggested, and put in to practice in a novel method, using multidimensional scaling (MDS), for visualising geometrically the dependency structure in a distributed representation.Engineering and Physical Science Research Counci