Distributed Construction and Maintenance of Bandwidth-Efficient Bluetooth Scatternets

Bluetooth networks can be constructed as piconets or scatternets depending on the number of nodes in the network. Although piconet construction is a well-defined process specified in Bluetooth standards, scatternet construction policies and algorithms are not well specified. Among many solution proposals for this problem, only a few of them focus on efficient usage of bandwidth in the resulting scatternets. In this paper, we propose a distributed algorithm for the scatternet construction problem, that dynamically constructs and maintains a scatternet based on estimated traffic flow rates between nodes. The algorithm is adaptive to changes and maintains a constructed scatternet for bandwidth-efficiency when nodes come and go or when traffic flow rates change. Based on simulations, the paper also presents the improvements in bandwidth-efficiency provided by the proposed algorithm

Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network

Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV) workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube demo at this: https://www.youtube.com/watch?v=zYypJPJipY

ANALISIS PERBANDINGAN KINERJA FORMASI BLUENET DAN FORMASI BLUETREES PADA BLUETOOTH SCATTERNET (Performance Comparison Analysis of Bluenet Formation and Bluetrees Formation in Bluetooth Scatternet)

ABSTRAKSI: Pada saat sekarang ini komunikasi wireless menjadi penting keberadaannya. Bluetooth merupakan suatu teknologi nirkabel menggunakan frekuensi 2.4 GHz dengan biaya dan penggunaan daya yang relatif rendah. Pada mulanya bluetooth ditujukan sebagai pengganti perangkat kabel, namun seiring dengan perkembangan teknologi bluetooth dapat membentuk suatu personal network yang dianalogikan sebagai piconet. Jaringan piconet yang terdiri atas satu master dan tujuh buah slave dapat membentuk jaringan yang lebih luas yang disebut dengan scatternet yang merupakan suatu multiple-piconet. Namun belum ada protokol formasi scatternet yang terdapat pada spesifikasi bluetooth secara terperinci. Selama ini telah banyak riset dilakukan mengenai protokol formasi pembentukan scatternet diantaranya adalah bluenet dan bluetrees.Pada kedua protokol formasi tersebut akan menghasilkan suatu topologi jaringan yang berbeda-beda. Perbedaan ini dapat dilihat dari jumlah peranan masing-masing node pada perangkat bluetooth yang dapat berperan sebagai node master, node slave dan node bridge. Bentuk topologi jaringan scatternet yang berbeda ini akan berpengaruh pada kinerja jaringan. Untuk itu perlu dilakukannya perbandingan terhadap performansi yang dihasilkan dari kedua protokol formasi tersebut. Sesuai pernyataan diatas pada tugas akhir ini akan dilakukan suatu perbandingan kinerja dari kedua algoritma formasi scatternet yakni bluenet dan bluetrees. Adapun yang menjadi parameter-parameter sebagai tolak ukur keefektifan didasarkan topologi scatternet yang dihasilkan oleh kedua algoritma. Secara umum parameter tersebut meliputi jarak lintasan terpendek, aliran maksimum, serta waktu pembentukan scatternet agar terhubung penuh.Pada simulasi akan memperlihatkan pengaruh dari scatternet parameter yang dihasilkan oleh kedua algoritma. Dan juga akan terlihat bahwa algoritma bluenet menghasilkan suatu topologi dengan performansi yang lebih baik pada indeks shortest path ratio mendekati nilai 1 dan pada maximum traffic flow yang berada pada level rata-rata mencapai 700 Kbps dengan deviasi dibawah 200 Kbps. Namun pada algoritma bluetrees, waktu pembentukan scatternet akan lebih singkat dibandingkan dengan algoritma bluenet yakni hanya dalam periode 6 s dalam jumlah 50 node.Kata Kunci : ABSTRACT: At this time the existence of wireless communication system was important thing to be occurred. Bluetooth is wireless technology that operates in 2.4 GHz of frequencies with low power and little cost. In the beginning time bluetooth wireless technology was used as roles of cable devices replacement, but in the grown of bluetooth technology, it could make personal network is called piconet. Piconet contains one master and seven unit slaves. It possibly builds a larger network which is called scatternet as a multiple-piconet. The scatternet formation protocol had not been specified in bluetooth specification. So many researches have been proposed in scatternet formation protocols which the one called bluenet and another one called bluetrees.Both of formation protocols have a different topology result. The different could be observed from the roles number of each node. The roles could be master, slave and bridge node. The different of scatternet topology structure will effect in network performance. Because of that reason is necessary to make comparison of performance by both of scatternet formation protocol resulted. In this final task will make some performance comparison from the both of scatternet formation bluenet and bluetrees. The index effectiveness based on scatternet topology structure from both algorithms. The index parameter consists of shortest path range, maximum flow, and scatternet formation time to get fully connected.Simulation show the effect of scatternet parameter resulted by both of algorithm. From that point it shows that bluenet algorithm build topology with better performance at shortest path ratio near up to 1 and more stable maximum traffic flow parameter up to 700 Kbps in mean and under 200 Kbps in deviation. However, in bluetrees algorithm scatternet formation time will be shorter than bluenet algorithm within 6 s periods at 50 numbers of nodes.Keyword

Adaptive role switching protocols for improving scatternet performance in Bluetooth radio networks

[[abstract]]Bluetooth is a low-power, low-cost, and short-range wireless technology. A well structured scatternet, with the appropriate number of piconets and bridges for a specific traffic pattern, increases the performance of a Bluetooth network. However, the structure of a scatternet is difficult to control or predefine because the scatternet is formed using a distributed procedure, with the master and slaves of each piconet connected at random. The participation of mobile Bluetooth devices in a scatternet at different times also increases the difficulty of maintaining a good structure. A badly structured scatternet exhibits the following characteristics: too many bridges in the scatternet creates a guard slot overhead associated with bridge switching among the participating piconets, increasing the probability that a packet is lost; too many piconets in a communicative range causes packet collision and thus degrades the performance; unnecessary piconets also lengthen the routing path, delaying the transmission of packets from source to destination. The paper proposes a distributed scatternet reconstruction protocol for dynamically reorganizing the scatternet. Unnecessary bridges and piconets can be dynamically removed by applying a role switching operation, improving the packet error rate, saving guard slots, and reducing the average routing length. By experiment, it is shown that the proposed protocol improves the data transmission performance of a Bluetooth scatternet.[[conferencetype]]國際[[conferencedate]]20040905~20040908[[booktype]]紙本[[conferencelocation]]Barcelona, Spai