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"Search and classification using multiple autonomous vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAV) under both deterministic and probabilistic frameworks. It serves as a first discussion of the problem of effective resource allocation using MAV with sensing limitations, i.e., for search and classification missions over large-scale domains, or when there are far more objects to be found and classified than there are autonomous vehicles available. Under such scenarios, search and classification compete for limited sensing resources. This is because search requires vehicle mobility while classification restricts the vehicles to the vicinity of any objects found. The authors develop decision-making strategies to choose between these competing tasks and vehicle-motion-control laws to achieve the proposed management scheme. Deterministic Lyapunov-based, probabilistic Bayesian-based, and risk-based decision-making strategies and sensor-management schemes are created in sequence. Modeling and analysis include rigorous mathematical proofs of the proposed theorems and the practical consideration of limited sensing resources and observation costs. A survey of the well-developed coverage control problem is also provided as a foundation of search algorithms within the overall decision-making strategies. Applications in both underwater sampling and space-situational awareness are investigated in detail. The control strategies proposed in each chapter are followed by illustrative simulation results and analysis. "

"Efficiency energy and stream data mining on Wireless Sensor Networks (WSNs) are a very interesting issue to be discussed. Routing protocols technology and resource-aware can be done to improve energy efficiency. In this paper we try to merge routing protocol technology using routing Distance Vector and Resource-Aware (RA) framework on heterogeneity wireless sensor networks by combining sun-SPOT and Imote2 platform wireless sensor networks. RA perform resource monitoring process of the battery, memory and CPU load more optimally and efficiently. The process uses Light-Weight Clustering (LWC) and Light Weight Frequent Item (LWF). The results obtained that by adapting Resource-Aware in wireless sensor networks, the lifetime of wireless sensor improve up to ± 16.62%.

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Efisiensi energi dan stream data mining pada Wireless Sensor Networks (WSN) adalah masalah yang sangat menarik untuk dibahas. Teknologi Routing Protocol dan Resource-Aware dapat dilakukan untuk meningkatkan efisiensi energi. Dalam penelitian ini peneliti mencoba untuk menggabungkan teknologi Routing Protocol menggunakan routing Distance Vector dan Resource-Aware (RA) framework pada Wireless Sensor Networks heterogen dengan menggabungkan sun-SPOT dan platform Imote2 Wireless Sensor Networks. RA melakukan proses pemantauan sumber daya dari memori, baterai, dan beban CPU lebih optimal dan efisien. Proses ini menggunakan Light-Weight Clustering (LWC) dan Light Weight Frequent Item (LWF). Hasil yang diperoleh bahwa dengan mengadaptasi Resource-Aware dalam Wireless Sensor Networks, masa pakai wireless sensor meningkatkan sampai ± 16,62%."

"Mikrokantilever mulai dilirik oleh para peneliti untuk diaplikasikan sebagai biosensor dalam dunia kesehatan, biologi,kimia dan lingkungan hidup. Mekanisme dan prinsip kerja mikrokantilever sensor ini terus digali untuk memperdalampengetahuan sistem sensor dan aplikasinya pada target obyek tertentu. Makalah ini memaparkan simulasi deteksimikrokantilever sensor berbasis persamaan Euler-Bernoulli dan perancangan sistem sensor berbasis piezoresistivemikrokantilever. Pada studi simulasi, perubahan frekuensi resonansi sebagai deteksi keberadaan obyek dihitung denganmenggunakan mikrokantilever yang mempunyai panjang 110 μm, lebar 50 μm, dan tebal 1 μm. Massa mikrokantileveradalah 12,815 nanogram (sudah termasuk massa receptor-nya). Target obyek yang dideteksi adalah bakteri, di manamassa untuk satu bakteri diasumsikan 0,3 pikogram. Saat terdeteksi, satu massa obyek bakteri menyebabkan nilaidefleksi sebesar 3,05355×10-11 m dan nilai frekuensi resonansi sebesar 118,90 kHz, sedangkan untuk empat obyekbakteri menyebabkan nilai defleksi sebesar 3,05445×10-11 m dan nilai frekuensi resonansi sebesar 118,68 kHz. Hasil inimenunjukkan bahwa bertambahnya massa bakteri akan menyebabkan naiknya nilai defleksi dan turunnya nilaifrekuensi resonansi. Hasil ujicoba sistem mikrokantilever sensor juga menunjukkan bahwa sistem bekerja normal dandapat digunakan untuk ujicoba terhadap target obyek tertentu."