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"Pada routing multi-hop, cluster head yang dekat dengan base station berperan sebagai node perantara bagi cluster head yang jauh dari base station untuk menyampaikan paket data dari node reguler ke base station. Cluster head sebagai relay akan menghabiskan energi lebih cepat sehingga menyebabkan masalah hotspot. Makalah ini mengusulkan algoritma routing multi-hop dinamis bernama Data Similarity Aware untuk Dynamic Multi-hop Routing Protocol DSA-DMRP untuk memecahkan masalah hotspot, meningkatkan masa hidup jaringan dan skalabilitas jaringan, dan memenuhi persyaratan aplikasi yang dipertimbangkan kesamaan data dari node yang berdekatan. DSA-DMRP menggunakan teknik agregasi fuzzy untuk mengukur kemiripan data mereka agar partisi jaringan menjadi cluster ukuran yang tidak sama. Dalam mekanisme ini, setiap node dapat mengenali dan mencatat simpul tetangga yang serupa. Selanjutnya, aturan K-hop Clustering Algorithm KHOPCA yang dimodifikasi digunakan untuk memilih cluster head dan membuat rute untuk transmisi intra cluster dan interkluster. DSA-DMRP dibandingkan dengan KHOPCA untuk menjustifikasi kinerjanya. Hasil simulasi menunjukkan bahwa, DSA DMRP dapat memperbaiki masa hidup jaringan dibanding KHOPCA, dan memecahkan masalah hotspot.

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In multi hop routing, cluster heads close to the base station role as intermediate nodes for farther cluster heads to relay the data packet from regular nodes to base station. The cluster heads as relays will deplete their energy more quickly that causes hot spot problem. This paper proposes a dynamic multi hop routing algorithm named Data Similarity Aware for Dynamic Multi hop Routing Protocol DSA DMRP to solve the hot spot problem, improve the lifetime and scalability of the network, and satisfy the requirement of applications that consider the data similarity of adjacent nodes. The DSA DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters.

In this mechanism, each node can recognize and note its similar neighbor nodes. Next, the modified K hop Clustering Algorithm KHOPCA rules by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra cluster and inter cluster transmission. The DSA DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA, and solve the hotspot problem."

"Providing travelers with accurate bus arrival time is an essential need to plan their traveling and reduce long waiting time for buses. In this paper, we proposed a new approach based on a Bayesian mixture model for the prediction. The Gaussian mixture model (GMM) was used as the joint probability density function of the Bayesian network to formulate the conditional probability. Furthermore, the Expectation maximization (EM) Algorithm was also used to estimate the new parameters of the GMM through an iterative method to obtain the maximum likelihood estimation (MLE) as a convergence of the algorithm. The performance of the prediction model was tested in the bus lanes in the University of Indonesia. The results show that the model can be a potential model to predict effectively the bus arrival time."