Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control

This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load-balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches

Energy Efficient Node Deployment in Wireless Ad-hoc Sensor Networks

We study a wireless ad-hoc sensor network (WASN) where $N$ sensors gather data from the surrounding environment and transmit their sensed information to $M$ fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs to minimize a Lagrange combination of the sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing-dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd algorithm to optimize node deployment. Simulation results show that, on average, the proposed algorithm outperforms the existing deployment algorithms.Comment: 7 pages, 6 figure

Q-LEACH: A New Routing Protocol for WSNs

Wireless Sensor Networks (WSNs) with their dynamic applications gained a tremendous attention of researchers. Constant monitoring of critical situations attracted researchers to utilize WSNs at vast platforms. The main focus in WSNs is to enhance network life-time as much as one could, for efficient and optimal utilization of resources. Different approaches based upon clustering are proposed for optimum functionality. Network life-time is always related with energy of sensor nodes deployed at remote areas for constant and fault tolerant monitoring. In this work, we propose Quadrature-LEACH (Q-LEACH) for homogenous networks which enhances stability period, network life-time and throughput quiet significantly

Mengenal pasti masalah pemahaman dan hubungannya dengan latar belakang matematik, gaya pembelajaran, motivasi dan minat pelajar terhadap bab pengawalan kos makanan di Sekolah Menengah Teknik (ert) Rembau: satu kajian kes.

Kajian ini dijalankan untuk mengkaji hubungan korelasi antara latar belakang Matematik, gaya pembelajaran, motivasi dan minat dengan pemahaman pelajar terhadap bab tersebut. Responden adalah seramai 30 orang iaitu terdiri daripada pelajar tingkatan lima kursus Katering, Sekolah Menengah Teknik (ERT) Rembau, Negeri Sembilan. Instrumen kajian adalah soal selidik dan semua data dianalisis menggunakan program SPSS versi 10.0 untuk mendapatkan nilai min dan nilai korelasi bagi memenuhi objektif yang telah ditetapkan. Hasil kajian ini menunjukkan bahawa hubungan korelasi antara gaya pembelajaran pelajar terhadap pemahaman pelajar adalah kuat. Manakala hubungan korelasi antara latar belakang Matematik, motivasi dan minat terhadap pemahaman pelajar adalah sederhana. Nilai tahap min bagi masalah pemahaman pelajar, latar belakang Matematik, gaya pembelajaran, motivasi dan minat terhadap bab Pengawalan Kos Makanan adalah sederhana. Kajian ini mencadangkan penghasilan satu Modul Pembelajaran Kendiri bagi bab Pengawalan Kos Makanan untuk membantu pelajar kursus Katering dalam proses pembelajaran mereka

Energy Efficiency in Two-Tiered Wireless Sensor Networks

We study a two-tiered wireless sensor network (WSN) consisting of $N$ access points (APs) and $M$ base stations (BSs). The sensing data, which is distributed on the sensing field according to a density function $f$, is first transmitted to the APs and then forwarded to the BSs. Our goal is to find an optimal deployment of APs and BSs to minimize the average weighted total, or Lagrangian, of sensor and AP powers. For $M=1$, we show that the optimal deployment of APs is simply a linear transformation of the optimal $N$-level quantizer for density $f$, and the sole BS should be located at the geometric centroid of the sensing field. Also, for a one-dimensional network and uniform $f$, we determine the optimal deployment of APs and BSs for any $N$ and $M$. Moreover, to numerically optimize node deployment for general scenarios, we propose one- and two-tiered Lloyd algorithms and analyze their convergence properties. Simulation results show that, when compared to random deployment, our algorithms can save up to 79\% of the power on average.Comment: 11 pages, 7 figure