Because of recent advances in wireless power transfer technologies, several key topics of wireless sensor networks (WSNs) need to be revisited. Traditional data collection methods for WSNs typically consider the balance of power consumption among sensors as a critical design criterion for avoiding uneven power depletion in the networks. I propose a solution for the uneven power consumption problem of data collection over WSNs. I designed a node‐Gosper island‐based scalable hierarchical cluster transmission method in conjunction with a wireless recharge plan for data collection over rechargeable WSNs. For the recharge plan, I used mobile wireless chargers to visit and recharge the batteries of sensors located on different levels of node‐Gosper curves with various frequencies. The duration of each recharging process for each layer of sensors was calculated to verify the feasibility of the proposed recharge plan. The simulation results indicate that my proposed data collection method outperforms several well‐known data collection methods in terms of energy consumption and that my proposed recharge plan is more efficient than previous approaches in terms of charge path length, number of alive nodes, and traveling efficiency.

Service systems used for various applications in home automation and security require estimating the locations precisely using certain sensors. Serving a mobile user automatically by sensing his/her locations in an indoor environment is considered as a challenge. However, indoor localization cannot be carried out effectively using the Global Positioning System (GPS). In recent years, the use of Wireless Sensor Networks (WSNs) in locating a mobile object in an indoor environment has become popular. Some physical features have also been discussed to solve localization in WSNs. In this paper, we inquire into received signal strength indication (RSSI)-based solutions and propose a new localization scheme called the closer tracking algorithm (CTA) for indoor localization. Under the proposed CTA, a mechanism on mode-change is designed to switch automatically between the optimal approximately closer approach (ACA) and the real-time tracking (RTT) method according to pre-tuned thresholds. Furthermore, we design a mechanism to move reference nodes dynamically to reduce the uncovered area of the ACA for increasing the estimation accuracy. We evaluate the proposed CTA using ZigBee CC2431 modules. The experimental results show that the proposed CTA can determine the position accurately with an error distance less than 0.9 m. At the same time, the CTA scheme has at least 87% precision when the distance is less than 0.9 m. The proposed CTA can select an adaptive mode properly to improve the localization accuracy with high confidence. Moreover, the experimental results also show that the accuracy can be improved by the deployment and movement of reference nodes.

In the paper, we propose a generic parallel computing model for the distributed computing platforms. This model is built on the top of the popular CORBA middleware in order to ensure easy distribution of the software components along the participating computers. We use the concept of factory objects in the model to create slave objects dynamically to fulfil the master/slave parallel computing paradigm. The speedup of the experiment programs using our proposed model is measured and compared with the corresponding MPI implementation. The experiment results show that our model is not only a feasible model for designing parallel programs with CORBA but also an efficient way in comparison to the MPI implementation

Let ${\cal S} = {C 1 , C 2 , . . . , C k }$ be a collection of pairwise disjoint subsets of U = { 1, 2, . . . , n} such that $\bigcup_{i = 1}^k C i = U. A partition matching of $\cal S$ consists of two subsets {a 1 , . . . , a m } and {b 1 , . . ., b m } of U together with a sequence of distinct partitions of $\cal S$: $({\cal A}_1, {\cal B}_1), \ldots, ({\cal A}_m, {\cal B}_m)$ such that a i is contained in a subset in the collection ${\cal A}_i$ and b i is contained in a subset in the collection ${\cal B}_i$ for all i = 1, . . . , m. An efficient algorithm is developed that constructs a maximum partition matching for a given collection $\cal S$. The algorithm can be used to construct optimal parallel routing between two nodes in interconnection networks.

Star networks have recently been proposed as attractive alternatives to the popular hypercube for interconnecting processors on a parallel computer. In this paper, we present an efficient algorithm that constructs an optimal parallel routing in star networks. Our result improves previous results for the problem.

Star networks were proposed recently as an attractive alternative to the well-known hypercube models for interconnection networks. Extensive research has been performed that shows that star networks are as versatile as hypercubes. This paper is an effort in the same direction. Based on the well-known paradigms, we study the one-to-many parallel routing problem on star networks and develop an improved routing algorithm that finds n-1 node-disjoint paths between one node and a set of other n-1 nodes in the n-star network. These parallel paths are proven of minimum length within a small additive constant, and the running time of our algorithm is bounded by O(n/sup 2/). More specifically, given a node s and n-1 other nodes {t/sub 1/, t/sub 2/, ..., t/sub n-1/} in the n-star network, our algorithm constructs n-1 node-disjoint paths P/sub 1/, P/sub 2/, ..., P/sub n-1/, where P/sub i/ is a path from s to t/sub j/ of length at most dist(s, t/sub j/)+6 and dist(s, t/sub j/) is the distance, i.e., the length of a shortest path, from s to t/sub j/, for i=1, 2, ..., n-1.The best bound on the path length by previously known algorithms for the same problem is 5(n-2)/spl ap/10/spl Delta//sub n//3, where /spl Delta//sub n/=max{dist(s, t)} is the diameter of the n-star network.