In this paper, we propose a new dynamic load balancing scheme according to load threshold adjustment and incentives mechanism. The proposed scheme adjusts the load threshold of a node by comparing it with a mean threshold of adjacent nodes, thereby increasing the threshold evenly. We also assign the incentives and penalties to each node through a comparison of the mean threshold of all the nodes in order to increase autonomous load balancing participation.

Recently, several methods to process continuous queries for mobile objects in broadcast environments have been proposed. We propose a new indexing method for processing continuous queries that uses vector information in broadcast environments. We separate the index structure according to the velocities of the objects to avoid unnecessary accesses. The index structure consists of the index files for the slow moving objects and the fast moving objects. By avoiding unnecessary accesses, we reduce the tuning time to process a query in broadcast environments. To show the superiority of the proposed method, we evaluate its performance from various perspectives.

In this paper, we propose a novel scheme to optimize the allocation of continuous queries in a sensor network with multiple sinks. The existing scheme compares the coverage areas of given queries and estimates the amount of sharing among them. It tries to allocate queries to the optimal sink that maximizes the amount of sharing and reduces the communication costs among sensor nodes and sinks. However, it inefficiently allocates continuous queries. The amount of sharing among continuous queries depends not only on their coverage area but also on their time-parameters like time-duration and time-interval. We define a new cost estimator with time-parameters for continuous queries and optimize their allocation in the sensor network. Simulation results show that our scheme performs the allocation of continuous queries efficiently and reduces the communication cost.

In sensor networks, many studies have been proposed to process in-network aggregation efficiently. Unlike general aggregation queries, skyline query processing compares multi-dimensional data for the result. Therefore, it is very difficult to process the skyline queries in sensor networks. It is important to filter unnecessary data for energy-efficient skyline query processing. Existing approaches get rid of unnecessary data transmission by deploying filters to whole sensors. However, network lifetime is reduced due to energy consumption for transmitting filters. In this paper, we propose a lazy filtering-based in-network skyline query processing algorithm to reduce energy consumption by transmitting filters. Our algorithm creates the skyline filter table (SFT) in the data gathering process which sends data from sensor nodes to the base station and filters out unnecessary data transmissions using it. The experimental results show that our algorithm reduces false positive by 53% and improves network lifetime by 44% on average over the existing method.

Many methods have been researched to prolong the lifetime of sensor networks that use mobile technologies. In the mobile sink research, there are the track based methods and the anchor points based methods as representative operation methods for mobile sinks. However, most existing methods decrease the Quality of Service (QoS) and lead to routing hotspots in the vicinity of the mobile sinks. The main reason is that they use static mobile sink movement paths that ignore the network environment such as the query position and the data priority. In this paper, we propose a novel mobile sink operation method that solves the problems of the existing methods. In our method, the probe priority of the mobile sink is determined from data priority to increase the QoS. The mobility of sink used to reduce the routing hotspot. Experiments show that the proposed method reduces the query response time and improves the network lifetime much more than the existing methods.

In this paper, we design an efficient P2P based mobile social network to facilitate contents search over mobile ad hoc networks. Social relation is established by considering both the locations and interests of mobile nodes. Mobile nodes with common interests and nearby locations are recommended as friends and are connected directly in a mobile social network. Contents search is handled by using social relationships of the mobile social network rather than those of the whole network. Since each mobile node manages only neighboring nodes that have common interests, network management overhead is reduced. Results of experiments have shown that our proposed method outperforms existing methods.

Due to most of the existing graph repartitioning methods are known for poor efficiency in distributed environments. In this paper, we introduce a new graph repartitioning method with two phases in distributed environments. In the first phase, a local method is designed to identify all the potential candidate vertices that should be moved to the other partitions at once in each partition locally. In the second phase, a streaming graph processing model is adopted to reassign the candidate vertices to achieve lightweight graph repartitioning. During the reassignment of the vertex, we propose an objective function to balance both the load balance and the number of crossing edges among the distributed partitions. The experimental results with a large set of real word and synthetic graph datasets show that the communication cost can be reduced by nearly 1 to 2 orders of magnitude compared with the existing methods.