In a wireless sensor network (WSN), a large number of sensor nodes are deployed over a wide area and multi-hop communications are required between the nodes. Managing numerous sensor nodes is a very complicated task, especially when the energy issue is involved. Even though a number of ad-hoc management and network structuring approaches for WSNs have been proposed, a management framework covering the entire network management infrastructure from the messaging protocol to the network structuring algorithm has not yet been proposed. In this paper we introduce a management framework for WSNs called SNOWMAN (SeNsOr netWork MANagement) framework. It employs the policy-based management approach for letting the sensor nodes autonomously organize and manage themselves. Moreover, a new light-weight policy distribution protocol called TinyCOPS-PR and policy information base (PIB) are also developed. To facilitate scalable and localized management of sensor networks, the proposed SNOWMAN constructs a 3-tier hierarchy of regions, clusters, and sensor nodes. The effectiveness of the proposed framework is validated through actual implementation and simulation using ns-2. The simulation results reveal that the proposed framework allows smaller energy consumption for network management and longer network lifetime than the existing schemes such as LEACH and LEACH-C for practical size networks.

Among several multiprocessor topologies, two-dimensional (2D) mesh topology has become popular due to its simplicity and efficiency. Even though a number of scheduling and processor allocation schemes for 2D meshes have been proposed in the literature, little study has been done aimed for real-time environment. In this paper, we propose an on-line scheduling and allocation scheme for real-time tasks that require the exclusive use of submeshes in 2D mesh system. By effectively manipulating the information on allocated or reserved submeshes, the proposed scheme can quickly identify the earliest available time of a free submesh for a newly arrived task. We employ a limited preemption approach to reduce the complexity of the search for a feasible schedule. Computer simulation reveals that the proposed scheme allows high throughput by decreasing the number of tasks rejected.

This letter proposes a Cell-based Hybrid Index (CHI) for energy conserving k Nearest Neighbor search on air. The proposed CHI provides global knowledge on data distribution for fast decision of the search space and local knowledge for efficient pruning of data items. Simulations show that CHI outperforms the existing indexing schemes in terms of tuning time and energy efficiency. With respect to access time, it outperforms them except the distributed indexing scheme optimized for access time.

Tree-based approach has been proven to be most scalable for one-to-many reliable multicast. It efficiently combines distributed recovery with local recovery over a logical tree of the sender and receivers. It has also been known that the performance of the tree-based protocols heavily depends upon the quality of the logical tree. In this paper, we propose an end-to-end scheme to further enhance the scalability of the tree-based approach. By exchanging packet loss information observed at the end hosts, the scheme constructs and maintains a logical tree congruent with the underlying multicast routing tree even in the presence of session membership and multicast route changes. The scheme also groups the tree nodes and assigns separate multicast addresses to them in order to enable efficient multicast retransmission for reducing both delay and exposure. We compare the proposed scheme with Tree-based Multicast Transport Protocol (TMTP), a static tree-based protocol. Extensive simulations up to 300 node sessions reveal that the proposed scheme reduces implosion and exposure more than 20% and 50%, respectively. The results also indicate that the scheme is highly scalable such that the improvement gets more significant as the size of the session increases.

Given a set of positive-weighted points and a query rectangle r (specified by a client) of given extents, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the total weights of all points covered by r is maximized. In this paper, we address the problem of processing MaxRS queries over road network databases and propose two new external memory methods. Through a set of simulations, we evaluate the performance of the proposed methods.

In this paper, we propose an efficient resource discovery scheme for large-scale ubiquitous computing environments, which supports scalable semantic searches and load balancing among resource discovery resolvers. Here, the resources are described based on the concepts defined in the ontological hierarchy. To semantically search the resources in a scalable manner, we propose a semantic vector space and semantic resource discovery network in which the resources are organized based on their respective semantic distances. Most importantly, landmarks are introduced for the first time to reduce the dimensionality of the vector space. Computer simulation with CAN verifies the effectiveness of the proposed scheme.