In this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Recently, as the size of sensor networks increases due to the growth of ubiquitous computing environments and wireless networks, building wireless sensor networks in a hierarchical configuration is put forth as a practical approach. Contrasted with the traditional approach of building networks in a “flat” structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher-capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by as the basis. In addition, the method considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing “false alarms”). We then present how to build a hierarchical sensor network that is optimalwith respect to the weighted sum of the two costs. This allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 1.002 - 3.210 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by
This paper describes the design and implementation of a novel 802.11-based self-organizing hierarchical ad-hoc wireless network (SOHAN), and presents some initial experimental results obtained from a proof-of-concept prototype. The proposed network has a three-tier hierarchy consisting of low-power mobile nodes (MNs) at the lowest layer, forwarding nodes (FNs) with higher power and multi-hop routing capability at the middle layer, and wired access points (APs) without power constraints at the highest layer. Specifics of new protocols used for bootstrapping, node discovery, and multi-hop routing are presented, and the overall operation of the complete hierarchical ad-hoc network is explained. A prototype implementation of the SOHAN network is outlined in terms of major hardware and software components, and initial experimental results are given. Clustering is an important characteristic of most sensor applications. In this paper we define COSMOS, the Cluster-based Heterogeneous Model for Sensor networks. The model assumes a hierarchical network architecture comprising of a large number of low cost sensors with limited computation capability, and fewer number of powerful clusterheads, uniformly distributed in a two dimensional terrain. The sensors are organized into single hop clusters, each managed by a clusterhead. The clusterheads are organized in a mesh-like topology. All sensors in a cluster are time synchronized, whereas the clusterheads communicate asynchronously. The sensors are assumed to have multiple power states and a wake-up mechanism to facilitate power management. To illustrate algorithm design using our model, we discuss implementation of algorithms for sorting and summing in sensor networks.