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
Representative examples of such hierarchical wireless sensor networks are PASTA(Power Aware Sensing, Tracking and Analysis) @cite_17 mentioned in COSMOS @cite_17 and SOHAN @cite_10 . PASTA is used in military applications for enemy movement surveillance and is configured with the server and about 400 intermediate tier nodes each clustering about 20 sensor nodes. SOHAN is used in traffic congestion monitoring applications to measure the traffic volume using roadside sensor nodes and is configured with the server and about 50 intermediate tier nodes each clustering about 200 sensor nodes.
@cite_10 @cite_17
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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. Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used for huge range of applications where the traditional infrastructure-based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume considerable amount of energy while transmitting the information. It is impractical and sometimes impossible to replace the battery and to maintain longer network life time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one such issue, which can reduce the energy consumption dramatically. Low energy adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold value and only CHs are allowed to send the information to the base station (BS). But in this approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy inference engine (Mamdani’s rule) is used to elect the chance to be the SCH. The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime. Mobile sensor nodes can be used for a wide variety of applications such as social networks and location tracking. An important requirement for all such applications is that the mobile nodes need to actively discover their neighbors with minimal energy and latency. Nodes in mobile networks are not necessarily synchronized with each other, making the neighbor discovery problem all the more challenging. In this paper, we propose a neighbor discovery protocol called U-Connect, which achieves neighbor discovery at minimal and predictable energy costs while allowing nodes to pick dissimilar duty-cycles. We provide a theoretical formulation of this asynchronous neighbor discovery problem, and evaluate it using the power-latency product metric. We analytically establish that U-Connect is an 1.5-approximation algorithm for the symmetric asynchronous neighbor discovery problem, whereas existing protocols like Quorum and Disco are 2-approximation algorithms. We evaluate the performance of U-Connect and compare the performance of U-Connect with that of existing neighbor discovery protocols. We have implemented U-Connect on our custom portable FireFly Badge hardware platform. A key aspect of our implementation is that it uses a slot duration of only 250μs, and achieves orders of magnitude lower latency for a given duty cycle compared to existing schemes for wireless sensor networks. We provide experimental results from our implementation on a network of around 20 sensor nodes. Finally, we also describe a Friend-Finder application that uses the neighbor discovery service provided by U-Connect. An emerging class of important applications uses ad hoc wireless networks of low-power sensor devices to monitor and send information about a possibly hostile environment to a powerful base station connected to a wired network. To conserve power, intermediate network nodes should aggregate results from individual sensors. However, this opens the risk that a single compromised sensor device can render the network useless, or worse, mislead the operator into trusting a false reading. We present a protocol that provides a secure aggregation mechanism for wireless networks that is resilient to both intruder devices and single device key compromises. Our protocol is designed to work within the computation, memory and power consumption limits of inexpensive sensor devices, but takes advantage of the properties of wireless networking, as well as the power asymmetry between the devices and the base station.