In-Network Outlier Detection in Wireless Sensor Networks

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy usage,(3) only uses single hop communication thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption.Comment: Extended version of a paper appearing in the Int'l Conference on Distributed Computing Systems 200

Design considerations in wireless sensor networks

We consider three problems in the design of wireless sensor networks: cross-layer optimization, neighbor discovery, and scheduling as a method of medium access control (MAC). Cross-layer optimization will be important for sensor networks, which typically have only one or two objectives to meet. We consider a sensor network which performs decentralized detection. We devise a method in which local observations by sensors are condensed into a single bit message and forwarded to a sink node which makes a final decision. The method involves unusual interactions between the application, the routing function, and the physical layer. Neighbor discovery is useful in sensor networks whose nodes are immobile, since routing and scheduling algorithms can make good use of neighbor information. We propose an asynchronous neighbor discovery algorithm. The algorithm is probabilistic: each node obtains a list of its neighbors which is possibly incomplete. Performance is analyzed and optimal parameter settings are obtained. Scheduling deserves consideration as a MAC in sensor networks, because MACs based on contention methods waste energy in retransmissions. We state a natural centralized scheduling problem, in which link demands are to be satisfied under signal-to-interference-and-noise-ratio (SINR) constraints, and transmit powers may be varied. We show that solving this minimum length scheduling problem is at least as hard as another problem we define, MAX-SINR-MATCHING, in the sense that if there is no polynomial-time algorithm to solve the latter then there is no polynomial-time algorithm to solve the former. We give evidence that MAX-SINR-MATCHING is a difficult problem. We add several theorems on the SINR model which exploit algebraic structure. The theorems predict what sets of links could be simultaneously activated in a wireless network and depend only on the SINR requirements of the nodes and the worst propagation loss in a network. These theorems apply to all wireless networks which can be described by SINR requirements, not only to sensor networks

Talk More Listen Less: Energy-Efficient Neighbor Discovery in Wireless Sensor Networks

Neighbor discovery is a fundamental service for initialization and managing network dynamics in wireless sensor networks and mobile sensing applications. In this paper, we present a novel design principle named Talk More Listen Less (TMLL) to reduce idle-listening in neighbor discovery protocols by learning the fact that more beacons lead to fewer wakeups. We propose an extended neighbor discovery model for analyzing wakeup schedules in which beacons are not necessarily placed in the wakeup slots. Furthermore, we are the first to consider channel occupancy rate in discovery protocols by introducing a new metric to trade off among duty-cycle, latency and channel occupancy rate. Guided by the TMLL principle, we have designed Nihao, a family of energy-efficient asynchronous neighbor discovery protocols for symmetric and asymmetric cases. We compared Nihao with existing state of the art protocols via analysis and real-world testbed experiments. The result shows that Nihao significantly outperforms the others both in theory and practice.Comment: 9 pages, 14 figures, published in IEEE INFOCOM 201