This article overviews the information-driven approach to sensor collaboration in ad hoc sensor networks. The main idea is for a network to determine participants in a “sensor collaboration ” by dynamically optimizing the information utility of data for a given cost of communication and computation. A definition of information utility is introduced, and several approximate measures of the information utility are developed for reasons of computational tractability. We illustrate the use of this approach using examples drawn from tracking applications.

This paper describes two novel techniques, informationdriven sensor querying (IDSQ) and constrained anisotropic diffusion routing (CADR), for energy-efficient data querying and routing in ad hoc sensor networks for a range of collaborative signal processing tasks. The key idea is to introduce an information utility measure to select which sensors to query and to dynamically guide data routing. This allows us to maximize information gain while minimizing detection latency and bandwidth consumption for tasks such as localization and tracking. Our simulation results have demonstrated that the information-driven querying and routing techniques are more energy efficient, have lower detection latency, and provide anytime algorithms to mitigate risks of link/node failures. 1

Ad hoc networks, where mobile nodes communicate via multihop wireless links, facilitate network connectivity without the aid of any pre-existing networking infrastructure. The intrinsic attributes of ad hoc networks, such as dynamic network topology, limited battery supply, constrained wireless bandwidth and quality, and large number of heterogeneous nodes, make network management significantly more challenging than stationary and wireline networks. In particular, the conventional client/serverbased manager-agent management paradigm falls short of addressing these issues. In this paper, we describe the Guerrilla Management Architecture to facilitate adaptive and autonomous management of ad hoc networks and demonstrate its capability via simulation. Apart from its functionalities, the management capability itself is scalable to accommodate the sheer number and heterogeneity of nodes, autonomous and survivable to adapt to network dynamics, and economical to minimize management overhead.

Abstract—In this paper, we consider two important problems for distributed fault-tolerant detection in wireless sensor networks: 1) how to address both the noise-related measurement error and sensor fault simultaneously in fault-tolerant detection and 2) how to choose a proper neighborhood size n for a sensor node in fault correction such that the energy could be conserved. We propose a fault-tolerant detection scheme that explicitly introduces the sensor fault probability into the optimal event detection process. We mathematically show that the optimal detection error decreases exponentially with the increase of the neighborhood size. Experiments with both Bayesian and Neyman-Pearson approaches in simulated sensor networks demonstrate that the proposed algorithm is able to achieve better detection and better balance between detection accuracy and energy usage. Our work makes it possible to perform energyefficient fault-tolerant detection in a wireless sensor network. Index Terms—Distributed event detection, fault tolerance, sensor fusion, energy-efficiency, wireless sensor networks. 1

We consider the problem of positioning data collecting base stations in a sensor network. We show that in general, the choice of positions has a marked influence on the data rate, or equivalently, the power efficiency, of the network. In our model, which is partly motivated by an experimental environmental monitoring system, the optimum data rate for a fixed layout of base stations can be found by a maximum flow algorithm. Finding the optimum layout of base stations, however, turns out to be an NP-complete problem, even in the special case of homogeneous networks. Our analysis of the optimum layout for the special case of the regular grid shows that all layouts that meet certain constraints are equally good. We also consider two classes of random graphs, chosen to model networks that might be realistically encountered, and empirically evaluate the performance of several base station positioning algorithms on instances of these classes. In comparison to manually choosing positions along the periphery of the network or randomly choosing them within the network, the algorithms tested find positions which significantly improve the data rate and power efficiency of the network.

This paper reports on the development of a utility-based mechanism for managing sensing and communication in cooperative multi-sensor networks. The specific application considered is that of GLACSWEB, a deployed system that uses battery-powered sensors to collect environmental data related to glaciers which it transmits back to a base station so that it can be made available world-wide to researchers. In this context, we first develop a sensing protocol in which each sensor locally adjusts its sensing rate based on the value of the data it believes it will observe. Then, we detail a communication protocol that finds optimal routes for relaying this data back to the base station based on the cost of communicating it (derived from the opportunity cost of using the battery power for relaying data). Finally, we empirically evaluate our protocol by examining the impact on efficiency of the network topology, the size of the network, and the degree of dynamism of the environment. In so doing, we demonstrate that the efficiency gains of our new protocol, over the currently implemented method over a 6 month period, are 470%, 250 % and 300 % respectively.

Distinct from wireless ad hoc networks, wireless sensor networks are data-centric, application-oriented, collaborative, and energyconstrained in nature. In this paper, formulate the problem of data transport in sensor networks as an optimization problem whose objective function is to maximize the amount of information (utility) collected at sinks (subscribers), subject to the flow, energy and channel bandwidth constraints. Also, based on a Markov model extended from [3], we derive the link delay and the node capacity in both the single and multi-hop environments, and figure them in the problem formulation. We study three special cases under the problem formulation. In particular, we consider the energy-aware flow control problem, derive an energy aware flow control solution, and investigate via ns-2 simulation its performance. The simulation results show that the proposed energy-aware flow control solution can achieve high utility and low delay without congesting the network. 1.

... an environment and communicate using wireless links. The lifetime of these networks is severely curtailed by the limited battery power of the sensors. One line of research in sensor network lifetime management has examined sensor selection techniques, in which applications judiciously choose which sensors' data should be retrieved and are worth the expended energy. In the past, many ad-hoc approaches for sensor selection have been proposed. In this paper, we argue that sensor selection should be based upon a tradeoff between application-perceived benefit and energy consumption of the selected sensor set. We propose

Advances in semiconductor technology have made it possible to build miniature but reliable biosensors. A network of such biosensors can be implanted in humans for health monitoring and prosthesis. However, such networks are fundamentally different from other wireless networks. They have a continuous but very small source of power. This energy constraint necessitates the use of highly energy-efficient communications protocols. We present two such protocols in the context of a biomedical application we are working on, namely, retinal prosthesis. Our first approach is a cluster-based protocol in which only a small fraction of the nodes make expensive long distance transmits to the external base station. Our second approach is a tree-based protocol. We also analyse the energy efficiency of these protocols while varying parameters such as number of nodes in the system and distance between nodes. Our analysis shows that the cluster-based protocol has better energy performance.

One of the main goals of sensor networks is to provide accurate information about a sensing field for an extended period of time. This requires collecting measurements from as many sensors as possible to have a better view of the sensor surroundings. However, due to energy limitations and to prolong the network lifetime, the number of active sensors should be kept to a minimum. To resolve this conflict of interest, sensor selection schemes are used. In this paper, we survey different schemes that are used to select sensors. Based on the purpose of selection, we classify the schemes into (1) coverage schemes, (2) target tracking and localization schemes, (3) single mission assignment schemes and (4) multiple missions assignment schemes. We also look at solutions to relevant problems from other areas and consider their applicability to sensor networks. Finally, we take a look at the open research problems in this field. 1.