In this paper, we examine the merit of a simple and lightweight stochastic transmission strategy based on the ALOHA protocol for underwater wireless sensor networks (UWSNs). We use a stochastic scheduling approach in which time is slotted, and each network component transmits according to some probability during each slot. We present objective functions for assigning the transmission probabilities that are aimed at optimizing network performance with respect to the overall network latency and the overall network reliability. We show that there is an easily distributed heuristic policy based on local network density that works well in practice. We also evaluate our approach using numerical simulations. The evaluation results show that even without using explicit control signaling, our lightweight stochastic scheduling method is effective for data transmission in underwater sensor networks.

In this paper, we present an algorithm for simultaneously refining a probability distribution function (PDF) for the pose of a sensor network (i.e. the locations of the sensors), and inferring the spatial variations of measured environmental parameters. Our approach iteratively refines a network pose PDF by assuming that environmental parameters vary smoothly. Both our physical experiments, which sensed wireless signal strength as the environmental variable, and our numerical simulations demonstrate that the approach has promise.

In this paper, we investigate a pure form of the topological mapping problem in mobile robotics. We consider the mapping ability of a robot navigating a graph-like world in which it is able to assign a relative ordering to the edges, leaving a vertex with reference to the edge by which it arrived but is unable to associate a unique label with any vertex or edge. Our work extends and builds upon earlier approaches in this problem domain, which are based on construction of exploration tree of plausible world models. The main contributions of the paper are improved exploration strategies that reduce model ambiguity, a new method of search through consistent models in the exploration tree that maintains a bounded set of likely hypotheses based on the principle of Occam's Razor, the incorporation of arbitrary feature vectors into the problem formulation, and an investigation of various aspects of this problem through numerical simulations.

In this paper we present an approach for localizing a sensor network augmented with a mobile robot which is capable of providing inter-sensor pose estimates through its odometry measurements. We present a stochastic algorithm that samples efficiently from the probability distribution for the pose of the sensor network by employing Rao-Blackwellization and a proposal scheme which exploits the sequential nature of odometry measurements. Our algorithm automatically tunes itself to the problem instance and includes a principled stopping mechanism based on convergence analysis. We demonstrate the favourable performance of our approach compared to that of established methods via simulations and experiments on hardware.

We present a method for inferring the topology of a sensor network given nondiscriminating observations of activity in the monitored region. This is accomplished based on no prior knowledge of the relative locations of the sensors and weak assumptions regarding environmental conditions. Our approach employs a two-level reasoning system made up of a stochastic expectation maximization algorithm and a higher level search strategy employing the principle of Occam's Razor to look for the simplest solution explaining the data. The result of the algorithm is a Markov model describing the behavior of agents in the system and the underlying traffic patterns. Numerical simulations and experimental assessment conducted on a real sensor network suggest that the technique could have promising real-world applications in the area of sensor network self-configuration.

When a network of robots or static sensors is emplaced in an environment, the spatial relationships between the sensing units must be inferred or computed for most key applications. In this paper we present a Monte Carlo expectation maximization algorithm for recovering the connectivity information (i.e. topological map) of a network using only detection events from deployed sensors. The technique is based on stochastically reconstructing samples of plausible agent trajectories allowing for the possibility of transitions to and from sources and sinks in the environment. We demonstrate robustness to sensor error and non-trivial patterns of agent motion. The result of the algorithm is a probabilistic model of the sensor network connectivity graph and the underlying traffic trends. We conclude with results from numerical simulations and an experiment conducted with a heterogeneous sensor network

We consider the problem of inferring sensor positions and a topological (i.e. qualitative) map of an environment given a set of cameras with non-overlapping fields of view. In this way, without prior knowledge of the environment nor the exact position of sensors within the environment, one can infer the topology of the environment, and common traffic patterns within it. In particular, we consider sensors stationed at the junctions of the hallways of a large building. We infer the sensor connectivity graph and the travel times between sensors (and hence the hallway topology) from the sequence of events caused by unlabeled agents (i.e. people) passing within view of the different sensors. We do this based on a first-order semi-Markov model of the agent's behavior. The paper describes a problem formulation and proposes a stochastic algorithm for its solution. The result of the algorithm is a probabilistic model of the sensor network connectivity graph and the underlying traffic patterns. We conclude with results from numerical simulations