We propose a method for estimating parameters of multiple target objects by using networked binary sensors whose locations are unknown. These target objects may have different parameters, such as size and perimeter length. Each sensors, which is incapable of monitoring the target object's parameters, sends only binary data describing whether or not it detects target objects coming into, moving around, or leaving the sensing area at every moment. We previously developed a parameter estimation method for a single target object. However, a straight-forward extension of this method is not applicable for estimating multiple heterogeneous target objects. This is because a networked binary sensor at an unknown location cannot provide information that distinguishes individual target objects, but it can provide information on the total perimeter length and size of multiple target objects. Therefore, we propose composite sensor nodes with multiple sensors in a predetermined layout for obtaining additional information for estimating the parameter of each target object. As an example of a composite sensor node, we consider a two-sensor composite sensor node, which consists of two sensors, one at each of the two end points of a line segment of known length. For the two-sensor composite sensor node, measures are derived such as the two sensors detecting target objects. These derived measures are the basis for identifying the shape of each target object among a given set of categories (for example, disks and rectangles) and estimating parameters such as the radius and lengths of two sides of each target object. Numerical examples demonstrate that networked composite sensor nodes consisting of two binary sensors enable us to estimate the parameters of target objects.

We investigated how to estimate the shape of a target object. For this problem, we propose pair-line composite sensor nodes consisting of multiple sensors on a pair of line segments, where each sensor generates binary information whether it detects the target object or not. We show that the proposed pair-line composite sensor nodes, which are randomly placed, can detect a certain range of angles; therefore, we also call them stochastic geometric filters. By random distribution of pair-line composite sensor nodes without GPS functions or careful placement at known locations, the information sent from the nodes enables us to estimate the boundary angles of the target object as well as its size and perimeter length. A composite sensor node can be conceptualized as between a sensor node equipped with GPS functions, or carefully placed sensors at known locations, and randomly deployed simple sensors without GPS functions.

An estimation method for the shape and size of a target object by using networked binary sensors whose locations are unknown is proposed. Each of those sensors, which individually are incapable of monitoring the target object's shape and size, sends only binary data describing whether or not it detects at every moment the target object. By using these data and the explicit formulas derived in this paper, we can estimate the size and the perimeter length of the target object even without sensor location information. An additional parameter, which is a function of the shape of the target object, can also be estimated when the target object is non-convex.

We study the connectivity in vehicular ad-hoc networks, where the motion of vehicles is constrained on a lattice-shaped road network. First, we theoretically investigate the connectivity under the Poisson-positioning assumption, where vehicles are positioned according to a Poisson process on each road at any arbitrary instants. We find that the Poisson-positioning assumption allows the existence of the finite critical-vehicle density; that is, if (and only if) the density of vehicles is greater than the finite critical density, then there exists a large (theoretically infinite) cluster of vehicles and an arbitrary pair of vehicles in the set is connected in single or multiple hops. We obtain an analytical expression for the critical density as a function of the transmission range of each vehicle and the distance between intersections. Next, we consider the connectivity under more realistic movement patterns of vehicles where the Poisson-positioning assumption does not hold. We numerically find that, even in non-Poisson-positioning cases, there exists the critical vehicle density. The critical density in non-Poisson-positioning cases is, however, larger than the one under the Poisson-positioning assumption. We also gain some insight on the efficiency of roadside-relay-station deployment to provide better connectivity between vehicles.