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.

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.

A doubly stochastic point process is proposed and analyzed. It is a switched batch Bernoulli process (SBBP) for modeling bursty and correlated input to discrete-time queuing systems. Through the investigation of the counting process, statistical characterization measures of the SBBP are explicitly obtained. A discrete-time single-server queue with SBBP input and general service time (SBBP/G/1) is then considered. The SBBP/G/1 queue has a potential applicability to a statistical multiplexer in the network. The supplementary variable technique is used to obtain the probability generating functions of performance measures in the SBBP/G/1 queue. It is shown how the SBBP is suitable for analyzing a discrete-time queue with bursty and correlated input.>