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Despite the introduction of novel energy harvesting technologies, the lifetime of a sensor platform remains one of its most important performance metrics. Performance, however, may also be assessed in terms of the fraction of events which may successfully/unsuccessfully be detected and reported within a time interval of interest, i.e., mission time. Such a performance metric, here termed event goodput, is key for all random event-driven networks, ranging from surveillance and intrusion detection applications operating in time critical scenarios, to mobile and wearable crowd-sensed ecosystems, where mobile sensors are utilized by a number of different applications. When reporting the appearance of a series of possible, but unknown, phenomena, predicting which event goodput may be obtained during the planned mission time is challenging. In this paper, we address this issue by reducing the network-wide problem to the analysis of the performance of individual nodes, in terms of their capability of handling given fractions of event arrivals under fixed probabilistic guarantees. This is obtained as a function of mission time, event arrival and energy consumption models of different, detection and communication schemes, all realistic.

In this work we model and provide the means to extend the lifetime of a barrier coverage sensor network deployed fortarget detection. We consider a scenario where sensors are randomly dropped on a bidimensional field in order to detect target traversals which occur in a stochastic way within a critical mission time. Once a target enters a sensor's detection area, the sensor transmits such information to a cluster head, in charge of receiving and retransmitting the messages received from the sensors deployed on the field. The contribution of this work is fourfold. We first identify the sensing nodes whose behavior is key to model the duration of sensing operations, assuming prior arrival and mobility models for target traversals. We then proceed, providing a heuristic estimation of the traffic received by the cluster head to quantify its energy requirements, resorting to specific lifetime definitions. We also evaluate the relationship between our probabilistic and heuristic models and the time until the barrier remains capable of detecting and reporting the traversal of any target to a sink, as obtained by simulation. Finally, we show how the lifetime of such network may be shaped, with the use of a sequential activation mechanism, for example to combat the traversals of adversaries exploiting the lifetime models obtained in this work.

In this work we model the lifetime of a clustered sensor network deployed for target detection. We assume sensors are randomly dropped on a bidimensional field in order to detect target traversals occuring stochastically. Once a target enters the sensing area of a sensor, the sensor transmits such information to a cluster head, in charge of receiving and retransmitting the messages received from the sensors deployed on the field. The contribution of this work is threefold. We first identify the sensing nodes whose behavior is key to model the duration of sensing operations. We then proceed, providing a heuristic estimation of the traffic received by the cluster head to quantify its energy requirements, resorting to specific lifetime definitions. We finally evaluate the relationship between our probabilistic and heuristic models and the time until when the network fulfills its purpose, i.e., it remains capable of detecting and reporting the traversal of any target to a sink, as obtained by simulation.

This work extends the state of the art in the modeling of the lifetime of a sensor network. This is performed concentrating on the peculiarities exhibited by a specific, but realistic scenario, surveillance sensor networks, sensor networks in charge of reporting the passage of targets across an area of interest. Focusing on surveillance sensor networks, we here show that a useful lifetime definition can be directly derived from the targets mobility model. In fact, instead of resorting to abstract lifetime or coverage indices, we here derive the lifetime of a sensor network as the time when a target is first able to cross the area of interest without being detected, studying such variable in terms of its stochastic model. To the best of our knowledge, this is the first contribution which approaches the problem of assessing the lifetime of a sensor network building upon the mobility model of its targets, while adopting a fully probabilistic approach to perform such type of analysis.