Abstract-Wireless Sensor and Actuator Networks (WSANs) employ mobile nodes in addition to stationary tiny sensors. Similarly, mobile sensors make it possible to have the flexibility of mobility in mobile sensor network (MSN) applications. Mobility can be exploited to connect partitioned WSANs and MSNs due to large scale damages or deployment problems. However, since mobility consume significant energy and it can be limited due to terrain constraints, the travel distance for the mobile nodes should be minimized in such a recovery effort. In this paper, we present a mathematical model which minimizes the total travel distance for connecting a given number of partitions. The idea is based on network flows and the problem is modeled as a mixed integer nonlinear program. The nonlinear terms in the model are linearized using a polygon approximation for computational efficiency. We evaluated the performance of the proposed approach in terms of total distance as well as the time to reconnect the partitions. The results show that our approach outperforms the heuristic approach in terms of total distance and delay and reveals various trade-offs involved in connecting multiple partitions.

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Previous nurse scheduling models have mainly focused on managerial constraints to minimize costs. Although some models incorporate nurse preferences and safety guidelines, human factors considerations related to performance of nurses (e.g., fatigue) have not been studied extensively. Fatigue has been linked to nursing injuries and medical errors, and shown to be impacted by schedule-related parameters (e.g., shift length). Thus, the objective of this article was to develop a nurse scheduling model incorporating quantitative models of fatigue. This model can help a nurse manager to make schedule-related decisions by highlighting trade-offs among many (conflicting) objectives including nurse shift preferences and nurse fatigue levels obtained from two different fatigue models, namely survey-based and circadian function-based fatigue models. The data used in the numerical experiments were obtained from real patient census data and various surveys of nurses working in different hospitals across the United States. Numerical results show that it is possible to obtain Pareto-optimal schedules where the nurse fatigue levels are significantly reduced for a slight decrement in nurse preferences.