(1) The problem of set cover maximization is modeled and solved by the mixed integer programming (MIP), and then a linear programming based heuristic computes the covers based on the solution returned by MIP. (2) A greedy heuristic solves the problem of set cover maximization
A greedy heuristic reinforced by the idea of critical targets (POIs with small neighbor-sensor sets) finds the maximum number of covers which is equal to the schedule makespan since sets provide coverage for the same unit of time
The column generation based heuristic finds covers and maximizes the sum of activation times of the covers for the problem modeled with a linear programming formulation
High-energy-first heuristic finds covers using the highest remaining battery states; the selection priorities change after every execution of the selected set, hence the variety of sets over the network lifetime
Two approaches—genetic algorithm and memetic algorithm with a two-dimensional representation of schedules (columns: covers; rows: sensors)—maximize the number of feasible covers satisfying the battery capacity restriction in the rows
The problem of set cover maximization is solved by the column generation based framework having embedded the greedy randomized adaptive search procedure and the variable neighborhood search
Simulated annealing with a two-dimensional representation of schedules (columns: covers; rows: sensors) maximizes the number of feasible covers satisfying the battery capacity restriction in the rows
Graph cellular automata approach for cover generation and makespan maximization where cells correspond to sensors, and the neighborhood relation between cells maps the existence of targets or areas commonly monitored by these sensors