The proposed sensor placement optimization framework can be extended to incorporate additional objectives or constraints by integrating multi-objective optimization techniques that account for various factors influencing sensor deployment. For instance, to include sensor installation costs, a new objective function can be introduced that minimizes the total cost associated with purchasing and installing sensors. This can be formulated as an additional constraint in the optimization model, ensuring that the total installation cost does not exceed a predefined budget.
Moreover, accessibility considerations can be integrated by introducing constraints that ensure sensors are placed in locations that are easily reachable for maintenance and data collection. This could involve defining a set of accessible nodes based on geographical data and incorporating these nodes into the optimization process. The framework could also utilize a weighted approach, where the importance of each objective (e.g., coverage, cost, accessibility) is defined, allowing the algorithm to balance trade-offs effectively. By employing techniques such as Pareto optimization, the framework can generate a diverse set of solutions that reflect the best trade-offs among the multiple objectives, thus enhancing the overall effectiveness of the sensor placement strategy.