Mobile-Edge Computation Servers Repairing for IoT Network Failures Under Uncertainty

Mobile-edge computation edge servers (ESs) act as crucial intermediaries between mobile devices (MDs) and cloud computing centers, ensuring low-latency computation and reliable IoT network performance. However, regional failures induced by natural or man-made disasters can severely disrupt ES operations. This paper focuses on devising an effective repair scheme for ESs after large-scale disasters, particularly under the uncertainty of secondary disasters and the stochastic demands of MDs. We formulate the edge server repair problem (ESRP) as a mixed-integer linear programming (MILP) model and prove its NP-hardness. To effectively solve this problem, we propose a solution algorithm based on Benders decomposition (BD). The MILP problem is decomposed into a more solvable master problem and a series of subproblems, which are solved alternately and iteratively. Furthermore, we enhance our BD algorithm by incorporating two types of initial cuts to reduce the iterative number and accelerate the convergence speed. The first type is known as supermodular cut, which exploits the inherent properties of the problem. The second type is called aggregation capacity constraint, which exploits the capacity characteristics of ESs. Extensive simulation results demonstrate that the proposed method excels in ES repairing under large-scale IoT network failure scenarios. Compared to other available algorithms, our BD-based approach more efficiently achieves the exact solution to the ESRP, minimizing network demand loss within limited resources.