ACCOP: Adaptive Cost-Constrained and Delay-Optimized Data Allocation over Parallel Opportunistic Networks
As wireless and mobile technologies are becoming increasingly pervasive, an uninterrupted connectivity in mobile devices is becoming a necessity rather than a luxury. When dealing with challenged networking environments, this necessity becomes harder to achieve in the absence of end-to-end paths from servers to mobiles. One of the main techniques employed to counteract such conditions is to simultaneously use parallel available networks. In this work, we tackle the problem of data-to-channel allocation in challenged networks, targeting a minimized delay while abiding by user preset budget. We propose ACCOP, an Adaptive, Cost-Constrained, and delay-OPtimized data-to-channel allocation scheme that efficiently exploits parallel channels typically accessible from the mobile devices. Our technique replaces the traditional, inefficient, and brute-force schemes through employing Lagrange multipliers to minimize the delivery delay. Furthermore, we show how ACCOP can dynamically adjust to the changing network conditions. Through analytical and experimental tools, we demonstrate that our system achieves faster delivery and higher performance while remaining computationally inexpensive.