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 such conditions is to simultaneously use parallel available networks. In this work, we tackle the problem of data allocation to parallel networks in challenged environments, 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

Adaptive Fuzzy Spray and Wait: Efficient Routing for Opportunistic Networks

The technological advancement in the area of wireless networking is ultimately envisioned to reach complete and seamless ubiquity, where every point on earth will need to be covered by Internet access. Low connectivity environments have emerged as a major challenge, and accordingly Opportunistic Networks arose as a promising solution. While these networks do not assume the existence of a path from the source to the destination, they opportunistically utilize any possible resource available to maximize throughput. Routing protocols in such environments have always tried to target an increased delivery probability, a shorter delay, and a reduced overhead. In this work, we try to balance these apparently conflicting goals by introducing “Adaptive Fuzzy Spray and Wait”, an optimized routing scheme for opportunistic networks. On top of the overhead reduction, we argue that the spray-based opportunistic routing techniques can attain higher delivery probability through integrating the adequate buffer prioritization and dropping policies. Towards that purpose, we employ a fuzzy decision making scheme. We also tackle the limitations of the previous approaches by allowing a full-adaptation to the varying network parameters. Extensive simulations using the ONE (Opportunistic Network Environment) simulator [1] show the robustness and effectiveness of the algorithm under challenged network conditions