ITSYSYITSep 14, 2015

Dynamic Network Control for Confidential Multi-hop Communications

arXiv:1301.10028 citationsh-index: 29
AI Analysis

For network operators requiring confidential multi-hop communications, this work provides a provably optimal dynamic control algorithm that integrates secrecy constraints into network utility maximization.

The paper develops a dynamic control algorithm for resource allocation in multihop networks with confidential communications, achieving optimal rates asymptotically with increasing block size and demonstrating efficacy through numerical studies.

We consider the problem of resource allocation and control of multihop networks in which multiple source-destination pairs communicate confidential messages, to be kept confidential from the intermediate nodes. We pose the problem as that of network utility maximization, into which confidentiality is incorporated as an additional quality of service constraint. We develop a simple, and yet provably optimal dynamic control algorithm that combines flow control, routing and end-to-end secrecy-encoding. In order to achieve confidentiality, our scheme exploits multipath diversity and temporal diversity due to channel variability. Our end-to-end dynamic encoding scheme encodes confidential messages across multiple packets, to be combined at the ultimate destination for recovery. We first develop an optimal dynamic policy for the case in which the number of blocks across which secrecy encoding is performed is asymptotically large. Next, we consider encoding across a finite number of packets, which eliminates the possibility of achieving perfect secrecy. For this case, we develop a dynamic policy to choose the encoding rates for each message, based on the instantaneous channel state information, queue states and secrecy outage requirements. By numerical analysis, we observe that the proposed scheme approaches the optimal rates asymptotically with increasing block size. Finally, we address the consequences of practical implementation issues such as infrequent queue updates and de-centralized scheduling. We demonstrate the efficacy of our policies by numerical studies under various network conditions.

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