In-Network Processing For Mission-Criticalwireless Networked Sensing And Control: A Real-Time, Efficiency, And Resiliency Perspective

As wireless cyber-physical systems (WCPS) are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). In this dissertation, we explore the potentials of two INP methods, packet packing and network coding, on improving network performance while satisfying application QoS requirements. We find that not only can these two techniques increase the energy efficiency, reliability, and throughput of WCPS while satisfying QoS requirements of applications in a relatively static environment, but also they can provide low cost proactive protection against transient node failures in a more dynamic wireless environment. We first study the problem of jointly optimizing packet packing and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. We design a distributed, online protocol tPack that schedules packet transmissions to maximize the local utility of packet packing at each node. We evaluate the properties of tPack in NetEye testbed. We find that jointly optimizing data delivery timeliness and packet packing and considering real-world aggregation constraints significantly improve network performance. We then work on the problem of minimizing the transmission cost of network coding based routing in sensor networks. We propose the first mathematical framework so far as we know on how to theoretically compute the expected transmission cost of NC-based routing in terms of expected number of transmission. Based on this framework, we design a polynomial-time greedy algorithm for forwarder set selection and prove its optimality on transmission cost minimization. We designed EENCR, an energy-efficient NC-based routing protocol that implement our forwarder set selection algorithm to minimize the overall transmission cost. Through comparative study on EENCR and other state-of-the-art routing protocols, we show that EENCR significantly outperforms CTP, MORE and CodeOR in delivery reliability, delivery cost and network goodput. Furthermore, we study the 1+1 proactive protection problem using network coding. We show that even under a simplified setting, finding two node-disjoint routing braids with minimal total cost is NP-hard. We then design a heuristic algorithm to construct two node-disjoint braids with a transmission cost upper bounded by two shortest node-disjoint paths. And we design ProNCP, a proactive NC-based protection protocol using similar design philosophy as in EENCR. We evaluate the performance of ProNCP under various transient network failure scenarios. Experiment results show that ProNCP is resilient to various network failure scenarios and provides a state performance in terms of reliability, delivery cost and goodput. Our findings in this dissertation explore the challenges, benefits and solutions in designing real-time, efficient, resilient and QoS-guaranteed wireless cyber-physical systems, and our solutions shed lights for future research on related topics

Optimizing Network Coding Algorithms for Multiple Applications.

Deviating from the archaic communication approach of treating information as a fluid moving through pipes, the concepts of Network Coding (NC) suggest that optimal throughput of a multicast network can be achieved by processing information at individual network nodes. However, existing challenges to harness the advantages of NC concepts for practical applications have prevented the development of NC into an effective solution to increase the performance of practical communication networks. In response, the research work presented in this thesis proposes cross-layer NC solutions to increase the network throughput of data multicast as well as video quality of video multicast applications. First, three algorithms are presented to improve the throughput of NC enabled networks by minimizing the NC coefficient vector overhead, optimizing the NC redundancy allocation and improving the robustness of NC against bursty packet losses. Considering the fact that majority of network traffic occupies video, rest of the proposed NC algorithms are content-aware and are optimized for both data and video multicast applications. A set of content and network-aware optimization algorithms, which allocate redundancies for NC considering content properties as well as the network status, are proposed to efficiently multicast data and video across content delivery networks. Furthermore content and channel-aware joint channel and network coding algorithms are proposed to efficiently multicast data and video across wireless networks. Finally, the possibilities of performing joint source and network coding are explored to increase the robustness of high volume video multicast applications. Extensive simulation studies indicate significant improvements with the proposed algorithms to increase the network throughput and video quality over related state-of-the-art solutions. Hence, it is envisaged that the proposed algorithms will contribute to the advancement of data and video multicast protocols in the future communication networks

Network Coding Meets TCP: Theory and Implementation

The theory of network coding promises significant benefits in network performance, especially in lossy networks and in multicast and multipath scenarios. To realize these benefits in practice, we need to understand how coding across packets interacts with the acknowledgment (ACK)-based flow control mechanism that forms a central part of today's Internet protocols such as transmission control protocol (TCP). Current approaches such as rateless codes and batch-based coding are not compatible with TCP's retransmission and sliding-window mechanisms. In this paper, we propose a new mechanism called TCP/NC that incorporates network coding into TCP with only minor changes to the protocol stack, thereby allowing incremental deployment. In our scheme, the source transmits random linear combinations of packets currently in the congestion window. At the heart of our scheme is a new interpretation of ACKs-the sink acknowledges every degree of freedom (i.e., a linear combination that reveals one unit of new information) even if it does not reveal an original packet immediately. Thus, our new TCP ACK rule takes into account the network coding operations in the lower layer and enables a TCP-compatible sliding-window approach to network coding. Coding essentially masks losses from the congestion control algorithm and allows TCP/NC to react smoothly to losses, resulting in a novel and effective approach for congestion control over lossy networks such as wireless networks. An important feature of our solution is that it allows intermediate nodes to perform re-encoding of packets, which is known to provide significant throughput gains in lossy networks and multicast scenarios. Simulations show that our scheme, with or without re-encoding inside the network, achieves much higher throughput compared to TCP over lossy wireless links. We present a real-world implementation of this protocol that addresses the practical aspects of incorporating network coding and decoding with TCP's wind ow management mechanism. We work with TCP-Reno, which is a widespread and practical variant of TCP. Our implementation significantly advances the goal of designing a deployable, general, TCP-compatible protocol that provides the benefits of network coding.National Science Foundation (U.S.) (Grant CNS-0627021)National Science Foundation (U.S.) (Grant CNS-0721491)National Science Foundation (U.S.) (Grant CCF-0915922)United States. Defense Advanced Research Projects Agency (Subcontract 18870740-37362-C)United States. Defense Advanced Research Projects Agency (Subcontract 060786)United States. Defense Advanced Research Projects Agency (Subcontract 069145)United States. Defense Advanced Research Projects Agency (Contract N66001-06-C-2020)Space and Naval Warfare Systems Center San Diego (U.S.) (Contract N66001- 08-C-2013