Distributed CSMA with pairwise coding

We consider distributed strategies for joint routing, scheduling, and network coding to maximize throughput in wireless networks. Network coding allows for an increase in network throughput under certain routing conditions. We previously developed a centralized control policy to jointly optimize for routing and scheduling combined with a simple network coding strategy using max-weight scheduling (MWS) [9]. In this work we focus on pairwise network coding and develop a distributed carrier sense multiple access (CSMA) policy that supports all arrival rates allowed by the network subject to the pairwise coding constraint. We extend our scheme to optimize for packet overhearing to increase the number of beneficial coding opportunities. Simulation results show that the CSMA strategy yields the same throughput as the optimal centralized policy of [9], but at the cost of increased delay. Moreover, overhearing provides up to an additional 25% increase in throughput on random topologies.United States. Dept. of Defense. Assistant Secretary of Defense for Research & EngineeringUnited States. Air Force (Air Force Contract FA8721-05-C-0002

Cross-layer optimizations for intersession network coding on practical 2-hop relay networks

Abstract-Full characterization of Intersession Network Coding (INC), i.e., coding across multiple unicast sessions, is notoriously challenging. Nonetheless, the problem can be made tractable when considering practical constraints that restrict the types of INC schemes of interest. This paper characterizes the INC capacity of 2-session wireless 2-hop relay networks with a packet erasure channel model and a round-based feedback schedule motivated by the usage of "reception reports" in practical protocols such as COPE. The capacity regions are formulated as linear programming problems, which admit simple concatenation with other competing techniques such as opportunistic routing (OpR), and cross-layer (CL) optimization. Extensive numerical evaluation is conducted on 1000 random topologies, which compares and quantifies the throughput benefits of INC, OpR, and CL, and their arbitrary combinations. The results show that by combining all three techniques of INC, OpR, and CL, the throughput of a wireless 2-hop relay network can be improved by 100-500% over the benchmark single-path routing solution depending on the number of sessions to be coded together

Joint Congestion Control and Scheduling in Wireless Networks with Network Coding

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Probabilistic network coding techniques for vehicular ad-hoc networks

vehicular ad hoc network (vanet) is an emerging technology that enables moving vehicles on the road to connect and communicate as network devices. vanets enhance roads safety measures and improve traffic efficiency. however, due to the lack of centralization and the large number of highly mobile nodes, vanets are considered as highly congested networks with significant packet collisions and retransmissions. on the other hand, network coding is an emerging technique known to effectively utilize network resources by significantly reducing the number of transmissions. in network coding, intermediate nodes minimize the number of transmission by combining different packets before transmitting. however, a fundamental problem for network coding relay when it receives a packet is whether to wait for a coding opportunity to reduce network congestion; or to send the packet immediately without coding to reduce packet delay. this thesis proposes network coding techniques to reduce the number of transmissions and the bandwidth consumption in vanet multi-hop scenario. it also presents an analytical study on the trade-off between the average packet delay and the network throughput in network coding. it proposes a probabilistic approach for the intermediate nodes and therefore develops an analytical framework to present the effect of using such technique on the network performance. the system stability conditions have also been investigated. moreover, flows with different and same priorities are considered and different mechanisms that consider the nature of the different applications are proposed. for fair delay, this thesis provides the optimum transmission probability which achieves the minimum fair delay and results in an optimum throughput. while for different priority flows, a queue state based probabilistic scheduling schemes are proposed to avoid unbounded packet delays. to highlight the result, for symmetric rate flows, fairness scheme shows that the optimum fair delay can be achieved with probability of transmission of 0.5. it also shows that despite the flow data rate, using this probability will result in 33% improvement in the bandwidth consumption, and in an equal hop delay for both flows that is 0.5/?, where ? is the average flow data rate. moreover, for asymmetric rate flows the work provides the optimum transmission probability and its corresponding fair delay and throughput improvement. simulation is carried out to verify the analytical results where it is closely matched the theoretical results

Survivability and performance optimization in communication networks using network coding

The benefits of network coding are investigated in two types of communication networks: optical backbone networks and wireless networks. In backbone networks, network coding is used to improve survivability of the network against failures. In particular, network coding-based protection schemes are presented for unicast and multicast traffic models. In the unicast case, network coding was previously shown to offer near-instantaneous failure recovery at the bandwidth cost of shared backup path protection. Here, cost-effective polynomial-time heuristic algorithms are proposed for online provisioning and protection of unicast traffic. In the multicast case, network coding is used to extend the traditional live backup (1+1) unicast protection to multicast protection; hence called multicast 1+1 protection. It provides instantaneous recovery for single failures in any bi-connected network with the minimum bandwidth cost. Optimal formulation and efficient heuristic algorithms are proposed and experimentally evaluated. In wireless networks, performance benefits of network coding in multicast transmission are studied. Joint scheduling and performance optimization formulations are presented for rate, energy, and delay under routing and network coding assumptions. The scheduling component of the problem is simplified by timesharing over randomly-selected sets of non-interfering wireless links. Selecting only a linear number of such sets is shown to be rate and energy effective. While routing performs very close to network coding in terms of rate, the solution convergence time is around 1000-fold compared to network coding. It is shown that energy benefit of network coding increases as the multicast rate demand is increased. Investigation of energy-rate and delay-rate relationships shows both parameters increase non-linearly as the multicast rate is increased

Efficient packet delivery in modern communication networks

Modern communication networks are often designed for diverse applications, such as voice, data and video. Packet-switching is often adapted in today’s networks to transmit multiple types of traffic. In packet-switching networks, network performance is directly affected by how the networks handle their packets. This work addresses the packet-handling issues from the following two aspects: Quality of Service (QoS) and network coding. QoS has been a well-addressed issue in the study of IP-based networks. Generally, nodes in a network need to be informed of the state of each communication link in order to make intelligent decisions to route packets according to their QoS demands. The link state can, however, change rapidly in a network; therefore, nodes would have to receive frequent link state updates in order to maintain the latest link state information at all times. Frequent link state updating is resource-consuming and hence impractical in network design. Therefore, there is a trade-off between the link state updating frequency and the QoS routing performance. It is necessary to design a link state update algorithm that utilizes less frequent link state updates to achieve a high degree of satisfaction in QoS performance. The first part of this work addresses this link state update problem and provides two solutions: ROSE and Smart Packet Marking. ROSE is a class-based link state update algorithm, in which the class boundaries are designed based on the statistical data of users’ QoS requests. By doing so, link state update is triggered only when certain necessary conditions are met. For example, if the available bandwidth of a link is fluctuating within a range that is higher than the highest possible bandwidth request, there is no need to update the state of this link. Smart Packet Marking utilizes a similar concept like ROSE, except that the link state information is carried in the probing packet sent in conjunction with each connection request instead of through link state updates. The second part of this work addresses the packet-handling issue by means of network coding. Instead of the traditional store-and-forward approach, network coding allows intermediate nodes in a multi-hop path to code multiple packets into one in order to reduce bandwidth consumption. The coded packet can later be decoded by its recipients to retrieve the original plain packet. Network coding is found to be beneficial in many network applications. This dissertation makes contributions in network coding in two areas: peer-to-peer file sharing and wireless ad-hoc networks. The benefit of network coding in peer-to-peer file sharing networks is analyzed, and a network coding algorithm – Downloader-Initiated Random Linear Network Coding (DRLNC) – is proposed. DLRNC shifts the coding decision from the seeders to the leechers; by doing so it solves the “collision” problem without increasing the field size. In wireless network coding, this work addresses the implementation difficulty pertaining to MAC layer scheduling. To achieve the ideal network coding gain in wireless networks, it requires perfect MAC layer scheduling. This dissertation first provides an algorithm to solve the ideal-case MAC layer scheduling problem. Since the ideal MAC layer schedule is often difficult to realize, a practical approach is then proposed to increase the network coding performance by modifying the ACK packets in the 802.11 MAC

Power management algorithms for IoT platforms

The Internet of Things (IoT) is a platform that connects various electronic systems such as home appliances, vehicles, and medical devices through wired or wireless communications. Without recharging the battery of sensors and mobile systems in IoT networks, their usage time is limited. In order to improve performance with finite battery energy, power management is used to conserve the energy dissipation of sensor networks and mobile systems. This dissertation addresses power management in two categories of systems within IoT: wireless sensor networks (WSNs) and electric vehicles (EVs). For power management in WSNs, this dissertation develops an algorithm using network coding (NC). When one sender transmits multiple packets to different receivers in a WSN, an NC algorithm reduces transmissions between the sender and the receivers by encoding many packets into one packet. Consequently, the total communication energy between the sender and the receivers is decreased. For further study about real energy gains generated by NC algorithms, we develop a wireless testbed by using mobile devices. Consequently, by varying different network variables such as transmission range of a sender and the number of receivers in the testbed network, we discover network conditions where communication energy saved by NC algorithms is increased. However, NC algorithms spend operational energy overheads for algorithm execution, encoding, and decoding. Hence, our research also shows the threshold conditions where the energy saved by the NC algorithms are larger than the energy overheads with consideration of communication variables or algorithm complexity in order to identify opportunities for energy savings. For power management of EVs, this dissertation develops an energy-efficient algorithm using neural networks which can be used for power management of EVs\u27 electronic control system. Power management saves energy consumption of the electronic control system by selectively activating electronic control units (ECUs) in the system. However, the energy savings generated by the power management could be less than the energy overheads used for the selective ECU activation and deactivation. Our algorithm experiences events where energy overheads were greater than energy savings and trains neural networks for the experienced events. The neural networks forecast energy-inefficient events and conserve energy overheads based on the predicted events. Our simulation study using real driving datasets shows that the algorithm improves the energy dissipation of the electronic control system by 5% to 7%