In recent years, network coding has emerged as an innovative method that helps a wireless network approach its maximum capacity, by combining multiple unicasts in one broadcast. However, the majority of research conducted in this area is yet to fully utilize the broadcasting nature of wireless networks, and still assumes fixed route between the source and destination that every packet should travel through. This assumption not only limits coding opportunities, but can also cause buffer overflow in some specific intermediate nodes. Although some studies considered scattering of the flows dynamically in the network, they still face some limitations. This paper explains pros and cons of some prominent research in network coding and proposes a Flexible and Opportunistic Network Coding scheme (FlexONC) as a solution to such issues. Furthermore, this research discovers that the conditions used in previous studies to combine packets of different flows are overly optimistic and would affect the network performance adversarially. Therefore, we provide a more accurate set of rules for packet encoding. The experimental results show that FlexONC outperforms previous methods especially in networks with high bit error rate, by better utilizing redundant packets spread in the network.
In inter-flow network coding, an intermediate node combines two packets if the next-hop of each packet has already received the other coding partner. To keep track of the packets received by each node, two types of information are used: deterministic information and probabilistic information. Deterministic information are provided by exchanging reception reports'' among nodes, where each node's reception report contains the packets that recently have been received or overheard by the node @cite_9 . These reception reports are usually piggybacked on data packets or broadcasted periodically.
@cite_9
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We present new distributed deterministic solutions to two communication problems in n-node ad-hoc radio networks: rumor gathering and multi-broadcast. In these problems, some or all nodes of the network initially contain input data called rumors, which have to be learned by other nodes. In rumor gathering, there are k rumors initially distributed arbitrarily among the nodes, and the goal is to collect all the rumors at one node. Our rumor gathering algorithm works in O((k + n) log n) time and our multi-broadcast algorithm works in O(k log3 n + n log4 n) time, for any n-node networks and k rumors (with arbitrary k), which is a substantial improvement over the best previously known deterministic solutions to these problems. As a consequence, we exponentially decrease the gap between upper and lower bounds on the deterministic time complexity of four communication problems: rumor gathering, multi-broadcast, gossiping and routing, in the important case when every node has initially at most one rumor (this is the scenario for gossiping and for the usual formulation of routing). Indeed, for k = O(n), our results simultaneously decrease the complexity gaps for these four problems from polynomial to polylogarithmic in the size of the graph. Moreover, our deterministic gathering algorithm applied for k = O(n) rumors, improves over the best previously known randomized algorithm of time O(k log n + n log2 n). Two tasks of communication in a multi-hop synchronous radio network are considered: point-to-point communication and broadcast (sending a message to all nodes of a network). Efficient protocols for both problems are presented. Even though the protocols are probabilistic, it is shown how to acknowledge messages deterministically. Let n, D, and ∆ be the number of nodes, the diameter and the maximum degree of our network, respectively. Both protocols require a setup phase in which a BFS tree is constructed. This phase takes O ((n + Dlogn)log∆) time. After the setup, k point-to-point transmissions require O ((k +D)log∆) time on the average. Therefore the network allows a new transmission every O (log∆) time slots. Also, k broadcasts require an average of O ((k +D)log∆logn) time. Hence the average throughput of the network is a broadcast every O(log∆logn) time slots. Both protocols pipeline the messages along the BFS tree. They are always successful on the graph spanned by the BFS tree. Their probabilistic behavior refers only to the running time. Using the above protocols the ranking problem is solved in O (nlognlog∆) time. The performance analysis of both protocols constitutes a new application of queueing theory. In this paper, we consider the CSMA CA multihop networks where the two end-nodes transmit their packets to each other and each intermediate node adopts network coding for delivering bidirectional flows. In addition, the neighbor nodes are randomly uniformly deployed with the Poisson Point Process. By varying the combination of the physical carrier-sensing range of the transmitter node and the target signal-to-interference ratio (SIR) set by the receiver node, we can control the interference level in the network and the degree of spatial reuse of a frequency band. The larger the carrier-sensing range is, the smaller the interference level, while the smaller the opportunity of getting a channel by a node. Similarly, the higher the target SIR value is, the more probable the retransmission (by the exponential random backoff) is, while the better the link quality on successful transmission is getting. Under this tradeoff context, we find the optimal combinations of these two factors that make the end-to-end throughput of the flow maximal for three different retransmission schemes. Energy harvesting (EH) two-hop communications are considered. The transmitter and the relay harvest energy from the environment and use it exclusively for transmitting data. A data arrival process is assumed at the transmitter. At the relay, a finite data buffer is used to store the received data. We consider a realistic scenario in which the EH nodes have only local causal knowledge, i.e., at any time instant, each EH node only knows the current value of its EH process, channel state, and data arrival process. Our goal is to find a power allocation policy to maximize the throughput at the receiver. We show that because the EH nodes have local causal knowledge, the two-hop communication problem can be separated into two point-to-point problems. Consequently, independent power allocation problems are solved at each EH node. To find the power allocation policy, reinforcement learning with linear function approximation is applied. Moreover, to perform function approximation two feature functions which consider the data arrival process are introduced. Numerical results show that the proposed approach has only a small degradation as compared to the offline optimum case. Furthermore, we show that with the use of the proposed feature functions a better performance is achieved compared to standard approximation techniques.