Network coding and opportunistic routing are two recognized innovative ideas to improve the performance of wireless networks by utilizing the broadcast nature of the wireless medium. In the last decade, there has been considerable research on how to synergize inter-flow network coding and opportunistic routing in a single joint protocol outperforming each in any scenario. This paper explains the motivation behind the integration of these two techniques, and highlights certain scenarios in which the joint approach may even degrade the performance, emphasizing the fact that their synergistic effect cannot be accomplished with a naive and perfunctory combination. This survey paper also provides a comprehensive taxonomy of the joint protocols in terms of their fundamental components and associated challenges, and compares existing joint protocols. We also present concluding remarks along with an outline of future research directions.Inter-flow network coding, opportunistic routing, network coding-aware routing, unicast traffic, multi-hop wireless mesh networks. I. INTRODUCTIONWireless mesh network (WMN) [1], [2] is a type of wireless communication networks aiming to realize the dream of a seamlessly connected world. In mesh infrastructure, radio nodes are connected via wireless links creating a multi-hop wireless network, and nodes can talk to each other and pass data over long distances. This is realized by forming long paths consisting of smaller segments and handing off data over mulitple hops. This cooperative data delivery is the key idea of mesh networks to share connectivity across a large area with inexpensive wireless technologies.Despite these advancements, users' expectations rise fast, and new applications require higher throughput and lower delay [3]. In addition, the performance of wireless networks is significantly restricted by interference, and the unreliability of the wireless channel. Also, it is adversely affected by the contention among different data flows and devices in sharing bandwidth and other network resources. However, since the last decade two promising approaches of "Opportunistic Routing" and "Network Coding" are proved to improve the performance of wireless networks significantly by creatively utilizing the broadcast nature of the wireless medium.Network coding (NC), more specifically inter-flow network coding (IXNC), is the process of forwarding more than one packet in each transmission. Doing so, it increases the "effective" capacity of the network [4] and improves the throughput. Opportunistic routing 1 (OR) also benefits from the broadcast nature of wireless networks via path diversity. In OR, in contrast to traditional forwarding, there is no fixed route, and nodes do not forward a packet to a specified pre-selected next-hop. In fact, a node first broadcasts the packet, and then the next-hop is selected among all neighbors that have received the packet successfully. In addition, as explained in Section II-B, OR can reduce the total number of transmissions by exploiting long but p...

The heritability of complex diseases including cancer is often attributed to multiple interacting genetic alterations. Such a non‐linear, non‐additive gene–gene interaction effect, that is, epistasis, renders univariable analysis methods ineffective for genome‐wide association studies. In recent years, network science has seen increasing applications in modeling epistasis to characterize the complex relationships between a large number of genetic variations and the phenotypic outcome. In this study, by constructing a statistical epistasis network of colorectal cancer (CRC), we proposed to use multiple network measures to prioritize genes that influence the disease risk of CRC through synergistic interaction effects. We computed and analyzed several global and local properties of the large CRC epistasis network. We utilized topological properties of network vertices such as the edge strength, vertex centrality, and occurrence at different graphlets to identify genes that may be of potential biological relevance to CRC. We found 512 top‐ranked single‐nucleotide polymorphisms, among which COL22A1, RGS7, WWOX, and CELF2 were the four susceptibility genes prioritized by all described metrics as the most influential on CRC.

Abstract-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.

Abstract-Network coding is an effective idea to boost the capacity of wireless networks, and a variety of studies have explored its advantages in different scenarios. However, there is not much analytical study on throughput and end-to-end delay of network coding in multi-hop wireless networks considering the specifications of IEEE 802.11 Distributed Coordination Function. In this paper, we utilize queuing theory to propose an analytical framework for bidirectional unicast flows in multi-hop wireless mesh networks. We study the throughput and end-to-end delay of inter-flow network coding under the IEEE 802.11 standard with CSMA/CA random access and exponential back-off time considering clock freezing and virtual carrier sensing, and formulate several parameters such as the probability of successful transmission in terms of bit error rate and collision probability, waiting time of packets at nodes, and retransmission mechanism. Our model uses a multi-class queuing network with stable queues, where coded packets have a non-preemptive higher priority over native packets, and forwarding of native packets is not delayed if no coding opportunities are available. Finally, we use computer simulations to verify the accuracy of our analytical model.

Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. This paper highlights the role of knowledge graphs in XAI models in healthcare, considering a state-of-the-art review. Based on our review, knowledge graphs have been used for explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interactions and to reduce the knowledge gap between healthcare experts and AI-based models. We also discuss how to leverage knowledge graphs in pre-model, in-model, and post-model XAI models in healthcare to make them more explainable.