Graph Theory with Applications to Engineering and Computer ... This outstanding introductory treatment of graph theory and its applications has had a long life in the instruction of advanced undergraduates and graduate students in all areas that require knowledge of this subject. The first nine chapters constitute an excellent overall introduction, requiring only some knowledge of set theory and matrix algebra.

Graph theory with applications to engineering and computer science, by Narsingh Deo. Pp xvii, 478. 1974. SBN 0 13 363473 6 (Prentice-Hall)

Software-defined networking (SDN) is an emerging architecture used in many applications because of its flexible architecture. It is expected to become an essential enabler for the Internet of Things (IoTs). It decouples the control plane from the data plane, and the controller manages the whole underlying network. SDN has been used in wireless sensor networks (WSNs) for routing. The SDN controller uses some algorithms to calculate the routing path; however, none of these algorithms have enough ability to obtain the optimized routing path. Therefore, reinforcement learning (RL) is a helpful technique to select the best routing path. In this article, we optimize the routing path of SDWSN through RL. A reward function is proposed that includes all required metrics regarding energy efficiency and network Quality-of-Service (QoS). The agent gets the reward and takes the next action based on the reward received, while the SDWSN controller improves the routing path based on the previous experience. However, the whole network is also controlled remotely through the Web. The performance of the RL-based SDWSN is compared with SDN-based techniques, including traditional SDN and energy-aware SDN (EASDN), QR-SDN, TIDE and non SDN-based techniques, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning and RL-based routing (RLBR). The proposed RL-based SDWSN outperforms in terms of lifetime from 8% to 33% and packet delivery ratio (PDR) from 2% to 24%. It is envisioned that this work will help the engineers for achieving the desired WSN performance through efficient routing. 

Improving the Software-Defined Wireless Sensor Networks Routing Performance Using Reinforcement Learning

In this paper, we propose a novel scheme for physical-layer network coding (PNC) in multiway relay channels (MWRC) from the perspective of sequential multiuser detection. We consider an uplink MWRC scenario where <inline-formula> <tex-math notation=\"LaTeX\">$N$ </tex-math></inline-formula> users, each equipped with a single antenna, simultaneously transmit their signal to a relay equipped with <inline-formula> <tex-math notation=\"LaTeX\">$K$ </tex-math></inline-formula> antennas (<inline-formula> <tex-math notation=\"LaTeX\">$K < N$ </tex-math></inline-formula>). Extraction of the network codes from the superimposed user signals at the relay node is formulated as an under-determined linear system. To solve this problem with low decoding complexity, the proposed method combines successive interference cancellation (SIC) with Babai estimation for regularized integer least squares (ILS). Specifically, SIC decoding is first employed to detect a selected subset of stronger user signals and remove their interfering effects. Babai estimation is then applied to extract the remaining user signals, which is formulated as an ILS problem with reduced dimension. We develop a power allocation scheme to enhance the performance of both SIC and ILS steps, and discuss an optimal user pairing strategy based on the average decoding error probability. Numerical results demonstrate the performance improvement of the proposed method in extracting network codes from multiple superimposed user signals.

/pdf/efficient-detection-scheme-for-physical-layer-network-coding-5cvcc96ds6.pdf

Efficient Detection Scheme for Physical-Layer Network Coding in Multiway Relay Channels

We propose a higher-order generalization of the well-known overall clustering coefficient for triples C ( 3 ) to any number of nodes. We give analytic formulae for the special cases of three, four, and five nodes and show that they have very fast runtime performance for small graphs. We discuss some theoretical properties and limitations of the new measure, and use it to provide insight into dynamic changes in the structure of U.S. airline networks.

https://hal-enac.archives-ouvertes.fr/hal-01821994/document

Cliques and a new measure of clustering: With application to U.S. domestic airlines

We use a greedy strategy to list the spanning trees of the fan graph, $F_n$, such that successive trees differ by pivoting a single edge around a vertex. It is the first greedy algorithm for exhaustively generating spanning trees using such a minimal change operation. The resulting listing is then studied to find a recursive algorithm that produces the same listing in O(1)-amortized time using O(n) space. Additionally, we present O(n)-time algorithms for ranking and unranking the spanning trees for our listing; an improvement over the generic $O(n^3)$-time algorithm for ranking and unranking spanning trees of an arbitrary graph.

A Pivot Gray Code Listing for the Spanning Trees of the Fan Graph

Random connected graphs are required to determine the performance of approximation algorithms, graph algorithms, algorithm for modeling and simulation, algorithms solving problems regarding database applications. Also, developing algorithms to generate all spanning trees required random connected graphs as input. In this paper, we have proposed an algorithm to generate random simple undirected graph instances that can be utilized to determine the performance and reliability of the above-mentioned algorithms. Our proposed algorithm creates random connected graphs with a given number of vertices and a distinct number of edges. We have been able to formulate the number of such generated graphs. The reliability and the time bound of the algorithm have also been established. We have included the computational results of the algorithm that are greatly encouraging.

Generation of Simple Undirected Connected Random Graphs

Relational database is a popular database for storing various types of information. But due to the ever-increasing growth of data, it becomes hard to maintain and process the database. So, the graph model is becoming more and more popular since it can store and handle big data more efficiently compared to relational database. But both relational database and graph database have their own advantages and disadvantages. To overcome their limitations, they are combined to make a hybrid model. This paper discusses relational database, graph database, their advantages, their applications and also talks about hybrid model.

Maumita Chakraborty

Papers

Generation of Simple Undirected Connected Random Graphs

A Study on Various Database Models: Relational, Graph, and Hybrid Databases