A Novel Mathematical Model for Radio Mean Square Labeling Problem

A radio mean square labeling of a connected graph is motivated by the channel assignment problem for radio transmitters to avoid interference of signals sent by transmitters. It is an injective map h from the set of vertices of the graph G to the set of positive integers N , such that for any two distinct vertices x , y , the inequality d x , y + h x 2 + h y 2 / 2 ≥ dim G + 1 holds. For a particular radio mean square labeling h , the maximum number of h v taken over all vertices of G is called its spam, denoted by rmsn h , and the minimum value of rmsn h taking over all radio mean square labeling h of G is called the radio mean square number of G , denoted by rmsn G . In this study, we investigate the radio mean square numbers rmsn P n and rmsn C n for path and cycle, respectively. Then, we present an approximate algorithm to determine rmsn G for graph G . Finally, a new mathematical model to find the upper bound of rmsn G for graph G is introduced. A comparison between the proposed approximate algorithm and the proposed mathematical model is given. We also show that the computational results and their analysis prove that the proposed approximate algorithm overcomes the integer linear programming model (ILPM) according to the radio mean square number. On the other hand, the proposed ILPM outperforms the proposed approximate algorithm according to the running time.

Orthognal Zones for Interference Migration in 2.4 GHz Mesh Backhaul

Managing interference in the multi-radio networks is critical challenge; problem becomes even more serious in 2.4 GHz band due to minimal availability of orthogonal channels. This work attempts to propose a channel assignment scheme for interference zones of 2.4 GHz backhaul of Wireless Mesh Networks (WMN). The static nodes of Infrastructure based Backhaul employing directional antennas to connect static nodes, orthogonal channel zones introducing Interference are formatted with the selection of single tire direct hop and two tier directional hopes. The effort maintain the orthogonality of channels on system thus reduce the co-channel interference between inter flow and intra flow links. Group of non-overlapping channels of selected band are obtained by a mathematical procedure, interference is modeled by directed graph and Channel assignment is carried out with the help of greedy algorithms. Experimental analysis of the technical proposal is done by simulation through OPNET 14. Our framework can act as an imperative way to enhance the network performance resulting a leading improvement in system throughput and reduction in system delay

Radio Labelings of Lexicographic Product of Some Graphs

Labeling of graphs has defined many variations in the literature, e.g., graceful, harmonious, and radio labeling. Secrecy of data in data sciences and in information technology is very necessary as well as the accuracy of data transmission and different channel assignments is maintained. It enhances the graph terminologies for the computer programs. In this paper, we will discuss multidistance radio labeling used for channel assignment problems over wireless communication. A radio labeling is a one-to-one mapping ℘ : V G ⟶ ℤ + satisfying the condition | ℘ μ − ℘ μ ′ | ≥ diam G + 1 − d μ , μ ′ : μ , μ ′ ∈ V G for any pair of vertices μ , μ ′ in G . The span of labeling ℘ is the largest number that ℘ assigns to a vertex of a graph. Radio number of G , denoted by r n G , is the minimum span taken over all radio labelings of G . In this article, we will find relations for radio number and radio mean number of a lexicographic product for certain families of graphs.

Goodput Modelling and Optimisation of Channel Assignment For Planning IEEE 802.11Wireless Backhaul Networks

<p>IEEE 802.11Wireless backhaul networks (WBNs) provide scalable and cost-effective solutions for interconnecting small-cell networks and backbone networks or Internet. With newer and farther reaching applications being developed in IEEE 802.11 WBNs, such as smart grids and intelligent transportation systems, users expect high goodput and better fairness. However, some performance issues in IEEE 802.11 protocols such as border effect, exposed nodes and hidden nodes are exacerbated as network densification occurs, leading to goodput degradation and severe unfairness such as flow starvation (extreme low goodput). These issues may cause an IEEE 802.11 WBN to form a bottleneck and impact the overall network performance. Therefore, in-depth study is required in order to improve the IEEE 802.11 WBN planning to achieve better goodput and fairness. This research aims to improve IEEE 802.11 WBN planning through goodput modelling and optimising channel assignment. A novel simple goodput distribution model is proposed to predict goodput and fairness in IEEE 802.11 WBNs. Simulation results show that the proposed goodput model accurately predicts goodput with consideration of carrier sensing effect and traffic demands. Based on this goodput model, a new interference model is proposed to more realistically reflect both local and global interference in IEEE 802.11 WBNs. With the proposed interference model, two anti-starvation channel assignments have been developed to prevent flow starvation. Simulation validations show that the new anti-starvation channel assignments effectively prevent flow starvation and improve network fairness in IEEE 802.11 WBNs. This research also optimises channel assignment to achieve desired fairness and goodput. A multi-objective optimisation problem is formulated and a new fitness function is designed to evaluate a channel allocation with accurate prediction of goodput and fairness. Utilising the new fitness function, two multi-objective channel assignments have been developed to achieve both fairness and goodput. Compared with existing channel assignments through simulation, the proposed multi-objective channel assignments provide a set of feasible solutions that meet desired fairness and goodput. This research helps network planners or service providers to improve the IEEE 802.11 WBN planning from predicting network performance to optimising goodput and fairness. The proposed goodput model, interference model, and fitness function are also useful for node placement, and optimising routing and scheduling in IEEE 802.11 WBNs.</p>

Goodput Modelling and Optimisation of Channel Assignment For Planning IEEE 802.11Wireless Backhaul Networks

<p>IEEE 802.11Wireless backhaul networks (WBNs) provide scalable and cost-effective solutions for interconnecting small-cell networks and backbone networks or Internet. With newer and farther reaching applications being developed in IEEE 802.11 WBNs, such as smart grids and intelligent transportation systems, users expect high goodput and better fairness. However, some performance issues in IEEE 802.11 protocols such as border effect, exposed nodes and hidden nodes are exacerbated as network densification occurs, leading to goodput degradation and severe unfairness such as flow starvation (extreme low goodput). These issues may cause an IEEE 802.11 WBN to form a bottleneck and impact the overall network performance. Therefore, in-depth study is required in order to improve the IEEE 802.11 WBN planning to achieve better goodput and fairness. This research aims to improve IEEE 802.11 WBN planning through goodput modelling and optimising channel assignment. A novel simple goodput distribution model is proposed to predict goodput and fairness in IEEE 802.11 WBNs. Simulation results show that the proposed goodput model accurately predicts goodput with consideration of carrier sensing effect and traffic demands. Based on this goodput model, a new interference model is proposed to more realistically reflect both local and global interference in IEEE 802.11 WBNs. With the proposed interference model, two anti-starvation channel assignments have been developed to prevent flow starvation. Simulation validations show that the new anti-starvation channel assignments effectively prevent flow starvation and improve network fairness in IEEE 802.11 WBNs. This research also optimises channel assignment to achieve desired fairness and goodput. A multi-objective optimisation problem is formulated and a new fitness function is designed to evaluate a channel allocation with accurate prediction of goodput and fairness. Utilising the new fitness function, two multi-objective channel assignments have been developed to achieve both fairness and goodput. Compared with existing channel assignments through simulation, the proposed multi-objective channel assignments provide a set of feasible solutions that meet desired fairness and goodput. This research helps network planners or service providers to improve the IEEE 802.11 WBN planning from predicting network performance to optimising goodput and fairness. The proposed goodput model, interference model, and fitness function are also useful for node placement, and optimising routing and scheduling in IEEE 802.11 WBNs.</p>

Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks

WLAN mesh networks are one of the key technologies for upcoming smart city applications and are characterized by a flexible and low-cost deployment. The standard amendment IEEE 802.11s introduces low-level mesh interoperability at the WLAN MAC layer. However, scalability limitations imposed by management traffic overhead, routing delays, medium contention, and interference are common issues in wireless mesh networks and also apply to IEEE 802.11s networks. Possible solutions proposed in the literature recommend a divide-and-conquer scheme that partitions the network into clusters and forms smaller collision and broadcast domains by assigning orthogonal channels. We present CHaChA (Clustering Heuristic and Channel Assignment), a distributed cross-layer approach for cluster formation and channel assignment that directly integrates the default IEEE 802.11s mesh protocol information and operating modes, retaining unrestricted compliance to the WLAN standard. Our concept proposes further mechanisms for dynamic cluster adaptation, including subsequent cluster joining, isolation and fault detection, and node roaming for cluster balancing. The practical performance of CHaChA is demonstrated in a real-world 802.11s testbed. We first investigate clustering reproducibility, duration, and communication overhead in static network scenarios of different sizes. We then validate our concepts for dynamic cluster adaptation, considering topology changes that are likely to occur during long-term network operation and maintenance.