Semantically Intelligent Distributed Leader Election (SIDLE) Algorithm for WSAN Part of IoT Systems

This paper introduces the deployment of a group of Wireless Sensor and Actuator Network (WSAN) part of Internet of Thing (IoT) systems in rural regions deployed by a drone dropping sensors and actuators at a certain position as a mesh of a hexagonal form. Nodes are heterogeneous in hardware and functionality thus not all nodes are able to transfer data directly to the base station. Primitive ones are only capable of collecting local data. However, ones that are more sophisticated are equipped with long-range radio telemetry and more computational power. Power optimization is one of the crucial factors in designing WSANs. Total power consumption must be minimized, as sensors are self-managed. It is not feasible to collect sensors on time bases and recharge the batteries. Therefore, energy consumption optimization and harvesting green energy are other factors that are considered. In this regard, protocols are designed in a way to support such requirements. The preprocessed data are first collected and combined by the leaders at each hexagonal cell. Then, the information packets are sent to the head clusters. Consequently, head clusters reprocess the received information and depict a better global view of the zone, using a variety of the received information. Finally, the processed information is sent to the nearest base station or a mobile drone.Comment: The First International Conference of Smart City, 2019, Apadana University, Shiraz, Iran https://www.civilica.com/Paper-SMARTCITYC01-SMARTCITYC01_100.htm

APPLICATION OF INTELLIGENT GAME THEORY APPROACH IN COGNITIVE RADIO AD HOC NETWORKS

Cognitive Radio (CR) technology is imagined to solve the problems in Wireless Ad-hoc NETworks (WANET) resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. Game theory is a process to analyze multi-person decision making situation, where each decision maker tries to maximize his own utility. In this paper, we illustrates how various interactions in Cognitive Radio Ad Hoc Network (CRAHN) can be modeled as a game. It also illustrates a problem with solution approach that uses intelligent game theory technique in CRAHN

Intelligent MANET optimisation system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the literature, various Mobile Ad hoc NETwork (MANET) routing protocols proposed. Each performs the best under specific context conditions, for example under high mobility or less volatile topologies. In existing MANET, the degradation in the routing protocol performance is always associated with changes in the network context. To date, no MANET routing protocol is able to produce optimal performance under all possible conditions. The core aim of this thesis is to solve the routing problem in mobile Ad hoc networks by introducing an optimum system that is in charge of the selection of the running routing protocol at all times, the system proposed in this thesis aims to address the degradation mentioned above. This optimisation system is a novel approach that can cope with the network performance’s degradation problem by switching to other routing protocol. The optimisation system proposed for MANET in this thesis adaptively selects the best routing protocol using an Artificial Intelligence mechanism according to the network context. In this thesis, MANET modelling helps in understanding the network performance through different contexts, as well as the models’ support to the optimisation system. Therefore, one of the main contributions of this thesis is the utilisation and comparison of various modelling techniques to create representative MANET performance models. Moreover, the proposed system uses an optimisation method to select the optimal communication routing protocol for the network context. Therefore, to build the proposed system, different optimisation techniques were utilised and compared to identify the best optimisation technique for the MANET intelligent system, which is also an important contribution of this thesis. The parameters selected to describe the network context were the network size and average mobility. The proposed system then functions by varying the routing mechanism with the time to keep the network performance at the best level. The selected protocol has been shown to produce a combination of: higher throughput, lower delay, fewer retransmission attempts, less data drop, and lower load, and was thus chosen on this basis. Validation test results indicate that the identified protocol can achieve both a better network performance quality than other routing protocols and a minimum cost function of 4.4%. The Ad hoc On Demand Distance Vector (AODV) protocol comes in second with a cost minimisation function of 27.5%, and the Optimised Link State Routing (OLSR) algorithm comes in third with a cost minimisation function of 29.8%. Finally, The Dynamic Source Routing (DSR) algorithm comes in last with a cost minimisation function of 38.3%

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Semantic reasoning in cognitive networks for heterogeneous wireless mesh systems

The next generation of wireless networks is expected to provide not only higher bandwidths anywhere and at any time but also ubiquitous communication using different network types. However, several important issues including routing, self-configuration, device management, and context awareness have to be considered before this vision becomes reality. This paper proposes a novel cognitive network framework for heterogeneous wireless mesh systems to abstract the network control system from the infrastructure by introducing a layer that separates the management of different radio access networks from the data transmission. This approach simplifies the process of managing and optimizing the networks by using extendable smart middleware that automatically manages, configures, and optimizes the network performance. The proposed cognitive network framework, called FuzzOnto, is based on a novel approach that employs ontologies and fuzzy reasoning to facilitate the dynamic addition of new network types to the heterogeneous network. The novelty is in using semantic reasoning with cross-layer parameters from heterogeneous network architectures to manage and optimize the performance of the networks. The concept is demonstrated through the use of three network architectures: 1) wireless mesh network; 2) long-term evolution (LTE) cellular network; and 3) vehicular ad hoc network (VANET). These networks utilize nonoverlapped frequency bands and can operate simultaneously with no interference. The proposed heterogeneous network was evaluated using ns-3 network simulation software. The simulation results were compared with those produced by other networks that utilize multiple transmission devices. The results showed that the heterogeneous network outperformed the benchmark networks in both urban and VANET scenarios by up to 70% of the network throughput, even when the LTE network utilized a high bandwidth