Coloured Petrinet for Modelling and Validation of Dynamic Transmission Range Adjustment Protocol for Ad Hoc Network

The IEEE 802.11 standard defines two operational modes for WLANs: infrastructure based and infrastructureless or ad hoc. A wireless ad hoc network comprises of nodes that communicate with each other without the help of any centralized control. Ad hoc implies that the network does not rely on a pre-existing infrastructure but rather each node participates in routing by forwarding data for other nodes. The decentralized nature improves the scalability of wireless ad hoc network as compared to wireless managed networks. Each node acts as either a host or router. A node that is within the transmission range of any other node can establish a link with the later and becomes its immediate neighbour. However, the nodes in the ad hoc networks are constrained with limited resources and computation capability. So it may not be possible for a node to serve more number of neighbours at some instant of time. This enforces a node to remain connected or disconnected with few of its existing neighbours supporting the dynamic restructuring of the network. The presence of dynamic and adaptive routing protocol enables ad hoc networks to be formed quickly. The Dynamic Transmission Range Adjustment Protocol (DTRAP) provides a mechanism for adjusting transmission range of the ad hoc nodes. They maintain a threshold number of registered neighbours based on their available resources. The node protects its neighbourhood relationship during data communication by controlling its transmission range. It registers or de-registers a communicating node as its neighbour by dynamically varying the transmission range. However a node has a maximum limit on its transmission range. If the distance between the node and its neighbour is less than the transmission range and; 1)if the number of neighbours of a node falls short of threshold value, the node dynamically increases its transmission range in steps until it is ensured of an optimal number of neighbours 2)if the number of neighbours of a node exceeds the threshold value, the node dynamically decreases its transmission range in steps until it is ensured of an optimal number of neighbours. Coloured Petri nets (CP-nets) is the modelling language tool used for systems having communication, synchronisation and resource sharing as significant aspects. It provides a framework for the design, specication, validation, and verication of systems. It describes the states in which the system may be in and the transition between these states. The CPN combines Petri nets and programming languages. Petri nets amalgamate the use of graphical notation and the semantical foundation for modelling in systems. The functional programming language standard ML provides the primitives for the definition of data types and manipulation of data values. Besides providing the strength of a graphical modelling language, CP-nets are theoretically well-founded and versatile enough to be used in practice for systems of the size and complexity of industrial projects

WiFo: A diagnostic tool for IEEE 802.11 MAC

WLANs are constantly undergoing extensive research and development, and scientists keep coming up with new methods to improve existing protocols and amend standards. Experimental assessment has been an important part of 802.11 research, however measuring the detailed behaviour of the medium and hardware has been challenging. In this paper we design a diagnostic tool, WiFo, for IEEE 802.11-based WLANs. This tool helps developers and researchers monitor and analyze the wireless signals and details such as backoff distribution in a user-friendly environment. Our solution is much cheaper and easier to use than existing tools, and provides more flexibility by allowing users to add functionality. We then use WiFo to study several aspects of some off-the-shelf hardware and their corresponding software drivers, and show some interesting results regarding how they apply standard specifications

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

SNR-Based OLSR Routing Protocol for Wireless Mesh Networks

Wireless Mesh Networks (WMNs) consist of a collection of mobile and fixed nodes that form a network. Nodes are capable of communicating with each other either with infrastructure, or infrastructureless, or in a hybrid mode. The major advantages of WMNs over the other wireless networks are the low-cost, self organization, self configuration, last mile internet solution, scalability, and reliability. These advantages have attracted the researcher over the last five years. WMNs technology is gaining an increased attention from the Institute of Electrical and Electronics Engineers (IEEE) community. This led the IEEE organization to emerge a special working group (IEEE 802.11s) in charge of the issues deriving from a completely wireless distribution system used to interconnect different Basic Service Sets (BSSs) through secure and performing links. In a multi-hop networks, like WMN, one of the main factors that influences the performance is the routing protocol. Generally speaking, routing protocols can be classified based-on the routing metric to 1) hop count-based routing protocols, like Adhoc on demand distance vector (AODV) where the optimum path is defined as the path that goes through the minimum number of nodes, 2) the link quality-based routing protocols, like OLSR where some metrics such as the bandwidth and the packet error rate are considered to define the optimum path to the destination. In this work the performances of a three commonly used routing protocols are compared. The main goal of this stag is to study the influence of different routing protocols in WMNs. The comparison is conducted with two scenarios of networks; a high mobility network and a low mobility network. (Open network) OPNET 11.5 modeler is used to build the WMNs. The performance of the network and the routing protocols has been studied in means of network throughput, End-to-End delay, routing protocol overhead and the mobility. The obtained results show that the Optimized link state routing protocol (OLSR) has the highestthroughput overDSR andAODVrouting protocols in WMNs. The unpredictable behavior of the wireless medium in WMNs environment demands the need for a routing protocol that is aware of the link conditions. Unfortunately the routing protocols used such as AODV and Dynamic source routing (DSR) are hop count-based; where the routing algorithm uses the number of nodes to determine the optimum path to the destination. In the second stage of this work a new routing technique for WMNs based-on Signal to noise ratio (SNR) as a new metric for OLSR routing protocol, is developed. The new metric has been implemented on the OLSR routing protocol module using OPNET simulator. The modified OLSR routing protocol is implemented in the comparison scenarios. The obtained results show that, when SNR is used as a routing metric in the OLSR routing protocol, the OLSR is getting the significantly higher network throughput over the DSR and AODV routing protocols. In the same time, the modified OLSR implemented with the SNR metric is showing a high improvement over the OLSR with the traditional hop-count metric. This thesis also studies the affect of different amounts of mobility in WMNs performance. VI

Design and implementation of a cognitive node for heterogeneous wireless ad-hoc

In this thesis, the design of a cognitive network layer solution for a scenario with mobile devices is presented. Cognitive networks are able to sense the environment and adapt in order to find the best performance of the network at any moment. The final objective is to carry out a design of a node of the network which has incorporated in it up to three different technologies, which are WLAN, Bluetooth and ZigBee. The node is able to determine whether a technology should be used or not based on the network state. In order to find out the network state, a routing protocol based on Link State to provide the full view of the network is designed. Adaptive routing metrics have been designed in order to determine the best performance of the network to meet the QoS requirements considering what service is being required by the application and therefore to choose what technology is more appropriated for the connection. Those metrics are based on the capacity of the link, which takes into account the technology, the delay and the packet error rate of itself, and the utilization level. Then, Dijkstras’ algorithm is computed to solve the routing problem based on the adaptive weights instead of using the traditional hop-based count as a cost function. Furthermore, a heterogeneous cognitive wireless ad-hoc network testbed is implemented to analyze the behavior of the cognitive network when different types of services are used. On top of the cognitive network layer, an application to arrange meetings is implemented. Meeting rooms offer two different type of service for the guests, video and data service. Thus, clients are able to configure a video conference with the meeting room in case they cannot attend the meeting