Fraser Cadger

Bio: Fraser Cadger is an academic researcher from Ulster University. The author has contributed to research in topics: Geographic routing & Dynamic Source Routing. The author has an hindex of 5, co-authored 10 publications receiving 259 citations.

A Survey of Geographical Routing in Wireless Ad-Hoc Networks

Abstract: Geographic routing offers a radical departure from previous topology-dependent routing paradigms through its use of physical location in the routing process. Geographic routing protocols eliminate dependence on topology storage and the associated costs, which also makes them more suitable to handling dynamic behavior frequently found in wireless ad-hoc networks. Geographic routing protocols have been designed for a variety of applications ranging from mobility prediction and management through to anonymous routing and from energy efficiency to QoS. Geographic routing is also part of the larger area of context-awareness due to its usage of location data to make routing decisions and thus represents an important step in the journey towards ubiquitous computing. The focus of this paper, within the area of geographic routing is on wireless ad-hoc networks and how location information can benefit routing. This paper aims to provide both a comprehensive and methodical survey of existing literature in the area of geographic routing from its inception as well as acting as an introduction to the subject.

Location and Mobility-Aware Routing for Improving Multimedia Streaming Performance in MANETs

Abstract: Device mobility is an issue that affects both Mobile ad hoc networks (MANETs) and opportunistic networks. While the former employs conventional routing techniques with some element of mobility management, opportunistic networking protocols often use mobility as a means of delivering messages in intermittently connected networks. If nodes are able to determine the future locations of other nodes with reasonable accuracy then they could plan ahead and take into account and even benefit from such mobility. In an ad hoc network, devices form a network amongst themselves and forward packets for each other without infrastructure. Ad hoc networks could be deployed in a disaster scenario to enable communications between responders and base camp to provide telemedicine services. However, most ad hoc routing protocols cannot meet the necessary standards for streaming multimedia because they do not attempt to manage quality of service (QoS). Node mobility adds an additional layer of complexity leading to potentially detrimental effects on QoS. Geographic routing protocols use physical locations to make routing decisions and are typically lightweight, distributed, and require only local network knowledge. They are thus less susceptible to the effects of mobility, but are not impervious. Location-prediction can be used to enhance geographic routing, and counter the negative effects of mobility, but this has received relatively little attention. Location prediction in combination with geographic routing has been explored in previous literature. Most of these location prediction schemes have made simplistic assumptions about mobility. However more advanced location prediction schemes using machine learning techniques have been used for wireless infrastructure networks. These approaches rely on the use of infrastructure and are therefore unsuitable for use in opportunistic networks or MANETs. To solve the problem of accurately predicting future location in non-infrastructure networks, we investigate the prediction of continuous numerical coordinates using artificial neural networks. Simulation using three different mobility models representing human mobility has shown an average prediction error of <1 m in normal circumstances.

MANET location prediction using machine learning algorithms

Abstract: In mobile ad-hoc networks where users are potentially highly mobile, knowledge of future location and movement can be of great value to routing protocols. To date, most work regarding location prediction has been focused on infrastructure networks and consists of performing classification on a discrete range of cells or access points. Such techniques are unsuitable for infrastructure-free MANETs and although classification algorithms can be used for specific, known areas they are not general or flexible enough for all real-world environments. Unlike previous work, this paper focuses on regression-based machine learning algorithms that are able to predict coordinates as continuous variables. Three popular machine learning techniques have been implemented in MATLAB and tested using data obtained from a variety of mobile simulations in the ns-2 simulator. This paper presents the results of these experiments with the aim of guiding and encouraging development of location-predictive MANET applications.

An Analysis of the Effects of Intelligent Location Prediction Algorithms on Greedy Geographic Routing in Mobile Ad-Hoc Networks

Abstract: Geographic routing uses the physical locations of devices for routing purposes instead of the conventional routing protocols which make use of logical addressing schemes representing an abstract hierarchy. Greedy geographic routing is a popular method favoured for its efficiency and its simplicity that considers only one hop neighbours. Greedy routing needs only minimal network information and as such is resilient to change and dynamic behaviour. Recent advances have seen the development of location prediction algorithms that use a variety of methods to determine a node’s future location based on their previous movements. Such prediction schemes can potentially benefit greedy geographic routing by allowing nodes to make routing decisions based on where a node will go rather than where it was according to the last update. The use of location prediction algorithms therefore allows geographic routing protocols to make decisions that are more intelligent, thus not only improving routing performance but providing a crucial step towards fully autonomous computer communications. The main contribution of this paper is the implementation and analysis of two existing location prediction schemes on top of the existing Greedy Perimeter Stateless Routing (GPSR) protocol ran in greedy mode.

Location and mobility-aware routing for multimedia streaming in disaster telemedicine

Abstract: Disaster telemedicine leverages communications networks to provide remote diagnosis of injured persons in areas affected by disasters such as earthquakes. However, telemedicine relies heavily on infrastructure, and in a disaster scenario there is no guarantee that such infrastructure will be intact. In an ad-hoc network, devices form a network amongst themselves and forward packets for each other without infrastructure. Ad-hoc networks could be deployed in a disaster scenario to enable communications between responders and base camp to provide telemedicine services. However, most ad-hoc routing protocols cannot meet the necessary standards for streaming multimedia because they do not attempt to manage Quality of Service (QoS). Node mobility adds an additional layer of complexity leading to potentially detrimental effects on QoS. Geographic routing protocols use physical locations to make routing decisions and are typically lightweight, distributed, and require only local network knowledge. They are thus less susceptible to the effects of mobility, but are not impervious. Location-prediction can be used to enhance geographic routing, and counter the negative effects of mobility, but this has received relatively little attention. Machine Learning algorithms have been deployed for predicting locations in infrastructure networks with some success, but such algorithms require modifications for us in ad-hoc networks. This paper outlines the use of an Artificial Neural Network (NN) to perform location-prediction in an ad-hoc network.

Location-Aware Communications for 5G Networks: How location information can improve scalability, latency, and robustness of 5G

Abstract: Fifth-generation (5G) networks will be the first generation to benefit from location information that is sufficiently precise to be leveraged in wireless network design and optimization. We argue that location information can aid in addressing several of the key challenges in 5G, complementary to existing and planned technological developments. These challenges include an increase in traffic and number of devices, robustness for mission-critical services, and a reduction in total energy consumption and latency. This article gives a broad overview of the growing research area of location-aware communications across different layers of the protocol stack. We highlight several promising trends, tradeoffs, and pitfalls.

Smart Cities: A Survey on Data Management, Security, and Enabling Technologies

Abstract: Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges.

A Survey on Mobile Anchor Node Assisted Localization in Wireless Sensor Networks

Abstract: Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints on cost and power consumption make it infeasible to equip each sensor node in the network with a global position system (GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use mobile anchor nodes (MANs), which are equipped with GPS units moving among unknown nodes and periodically broadcasting their current locations to help nearby unknown nodes with localization. A considerable body of research has addressed the mobile anchor node assisted localization (MANAL) problem. However, to the best of our knowledge, no updated surveys on MAAL reflecting recent advances in the field have been presented in the past few years. This survey presents a review of the most successful MANAL algorithms, focusing on the achievements made in the past decade, and aims to become a starting point for researchers who are initiating their endeavors in MANAL research field. In addition, we seek to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful advances in this research field.