A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Cognitive Radio Network with a distributed control channel and quality-of-service solution

The proliferation of wireless access and applications to the Internet and the advent of a myriad of highly evolved portable communication devices; creates the need for an efficiently utilized radio spectrum. This is paramount in the licensed and unlicensed radio frequency bands, that spawn an exponential growth in Dynamic Spectrum Access (DSA) research, Cognitive Radio (CR) and Cognitive Radio Networks (CRN) research. DSA research has given way to the paradigm shift toward CR with its dynamic changes in transmission schemas. This paradigm shift from a fixed and centralized frequency spectrum environment has morphed into a dynamic and decentralized one. CR provides wireless nodes the capability to adapt and exploit the frequency spectrum. The spectrum information obtained is scanned and updated to determine the channel quality for viability and a utilization/availability by the licensed (primary) user. To take advantage of the CR capabilities, previous research has focused on a Common Control Channel(CCC) for the control signals to be used for spectrum control. This utilization generates channel saturation, extreme transmission overhead of control information, and a point of vulnerability. The traditional designs for wireless routing protocols do not support an ad hoc multi-hop cognitive radio network model. This research focuses on a real world implementation of a heterogeneous ad hoc multi-hop Cognitive Radio Network. An overall model, coined Emerald, has been designed to address the architecture; the Medium Access Control layer, E-MAC; and the network layer, E-NET. First, a Medium Access Control(MAC) layer protocol is provided to avoid the pitfalls of a common control channel. This new design provides CRNs with network topology and channel utilization information. Spectrum etiquette, in turn, addresses channel saturation, control overhead, and the single point of vulnerability. Secondly, a routing model is proposed that will address the efficiency of an ad hoc multi-hop CRN with a focus on the Quality-of-Service(QoS) of the point-to-point as well as end-to-end communication. This research has documented weaknesses in spectrum utilization; it has been expanded to accommodate a distributed control environment. Subsets of the model will be validated through Network Simulator-2(NS/2) and MatLab© simulations to determine point-to-point and end-to-end communications

Quality-of-service management in IP networks

Quality of Service (QoS) in Internet Protocol (IF) Networks has been the subject of active research over the past two decades. Integrated Services (IntServ) and Differentiated Services (DiffServ) QoS architectures have emerged as proposed standards for resource allocation in IF Networks. These two QoS architectures support the need for multiple traffic queuing systems to allow for resource partitioning for heterogeneous applications making use of the networks. There have been a number of specifications or proposals for the number of traffic queuing classes (Class of Service (CoS)) that will support integrated services in IF Networks, but none has provided verification in the form of analytical or empirical investigation to prove that its specification or proposal will be optimum. Despite the existence of the two standard QoS architectures and the large volume of research work that has been carried out on IF QoS, its deployment still remains elusive in the Internet. This is not unconnected with the complexities associated with some aspects of the standard QoS architectures. [Continues.

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Integration of ICN and MEC in 5G and beyond networks : mutual benefits, use cases, challenges, standardization, and future research

Multi-access Edge Computing (MEC) is a novel edge computing paradigm that moves cloudbased processing and storage capabilities closer to mobile users by implementing server resources in the access nodes. MEC helps fulfill the stringent requirements of 5G and beyond networks to offer anytimeanywhere connectivity for many devices with ultra-low delay and huge bandwidths. Information-Centric Networking (ICN) is another prominent network technology that builds on a content-centric network architecture to overcome host-centric routing/operation shortcomings and to realize efficient pervasive and ubiquitous networking. It is envisaged to be employed in Future Internet including Beyond 5G (B5G) networks. The consolidation of ICN with MEC technology offers new opportunities to realize that vision and serve advanced use cases. However, various integration challenges are yet to be addressed to enable the wide-scale co-deployment of ICN with MEC in future networks. In this paper, we discuss and elaborate on ICN MEC integration to provide a comprehensive survey with a forward-looking perspective for B5G networks. In that regard, we deduce lessons learned from related works (for both 5G and B5G networks). We present ongoing standardization activities to highlight practical implications of such efforts. Moreover, we render key B5G use cases and highlight the role for ICN MEC integration for addressing their requirements. Finally, we layout research challenges and identify potential research directions. For this last contribution, we also provide a mapping of the latter to ICN integration challenges and use cases

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

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

Management system for Unmanned Aircraft Systems teams

This thesis investigates new schemes to improve the operability of heterogeneous Unmanned Aircraft Systems (UAS) teams through the exploitation of inter-vehicular communications. Releasing ground links from unnecessary data exchanges saves resources (power, bandwidth, etc) and alleviates the inherent scalability problem resulting from the increase in the number of UAS to be controlled simultaneously. In first place, a framework to classify UAS according to their level of autonomy is presented along with efficient methodologies to assess the autonomy level of either individual or multiple UAS. An architecture based on an aerial Mobile Ad-hoc Network (MANET) is proposed for the management of the data exchange among all the vehicles in the team. A performance evaluation of the two most relevant MANET approaches for path discovery (namely, reactive and proactive) has been carried out by means of simulation of two well-known routing protocols: Ad-hoc On-demand Distance Vector (AODV) and Destination Sequenced Distance Vector (DSDV). Several network configurations are generated to emulate different possible contingencies that might occur in real UAS team operations. Network topology evolution, vehicle flight dynamics and data traffic patterns are considered as input parameters to the simulation model. The analysis of the system behaviour for each possible network configuration is used to evaluate the appropriateness of both approaches in different mission scenarios. Alternative network solutions based on Delay Tolerant Networking (DTN) for situations of intermittent connectivity and network partitioning are outlined. Finally, an assessment of the simulation results is presented along with a discussion about further research challenges