Design for Wireless Sensor Network-Based Intelligent Public Transportation System

Intelligent public transportation system is an effective way to improve the quality of our country transportation. Wireless sensor network is a novel technology made by the convergence of sensor technology, micro electro mechanism technology, wireless telecommunication technology and network technology. It is suitable to transportation scenarios for the characteristic of rapidly deployed and self organized. An improved solution combined the wireless sensor network and internet technology is presented in the article. It effectively solves three critical problems of the wireless sensor network, including energy saving, localization and communication distance. By using the low cost and high stability microchip, a high reliability and low cost intelligent public transportation system based on wireless sensor network can be easily established

Design, Analysis, and Performance Evaluation for Handshaking Based MAC Protocols in Underwater Acoustic Networks

Ph.DDOCTOR OF PHILOSOPH

Development of an Adaptive Environmental Management System for Lejweleputswa District: A Participatory Approach through Fuzzy Cognitive Maps

Published ThesisEnvironmental pollution caused by mines within the district of Lejweleputswa in Free State is a major contributor to health issues and the inability to grow crops within the mining communities. Mining industries continue to develop environmental management systems/plans to mitigate the impact their operations has on the society. Even with these plans, there are still issues of environmental pollution affecting the society. Though there are Information Communication and Technology (ICT) based pollution monitoring solutions, their use is dismal due to lack of appreciation or understanding of how they disseminate information. Furthermore, non-adopting community members are being regarded as inherently conservative or irrational, but these community members argue that the recommendations and technologies brought to them are not always appropriate to their circumstances. There was concern that local people’s knowledge of their environment, farming systems, and their social as well as economic situation had been ignored and underestimated when ICTs solutions are being implemented (Warburton & Martin, 1999). Another challenge is that there is no station to monitor pollution for small communities such as Nyakallong in the district. This result in mining communities depending on their own local knowledge to observe and monitor mining related environmental pollution. However, this local knowledge has never been tested scientifically or analysed to recognize its usability or effectiveness. Mining companies tend to ignore this knowledge from the communities as it is treated like common information with no much scientific value. As a step towards verifying or validating this local knowledge, fuzzy cognitive maps were used to model, analyse and represent this linguistic local knowledge. Although this local knowledge assists in mitigating environmental pollution, incorporating it with scientific knowledge will improve its relevance, trustworthiness and acceptability by majority of community members and policy-makers. Information and Communication Technologies (ICTs) can accelerate this integration; this is the focus of this research. The increased usages of Information Technology being witnessed today makes it the most important factor for the world to depend on for solutions to many of today’s and tomorrow’s problems. These solutions make use of various forms for dissemination purposes, one of the most versatile dissemination device is a mobile phone since majority of the world’s population do own a mobile phone. In this way information is easily accessible by almost everyone that needs it. A novel environmental management solution was designed to work within the mining communities of Lejweleputswa. The research started off by designing a unique integration framework that creates the much-needed link between local knowledge and scientific knowledge. The framework was then converted into an adaptable environmental pollution management system prototype made up of three components; (1) gathering environmental pollution knowledge; (2) environmental monitoring and; (3) environmental dissemination and communication. To achieve sustainability, relevance and acceptability, local knowledge was integrated in each of the three components while mobile phones were used as both input and output devices for the system. In order to facilitate collection and conservation of local knowledge on environmental monitoring, an elaborate android-based mobile application was developed. Wireless sensor-based gas sensor boards were acquired, and deployed as a compliment to conventional monitoring stations, they were used to gather scientific knowledge. To allow for public access to the system’s data, a web portal and an SMS-based component were also implemented. In order to collect local knowledge from community, a case study of Nyakallong community in Lejweleputswa was carried out. On completion of the system prototype, it was evaluated by participants from the community; 90% of respondents gave a score of ‘excellent ‘

Situation-aware Edge Computing

Future wireless networks must cope with an increasing amount of data that needs to be transmitted to or from mobile devices. Furthermore, novel applications, e.g., augmented reality games or autonomous driving, require low latency and high bandwidth at the same time. To address these challenges, the paradigm of edge computing has been proposed. It brings computing closer to the users and takes advantage of the capabilities of telecommunication infrastructures, e.g., cellular base stations or wireless access points, but also of end user devices such as smartphones, wearables, and embedded systems. However, edge computing introduces its own challenges, e.g., economic and business-related questions or device mobility. Being aware of the current situation, i.e., the domain-specific interpretation of environmental information, makes it possible to develop approaches targeting these challenges. In this thesis, the novel concept of situation-aware edge computing is presented. It is divided into three areas: situation-aware infrastructure edge computing, situation-aware device edge computing, and situation-aware embedded edge computing. Therefore, the concepts of situation and situation-awareness are introduced. Furthermore, challenges are identified for each area, and corresponding solutions are presented. In the area of situation-aware infrastructure edge computing, economic and business-related challenges are addressed, since companies offering services and infrastructure edge computing facilities have to find agreements regarding the prices for allowing others to use them. In the area of situation-aware device edge computing, the main challenge is to find suitable nodes that can execute a service and to predict a node’s connection in the near future. Finally, to enable situation-aware embedded edge computing, two novel programming and data analysis approaches are presented that allow programmers to develop situation-aware applications. To show the feasibility, applicability, and importance of situation-aware edge computing, two case studies are presented. The first case study shows how situation-aware edge computing can provide services for emergency response applications, while the second case study presents an approach where network transitions can be implemented in a situation-aware manner

Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

Energy Efficient Cloud Computing Based Radio Access Networks in 5G. Design and evaluation of an energy aware 5G cloud radio access networks framework using base station sleeping, cloud computing based workload consolidation and mobile edge computing

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increase energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices cause a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS

Techniques for Decentralized and Dynamic Resource Allocation

abstract: This thesis investigates three different resource allocation problems, aiming to achieve two common goals: i) adaptivity to a fast-changing environment, ii) distribution of the computation tasks to achieve a favorable solution. The motivation for this work relies on the modern-era proliferation of sensors and devices, in the Data Acquisition Systems (DAS) layer of the Internet of Things (IoT) architecture. To avoid congestion and enable low-latency services, limits have to be imposed on the amount of decisions that can be centralized (i.e. solved in the ``cloud") and/or amount of control information that devices can exchange. This has been the motivation to develop i) a lightweight PHY Layer protocol for time synchronization and scheduling in Wireless Sensor Networks (WSNs), ii) an adaptive receiver that enables Sub-Nyquist sampling, for efficient spectrum sensing at high frequencies, and iii) an SDN-scheme for resource-sharing across different technologies and operators, to harmoniously and holistically respond to fluctuations in demands at the eNodeB' s layer. The proposed solution for time synchronization and scheduling is a new protocol, called PulseSS, which is completely event-driven and is inspired by biological networks. The results on convergence and accuracy for locally connected networks, presented in this thesis, constitute the theoretical foundation for the protocol in terms of performance guarantee. The derived limits provided guidelines for ad-hoc solutions in the actual implementation of the protocol. The proposed receiver for Compressive Spectrum Sensing (CSS) aims at tackling the noise folding phenomenon, e.g., the accumulation of noise from different sub-bands that are folded, prior to sampling and baseband processing, when an analog front-end aliasing mixer is utilized. The sensing phase design has been conducted via a utility maximization approach, thus the scheme derived has been called Cognitive Utility Maximization Multiple Access (CUMMA). The framework described in the last part of the thesis is inspired by stochastic network optimization tools and dynamics. While convergence of the proposed approach remains an open problem, the numerical results here presented suggest the capability of the algorithm to handle traffic fluctuations across operators, while respecting different time and economic constraints. The scheme has been named Decomposition of Infrastructure-based Dynamic Resource Allocation (DIDRA).Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Traffic and task allocation in networks and the cloud

Communication services such as telephony, broadband and TV are increasingly migrating into Internet Protocol(IP) based networks because of the consolidation of telephone and data networks. Meanwhile, the increasingly wide application of Cloud Computing enables the accommodation of tens of thousands of applications from the general public or enterprise users which make use of Cloud services on-demand through IP networks such as the Internet. Real-Time services over IP (RTIP) have also been increasingly significant due to the convergence of network services, and the real-time needs of the Internet of Things (IoT) will strengthen this trend. Such Real-Time applications have strict Quality of Service (QoS) constraints, posing a major challenge for IP networks. The Cognitive Packet Network (CPN) has been designed as a QoS-driven protocol that addresses user-oriented QoS demands by adaptively routing packets based on online sensing and measurement. Thus in this thesis we first describe our design for a novel ``Real-Time (RT) traffic over CPN'' protocol which uses QoS goals that match the needs of voice packet delivery in the presence of other background traffic under varied traffic conditions; we present its experimental evaluation via measurements of key QoS metrics such as packet delay, delay variation (jitter) and packet loss ratio. Pursuing our investigation of packet routing in the Internet, we then propose a novel Big Data and Machine Learning approach for real-time Internet scale Route Optimisation based on Quality-of-Service using an overlay network, and evaluate is performance. Based on the collection of data sampled each $2$ minutes over a large number of source-destinations pairs, we observe that intercontinental Internet Protocol (IP) paths are far from optimal with respect to metrics such as end-to-end round-trip delay. On the other hand, our machine learning based overlay network routing scheme exploits large scale data collected from communicating node pairs to select overlay paths, while it uses IP between neighbouring overlay nodes. We report measurements over a week long experiment with several million data points shows substantially better end-to-end QoS than is observed with pure IP routing. Pursuing the machine learning approach, we then address the challenging problem of dispatching incoming tasks to servers in Cloud systems so as to offer the best QoS and reliable job execution; an experimental system (the Task Allocation Platform) that we have developed is presented and used to compare several task allocation schemes, including a model driven algorithm, a reinforcement learning based scheme, and a ``sensible’’ allocation algorithm that assigns tasks to sub-systems that are observed to provide lower response time. These schemes are compared via measurements both among themselves and against a standard round-robin scheduler, with two architectures (with homogenous and heterogenous hosts having different processing capacities) and the conditions under which the different schemes offer better QoS are discussed. Since Cloud systems include both locally based servers at user premises and remote servers and multiple Clouds that can be reached over the Internet, we also describe a smart distributed system that combines local and remote Cloud facilities, allocating tasks dynamically to the service that offers the best overall QoS, and it includes a routing overlay which minimizes network delay for data transfer between Clouds. Internet-scale experiments that we report exhibit the effectiveness of our approach in adaptively distributing workload across multiple Clouds.Open Acces

Design of rate-adaptive MAC and medium aware routing protocols for multi-rate, multi-hop wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The IEEE 802.11 standard conformant wireless communication stations have multi-rate transmission capability. To achieve greater communication efficiency, multi-rate capable stations use rateadaptation to select appropriate transmission rate according to variations in the channel quality. The thesis presents two rate-adaptation schemes, each belonging to one of the two classes of rateadaptation schemes i.e.(1) the frame-transmission statistics based schemes, and (2) Signal-to-Noise Ratio (SNR) based, closed loop schemes. The SNR-based rate-adaptation scheme, proposed in this thesis uses a novel mechanism of delivering a receiver’s feedback to a transmitter; without requiring any modification in the standard frames as suggested by existing research. The frame-transmissionstatistics based rate adaptation solution uses an on-demand incremental strategy for selecting a rate-selection threshold. This solution is based on a cross-layer communication framework, where the rate-adaptation module uses information to/from the Application layer along with relevant information from the Medium Access Control (MAC) sub-layer. The proposed solutions are highly responsive when compared with existing rate-adaptation schemes; responsiveness is one of the key factors in the design of such protocols. The novel feedback mechanism makes it possible to achieve frame-loss differentiation with just three frames, avoiding the use of Request To Send/ Clear To Send (RTS/CTS) frames and further delays in this process. Performance tests have affirmed that the proposed rate-adaptation schemes are energy efficient; with efficiency up to 19% in specific test scenarios. In terms of throughput and frame loss-differentiation mechanisms, the proposed schemes have shown significantly better performance.Routing protocols for Mobile Ad-Hoc Networks (MANETs) use broadcast frames during the route discovery process. The 802.11 mandates the use of different transmission rates for broadcast and unicast (data-) frames. In many cases it causes creation of communication gray zones, where stations which are marked as ‘reachable neighbours’ using the broadcast frames (using lower transmission rate) are not accessible during normal, unicast communication (mainly at a higher rate). Similarly, higher device density, interference and mobility cause variable medium access delays. The IEEE 802.11e introduces four different MAC level queues for four access categories, maintaining service priority within the queues; which implies that frames from a higher priority queue are serviced more frequently than those belonging to lower priority queues. Such an enhancement at the MAC sub-layer introduces uneven queuing delays. Conventional routing protocols are unaware of such MAC specific constraints and as a result these factors are not considered which result in severe performance deterioration. To meet such challenges, the thesis presents a medium aware distance vector (MADV) routing protocol for MANETs. MADV uses MAC and physical layer (PHY) specific information in the route metric and maintains a separate route per-AC-per-destination in its routing tables. The MADV-metric can be incorporated into various routing rotocols and its applicability is determined by the possibility of provision of MAC dependent arameters that are used to determine the hop-by-hop MADV-metric values. Simulation tests and omparison with existing MANET protocols demonstrate the effectiveness of incorporating the medium dependent parameters and show that MADV is significantly better in terms of end-to-end delay and throughput