Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP’s profit under varying cloud environment conditions

A performance comparison of smart probabilistic broadcasting of ad hoc distance vector (AODV).

Broadcast is a common operation used in Mobile Ad hoc Networks (MANETs) for many services, such as, routdiscovery and sending an information messages. The direct method to perform broadcast is simple flooding, which itcan dramatically affect the performance of MANET. Recently, a probabilistic approach to flooding has beenproposed as one of most important suggested solutions to solve the broadcast storm problem, which leads to thecollision, contention and duplicated messages. This paper proposed new probabilistic method to improve theperformance of existing on-demand routing protocol by reduced the RREQ overhead during rout discoveryoperation. The simulation results show that the combination of AODV and a suitable probabilistic rout discoverycan reduce the average end- to- end delay as well as overhead and still achieving low normalized routing load,comparing with AODV which used fixed probability and blind floodin

Towards an Energy Balancing Solution for Wireless Sensor Network with Mobile Sink Node

The issue of energy holes, or hotspots, in wireless sensor networks is well referenced. As is the proposed mobilisation of the sink node in order to combat this. However, as the mobile sink node may communicate with some nodes more than others, issues remain, such as energy spikes. In this study we propose a lightweight MAC layer solution-Dynamic Mobility and Energy Aware Algorithm (DMEAAL). Building on existing solutions utilising a communication threshold between static nodes and a sink node using a predictable mobility pattern, DMEAAL takes knowledge of optimum energy consumption levels and implements a cross-layer approach, utilising current energy consumption and dynamically adjusting communication threshold size based on target energy consumption. This approach is shown to balance energy consumption across individual nodes without increasing overall energy consumption compared to previous solutions. This without detrimentally affecting frame delivery to the sink. As such, network lifetime is improved. In addition we propose Mobile Edge Computing (MEC) applications for this solution, removing certain functionality from static nodes and instead deploying this within the mobile sink at the network edge

A New Load-Balancing Aware Objective Function for RPL’s IoT Networks

The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized as the de facto solution for routing in the context of the emerging Internet of Things (IoT) paradigm. RPL, along with other standards, has provided a baseline framework for IoT that has helped advance communications in the world of embedded resource-constrained networks. However, RPL still suffers from issues that may limit its efficiency such as the absence of an efficient load-balancing primitive. In this study, we show how RPL suffers from a load-balancing problem that may harm both the reliability of the protocol and its network lifetime. To address this problem, a novel load-balancing scheme is introduced that significantly enhances the reliability of RPL and fosters the protocol’s efficiency in terms of power consumption

Machine Learning-driven Optimization for SVM-based Intrusion Detection System in Vehicular Ad Hoc Networks

Machine Learning (ML) driven solutions have been widely used to secure wireless communications Vehicular ad hoc networks (VANETs) in recent studies. Unlike existing works, this paper applies support vector machine (SVM) for intrusion detection in VANET. The structure of SVM has many computation advantages, such as special direction at a finite sample and irrelevance between the complexity of algorithm and the sample dimension. Intrusion detection in VANETis nonconvex and combinatorial problem. Thus, three intelligence optimization algorithms are used for optimizing the accuracy value of SVM classifier. These optimization algorithms include Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Our results demonstrate that GA outperformed other optimization algorithms

MOSAIC: Simultaneous Localization and Environment Mapping using mmWave without a-priori Knowledge

Simultaneous Localization and environment mapping (SLAM) is the core to robotic mapping and navigation as it constructs simultaneously the unknown environment and localizes the agent within. However, in millimeter wave (mmWave) research, SLAM is still at its infancy. In this paper, we introduce MOSAIC a new approach for SLAM in indoor environment by exploiting the map-based channel model. More precisely, we perform localization and environment inference through obstacle detection and dimensioning. The concept of Virtual Anchor Nodes (VANs), known in literature as the mirrors of the real anchors with respect to the obstacles in the environment, is firstly introduced. Then, based on these VANs, the obstacles positionsand dimensions are estimated by detecting the zone of pathsobstruction, points of reflection and obstacle vertices estimation.Cramer-Rao Lower Bounds (CRLB) are then derived to find the optimal number of anchor nodes and measurements points that improve the localization and mapping accuracy. Simulation results have shown high localization accuracy and obstacle detection in different environments using mmWave technology

Mobility Aware Duty Cycling Algorithm (MADCAL) A Dynamic Communication Threshold for Mobile Sink in Wireless Sensor Network

The hotspot issue in wireless sensor networks, with nodes nearest the sink node losing energy fastest and degrading network lifetime, is a well referenced problem. Mobile sink nodes have been proposed as a solution to this. This does not completely remove the hotspot problem though, with nodes the sink passes most closely still expending more energy than others. This study proposes a lightweight algorithm, located in the MAC layer of static nodes and utilising knowledge of predictable sink node mobility. This in order to create a dynamic communication threshold between static nodes and the sink, within which static nodes awaken. Lessening competition for sink communication between nodes. In utilising predictable mobility and factors already known to the static node, such as location and interference range, there is no need for energy consuming messaging. Analysis and simulation results, tested on a lightweight implementation of a carrier-sense multiple access based MAC protocol, shows a significant improvement in energy consumption in both controlled and random environments. With frame delivery improved to the point where sink speed is negated. This when compared to the existing duty cycling approach

Wireless Body Area Network (WBAN): A Survey on Reliability, Fault Tolerance, and Technologies Coexistence

Wireless Body Area Network (WBAN) has been a key element in e-health to monitor bodies. This technology enables new applications under the umbrella of different domains, including the medical field, the entertainment and ambient intelligence areas. This survey paper places substantial emphasis on the concept and key features of the WBAN technology. First, the WBAN concept is introduced and a review of key applications facilitated by this networking technology is provided. The study then explores a wide variety of communication standards and methods deployed in this technology. Due to the sensitivity and criticality of the data carried and handled by WBAN, fault tolerance is a critical issue and widely discussed in this paper. Hence, this survey investigates thoroughly the reliability and fault tolerance paradigms suggested for WBANs. Open research and challenging issues pertaining to fault tolerance, coexistence and interference management and power consumption are also discussed along with some suggested trends in these aspect

Reliability and Energy Efficiency Enhancement for Emergency-Aware Wireless Body Area Networks (WBAN)

Medium Access Control (MAC) protocols based on Time Division Multiple Access (TDMA) can improve the reliability and efficiency of WBAN. However, traditional static TDMA techniques adopted by IEEE 802.15.4 and IEEE 802.15.6 do not sufficiently consider the channel status or the buffer requirements of the nodes within heterogeneous contexts. Although there are some solutions that have been proposed to alleviate the effect of the deep fade in WBAN channel by adopting dynamic slot allocation, these solutions still suffer from some reliability and energy efficiency issues and they do not avoid channel deep fading. This paper presents two novel and generic TDMA based techniques to improve WBAN reliability and energy efficiency. Both techniques synchronize nodes adaptively whilst tackling their channel and buffer status in normal and emergency contexts. Extensive simulation experiments using various traffic rates and time slot lengths demonstrate that the proposed techniques improve the reliability and the energy efficiency compared to IEEE 802.15.4 and IEEE 802.15.6 in both situations, the normal and emergency contexts. This improvement has been achieved in terms of packet loss, up to 90% and energy consumption, up to 13%, confirming the significant enhancements made by the developed scheduling techniques

A Mobility Aware Duty Cycling and Preambling Solution for Wireless Sensor Network with Mobile Sink Node

Utilising the mobilisation of a sink node in a wireless sensor network to combat the energy hole, or hotspot issue, is well referenced. However, another issue , that of energy spikes may remain. With the mobile sink node potentially communicating with some nodes more than others. In this study we propose the Mobility Aware Duty Cycling and Dynamic Preambling Algorithm (MADCaDPAL). This algorithm utilises an existing solution where a communication threshold is built between a mobile sink node using predictable mobility and static nodes on its path. MADCaDPAL bases decisions relating to node sleep function, moving to clear channel assessment and the subsequent sending of preambles on the relation between the threshold built by the static node and the position of the mobile sink node. MADCaDPAL achieves a reduction in average energy consumption of up to 80%, this when used in conjunction with a lightweight carrier-sense multiple access based MAC implementation. Maximum energy consumption amongst individual nodes is also brought closer to the average, reducing energy spikes and subsequently improving network lifetime. Additionally, frame delivery to the sink is improved overall