Energy Efficient Service Embedding In IoT over PON

In this paper, we present an energy efficient service embedding framework in Internet of Things (IoT) network using Mixed Integer Linear Programming (MILP). The framework enables an energy efficient smart road paradigm for different simultaneous applications supported by a passive optical network (PON) and wireless communication in the smart city. It optimizes the infrastructure's resources including the access network, IoT, fog and cloud computing. We consider an event-driven paradigm in a Service Oriented Architecture (SOA) in our framework in order to provide service abstraction of basic services which can be composed into complex services and exploited by the upper application layers. The framework results show that it is possible to reduce the power consumption by optimizing the selection of computing nodes and traffic distribution in the network while satisfying the service requirements

Energy Efficiency of Fog Computing Health Monitoring Applications

Fog computing offers a scalable and effective solution to overcome the increasing processing and networking demands of Internet of Thing (IoT) devices. In this paper, we investigate the use of fog computing for health monitoring applications. We consider a heart monitoring application where patients send their 30 minute recording of Electrocardiogram (ECG) signal for processing, analysis, and decision making at fog processing units within the time constraint recommended by the American Heart Association (AHA) to save heart patients when an abnormality in the ECG signal is detected. The locations of the processing servers are optimized so that the energy consumption of both the processing and networking equipment are minimised. The results show that processing the ECG signal at fog processing units yields a total energy consumption saving of up to 68% compared to processing the at the central cloud

Energy Efficient Virtual Machine Services Placement in Cloud-Fog Architecture

The proliferation in data volume and processing requests calls for a new breed of on-demand computing. Fog computing is proposed to address the limitations of cloud computing by extending processing and storage resources to the edge of the network. Cloud and fog computing employ virtual machines (VMs) for efficient resource utilization. In order to optimize the virtual environment, VMs can be migrated or replicated over geo-distributed physical machines for load balancing and energy efficiency. In this work, we investigate the offloading of VM services from the cloud to the fog considering the British Telecom (BT) network topology. The analysis addresses the impact of different factors including the VM workload and the proximity of fog nodes to users considering the data rate of state-of-the-art applications. The results show that the optimum placement of VMs significantly decreases the total power consumption by up to 75% compared to a single cloud placement

Energy consumption in wireless IoT- a review

The Internet of Things (IoT) is a rising innovation, including a wide range of uses identified with modern control, savvy metering, home mechanization, horticulture, health, etc. For these applications to run independent the IoT gadgets are required to make do for a considerable length of time and years under severe vitality imperatives. When growing such applications, it is significant for the application to think about its own energy consumption. In this work, we propose and assess an energy consumption estimation approach for occasional detecting applications running on the IoT gadgets. Our methodology depends on three stages. In the main stage, we distinguish the unmistakable exercises, for example, rest, transmit, detect and process in a detecting cycle. In the subsequent stage, we measure the power consumption of these exercises before the IoT gadget has been conveyed in the arrange. The third stage happens at run-time once the IoT gadget has been sent, to convey the energy consumption of a detecting cycle. The energy consumption is determined by utilizing the exercises control and their spans acquired at run-time. The proposed methodology is basic and conventional on the grounds that it doesn’t include any intricate equipment for runtime control estimation. Besides, this methodology likewise consolidates the dynamic idea of detecting applications by run-time estimation of energy consumption

A development of optical network unit power consumption model considering traffic load effect

Accurate and precise measurement of energy consumption for the deployment of fiber-to-the-home (FTTH) network using Gigabit passive optical network (GPON) is vital to the research community to develop models for the synthesis of energy-efficient protocols and algorithms for the access network. However, lack of power consumption measurement of optical network devices in the past has led to unrealistic and/or oversimplified model being used in simulations. Usually the access network devices are assumed always on and their consumption is both traffic and time independent. Therefore, in this paper we propose an experimentally-driven approach to i) characterize the Optical Network Unit (ONU) from the power consumption standpoint and ii) develop more accurate power consumption model for the ONU. We focus on ONU since it represents the main contributor to the energy consumption of optical access network. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a GPON network testbed. The measurement is limited to a maximum 100 Mbps data rate due to a limitation in the sampling rate and precision of the measurement device. However, validation has been done with theoretical power consumption model in order to prove the feasibility of the proposed model. Our measurements show that the power consumption of the ONU exhibits a linear dependence on the traffic in which the power consumption at idle mode is 11.51 W while in low power mode the power consumption is around 7.52 W

Impact of Distributed Processing on Power Consumption for IoT Based Surveillance Applications

With the rapid proliferation of connected devices in the Internet of Things (IoT), the centralized cloud solution faces several challenges, out of which, power consumption is one of the top priorities among the research community. In this paper, we evaluate the impact of demand splitting over heterogeneous processing resources in an IoT platform, supported by Fog and Cloud infrastructure. We develop a Mixed Integer Linear Programming (MILP) model to study the gains of splitting resource intensive demands among IoT nodes, Fog devices and Cloud servers. A surveillance application is considered, which consists of multiple smart cameras capable of capturing and analyzing real-time video streams. The PON access network aggregates IoT layer demands for processing in the Fog or the Cloud, which is accessed through the IP/WDM network. For typical video analysis workloads, the results show that, splitting medium demand sizes among IoT and Fog resources yields a total power consumption saving of up to 32%, even if these layers can host only 10% of the total workload and this can reach up to 93% for lower number of demands, compared to the centralized cloud solution. However, the gains in power savings from splitting decreases as the number of splits increases

Energy Minimized Federated Fog Computing over Passive Optical Networks

The rapid growth of time-sensitive applications and services has driven enhancements to computing infrastructures. The main challenge that needs addressing for these applications is the optimal placement of the end-users’ demands to reduce the total power consumption and delay. One of the widely adopted paradigms to address such a challenge is fog computing. Placing fog units close to end-users at the edge of the network can help mitigate some of the latency and energy efficiency issues. Compared to the traditional hyperscale cloud data centres, fog computing units are constrained by computational power, hence, the capacity of fog units plays a critical role in meeting the stringent demands of the end-users due to intensive processing workloads. In this paper, we first propose a federated fog computing architecture where multiple distributed fog cells collaborate in serving users. These fog cells are connected through dedicated Passive Optical Network (PON) connections. We then aim to optimize the placement of virtual machines (VMs) demands originating from the end-users by formulating a Mixed Integer Linear Programming (MILP) model to minimize the total power consumption. The results show an increase in processing capacity and a reduction in the power consumption by up to 26% compared to a Non-Federated fogs computing architecture

Presence Aware Power Saving Mode (PA-PSM) enhancement for IoT devices for energy conservation

The Internet of Things has brought a vision to turn the digital object into smart devices by adding an intelligence system and thereafter connecting them to the internet world. These smart devices accumulate environmental information with the help of sensors and act consequently without human intervention. The Internet of Thing is a rapidly growing industry with expected 50 - 200 billion smart devices to connect to the internet. Multi-billions of smart devices will produce a substantial amount of data to provide services to human society, although, it will lead to increase energy consumption at the highest level and drive to high energy bills. Moreover, the flood of IoT devices may also lead to energy scarcity. IoT is nowadays mainly focused on the IT industry and researchers believe the next wave of IoT may connect 1 trillion sensors by 2025. Even if these sensors would have 10 years of battery life, it will still require 275 million batteries to be replaced every day. Therefore, it is a necessity to reduce energy consumption in smart devices. “Presence Aware Power Saving Mode (PA-PSM) Enhancement for IoT Devices for Energy Conservation”, a proposed novel approach in this research paper by the help of a proposed algorithm in this research paper to reduce power consumption by individual devices within smart homes. In the proposed approach, a centralized automation controller keeps the less priority smart devices into deep sleep mode to save energy and experiments suggest the proposed system may help to reduce 25.81% of the energy consumed by smart devices within the smart home