From serendipity to sustainable Green IoT: technical, industrial and political perspective

Recently, Internet of Things (IoT) has become one of the largest electronics market for hardware production due to its fast evolving application space. However, one of the key challenges for IoT hardware is the energy efficiency as most of IoT devices/objects are expected to run on batteries for months/years without a battery replacement or on harvested energy sources. Widespread use of IoT has also led to a largescale rise in the carbon footprint. In this regard, academia, industry and policy-makers are constantly working towards new energy-efficient hardware and software solutions paving the way for an emerging area referred to as green-IoT. With the direct integration and the evolution of smart communication between physical world and computer-based systems, IoT devices are also expected to reduce the total amount of energy consumption for the Information and Communication Technologies (ICT) sector. However, in order to increase its chance of success and to help at reducing the overall energy consumption and carbon emissions a comprehensive investigation into how to achieve green-IoT is required. In this context, this paper surveys the green perspective of the IoT paradigm and aims to contribute at establishing a global approach for green-IoT environments. A comprehensive approach is presented that focuses not only on the specific solutions but also on the interaction among them, and highlights the precautions/decisions the policy makers need to take. On one side, the ongoing European projects and standardization efforts as well as industry and academia based solutions are presented and on the other side, the challenges, open issues, lessons learned and the role of policymakers towards green-IoT are discussed. The survey shows that due to many existing open issues (e.g., technical considerations, lack of standardization, security and privacy, governance and legislation, etc.) that still need to be addressed, a realistic implementation of a sustainable green-IoT environment that could be universally accepted and deployed, is still missing

Energy-efficient vertical handover parameters, classification and solutions over wireless heterogeneous networks: a comprehensive survey

In the last few decades, the popularity of wireless networks has been growing dramatically for both home and business networking. Nowadays, smart mobile devices equipped with various wireless networking interfaces are used to access the Internet, communicate, socialize and handle short or long-term businesses. As these devices rely on their limited batteries, energy-efficiency has become one of the major issues in both academia and industry. Due to terminal mobility, the variety of radio access technologies and the necessity of connecting to the Internet anytime and anywhere, energy-efficient handover process within the wireless heterogeneous networks has sparked remarkable attention in recent years. In this context, this paper first addresses the impact of specific information (local, network-assisted, QoS-related, user preferences, etc.) received remotely or locally on the energy efficiency as well as the impact of vertical handover phases, and methods. It presents energy-centric state-of-the-art vertical handover approaches and their impact on energy efficiency. The paper also discusses the recommendations on possible energy gains at different stages of the vertical handover process

Reputation-based network selection solution for improved video delivery quality in heterogeneous wireless network environments

The continuous innovations and advances in both high-end mobile devices and wireless communication technologies have increased the users demand and expectations for anywhere, anytime, any device high quality multimedia applications provisioning. Moreover, the heterogeneity of the wireless network environment offers the possibility to the mobile user to select between several available radio access network technologies. However, selecting the network that enables the best user perceived video quality is not trivial given that in general the network characteristics vary widely not only in time but also depending on the user location within each network. In this context, this paper proposes a user location-aware reputation-based network selection solution which aims at improving the video delivery in a heterogeneous wireless network environment by selecting the best value network. Network performance is regularly monitored and evaluated by the currently connected users in different areas of each individual network. Based on the existing network performance-related information and mobile user location and speed, the network that offers the best support for video delivery along the user’s path is selected as the target network and the handover is triggered. The simulation results show that the proposed solution improves the video delivery quality in comparison with the case when a classic network selection mechanism was employed

OFLoad: An OpenFlow-based dynamic load balancing strategy for datacenter networks

The latest tremendous growth in the Internet traffic has determined the entry into a new era of mega-datacenters, meant to deal with this explosion of data traffic. However this big data with its dynamically changing traffic patterns and flows might result in degradations of the application performance eventually affecting the network operators’ revenue. In this context there is a need for an intelligent and efficient network management system that makes the best use of the available bisection bandwidth abundance to achieve high utilization and performance. This paper proposes OFLoad, an OpenFlow-based dynamic load balancing strategy for datacenter networks that enables the efficient use of the network resources capacity. A real experimental prototype is built and the proposed solution is compared against other solutions from the literature in terms of load-balancing. The aim of OFLoad is to enable the instant configuration of the network by making the best use of the available resources at the lowest cost and complexity

Power-friendly access network selection strategy for heterogeneous wireless multimedia networks

Apart from the number and types of applications available to users of diverse devices with various characteristics, a highly relevant issue in current and future wireless environment is the coexistence of multiple networks supported by various access technologies deployed by different operators. In this context, the aim is to keep the mobile users “always best connected” anywhere and anytime in such a multi-technology multi-application multi-terminal multi-user environment. Multimedia streaming to battery powered mobile devices has become widespread. However, the battery power capability has not kept up with the advances in other technologies and it is rapidly becoming a concern. Since multimedia applications are known to be high energy consumers and since the battery lifetime is an important factor for mobile users, this paper proposes a network selection algorithm which bases its decision on the estimated energy consumption. The proposed solution enables the multimedia stream to last longer while maintaining an acceptable user perceived quality by selecting the least power consuming network

An innovative machine learning-based scheduling solution for improving live UHD video streaming quality in highly dynamic network environments

The latest advances in terms of network technologies open up new opportunities for high-end applications, including using the next generation video streaming technologies. As mobile devices become more affordable and powerful, an increasing range of rich media applications could offer a highly realistic and immersive experience to mobile users. However, this comes at the cost of very stringent Quality of Service (QoS) requirements, putting significant pressure on the underlying networks. In order to accommodate these new rich media applications and overcome their associated challenges, this paper proposes an innovative Machine Learning-based scheduling solution which supports increased quality for live omnidirectional (360◦) video streaming. The proposed solution is deployed in a highly dy-namic Unmanned Aerial Vehicle (UAV)-based environment to support immersive live omnidirectional video streaming to mobile users. The effectiveness of the proposed method is demonstrated through simulations and compared against three state-of-the-art scheduling solutions, such as: Static Prioritization (SP), Required Activity Detection Scheduler (RADS) and Frame Level Scheduler (FLS). The results show that the proposed solution outperforms the other schemes involved in terms of PSNR, throughput and packet loss rate

A real-time power monitoring and energy-efficient network/interface selection tool for android smartphones

Energy efficiency in wireless and cellular networks has become one of the most important concerns for both academia and industry due to battery dependence of mobile devices. In this regard, Wireless Network Interface Cards (WNICs) of mobile devices have to be taken into account carefully as they consume an important chunk of the system's total energy. In this paper, we propose a real-time network power consumption profiler and an energy-aware network/interface selection tool for Android-based smartphones. The tool has been freely released on the Android Play Store. The proposed solution reports the power consumption levels of different network interfaces (Wi-Fi and Cellular) by making use of actual packet measurements and precise computations, and enables the devices to handover horizontally/vertically in order to improve the energy efficiency. In this context, widespread analyses have been executed to show the accuracy of the proposed tool. The results demonstrate that the proposed tool is very accurate for any type of IEEE 802.11 wireless or cellular stations, regardless of having different amount of channel utilization, transmission rates, signal strengths or traffic types

Performance of an adaptive multimedia mechanism in a wireless multi-user environment

With the increasing popularity of accessing multimedia services over different wireless networks, researchers have been trying to develop different adaptive multimedia mechanisms in order to mitigate the impact of fluctuating radio resources. This paper considers the case when multiple users stream video over the same IEEE 802.11b WLAN using a newly proposed Signal Strength-based Adaptive Multimedia Delivery Mechanism (SAMMy). SAMMy makes use of the IEEE 802.11k standard and uses estimated signal strength, location, and packet loss as part of its adaptive mechanism in order to increase user perceived quality for multimedia streaming applications in wireless networks. SAMMy is evaluated by modeling and simulations and compared with another adaptive multimedia delivery mechanism TFRC, in terms of aggregate throughput and fairness. The results show that the proposed signal strengthbased adaptive multimedia delivery scheme outperforms the other scheme in terms of both throughput and fairness when performing video streaming in WLAN

E³DOAS: balancing QoE and energy-saving for multi-device adaptation in future mobile wireless video delivery

Smart devices (e.g. smartphones, tablets, smart-home devices, etc.) have become important companions to most people in their daily activities, and are very much used for multimedia content exchange (i.e. video sharing, real-time/non-real-time multimedia streaming), contributing to the exponential increase in mobile traffic over the current wireless networks. While the next generation mobile networks will provide higher capacity than the current 4G systems, the network operators will face important challenges associated with the outstanding increase of both video traffic and user expectations in terms of their levels of perceived quality or Quality of Experience (QoE). Furthermore, the heterogeneity of mobile devices (e.g. screen resolution, battery life, hardware performance) also impacts severely the end-user QoE. In this context, this paper proposes an Evolved QoE-aware Energy-saving Device-Oriented Adaptive Scheme (E3DOAS ) for mobile multimedia delivery over future wireless networks. E3DOAS makes use of a coalition game-based rate allocation strategy within the multi-device heterogeneous environment, and optimizes the trade-off between the end-user perceived quality of the multimedia delivery and the mobile device energy-saving. Testing has involved a prototype of E3DOAS, a crowd-sourcing-based QoE assessment method to model non-reference perceptual video quality, and an energy measurement testbed introduced to collect power consumption parameters of the mobile devices. Simulation-based performance evaluation showed how E3DOAS outperformed other state of the art multimedia adaptive solutions in terms of energy saving, end-to-end Quality of Service (QoS) metrics and end-user perceived quality

MiceTrap: scalable traffic engineering of datacenter mice flows using OpenFlow

Datacenter network topologies are inherently built with enough redundancy to offer multiple paths between pairs of end hosts for increased flexibility and resilience. On top, traffic engineering (TE) methods are needed to utilize the abundance of bisection bandwidth efficiently. Previously proposed TE approaches differentiate between long-lived flows (elephant flows) and short-lived flows (mice flows), using dedicated traffic management techniques to handle elephant flows, while treating mice flows with baseline routing methods. We show through an example that such an approach can cause congestion to short-lived (but not necessarily less critical) flows. To overcome this, we propose MiceTrap, an OpenFlow-based TE approach targeting datacenter mice flows. MiceTrap employs scalability against the number of mice flows through flow aggregation, together with a software-configurable weighted routing algorithm that offers improved load balancing for mice flows