Adaptive testing for video quality assessment

Optimizing the Quality of Experience and avoiding under or over provisioning in video delivery services requires understanding of how different resources affect the perceived quality. The utility of resources, such as bit-rate, is directly calculated by proportioningthe improvement in quality over the increase in costs. However, perception of quality in video is subjective and, hence, difficultand costly to directly estimate with the commonly used ratingmethods. Two-alternative-forced choice methods such asMaximum Likelihood Difference Scaling (MLDS) introduces less biases and variability, but only deliver estimates for relativedifference in quality rather than absolute rating. Nevertheless, thisinformation is sufficient for calculating the utility of the resourceon the video quality. In this work, we are presenting an adaptiveMLDS method, which incorporates an active test selectionscheme that improves the convergence rate and decreases theneed for executing the full range of tests

Adaptive testing for video quality assessment

Optimizing the Quality of Experience and avoiding under or over provisioning in video delivery services requires understanding of how different resources affect the perceived quality. The utility of resources, such as bit-rate, is directly calculated by proportioningthe improvement in quality over the increase in costs. However, perception of quality in video is subjective and, hence, difficultand costly to directly estimate with the commonly used ratingmethods. Two-alternative-forced choice methods such asMaximum Likelihood Difference Scaling (MLDS) introduces less biases and variability, but only deliver estimates for relativedifference in quality rather than absolute rating. Nevertheless, thisinformation is sufficient for calculating the utility of the resourceon the video quality. In this work, we are presenting an adaptiveMLDS method, which incorporates an active test selectionscheme that improves the convergence rate and decreases theneed for executing the full range of tests

Improving Availability of Mobile Code Systems by Decoupling Interaction from Mobility

Resource availability in pervasive environments is restricted by many either mobility- and/or security-related factors. Multi-agent systems deployed in such environments would have to rely on a potentially low number of hosts allowing and supporting the arrival and execution of foreign code. To address this issue, this paper proposes to decouple interaction of executing programs and services from the actual software mobility pattern used to realize this interaction. The proposed system (MoDeS - Mobility Decision System) dynamically decides on the best mobility method to implement a series of software interactions while satisfying the appropriate software constraints. The system takes as input an interaction plan and produces the corresponding mobility plan. A series of simulations were performed on single- and multi-hop scenarios which showed that MoDeS can significantly increase the availability of software interactions even in highly constraint environments.</p

Predicting battery depletion of neighboring wireless sensor nodes

With a view to prolong the duration of the wireless sensor network, many battery lifetime prediction algorithms run on individual nodes. If not properly designed, this approach may be detrimental and even accelerate battery depletion. Herein, we provide a comparative analysis of various machine-learning algorithms to offload the energy inference task to the most energy-rich nodes, to alleviate the nodes that are entering the critical state. Taken to its extreme, our approach may be used to divert the energy-intensive tasks to a monitoring station, enabling a cloud-based approach to sensor network management. Experiments conducted in a controlled environment with real hardware have shown that RSSI can be used to infer the state of a remote wireless node once it is approaching the cutoff point. The ADWIN algorithm was used for smoothing the input data and for helping a variety of machine learning algorithms particularly to speed up and improve their prediction accuracy

Impact of Transmission Power Control in multi-hop networks

Many Transmission Power Control (TPC) algorithms have been proposed in the past, yet the conditions under which they are evaluated do not always reflect typical Internet-of-Things (IoT) scenarios. IoT networks consist of several source nodes transmitting data simultaneously, possibly along multiple hops. Link failures are highly frequent, causing the TPC algorithm to kick-in quite often. To this end, in this paper we study the impact that frequent TPC actions have across different layers. Our study shows how one node’s decision to scale its transmission power can affect the performance of both routing and MAC layers of multiple other nodes in the network, generating cascading packet retransmissions and forcing far too many nodes to consume more energy. We find that crucial objectives of TPC such as conserving energy and increasing network capacity are severely undermined in multi-hop networks

Modelling electricity storage systems management under the influence of demand-side management programmes

Electricity storage systems (ESS) for bulk energy storage are principally used for load levelling purposes or for relieving the intermittency of renewables. Another use is electricity arbitrage through the rule of ‘buy low, sell high’. This operation tracks the market-clearing price (MCP) profiles and produces profit by exploiting the differences between peak and off-peak prices. The profits made in this way depend on technology characteristics and the market competition level. We investigate the influence of demand-side management (DSM) on ESS profitability when the only income is from provision of electricity arbitrage services, by optimizing the time allocation of the charge and discharge operations. Two scenarios of DSM in the market have been selected for two management periods (MP): 1 day and 3 days. The longer MP is examined in order to investigate the potential for higher economic value when energy transfer to the next day is permitted. The key finding is that a very small load shifting from peaks to off-peaks, due to DSM, significantly affects the ESS profit. The significant profit losses the ESS showed are a result of the high capital costs and the small difference of the peak and off-peak electricity prices in the Greek market. Therefore, under the assumptions we have made for this research, any attempt to use ESS in ‘buy low, sell high’ operation is not profitable

Spontaneous networks

How can we increase the capillarity of the Net without facing the daunting issues that come with large-scale infrastructures? Can we embed all necessary protocols into our terminals and then use the terminals themselves to relay packets? This chapter develops the vision of ubiquitous connectivity, pinpointing foundations and problems. Networks made without any dedicated hardware are possible, but require new protocols. Here, we discover how to build spontaneous, ad hoc networks starting from extremely simple mechanisms

Modelling electricity storage systems management under the influence of demand-side management programmes

Electricity storage systems (ESS) for bulk energy storage are principally used for load levelling purposes or for relieving the intermittency of renewables. Another use is electricity arbitrage through the rule of ‘buy low, sell high’. This operation tracks the market-clearing price (MCP) profiles and produces profit by exploiting the differences between peak and off-peak prices. The profits made in this way depend on technology characteristics and the market competition level. We investigate the influence of demand-side management (DSM) on ESS profitability when the only income is from provision of electricity arbitrage services, by optimizing the time allocation of the charge and discharge operations. Two scenarios of DSM in the market have been selected for two management periods (MP): 1 day and 3 days. The longer MP is examined in order to investigate the potential for higher economic value when energy transfer to the next day is permitted. The key finding is that a very small load shifting from peaks to off-peaks, due to DSM, significantly affects the ESS profit. The significant profit losses the ESS showed are a result of the high capital costs and the small difference of the peak and off-peak electricity prices in the Greek market. Therefore, under the assumptions we have made for this research, any attempt to use ESS in ‘buy low, sell high’ operation is not profitable

On the way to the pervasive web

Web applications bring about extraordinary breakthroughs regarding our digital ecosystem. Pretty much anything with a chip and a radio interface can connect to the Web. However, many advocate a complete overhaul of the Internet as the only means to sustain innovation and productivity. Nobody knows what the next-generation of the Internet will look like; though important clues are visible as years of research have already generated phenomenal ideas. Together, we'll bring a range of network mechanisms "out of the lab" that can make the Net more proactive, reactive, robust and, ultimately, more pervasive than it is today. Our journey starts by scrutinizing the inexorable transformation of Web Applications in order to unveil the intrinsic limitations of the Internet