Rising energy costs, losses in the present-day electricity grid, risks from nuclear power generation, and global environmental changes are motivating a transformation of the conventional ways of generating electricity. Globally, there is a desire to rely more on renewable energy resources (RERs) for electricity generation. RERs reduce green house gas emissions and may have economic benefits, e.g., through applying demand side management with dynamic pricing so as to shift loads from fossil fuel-based generators to RERs. The electricity grid is presently evolving towards an intelligent grid, the so-called smart grid (SG). One of the major goals of the future SG is to move towards 100% electricity generation from RERs, i.e., towards a 100% renewable grid. However, the disparate, intermittent, and typically widely geographically distributed nature of RERs complicates the integration of RERs into the SG. Moreover, individual RERs have generally lower capacity than conventional fossil-fuel plants, and these RERs are based on a wide spectrum of different technologies. In this article, we give an overview of recent efforts that aim to integrate RERs into the SG. We outline the integration of RERs into the SG along with their supporting communication networks. We also discuss ongoing projects that seek to integrate RERs into the SG around the globe. Finally, we outline future research directions on integrating RERs into the SG.

Detecting and dealing with congestion in delay-tolerant networks (DTNs) is an important and challenging problem. Current DTN forwarding algorithms typically direct traffic towards more central nodes in order to maximise delivery ratios and minimise delays, but as traffic demands increase these nodes may become saturated and unusable. We pro- pose CafRep, an adaptive congestion aware protocol that detects and reacts to congested nodes and congested parts of the network by using implicit hybrid contact and resources congestion heuristics. CafRep exploits localised relative utility based approach to offload the traffic from more to less congested parts of the network, and to replicate at adaptively lower rate in different parts of the network with non-uniform congestion levels. We extensively evaluate our work against benchmark and competitive protocols across a range of metrics over three real connectivity and GPS traces such as Sassy [44], San Francisco Cabs [45] and Infocom 2006 [33]. We show that CafRep performs well, independent of network connectivity and mobility patterns, and consistently outperforms the state-of-the-art DTN forwarding algorithms in the face of increasing rates of congestion. CafRep maintains higher availability and success ratios while keeping low delays, packet loss rates and delivery cost. We test CafRep in the presence of two application scenarios, with fixed rate traffic and with real world Facebook application traffic demands, showing that regardless of the type of traffic CafRep aims to deliver, it reduces congestion and improves forwarding performance

Pervasive gaming is a new form of multimedia entertainment that extends the traditional computer gaming experience out into the real world. Through a combination of personal devices, positioning systems and other multimedia sensors, combined with wireless networking, a pervasive game can respond to a player’s movements and context and enable them to communicate with a game server and other players. We review recent examples of pervasive games in order to explain their distinctive characteristics as multimedia applications. We then consider the challenge of scaling pervasive games to include potentially very large numbers of players. We propose a new approach based upon a campaign model in which individuals, local groups and experts draw on a combination of pervasive games, online services and broadcasting to take part in national or even global events. We discuss the challenges that this raises for further researc

Purpose: This paper presents an initial development of a personal data attitude (PDA) measurement instrument based on established psychometric principles. The aim of the research was to develop a reliable measurement scale for quantifying and comparing attitudes towards personal data that can be incorporated into cybersecurity behavioural research models. Such a scale has become necessary for understanding individuals’ attitudes towards specific sets of data, as more technologies are being designed to harvest, collate, share and analyse personal data.Design/methodology/approach: An initial set of 34 five-point Likert-style items were developed with eight subscales and administered to participants online. The data collected were subjected to exploratory and confirmatory factor analyses and MANOVA. The results are consistent with the multidimensionality of attitude theories and suggest that the adopted methodology for the study is appropriate for future research with a more representative sample.Findings: Factor analysis of 247 responses identified six constructs of individuals’ attitude towards personal data: protective behaviour, privacy concerns, cost-benefit, awareness, responsibility and security. This paper illustrates how the PDA scale can be a useful guide for information security research and design by briefly discussing the factor structure of the PDA and related results.Originality/value: This study addresses a genuine gap in research by taking the first step towards establishing empirical evidence for dimensions underlying personal data attitudes. It also adds a significant benchmark to a growing body of literature on understanding and modelling computer users’ security behaviours.

Oceanic eddy is the ubiquitous ocean flow phenomenon, which has been the key factor in the transportation of ocean energy and materials. Consequently, oceanographic understanding can be enhanced by the intelligent identification of eddy. The state-of-the-art deep learning technologies are gradually improving the identification methods. This letter proposes the pyramid split attention(PSA) eddy detection U-Net architecture (PSA-EDUNet) that targets oceanic eddy identification from ocean remote sensing imagery. As for the PSA-EDUNet, its inspiration comes from U-Net, which contains encoder and decoder parts, making the integration of inferior and senior features efficiently and ensuring the feature information will not be lost in large quantities through non-linear connection mode. Meanwhile, PAS module is introduced to enhance feature extraction. In terms of the fusion data, the sea surface feature is the main criterion of eddy identification, including sea surface temperature (SST) and sea level anomaly (SLA). The experiments are implemented on the Kuroshio Extension and the South Atlantic regions, the results demonstrate that the proposed method can outperform other methods, especially for eddy edges and small-scale eddies.