Athen Ma

FORUM: Ecological networks: the missing links in biomonitoring science

Summary Monitoring anthropogenic impacts is essential for managing and conserving ecosystems, yet current biomonitoring approaches lack the tools required to deal with the effects of stressors on species and their interactions in complex natural systems. Ecological networks (trophic or mutualistic) can offer new insights into ecosystem degradation, adding value to current taxonomically constrained schemes. We highlight some examples to show how new network approaches can be used to interpret ecological responses. Synthesis and applications. Augmenting routine biomonitoring data with interaction data derived from the literature, complemented with ground‐truthed data from direct observations where feasible, allows us to begin to characterise large numbers of ecological networks across environmental gradients. This process can be accelerated by adopting emerging technologies and novel analytical approaches, enabling biomonitoring to move beyond simple pass/fail schemes and to address the many ecological responses that can only be understood from a network‐based perspective.

Improving the Energy-Efficiency of GPS Based Location Sensing Smartphone Applications

Smartphones with an embedded GPS sensor are being increasingly used for location determination to enable Location based services (LBS) deliver location context pervasive computing services such as maps and navigation. Although a Smartphone GPS provides adequate accuracy, it has limitations such as high energy consumption and is unavailable in locations with an obscured view of GPS satellites. Use of alternate location sensors such as Wi-Fi and GSM can be used to augment GPS and to alleviate these GPS limitations, but they can increase the average localization error. The novelty of our contribution is twofold. First we present an accelerometer based architecture that reduces GPS energy-consumption without compromising on either the location accuracy or sampling rate. Evaluation of our system shows energy-savings of up to 27% in typical circumstances. Second, as a users mobility state is complex we also propose a method to not only detect that a user is non-stationary but also classify a representative set of mobility states.

Networking our way to better ecosystem service provision

The ecosystem services (EcoS) concept is being used increasingly to attach values to natural systems and the multiple benefits they provide to human societies. Ecosystem processes or functions only become EcoS if they are shown to have social and/or economic value. This should assure an explicit connection between the natural and social sciences, but EcoS approaches have been criticized for retaining little natural science. Preserving the natural, ecological science context within EcoS research is challenging because the multiple disciplines involved have very different traditions and vocabularies (common-language challenge) and span many organizational levels and temporal and spatial scales (scale challenge) that define the relevant interacting entities (interaction challenge). We propose a network-based approach to transcend these discipline challenges and place the natural science context at the heart of EcoS research.

Using a Smart City IoT to Incentivise and Target Shifts in Mobility Behaviour—Is It a Piece of Pie?

Whilst there is an increasing capability to instrument smart cities using fixed and mobile sensors to produce the big data to better understand and manage transportation use, there still exists a wide gap between the sustainability goals of smart cities, e.g., to promote less private car use at peak times, with respect to their ability to more dynamically support individualised shifts in multi-modal transportation use to help achieve such goals. We describe the development of the tripzoom system developed as part of the SUNSET—SUstainable social Network SErvices for Transport—project to research and develop a mobile and fixed traffic sensor system to help facilitate individual mobility shifts. Its main novelty was its ability to use mobile sensors to classify common multiple urban transportation modes, to generate information-rich individual and group mobility profiles and to couple this with the use of a targeted incentivised marketplace to gamify travel. This helps to promote mobility shifts towards achieving sustainability goals. This system was trialled in three European country cities operated as Living Labs over six months. Our main findings were that we were able to accomplish a level of behavioural shifts in travel behaviour. Hence, we have provided a proof-of-concept system that uses positive incentives to change individual travel behaviour.

Anatomy of funded research in science

Significance The study of scientific collaborations has been predominately focused on characterizing publication coauthorships. Here, we study instead collaboration networks by looking at project partnerships funded over the years and show clearly the substantial impact of funding shifts on the pattern of interactions. We find that the leading universities form a cohesive clique among themselves and occupy brokerage positions between otherwise disconnected entities, and as the inequality in the distribution of funding grows over time, so does the degree of brokerage. Specifically, the elites overattract resources but they also reward in variety of research and quality. We are the first to our knowledge to systematically quantify the far-reaching effects of external forces on the complex interactions in team research that underpin the production and evolution of science. Seeking research funding is an essential part of academic life. Funded projects are primarily collaborative in nature through internal and external partnerships, but what role does funding play in the formulation of these partnerships? Here, by examining over 43,000 scientific projects funded over the past three decades by one of the major government research agencies in the world, we characterize how the funding landscape has changed and its impacts on the underlying collaboration networks across different scales. We observed rising inequality in the distribution of funding and that its effect was most noticeable at the institutional level—the leading universities diversified their collaborations and increasingly became the knowledge brokers in the collaboration network. Furthermore, it emerged that these leading universities formed a rich club (i.e., a cohesive core through their close ties) and this reliance among them seemed to be a determining factor for their research success, with the elites in the core overattracting resources but also rewarding in terms of both research breadth and depth. Our results reveal how collaboration networks organize in response to external driving forces, which can have major ramifications on future research strategy and government policy.

Rich-Cores in Networks

A core comprises of a group of central and densely connected nodes which governs the overall behaviour of a network. It is recognised as one of the key meso-scale structures in complex networks. Profiling this meso-scale structure currently relies on a limited number of methods which are often complex and parameter dependent or require a null model. As a result, scalability issues are likely to arise when dealing with very large networks together with the need for subjective adjustment of parameters. The notion of a rich-club describes nodes which are essentially the hub of a network, as they play a dominating role in structural and functional properties. The definition of a rich-club naturally emphasises high degree nodes and divides a network into two subgroups. Here, we develop a method to characterise a rich-core in networks by theoretically coupling the underlying principle of a rich-club with the escape time of a random walker. The method is fast, scalable to large networks and completely parameter free. In particular, we show that the evolution of the core in World Trade and C. elegans networks correspond to responses to historical events and key stages in their physical development, respectively.

Weighting and indirect effects identify keystone species in food webs

Abstract Species extinctions are accelerating globally, yet the mechanisms that maintain local biodiversity remain poorly understood. The extinction of species that feed on or are fed on by many others (i.e. ‘hubs’) has traditionally been thought to cause the greatest threat of further biodiversity loss. Very little attention has been paid to the strength of those feeding links (i.e. link weight) and the prevalence of indirect interactions. Here, we used a dynamical model based on empirical energy budget data to assess changes in ecosystem stability after simulating the loss of species according to various extinction scenarios. Link weight and/or indirect effects had stronger effects on food‐web stability than the simple removal of ‘hubs’, demonstrating that both quantitative fluxes and species dissipating their effects across many links should be of great concern in biodiversity conservation, and the potential for ‘hubs’ to act as keystone species may have been exaggerated to date.

A Method to Evaluate the Energy-Efficiency of Wide-Area Location Determination Techniques Used by Smartphones

Location-based services (LBS) are one of the most useful applications for smart phone users, but there is a significant energy cost in acquiring the user location. Continuous location sampling using GPS, Wi-Fi positioning system (WPS), and Global System for Mobile Communications Positioning System (GSMPS) typically deplete the battery within 12, 46, and 63 hours respectively, compared to 284 hours when GPS and Wi-Fi location positioning are turned-off. We present the design, implementation, and evaluation of a novel method to evaluate the energy-efficiency of GPS, WPS, and GSMPS location sensing technologies used by smart phones based upon a user-centred metric, battery depletion time. This metric depends upon three main factors: location sensor usage, user mobility context determination and location accuracy. Using solely the embedded smart phone accelerometer our pattern recognition model can within 2 seconds identify the user mobility state which in-turn manages the activation and deactivation of the location determination. Our results show that our hybrid energy efficient location sensing architecture can achieve energy-savings of up to 57% in typical circumstances.

Intelligent resource optimisation using semi-smart antennas in LTE OFDMA systems

The next generation 3GPP long-term evolution (LTE) mobile system will use orthogonal frequency division multiplexing access (OFDMA) as the key technology to improve the spectrum efficiency and flexible user resource allocation. One of the challenges of OFDMA systems is the interference at the cell edge where users are likely to get high interference from neighbouring cells. In a network with non-uniform traffic distribution, the problem becomes even more challenging. In this paper, a semi-smart antenna based resource allocation scheme is proposed for the LTE OFDMA system. The scheme features a centralised Genetic Algorithm (GA) algorithm that changes the cellular coverage in a coordinated manner to improve the system capacity and mitigate network interference. Simulation experiments have been carried out to evaluate the performance of the proposed scheme, the results showing significant performance improvement in terms of total traffic load achieved for all cells compared to a system with fixed coverage pattern.

Short-Term Traffic Volume Prediction for Sustainable Transportation in an Urban Area

AbstractAccurate short-term traffic volume prediction is essential for the realization of sustainable transportation as providing traffic information is widely known as an effective way to alleviate congestion. In practice, short-term traffic predictions require a relatively low computation cost to perform calculations in a timely manner and should be tolerant to noise. Traffic measurements of variable quality also arise from sensor failures and missing data. There is no optimal prediction model so far fulfilling these challenges. This paper proposes a so-called absorbing Markov chain (AMC) model that utilizes historical traffic database in a single time series to carry out predictions. This model can predict the short-term traffic volume of road links and determine the rate in which traffic eases once congestion has occurred. This paper uses two sets of measured traffic volume data collected from the city of Enschede, Netherlands, for the training and testing of the model, respectively. The main advantag...