In this paper, we focus on a self-organizing horizontal placement problem of unmanned aerial vehicles (UAVs) which offer network coverage to selected user devices (UDs) in large areas. Specifically, we apply a virtual force field (FF) concept to allow a swarm of UAVs to autonomously locate themselves in proximity to UDs. Unlike the previous work on the FF approach for the selected scenario, we address the incomplete connectivity of network nodes in terms of temporal and spatial couplings under the condition where the UD associations need to be prioritized. We propose a layered FF approach that combines two loosely coupled but complementary FFs. These include a remote FF (RFF) built upon outdated network status information of the whole network and a proximate FF (PFF) that relies on the prompt status of a local subset of nodes in proximity to a particular UAV. We also derive balancing conditions for both RFF and PFF that prevent the UAV swarm to escape from the vicinity of prioritized UDs. The simulation results first characterize the ability of the PFF and RFF to approach the prioritized UDs and then demonstrate the potential gain to improve the coverage probability using a layered FF structure.

When deploying a wireless sensor network (WSN) identifying optimal locations for the nodes is challenging, especially so if the environment is either (i) unknown or (ii) obstacle-rich. We proposes BISON (Bio-Inspired Self-Organizing Network), a variant of the Voronoi algorithm to address this challenge. Among the features of the approach are the restrictions placed upon the abilities of the nodes: (i) all information is sensed locally as well as (ii) subject to communication noise. Performance is measured as (i) the percentage of area covered, (ii) the total distance traveled by the nodes, (iii) the cumulative energy consumption and (iv) the uniformity of nodes’ distribution. Obstacle constellations and noise levels are studied systematically and a collision-free recovery strategy for failing nodes is proposed. Results obtained from extensive simulations show the algorithm outperforming previously reported approaches in both, convergence speed, as well as deployment cost.

Interactive learning through computer quiz games has been introduced to improve teaching and learning in a freshman engineering chemistry course. This is accomplished through a series of JAVA-based, weekly general chemistry quiz games, developed and implemented as a supplemental and augmentative tool in delivering freshmen chemistry for engineering majors. The introduction of quiz game is anticipated to increase the engagement of students in chemistry classes by improving their attitudes towards chemistry and their overall chemistry achievements. Kolb's four-stage learning cycle is introduced as a possible model for a role the computer quiz games could have in the overall learning process, where the games are anticipated to serve as alternate mode of learning and as an additional motivational tool. In addition to introducing weekly computer quiz games, we report on the effort made to assess their effectiveness. For this purpose, we propose a novel chemistry achievement test developed in this study, Freshman Engineering Chemistry Aptitude Test (FECAT), which aims to comprehensively cover the freshmen engineering chemistry course. Additionally, we implemented an attitude questionnaire. The findings of this pilot study help to advance our understanding of the practical contribution of game-based learning to students' achievement in chemistry and also their attitudes towards learning chemistry at university. At the time of the pilot implementation, we hypothesized that the major benefit of the interactive game would be an increase in students' overall interest in chemistry, with an associated improvement in achievements in the course.

It is sometimes assumed that the total amount of a resource being consumed is the key consideration when attempting to devise the most efficient management strategy.We explore a case in which the rate and timing of resource utilization are also susceptible to impact on performance and illustrate how bringing random fluctuations under control can help maximize efficiency in a simple client-server scenario.The role of selforganization and distributed control methods in achieving this goal is briefly discussed.

In this article, we present a collective decision-making framework inspired by biological swarms and capable of supporting the emergence of a consensus within a population of agents in the absence of environment-mediated communication (stigmergy). Instead, amplification is the result of the variation of a confidence index, stored in individual memory and providing each agent with a statistical estimate of the current popularity of its preferred choice within the whole population. We explore the fundamental properties of our framework using a combination of analytical and numerical methods. We then use Monte Carlo simulation to investigate its applicability to host selection in the presence of multiple alternatives, a problem found in application migration scenarios. The advantages of self-organization and the use of statistically predictive methods in this context are also discussed.

This article distinguishes two aims: (1) to showcase an approach to identify clusters of users of mobile devices on the basis of shared common attributes, and (2) to investigate a social network-based awareness campaign which shows significant promise to reduce the individual's as well as the group's energy consumption. One possible way of progressively driving down the power consumption of individuals is to improve real-time and fine-grained awareness of their energy signature (or carbon footprint) relative to their peers. In this paper, we explore the possibility to support such an awareness program using gossiping in a network of mobile clients. We identify and address key challenges, namely how to propagate required information and, critically, how to filter out meaningless and / or counter-productive comparisons by detecting (through machine-learning techniques) boundaries between user communities. Results of a preliminary simulation study designed for evaluating performance in terms of our framework's ability to identify suitable comparison groups and to subsequently drive down the number of false positives / negatives are presented.

A new paradigm of thinking pertaining to a novel holistic intelligent campus (iCampus) environment is proposed, in this paper, in order to enrich and enhance, as well as to transform, the end-to-end learning lifecycle of a knowledge ecosystem. Analogous to the different functions of a biological brain, the central digital nervous system of the campus is comprised of various different interconnected functional intelligences. Each of these intelligent areas is set to perform its specified functional role in a dynamic and coherent inter- and intra-integrative manner within the environment itself. A generalized roadmap has also been devised to encapsulate the concept from within an existing or a new campus setting. Note that the nature of the iCampus proposition is inherently multi-disciplinary and has multi-applicability to other forms of intelligent environment. To capture part of the essence of the concept, some of the key challenges pertinent to the iCampus ecosystem have also been highlighted within the campus value proposition framework.

In this paper, we investigate the global self-aggregation dynamics arising from local decision-based rewiring of an overlay network, used as an abstraction for an autonomic service-oriented architecture. We measure the ability of a selected set of local rules to foster self-organization of what is originally a random graph into a structured network. Scalability issues with respect to the key parameters of system size and diversity are extensively discussed. Conflicting goals are introduced, in the form of a population of nodes actively seeking to acquire neighbours of a type different from their own, resulting in decreased local homogeneity. We show that a ‘secondary’ self-organization process ensues, whereby nodes spontaneously cluster according to their implicit objective. Finally, we introduce dynamic goals by making the preferred neighbour type a function of the local characteristics of a simulated workload. We demonstrate that in this context, an overlay rewiring process based purely on local decisions and interactions can result in efficient load-balancing without central planning. We conclude by discussing the implications of our findings for the design of future distributed applications, the likely influence of other factors and of extreme parameter values on the ability of the system to self-organize and the potential improvements to our framework.

In this demonstration, we present B 3 (BroadBand Broker), a QoS management framework capable of smoothing QoS distribution across multiple competing applications through differential allocation of the contended resource (i.e. bandwidth). I. BACKGROUND With the development of the connected home and liquid bandwidth paradigms, there is a distinct need for simple methods to manage quality of service (QoS) across multiple applications at the end user level (i.e. within the confines of the individual subscriber's environment). Such methods need to feature two fundamental properties: • They must be capable of dealing with the fact that every network application has different QoS requirements and that QoS cannot always be measured using simple technical criteria (e.g. packet loss, jitter…) but has to reflect the perception of the user.

Autonomic communications seek to improve the ability of network and services to cope with unpredicted change, including changes in topology, load, task, the physical and logical characteristics of the networks that can be accessed, and so forth. Broad-ranging autonomic solutions require designers to account for a range of end-to-end issues affecting programming models, network and contextual modeling and reasoning, decentralised algorithms, trust acquisition and maintenance---issues whose solutions may draw on approaches and results from a surprisingly broad range of disciplines. We survey the current state of autonomic communications research and identify significant emerging trends and techniques.

This paper shows how self-mechanisms give rise to complex but predictable and therefore steerable global system behavior in a cooperative computing environment. The operation of and the interactions between a set of networked autonomic devices are simulated. These are used as access points to a number of services, have the ability to accept or delegate execution of the associated tasks, and can adjust their internal state in response to the demand. At initialization, all devices are assigned a random internal state, i.e., there is no correlation between their configuration and the tasks that they are expected to perform. The authors study the emergence of cooperation and find that it spontaneously occurs when specific conditions are met, which allow individual devices to focus on performing a single task, sacrificing their ability to efficiently perform others. A relatively simple model that can be completely and thoroughly analyzed was chosen so as to demonstrate how the methodology developed to study complex adaptive systems in biology can be a powerful tool when planning the deployment of large ensembles of interacting autonomic devices

This paper shows how self-(*) mechanisms give rise to complex but predictable and therefore steerable global system behavior in a cooperative computing environment. The operation of and the interactions between a set of networked autonomic devices are simulated. These are used as access points to a number of services, have the ability to accept or delegate execution of the associated tasks, and can adjust their internal state in response to the demand. At initialization, all devices are assigned a random internal state, i.e., there is no correlation between their configuration and the tasks that they are expected to perform. The authors study the emergence of cooperation and find that it spontaneously occurs when specific conditions are met, which allow individual devices to focus on performing a single task, sacrificing their ability to efficiently perform others. A relatively simple model that can be completely and thoroughly analyzed was chosen so as to demonstrate how the methodology developed to study complex adaptive systems in biology can be a powerful tool when planning the deployment of large ensembles of interacting autonomic devices.