Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.

Ability of students to recognize the relationship between using mobile apps for learning during fieldwork and the development of graduate attributes

The increasing importance of employability in Higher Education curricula and the prevalence of using mobile devices for fieldbased learning prompted an investigation into student awareness of the relationship between the use of mobile apps for learning and the development of graduate attributes (GAs) (and the link to employability). The results from post-fieldwork focus groups from four field courses indicated that students could make clear links between the use of a variety of mobile apps and graduate attribute development. The study suggests a number of mobile apps can align simultaneously with more than one graduate attribute. Furthermore, prior experience and the context of use can influence students’ perceptions of an app and its link with different GAs.

Effectiveness of mobile apps in teaching field-based identification skills

It has been suggested that few students graduate with the skills required for many ecological careers, as field-based learning is said to be in decline in academic institutions. Here, we asked if mobile technology could improve field-based learning, using ability to identify birds as the study metric. We divided a class of ninety-one undergraduate students into two groups for field-based sessions where they were taught bird identification skills. The first group has access to a traditional identification book and the second group were provided with an identification app. We found no difference between the groups in the ability of students to identify birds after three field sessions. Furthermore, we found that students using the traditional book were significantly more likely to identify novel species. Therefore, we find no evidence that mobile technology improved students’ ability to retain what they experienced in the field; indeed, there is evidence that traditional field guides were more useful to students as they attempted to identify new species. Nevertheless, students felt positively about using their own smartphone devices for learning, highlighting that while apps did not lead to an improvement in bird identification ability, they gave greater accessibility to relevant information outside allocated teaching times.

Moving on in Neolithic studies: understanding mobile lives

Mobility is a fundamental facet of being human and should be central to archaeology. Yet mobility itself and the role it plays in the production of social life, is rarely considered as a subject in its own right. This is particularly so with discussions of the Neolithic people where mobility is often framed as being somewhere between a sedentary existence and nomadic movements. This volume examines the importance and complexities of movement and mobility, whether on land or water, in the Neolithic period. It uses movement in its widest sense, ranging from everyday mobilities – the routines and rhythms of daily life – to proscribed mobility, such as movement in and around monuments, and occasional and large-scale movements and migrations around the continent and across seas. Papers are roughly grouped and focus on ‘mobility and the landscape’, ‘monuments and mobility’, ‘travelling by water’, and ‘materials and mobility’. Through these themes the volume considers the movement of people, ideas, animals, objects, and information, and uses a wide range of archaeological evidence from isotope analysis; artefact studies; lithic scatters and assemblage diversity.

Mobile sink based fault diagnosis scheme for wireless sensor networks

Network diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results.