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Mobile agent (MA), a part of the mobile computing paradigm, was recently proposed for data gathering in Wireless Sensor Networks (WSNs). The MA-based approach employs two algorithms: Single-agent Itinerary Planning (SIP) and Multi-mobile agent Itinerary Planning (MIP) for energy-efficient data gathering. The MIP was proposed to outperform the weakness of SIP by introducing distributed multi MAs to perform the data gathering task. Despite the advantages of MIP, finding the optimal number of distributed MAs and their itineraries are still regarded as critical issues. The existing MIP algorithms assume that the itinerary of the MA has to start and return back to the sink node. Moreover, each distributed MA has to carry the processing code (data aggregation code) to collect the sensory data and return back to the sink with the accumulated data. However, these assumptions have resulted in an increase in the number of MA’s migration hops, which subsequently leads to an increase in energy and time consumption. In this paper, a spawn multi-mobile agent itinerary planning (SMIP) approach is proposed to mitigate the substantial increase in cost of energy and time used in the data gathering processes. The proposed approach is based on the agent spawning such that the main MA is able to spawn other MAs with different tasks assigned from the main MA. Extensive simulation experiments have been conducted to test the performance of the proposed approach against some selected MIP algorithms. The results show that the proposed SMIP outperforms the counterpart algorithms in terms of energy consumption and task delay (time), and improves the integrated energy-delay performance.

In this paper, we propose a solution for the problem of searching for multiple targets by a group of mobile agents with sensing errors of the first and the second types. The agents’ goal is to plan the search and follow its trajectories that lead to target detection in minimal time. Relying on real sensors’ properties, we assume that the agents can detect the targets in various directions and distances; however, they are exposed to first- and second-type statistical errors. Furthermore, we assume that the agents in the group have errorless communication with each other. No central station or coordinating agent is assumed to control the search. Thus, the search follows a fully distributed decision-making process, in which each agent plans its path independently based on the information about the targets, which is collected independently or received from the other agents. The suggested solution includes two algorithms: the Distributed Expected Information Gain (DEIG) algorithm, which implements dynamic Voronoi partitioning of the search space and plans the paths by maximizing the expected one-step look-ahead information per region, and the Collective Q-max (CQM) algorithm, which finds the shortest paths of the agents in the group by maximizing the cumulative information about the targets’ locations using deep Q-learning techniques. The developed algorithms are compared against previously developed reactive and learning methods, such as the greedy centralized Expected Information Gain (EIG) method. It is demonstrated that these algorithms, specifically the Collective Q-max algorithm, considerably outperform existing solutions. In particular, the proposed algorithms improve the results by 20% to 100% under different scenarios of noisy environments and sensors’ sensitivity.

The delivery of ecosystem services by mobile organisms depends on the distribution of those organisms, which is, in turn, affected by resources at local and landscape scales. Pollinator‐dependent crops rely on mobile animals like bees for crop production, and the spatial relationship between floral resources and nest location for these central‐place foragers influences the delivery of pollination services. Current models that map pollination coverage in agricultural regions utilize landscape‐level estimates of floral availability and nesting incidence inferred from expert opinion, rather than direct assessments. Foraging distance is often derived from proxies of bee body size, rather than direct measurements of foraging that account for behavioral responses to floral resource type and distribution. The lack of direct measurements of nesting incidence and foraging distances may lead to inaccurate mapping of pollination services. We examined the role of local‐scale floral resource presence from hedgerow plantings on nest incidence of ground‐nesting bees in field margins and within monoculture, conventionally managed sunflower fields in California's Central Valley. We tracked bee movement into fields using fluorescent powder. We then used these data to simulate the distribution of pollination services within a crop field. Contrary to expert opinion, we found that ground‐nesting native bees nested both in fields and edges, though nesting rates declined with distance into field. Further, we detected no effect of field‐margin floral enhancements on nesting. We found evidence of an exponential decay rate of bee movement into fields, indicating that foraging predominantly occurred in less than 1% of medium‐sized bees' predicted typical foraging range. Although we found native bees nesting within agricultural fields, their restricted foraging movements likely centralize pollination near nest sites. Our data thus predict a heterogeneous distribution of pollination services within sunflower fields, with edges receiving higher coverage than field centers. To generate more accurate maps of services, we advocate directly measuring the autecology of ecosystem service providers, which vary by crop system, pollinator species, and region. Improving estimates of the factors affecting pollinator populations can increase the accuracy of pollination service maps and help clarify the influence of farming practices on wild bees occurring in agricultural landscapes.

We propose a solution to the problem of herding by caging: given a set of mobile robots (called herders) and a group of moving agents (called sheep), we guide the sheep to a target location without letting them escape from the herders along the way. We model the interaction between the herders and the sheep by defining virtual “repulsive forces” pushing the sheep away from the herders. This enables the herders to partially control the motion of the sheep. We formalize this behavior topologically by applying the notion of caging, a concept used in robotic manipulation. We demonstrate that our approach is provably correct in the sense that the sheep cannot escape from the robots under our assumed motion model. We propose an RRT-based path planning algorithm for herding by caging, demonstrate its probabilistic completeness, and evaluate it in simulations as well as on a group of real mobile robots.

In a rapidly changing transnational eduscape, it is timely to consider how best to conceptualize international education. Here we argue for a conceptual relocation from international student to international study as a means to bridge the diverse literatures on international education. International study also enables recognition of the multiple contributions (and resistances) of international students as agents of knowledge formation; it facilitates consideration of the mobility of students in terms of circulations of knowledge; and it is a means to acknowledge the complex spatialities of international education, in which students and educators are emotionally and politically networked together through knowledge contributions.

The technologies, architectures, and methodologies traditionally used to develop distributed applications exhibit a variety of limitations and drawbacks when applied to large scale distributed settings (e.g., the Internet). In particular, they fail in providing the desired degree of configurability, scalability, and customizability. To address these issues, researchers are investigating a variety of innovative approaches. The most promising and intriguing ones are those based on the ability of moving code across the nodes of a network, exploiting the notion of mobile code. As an emerging research field, code mobility is generating a growing body of scientific literature and industrial developments. Nevertheless, the field is still characterized by the lack of a sound and comprehensive body of concepts and terms. As a consequence, it is rather difficult to understand, assess, and compare the existing approaches. In turn, this limits our ability to fully exploit them in practice, and to further promote the research work on mobile code. Indeed, a significant symptom of this situation is the lack of a commonly accepted and sound definition of the term mobile code itself. This paper presents a conceptual framework for understanding code mobility. The framework is centered around a classification that introduces three dimensions: technologies, design paradigms, and applications. The contribution of the paper is two-fold. First, it provides a set of terms and concepts to understand and compare the approaches based on the notion of mobile code. Second, it introduces criteria and guidelines that support the developer in the identification of the classes of applications that can leverage off of mobile code, in the design of these applications, and, finally, in the selection of the most appropriate implementation technologies. The presentation of the classification is intertwined with a review of state-of-the-art in the field. Finally, the use of the classification is exemplified in a case study.

The book focuses on mobile agents, which are computer programs that can autonomously migrate between network sites. This text introduces the concepts and principles of mobile agents, provides an overview of mobile agent technology, and focuses on applications in networking and distributed computing.

We present a framework for testing applications for mobile computing devices. When a device is moved into and attached to a new network, the proper functioning of applications running on the device often depends on the resources and services provided locally in the current network. This framework provides an application-level emulator for mobile computing devices to solve this problem. Since the emulator is constructed as a mobile agent, it can carry applications across networks on behalf of its target device and allow the applications to connect to local servers in its current network in the same way as if they had been moved with and executed on the device itself. This paper also demonstrates the utility of this framework by describing the development of typical network-dependent applications in mobile and ubiquitous computing settings.

Evolutionary game theory in the realm of network science appeals to a lot of research communities, as it constitutes a popular theoretical framework for studying the evolution of cooperation in social dilemmas. Recent research has shown that cooperation is markedly more resistant in interdependent networks, where traditional network reciprocity can be further enhanced due to various forms of interdependence between different network layers. However, the role of mobility in interdependent networks is yet to gain its well-deserved attention. Here we consider an interdependent network model, where individuals in each layer follow different evolutionary games, and where each player is considered as a mobile agent that can move locally inside its own layer to improve its fitness. Probabilistically, we also consider an imitation possibility from a neighbor on the other layer. We show that, by considering migration and stochastic imitation, further fascinating gateways to cooperation on interdependent networks can be observed. Notably, cooperation can be promoted on both layers, even if cooperation without interdependence would be improbable on one of the layers due to adverse conditions. Our results provide a rationale for engineering better social systems at the interface of networks and human decision making under testing dilemmas.