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Smart Workshops are experiencing the need of a mobile intelligence for mining both learning patterns and knowledge from the wide sea of data generated by both mobile users and mobile technologies. Indeed, mobile intelligence would represent the ideal substratum for providing "agentified" robots with a plethora of advanced capabilities (e.g., visual recognition, fault detection, self-recovery) and, hence, with high-level functionalities, like production line control, asset movement, connectivity restore. Besides the operational plane, however, mobile intelligence can be successfully exploited also in organizational tasks, like the formation of temporary, ad-hoc teams for accomplishing a given target. The complexity of some industrial operations, indeed, often demands the involvement of several, heterogeneous group of robots and the adequate representation of the reciprocal trustworthiness represents a key pre-requisite. It holds particularly for the Automated Guided Vehicles (AGVs) which are increasingly involved in collaborative activities aimed to optimise storage, picking, and transport functions in a wide variety of workshop areas. Therefore, in this paper we define a trustworthiness model for agentified AGVs based on the mix of their reputation and reliability and we present an agent-based framework implementing the related team formation strategy. The improvements obtained in terms of effectiveness and efficiency from the AGV team are observed and measured through a simulation activity, in which realistic settings for an industrial applications have been considered.

IoT devices dealing with complex tasks require powerful hardware capabilities or to get resources on the cloud. When an IoT device is “virtualized” on the Cloud, it can rely on one or more software agent that can exploit its social attitude to interact and cooperate. In this context, the choice of a partner to cooperate is a sensitive question but when an agent cannot perform a reliable choice then, like real communities, it can ask information to other agents it considers as trustworthy. This process can be improved by partitioning the agents in groups by using trust relationships to allow agents to interact with the most reliable partners. To this aim, we designed an algorithm to form agent groups based on reliability and reputation information and the results of some simulations confirmed its potential advantages.

In this paper, we describe how the microservices paradigm can be used to design and implement distributed edge services for Internet of Things applications. As a case study, traditionally monolithic user mobility analysis service is developed, with distributed and extendable microservices, for the standardized ETSI MEC system reference architecture. In each of the edge system three tiers, microservices implement the service logic with components for movement trace analysis, movement prediction and visualization of the results. The distributed service is implemented with Docker containers and evaluated on real-world settings with low capacity edge servers and real user mobility data. The results show that the edge promise of low latency can be met in such as implementation. The integration of a software development technology with a standardized edge system provides solid background for further development.

Wearable devices and wireless communications, combined with personalized health management, are future trends that healthcare practices and procedures are heading toward. To facilitate this progress, new technologies and methods are required to provide reliable measurements, end-to-end communications, and data analysis mechanisms from the data source to medical health records. The wireless body area network (WBAN) is the major element in this process. This Special Section is not only limited to on-body WBAN devices but also focuses on benefitting technologies which can distribute vital information inside a human body or allow control of implantable gadgets. Dependable wireless communications combined with versatile application areas, such as accurate localization or behavior analysis techniques, remote monitoring, adoption of vital sensors, and actuators, can benefit the increased use of new WBAN technologies in various healthcare-related studies. In the end, this will make healthcare processes more effective and user friendly, and simultaneously increase the safety of (out)patients. This Special Section focuses on various theoretical and experimental views on WBAN applications, technologies, implementations, and utilizations based on invited, extended versions of the best-evaluated articles from BodyNets 2018, held at Oulu, Finland, in October 2018, as well as quality articles submitted from the Open Call. This Special Section includes 16 accepted articles which have undergone a rigorous peer-review process. Three of the accepted articles were invited extensions of the articles presented at the BodyNets 2018 conference. The total number of submissions we received for this Special Section was 45; thus, the acceptance rate was 36%.

Software agents have been exploited to handle the inherent dynamicity in the Internet of Things (IoT) systems, as agents are capable of autonomous, reactive and proactive operation in response to changes in their local environment. Agents, operating at the network edge, enable leveraging cloud resources into the proximity of the user devices. However, poor interoperability with the existing IoT systems and the lack of a systematic methodology for IoT system development with the agent paradigm have hindered the utilization of software agent technologies in IoT. In this paper, we describe the development process and the system architecture of a mobile crowdsensing service, provided by an agent-based smart object that comprises agents in both edge and user devices. Mobile crowdsensing is an example of such an application that relies on large-scale participatory sensor networks, where participants have active roles in producing information about their environment with their smartphones. This scheme introduces challenges in handling dynamic opportunistic resource availability, due to mobility and unpredicted actions of the participants. We present how ACOSO-Meth (Agent-oriented Cooperative Smart Object-Methodology) guidelines the development process systematically from the analysis to the actual agent-based implementation of a crowdsensing service. The implementation is done with the ROAgent framework that utilizes resource-oriented architecture and REST principles to integrate agent-based smart objects seamlessly with the programmable Web.

Open interoperability delivers on the promise of enabling vendors and developers to interact and interoperate, without interfering with anyone’s ability to compete by delivering a superior product and experience. In the absence of global IoT standards, the INTER-IoT voluntary approach will support and make it easy for any IoT stakeholder to design open IoT devices, smart objects, services, and complex systems and get them to be operative and interconnected quickly, thus creating new IoT interoperable ecosystems by using a bottom-up approach. In particular, INTER-IoT is based on hardware/software tools (INTER-Layer) granting multi-layer interoperability among IoT system layers (i.e. device, networking, middleware, application service, data and semantics), on frameworks for open IoT application and system programming and deployment (INTER-FW), and on a full-fledged CASE tool-supported engineering methodology for IoT systems integration (INTER-Meth). The INTER-IoT approach is notably exemplified through two use cases: INTER-LogP, involving interoperability of port logistics ecosystems, and INTER-Health, encompassing integration between e-Health at home and in mobility infrastructures.

The Special Section in IEEE Access on Body Area Networks (BAN) collects the extended versions of the best manuscripts submitted to and presented at “Bodynets 2016,” a conference that “aims to provide a world-leading and unique opportunity for bringing together researchers and practitioners from diverse disciplines to plan, analyze, design, build, deploy and experiment with/on Body Area Networks (BANs)” http://bodynets.org/2016/show/home, held in December 2016 in Torino, Italy.

The Agent-based Cooperating Smart Objects methodology (ACOSO-Meth) fully supports the systematic development of Internet of Things (IoT) systems from analysis to implementation by tackling their manifold requirements (e.g., self-management, distributed smartness, interoperability). At the same time, ACOSO-Meth allows the re-engineering of existing IoT systems, thus enhancing their maintainability, reusability and extensibility. In such direction, this paper (i) first presents the integration of the resource-oriented agent framework complying with the IETF Constrained RESTful Environment (CoRE) framework into ACOSO-Meth; then (ii) reports a case study to exemplify the re-engineering of a resource-constrained agent application through the ACOSO-Meth metamodel-driven approach.

Transmission Power Control (TPC) is employed in the sensor nodes with the main objective of minimizing transmission power consumption. However, major drawbacks with well-known TPC are time consuming and energy inefficient initialization phase. Moreover, they employ Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI) metrics for initialization phase that are sensitive to environmental conditions and hence are not appropriate parameters to adjust the power. To overcome these shortcomings of existing TPC, we propose a novel TPC algorithm dubbed as Data-Driven Transmission Power Control (DA-TPC) that has shorter initialization phase and uses priority of data as the only metric to adjust the power level. The two main aims of this paper are to minimize power consumption during initialization phase and to show how by utilizing priority of data as a sole metric for power adaptation improves reliability and decreases not only latency but also overall energy consumption while transmitting data packets.

Designing energy-efficient communication protocols is one of the main challenges in wireless sensor networks. This work presents an adaptive radio scheduling schema employing a reinforcement learning algorithm for reducing the energy consumption while preserving the other network performances. By means of a decentralized on-line approach, each nodes determines the most beneficial radio schedule by dynamically adapting to its own traffic load and to the neighbors’ communication activities. We compare our approach with other learning-based MAC protocols as well as conventional MAC approaches and show that, under different simulating scenarios and traffic conditions, our protocol achieves better trade-offs in terms of energy consumption, latency and throughput.

The aim of this study was to investigate changes in haematological and biometric parameters in relations to monthly variations in sea bream and sea bass reared in Mediterranean land off-shore tanks. For this purpose, each month (from October 2010 until June 2011), haematological profile was assessed on 25 Sparus aurata and on 25 Dicentrarchus labrax randomly captured from the same batches in Sicilian farm. The application of one way ANOVA showed a strong significant effect of time on all blood parameters in both species that showed similar trend in Red Blood cell count (RBC), Haematocrit (Hct), Haemoglobin concentration (Hgb) and in White Blood Cell count (WBC). RBC and Hct showed highest values in January; from December until February Hgb was higher respect to other months; in sea bream WBC assumed highest values in February while in sea bass WBC was highest in January. Thrombocyte Count (TC) was highest in November and after a decrease during cold months, it rose again in spring. These results obtained could improve the understanding of the influence of seasonal variations on the haemotological and biometric parameters in fish in order to optimize the husbandry practices.

This paper introduces challenges in contemporary situation assessment using collaborative inference and discusses solutions that are based on workflows between distributed processing nodes. The paper exposes the necessary conditions that workflows have to satisfy in order to support accurate situation assessment and provides a systematic approach to verification of the workflows. In particular, we emphasize the link between the complexity of the domain and the complexity of the workflows in terms of data and control coupling. With the help of graphical representations, we characterize the complexity of the domains and identify critical relations that have to be captured by collaborating processes in a workflow supporting correct situation assessment.

The overall objective of PURE is to provide practical IPM solutions to reduce dependence on pesticides in selected major farming systems in Europe. This paper summarises methodological advances with regards to the design and assessment of IPM solutions. The presented case studies include major crops (cropping systems based on wheat or maize), field vegetables, orchards, vineyard and Controlled Environment Agriculture systems. - Ecological modelling. A software package (Universal Simulator) for collaborative ecological modelling is now available:http://www.ecolmod.org/. - Modelling for ex-ante and ex post assessment of IPM solutions. A multi-criteria model (DEXIPM) for sustainability assessment of innovative crop protection strategies has been developed along with SYNOPS, a web-based model for scaling up ex-post pesticide risk assessments at the individual crop level to the farm and regional levels. In addition, a model for ex-ante evaluation of IPM solutions is currently under development specifically for orchards (PREMISE). - Multiple pest modelling. An interactive generic modelling platform to help design models that simulate yield losses caused by an injury profile in a given production situation (X-PEST) is currently under development. Moreover, theoretical mathematical modelling approaches are conducted to represent the interactions between generalist biological control agents and multiple pests. - Optimisation techniques. Reinforcement learning methods have been adapted and applied to IPM. Multiobjective optimisation algorithms for model-based design of IPM solutions are being developed. The Graph based Markov Decision Process framework is being used for the optimisation of sequential decisions under uncertainty in a spatial context. - Cost-benefit analysis and consumers' willingness to pay. Cost-benefit analyses are conducted for IPM solutions tested in the PURE project. An experimental approach is planned to characterise consumers' willingness to pay for agricultural goods produced under IPM solutions as a function of their level of information on the mode of production. It is important to state that the methodological breakthroughs produced in this work package will not only benefit to the PURE project, but also aim at contributing to the design of practical IPM solutions to reduce dependence on pesticides for a wider range of farming systems. This is made possible by ensuring as much as possible genericity in the developed approaches.

With the large diffusion of Business Process Management (BPM) automation suites, the possibility of managing process-related risks arises. This paper introduces an innovative framework for process-related risk management and describes a working implementation realized by extending the YAWL system. The framework covers three aspects of risk management: risk monitoring, risk prevention, and risk mitigation. Risk monitoring functionality is provided using a sensor-based architecture, where sensors are de¿ned at design time and used at run-time for monitoring purposes. Risk prevention functionality is provided in the form of suggestions about what should be executed, by who, and how, through the use of decision trees. Finally, risk mitigation functionality is provided as a sequence of remedial actions (e.g. reallocating, skipping, rolling back of a work item) that should be executed to restore the process to a normal situation.

Recent years have seen rapid advancements in wireless sensor networks (WSNs) and software agents resulting, among others, in maturation of their technology platforms. Furthermore, benefits of combining these research areas have been analyzed. The MAPS agent platform allows fusion of agents and WSNs. However, due to the hardware limitation, MAPS misses important functionalities needed, for instance, in advanced decision support. Such functions are available, among others, in the JADE agent platform, geared towards more powerful computing devices. Therefore, integration of MAPS and JADE had to be considered. The aim of this paper is to discuss technical issues involved in achieving this goal.

Moving large quantities of data between distributed parties is a frequently invoked process in data intensive applications, such as collaborative digital media development. These transfers often have high quality requirements on the network services, especially when they involve user interactions or require real time processing on large volumes of data. The best effort services provided by IP-routed networks give limited guarantee on the delivery performance. Advanced networks such as hybrid networks make it feasible for high level applications, such as workflows, to request network paths and service provisioning. However, the quality of network services has so far rarely been considered in composing and executing workflow processes; applications tune the execution quality selecting only optimal software services and computing resources, and neglecting the network components. In this chapter, the authors provide an overview on this research domain, and introduce a system called NEtWork QoS Planner (NEWQoSPlanner) to provide support for including network services in high level workflow applications.

In the context of the development of wireless sensor network (WSN) applications, effective programming frameworks and middlewares for rapid and efficient prototyping of resource-constrained applications are highly required. Mobile agents are an effective distributed programming paradigm that is being used for WSN programming. However its diffusion is limited mainly due to the scarce availability of usable mobile agent systems for WSNs. This paper proposes TinyMAPS, a mobile agent system for programming WSNs based on the Sentilla sensor platform. TinyMAPS derives from MAPS (Mobile Agent Platform for Sun SPOT) and is specifically tailored for sensors more constrained than the Sun SPOTs. After providing a description of TinyMAPS and its comparison with MAPS, a simple yet effective case study implemented with TinyMAPS and concerning a real-time WBSN-based system for human activity monitoring is described.

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run-time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.

This paper proposes an architecture conceived for supporting rapid design and implementation of Wireless Body Sensor Network (WBSN) applications having autonomic characteristics. WBSNs represent the most suitable systems for monitoring and controlling physical and biochemical parameters on the human body, and thus supporting high-impact applications in a variety of human-centered domains The effectiveness of WBSN applications is a critical issue since their correctness and efficiency have to be assured not only at design/implementation time but notably at execution time. The autonomic computing paradigm can perfectly meet the critical requirements of WBSN applications such as fault tolerance, adaptability, and reliability, due to its fundamental self-* properties. In particular, our solution is based on the SPINE2 framework and its important distinctive feature is an architecture for guaranteeing the separation of concerns between the user-defined application logic and the autonomic-related operations. Our proposal is finally exemplified through a case study concerning the development of an autonomic application for human activity monitoring.

This paper proposes an overview and comparison of mobile agent platforms for the development of wireless sensor network applications. In particular, the architecture, programming model and basic performance of two Java-based agent platforms, Mobile Agent Platform for Sun SPOT (MAPS) and Agent Factory Micro Edition (AFME), are discussed and evaluated. Finally, a simple yet effective case study concerning a mobile agent-based monitoring system for remote sensing and aggregation is proposed. The proposed case study is developed both in MAPS and AFME so allowing to analyze the differences of their programming models.

In this paper, the concept of Interactive Process Simulation is introduced as a specialisation of Business Gaming. A specific gaming and agent development framework, based oil interactive simulation and a specific modelling langauge, is shortly presented. The concepts of the language are explained via a simple example illustrating ail interactive process in a logistic environment. Finally, the statement that process simulation leads to organisational learning is argumented.