According to both academic and industry perspectives, the Fourth Industrial Revolution has brought about a paradigm shift in the manufacturing sector enabling companies to enhance their competitiveness in the global market. To achieve this goal, manufacturing companies will need to undertake a deep digital transformation, primarily by introducing advanced Information Technology into traditionally less digitalized departments, such as shop floors, where Operational Technology currently dominate. For the full achievement of Industry 4.0 revolution objectives, practitioners believe in the strong requirement of a progressive and tight integration between IT and OT departments. In the depicted scenario, communication technologies are expected to play a pivotal role in facilitating the integration process, but other more recent and advanced IT have also proven helpful. In particular, the topic of IT/OT integration has attracted significant attention from various research communities that have sought to identify both the opportunities and challenges associated with its implementation. Although some good surveys of those works have appeared in the literature, to the best of our knowledge, no comprehensive review has yet been conducted that is fully dedicated to the topic of IT/OT convergence. In this paper, we propose a holistic approach to examine the various dimensions of IT/OT integration, which we classify into five interconnected realms, Communication, IT-Driven Support to OT, Human Centricity, Advanced Industrial Control Systems, and cybersecurity. Furthermore, we develop a realm-oriented taxonomy to organize the surveyed works in a structured manner, offering readers a clear overview of the current state of the literature, along with insights into unexplored opportunities and future directions for IT/OT integration.

The Fog computing paradigm makes use of dispersed, diverse, and resource-limited devices located at the network edge to effectively implement Internet of Things (IoT) application services that demand low latency and substantial bandwidth. At the same time, the adoption of microservice-based architectures in the IoT domain is on the rise due to their ability to align with the swift evolution and deployment demands of highly dynamic IoT applications and to elastically scale to fulfill load demands. In complex environments like Fog federations, characterized by highly heterogeneous computing and networking resources, the effective allocation of microservices to available nodes, while ensuring compliance with required Quality of Service (QoS) constraints, represents a significant challenge. In this paper, we present the design and implementation of OptiFog, a comprehensive framework that enables users to model, simulate, and validate microservice placement solutions within a realistic testbed environment. Compared to state-of-the-art approaches, OptiFog offers developers a controlled environment for experimenting with placement solutions while providing the assurance that the resulting deployments will meet the targeted QoS requirements in real-world scenarios, specifically in terms of service execution time and energy consumption of Fog nodes. To demonstrate the feasibility of the proposed approach, we implemented and evaluated a representative use case, involving both sub-optimal and optimal microservice placement, and utilizing real-world microservices drawn from the IoT domain.

In the last years, we have seen an increase in the number of Artificial Intelligence (AI)-powered applications for information retrieval and data science. This fact led to an increasing reliance on distributed computing infrastructures, including Cloud, Edge, and IoT environments. These architectures enable powerful and scalable solutions but also introduce new security and privacy risks that must be addressed at both the system and data levels. Even a single breach on any of the links of the data-service-infrastructure chain may seriously compromise the security of the end-user application. With such a wide attack surface, security must definitely be approached in a holistic way and addressed in any layer where concerns may potentially arise. SIoTEC solicits novel and innovative ideas, proposals, positions and best practices that address the modelling, design, implementation, and enforcement of security in Cloud/Edge/IoT environments. Workshop website: https://siotec.netsons.org/

The microservices architecture has transformed application development by providing scalability, flexibility, and resilience. However, as organizations scale their infrastructure, deploying microservices across multiple clusters - whether for fault tolerance, geographic distribution, or workload optimization — presents several challenges. Efficient orchestration in these multi-cluster environments is essential to ensure seamless service provisioning, workload distribution, and inter-cluster communication. In this paper, we propose an extension of the OASIS TOSCA standard to support the need of application owners to define deployment schemes that enable them to distribute application components across multiple clusterized environments. To test the viability of the proposed extension, we set up a small-scaled, multi-cluster environment powered with Kubernetes and employed an orchestrator of microservice-based applications that implements the mentioned capability. For the test purpose, a real application from the logistics domain was employed.

Open Cloud (O-Cloud) is the Open Radio Access Network (O-RAN) computing infrastructure spanning edge to cloud sites defined by the O-RAN Alliance to support and coordinate RAN infrastructure management shared among multiple Mobile Network Operators (MNO). In the standard definition, O-Cloud ensures reliable connectivity, active coordination, and efficient distribution of the Radio Access Network (RAN) deployments. Thanks to these properties, O-Cloud can support Mobile Network and Infrastructure operators to achieve fully automated infrastructure management, self-service MNO portals, enhanced security measures, and O-Cloud infrastructure-agnostic management. In this paper, we present TORNADO: TOSCA-enabled Orchestration for RAN in Next-Generation Networks Automating DevOps in O-Cloud, an O-Cloud automation infrastructure designed to ease DevOps deployment and operation phases of RAN network functions for multiple MNOs. Our solution introduces infrastructure-agnostic automation for multi-site, multi-MNO RAN components, offering high-level, secure self-service MNO portals for defining RAN deployments. Performance evaluation of our solution for various RAN network functions demonstrated the capability of TORNADO to automate the deployment in a multi-site, multi-MNO heterogeneous infrastructure.

The Software-Defined Networking (SDN) paradigm disaggregates the data plane, embodied by switches that only forward data, from the control plane, embodied by SDN controllers that communicate with said switches. SDN also proposes a third, application layer, which implements various functions such as firewalls or service discovery by communicating with the controllers through the northbound interface. However, while state-of-the-art works propose the deployment of multiple, distributed SDN controllers, the software architecture of these controllers is still monolithic, requiring not only the controller runtime but also all the network-level applications to be deployed across all SDN controller hardware. On the other hand, state-of-the-art SDN controllers such as MSN allow treating network-level applications as microservices, which comes with the challenge of orchestrating the microservices across the network. In this paper, we present Grex, a framework to orchestrate network-level applications across microservice-based SDN controllers. We test and validate the optimization model of Grex by performing experiments in a realistic network testbed using the MSN controller.

In recent years, Software-Defined Networking (SDN) research literature has proposed the integration of multiple SDN controllers into the same network, improving the scalability and reliability of the network. However, while this evolution has focused on control plane hardware, the architecture of SDN controller software is still monolithic, and its communication with the application plane through the northbound interface is done by the integration of the network-level applications' codebase with the controller software. The proposal of SDN Mi-croservices Architectures (SDN MSAs) is aimed at transforming the application plane, from a monolithic architecture to a set of independently deployable modules named SDN microservices. However, the promising paradigm of SDN MSAs also increases the complexity of network management, as these microservices must be placed through the SDN controllers. This placement is especially complex due to its NP-hard nature. In this work, we present Genetic Algorithm for SDN MSA (GASM), an evolutionary computation-based heuristic to solve this issue in tractable times. Experimental results show that GASM represents an average speed-up of 846.33 × compared to optimal solvers.

The adoption of the Cloud Continuum pattern, leveraging computational resources from both Cloud and Edge infrastructures provided by various vendors, is exacerbating the challenge of managing infrastructure and services. To forefront this complexity, Cloud providers and customers are evolving their usual approach to service development and deployment, seeking faster and easier paths that can sustain time-to-market business needs. Many Cloud providers are shaping their offerings to incorporate an ever-increasing number of Serverless Computing services, wherein they handle every aspect of customer service and infrastructure management. In this work, we aim to extend the principles of Serverless computing service development to Cloud continuum environments and put forth a proposal for enforcing smart deployment of Serverless applications over heterogeneous computing environments. Functional to this goal is the heuristic component, a founding element of our architectural proposal that is capable of building applications "footprint" in different execution environments offered by the provider infrastructure. In this preliminary work, we discuss the results obtained from testing the above-mentioned component on a sample set of functions.

IoTwins is a recently terminated EU H2020 research project for helping SMEs adopt Digital Twins in their Industry 4.0 processes and applications. Based on the primary guidelines of distributed and hybrid digital twins (exploiting cloud continuum virtualized resources and the synergy between machine learning and simulation models), IoTwins has successfully delivered twelve industrial test-beds to showcase the feasibility and re-usability of its proposed approach and platform. This paper summarizes the proposed IoTwins methodology, and discusses a practical use case of development of an IoTwins-based I4.0 application. We believe that this contribution could significantly help in lowering the technological barriers that slow down the utilization of the digital twin benefits, in particular for small and medium enterprises.

The Software-Defined Networking (SDN) paradigm enables network administrators to manage the behavior of the network thanks to a centralized control plane. By programming network-level applications, it is possible to determine how traffic flows must be handled programmatically. In the same manner that computing applications have evolved from monolithic to microservice-based architectures, network-level applications are expected to evolve into microservice-based SDN controllers, implementing each application as a replicable and individually deployable component of the SDN controller. In such a scenario, the Quality of Service (QoS) experienced by traffic flows depends on how these microservices are placed and deployed through the network topology. In this work, we provide a system to optimize the QoS of the traffic in microservice-based SDN networks by optimally placing and replicating the network-level microservices. Experimental results show the effectiveness of the proposed solution over a real network topology with varying traffic loads.

Fog computing emerged as a novel approach to deliver micro-services that support innovative applications.This paradigm is consistent with the modern approach to application development, that leverages the composition of small micro-services that can be combined to create value-added applications.These applications typically require the access from distributed data sources, such as sensors located in multiple geographic locations or mobile users.In such scenarios, the traditional cloud approach is not suitable because latency constraints may not be compatible with having time-critical computations occurring on a far away data-center; furthermore, the amount of data to exchange may cause high costs imposed by the cloud pricing model.A layer of fog nodes close to application consumers can host pre-processing and data aggregation tasks that can reduce the response time of latency-sensitive elaboration as well as the traffic to the cloud data-centers.However, the problem of smartly placing micro-services over fog nodes that can fulfill Service Level Agreements is far more complex than in the more controlled scenario of cloud computing, due to the heterogeneity of fog infrastructures in terms of performance of both the computing nodes and inter-node connectivity.In this paper, we tackle such problem proposing a mathematical model for the performance of complex applications deployed on a fog infrastructure.We adapt the proposed model to be used in a genetic algorithm to achieve optimized deployment decisions about the placement of micro-services chains.Our experiments prove the viability of our proposal with respect to meeting the SLA requirements in a wide set of operating conditions.

With the increase of the volume of data produced by IoT devices, there is a growing demand of applications capable of elaborating data anywhere along the IoT-to-Cloud path (Edge/Fog). In industrial environments, strict real-time constraints require computation to run as close to the data origin as possible (e.g., IoT Gateway or Edge nodes), whilst batch-wise tasks such as Big Data analytics and Machine Learning model training are advised to run on the Cloud, where computing resources are abundant. The H2020 IoTwins project leverages the digital twin concept to implement virtual representation of physical assets (e.g., machine parts, machines, production/control processes) and deliver a software platform that will help enterprises, and in particular SMEs, to build highly innovative, AI-based services that exploit the potential of IoT/Edge/Cloud computing paradigms. In this paper, we discuss the design principles of the IoTwins reference architecture, delving into technical details of its components and offered functionalities, and propose an exemplary software implementation.

Abstract The growth in the number and types of cloud-based services offered to IT customers is supported by the constant entry of new actors in the market and the consolidation of disruptive technologies such as AI, Big Data and Micro-services. From the customer’s perspective, in a market landscape where the cloud offer is highly diversified due to the presence of multiple competing service providers, picking the service that best accommodate their specific needs is a critical challenge. Once the choice is made, so called “cloud orchestration tools” (orchestrators) are required to take care of the customer application’s life-cycle. While big players offer their customers proprietary orchestrators, in the literature quite a number of open-source initiatives have launched multi-cloud orchestrators capable of transparently managing applications on top of the most representative cloud platforms. In this paper, we propose TORCH, a TOSCA-based framework for the deployment and orchestration of cloud applications, both classical and containerised, on multiple cloud providers. The framework assists the cloud customer in defining application requirements by using standard specification models. Unlike other multi-cloud orchestrators, adopts a strategy that separates the provisioning workflow from the actual invocation of proprietary cloud services API. The main benefit is the possibility to add support to any cloud platforms at a very low implementation cost. In the paper, we present a prototypal implementation of TORCH and showcase its interaction with two different container-based cluster platforms. Preliminary performance tests conducted on a small-scale test-bed confirm the potential of TORCH.

Nowadays, the exploitation of distributed ledger technology (DLT) is increasing among different domains and use cases. Not only within the context of cryptocurrencies, DLT could help the cooperation among untrusted parties in a wide variety of application scenarios. In particular, crowdsensing platforms can benefit from DLT because they need to federate systems belonging to different organizations to share end-user profiles, finally free to move within different domains, maintaining their identity. In this paper, we propose an edge-based distributed ledger architecture for supporting decentralised incentives in a specific mobile crowdsensing paltform called ParticipAct. To motivate the choice we describe two different deployments of ParticipAct, one based on a classical client-server architecture and the other one based on an edge-based model, and we highlight their pro and cons. In particular, our more notable findings rely on an approach based on edge computing and highlight how the three-tier solution improves the scalability, the performance, the security and the fault tolerance of the infrastructure responsible for the management of the federation among untrusted crowdsensing platforms.

The technological progress is leading to an increase of instrument sensitivity in the field of rotational spectroscopy. A direct consequence of such a progress is an increasing amount of data produced by instruments, for which the currently available analysis software is becoming limited and inadequate. In order to improve data analysis performance, parallel computing techniques and distributed computing technologies like Cloud and High Performance Computing (HPC) can be exploited. Despite the availability of computer resources, neither Cloud nor HPC have been fully investigated for identifying unknown target spectra in rotational spectrum. This paper proposes the design and implementation of a Highly Scalable AUTOFIT (HS-AUTOFIT), an enhanced version of a fitting tool for broadband rotational spectra that is capable of exploiting the resources offered by multiple computing nodes. Compared to the old program version, the new one is capable of scaling on multiple computing nodes, thus guaranteeing higher accuracy of the fit function and consistent boost of execution time. The result of tests conducted in real Cloud and HPC environments show that HS-AUTOFIT is a viable solution for the analysis of huge amount of data in the addressed scientific field.

Although cloud computing has been around for over a decade now, many issues of the first hour still persist. Vendor lock-in, poor interoperability and portability hinder users from taking full advantage of main cloud features. On the industry side, the big players often adopt proprietary solutions to guarantee a seamless management and orchestration of cloud applications; on the research side, the TOSCA specification has emerged as the most authoritative effort for the interoperable description of cloud services. In this paper, we discuss the design and implementation of a TOSCA-based orchestration framework for the automated service provisioning. We also compare this approach to two open-source orchestration tools (Heat, Cloudify) with respect to the deployment of a two-tier application on an OpenStack environment. Test results show our method performs comparably to the aforementioned tools, while maintaining full compliance with TOSCA.

Hybrid and multi-cloud become prominent infrastructure strategy of enterprise. However, the complexity of such infrastructure is a considerable challenge. It is common to see that multi-cloud infrastructure is divided into several smaller units to facilitate the management. The division criteria are geolocation, cost, security, etc. Therefore, how to manage application deployment in such partitioned environment is an intriguing topic of multi-cloud management. We propose a policy-based deployment in multi-cloud infrastructure, which contains policy evaluation and TOSCA standard based orchestration. The system architecture is introduced and a case study with two empirical scenarios is discussed. The results indicate that the proposed policy-based deployment is useful in finding suitable resource and improving deployment efficiency.

Cloud computing is nowadays a consolidated paradigm that enables scalable access to computing resources and complex services. One of the greatest challenges Cloud providers have to deal with is to efficiently automate the service "provisioning" activities through Cloud orchestration techniques. By relying on TOSCA, a well-known standard specification for the interoperable description of Cloud services, we developed a fault-aware orchestrator capable of automating the workflow for service provisioning. The BPMN notation was used to define both the workflow and the data associated with workflow elements. To corroborate the proposal, a software prototype was developed and tested with a sample use case which is discussed in the paper.

The wide spread adoption of IoT technologies has resulted in generation of huge amount of data, or Big Data, which has to be collected, stored and processed through new techniques to produce value in the best possible way.Distributed computing frameworks such as Hadoop, based on the MapReduce paradigm, have been used to process such amounts of data by exploiting the computing power of many cluster nodes.Unfortunately, in many real big data applications the data to be processed reside in various computationally heterogeneous data centers distributed in different locations.In this context the Hadoop performance collapses dramatically.To face this issue, we developed a Hierarchical Hadoop Framework (H2F) capable of scheduling and distributing tasks among geographically distant clusters in a way that minimizes the overall jobs execution time.In this work the focus is put on the definition of a job scheduling system based on a one-point iterative search algorithm that increases the framework scalability while guaranteeing good job performance.

Effective big data analysis is one of the most notable research challenge of the latest few years. Hadoop, the most popular implementation of the MapReduce framework, has today become widespread used for processing large data sets using cloud resources. However, in many scenarios, data are geographically distributed over data centers and moving them to a single site for processing may result extremely expensive when not feasible at all. A key challenge for running applications in such a geographically distributed environment is how to efficiently schedule the computation over the different datacenters. In this work we present a job scheduler for a Hierarchical Hadoop Framework (H2F) that allows the management of multiple requests of job execution ensuring an efficient use of the available resources. Our experimental evaluations show that using H2F significantly improves processing time for geodistributed data sets with respect to a plain Hadoop system.

Big data analysis requires adequate infrastructure and programming paradigms capable of processing large amount of data. Hadoop, the most known open-source implementation of the MapReduce paradigm, is widely employed in big data analysis frameworks. However, in many recent application scenarios data are natively distributed over different geographic regions in data centers which are inter-connected through network links with very lower bandwidth than those of the computing environments where traditionally Hadoop deployments are supposed to work. In such a context, Hadoop applications perform very poorly. To cope with these issues, we developed a Hierarchical Hadoop Framework (H2F) specifically designed to work on geodistributed data. In this work, we compare the performance of H2F with that of a plain Hadoop implementation. First results show that for very large amount of data the H2F solution performs better than the Hadoop.

TEST 02 - Elsevier's Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.

In recent years we have been witnessing significant advances in the medical imaging techniques. With respect to the past, current imaging processes enable physicians to reach higher precision level when it comes to diagnosing patients' diseases. Such a precision is supported by sophisticated imaging machinery, whose cost increases with the technological advances. Also, most of imaging scans expose patients to ionizing radiation which in certain cases may seriously harm their health. In this panorama, ordering inappropriate imaging examinations has a great impact on both the economy of a nation and on its citizens' health. Many governments have published guidelines which physicians should follow when deciding which imaging technique is to be prescribed to best answer to a clinical question. The COLLABORADI project is a recently concluded research project which studied the phenomenon of inappropriate imaging prescriptions in Sicily (Italy), and proposed the design and implementation of a decision support system to help physicians to set up the most appropriate diagnostic route for their patients. This paper discusses the work carried out within the project.

In the past two decades there has been a growing interest over the definition of new distributed computational paradigms capable to serve the need of manipulating and analyzing huge amounts of data. Among the others, the MapReduce outstands for popularity. Its open-source implementation Hadoop is widely used in academic environments and is also greatly supported by huge IT players. There are many application scenarios where the data to be manipulated resides on data centers which are heterogeneous in term of computing capacity and are geographically distant from each other's. Unfortunately, in this contexts Hadoop performs very poorly. In this paper we propose to leverage on a hierarchical computing framework to boost the Hadoop performance in geo-distributed computing environments. The framework we propose drains fresh information from the distributed computing context and exploits it to carry out a smart job scheduling strategy. In this work, the focus is put on the study and definition of the application profile of the jobs. We implemented a software prototype of the proposed hierarchical Hadoop framework. Tests run on the prototype proved the capability of the job scheduling system to compute the job's execution path and estimate its completion time.

TEST 02 - Elsevier's Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.

Currently in the cloud market resources are mainly allocated according to the fixed-price, direct selling model. Market principles such as the supply-demand rate are not taken into consideration by cloud providers. In the literature several market-based resource allocation models and algorithms have been proposed, showing that dynamic pricing models might result more convenient for both the providers and the consumers of cloud resources. In this paper, we discuss about the use of alternative auction-based mechanisms to sell the “residual” computing capacity, i.e., the capacity which the provider is not able to allocate through direct-selling. The design of a procurement market for computing resources is proposed: in such a market we devised an adaptive bidding strategy that suggests to the provider the right actions useful to attain its business objective. The resource overbooking mechanism is also proposed to overcome the problem of resource underutilization. A software simulator was implemented with the aim of testing and validating the proposed mechanisms. Results show that, by fine-tuning their own strategy, providers manage to pursue specific objectives.

TEST 02 - Elsevier's Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.

Nowadays sensors and sensor networks are deployed in many application domains to accomplish well defined, field-oriented monitoring tasks. Very often, sensors which have been deployed to monitor specific contexts are required to interoperate to third-party sensor systems to serve more complex and demanding monitoring tasks. The heterogeneity of sensor platforms offered by vendors raises the need for a common framework where sensors can be regarded as vendoragnostic services with well defined and open functionality. To this end, this work proposes a semantic approach to foster the characterization of heterogeneous sensors. Also, an effort is made to semantically characterize the geographic context where sensors may be deployed, in a way that in the resulting semantic framework functional and geographic properties of sensors may be combined together. The exploitability of such an idea is then investigated through a practical use case in the domain of civil protection. The implemented scenario shows that the combination of sensors' functional and geographic properties is useful to the domain's monitoring purpose.

Cloud computing has reached a high level of acceptance, both in academia and in industry. The maturity of the technology, along with the considerable business opportunity that has been promised, is the main responsible for its success. Nevertheless, today only few, very big players dominate the commercial panorama and take over market shares. From that position, they impose rigid pricing policies and quite inflexible negotiation schemes. When the ongoing cloud standardization process will complete, and thus full interoperability among clouds will be accomplished, new players will come into play. A real competition among cloud providers will then start based on key factors like the capability of providing flexible services tailored to specific, fine-grained customers' requirements. In this market scenario a mechanism must be devised to support the matchmaking between what providers offer and what customers demand. In this work we define a semantic model to help customers and providers to characterize their demands/offers, and propose the use of semantic tools to perform the matchmaking in such a way to maximize both the provider's and the customer's satisfaction.

Cloud computing poses several new security and privacy challenges, mainly related to resource sharing, interoperability and dinamicity among different providers. Although policy specification languages address some of these challenges,many issues still have to be faced with. Policy matching is today performed by way of syntactical approaches, which may limit the selection of suitable services on the one hand, and the flexibility and the dinamicity of the matching process on the other one. In this work we propose a semantic approach that,by means of semantic annotations to WS-Policy documents,allows for an improved matching of security requirements and capabilities based on their actual meaning. The proposed approach has been validated through a case study that shows how a pure syntactic-based mechanism of WS-Policy would have failed in matching two actually compatible policies.

In SOA environments, as the number of published Web Services grows, the provision of a robust, scalable and efficient discovery service is still an unresolved issue. In this paper we present a P2P-based infrastructure that leverages the semantic technologies to support a scalable and accurate service discovery process. The key concept of the presented idea is the creation of an overlay network organized in several semantic groups of peers, each specialized in answering queries pertaining to specific applicative domains. Groups are formed by clustering together peers offering services that are semantically related. A semantic query routing mechanism ensures the delivery of queries to the groups that are semantically "worth" to respond. In this paper the architecture details of the proposed solution is presented. A system prototype has also been implemented and validated through a case study deployed on the PlanetLab testbed.

Semantic web services have recently appeared as an enabling technology for the challenging task of automated service discovery and composition. In addition, the growing number of web services demands for distributed discovery mechanisms which do not suffer of the scalability problems of the centralized ones. A framework based on the P2P paradigm to enhance the service discovery process has been designed: it is mainly based on the grouping of peers which are semantically "close" to each other. In this paper, we focus on the semantic characterization of a peer, which is built by taking into account the set of services it provides. In order to reach an high level of service integration, our framework builds an abstract "expertise" of the peer, which is independent of the specific semantic language used to annotate the service.

A large number of heterogeneous and computing devices, such as PCs, PDAs, and cell phones, nowadays are used to access the same information. Currently, designers designing such multi-device user interfaces have to design a UI separately for each device, which is a time consuming and error prone activity. This paper discusses our approach to the multi-device interface development. In particular we describe how abstract UI descriptions and task model management systems can be combined to develop adaptive UIs for a wide range of devices. The designed software framework allows generating the concrete user interface at runtime, by adapting it to the client's execution environment. As shown in the example application, three different environments have been the target of our implementation work: standard PCs, PDAs and mobile phones equipped with Java micro edition

The availability of powerful personal computers and high bandwidth home connectivity is changing the way users cooperate and exchange information on the network. Peer to peer computing is becoming a common paradigm for many distributed applications, allowing for extensive resource sharing and direct communication among peers. Recently, the wide spreading of new wireless communication technologies and personal computing devices, enables the adoption of the P2P paradigm also in mobile environments. However, several questions arise when trying to develop such systems: existing solutions do not address all the requirements of these environments. In this paper we present a framework to develop mobile applications which exploits the P2P paradigm: it is designed to work on J2ME enabled mobile devices, interacting with each other in an ad-hoc fashion. The designed framework is also interoperable with JXTA, a well-known P2P open platform.

Abstract To implement seamless mobility inside an integrated, multiple (e.g., GPRS/WiFi) access system, a vertical handover policy has to be devised. This is usually done at the mobile terminal, allowing it to be customized from an end‐user's perspective, in order to fit individual needs/preferences. We propose a new approach in taking vertical handover decisions, which are not anymore exclusively based on the knowledge of the available access networks' characteristics but also on higher level parameters which fall in the transport and application layers. To this extent, in this paper a model has been realized and simulations have been run in order to evaluate the impact of the vertical handover and its frequency on a set of typical user's network applications/services. We also take into account the user preferences in terms of cost and quality of service. We believe this approach reflects the optimal settings from the user's point of view with regard to his running services and applications. Our aim is to understand how to define a metric to be used in order to devise a solution which should try to balance the overall cost of vertical handovers with the actual benefits they bring to actual user's networking needs. This way, each mobile user could autonomously apply the handover decision policy, which is more convenient to his specific needs. Copyright © 2005 John Wiley & Sons, Ltd.

Peer-to-peer (P2P) technology has arisen as a simple and user-friendly mechanism not relying on a central server. In this paper an attempt to exploit this emerging technology in the area of centralization in a local region with flexibility of P2P mechanism on a broader level. The implementation of the proposed approach, which has been built on top of the MAP agent system, is compared with more traditional techniques for agent location, based on a naming service. Comparisons between the two approaches are presented together with some hints helping to identify the best approach in each considered scenario.

The combination of personal computing devices and wireless ad-hoc networks allows the concept of mobile ad-hoc information system, consisting of a highly dynamic, decentralized and self-organizing network of autonomous and mobile devices that interact as peers [M. Hannicainen et al. (2002), L. Kleinrock, (2000)]. Here we define a middleware layer, named Expeerience. We have decided to adopt as a basic software framework, an emerging P2P open technology such as JXTA. The second issue we address in this work is the integration of some code mobility support in the developed middleware to allow the distribution and execution of services on peers that originally do not own the service code. This has been introduced in our platform by adding a mobile code service, which enables to download and install new services dynamically only if necessary.

The third-generation mobile networks will provide new and advanced services for mobile users: in this context, the virtual home environment (VHE), currently being widely investigated, is a concept which will enable users to access and personalize their subscribed services whatever the terminal they use and whatever the underlying network used. Adaptation is a key feature for these architectures: services should be designed and developed only once, independently of the actual user terminal and its capabilities. We show how an adaptation framework could benefit of code mobility, especially for services providing multimedia content. The base architecture is shown, and specific scenarios are discussed. The work in this paper has been done in the context of the European IST project VESPER.