Abstract Generative AI is regarded as a major disruption to software development. Platforms, repositories, clouds, and the automation of tools and processes have been proven to improve productivity, cost, and quality. Generative AI, with its rapidly expanding capabilities, is a major step forward in this field. As a new key enabling technology, it can be used for many purposes, from creative dimensions to replacing repetitive and manual tasks. The number of opportunities increases with the capabilities of large-language models (LLMs). This has raised concerns about ethics, education, regulation, intellectual property, and even criminal activities. We analyzed the potential of generative AI and LLM technologies for future software development paths. We propose four primary scenarios, model trajectories for transitions between them, and reflect against relevant software development operations. The motivation for this research is clear: the software development industry needs new tools to understand the potential, limitations, and risks of generative AI, as well as guidelines for using it.

Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing data, classification, logistic optimization and infrastructure optimization. Depending on the application at hand, a multitude of extensions to the clustering problem may be necessary. In this article, we propose a number of novel extensions to PACK, a recent capacitated partitional spatial clustering method which uses an optimization algorithm that is based on linear programming tasks. These extensions relate to the relocation and location preference of cluster centers, outliers, and non-spatial attributes, and they can be considered jointly. In the context of edge server placement, these improve the spatial location of servers while considering, for example, application placement on the servers in response to spatial application usage patterns. We demonstrate the usefulness of an extended version of PACK with an example with simulated data, as well as a real world example in edge server placement for a city region with various different setups. These setups are evaluated with summary statistics about spatial proximity and attribute similarity. As a result, the similarity of the clusters was improved by 53% at best while simultaneously the proximity degraded only by 18%. The extensions provide valuable means for including non-spatial information in the cluster analysis, and to attain better overall proximity and similarity.

General-purpose computing on graphics processing units (GPGPU) is a promising way to speed up computationally intensive network functions, such as performing real-time traffic classification based on machine learning. Recent studies have focused on integrated graphics units and various performance optimizations to address bottlenecks such as latency. However, these approaches tend to produce architecture-specific binaries and lack the orchestration of functions. A complementary effort would be a GPGPU architecture based on standard and open components, which allows the creation of interoperable and orchestrable network functions.This study describes and evaluates such open architecture based on the cross-platform Vulkan API, in which we execute hand-written SPIR-V code as a network function. We also demonstrate a multi-node orchestration approach for our proposed architecture using Kubernetes. We validate our architecture by executing SPIR-V code performing traffic classification with random forest inference. We test this application both on discrete and integrated graphics cards and on x86 and ARM. We find that in all cases the GPUs are faster than the baseline Cython code.

The deployment of edge computing infrastructure requires a careful placement of the edge servers, with an aim to improve application latencies and reduce data transfer load in opportunistic Internet of Things systems. In the edge server placement, it is important to consider computing capacity, available deployment budget, and hardware requirements for the edge servers and the underlying backbone network topology. In this paper, we thoroughly survey the existing literature in edge server placement, identify gaps and present an extensive set of parameters to be considered. We then develop a novel algorithm, called PACK, for server placement as a capacitated location–allocation problem. PACK minimizes the distances between servers and their associated access points, while taking into account capacity constraints for load balancing and enabling workload sharing between servers. Moreover, PACK considers practical issues such as prioritized locations and reliability. We evaluate the algorithm in two distinct scenarios: one with high capacity servers for edge computing in general, and one with low capacity servers for Fog computing. Evaluations are performed with a data set collected in a real-world network, consisting of both dense and sparse deployments of access points across a city area. The resulting algorithm and related tools are publicly available as open source software.

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM’s performance deterioration with the increase of sequence length. A sliding window enables predicting the long¬term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.

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.

Prevalent weather prediction methods are based on sensor data, collected by satellites and a sparse grid of stationary weather stations. Various initiatives improve the prediction models by including additional data sources such as mobile weather sensors, mobile phones, and wireless sensor networks (WSN) of, for example, smart homes. The underlying computing paradigm is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable. When the spatial and temporal density of weather sensor data grows, the required data transmission capacities and computational resources become unfeasible. We identify the challenges posed by spatial distribution of a weather prediction model, and suggest solutions for those challenges. We propose EDISON: an edge-native interpolation approach based on AI methods, distributed horizontally on edge servers. Finally, we demonstrate EDISON with a simple, simulated setup.

Edge computing in Internet of Things enhances application execution by retrieving cloud resources to the close proximity of resource-constrained end devices at the edge and by enabling task offloading from these devices to the edge. In this paper, edge computing platforms are extended into the data producing end devices, including wireless sensor network nodes and smartphones, with mobile agents. Mobile agents operate, as a multi-agent system, on the opportunistic network of heterogeneous end devices. The benefits include autonomous, asynchronous and adaptive execution and relocation of application-specific computational tasks, while taking into account the local resource availability. In addition to the vertical edge connectivity, mobile agents enable horizontal sharing of information between these devices. Use cases are presented where mobile agents address challenges in current edge computing platforms. An edge application is evaluated where mobile agents as a multi-agent system process sensor data in a heterogeneous set of end devices, control the operation of the devices and share their tasks results in the system. The mobile agents operate atop a REST-compliant software agent framework that relies on embedded Web services for interoperability. A real-world evaluation and large-scale simulations show that energy consumption is reduced significantly, up to 60%, in the edge application execution.

Semantics associates meaning with Internet of Things (IoT) data and facilitates the development of intelligent IoT applications and services. However, the big volume of the data generated by IoT devices and resource limitations of these devices have given rise to challenges for applying semantic technologies. In this article, we present Cloud and edge based IoT architectures for semantic reasoning. We report three experiments that demonstrate how edge computing can facilitate IoT systems in terms of data transfer and semantic reasoning. We also analyze how distributing reasoning tasks between the Cloud and edge devices affects system performance.

Promoted by sensor, big data and mobile computing technologies, the number of Internet of Things (IoT) applications and services is increasing rapidly. The massive amounts of heterogeneous data produced by a large variety of IoT devices require us to re-think its influence on the network. In this paper, we study the characteristics of IoT data traffic in the context of smart city. We generate data traffic according to the characteristics of different IoT applications. We propose a Gamma modulated wavelet method for statistical characterization of both IoT data and the aggregated traffic, aiming at analyzing the influence of IoT data traffic on the access and core network. By using Gamma function to modulate the coefficients of the wavelet, both the long range and short range dependency of the IoT data traffic can be described through fewer parameters. The Gamma modulation also reduces the independency of the coefficients and improves the accuracy of the Wavelet model.

The quality of service of a mobile application is critical to ensure user satisfaction. Techniques have been proposed to accomplish adaptation of quality of service dynamically. However, there is still a limited understanding about how to provide a utility model for code execution. One key challenge is modeling the level of quality in the code execution that can be provisioned by the cloud. Since the allocation of cloud resources has a cost, it is important to optimize cloud usage. We propose a software-defined networking approach that allows modeling and controlling code acceleration of a mobile application deployed across multiple type of devices. By segregating the computational requirements of the mobile application into groups, we were able to define the acceleration needed by each group of devices. As the computational requirements of a device can change across time, a mobile device can be re-assigned to another group based on demand. Our SDN approach implements a model that allows the system to predict workload based on acceleration groups. Evaluating our system in a real testbed showed that it is possible to predict workload and allocate optimal resources to handle that workload with 87.5% accuracy.

The combination of Device-to-Device (D2D) Communication in 5G Cellular Networks and vehicular networks will not only increase the performance of vehicular networks, but also increase the revenues for network operators and services providers. This paper proposes a 5G D2D routing method oriented to vehicular networks, which can increase the connectivity and scalability of vehicular networks while alleviating the traffic load of 5G base stations. Vehicular nodes are clustered according to their communication range, the speed and direction of the movements. Data are transferred among vehicles in the same and neighbor clusters in the D2D mode, only certain data packets are needed to be transferred through the 5G base stations. In this way, the traffic load of 5G based stations can be reduced greatly, whereas the data transmission delay of the vehicular networks can also be guaranteed. Simulation results show that our cluster-based routing mechanism is stable and inexpensive, and is suitable for the vehicular networks.

Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for Internet of Things (IoT) applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resource-constraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.

The infrastructure built in the City of Oulu provides rich information about the city environment and objects moving in it. We utilize this infrastructure in building an IoT system for data-intensive smart city services; by collecting data from real city environment and developing analysis methods for these data. We are building Smart City Traffic Pilot on top of the infrastructure to provide the functionality to collect the data and perform the analysis. Based on this experience, we present in this article requirements for data-intensive smart city services. Moreover, we describe four implemented use cases for utilizing rich data sources available in the smart city: situational picture, driving coach, real time reasoning, and mobile code. A lively collaboration between a large number of different actors is essential in realizing these use cases. Finally, we discuss how the use cases fulfill the requirements and the lessons we have learnt.

The Internet of Things (IoT) architecture is expected to evolve into a horizontal model containing various open systems, integrated environments, and platforms. However, not much research effort has been devoted to developing architectures for horizontal IoT solutions so far. This paper presents an IoT architecture based on Software-Defined Networking (SDN). In this architecture, devices, gateways, and data are open and programmable to IoT application developers and service operators. Moreover, IoT data provision and interoperability are supported at different levels. We present an implementation of the proposed architecture. Our implementation shows that the proposed architecture enables rapid creation of IoT applications by reusing ready applications and data. The measurement and evaluation results demonstrate the feasibility of the proposed architecture.

We present how NFC transponders can be used for energy efficient programming of closed-loop sensor networks, to update or augment the existing functionality. Use cases include road tunnel inspection, water pipeline monitoring and maintaining safety information on behalf of mine workers. We utilize opportunistic movement of the human operator, the flow of fluid in a pipeline or material in mines, to move the NFC transponder in the system effortlessly and without external network connectivity. Transponders contain mobile agents in their memory, which are injected into the system when transponder comes to the proximity of a node with NFC reader component. Then mobile agents autonomously operate their tasks, i.e. collect and process sensor data in the devices, detect events from data, control physical components and report their results. Mobile agents can adapt to the operational conditions of the system and physical environment, e.g. to save energy or operate in isolated network segments in fault situations. Real-world evaluation shows that this method is energy efficient in comparison with communications atop similar wireless sensor network.

We introduce a novel interaction method which integrates humans into Internet of Things and advances from the existing client-server paradigm. We take advantage of the shared physical space to facilitate decentralized and seamless human-to-thing, human-to-human and thing-to-thing interactions. We build these interactions upon two core technologies: mobile agents and NFC. Mobile agents realize autonomous execution of user-specific interaction tasks among things while facilitating cooperation and interoperability. Humans initiate and control interactions through physical actions that trigger communications, e.g. mobile agent migration, between NFC devices even over disparate systems. Human social relationships are utilized to disseminate tasks further in the IoT system. We discuss the benefits of this method in comparison with the common smartphone-based control of smart spaces. Real-world evaluation shows that this interaction method is feasible for resource-constrained embedded IoT devices.

Demand for new efficient methods for processing large-scale heterogeneous data in real-time is growing. Currently, one key challenge in Big Data is performing low-latency analysis with real-time data. In vehicle traffic, continuous high speed data streams generate large data volumes. Harnessing new technologies is required to benefit from all the potential this data withholds. This work studies the state-of-the-art in distributed and parallel computing, storage, query and ingestion methods, and evaluates tools for periodical and real-time analysis of heterogeneous data. We also introduce a Big Data cloud platform with ingestion, analysis, storage and data query APIs to provide programmable environment for analytics system development and evaluation.

Summary The development of Internet of Things (IoT) applications can be facilitated by encoding the meaning of the data in the messages sent by IoT nodes, but the constrained resources of these nodes challenge the common Semantic Web solutions for doing this. In this article, we examine enabling technologies for adding semantics to the IoT. Especially, we analyze data formats, which enable IoT applications consume semantic IoT data in a straightforward and general fashion, and evaluate resource usage of different alternatives with a sensor system. Our experiment illustrates encoding and decoding of different data formats and shows how big a difference a data format can make in energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we propose a novel vibration-based communication system for Bluetooth-equipped smartphones. Smartphones are commonly utilized through the display; however, sometimes users would like to exchange pieces of information with nearby peers, without shifting their focus from current task. Additionally, this enables communication in cases without visual or sound contact. For this purpose, we developed a novel application called Hand-to-Hand on Bluetooth Communication (H2BCom). We use the common gestures of tapping and touching the smartphone display for sending a Morse coded message over a Bluetooth channel. On the receiving side, the Morse coded message is presented as vibration of the smartphone. We show the design, implementation and evaluation of H2BCom on Android phones. In our user study, we found that using the Morse code was difficult for beginners, but the users'skill improved after taking a tutorial of the Morse code.

We report a pre-study and a three-week in-the-wild deployment of a non-moderated interactive public display prototype designed as a communication extension for an established community. A pre-study was conducted to map existing practices in order to ground the design. We explore the adoption process of the display prototype as well as rhythms of usage. We discuss findings related to extensions of presence within the community, the impact of the display on the community's activities, as well as aspects of appropriation and co-design. We illustrate how the display was used to extend one's presence within the community in addition to existing means of communication. This opens up new design possibilities when social dynamics are carefully negotiated.

We demonstrate interoperable mobile agents for language- and platform-independent wireless sensor network programming, with low-power resource-constrained embedded devices as static nodes and Android-based smartphones as mobile nodes, over disparate networks: 6LoWPAN and Wi-Fi. Representational state transfer architectural principles are applied in agent composition, control and migration and exposing the system resources to the Web: the devices, agents, sensor data, tasks and data processing results. The adaptable agent composition includes the task code in any programming language, the agent migrates according to a resource list and the state, i.e. the intermediate task result, represents the agent as a system resource. Mobile agents are then utilized in two tasks: first to collect light sensor data cooperatively in location, saving the mobile node battery whenever possible, and secondly the magnetometer sensor data with the scanned Wi-Fi access points' signal strength is used to detect groups of mobile nodes moving in the same direction in real-time.

We demonstrate interoperable mobile agents to integrate Internet of Things and wireless sensor networks with resource-constrained low-power embedded networked devices. We introduce adaptable composition for the mobile agent, complying with the Representational State Transfer principles, which are then used for agent migration, controlling the agent and exposing the data, system resources, tasks and services, to the Web. We gather requirements for the system and heterogeneous networked devices and present an application programming interface to enable mobile agents in these systems. The agents are demonstrated in a real-world prototype with smartphones and embedded networked devices, where we utilize a proxy component to expose system resources to the Web for human-machine interactions. Spanning over disparate networks and protocols, the proxy translates messages including the agent composition, between HTTP and Constrained Application Protocol. Lastly, we suggest an evaluation method for the agent communication and migration costs, considering the different types of system resources and utilization.

Near Field Communication (NFC) technology enables interactive, participatory learning applications that integrate digital learning material seamlessly to our everyday environment. NFC permits tagging objects in the environment and interacting with applications by touching the tagged objects with mobile devices. Touching a tag triggers an application in the mobile device to perform an operation that is related to the touched object. In this paper, we report how NFC can be used to create interactive applications that help children in their learning process and persuade them to do physical exercise. We report fully working applications we have developed to support children in learning to read, in learning a foreign language, in studying biology, and one application developed to persuade children to run outdoors.

This paper proposes context modelling and reasoning to enable intelligent services in a ubiquitous campus. An ontology-based modelling includes upper level context modelling and domain-specific modelling for the campus area. Ontological and rule-based inferencing, which facilitate ubiquitous functionality for daily life, are implemented by utilizing the context model developed. A student assistant scenario is presented, demonstrating the usefulness of ontological context modelling and reasoning for highly distributed environments, such as a university campus.

With the advancement of mobile devices' capabilities, it is possible to implement knowledge-based systems on the mobile devices. This development introduces a challenge of transferring knowledge between mobile devices and knowledge-based systems on the server side. This paper presents a novel representation, Entity Notation, to tackle this challenge. It can represent ontology knowledge in a straightforward fashion and allows incremental transfer of ontology. This unique feature makes Entity Notation an ideal solution for transferring knowledge in highly dynamic ubiquitous environments. Moreover, Entity Notation has a short format suitable for communication when resources are constrained. We address the design issues of the representation, demonstrate its usability by a small ontology, and evaluate it based on a set of ubiquitous ontologies.

In this paper, we suggest utilizing modern social networking services for building versatile applications for interactive public displays. We demonstrate the functionality and potential of this approach by presenting a set of services deployed on top of a network of public displays, utilized in a longitudinal study in an authentic city setting. We further propose utilizing users' personal online profiles for building personalized and appealing public social services, and suggest that this may enhance the attractiveness of interactive public displays. Results of this study indicate that using interactive public displays is inherently a social event, and that services supporting group use and sociality succeed in urban smart spaces.

In this paper, we present our work towards achieving context-awareness in mobile devices by combining Semantic Web technology with sensory data. Our investigation shows that some context data pertaining to the user, such as location, time, and physical surroundings, is vital for the realization of intelligent maps. Hence, embedding context-awareness into intelligent maps may prompt the usability of mobile map applications. Aiming at this goal, we suggest Semantic Web technology-based solution. We present a data representation, Entity Notation, to connect sensors to Resource Description Framework (RDF), the basis of Semantic Web data, in the data interchange level. At the same time, our data representation is lightweight enough that any resource-constrained sensors can support and process it. Ontology and ontology-based inference engine are developed to reason on the sensory data. Finally, intelligent maps could utilize its inference output to achieve context-awareness. We demonstrate our methods with a simulator and discuss the future work.

Auditory contexts are recognized from mixtures of sounds from mobile userspsila everyday environments. We describe our implementation of auditory context recognition for mobile devices. In our system we use a set of support vector machine classifiers to implement the recognizer. Moreover, static and runtime resource consumption of the system are measured and reported.

This paper presents a new architecture, Reach4Cloud, for an existing system which allows the building of user friendly interfaces into interactive spaces. Our new system facilitates the controlling of local resources and services using physical user interfaces. In this paper we focus our attention on applying cloud computing architectures used in botnets and malware to our previous distributed computing system. We have identified the command-and-control message bus as the common denominator of botnets. We have also selected the IRC as the message bus and have applied this model as distributed system architecture to a previous version of the system, partially re-architecting it to communicate over the IRC. We have investigated botnets and compare the botnet-based Reach4Cloud system to the original REACHeS architecture while reporting our observations along the way.

This paper documents the demonstration showcasing the ScreenSpot resource discovery framework and the Mobile-Vue social media sharing application. The key points of the demonstration include: requesting multidimensional resource availability information from a smart space and visualizing this inform

Big challenge related to the contemporary research on ubiquitous and pervasive computing is that of seamless integration. For the next generation of ubiquitous and distributed applications to emerge, disruptive functionality towards opportunistic and heterogeneous device ensembles is required on all

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The breakthrough of web 2.0 and sudden uprising of hundreds of different social networking sites have modified the way we communicate and share our experiences and lives with other people. Despite the undeniable networking advantages this digital world offers, it cannot be seen as purely positive development. Due to a prospering online "hang around" culture, we have lost genuine rich human social experiences to some degree. This paper proposes how to utilize the digital identities and content people have created within these services to improve and encourage natural communication and lower the social barrier of natural interaction in public settings. We propose a system that combines the content in the virtual world with the presence in real world settings. SocS (Social Surroundings) brings out digital information and media content of users to the current physical space, thus attempting to make individuals more approachable by other participants. The requirements, design principles and design of the proposed application are presented. We also discuss the usage and current state of social networking sites as a motivator for new genre of intelligent services.

Here, we present Touch & Share, an application for sharing multimedia content in our everyday environment. Our main design criterion is ease of use. Visual icons placed in the environment act as data storage (file containers). Users can drop multimedia files into the containers and pick files from them by touching the icons with their mobile terminals. The icons are placed on, or near, the physical objects the files are related to. RFID tags are placed behind the icons and they store metadata information about the files available in the corresponding containers. When users bring their terminals near the icons, the terminals' RFID readers communicate with the tags, read the contents of a container from the tag and possibly write information about new files. The actual files are stored in a server, but the user experiences the files to be in the containers. The main contributions of this work are a general application for storing multimedia content in nearly any place or everyday object and a fully functional prototype built using only commercial off-the-self technology.

Multimedia content is commonly manipulated using a keyboard and a mouse. We propose an innovative way of controlling multimedia services using mobile phones equipped with NFC (Near Field Communication) compliant RFID readers. A user activates multimedia content by touching an RFID tag. This action configures the mobile phone as a remote control for the multimedia content. The user sends commands such as play, pause and stop, using the phone's UI. The multimedia content is shown on a wall display. This approach has several benefits: a common and easy-to-use user interface, off-the-self components, and lightweight implementation. We have not created any new technology, but our system makes use of widely used ones. The innovation of our system comes up on how all these technologies are glued together to get a light and flexible system. We present three prototypes: an interactive advertising catalogue, a video player and a slideshow viewer.

One of the challenges in pervasive computing is communication between a mobile user's terminal and the continuously changing local environment. Bluetooth is one potential option for providing connectivity, but its usage is hindered by the time consuming device discovery and service discovery processes. We propose using RFID technology to enhance the Bluetooth connection establishment procedure. We present quantitative evaluation and qualitative user evaluation of our system compared to the standard Bluetooth mechanism. Measurements show that our approach dramatically increases the performance when establishing a Bluetooth connection between two devices. Also users prefer our approach because it is faster to use, there is no need for menu selections, and it is considered easier and more pleasant to use than the standard approach

A HTTP input channel can adapt its behavior according to the changing network status in a context-aware fashion. The adaptation includes both selecting and dynamically switching the underlying connection used, as well as coping with disconnection situation. This paper describes the architecture and design of HTTP input channel for multi-access wireless network. A series of experiments were performed in a GPRS-WLAN integrated environment. Performance metrics were measured and analyzed. In particular, the time delay of channel creation and connection switching was investigated. For channel creation, the majority of time delay comes from the normal operation of HTTP URL connection. More time is needed for best connection evaluation and default gateway changing when more than one connection is available. For passive switching delay both event detection and connection evaluation contribute to the most besides the normal HTTP connection opening

This paper presents a software architecture for the network-transparent control of distributed robotic systems. The system consists of two main components: a generic and easily extensible CORBA-based interface to distributed services, and a high-level XML-based description language for specifying the behavior of the robots. The architecture makes it possible to create dynamically modifiable, extensible control software with ease. It is successfully utilized in implementing a coffee serving system in which the co-operation of two very different robots and two other distributed services are needed.

We present a context-aware middleware for mobile multimedia applications. The middleware offers functionality for service discovery, asynchronous messaging, publish/subscribe event management, storing and management of context information, building the user interface, and handling the local and network resources. It supports a wide range of context information including location, time, and user's preferences. Further, it allows controlling the connectivity of the device; the middleware is capable of switching traffic from one network connection to another. It can locate the services and software components as well. It facilitates development of multimedia applications by handling such functions as capture and rendering, storing, retrieving and adapting of media content to various mobile devices. The middleware offers facilities for media alerts, which are multimedia messages that are generated when predefined context is recognized. With these capabilities, it enables development of complex context-aware multimedia applications for mobile devices.

While peer-to-peer (P2P) has emerged as a new hot communication concept among the Internet users, mobile usage of P2P applications is still taking its first steps. This article first elaborates the evolutionary process that P2P architectures are going through. Challenges and requirements for mobile P2P are then identified, followed by a definition of a novel Plug-and-Play Application Platform (PnPAP). This platform enables dynamic selections between diverse P2P and session management protocols while preserving the best available network connectivity through Holistic Connectivity (HCon) management. On-the-fly reconfiguration and run-time parameter optimization can be done with a lightweight interpretable state machine. The concept enables flexible and seamless communications for mobile devices in P2P networks.

In context-aware pervasive networking (Capnet) environments the user interface of a service application has to be adapted to whatever type of a device the user is using, taking into account the situation at hand, i.e. the user's current context, as well as user's personal preferences. In addition, new and more natural interaction styles have to be provided for the user. When designing and implementing user interfaces, or generating them automatically at runtime, some a priori knowledge of the capabilities of user's device and her personal preferences are required. Furthermore, runtime adaptation calls for mechanisms for recognizing context and delivering it. This paper presents the support that could be provided in middleware level for implementing context-aware multimodal user interfaces and the Capnet middleware user interface architecture model for providing that support. Adaptation algorithms are not discussed. The Capnet middleware backbone provides support for context-awareness via context recognition and automatic delivery of context data through a notification mechanism. For multimodal input and output third party components are required. The proposed architecture model is partly implemented and tested with software prototypes.

An infrastructure based on multiple heterogeneous access networks is one of the leading enablers for the emerging pervasive and ubiquitous computing paradigm, in which the optimal management of diverse networking resources is a challenging problem. This paper presents a context-aware policy mechanism and related end-to-end evaluation algorithm for adaptive connectivity management of multi-access wireless networks. A policy is used to express the criteria for adaptive selection of the best local and remote network interfaces. The best connection can then be used for the establishment of a channel as well as the maintenance of on-going data transmission. Rich context information is considered in the policy representation with respect to user profile and preference, application characteristics, device capability, and network QoS condition. The decision of the best access networks to be used is made on the basis of an end-to-end evaluation process. The decision can be made in both Master-Slave and Peer-to-Peer modes. The paper focuses on the methods for policy representation and connection evaluation algorithm. A case study is presented to show the usability of the proposed policy mechanism and decision-making algorithm in the adaptive management of heterogeneous networking resources.

Presents a general architectural solution for concurrent real-time color-based tracking of multiple objects. The presented architecture, called Cocoa, is suggested for a mobile robot interacting with humans. The main contributions of the architecture are markers and color judges. Markers are a situated representation of the robot's environment. Color judges separate color-based segmentation methods from the process of labeling images. Furthermore, methodology for coping with objects entering and leaving the robot's field of view is suggested. Tracking experiments performed with a system implemented on a Nomad XR4000 robot are presented as well.

In this paper we describe a behavior-based control architecture capable of reactive task execution; that is, it reasons out actions based on task constraints and reacts quickly to unexpected events in the environment. Reactive task execution requires versatile behavior coordination methods. We suggest that the required behavior coordination can be achieved by combining action maps. The main advantages of this method are that it enables executing several tasks in parallel and facilitates considering environment dynamics. These properties make this behavior coordination method suitable for reactive task execution.

In this paper we describe an extended behavior-based architecture capable of executing purposive tasks. The key to this capability is a novel mechanism for sharing data between behaviors. Simple processing elements called markers ground task related data on sensor data flow and communicate it to the behaviors. A control system based on the architecture controls a mobile robot to help other robots in their transfer tasks. In addition to robot movements the control system controls a stereo gaze platform performing real time stereo tracking. In this paper we concentrate on behavior coordination by markers and realistic behavior-based systems. We present experimental results using a real robot.

This paper presents some advanced examples of reactive vision-based cooperative behaviors: 1) chasing and posing against another robot among others; 2) unblocking the path of another robot by removing an obstacle; and 3) passing an object from one to another. These behaviors are demonstrated using real mobile robots equipped with CCD cameras, in a complex environment, and with no central controller or explicit communication among the robots. The action observation is based on real time processing of optical flow analysis and stereo tracking. An extended behavior-based architecture for "cooperation by observation" is presented. The core extension consists of a mobile space buffer and an image space buffer with manipulable markers which control the internal flow of information, thereby coordinating parallel behaviors and achieving purposive tasks in complex and dynamic environments.>

Implementing a control system for an autonomous machine is a challenging task. Several techniques have to be applied, such as task planning, hierarchical and/or distributed control, and advanced sensing techniques. In addition, to be useful these various techniques have to be integrated into a system that has to operate more or less in real-time. The authors present a control scheme based on hierarchically organized planning-executing-monitoring-cycles which is used to solve some of the problems related to real-time control of an autonomous machine. The implementation is also presented in which the control system is applied in a pilot system based on an industrial robot.>