The human thermal plume impacts embedded system performance, especially in portable or wearable devices with multiple sensors near or on the body, affecting measurement accuracy. These devices have many indoor uses, providing insights like thermal comfort and indoor air quality. They can also benefit sustainability and energy consumption by linking sensor measurements to room or skin temperature assessments. Current research investigates thermal plumes under realistic conditions, and a significant advancement is a set of equations that describe body and environmental temperatures simultaneously. These equations link room temperature to sensor data from wearable devices in the human thermal plume, enabling smarter sensor systems. The thermal model, based on computational fluid dynamics (CFD), accounts for the effects of human breathing on measurements and includes air movement, convection, and conduction mechanisms. It simulates how the human thermal plume spreads under different conditions, such as varying temperatures and levels of clothing insulation, especially when a sensor is placed on the arm or wrist. An experimental setup calibrates and observes temperature distributions in a realistic room-scale environment, also highlighting the impact of human breath on sensor measurements.

Considering the significant implications of indoor air quality on health, productivity, comfort and well-being, accurate forecasting of indoor air contaminants is of the utmost importance for devising effective strategies and proactive control of ventilation in buildings. CO2is a common air pollutant in indoor settings, with human exhalation serving as its primary source. This study investigates the potential of machine learning models—particularly Gradient Boosting—for short-term indoor CO2forecasting using an extensive dataset of air quality measurements collected from a hospital in Greece. The predictive performance of Gradient Boosting was evaluated against a baseline model, which interpolates the latest observed CO2levels, as well as against linear models that capture recent temporal patterns in CO2concentrations, over forecasting horizons of 5, 10, and 15 minutes. The Gradient Boosting model significantly outperformed the baseline, achieving a 44.53% reduction in Root Mean Squared Error (RMSE) and a corresponding decrease in Mean Absolute Error (MAE). In contrast, both the baseline and the linear models exhibited relatively low errors for 5- and 10-minute forecasts, suggesting that their low complexity and minimal computational demands make them reliable, lightweight alternatives for short-term CO2prediction. Moreover, the findings underscore the value of incorporating occupancy factors in forecasting indoor CO2levels, thereby paving the way for improved data-driven indoor environmental management strategies.

Thermal comfort (TC) serves as a key measure of how an individual feels about the temperature in an indoor environment, and it is influenced by a combination of physiological and environmental factors. Therefore, people can have different perceptions of the temperature within the same indoor space. An accurate and timely assessment of an individual’s TC can lead to benefits such as increased energy flexibility, improved energy efficiency, and reduced energy costs. This paper showcases the accurate TC assessment within indoor environments utilizing a wrist-worn wearable device that has been developed to continuously capture in a non-interventional manner the environmental conditions and the human biological indicators that affect the individual’s TC. The Heart Rate Variability (HRV) biological indicator has been employed for the validation of TC outcomes while the accuracy and the reliability of the device’s TC outcomes is based on the consistency with the existing research literature.

This work presents the concept and methodology as well as the architecture and physical implementation of an integrated node for smart-city applications. The presented integrated node lies on active RFID technology whereas the use case illustrated, with results from a small-scale verification of the presented node, refers to common-type waste-bins. The sensing units deployed for the use case are ultrasonic sensors that provide ranging information which is translated to fill-level estimations; however the use of a versatile active RFID tag within the node is able to afford multiple sensors for a variety of smart-city applications. The most important benefits of the presented node are power minimization, utilization of low-cost components and accurate fill-level estimation with a tiny data-load fingerprint, regarding the specific use case on waste-bins, whereas the node has to be deployed on public means of transportation or similar standard route vehicles within an urban or suburban context.

This work presents the architecture, modelling, simulation, and physical implementation of a versatile, scalable system for use in common-type waste-bins that can perform and transmit accurate fill-level estimates while consuming minimal power and consisting of low-cost embedded components. The sensing units are based on ultrasonic sensors that provide ranging information which is translated to fill-level estimations based on extensive simulations in MATLAB and physical experiments. At the heart of the proposed implementation lies RFID technology with active RFID tags retrieving information and controlling the sensors and RFID readers receiving and interpreting information. Statistical processing of the simulation in combination with physical experiments and field tests verified that the system works accurately and efficiently with a tiny data-load fingerprint.

In the age of automation the need for indoor navigation has become quite important for a rising number of applications. In this work a strapdown INS (SINS) in terms of Inertial Navigation System is discussed. INS is an electronic device, containing an Inertial Measurement Unit (IMU), implementing a Dead Reckoning (DR) algorithm. This algorithm estimates position, velocity and orientation utilizing readings from motion sensors. The logic behind DR algorithm is to continually adding detected changes from the sensors to its previously calculated results. This is the main reason why INS is limited to measure relative position and orientation. Thus the accuracy of the propagated position depends heavily on the quality of the initial conditions. In addition, if absolute position or orientation updates are obtained by another sensor source at a high rate, the INS can be used to deliver high precision positions. In this paper, an INS fused with the new Bluetooth v4.0 technology is described.

A fuzzy rule-based and energy-efficient method for estimating the free size of parking places in smart cities is proposed. In particular, the estimation is the output value of a fuzzy inference system (FIS) which was implemented on each wireless sensor mote (WSM) of a wireless sensor network (WSN) placed in the ground below the parking places and it was observed that it requires 8.9 Kbytes and 5 Kbytes of flash program memory and RAM respectively. Moreover, the fuzzy rules were formulated by using real numerical data obtained from the WSN. Next, the well known network simulator 2 (NS-2) was used for acquiring simulation results concerning the energy consumption of the battery of the WSMs. In order to model the error of the output value of the FIS and the energy consumption of the battery, a data fitting problem was solved and the cubic polynomial was fitted to the real and simulation data. As a result, a real-valued cost function was formed which was minimized to get the optimal values for the distance between the WSMs and the sampling period based on which the output of the FIS should be re-computed.

Logistics or supply-chain services provide enterprises and organizations with the necessary level of flexibility and efficiency in order to retain competitiveness under the increasingly turbulent e-business area.Web-Services are utilized by organizations in order to integrate high and low level applications, thus providing a collaborative environment without affecting inter-and intra-enterprise processes.Nevertheless, the above context should be enhanced in order to comply with the Web-of-Things concept.This paper describes a sustainable approach towards the above requirement by employing ONS based services able to provide targeted information regarding RFID-enabled physical objects that are handled in an organization agnostic collaborative environment.

Efficient collection of urban solid waste is a growing problem for cities and urban areas. Optimizing service routes based on actual waste bin fill level information can result in significant cost savings, reduced emissions, and improved city image. Fill level sensor installation and networking at an urban scale poses unique challenges. We describe Dynacargo, an innovative smart city application that leverages RFID technology in the waste bins and delay-tolerant networking (DTN) in roaming vehicles acting as mobile sinks. Dynacargo collects and transmits waste bin status information to the backend systems utilizing already existing network infrastructures and single-hop communications thus, reducing associated costs and time for installation, operation, and management of the necessary networks and ICT systems.

In this paper, a modification of the protocol stack of the device profile for web services (DPWS) is proposed which can be applied in wireless sensor networks (WSNs) that comply with the IPv6 over low-power wireless personal area networks (6LoWPAN) architecture. The modification is based on a new format for the DPWS message exchanges without prohibiting the usage of the web services (WS) and the extensible markup language (XML) set of rules. The modified DPWS was implemented on the SunSPOT wireless sensor mote (WSM) and it was observed that it processes XML documents with a mean computation time less by 53% than the respective computation time of the DPWS while it consumes less EEPROM and RAM by 84% and 85%, respectively. Furthermore, its network performance was assessed by testing it over a real 6LoWPAN-based WSN with its maximum number of WSMs being 12. In order to validate these results and extend them to larger-scale 6LoWPAN-based WSNs, the network simulator 2 (NS-2) was used by enhancing it with a developed 6LoWPAN object. The NS-2 was also utilized for comparing the modified DPWS, the DPWS and a binary-based DPWS in terms of packet delivery ratio and maximum transmission delay. Simulation results have shown that the modified DPWS presents better performance than the DPWS and offers inferior results only when it is compared with the binary-based DPWS which, however, does not retain the WSs interoperability feature as it does not use XML documents.

Wireless Sensor Networks (WSNs) have limited power capabilities, whereas they serve applications which usually require specific packets, i.e. High Priority Packets (HPP), to be delivered before a deadline. Hence, it is essential to reduce the energy consumption and to have real-time behavior. To achieve this goal we propose a hybrid technique which explores the benefits of data aggregation without data size reduction in combination with prioritized queues. The energy consumption is reduced by appending data from incoming packets with already buffered Low Priority Packets (LPP). The real-time behavior is achieved by directly forwarding the HPP to the next node. Our study explores the impact of the proposed hybrid technique in several all-to-one data flow scenarios with various traffic loads, wait time intervals and percentage of HPP. Our results show gain up to 23,3% in packet loss and 36,6% in energy consumption compared with the direct forwarding of packets.

Wireless Sensors Networks (WSNs) emphasize the necessity of accurate event detection in realistic environments, while health monitoring comprise one of their most pronounced applications. Although respective fuzzy logic systems have been proposed based on vital and environmental sensors, there is a significant lack of investigation on the reliability of such systems when applied in error prone wireless environments. Therefore, this paper aims to address this need by porting an adequate fuzzy inference system (FIS) to a WSN simulation framework. The considered FIS has been validated in previous work and is implemented in a TelosB emulated mote, which evaluates the health status of a monitored person. Such implementation allows the thorough performance evaluation of the FIS under a wide range of communication conditions. Respective results and comparative evaluation against Matlab environment reveal strong dependencies to critical WSN network parameters.

Wireless Sensor and Actor Networks emphasize the necessity for cooperative communication between actors as well as sensors in order to achieve ubiquitous monitoring and efficient interaction in realistic physical environments and appropriate acting on them. This collaboration aims to assure appropriate decision making related to specific event detection, appropriate classification as well as selection of the optimal actor able to react to the event. Therefore this paper aims to propose a suitable architecture providing a data fusion model necessary for the first goal as well as a task assignment method comprising an essential component for energy efficient communication between the actors so as to allocate the acting tasks.

A modular architecture for power constrained embedded devices which leverages an agent based middleware in order to impose intelligence over critical infrastructures which require real time actions is always desirable in the case of Wireless Sensor Networks. The main objective of such architecture is the integration of a Wireless Sensor Network with the internet. This objective indicates the need of Ipv6 ready devices as well as the utilization of newly emerged standards such as the 6lowpan which forms an abstraction layer between the Medium Access control and the IP layers.

The benefits of utilizing service oriented architectures (SOA) in industrial automation are well recognized. Nevertheless these architectures imply connection establishments and message exchanges with a large amount of overhead which is inappropriate when considering the real time characteristics of an industrial application. Furthermore, wireless sensor networks (WSNs) which are often part of such applications cannot handle the need for continuously parsing and storing of SOA messages whose data volume is high. In this paper an innovative system approach is proposed capable of retaining all the interoperability issues that SOA provides. In this system a new bridge is used for mapping WSNs to IP systems and a novel compression technique is applied that reduces the size of the exchanged messages but nonetheless not prohibiting the usage of SOA standards. Finally a SOA-based technology is proposed which is embedded on wireless sensors motes and enables them to communicate with other devices regardless their implementation platform. Simulation results have shown that the proposed system can operate with real time responses when the number of sensors does not exceed a specific limit and the required sampling period is within particular time intervals in contrast with a classical SOA-based system.

This paper proposes an advanced two-part middleware solution to the problem of integrating resource constrained devices located in the field of factory automation, such as wireless sensor networks, into an enterprise information system. These networks seem to present the most difficult part to be integrated into such information systems. The first part of the proposed middleware is implemented at the client side and provides a service oriented architecture connection to the Internet. The second part provides to the wireless sensors a service oriented architecture connection to the Internet by enhancing the exchanged information with semantic expressivity. Both parts are based on the device profile for Web services which is a service oriented architecture technology at the device level. By utilizing the proposed two-part middleware, we explain how such a wireless sensor network can interact with a client with no previous knowledge on each other's services achieving in this way an automatic integration of the wireless sensor network into the enterprise information system.

As dynamicity in networks and interoperability in DRM systems become the critical aspects in networked ecosystems, new emerging frameworks for secure, user-converged digital content delivery are required. In this work, we argue that the IPMP and REL components of MPEG-21 framework should be utilized in order to achieve a user-adaptive and interoperable DRM architecture.

As networks become more and more complicated and applications more and more demanding, a very common network topology for state-of-the-art multimedia applications supporting emerging user - centric frameworks is a heterogeneous wired/wireless architecture. A network architecture solution for indoor/outdoor heterogeneous networking applications, to support both multimedia and sensor applications suitable for such frameworks is proposed in this paper. Furthermore, an integrated DRM system architecture is proposed for the protection of intellectual property, characterized by its interoperability aspect.

The manufacturing environment is characterized by a high degree of heterogeneity. The seamless integration of MES and Shop-Floor layers poses a significant challenge. Industrial manufacturing ontologies provide the necessary semantics for such and integration. A gap nevertheless exists between the industrial ontology semantics and the behaviour and activity model utilized by a multi-agent system that implements the MES layer. Work presented in this paper presents a way to close this gap through the enforcement of a sound methodology utilizing an appropriate meta-ontology. The applicability of the methodology is evaluated against a Multi-Agent System paradigm modeled according to the GAIA methodology.

The paper deals with the problem of modeling and implementation of distributed automation systems for manufacturing applications. In particular, a novel approach has been conceived. A suitable meta-model of the production system has been derived, compliant with the state-of-the-art industrial standards. After the compilation and validation stages, an ontology is derived containing all the necessary definitions of modeling entities and constraints, to be used directly and explicitly in the execution of the control code. This entitles the control application to be defined as "model-driven" or better "ontology-driven". The results are obtained within the running EU project PABADIS'PROMISE(P2).

This paper discusses the design and the development of a control architecture for reconfigurable manufacturing systems. It presents the approach proposed by the PABADIS' PROMISE European research project which targets the issues of reconfigurability, flexibility, and efficiency in the context of the interaction among the three levels of the control pyramid (ERP, MES, Field). The key feature of the overall control architecture is a distributed organization based on agent technology and on a specific ontology for manufacturing systems.

Collaborative B2B e-commerce provides enterprises with the necessary level of flexibility and efficiency to retain competitiveness under the increasingly turbulent business environment. Widely accepted XML-based frameworks like RosettaNet may be utilized to provide generic inter-enterprise B2B infrastructure. This paper presents an innovative approach towards utilizing RosettaNet in combination with Web services, ontologies and workflow management systems for the enforcement of the continuous replenishment planning process.

The need for interoperability is prominent in the industrial enterprise environment. Different applications and systems that cover the overall range of the industrial infrastructure from the field to the enterprise level need to interoperate. This quest is driven by the enterprise need for greater flexibility and for the wider possible integration of the enterprise systems. This paper presents a distributed system architecture that utilizes dominant state-of-the-art standard technologies, such as workflows, ontologies, and web services, in order to address the above quest in an efficient way.

Collaborative e-commerce provides an increased level of flexibility and dynamicity and generates an entrepreneurial competitive edge. Widely accepted implementation frameworks like RosettaNet may be utilized to provide generic inter-enterprise B2B infrastructure. This paper presents an innovative approach towards utilizing RosettaNet in combination with Web services and ontologies for the enforcement of the continuous replenishment planning model