Location-Based Services (LBS) are increasingly important across different applications, particularly within the growing Internet of Things (IoT) domain, as they provide accurate context-aware information, i.e., position about the devices moving across a network. One of the most used LBS technologies exploits the distance between pairs of network nodes to determine the position of the target. Although several methods exist to compute such distances, the Received Signal Strength Indicator (RSSI) is the one with the lowest computational complexity, which is desirable in IoT-based applications. High-quality experimental data is crucial to effectively researching new strategies for positioning and navigation in interference-prone environments such as industrial ones. This work presents an experimental methodology comprising multiple wireless technologies, WiFi and Bluetooth Low Energy (BLE), to generate a dataset of RSSI measurements in an indoor industrial research laboratory. This dataset includes detailed ground truth data on target positions, orientations, and velocities, enabling a thorough evaluation of different positioning methods and advancing the development of accurate and robust solutions. In particular, this paper validates the dataset through experimental tests employing a fingerprinting-based approach.

The healthcare sector is experiencing a profound transformation, fueled by the rapid evolution of sixth-generation (6G) cellular networks and Internet of Things (IoT) technologies. At the heart of this shift lies the concept of medical digital twins (MDTs), which serve as dynamic virtual representations of physical systems or biological processes. MDTs offer a secure environment to simulate and evaluate therapeutic strategies, leading to reduced costs and more informed clinical decision-making. They also enable real-time support and in-depth data analysis, setting new standards for patient care. Nonetheless, realizing the full capabilities of MDTs remains challenging due to the inherent complexity of human life cycles. Crucial aspects include selecting appropriate data sources and defining robust communication protocols between the physical and digital realms. In particular, integrating wearable technologies with edge computing and WiFi-based Channel State Information (CSI) can significantly enhance health monitoring and activity recognition for elderly individuals within indoor settings. The synergy of IoT advancements and 6G networks paves the way for improved data exchange and continuous synchronization between digital and physical counterparts. This paper, part of the HIPPOCRATES project, presents an IoT-driven architecture for MDTs that incorporates wearable sensors and CSI data to strengthen health monitoring and early intervention strategies, with a focus on elderly care.

This paper examines the challenges posed by constructive-destructive interference and Denial of Service (DoS) jamming attacks on multilateration-based localization algorithms, particularly concerning Least Square (LS)-based approaches. Despite the increasing interest in localization technologies in a wide range of applications, the vulnerabilities of these systems to such attacks have been insufficiently explored in existing literature. To evaluate the impact of jamming, scenarios with varying numbers of malicious nodes are considered in this paper to assess their effects on localization accuracy. The results reveal a significant degradation in accuracy as the number of compromised anchors increases. However, the implementation of mitigation strategies leads to a substantial performance improvement, effectively reducing the impact of interference and maintaining lower position estimation errors.

This paper analyzes the impact of specular signal reflections on the accuracy of Received Signal Strength (RSS)-based localization for Internet of Things (IoT) devices using the weighted least squares (WLS) regression algorithm within a two-ray propagation channel. Simulations with realistic WiFi/BLE settings, considering distance, antenna heights, and carrier frequency, reveal that localization accuracy is significantly influenced by deep fades caused by surface reflections, which depend on the geometry of anchor-target positions. A pseudo-outlier elimination approach based on feasible localization distances effectively mitigates this issue, significantly reducing localization error. These findings offer practical insights into the performance of WLS-based IoT localization in two-ray environments and lay the groundwork for GPS-free or GPS-denied localization systems in challenging scenarios, such as overwater environments, where two-ray propagation is predominant.

Climate change mitigation requires the urgent reduction of Greenhouse Gas (GHG) emissions, with the building sector as a significant contributor. This study develops a system to identify appliance profiles from smart meter data, enhancing energy consumption awareness and management. These profiles provide valuable insights into users’ consumption patterns and habits, enabling more accurate load consumption prediction and effective appliance scheduling strategies. The proposed approach employs feature extraction techniques to characterise energy consumption profiles, followed by k-means clustering to identify distinct appliance profiles. Eleven representative features are identified, offering comprehensive insights into occupants’ energy usage habits. The evaluation with real-case data shows accurate consumption cycle approximations for each profile, with errors consistently below 10%. Performance assessment using classification metrics indicates well-characterised and representative profiles, outperforming state-of-the-art methods with average values exceeding 0.88 for all considered metrics. This system helps raise occupants’ awareness of appliance energy usage and facilitates optimised scheduling through an Energy Management System (EMS). By promoting more efficient energy consumption, the proposed approach contributes to overall energy reduction and, consequently, lower GHG emissions in the building sector. • Development of a system to monitor consumption habits for different appliances. • Design a methodology to identify key features for diverse appliance profiles. • Identification of 11 robust features representing consumption profiles effectively. • Integration of k-means clustering to group appliances by consumption and behaviour. • Validation of the proposed system using a real-case dataset.

Human Activity Recognition (HAR) techniques play a key role in identifying and categorizing human activities based on environmental information. More recently, the use of Channel State Information (CSI) has gained momentum because this information can be extracted in a non-intrusive manner. CSI-based algorithms leverage the correlation between CSI dynamics of wireless transmissions and human body movements. However, privacy concerns may arise, as this approach may inadvertently disclose sensitive information about individuals’ movements, habits, and behaviors. In this context, this study investigates the challenge of preserving user privacy in CSI-based HAR for eHealth Ambient Assisted Living (AAL) applications. More specifically, the impact of simple filter-based CSI obfuscation is evaluated on the accuracy performance of a HAR model that makes use of a Long-Short-Term Memory (LSTM) algorithm. Using a publicly available dataset, the accuracy between the original and the obfuscated versions of the dataset generated by simple filtering techniques is compared. The results show significant performance degradation when data is obfuscated, with a HAR accuracy degradation compared to the original results ranging from a minimum of 17.9% to a maximum of nearly 90%. Such results prove that, even with simple obfuscation techniques, the privacy of CSI-enabled HAR-based systems can be sufficiently preserved.

This paper addresses the problem of data privacy in Human Activity Recognition (HAR) applications for eHealth. Cryptography, a proven privacy safeguard on the Internet, remains underutilized in the HAR context, as observed in the existing literature. This study highlights the importance of cryptographic practices by conducting a performance analysis, focusing on the accuracy of HAR with and without data en-cryption. This paper proves that even by introducing a very simple cryptographic mechanism, a potential eavesdropper would experience a reduction of more than 20% of accuracy in the activity recognition task as compared to the case where no encryption is used, at the price of a limited increase in the energy consumption for the involved sensors. Such preliminary results demonstrate the effectiveness of encryption for applications of this type, encouraging further exploration and refinement in this direction.

Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to thebestdevices, according to the context and task requirements. The main purpose of this article is to study the different connotations of the concept of TAefficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. This article first analyzes the domains of connected devices where TA has an important role, which brings to this classification: 1) Internet of Things (IoT); 2) sensor and actuator networks (SANs); 3) multirobot systems (MRSs); 4) mobile crowdsensing (MCS); and 5) unmanned aerial vehicles (UAV). This article then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. This article also discusses the issues that need to be addressed by future research activities, i.e., allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; the list of tasks performed by objects; and designing TA strategies where network service providers have a role in TA functionalities’ provisioning.

It has been largely demonstrated how human be-haviour can have a great impact on the quality of life. Some habits, such as those related to sleeping, exercising and working, directly affect people's psycho-physical health, either positively or negatively. The research has been increasingly focusing on better understanding how some behaviours, or changes in someone's usual habits, can be triggers to early recognising and predicting bad health conditions that might even be the warning signal of pathological conditions such as depressive disorders or neurodegenerative diseases. Non-invasive wearable health monitoring systems have the potential of being a key technology to this purpose, because they are easy-to-use and are not perceived as intrusive by users. In this paper, a system that makes use of popular commercial wrist-wearable devices to find the correlation between the monitored users' activities and their stress and well-being conditions, as subjectively self-assessed by them, is proposed. The paper aims to present a methodology to automatically learn which users' activities can be associated with positive and negative health conditions so that they can be later predicted as soon as the first signals are detected by wearable devices. The paper further presents the implementation and preliminary results of a first prototype of the proposed system, which monitors users' sleep and activity and assesses the correlation with stress levels and illness conditions.

In this work, we propose a remuneration-aided Game theoretical solution for task allocation in cognitive radio (CR) enabled Internet of things (IoT) scenarios, where cognitive nodes (CNs) in close proximity and with similar sensing capabilities are clustered around a cluster head (CH). We consider a framework in which task allocation in the system is driven by CNs with spectrum sensing capabilities. In the proposed approach, the CH assigns a remuneration to CNs for their contribution in spectrum sensing prior to initiating the task allocation procedure. Such remunerations can be used by CNs in proposing the bids to win the task in the Game. Hence a non-cooperative Game approach modelled as an auction process is proposed. We show that the proposed framework is able to exploit cognitive behaviour efficiently in conditions suitable for cognitive radios (low spectrum occupancy), and under the same conditions the overall system utility increases by 29% w.r.t the case when licensed users (LUs) occupy the band 70% of the time. Additionally, the framework allows the system to reap benefits of energy efficiency while experimenting cognitivity.

The Social Internet of Things (SIoT) is a novel communication paradigm according to which the objects connected to the Internet create a dynamic social network that is mostly used to: route information and service requests, disseminate data, and evaluate the trust level of each member of the network. In this paper, the SIoT paradigm is applied to a scenario where geolocated sensing tasks are assigned to fixed and mobile devices, providing the following major contributions. The SIoT model is adopted to find the objects that can contribute to the IoT application by crawling the social network through the nodes profile and trust level. A new algorithm to address the resource management issue is proposed so that sensing tasks are fairly assigned to the objects in the SIoT. To this, an energy consumption profile is created per device and task, and shared among nodes of the same category through the SIoT. The resulting solution is also implemented in the SIoT-based Lysis platform. Emulations have been performed, which showed an extension of the time needed to completely deplete the battery of the first device of more than 40% with respect to alternative approaches.

In this study we consider the scenario of sensors belonging to different platforms and owned by different owners that join the efforts in an opportunistic way to improve the overall sensing capabilities in a given geographical area by forming clusters of nodes. The considered nodes have cognitive radio and exploit device-to-device communications. A solution is proposed which relies on a Cluster Head (CH) that guides the whole task allocation strategy. The addressed challenges are the following: i) collaborative spectrum sensing for effective communications within the cluster; ii) assignment of each request of sensing tasks to a single node in the cluster. The first challenge is addressed by proposing a collaborative sensing procedure where each node communicates to the CH the received signal energy of licensed users so that the latter makes a decision on the availability of the band by fusing the received information towards a minimisation of the uncertainty in detecting the free spectrum. The second challenge is addressed by proposing a non-cooperative Game theory based approach in which cluster nodes make effort to selfishly increase utility by winning the task. Each node takes part to the competition by considering two elements: the gain that is won for its contribution to sensing and for the execution of the task (in case it wins the competition); the cost in terms of energy to be consumed in case the task is executed. A Nash Equilibrium Point (NEP) is found for the aforementioned game in which each object has no incentive to deviate uni-laterally from the NEP. Extensive simulations are performed to evaluate the impact of probability of false alarm, utility function weighting factors and presence of licensed users on the cumulative system utility.

The new generation of LTE (Long Term Evolution) networks provides ubiquitous broadband access to mobile devices matching land communications in quality and speed. However, to optimize network resource usage in a dynamic environment network operators need models and strategies to constantly assess and manage the end-user's Quality of Experience (QoE). Given the importance of these activities, in the current paper, we focus on quality monitoring and the usage of QoE-agents in an LTE-Advanced Pro network. Specifically, we identify the location and the operation of the QoE-Agents based on the accuracy of the measurements and the load in the network considering the frequency of the measurements and the running applications. Emulations have been also carried out to evaluate two scenarios with different network conditions and we made experiments with different quality sampling rates and different application configurations. The preliminary results have shown that the proposed strategy brings to acceptable errors from our measurements, low CPU utilization and acceptable memory utilization.

In the last decade, we have been experiencing an increasing level of intelligence that the objects in the Internet of Things (IoT) have been augmented with, especially in the direction of giving them cognitive and socialization capabilities. We believe that this evolution should go further in the direction of the Emotional Intelligence, which allows humans to be successful in their lives. EmIoT is the resulting paradigm that we propose, which is aimed to increase the Quality of Experience delivered by IoT applications by making IoT capable of: understanding peoples needs by observing them; better management of its own resources on the basis of users emotional state; creating a level of affection to be used for leveraging the level of interaction between IoT and users. The paper's contribution lies in the definition of the paradigm, the analysis of the new functionalities the IoT should be augmented with, and the preliminary investigation of the possible EmIoT architecture.

In this paper, we illustrate a Software Defined Network (SDN)-based architecture for Quality of Experience (QoE) management that solves two of the major problems of current networking technologies which are related to the limitations in scalability and flexibility. Its advantage is the exploitation of the virtualization features of the network nodes and devices to flexibly deploy monitoring and control functions in the different points of the network according to the SDN control functions. As a result the QoE monitoring and management is deployed at the application layer on top of the controller. In order to evaluate the proposed framework and architecture, a platform has been developed, which is called QoE-MoMa (QoE-Monitoring and Management) platform, making use of the Opendaylight solution and Mininet emulation environment. To evaluate QoE-MoMa, we focused on the video streaming service, whose final quality has been evaluated using the estimated MOS (eMOS) model that mostly considers rebuffering events, duration of the rebuffering, switch quality rates, video resolution, and quantization parameter. The results show the efficiency of the proposed approach observing that higher QoE level is achieved if we consider application and network parameters. In conclusion, we consider that QoE-MoMa is useful as a QoE monitoring and management tool for a variety of services and can be deployed on a real network conveniently.

Future networks will be accompanied by new heterogeneous requirements in terms of end-users Quality of Experience (QoE) due to the increasing number of application scenarios being deployed. Network softwarization technologies such as Software Defined Networks (SDNs) and Network Function Virtualization (NFV) promise to provide these capabilities. In this paper, a novel QoE-driven resource allocation mechanism is proposed to dynamically assign tasks to virtual network nodes in order to achieve an optimized end-to-end quality. The aim is to find the best combination of network node functions that can provide an optimized level of QoE to the end users though node cooperation. The service in question is divided in tasks and the neighbor nodes negotiate the assignment of these considering the final quality. In the paper we specifically focus on the video streaming service. We also show that the agility provided by SDN/NFV is a key factor for enhancing video quality, resource allocation and QoE management in future networks. Preliminary results based on the Mininet network emulator and the OpenDaylight controller have shown that our approach can significantly improve the quality of a transmitted video by selecting the best path with normalized QoS values.

In this paper, a Quality of Experience (QoE)-aware Smart Home Energy Management (SHEM) system is proposed. Firstly, a survey has been conducted on 64 people to investigate the degree of satisfaction perceived when the starting time of appliances was postponed or anticipated with respect to the preferred time. Secondly, the results were clustered in different profiles using the k-means algorithm to control appliances' working time according to the detected user profile. Thirdly, a SHEM system is run that relies on two algorithms: the QoE-aware Cost Saving Appliance Scheduling (Q-CSAS) and the QoE-aware Renewable Source Power Allocation (Q-RSPA). The former is aimed at scheduling controllable loads based on users' profile preferences and Time-of-Use (TOU) electricity prices, thus taking into account the level of annoyance perceived when a task is postponed or anticipated. The latter re-allocates the starting time of appliances whenever a surplus of energy has been made available by Renewable Energy Sources (RES). This re-allocation takes place using a distributed max-consensus negotiation algorithm. The objective is that of scheduling the appliances starting time so that a trade-off between cost saving and annoyance perceived is achieved. As demonstrated by simulation results, the two algorithms ensure a cost saving that goes from 19% to 84% depending on the presence of RES, with a resulting average annoyance factor value of 1.01 to 1.03.

Energy usage optimization in Smart Homes is a critical problem: over 30% of the energy consumption of the world resides in the residential sector. Usage awareness and manual appliance control alone are able to reduce consumption by 15%. This result could be improved if appliance control is automatic, especially if renewable sources are present locally. In this paper, a Smart Home Energy Management system that aims at automatically controlling appliances in groups of smart homes belonging to the same neighborhood is proposed. Not only is electric power distribution considered, but also renewable energy sources such as wind micro-turbines and solar panels. The proposed strategy relies on two algorithms. The Cost Saving Task Scheduling algorithm is aimed at scheduling high-power controllable loads during off-peak hours, taking into account the expected usage of the non-controllable appliances such as fridge, oven, etc. This algorithm is run whenever a new need of energy from a controllable load is detected. The Renewable Source Power Allocation algorithm re-allocated the starting time of controllable loads whenever surplus of renewable source power is detected making use of a distributed max-consensus negotiation. Performance evaluation of the algorithms tested proves that the proposed approach provides an energy cost saving that goes between 35% and 65% with reference to the case where no automatic control is used.

The realization of the Internet of Things (IoT) paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. In order for objects to dynamically cooperate to IoT applications' execution, they need to make their resources available in a flexible way. However, available resources such as electrical energy, memory, processing, and object capability to perform a given task, are often limited. Therefore, resource allocation that ensures the fulfilment of network requirements is a critical challenge. In this paper, we propose a distributed optimization protocol based on consensus algorithm, to solve the problem of resource allocation and management in IoT heterogeneous networks. The proposed protocol is robust against links or nodes failures, so it's adaptive in dynamic scenarios where the network topology changes in runtime. We consider an IoT scenario where nodes involved in the same IoT task need to adjust their task frequency and buffer occupancy. We demonstrate that, using the proposed protocol, the network converges to a solution where resources are homogeneously allocated among nodes. Performance evaluation of experiments in simulation mode and in real scenarios show that the algorithm converges with a percentage error of about±5% with respect to the optimal allocation obtainable with a centralized approach.

Nodes in Wireless Sensor Networks (WSNs) are becoming more and more complex systems with the capabilities to run distributed structured applications. Which single task should be implemented by each WSN node needs to be decided by the application deployment strategy by taking into account both network lifetime and execution time requirements. In this paper, we propose an adaptive decentralised algorithm based on noncooperative game theory, where neighbouring nodes negotiate among each other to maximize their utility function. We then prove that an increment of the nodes utility corresponds to the same increment of the utility for the whole network. Simulation results show significant performance improvement with respect to existing algorithms.

We consider the scenario of a Wireless Sensor Networks (WSN) where the nodes are equipped with a programmable middleware that allows for quickly deploying different applications running on top of it so as to follow the changing ambient needs. We then address the problem of finding the optimal deployment of the target applications in terms of network lifetime. We approach the problem considering every possible decomposition of an application's sensing and computing operations into tasks to be assigned to each infrastructure component. The contribution of energy consumption due to the energy cost of each task is then considered into local cost functions in each node, allowing us to evaluate the viability of the deployment solution. The proposed algorithm is based on an iterative and asynchronous local optimization of the task allocations between neighboring nodes that increases the network lifetime. Simulation results show that our framework leads to considerable energy saving with respect to both sink-oriented and cluster-oriented deployment approaches, particularly for networks with high node densities and non-uniform energy consumption or initial battery charge.