In emergency scenarios such as earthquakes and floods, Internet-of-Things (IoT) systems composed of heterogeneous devices—including wearable medical sensors, ground gateways, and unmanned aerial vehicles (UAVs)—are increasingly used to collect and relay real-time vital signs to edge nodes for rapid analytics and to cloud services for long-term storage. Although clustering is widely adopted to organize UAV ad hoc networks for efficient IoT data routing, the failure of a cluster head (CH) can fragment the network and result in severe data loss. Existing routing protocols often prioritize latency reduction while overlooking CH continuity and reliability, which are critical in crisis-driven e-health applications. To address these challenges, this work proposes a multi-agent redundant clustering strategy that enhances CH availability while maintaining efficient data delivery. A weighted clustering metric is introduced, incorporating distance stability, reward-based stability, velocity stability, and energy stability to improve cluster formation. In addition, a redundant CH is pre-designated to seamlessly assume routing responsibilities in the event of a CH failure, ensuring uninterrupted data transmission. The proposed protocol is implemented and evaluated through MATLAB simulations and compared against the LEACH protocol. Simulation results demonstrate that the proposed approach improves packet delivery ratio and throughput by approximately 0.7-1.0% and enhances forwarding efficiency by about 0.8%, indicating more reliable and effective data forwarding. While a marginal increase in average end-to-end delay is observed, the delay remains within acceptable limits for e-health monitoring scenarios. Overall, the proposed redundant clustering strategy significantly improves routing reliability and data delivery performance, thereby enhancing the dependability of emergency e-health communication systems.

Ensuring secure and efficient surveillance in maritime border security is critical to addressing threats such as illegal trafficking, unauthorized vessel movements, and piracy. This paper presents a novel Autonomous Aerial Vehicle (AAV)-assisted surveillance framework that leverages QoS-aware edge computing to enhance real-time situational awareness, task offloading, and secure trajectory optimization. The proposed system integrates Twin-Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning for adaptive AAV trajectory planning, ensuring optimal coverage and minimal energy consumption. Enhanced Particle Swarm Optimization (EPSO) is also employed for intelligent task offloading, efficiently balancing computational workloads between AAVs and edge nodes. It is evaluated through simulations with real-world maritime surveillance scenarios, demonstrating reduced latency and improved energy efficiency compared to conventional surveillance and task management strategies.

Green communication and sustainable operations have become critical objectives in Information and Communications Technology (ICT) systems, particularly when integrating energy-intensive technologies such as Autonomous Aerial Vehicles (AAVs). Therefore, this paper introduces a dynamic resource allocation framework for AAV-enabled green edge networks that adaptively manages bandwidth and computational power while optimizing AAV trajectories. By explicitly formulating the problem as a Markov Decision Process (MDP) and employing Deep Reinforcement Learning (DRL) with Proximal Policy Optimization (PPO), the proposed system strikes a balance between high data synchronization demands and strict energy constraints, leading to improved throughput and sustainability. The simulation results reveal that this approach significantly boosts data throughput and communication efficiency while reducing energy consumption. These findings pave the way for environmentally responsible edge networks that meet both performance requirements and sustainability targets.

Multi-object tracking (MOT) is crucial for video analysis and various computer vision applications. Traditional MOT methods primarily rely on unidirectional trajectory prediction, which can be severely affected by occlusions and short-term object losses, leading to tracking failures or incorrect associations. A significant challenge in MOT is handling these issues while maintaining accurate and consistent tracking over long durations. To address this issue, we propose a novel Bidirectional Constraint-Driven Online Multi-Object Tracking (BCDTrack) method that improves long-term trajectory association. The proposed method performs forward and backward tracking on video sequences using a sliding window approach. In each window, forward tracking is executed first, followed by backward tracking, and the motion information is used to fuse the forward and backward trajectories. Furthermore, to ensure identity (ID) consistency during the fusion process, we design a trajectory fusion strategy that utilizes Kalman filter to perform forward and backward predictions. Extensive experiments and ablation studies on the MOT20 datasets demonstrate that the proposed approach significantly enhances long-term tracking performance, particularly in dynamic and occlusion-prone scenarios, offering superior robustness against object loss and tracking failures.

The proposed system known as Vision Transformer-Masked Autoencoder-Cheetah Optimization Algorithm (ViT-MAE-COA) uses image preprocessing techniques in addition to segmentation and classification features and hyperparameter optimization capabilities to classify Otitis Media. The framework starts by improving image quality through the Wiener filter (WF) that minimizes mean squared error between original images and noisy images for noise reduction purposes. The W-Net architecture processes segmented data to maintain essential localization data and content information through a strategy that decreases parameters with max pooling. The results indicate that the model exhibited better prediction accuracy than other Deep Learning models.

The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time monitoring, diagnostics, and secure data-driven decision-making. However, IoMT networks are vulnerable to adversarial attacks, data breaches, and privacy threats, making secure and optimized node deployment a critical challenge. This paper presents a novel framework integrating 3D Voronoi diagrams and K-means clustering with a hybrid Particle Swarm Optimization-Genetic Algorithm (PSOGA) to optimize IoMT node placement while enhancing security and resilience. Initially, K-means clustering distributes nodes, followed by spatial partitioning with 3D Voronoi diagrams. The PSO-GA hybrid algorithm then iteratively refines node positions, balancing rapid convergence with global exploration to achieve optimal configurations that improve coverage, energy efficiency, and secure data exchange. Additionally, the proposed approach integrates risk assessment techniques and privacypreserving mechanisms to mitigate adversarial threats, ensuring robustness against poisoning and evasion attacks. By dynamically adapting to changing healthcare environments, the framework enhances network resiliency while aligning with AI security and privacy-by-design principles. Experimental results validate the algorithm's scalability and effectiveness, making it a promising solution for real-world IoMT applications in secure medical monitoring, diagnostics, and AI-driven threat intelligence.

In Intelligent Transportation Systems (ITS), driver recognition presents challenges of data privacy, computational efficiency, and energy consumption. Optimizing energy use in ITS has become crucial with the rise of environmentally conscious Information and Communication Technology (ICT) practices. Therefore, this paper introduces a privacy-preserving and energy-efficient task offloading strategy using Deep Reinforcement Learning (DRL) and Federated Learning (FL) within a network leveraging Smart Traffic Cameras (STCs) for edge computing. Initially, the public transports employ a DRL-based strategy to offload tasks to STCs, optimizing network resources and minimizing energy use. At the second phase, allows private vehicles to train models locally, offloading only model parameters, thus ensuring data privacy and reducing communication energy costs. Finally, aggregates these parameters at a central cloud server, refining a Network-Wide Model (NWM). The proposed framework enhances model performance, preserves privacy, and improves computational efficiency, reducing the carbon footprint of ITS operations. Simulations demonstrate that DRL's Actor-Critic Algorithm (ACA) reduces task latency and energy consumption while FL ensures efficient model training with minimal communication overhead.

A swarm of unmanned aerial vehicles (UAVs) is a wireless network made up of UAVs cooperating to upload data to the internet. During critical scenarios like earthquakes, one or more UAV could get damaged. Clustering is one of the most dependable routing systems since the network is divided into clusters with a cluster head (CH) and cluster members (CM). Having a damaged CH results in a disconnected cluster and loss of valuable data. Ensuring a functional CH is a crucial topic that is rarely covered in routing schemes. Our proposed scheme targets ensuring a functional CH through multiple redundancies while redeucing the delays. This paper suggests an enhanced redundant routing scheme based on three weighted parameters, which are the distance stability, the rewarding stability, and the energy stability. The weighted calculation will result in a new metric named cluster index which will be utilized in CH and CMs selection. To guarantee connection stability, whenever the primary CH is not operating, a redundant CH handles the routing responsibilities. The proposed protocol was simulated using MATLAB. The results obtained demonstrated that our proposed scheme is promising in optimizing the cluster formation while reducing total delays and ensuring a functional CH and data delivery.

A UAV-based wireless network is made up of UAVs working together to upload data to the internet. During crisis scenarios like earthquakes or floods, UAVs are highly used in transmitting valuable health-related data. Basic physiological life-saving parameters like blood pressure, temperature, and respiration rate can be tracked by sensors built into UAVs. One of the most reliable routing techniques in UAVs is clustering, where the network is separated into clusters, each of which has a cluster head (CH) and cluster members (CM). Having a damaged CH results in a disconnected cluster and loss of data. Several routing schemes target reducing delays, but ensuring a functional CH is a topic rarely discussed. Our proposed scheme targets both reducing delays and ensuring a functional$\mathbf{C H}$through multiple redundancy and node type variation to ensure health data transmission. This paper suggests an enhanced redundant routing scheme based on four weighted parameters, which are the distance stability, the rewarding stability, the velocity stability, and the energy stability. The network will be formed of UAVs and vehicles to maximize the likelihood of a functional$\mathbf{C H}$. The weighted calculation will result in a customized metric utilized in cluster formation. To guarantee connection stability, whenever the primary$\mathbf{C H}$is not operating, a redundant$\mathbf{C H}$handles the routing responsibilities. The proposed protocol was simulated using MATLAB. The results obtained demonstrated that our proposed scheme is promising in optimizing the cluster formation while reducing total delays and ensuring a functional CH.

Network slicing has emerged as a transformative enabler for meeting the diverse requirements of 5G and beyond networks, including 6G. However, network slices’ dynamic and virtualized nature introduces significant security challenges, particularly against evolving cyber threats. We propose a Deep Reinforcement Learning (DRL)–based Moving Target Defense (MTD) strategy tailored for secure network slicing to address these challenges. Our approach utilizes a Q-Learning framework to manage MTD actions dynamically, optimizing security while maintaining service quality. Extensive simulations demonstrate the effectiveness of our framework in minimizing attack success rates and ensuring operational stability, significantly outperforming baseline methods such as random decision-making.

This paper addresses the challenge of device localization within a Wi-Fi network. It introduces a novel localization technique, using a pre-constructed map with predefined positions around an Access Point (AP). The Wi-Fi client devices (STAs), are positioned on the map utilizing the capabilities of the Fine Time Measurement (FTM) protocol (IEEE 802.11mc), and tracked using the Pedestrian Dead Reckoning (PDR) method, which leverages the device’s Micro-Electro-Mechanical Systems (MEMS). It also harnesses the capabilities of Neighbor Awareness Networking (NAN) technology, to discover the distances between Wi-Fi Aware certified devices. This method requires no additional equipment or prior setup, since one AP is sufficient to determine the STA’s position. The findings suggest that this approach provides a scalable and efficient solution for IoT applications.

Modern IoT and Fog environments are complex and diverse ecosystems that consist of numerous devices. Some of these devices can receive and process offloaded tasks. For such devices to operate at the highest capacity levels, there is a need for mechanisms that could optimize their performance with offloaded tasks. That includes, but is not limited to, such aspects as resource management, workload balancing and scheduling. Unlike local tasks, offloaded ones are not a part of device’s environment. Therefore, processing them should not irreparably disrupt a device’s functionality. This requires devices to have a mechanism for managing offloaded tasks differently from their local. The current work attempts to research possible ways to optimize in-device execution of offloaded tasks, while reducing detrimental effects to a device’s state. To achieve that, the solution involves application of Reinforcement Learning techniques. The work proposes to utilize Deep Deterministic Policy Gradient (DDPG) Actor/Critic method, to allow devices continuously learn optimal scheduling strategies for offloaded tasks. The contribution of this work is in its exploration of the impact machine learning makes on in-device scheduling, application feasibility and the overall execution time optimization of offloaded tasks.

Smart spaces integrate advanced technologies like the Internet of Things (IoT), Machine Learning, and Artificial Intelligence (AI) to enhance automation and control within various environments. Effective deployment of IoT nodes is crucial for maximizing coverage, minimizing costs, and ensuring network stability in these spaces. This paper presents a novel approach combining 3D Voronoi diagrams with a modified Genetic Algorithm (GA) to optimize IoT node placement in three-dimensional environments. The proposed method starts with node placement using a homogeneous Poisson Point Process (PPP) and partitions the space into Voronoi cells, followed by iterative adjustments using the modified GA. The method achieves a 15% improvement in coverage ratio, a 10% reduction in deployment effort, and a 20% increase in network stability compared to existing algorithms, with results statistically significant at 5%. Moreover, optimising sensor placements indirectly enhances network security by reducing redundant data paths and strengthening network resilience. This study provides a scalable, efficient solution for IoT network deployment in complex environments, addressing key challenges in smart space optimization and paving the way for more secure and robust IoT infrastructures.

The proliferation of virtual reality (VR) content within social networks amplifies the importance of reducing rendering delay, as seamless interactions in shared virtual spaces are crucial for fostering social connections. Integrating VR streaming with social networks requires innovative solutions to address the unique challenges arising from the interaction between immersive experiences and social interactions. In this context, our research focuses on minimizing rendering delay for VR streaming in social networks, leveraging the synergistic benefits of edge computing. However, in scenarios where end-users experience poor channel quality, the rendering delay is prolonged due to lower data rates. To this end, a double reconfigurable intelligent surface (RIS) is employed to assist in improving the channel efficiency for end-user devices located in weak signal reception zones. We formulate an optimization problem related to the rendering resource allocation in edge servers and the distribution of downlink bandwidth for VR content from the edge server as a quadratically constrained quadratic problem. The non-convex optimization problem has been solved by dividing the problem into three sub-problems and solved using the block coordinate descent (BCD) method. In this study, we aim to enhance the overall quality of VR experiences in social settings, paving the way for more compelling and interactive virtual interactions where end-users have low wireless channel reception Quality.

Swarm of UAVs (S-UAVs) is the term used to describe an unplanned gathering of un-manned aerial vehicles (UAVs) that collaborate to complete predefined missions. Any UAV has a risk of being damaged in crisis situations like fire. Clustering is one of the most often used routing algorithms in S-UAVs. The clustering scheme divides the UAVs into clusters, with a cluster head (CH) and cluster members (CM) in each cluster. The CH plays a critical role within inter-cluster communication, and because of this, its selection is an ongoing area of study. A clustered weighted scheme with redundancy and dynamic weight adjustment is proposed in this paper. The selection of the principal CH, redundant CHs, and CMs is based on a weighted formula composed of distance, speed, and rewarding index. Whenever the primary CH is damaged, the redundant CH takes its place immediately. After the first clustering process, the proposed scheme dynamically and autonomously adjusts the weights to optimize the UAV role selection. According to the outcomes of the carried-out simulation, this is a promising scheme that reduces data loss in a crisis-case scenario and optimizes the time delay through the dynamic adjustments of the weights.

Integrating Unmanned Aerial Vehicles (UAVs) into the emerging sixth-generation and beyond (6G+) cellular networks as aerial base stations represents a significant technological advancement. This integration offers numerous benefits, including widespread accessibility, enhanced navigation, and simplified monitoring and management. A key element of this integration involves the instantaneous distribution of vital information throughout the transportation infrastructure. Characterized by their agility, mobility, and flexibility, UAVs play a crucial role in relieving data traffic loads, thereby offering additional access points. This function is essential for making prompt, precise, and well-informed decisions in Intelligent Transportation Systems (ITS), utilizing data-centric insights. Deploying versatile Road Side Units (RSUs) for secure data collection and dissemination requires a robust framework for safe data transfer. Ensuring data governance in the Internet of Vehicles (IoV) network relies heavily on specific interactions between trusted parties. In response, we introduce an advanced encryption approach to promote secure data exchange in ITS, thus supporting the confidential transfer of information in IoV communications. This innovative encryption method can also perform encryption and decryption of ciphertexts, encompassing confidential data and facilitating secure communication.

This study presents an innovative approach to enhancing the efficiency of Unmanned Aerial Vehicles (UAV) in IoT networks. Employing UAVs as flying relays focuses on their role in data collection and support for terrestrial cellular networks. The central innovation lies in the application of Federated Learning (FL), which processes data while ensuring user privacy and reducing communication overhead. Addressing the challenge of UAVs’ limited battery life, which restricts sustained FL operations, we introduce the Enhanced UAV Network optimization Algorithm with Adaptive Spatial Play (ENUO-ASP). ENUOASP incorporates a modified Particle Swarm optimization (PSO) technique to determine optimal UAV placements, enhancing data collection by focusing on the Signal-to-Interference Ratio (SINR). Additionally, the paper utilizes the Deep Deterministic Policy Gradient (DDPG) method for dynamic resource allocation, optimizing energy consumption and reducing link latency between the UAV network and users. The findings indicate that the ENUO algorithm outperforms existing methods by achieving higher data rates and balanced SINR. Furthermore, the ASP resource allocation strategy improves FL execution, significantly lowering latency and energy use. This research contributes to the UAV-enabled communication field, offering a more efficient and performance-driven solution for advanced IoT applications.

The aim of this study is to investigate into $\mu \mathrm{AFL}$, a non-intrusive, feedback-driven fuzzing framework, evaluated on Cortex M4 embedded systems and Unix platforms, focusing on the STM32F407VE Cortex M4 microcontroller. By leveraging the SEGGER J-Trace Pro for trace collection, it demonstrates $\mu$AFL’s utility beyond its traditional scope, showcasing its efficacy in both embedded and general-purpose computing environments. Our analysis, enriched by juxtaposing $\mu$AFL’s capabilities with traditional AFL, emphasizes the adaptability and effectiveness of fuzzing methodologies in firmware security enhancement. Furthermore, the study provides a deep understanding of fuzzing execution on different hardware, presenting an execution strategy for the STM32F407VE that highlights the framework’s potential in identifying vulnerabilities, evidenced by tests on specific firmware programs such as an LED blinking program integrated with semihosting breakpoints and ETM tracing. The use of uninitialized memory sections and strategically placed break-points offers significant insights into the firmware’s execution flow. The results of our comparative analysis clearly show that $\mu \mathrm{AFL}$ excels at uncovering vulnerabilities, reinforcing the need for evolving fuzzing methodologies to build stronger security systems for embedded devices. This contribution underscores the importance of refining fuzzing techniques to meet the intricate security demands of contemporary computing environments.

This paper explores the integration of Quantum Communication Networks (QCNs), semantic communication, and covert communication within the context of 6G and future wireless networks. Introducing a new Quantum Semantic Communications (QSC) framework that capitalizes on advancements in quantum machine learning and semantic representations, this framework dramatically enhances resource efficiency in QCN s. It does so by embedding only relevant classical data into compact, high-dimensional quantum states for transmission, achieving a potential resource reduction of 50-75% while boosting quantum semantic fidelity. The paper further examines Artificial Intelligence's (AI) transformative role in upgrading tra-ditional communication paradigms into more efficient semantic communication systems, utilizing deep learning and end-to-end methodologies to ensure precise conveyance and interpretation of meanings in transmitted information. Additionally, it explores incorporating covert communication strategies within systems supported by a Reconfigurable Intelligent Surface (STAR-RIS) and Non-Orthogonal Multiple Access (NOMA), emphasizing the enhanced security and stealth necessary for modern networks. By merging these sophisticated communication strategies, the paper anticipates a new era of telecommunications that significantly surpasses existing security, efficiency, and semantic accuracy capabilities, marking a progressive step towards future networks optimized for secure, efficient, and meaning-focused communication in the quantum and AI era.

Security assurance is the capacity of any teleinformatic system to demonstrate that the system is secure. It is provided by testing the system by an independent laboratory. Such an evaluation gives to the customer certainty that the system or product is secure enough for the intended use. In this paper we discuss the security assurance that the automotive sector requires to the 5G network for the secure communication of the Intelligent Transport System applications. In this case, the automotive sector takes the role of the customer of the network operator that provides connectivity of cars, trucks, bicycles, pedestrians and traffic infrastructure for creating vehicle to everything platform. Concretely, in this paper we (1) show new methodologies for evaluating network security, (2) provide a new strategy for the customer (automotive company) to select the underlying system based on network assurance levels, (3) survey security functionalities of a network devoted to automotive applications and demonstrate how automotive applications, 5G network and physical infrastructure, cooperate for enhancing security of the end-to-end system, and (4) provide example security evaluation results at different assurance levels. The results show the necessity of providing different levels of network assurance during the certification process in order that the automotive customer will be able to select the best products and sub-systems that may demonstrate (assure) enough security to the complete system.

The three pillars of health data exchange, confidentiality, integrity and availability, pose a significant challenge to the efficiency and robustness of the healthcare ecosystem. By utilising fragmentation, sensitive attributes dissociate, and thus, data security can be enhanced, and data utility can be improved. Throughout this research, confidentiality was performed by deploying polynomials and Newton-Gregory's divided difference interpolation to enable encryption of confidential data values such as patients' IDs. The fragmentation technique was utilised to achieve integrity, and the utility method enabled end-user availability. Extensive evaluations show that the precision, recall, and Fl-score under different values of correlation index ϒ of the proposed methodology outperform state-of-the-art approaches. Also, a time complexity comparison for overhead tasks was implemented between these approaches.

A swarm of unmanned aerial vehicles (S-UAVs) consists of UAVs flying together with the target of accomplishing a certain task in a faster and more reliable way as compared to a single UAV. In a crisis scenario, UAVs have been widely used in rescue missions. Clustering is one of the most reliable routing schemes for S-UAVs. The UAVs are grouped into clusters with a cluster-head (CH) and cluster-members (CM). The CH plays a major role in clustering schemes as it handles all inter-cluster communication. In a crisis case, any UAV is at risk of getting non-functional, thus resulting in a disconnected cluster. This paper proposes a new clustering scheme based on K-means and weighted formulas. The K-means protocol is applied to generate pilot phase clusters. Afterward, whenever the metrics of the networks are established, the weighted formula is applied for cluster formation and CH selection. The weighted formula is based on the performance index, the relative movement, and the remaining energy. To ensure end-to-end communication despite CH non-functionality, our proposed protocol selects a redundant CH for every CH. This protocol had been simulated using MATLAB. The results obtained and analyzed towards the end of this paper demonstrate that the proposed scheme is very promising.

The endeavours to interpret the acquired data are combined with the efforts to strengthen the smart city's multidimensional framework. As the name implies, smart cities are built atop more intelligent data. However, it is a significant challenge because Big Data needs to be evaluated to provide interpretation for a posterior evolution of the current technology. Therefore, using the right building blocks is vital, aligned with clear and convincing guidelines on best practices. To achieve a scale of evaluation, we need standards. The intertwining development drivers need to define how we see and measure the world around us and how this Big Data in the era of IoT informs the decision-making processes. Hence, we are introducing an evaluation model for Big Data obtained from the assessment of Quality of Service (QoS) and Quality of Experience (QoE) delivery in an urban environment. Using the Best-Worst Method (BWM) combined with the orientation of Intuitionistic fuzzy sets. We obtained intuitive preference information based on various criteria. Thus, by prioritizing these end-user predilections and transmitting them into adaptable technological improvements, we achieved a significant step toward sustainable Smart Cities.

A spontaneous group of unmanned aerial vehicles (UAVs) is denoted as a swarm of UAVs (S-UAVs). The UAVs communicate wirelessly and cooperate to accomplish tasks. In crisis scenarios such as flooding or earthquakes, all UAVs are at risk of getting damaged and thus non-functional. A non- functional UAV will result in a disconnected network, especially if that UAV is highly responsible for packet forwarding. S- UAV s are dynamic networks; clustering is one of the most adopted routing schemes in S-UAVs. The clustering scheme groups the UAVs into clusters where each cluster is formed of a cluster head (CH) and cluster members (CMs). Only the CH can handle inter-cluster communication. Due to the crucial role played by the CH, its selection is a continuous field of research. This paper proposes an enhanced clustered weighted scheme with redundancy to ensure end-to-end communication. The proposed scheme is based on a weighted formula for the primary CH, redundant CH, and CMs selection. The weighted formula calculates a cluster index based on the distance, the speed, and the reward index. A new component is added to the reward index which is performance. The redundant CH is selected to automatically replace the primary CH whenever it is damaged. If the redundant CH becomes inoperable, the second redundant CH will take over. Each cluster is formed of n CMs and will have n-2 redundant CHs. The results obtained from the conducted simulation experiments concluded that this promising scheme decreases data loss in a crisis case scenario.

Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area.

As the fields of machine learning and computer vision are developing, facial recognition systems are becoming increasingly popular and are slowly being widely used in various fields like security, surveillance and medicine. This paper presents the design and development of a facial recognition solution that works in a distributed context, given that the devices used for capturing the images do not have the ability to train models capable of achieving good enough accuracy on large amounts of data. Thus, a method is presented in which the detection of human faces and the characteristics extraction are done locally based on a pre-trained FaceNet model. These characteristics are sent to a strong processing unit where a global model is trained and then transferred back to the clients, where it can be used for recognition. Through experimental evaluation, we show that our solution is efficient and exhibits high accuracy values.

Smart healthcare systems require secure and robust data computations for providing uninterrupted monitoring, recommendation, and assistance. Wearable sensor (WS) data sources serve as the prime aggregator for data handling. Considering the security demands in sensitive healthcare data, this article introduces a Definitive Privacy-Preserving Analytical Scheme (DP2AS). The proposed scheme exploits the data classification feature based on false positives and replication. The suggested method detects redundant data in healthcare by comparing open and secure aggregation scenarios. Classifying data features as either continuous or replicating helps prevent fraudulent data insertion. By employing tree classifiers, the data attributes are accounted for in different WS aggregation intervals preventing replications. The computations are independent of false data and application-specific computations, retaining the WS privacy. In this analysis process, the error-free/ false positive fewer data chunks are concealed with user adaptable security mechanism for preventing data poisonings. The analytical model considers the previous data state with the current processing data for avoiding erroneous interruptions. The state classffier’s maximum replication mitigation provides application-specific data transfers with fast computation possibility. The proposed scheme’s performance is analyzed using the metrics false rate, data utilization, and analysis time.

Sixth-generation networks and vehicular Ad hoc networks advancement brought us to the cusp of a new era. Autonomous Vehicles with a plethora of advanced applications require a substantially enhanced vehicle-to-everything communication network. The infrastructure should efficiently support hyper-fast, ultra-reliable, and low-latency massive data exchange. Roadside units were initially exploited as a promising communication solution to overcome this challenge. However, the challenging integration with the infrastructure led to the investigation of additional solutions. Unmanned aerial vehicles are one of the most dominant assistive solutions due to their inherent advantage of mobility. Moreover, air-to-air and ground-to-air networks are more efficient than ground-to-ground. Nevertheless, it is a prerequisite to leverage the potential of unmanned aerial vehicles to attain nationwide Vehicle-to-Everything connectivity. Therefore, we focused our research orientation on developing a strategy to optimize the network’s coverage within the intelligent transportation systems framework. In particular, we have deployed an innovative algorithmic technique that constructs Voronoi diagrams using circles. Besides, we applied the poison point process to determine the optimum locations of the transceivers’ establishment. Simulation results illustrate full network coverage for the tested area after the required iterations. Also, time complexity evaluation proved the simplicity of the proposed algorithm.

End-users under poor wireless network coverage generally suffer from underutilization of bandwidth. This adversely affects the overall performance of task offloading to the edge server. In this work, we study a Reconfigurable Intelligent Surface (RIS)-assisted wireless network that enables end-user's devices under weak signal reception areas to enhance their offloading opportunities for delay minimization. It becomes a challenging task to allocate uploading bandwidth allocation for the offloaded tasks from end-user devices under different signal coverage areas. We formulate the optimization problem of bandwidth allocation for the offloaded tasks in the edge server and the offloading decisions as a quadratically constrained quadratic problem. We exploit a semi-definite relaxation (SDR) method to solve the problem. Moreover, during optimization, we minimize the adverse impact of bandwidth allocation for poor end-users on good end-users performance. From extensive simulation results, we show remarkably elevated improvement in delay reduction with RIS assistance compared to other baselines, increasing the number and ratio of end-user devices under good and poor signal reception areas.

A swarm of UAVs (S-UAVs) is a spontaneous wireless network composed of unmanned aerial vehicles (UAVs) cooperating together to upload data to the internet. In crisis cases such as flooding or earthquakes, one or more UAVs might be non-functional, resulting in a disconnected network. Due to the continuous changes in S-UAVs, one of the most reliable routing schemes is clustering. Clustering scheme splits the network into clusters with a cluster head and cluster members. The cluster head is responsible for all intra-cluster communication, which consists of communication between the clusters. The selection of the cluster head has a high impact on the performance of the routing scheme. This paper proposes a new clustered weighted scheme with redundancy to ensure end-to-end communication even with some non-functional UAVs. The proposed protocol has a new weighted formula for the primary cluster head selection. In addition, the proposed protocol selects the secondary cluster heads based on an elimination process. The parameters of the weighted formula are the distance, the speed, and the rewarding index. The rewarding index is calculated based on the latency. This protocol proposes the presence of a secondary cluster head that automatically replaces the primary cluster head whenever it is non-functional, thus ensuring the stability of the connection. The proposed scheme is simulated using MATLAB. The results obtained are discussed towards the end of this paper. The obtained results conclude that this is a promising protocol to decrease data loss in a crisis case scenario.

Since the outbreak of the COVID-19 pandemic, indoor air quality has become increasingly important. The interdisciplinary grouping of academic majors focused on the pursuit of solutions that identify or prevent the airborne transmission and inhalation, initially of Coronavirus and secondarily of viruses such as influenza. Throughout the research work, we aim to contribute by elaborating the teaching-learning technique to select and identify the optimal attributes of viruses’ variants of the indoor atmosphere. The novelty is based on the objective to enable real-time identification of the density of the airborne molecules to prevent virus propagation. Several sensors and systems came into the spotlight by conducting a systematic literature review that, in conjunction with our innovative idea, could construct a revolutionary new solution that could eliminate the risk of exposure to viable viruses. The proposed teaching-learning based attribute selection optimisation is among the most popular bio-inspired meta-heuristic methods. Therefore, evolutionary logic and provocative performance can be widely utilised to solve the aforementioned humanitarian problem. The proposed frame constitutes three pivotal steps: the new update mechanism, the novel method of selecting the principal teacher in the teacher’s phase, and the support vector machine method to compute the fitness function of optimisation.

Unmanned aerial vehicles (UAVs) which are commonly known as drones are highly reliable in data transportation, especially whenever terrestrial access is unavailable due to geographical limitations. Several UAVs might collaborate to perform tasks in several applications such as rescue, military, and others. This collection of UAVs is known as a swarm of unmanned aerial vehicles. Drones can be used as aerial relays in complex communication scenarios thus establishing a reliable end-to-end transmission. Wireless Sensor Network (WSN) consists of sensor nodes and base stations. The sensors of WSN are widely dispersed in predefined geographical fields. These sensors might be used for continuous monitoring for example temperature or might be used for event triggers example whenever the temperature exceeds a certain threshold an alarm is fired. In a crisis case scenario, the base station which acts as a relay to upload the sensed data to the internet might be nonfunctional thus resulting in a disconnected network and loss of sensed data. This paper proposes the usage of a swarm of UAVs to upload data from the WSN network to the internet whenever the base station is nonfunctional. This paper will study the mathematical correlation between the parameters of this network taking into consideration the constraint to avoid collisions between the UAVs. A simulation using MATLAB studies the effect of the number of levels in the WSN, the range of communication, UAV’s velocity, and UAv’s trajectory on the network performance.

In this paper, we study the deadline-aware task data offloading in edge-cloud computing systems. The hard-deadline tasks strictly demand to be processed within their delay deadline, whereas the deadline can be relaxed for the soft-deadline tasks. Generally, edge computing aims to shorten the transmission delay between the remote cloud and the end-user, however, at the cost of limited computing capability. Therefore, it is challenging to decide where to offload the hard- and soft-deadline tasks based on the average delay and the service price set by the edge and cloud servers. Both edge and cloud servers aim to maximize their revenue by selling the computational resources at the optimal price. Interestingly, a Wardrop equilibrium is reached, considering that each task is considered independently to be offloaded to a suitable location. The numerical results demonstrate that the proposed price- and deadline-sensitive task offloading policy reaches the equilibrium and finds the optimal location for processing while maximizing the revenue of both edge and cloud servers.

The implementation of a variety of complex and energy-intensive mobile applications by resource-limited mobile devices (MDs) is a huge challenge. Fortunately, mobile edge computing (MEC) as a new computing paragon can offer rich resources to perform all or part of the MD’s task, which greatly reduces the energy consumption of the MD and improves the quality of service (QoS) for applications. However, offloading tasks to the edge server is vulnerable to attacks such as tampering and snooping, resulting in a deep learning (DL) security feature developed by major cloud service providers. An effective security strategy method to minimize ongoing attacks in the MEC setting is proposed. The algorithm is based on the synthetic principle of a special set of strategies, and it can quickly construct suboptimal solutions even if the number of targets achieves hundreds of millions. In addition, for a given structure and a given number of patrollers, the upper bound of the protection level can be obtained, and the lower bound required for a given protection level can also be inferred. These bounds apply to universal strategies. By comparing with the previous three basic experiments, it can be proved that our algorithm is better than the previous ones in terms of security and running time.

According to the current unprecedented pandemic, we realise that we cannot respond to every contagion novel virus as fast as possible, either by vaccination or medication. Therefore, it is paramount for the sustainable development of antiviral urban ecosystems to promote early detection, control, and prevention of an outbreak. The structure of an antivirus-based multi-generational smart-city framework could be crucial to a post-COVID-19 urban environment. Humanitarian efforts in the pandemic's framework deployed novel technological solutions based on the Internet of Things (IoT), Machine Learning, Cloud Computing and Artificial Intelligence (AI). We aim to contribute by improving real-time detection using data mining in collaboration with machine learning techniques through our research work. Initially, for detection, we propose an innovative system that could detect in real-time virus propagation based on the density of the airborne COVID-19 molecules-the proposal based on the detection through the isothermal amplification RT-Lamp [1]. We also propose real-time detection by spark-induced plasma spectroscopy during the internal airborne transmission process [17]. The novelty of this research work, called characteristic subset selection, is based on identifying irrelevant data. By deducting the unrelated information dimension, machine learning algorithms would operate more efficiently. Therefore, it optimises data mining and classification in high-dimensional medical data analysis, particularly in effectively detecting COVID-19. It can play an essential role in providing timely detection with critical attributes and high accuracy. We elaborate the teaching-learning method optimisation to achieve the optimal set of features for the detection.

Flying Ad hoc Network (FANET) is the result of Unmanned Aerial Vehicles (UAVs) that collaborate to perform tasks in several applications such as rescue, military, and others. The unmanned aerial vehicle which is also known as a drone improves routing as the line-of-sight probability is increased due to its three-dimensional movement capability. Drones can be used as aerial relays in complex communication scenarios. Routing protocols are necessary for FANETs to establish communication between UAVs. Various routing protocols have been reported for FANETs targeting to find the routing protocol with minimal overhead while establishing a reliable end-to-end transmission. Wireless Sensor Network (WSN) consists of sensor nodes and base stations. The wireless sensors collect different types of information for their surroundings. Then, this information is uploaded to the internet through the base station. In a crisis case scenario, the base station might be nonfunctional resulting in a data upload failure. This paper proposes to use the UAV as an aerial base station to compensate for the nonfunctional terrestrial base station. As delay is one of the major metrics in routing protocols this paper targets to study theoretically the transmission time of packets traveling from the sensors in the WSN to the UAV. Besides, a simulation had been carried using MATLAB to study the total transfer time with respect to several network variables such as the coverage radius, the number of levels in WSN, the angle of propagation of the UAV, and the UAV's speed.

Coronavirus disease 2019 (COVID-19) is currently the most crucial emerging virus in the world. The absence of licensed medication or vaccination leads to alternative strategies. A fundamental response plan implemented by all countries was the detection and isolation of infected cases. Contact tracing of infected citizens and testing every suspected case is a prerequisite to avoid new quarantine measures. Infected cases called ‘Orphan cases’ with no epidemiological connection are more worrying. The initial method to identify them should be knowing the probability for a citizen to be infected, given that presents specific symptoms, to be tested as a suspected case and not as random. This article proposes a cloud-based identification system that studies suspected cases to increase the likelihood that a positive result is correct. Also, it introduces an innovative solution to prevent and control the further spread of Corona-virus disease based on smartphones through the deployment of cutting-edge computing systems in the framework of a Naive Bayesian Network (NBN). Furthermore, the integration of Google Maps could provide geolocation risk assessment and early inferences to government health authorities to raise the test rates in risk- prone areas.

In this paper, we propose a hybrid OFDM-SNM scheme, named joint-mapping OFDM-SNM (JM-OFDM-SNM), to avoid transmitting variable lengths of information bits. In JM-OFDM-SNM, the signal vectors are generated by jointly considering subcarrier activation patterns and constellation symbols. To relieve the high computational complexity of the optimal maximum-likelihood (ML) detection, we design a low-complexity detection method via resorting to the log-likelihood ratio criterion. We also analyze the upper bound on the bit error rate of JM-OFDM-SNM. To further enhance the utilization of frequency resource, we propose a more general scheme, named adaptive JM-OFDM-SNM (AJM-OFDM-SNM), to accommodate the constellation orders for different numbers of activated subcarriers. Simulation results show that AJM-OFDM-SNM achieves better performance than both JM-OFDM-SNM and OFDM-SNM at the same spectral efficiency. The low-complexity detection method of JM-OFDM-SNM achieves very close performance to the optimal ML detection, and the theoretical curves well match the simulation curves in the high signal-to-noise ratio region.

Significant advances in wireless detection, networking, and IoT technologies presuppose network security and confidentiality demand. Therefore, we develop a novel text encryption framework that has provable security against attacks on cryptosystems. The framework is based on fundamental mathematics and specifically on the Pell-Lucas sequence in conjunction with elliptic curves. We elaborate the plain text by the implementation of three basic steps. Initially, by applying a cyclic shift on the symbol set, we obtain a meaningless plain text. After that, we conceal the elements of the scattered plain text from the adversaries by using the Pell-Lucas sequence, a weight function, and a binary sequence. The binary sequence encodes each component of the diffused plain text into real numbers. In the final step, the encoded scattered plain text is confused by creating permutations over elliptic curves. We then prove that the proposed encryption framework has provable security against brute-force and known-plaintext attack. It is also extremely secure compared with fundamental spacing analysis.

This research analyses new approaches to security enforcement in fifth generation (5G) architecture from end to end perspective. With the aim of finding a suitable and effective unified schema across the different network domains, it shows that policy control framework may become the cornerstone for the definition and enforcement of security policies in new 5G networks. The 5G core network architecture reference model is defined as a Service Based Architecture (SBA). The Policy Control Function (PCF) is a Network Function (NF) that constitutes, within the SBA architecture, a unique framework for defining any type of policies in the network and delivering those to other control plane NFs. In previous generations the policy control approach has been restricted to Quality of Service (QoS) and charging aspects. In contrast, the 5G system is now based on a unified policy control scheme that allows to build consistent policies covering the entire network. By utilizing the unified 5G policy framework we have found an effective security enforcement schema flexible to create new security policies, and agile to react to the constantly changing environment, across the end to end architecture. Within this schema we have defined mechanisms to apply the QoS principles to security use cases. We have also set up the user plane security enforcement within the session management and established security policies. Finally we have made proposals to extend the network analytics to security analytics. Our overall vision is to consider security as a quality element of the network.

Modern Living, as we know it, has been impacted meaningfully by the Internet of Things (IoT). IoT consists of a network of things that collect data from machines (e.g., mobile devices) and people. Mobile application development is a flourishing tendency, given the increasing popularity of smartphones. Nowadays, users are accessing their desired services on the smartphone by means of dedicated applications as the latter offers a more customized and prompt service. In addition, companies are also looking to persuade users by offering interactive and effective mobile applications. Mobile application developers are using IoT to develop better applications. However, there is no generalized consensus on the selection of best architecture or even the most suitable communications protocols to be used on an IoT application development. Therefore, this article aims at presenting approaches, architectures, and methodologies relevant to the development of mobile IoT solutions.

Industry 4.0 is a promising evolution in the field of smart farming by improving the productivity and reducing human intervention to modernize agriculture. This smart paradigm incorporates different levels of the automation from cropping to production yield through sophisticated techniques. Different intelligent computing techniques and communication technologies are augmented with the industry paradigm for improving the efficiency of agriculture systems. This letter introduces information scheduling and optimization framework (ISOF) for optimizing the communication and information layer process in industry 4.0 architecture. Information scheduling and classification of agriculture information are optimized through this framework for reducing process latency and stagnancy. The control flexibility of a smart farm is determined using the latency and stagnancy at the end of yields. The classification part segregates information based on processing and completion time to reduce backlogs through offloading process. The advantage of this framework is that it inherits the advantages of Internet of Things (IoT) and edge computing (EC) technologies with interoperable feature to aid information processing, information classification, offloading, and periodic updates. The performance of the proposed framework is tested in a corn farm and some common metrics, such as delayed information, processing time, audit data, and information distribution, are analyzed for proving the reliability of the framework.

Over recent years the focus on the comprehensive health-care system in IoT has become increasingly important, which considers in many ways a significant concept to promote health-care. It plays a positive role in increasing the highlight of the issue of medical disadvantage that threatens the medical diagnosis. Medical image constitutes a crucial carrier of the patient's diagnosis information. It nonetheless is exposed to several kinds of noise through transmission, and storage, which leads to impeding the full diagnosis for the patient and a loss of its quality as a medical digital image. Noise is a key factor in decreasing the image quality of different sorts of medical images (X ray, CAT scan, and MRI). Many techniques have been applied for image de-noising. The Discrete wavelet transform which is regarded as the most recent and optimum technique. This paper has been presented four levels of a discrete wavelet transform for the removal of Gaussian noise from several medical images based on diverse wavelet family transforms and median filtering. The proposed method submitted admissible results with regard to removing noise from medical images. The performance evaluation of the proposed algorithm is done by measuring the values PSNR, MSD, and NC.

In this paper, we study the priority-aware task data offloading in edge computing-assisted healthcare service provisioning. The edge server aims to provide additional computing resources to the end-users for processing the delay-sensitive tasks. However, at the same time, it becomes a challenging issue when some of the tasks demand lower response time compared to the other tasks. We present a priority-aware task offloading and scheduling strategy that allocates the computing resources to the high-priority tasks. The hard-deadline tasks are processed first. Later, the remaining computing resources are used to tolerate longer average response time for the soft-deadline tasks. Moreover, we derive a lower bound of the average response time for all hard- and soft-deadline tasks. Through extensive simulations, we show that the proposed task scheduling manages to allocate the computing resources of both end-users and edge server to the hard-deadline tasks while scheduling the soft-deadline tasks with low priority.

In fog computing, end-users can offload the computation-intensive tasks to the fog node in the proximity. Additionally, the fog nodes also offload these tasks to the cloud and neighboring fog node to seek additional computational resources. In this paper, we propose an offloading strategy in fog computing to minimize the cost that is a weighted sum of energy consumption and total delay for the task processing per end-user. We take the heterogeneous nature of the fog computing nodes that have different CPU frequency to process the tasks. We aim to find an optimal amount of task data to be either locally processed or offloaded to the preferable fog node and the remote cloud under the energy and delay constraints. We then formulate the optimization problem into a non-convex quadratically constrained quadratic program. We further provide an efficient solution to this problem by semidefinite relaxation. Finally, our proposed offloading scheme is evaluated by the simulation to demonstrate the offloading profile and optimal cost of the offloading with a wide range of parameter settings.

Breakdown or interruption of communication infrastructure is one of the most immediate and significant impacts of natural disasters. This paper is devoted to the modeling of the successful uplink in Vehicular ad-hoc networks (VANET) in disaster cases where vehicles encounter disrupted communication. The packets' size to be transmitted, the vehicles' speed, the infrastructure coverage range, the disconnected distance, and the number of copies sent have a strong influence on the probability of success for uploading the packets. A simple connectionless retransmission scheme is proposed where several copies of the same packet are transmitted to make sure that the vehicle will upload successfully at least one copy of the packet. The optimum number of retransmission and the choice of inter-frame gap required between successive copy packets are also found. In this respect, this paper studies the mentioned parameters' effect on the probability of success for the uplink and the throughput. MATLAB was used to run a simulation and validate the theoretical analysis.

VANET is a spontaneous creation of a wireless network among vehicles to exchange data. Clustering is one of the most common networking protocols for data propagation in those networks. The application of the clustering algorithm is effective in VANET because the algorithm makes it a more robust and scalable network. However, due to the high mobility of nodes, it is difficult to obtain stable clusters. One of the major challenges in clustering is the cluster head (CH) election since the CH has a critical role in data routing. For stable cluster formation in VANET, some constraints such as vehicles' velocity and vehicles' separation distance must be considered while selecting the CH. This paper proposes a new clustering algorithm based on a weighted formula for calculating the probability of cluster head selection. The weighted formula is based on three parameters: the trust, the distance, and the velocity. The trust is a newly added metric that is calculated by each vehicle and broadcasted to all neighbors. Whereas, the distance and the velocity are previously treated by other papers.

Continuous service provision, minimization of energy consumption and improvement of the provided security are the three fields that are tormenting sensors in a smart home. In the current work we propose and evaluate a model that continue to provide its services in a smart home environment even in the case of a fault. Our proposed solution achieves high energy efficiency and guarantees minimum security specifications that describe a reliable smart home model by utilizing blockchain.

The use of Social Network Centrality alongside Content Centric Networking is used towards improving user's access to content, as well as maximizing the efficient exploitation of network resources. This work demonstrates how online Social Network Centrality can be used with content centric solutions for efficient content distribution in AAL environments. The benefits of this approach are highlighted, by conducting simulations and providing results on the performance of the proposed research approach. In particular, this work shows how the proposed resource allocation method improves the regular IP-based content delivery allocation in the distribution of content. Facebook data is used as a super-set of AAL data, in order to map users to a network graph and using their social proximity to determine where content can be effectively cached. The efficiency of the proposed scheme is validated for its performance through simulations, indicating the level of the offered efficiency in contrast to content distribution.

Security requirements applicable to the Internet of Things (IoT) should aim to ensure integrity, authenticity and authorization, confidentiality/privacy, nonrepudiation, and last but not least, availability. Classic data analysis algorithms are no longer valid for assuring security at all levels and a new approach to data sciences is required, which would consider the complex heterogeneous nature of the IoT, taking also into consideration, its potential to deploy cross layer for security assessment mechanisms. Furthermore, data collected from sensors should be processed and analyzed nearly in real time. The classical algorithms have two main drawbacks: 1) they deal with unidimensional data and 2) they fail to assume limited information available in the stream data processing. In this article, new solutions are discussed and presented that detect anomalies in data streams nearly in real time. Specifically, we propose: 1) an event detection method used in unidimensional data streams and relying on the event strength function, which is an extension of the typical “True or False” decision-making scheme; 2) multiple-criteria event detection approaches based on the Dynamic Pareto Set, introducing a time-depending decision set; and 3) an anomaly detection method based on multicriteria temporal graphs, combining the dynamics of decision making and multicriteria. All the proposed algorithms are presented by means of their formal description and are illustrated with examples.

With the emergence of the Internet of Things (IoT), billions of heterogeneous physical objects are connected through a network for collecting and sharing information, which can improve various aspects of daily lives, including smart living and transportation, and smart ambient environment, including smart city, smart home, smart agriculture, smart water, waste management, etc. The main objective of the IoT devices is to provide seamless services to the users without their intervention. The all-connected paradigm (i.e., connecting people, things, processes, and data in the network) is based on near Internet ubiquity and includes three types of communication: 1) machine-to-machine; 2) person-to-machine; and 3) person-to-person, and consists as the base for reliable services provision to the end devices at the edge. Fog paradigm implements effectively and efficiently the all-data requests to be shared in a reliable manner as fog implementations complement the cloud computing paradigm by extending computing and caching capabilities to the edges of the network, and it facilitates smart localization decisions and rapid responses. The wide range of IoT services calls for a disruptive, highly efficient, scalable, and flexible communication network able to cope with the increasing demands and the number of connected devices, as well as the diverse and stringent application requirements.

As the industrial sector is becoming ever more flexible in order to improve productivity, legacy interfaces for industrial applications must evolve to enhance efficiency and must adapt to achieve higher elasticity and reliability in harsh manufacturing environments. The localization of machines, sensors and workers inside the industrial premises is one of such interfaces used by many applications. Current localization-based systems are unable to deal with highly variable conditions, meaning that the solutions working well in stationary systems suffer from considerable difficulties in harsh environments, such as factories. As a result, the precision of localization techniques is not satisfactory in most industrial applications. This paper fills in the existing gap between static approaches and dynamic indoor positioning systems, by presenting a solution adapting the system to highly changeable conditions. The proposed solution makes use of a Machine Learning-based feedback loop that learns the variability of the environment. This feedback makes continuous fingerprint calibration feasible even in the presence of different machines and Industrial Internet of Things sensors that introduce variations to the electromagnetic environment. This paper also presents a comprehensive indoor positioning system solution that reduces complexity of hardware, meaning that a multi-standard-transceiver infrastructure may be adopted with reduced capital and operational expenditures. We have developed the system from scratch and have conducted an extensive range of testbed experiments showing that the multi-technology transceiver feature is capable of increasing positioning accuracy, as well as of introducing permanent fingerprints calibration at harsh industrial premises.

Fog computing leverages the computational resources at the network edge to meet the increasing demand for latency-sensitive applications in large-scale industries. In this article, we study the computation offloading in a fog computing network, where the end users, most of the time, offload part of their tasks to a fog node. Nevertheless, limited by the computational and storage resources, the fog node further simultaneously offloads the task data to the neighboring fog nodes and/or the remote cloud server to obtain the additional computing resources. However, meanwhile, the offloaded tasks from the neighboring node incur burden to the fog node. Moreover, the task offloading to the remote cloud server can suffer from limited communication resources. Thus, to jointly optimize the amount of tasks offloaded to the neighboring fog nodes and communication resource allocation for the offloaded tasks to the remote cloud, we formulate a latency-driven task data offloading problem considering the transmission delay from fog to the cloud and service rate that includes the local processing time and waiting time at each fog node. The optimization problem is formulated as a quadratically constraint quadratic programming. We solve the problem by semidefinite relaxation. The simulation results demonstrate that the proposed strategy is effective and scalable under various simulation settings.

In this work the problem of energy conservation in wireless Internet of Things (IoT) devices is being addressed for machine-to-machine (M2M) communication. IoT connected devices (i.e. glasses, set-top-boxes, home appliances etc.) can affect the energy levels of the IoT ecosystem and can play an active role in the level of QoS/QoE provided to the end-users, for any service demands, on-the-move. To this end, this work proposes a novel offloading methodology that hosts a “resource-aware” recommendation scheme, which allows the efficient monitoring of energy draining applications that run in an IoT ecosystem. The proposed framework allows users to have a continuous on-demand service provision where devices can actively provide the available resources to be exploited in the IoT ecosystem. Considering the latter, this work utilises an Edge-based Computing offload mechanism in M2M communication for resource-aware recommendation. The work assesses the proposed framework in the context of (i) the offered reliability for IoT services by assistive recommendation scheme and (ii) the energy conservation for a number of devices, forming the IoT ecosystem during the offloading process.

Efficient image transfer in the Internet of Things (IoT) Era, has many requirements that need to be satisfied based on the nature of each underlying application. Internet of Things is a research paradigm that empowers data, data fusion and encompasses embedded images, which need to be transmitted to numerous interconnected devices. The risks of contravention of owner's rights are increasing, while data transfer creates new demands for securing the Internet of Things. In this respect, watermarking schemes can be used to save those rights from illegal usage and copying of digital image data. For IoT applications digital watermarking can be used to guarantee that the data used is protected and the rights of the owner are secured (i.e. both user and machine generated). This work proposes a novel watermarking scheme based on the biorthogonal family, (biorthogonal 2.2, biorthogonal 3.5 and biorthogonal 5.5) wavelet transform, while it uses a convolution for symlets wavelet transform and coiflets wavelet transform. These wavelet family approaches are highly robust against various types of attacks (both passive and active), for the prevention of the piracy and authentication of the data over IoT ecosystems. The proposed framework is thoroughly evaluated showing great robustness against attacks and allowing a higher level of protection compared to other available frameworks and schemes.

The low weight of fetus at birth is considered one of the most critical problems in pregnancy care, affecting the newborn's health and leading it to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict problems related to the fetus' health state during entire gestation, including at birth. Hence, this paper proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The results show that the hybrid model, named bagged tree, achieved excellent results concerning accuracy and area under the receiver operating characteristic curve, to know, 0.849 and 0.636, respectively. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.

Computational offloading becomes an important and essential research issue for the delay-sensitive task completion at resource-constraint end-users. Fog computing that extends the computing and storage resources of the cloud computing to the network edge emerges as a potential solution towards low-latency task provisioning via computational offloading. In our offloading scenario, each end-user will first offload the task to its primary fog node. When the primary fog node cannot meet the tolerable latency, it has the possibility to offload to the cloud and/or assisting fog node to obtain extra computing resource to shorten the computing latency at the expense of additional transmission latency. Therefore, a trade-off needs to be carefully made in the offloading decision. At the same time, in addition to the task data from the end-users under its primary coverage, the primary fog node receives the tasks from other end-users via its neighbor fog nodes. Thus, to jointly optimize the computing and communication resources in the fog node, we formulate a delay-sensitive data offloading problem that mainly considers the local task execution delay and transmission delay. An approximate solution is obtained via Quadratically Constraint Quadratic Programming (QCQP). Finally, the extensive simulation results demonstrate the effectiveness of the proposed solution, while guaranteeing minimum end-to-end latency for various task processing densities and traffic intensity levels.

The high proliferation of on-line gaming along with the high demands of availability of network resources, created the need for the development of Cloudified services that will augment computation capabilities of mobile devices. To this end, this work elaborates on the design, the development and the comparative evaluation with other similar models, as well as with real-time comparisons through emulators, of a process-offloading scheme that is based on a mobile opportunistic cloud computing approach. According to the proposed approach, each mobile device with access to interactive -delay sensitive- multimedia content (i.e. online gaming with processing power requirements) exploits several network-centric parameters, by using Nano-Mobile Data Centers for an interactive, collaborative and real-time manipulation of the available resources. The communication and the social context is used by the mobile nodes with other communication related parameters, towards achieving the efficient execution of the offloading process in order to support adequate quality of service. The proposed scheme allows interactive mobile users to efficiently exploit their resources, while the processes that cannot be locally handled (by each device), are effectively offloaded. The scheme aims at prolonging the lifetime of each mobile device and maximizing the efficiency in running context interactive applications. The efficiency of the proposed scheme is validated through comparative performance evaluations with other similar schemes, indicating the level of the mobile nodes lifetime extensibility that is offered, in contrast to existing approaches.

Internet of Things (IoT) is a set of massively emerging technologies that aim to make our lives better. One of the most suitable application domains is healthcare and wellbeing, where IoT technologies can enhance living environments (ELE). IoT applications are deployed into an IoT ecosystem, which is supported by a specific architecture. The usual IoT architecture model has three different layers (Acquisition, Network and Application), where all the different IoT elements are organized. This architecture has been designed for general purposes, but the deployment of IoT healthcare applications supported by this architecture model exhibits important drawbacks. In particular, this architecture model does not consider IoT devices coexisting with wearables devices, which hinders the design of smart habitats. In this paper we present an IoT-aware architectural model for smart habitats with special emphasis on healthcare domain. The new architecture adds a fourth layer to the traditional IoT architecture, describing the software artefacts and tips needed to support context-aware habitats through the interaction between wearable computing devices and IoT devices. The main goal of the IoT architecture presented is to enhance living environments for elderly people.

Mobile cloud computing is being accepted as the model for mobile users to ubiquitously access a shared pool of cloud computing resources, data and services on-demand. In this context, elasticity debt analytics can be harnessed as a measure for efficient scheduling of cloud resources and guarantee of quality of service requirements. This paper proposes a novel green-driven, game theoretic approach to minimizing the elasticity debt on mobile cloud-based service level, investigating the case when a task is offloaded, scheduled and executed on a mobile cloud computing system. The decision to offload a mobile device user's task on cloud affects the level of elasticity debt minimization for the provided services. The research problem is formulated as an elasticity debt quantification game, elaborating on an incentive mechanism to: (a) predict elasticity debt and mitigate the risk of service overutilization, (b) achieve scalability as the number of mobile device user requests for cloud resources increases or decreases accordingly, and (c) optimize cloud resource provisioning, parameterizing the current pool of active users per service. The experimental results prove the effectiveness of the equilibrium model, which allocates the mobile device user requests to high elasticity debt-level services and facilitate elasticity debt minimization for greener mobile cloud computing environments.

This paper presents the Dynamic Adaptive Streaming over CoAP (DASCo), a solution for adaptive media streaming in the Internet of Things (IoT) environment. DASCo combines DASH (Dynamic Adaptive Streaming over HTTP), the widespread open standard for HTTP-compliant streaming, with Constrained Application Protocol (CoAP), the vendor-independent web transfer protocol designed for resource-constrained devices. The proposed solution uses DASH formats and mechanisms to make media segments available for consumers, and exploits CoAP to deliver media segments to consumers’ applications. Consequently, the DASCo player offers native interoperability with IoT devices that are accessed via CoAP, thus it allows easy access to data collected by different sensors in order to enrich the multimedia services. In the paper we present an overview of constraints of default CoAP implementation with respect to media streaming, and propose guidelines for development of adaptive streaming service over CoAP. Moreover, we discuss the features of CoAP that can be investigated when designing an efficient adaptive algorithm for DASCo. Presented experimental results show poor performance of DASCo when default values of CoAP transmission parameters have been used. However, adjusting the parameters according to the network conditions considerably improves DASCo efficiency. Furthermore, in bad network conditions the enhanced DASCo is characterized by a more stable download rate compared to DASH. This feature is important in the context of dynamic media adaptation, since it allows an adaptation algorithm to better fit media bit rate with download conditions.

Virtualization deployment in I-IoT domain is associated with many potential benefits. However, to achieve benefits from virtualization, which is nowadays a well-known technology, it is necessary to verify usability of virtualization platforms in context of I-IoT. In this article, we present a quantitative comparison of two leading open-source hypervisors, XEN and KVM, focusing on throughput and latency, which are key factors from I-IoT point of view. This paper analyzes the methodology for calculating throughput and latency in an computing device that hosts multiple I-IoT subsystems.

The utilization of social parameters, such as the centrality principle, is vital to be considered, in order to optimally exploit the behaviour of Mobile Cloud devices. This work elaborates on the design and exploitation of social and device-centric parameters, as a typical prediction-based method in Mobile Cloud Systems with delay constraints. Based on this consideration, this work introduces a Content-oriented Social Offloading (CSO) scheme. The proposed CSO scheme is based on utilizing the complex characteristics of social communities, in order to optimally choose the mobile node for applying the offloading process, resulting in minimized energy consumption for each mobile device. The proposed scheme aims at saving resources on the user's device, thus minimizing energy consumption and prolonging the device's lifetime. A thorough assessment has been performed, in order to evaluate the performance of the proposed scheme, while delay-sensitive “critical-process executions” are taking place. Furthermore, we carry out a comparison against other similar schemes under the same conditions, in order to validate the efficiency of the proposed framework, with respect to the energy conservation of the mobile nodes.

The advances in Internet of Everything (IoE) and the market-oriented cloud computing have provided opportunities to resolve the challenges caused by the Internet of Things (IoT) infrastructure virtualization, capacity planning, data storage or complexity. The volume and types of IoT data motivate the need for a data storage framework towards the integration of both structured and unstructured data. In this paper, we propose a novel game theoretic technique for efficient and dynamic storage allocation in cloud-centric IoT systems. The benefit maximization problem is formulated as a cost-benefit analysis game investigating the storage capacity currently used in the cloud. In view of each player's strategy to lease additional storage capacity, the game property is analyzed and we prove that the game always admits a pure strategy Nash equilibrium. Since the player's decision affects the level of benefit maximization, we elaborate on a cost-optimal storage allocation incentive mechanism, which scales effectively once non-linear or linear demand for storage capacity occurs, towards achieving optimal leasing conditions on cloud storage and computing capacity level. The experimental validation tests prove the effectiveness of the proposed game theoretic approach allocating the requests for more storage capacity in a cost-effective manner, which achieves to maximize the benefits.

Summary Opportunistic network applications are usually assumed to work only with unordered immutable messages, like photos, videos, or music files, while applications that depend on ordered or mutable messages, like chat or shared contents editing applications, are ignored. In this paper, we examine how total ordering can be achieved in an opportunistic network. By leveraging on existing dissemination and causal order algorithms, we propose a commutative replicated data type algorithm on the basis of Logoot for achieving total order without using tombstones in opportunistic networks where message delivery is not guaranteed by the routing layer. Our algorithm is designed to use the nature of the opportunistic network to reduce the metadata size compared to the original Logoot, and even to achieve in some cases higher hit rates compared to the dissemination algorithms when no order is enforced. Finally, we present the results of the experiments for the new algorithm by using an opportunistic network emulator, mobility traces, and Wikipedia pages.

Critical applications which need to deliver multimedia through the Internet, may achieve the required quality of service thanks to the Content-Aware Networks (CAN). The key element of CAN is an efficient decision algorithm responsible for the selection of the best content source and routing paths for content delivery. This paper proposes a two-phase decision algorithm, exploiting the Evolutionary Multiobjective Optimization (EMO) approach. It allows to consider valid information in different time scales, adapting decision-maker to the evolving network and server conditions as well as to get the optimal solution in different shapes of Pareto front. The simulation experiments performed in a large-scale network model, confirm the effectiveness of the proposed two-phase EMO algorithm, comparing to other multi-criteria decision algorithms used in CAN.

The papers in this special section focus on the development of algorithms and computational models for sustainable computing in cloud and data centers. These papers bring together high quality contributions covering the topics of energy efficiency cloud computing, advances in sustainable cloud and data centers, energy aware applications at various levels of the computational and data storage processes, and emerging trends in sustainable computing. New research findings, novel approaches, and algorithms are presented for energy-efficient Cloud computing, energy-aware memory management solutions for data centers and processing of large data sets, energy aware scheduling approaches for data centers and processing of large data sets, energy consumption computational models, analytics and performance evaluations, and applications as well as emerging trends and applications.

As network bandwidth and coverage continue to increase, the adoption rates of mobile devices are growing over time and the mobile technology is becoming increasingly industrialized. In mobile cloud marketplaces, the cloud-supported mobile services can be leased off. However, the mobile service selection may introduce technical debt (TD), which is essential to be predicted and quantified. In this context, this paper examines the incurrence of technical debt in the future when leasing cloud-based mobile services by proposing a novel quantitative model, which adopts a linear and symmetric approach as a linear growth in the number of users is predicted. The formulation of the problem is based on a cost-benefit analysis, elaborating on the potential profit that could be obtained if the number of users would be equal to the maximum value. The probability of overutilization of the selected service in the long run is also researched. Finally, a quantification tool has been developed as a proof of concept (PoC), which initiates the technical debt analysis and optimization on mobile cloud-based service level and aims to provide insights into the overutilization or underutilization of a web service when a linear increase in the number of users occurs.

This paper analyzes a novel methodology for calculating the throughput in a device, which hosts multiple virtualized network interconnect devices (i.e. virtual routers). The proposed methodology, which extends the well-known procedure (for non-virtualized IP routers) adopted from RFC 2544, considers the impact of heterogeneity of the offered load at the level of virtual routers. The utility of this methodology is demonstrated, analyzing the throughput of virtualized routers by four different virtualization platforms that use two different techniques, which are the paravirtualization (Xen and Citrix Xen) and the OS-level virtualization (Linux Containers and Jails). The results indicate that the virtualization platforms behave differently to distribution of traffic load among virtual routers. Finally, the need for the proposed methodology is motivated by performing extensive throughput tests on the aforementioned platforms at different work points of the network device (i.e. different offered traffic load distribution between virtual routers).

The traditional pub/sub paradigm is inadequate for dissemination in mobile networks, since any node is able to publish content at any time, thus easily leading to congestion. Therefore, a dissemination paradigm where mobile nodes contribute with a fraction of their resources is needed through the use of opportunistic networks. Moreover, as shown in recent work, a suitable organization for data dissemination in mobile networks should be centered around interests. Thus, we propose an interest-based dissemination framework for opportunistic networks entitled Interest Spaces. We focus on the first step required for implementing it in real life: data aggregation. Furthermore, we propose a method for aggregating data from encountered peers, in order for opportunistic nodes to have an informed view of the network and to avoid storing excessive amounts of information or performing many data exchanges.

In the last decade vehicular communications have been the focus of research, not only by the related scientific community but also by the automotive industry. Several architectures were proposed in order to overcome some issues found in such networks. Vehicular Delay-Tolerant Networks (VDTNs) try to improve vehicular communications by deploying the Delay-Tolerant Networks (DTNs) store-carry-and-forward model and assuming the bundle layer placement under the network layer. Although all the improvements already achieved by VDTNs, there are still several challenges that must be overcome. One of these challenges is how to stimulate nodes to cooperate and minimize the impact of misbehavior nodes on the network performance. A node may be unwilling to cooperate due to a selfish behavior or to save its own resources from being compromised. To detect, isolate, and exclude this type of nodes a reputation system with different mechanisms (allowing to punish selfish nodes in different ways) was implemented in VDTNs. This paper adapts the already proposed reputation system to perform together with a hybrid system, which main goal is to incentive selfish nodes to share their resources with others, instead of immediately excluding them from the network. With the proposal of this hybrid system, two incentive mechanisms were also created. Across all the experiments, it was shown that by incentive selfish nodes to cooperate contributes to an increase of the overall network performance, when compared to an approach that excludes them immediately from the network.

This paper proposes two algorithms adopted in a prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting a novel resource prediction engine. The proposed architecture exploits both algorithms for the prediction of future multimedia services demands, by providing the ability to keep optimal the distribution of the streaming data, among Content Delivery Networks, cloud-based providers and Home Media Gateways. In addition, the prediction of the upcoming fluctuations of the network, provides the ability to the proposed network architecture, achieving optimized Quality of Service (QoS) and Quality of Experience (QoE) for the end users. Both algorithms were evaluated to establish their efficiency, towards effectively predicting future network traffic demands. The experimental results validated their performance and indicated fields for further research and experimentation.

The efficient support of the massive device transmission is challenging in Machine-to-Machine (M2M) communications. A large number of devices activate transmissions within a short period of time in M2M communications, which in turn cause high radio access congestions and severe wireless access medium delay. In this context, this work proposes a hybrid MAC approach, based on the well-known ALOHA protocol, and intended to be applied in 5G mobile networks. The topology changes dynamically in the proposed rate-adaptive ALOHA scheme, whereas the bandwidth of a channel is utilized effectively aiming to improve the overall performance of the M2M mobile environment. A random distribution model was exploited during the conducted experiments in order to perform a systematic evaluation of bidirectional communication within a 5G mobile environment. The major contribution of the proposed scheme is the improvement of the network throughput as multiple connections are possible. The experimental results indicate the scheme's efficiency by offering high throughput as opposed to the delay variations between packets, while the proposed scheme aims at maximizing the efficiency of resource exchange between mobile peers.

This paper studies the evaluation of the video streaming over Mobile Peer-to-Peer (MP2P) networks using Scalable Video Coding. The proposed research framework exploits the MP2P diversity characteristics where each node acts as a Mobile Peer for every neighboring node in a relay-based communicating path. Mobile Peers use a common look-up table to request video streaming resources in order to estimate the video packets transfer duration in the end-to-end path. The collaborative streaming is achieved through the data packets replication policy, which uses a bounded upper time limitation for the video packets of each layer (Use of Scalable Video). Each node/device utilizes a specified amount of capacity for both channel and storage purposes. Different scenarios have been introduced in this paper towards evaluating the video streaming policy in the MP2P system, using a real-time probabilistic Fractional Brownian Motion (FBM) and a Random Waypoint (RWP) Mobility model. In both models, nodes move according to certain probabilities, location and time. Intermittent connectivity occurs while nodes claim video streams, whereas promiscuous caching policy enables -where possible- recoverability of lost packets offering priority to packets from the base layer. The simulation results show that FBM provides better objective and subjective quality results for two video sequences resolution, in the MP2P network configuration.

This paper proposes a delay-assisted cooperative scheme for optimal TV White Spaces (TVWS) exploitation and maximum energy conservation in a 5G mobile cognitive radio (CR) network architecture. This architecture utilizes a radio spectrum broker, which administrates the process of network resources management among several 5G base-stations to support Quality of Service (QoS) provision and minimum energy consumption. The proposed scheme is based on the comparison of the delays of both the secondary nodes and the Radio Access Points, when a delay sensitive transmission is requested. The validity of the proposed scheme is verified through several experimental tests, performed under controlled simulation conditions. The performance evaluation results include the energy consumption level and the lifetime span of each wireless node, the throughput response of the system during the delay-sensitive resource exchange process, as well as quantitative measurements of the energy efficiency levels of the proposed scheme.

Data dissemination in opportunistic networks poses a series of challenges, since there is no central entity aware of all the nodes' subscriptions. Each individual node is only aware of its own interests and those of a node that it is contact with, if any. Thus, dissemination is generally performed using epidemic algorithms that flood the network, but they have the disadvantage that the network overhead and congestion are very high. In this paper, we propose ONSIDE, an algorithm that leverages a node's online social connections (i.e. friends on social networks such as Facebook or Google+), its interests and the history of contacts, in order to decrease congestion and required bandwidth, while not affecting the overall network's hit rate and the delivery latency. We present the results of testing our algorithm using an opportunistic network emulator and three mobility traces taken in different environments.

This paper proposes an energy-efficient routing scheme that enables energy conservation and efficient data flow coordination, among communication nodes with heterogeneous spectrum availability in distributed cognitive radio networks. Effective routing scheme operation, as a matter of maximum energy conservation and traffic manipulation is obtained, by utilizing backward traffic activity evaluation, developed based on a simulation scenario. This simulation scenario includes a number of secondary communication nodes, operating over television white spaces (TVWS), under “spectrum of commons” regulation policy. The validity of the proposed energy-efficient routing scheme is verified, by conducting experimental simulations and obtaining performance evaluation results. Simulation results validated its efficiency for minimizing energy consumption and maximizing resources exchange among secondary communication nodes.

With the gigantic growth of social-enabled web during the past years and the creation of new platforms that encompass the social communication, a new application paradigm is utilized. This application paradigm is enforced with the users’ tendency to associate social behavior with their daily demands. As this paradigm can be utilized in all communication environments (wired and wireless), there is a need to eliminate the opportunistic aggravation of the resource-sharing process in wireless communication and enable a mechanism that will consider the problem of content distribution by these devices. As wireless devices experience intermittent connectivity, resource exchange by these devices can rarely be successful due to the frequent variations in the communication and the dynamic changes in the topology. This work takes into account the proposed socio-technical model in order to strengthen the interactions among devices for enabling reliability and availability of the requested resources. In addition, the scheme considers the users’ modeled interaction metrics and their location profiling based on social parameters and allows on-demand resource sharing through the opportunistic socially oriented caching. Different concepts of social centralities are introduced and modeled in order to meet the improvements of resource sharing considering the temporal centrality characteristic of each node and the social interactions among users. The proposed model enables one, via the users’ social and location-aware interactions, and the introduced aging factor, to strengthen or weaken the diffusion process of highly ranked requested resources. The proposed collaborative opportunistic resource-sharing scheme is thoroughly evaluated through experimental simulation for the effectiveness of the resource-sharing efficiency as well as for the accuracy of end-to-end reliability and resource-sharing completeness.

File-sharing systems have, in the past few years, witnessed an upsurge in research development and implementation of new technologies, and in the deployment of actual solutions and technologies. The type of protocol used to interconnect embedded nodes has a decisive impact on whether the system can operate in a deterministic way. File chunk streaming policy plays a major role for the quality of the end-to-end service provision, where, along with the diffusion process, they both aim to provide availability of the resources on-demand. Considering that reliability and availability are affected by the temporal characteristics, this work proposes an embedded framework using a k-hop collaborative outsourcing scheme for enabling and preserving the reliability in any requested path and for any requested resources. The proposed replication scheme takes into account the pre-determined vehicular mobility—as real-time users—in a k-hop path. The outsourcing strategy uses a sequential scheme for caching requested high-ranked resources onto neighboring nodes. The scheme takes into consideration the community-oriented triggered feedback for cluster formation and topology updates, the different file chunks’ capacities, the relay epoch and mobility considerations of each node in the k-hop path for evaluating the device's outsourcing schedules in order to enable end- to-end reliable streaming. Simulation experiments show that the proposed model, through the efficiency of the proposed collaborative streaming policy, can provide scalability and increases the successful delivery rate while it improves significantly the throughput response of the system.

Current approaches for tuning the energy conservation do not take into consideration the storage requirements of each device, the traffic and channel data rate as well as parameters concerning the caching activity for packet forwarding. In this work, an evaluation scheme is proposed based on each node's self-scheduling energy management. This scheme is associating crucial metrics of an ad-hoc wireless host with the remaining capacity units of each device, communicating via asymmetrical wireless links. Promiscuous caching is used in the ad-hoc based connectivity scenario where the-so called- promiscuous caching threshold parameter is introduced in order to bound the associated mobility with storage/capacity and traffic characteristics and figure out a suitable pattern for optimal energy conservation. Through experimental simulation, the proposed energy management scheme is thoroughly evaluated in order to meet the parameters' values where the optimal energy conservation is achieved.

Advances in wireless networks enable high rates interaction between mobile devices. Short range wireless communication technologies such as wearable PCs demand low latency and reliability as the first thing for considering QoS. Mobile peer-to-peer devices as an autonomous system of mobile routers that are self-organized, self-configured and completely decentralized are characterized by bounded resource sharing reliability. Due to the uncertainty in available resources wireless networks could rarely host file sharing applications in a reliable manner. This paper examines the response of a gossip-based data replication scheme for reliable file sharing under specified patterns and conditions, using the hybrid mobile infostation system (HyMIS). This scheme is based on the advantages of mobile infostations. Combining the strengths of autonomic gossiping and the hybrid -entirely mobile- infostation concept, this scheme enables end to end reliability. Examination is performed for the response, the robustness and the offered reliability while examining the effectiveness of the proposed scheme for facing mobility limitations using the gossip-based 'selection' of users

Peer-to-peer applications have become highly popular in today's pervasive environments due to the spread of different file sharing platforms. In such a multiclient environment, if users have mobility characteristics, asymmetry in communication causes a degradation of reliability. This work proposes an approach based on the advantages of epidemic selective resource placement through mobile Infostations. Epidemic placement policy combines the strengths of both proactive multicast group establishment and hybrid Infostation concept. With epidemic selective placement we face the flooding problem locally (in geographic region/landscape) and enable end to end reliability by forwarding requested packets to epidemically 'selected' mobile users in the network on a recursive basis. The selection of users is performed based on their remaining capacity, weakness of their signal and other explained mobility limitations. Examination through simulation is performed for the response and reliability offered by epidemic placement policy which reveals the robustness and reliability in file sharing among mobile peers.