The Internet of Drones (IoD) has become increasingly important in applications such as forest inventory, leveraging advanced sensors and internet connectivity to enable efficient data collection. Compared to traditional methods, IoD offers superior cost-effectiveness. However, its reliance on public channels, unreliable connectivity, and dynamic environments poses significant security and privacy challenges. Safeguarding forest inventory data is essential to maintaining accuracy, preventing unauthorized access, and mitigating the risk of data manipulation, which can lead to suboptimal management decisions. To address these concerns, it is essential to design a lightweight authentication protocol that secures IoD communication, accounts for network bandwidth limitations and scalability, and supports integration with emerging technologies. This manuscript introduces a new Authentication and Key Agreement (AKA) protocol specifically designed for the Internet of Drones (IoD), leveraging asymmetric cryptography and aggregate signatures to enhance security and privacy in forest inventories with fog computing. Its robustness was confirmed through informal and formal security analyses by the AVISPA tool and the ROR model, demonstrating resistance to known attacks and superior communication, computational, and energy performance compared to existing protocols.

Abstract Video-based Cognitive radio networks (CRNs) are a sub-type in which some video users send video traffic. In video CRN context, cognitive users must find an optimal rate/power assignment strategy by solving an optimization problem to maximize their perceived quality of experience (QoE) under interference and energy constraints. Due to large dimension of parameter space in dense CRNs, solving this problem using traditional methods such as game theory or other analytical gradient descent-based approaches can lead to large computational burden. Basically, in such scenarios, each cognitive video user actually faces with mean interference effect from its surrounding nodes and must adopt its behavior (rate/power optimization) strategy based on this mean-field interference effect. Because of inherent nature of mean-field game (MFG) theory in distributed solving of high-dimensional optimization problems, it seems to be a good solution candidate in this context. So, in the current paper, we have used MFG for green (energy-efficient) quality control of cognitive users in dense CRNs. We design two different solution approaches based on a finite-difference method (named GMFQ) and machine learning (named D2GMFQ). The first approach is a standard MFG solution but lacks good scalability in very dense CRN scenarios. So, we introduce the second methodology which is fast enough to tackle such cases. Numerical results show that the proposed methods, outperforms similar ones in maximizing sum perceived cognitive user QoEs under energy-efficiency constraints. Specifically, it is determined that about 11 dB and 15 dB gains can be achieved by GMFQ in average comparing with the traditional TCP and UDP streaming scenarios respectively.

Conventional clustering algorithms in wireless sensor networks (WSNs) often face several challenges, including energy imbalance, routing, and the hot-spot problem, especially in networks with varying node densities and energy levels. To address these challenges, we propose the adaptive type-2 fuzzy unequal clustering algorithm with energy-aware routing (AT2UCAR). It employs a fuzzy inference mechanism to dynamically establish cluster sizes and select optimal cluster heads based on residual energy, node density, and distance from the base station. By creating a set of fuzzy rules, this algorithm enables the development of unequal clusters, allowing nodes situated farther from the BS to form larger clusters, while those nearer to the BS maintain smaller clusters to minimize overall energy consumption. AT2UCAR is proposed to optimize data transmission paths based on residual energy and further optimizes multi-hop routing to improve energy efficiency and load balancing. Simulation results indicate that AT2UCAR significantly improves load balancing and enhances energy efficiency in comparison to other clustering algorithms.

In recent years, the Internet of Things (IoT) has witnessed an explosion, which enables new applications based on connecting numerous devices to the Internet. However, the increasing number of IoT devices utilizing various Radio Access Technologies (RATs), each with distinct communication requirements, presents significant challenges in ensuring reliable Quality of Service (QoS) in such a heterogeneous, multi-technology, multi-user, and multi-application environment. Therefore, for a proper RAT selection, this paper proposes a novel hybrid and adaptive approach based on Deep Reinforcement Learning (DRL) that combines QoS and Quality of Experience (QoE) factors. Unlike previous research, our approach integrates QoS parameters and an adaptive network trust factor that reflects the user's QoE based on reliable past device experiences, ultimately providing users with consistently better connectivity. Within our framework, an IoT device dynamically selects its RAT in a distributed manner based on service requirements and the trust score it assigns to RATs, reflecting its satisfaction with past RAT behavior. The proposed hybrid and adaptive RAT selection approach meets QoS requirements, for various applications at any time as indicated by simulations run using Network Simulator 3 (NS3). Compared to other methods, the energy consumption was reduced by 64.12%, network handovers were decreased by 37.73%, while the average packet delivery ratio was improved by 17.85%. This demonstrates the effectiveness of the proposed approach in optimizing both energy efficiency and network performance, ensuring a more reliable and sustainable system for IoT applications.

Wireless Sensor Networks (WSNs) are a primary means of collecting data in Internet of Things (IoT) systems. Clustering is a highly effective strategy to reduce energy consumption in IoT-based WSNs. In multi-hop clustering, individual sensor nodes transmit their data to designated cluster heads (CHs), which aggregate the data from their member nodes and forward it to the base station (BS) via other CHs. However, a significant challenge in such networks is the hot-spot problem, where CHs located closer to the BS handle increased traffic, leading to faster energy depletion. To address this, the present paper proposes the Interval Type-2 Fuzzy Unequal Clustering and Sleep Scheduling (IT2FUSS) method, which uniquely integrates Interval Type-2 Fuzzy Sets (IT2FS) to model uncertainties in residual energy (RE), node density (ND), and relative distance to the BS (RDBS), dynamic unequal clustering that adjusts cluster sizes in real time to balance CH workloads, and adaptive sleep scheduling to minimize idle energy consumption. Unlike Type-1 fuzzy systems, IT2FUSS leverages the Footprint of Uncertainty (FOU) to more robustly handle sensor data variability, while the co-design of unequal clustering and sleep scheduling helps mitigate hot-spot effects. A fuzzy inference system generates outputs to optimize CH selection, determine cluster sizes, and improve energy efficiency. Simulation results demonstrate that IT2FUSS achieves superior performance in balancing energy consumption and enhancing overall network longevity compared to existing clustering algorithms .

Identifying the spectrum of fruit and leaf disease is one of the important pillars for farmers to correctly diagnose orange disease and quickly treat it so that it does not spread to other orange trees. For this purpose, we designed an Android and iOS application that can detect orange fruit and leaf diseases, such as melanosis, black spots, canker, and greening, as well as detect healthy oranges. It also declares everything but oranges as Not Orange’. In this case, farmers no longer need specialists to diagnose fruit disease. A new deep learning model has been proposed and built to diagnose the type of orange fruit disease. KIVY framework and KV language were used for graphical programming in Android, iOS, Windows, Linux, and Raspberry Pi operating systems. To learn the proposed model, an image dataset of orange disease and orange tree leaves, containing 5073 images, was used. We have provided data images to the proposed deep learning model algorithm so that the new model can be trained by processing these images. Powerful hardware is required for image processing operations in deep learning networks. For this purpose, we used Google Collaboratory. The output of this newly trained model can be used in Windows and Linux. However, they cannot be used in mobile hardware or Android OS. Therefore, we converted the proposed new trained model to TensorFlow lite, which can be implemented in mobile phones and Android and iOS operating systems and has been optimized for this purpose. We then created an Android package using the python-for-Android project. The Buildozer tool was used to automate the entire process. The trained model could detect canker, melanosis, greening, and black spots with 98.29% accuracy. This application is easily available to farmers, who can easily detect diseases in oranges and orange tree leaves

We propose a novel approach to energy-efficient radio frequency (RF) communication based on uncertainty-aware semantic filtering and multi-task learning. The framework utilizes a Bayesian Neural Network (BNN) with Monte Carlo Dropout to estimate predictive uncertainty and filter semantically redundant RF frames. This adaptive mechanism enables efficient data reduction based on confidence levels, optimizing bandwidth without sacrificing performance. The retained high-confidence frames are used for multi-class modulation classification and signal-to-noise ratio (SNR) prediction, supporting intelligent transmission under varying channel conditions while minimizing unnecessary processing. Experimental results show that entropy-based filtering achieves bandwidth savings of up to 40%, maintaining 90.80% classification accuracy, and reducing energy consumption by 10% compared to the baseline. Uniform Manifold Approximation and Projection (UMAP) visualizations confirm improved latent space separability after filtering. Energy consumption is tracked using CodeCarbon, showing a significant reduction in energy per transmitted frame. The proposed framework is lightweight, ideal for real-time inference in resource-constrained environments, and offers applications in edge artificial intelligence (AI), low-power Internet of Things (IoT), and vehicular networks (V2X), paving the way for energy-efficient communication in future 6G systems.

The Internet of Things (IoT) and its industrial counterpart, the Industrial Internet of Things (IIoT), have transformed sectors such as home automation, healthcare, and manufacturing by enhancing data management through advanced networking. However, the rapid growth of IIoT has introduced significant cybersecurity challenges, necessitating a comprehensive approach to securing data across the TCP/IP model. This paper presents a novel cybersecurity investment strategy formulated as a bi-objective optimization problem, validated through genetic and iterative algorithms. The strategy effectively balances security and cost, achieving nearly 50% efficiency in solution effectiveness. By utilizing these optimization techniques, the approach provides a practical and cost-effective solution to improve IIoT security within budget constraints, offering valuable insights for cybersecurity professionals seeking robust and economically viable solutions.

Abstract In this paper, a modern and effective approach with high resilience for microwave imaging of breast tumor detection using 12 antennas is presented. The proposed antenna array operates from 3.1 GHz to 11.6 GHz according to FCC frequencies, which enables the imaging system to mix two advantages at the same time, deep penetration, and high resolution for accurate image acquisition. The suggested antenna array has a compact size of 21 × 21 × 12 cm 3 , this allows for the integration of as many elements as feasible, provided that the coupling between them remains within an acceptable range. This ensures improved accuracy and resolution in the resulting image. The 3D system is arranged in a circular manner around the phantom to cover all breast positions. A measurement campaign will then be made in different scenarios to detect the tumor using two artificial breasts with and without tumor, their dielectric properties and physiological arrangement approximate those of a natural breast. The proposed microwave imaging system can detect and identify all targets, making it one of the most effective systems for detecting breast tumors.

Blockchain enhances trust and collaboration among entities through its inherent features of transparency, immutability, and traceability, leading to its extensive integration into Multi-access Edge Computing (MEC). However, existing transaction propagation mechanisms require MEC devices to consume significant computing resources for complex transaction verification, increasing their vulnerability to malicious attacks. Adversaries can exploit this by flooding the blockchain network with spam transactions, aiming to deplete device energy and disrupt system performance. To cope with these issues, this paper proposes a reputation-based energy-efficient transaction propagation mechanism that alleviates spam transaction attacks while reducing computing resources and energy consumption. Firstly, we design a subjective logic-based reputation scheme that assesses node trust by integrating local and recommended opinions and incorporates opinion acceptance to counteract false evidence. Then, we optimize the transaction verification method by adjusting transaction discard and verification probabilities based on the proposed reputation scheme to curb the propagation of spam transactions and reduce verification consumption. Finally, we enhance the transaction transmission strategy by prioritizing nodes with higher reputations, enhancing both resilience to spam transactions and transmission reliability. A series of simulations demonstrate the effectiveness of the proposed mechanism.

In this project, we aim to detect gas emissions in the decomposition of Dosidicus gigas through low-cost sound monitoring. Quality Control is a set of activities that verify whether the developed product meets the minimum quality requirements and identifies any problems. This research investigates product quality control and health protection. The Humboldt squid (Dosidicus gigas), also called giant squid, is a large cephalopod mollusk abundant on the Peruvian, Mexican, and Chilean coasts. It is a fast-growing invertebrate with a complex nervous system and a well-developed visual system. The nutritional and health properties of the giant squid are of great importance. The squid or giant squid (Dosidicus gigas) is a crucial fishing resource for Peruvians and other surrounding coasts of other countries. It contributes to solving food and nutritional problems, particularly combating chronic malnutrition and childhood chronic anemia. Therefore, this research focuses on analyzing gas emissions during decomposition through low-cost soundproofing.

[EN] Wireless Sensor Networks (WSNs) are vital for collecting data from remote environments. Nevertheless, the limited energy resources of sensor nodes render energy-efficient routing a critical concern for the successful operation of WSNs. To address these concerns, clustering, and routing are essential tasks in WSNs; clustering aims to organize sensor nodes into groups or clusters to minimize energy usage and prolong the network's lifespan. On the other hand, routing involves determining the optimum paths for transmitting data from the source nodes to the destination nodes. Nonetheless, it has been established that the current energy-efficient routing problem is an NP-hard, requiring a trade-off between energy and overall network performance. In this paper, we proposed a Hybrid Whale-Ant Optimization Algorithm (WAOA) for energy-efficient routing in WSNs. The proposed WAOA utilizes the Whale Optimization Algorithm (WOA) to find the suitable cluster head in the predefined search space, while the Ant Colony Optimization (ACO) searches the optimal route from the source cluster sensors to the cluster head within its predefined space. Linear programming construction is employed to formulate optimization problems for cluster head selection and search for the optimal route. The performance analysis demonstrates that the proposed WAOA performs better than MOORP, MMABC, and AZEBR by 5.78 %,16.11 %, and 18.52 %, respectively, in terms of network lifetime.

Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings.

Special Issue on Deep Learning for Intelligent Human Computer InteractionDeep Learning ( DL ) is growing at a fast pace with a plethora of related research being conducted and industrial applications being developed.Robotics, computer graphics, computer vision including areas such as feature extraction/matching, 3D reconstruction, manufacturing, medicine, knowledge acquisition, control theory, planning and scheduling, among others, have uncovered the potential of DL.The reason for such success mainly stems from two aspects.On the one hand, the theoretical advances in related disciplines such as optimization methods, pattern recognition, and hardware (GPUs) have undergone great breakthroughs.On the other hand, the technical applications in industry have addressed many actual problems that have in turn accelerated DL development.Leading technology companies like Microsoft, Apple, Google, Facebook, Nvidia, and Amazon all launched cutting-edge industrial products offering advanced functionalities that were only made possible thanks to the employment of DL algorithms.The effort in establishing new media and network technologies led to the identification of a niche for ML in the field of humancomputer interaction.Human-computer interaction ( HCI ) is a multidisciplinary field of study focusing on the design of computer technology through the analysis and understanding of how humans interact with computers.HCI addresses various areas, such as user interface design or computer-supported cooperative work for data modeling, system adaptation and optimization to fit user needs, complex systems, the development of smart equipment to ease utilization, and the design of smart environments to promote user comfort and safety.Nowadays, DL is largely employed in HCI to model human behavior as they have shown to outperform traditional modeling techniques.Although their potential is undoubtedly broad, there are several important research challenges that remain unaddressed.The explainability and interpretability of the decision-making process of DL approaches is key to assess models' behaviors, especially when unexpected or incorrect outputs occur.The complexity and the dimensionality of the underlying mathematical models are among the principal causes that hinder understanding.Privacy must be guaranteed as sensible private data is typically continually collected, processed, and stored over the Cloud in order to provide improved user-specific experiences.Such continual learning for DL approaches is itself a challenge as models may suffer from catastrophic forgetting, but it is a key ingredient that needs to be taken into consideration to enable scalability.Data annotation done at large scale is costly and hardly sustainable, therefore particular attention must be put on self-supervised or unsupervised learning mechanisms.DL-based user interfaces, such as gesture recognition, must be supported

Abstract The modern communication network has advanced to such an extent that it is now possible for devices within a wireless personal area network (WPAN) to communicate among themselves directly. However, the limited shared radio resources of a WPAN lead to numerous issues, such as cross-layer interference and data collisions, which wind up affecting the quality of communication. A load based dynamic channel allocation (LB-DCA) model has been proposed to enhance the performance of device-to-device communication in WPAN. This model uses several control schemes in collaboration with interference estimation and channel load balancing mechanisms to allocate and manage the radio resources efficiently. The objective of this model is to achieve high throughput, low interference and low energy consumption. The control schemes implemented are based on distributed coordination and a cell-splitting approach. These schemes are utilized to estimate the channel usage and number of active nodes in a network. The interference estimation is done by using a new efficiency formula. Further, channel load balancing takes into account the hops and load factor values. The proposed model obtained 98.58% CSI, 95.86% MCC, 96.35% delta-P, 97.96% FMI, 99.83% BMI, 21.52% enhanced spectrum efficiency, 16.38% enhanced scalability, 18.79% enhanced signal quality, 18.64% enhanced power control and 18.89% enhanced energy efficiency.

Nowadays, almost all applications use Internet of Things (IoT) to modernize their process of data communication. The area of smart cities is considered as an industry whose the exploitation of IoT is in constant increasing. Due to the specific requirements of the smart cities' applications, especially in terms of QoS (Quality of Services), IoT communications face multiple constraints. The limited resources, whether for devices or for communication links, is considered one important constraint. The frequent changes in the states and situations of devices and communication links make this constraint more complex. Considering this obstacle when designing communication algorithms for the IoT is an active research axis that is conducted in the IoT context. Given the unpredictable and uncertain nature of the situations that can be occurred in the network, it is important that these algorithms be adaptive and intelligent. The objective of the presented work in this paper is the proposition of a new communication solution, named IAAC-IoT (Intelligent and Adaptive Algorithms for IoT Communications), gathering adaptive and intelligent algorithms to improve the QoS and energy consumption within IoT. During the performance evaluation of the IAAC-IoT, we obtained satisfactory performance results in terms of QoS and energy efficiency.

The importance of vertical farming is increasing day by day, given its numerous benefits including the possibility to cultivate a crop in the minimum available space. Similarly, the primary sector also needs technology incorporation to ensure sustainability. Therefore, this paper presents the design and deployment of a wireless sensor network (WSN) responsible for monitoring the quality of irrigation water in terms of salinization for vertical farming. The study presents 2 prototypes of salinity sensors, based on a single coil, which can determine the salt levels of the liquid in which they are introduced. Both prototypes are tested and compared with professional equipment, and then subjected to a validation process. Finally, the network traffic generated by such an installation for different numbers of nodes is analyzed, and the results show that the bandwidth consumed by a node like ESP32 is extremely low, with average bandwidth values of around 700 Bps.

With the rise of sensor networks and the concern about how to leverage data from deployed networks in different environments, the use of storage and management platforms for these data becomes necessary. Currently, there are commercial solutions available (some of them free but with significant limitations). However, this fact forces you to always depend on this platform for the proper performance of the service or application. For this reason, this article presents a novel hierarchical system that allows the design and deployment of a customizable platform for monitoring networks and the Internet of Things (IoT) for the collection, storage, and visualization of real-time data. Throughout the article, the network architecture, as well as the protocols, tasks, and services deployed in each layer, will be presented. Finally, the platform will be tested in terms of the performance of different services and the network traffic recorded based on the number of nodes that would constitute the monitoring network.

The cloud-based Internet of Things (IoTs) storage enables patients to monitor their health remotely and offers services for physicians of various Medical Institutions (MIs) to diagnose and treat them on time. As a matter of trust, patients are legally expected to hide their real identity and ensure data privacy in the cross-domain of IoT-healthcare, whether it is stored correctly or modified due to external and internal attacks in the cloud. Additionally, physicians treat patients and continuously store duplicated data in cloud storage, which increases the cost of computing. In this context, this paper presents HIDE-Healthcare IoT Data privacy trust management framework, focusing on attributes. Patients’ attributes are used to encrypt and decrypt sensory data between patients and different entities by incorporating the idea of trustworthy and secure shared keys. HIDE uses an intelligent object’s pointer to store the same patient’s sensory data in various versions to prevent data duplication, which will help track MIs that treat patients. An intelligent content-based emergency data access control is developed to monitor multiple patient health criticalities in HIDE. The security analysis and experimental evaluation attest to the benefits of the proposed HIDE framework, considering security and privacy metrics.

In recent days, due to the emergence of communicable infectious diseases, healthcare, and medical technologies are expected to play a critical role. The advancement of communication and sensor network technologies has accelerated mass screening systems to combat the disease. Human temperature detection is one of the measurements for crowd screening in public places. Nevertheless, it is challenging to design a fast, lightweight, and easy-to-deploy contact-less crowd screening system in the outdoor environment due to several factors, such as environmental effect, background temperature, deployment cost, and remote operation. The state-of-the-art is mainly based on either hand-held devices or high-cost infrared cameras in only designated places. This article presents an end-to-end contactless assistive method for human body temperature screening systems, starting from collecting raw temperature data using a thermal camera to identify the suspected individual for combating communicable infectious diseases. We leverage the computing, storage, and communication resources offered by edge computing. In particular, we deploy a lightweight version of MobileNet v2 in resource-constrained Raspberry Pi 4B to detect the human’s head and body from the thermal image and use a classifier to determine the temperature from the raw temperature data. Moreover, we leverage a low-power and long-range wireless network for the exchange of model parameters between Raspberry Pi and the remote server. The experiments show that although the detection accuracy is not very high, we can reduce the bottleneck from screening time and reduce the exposure for the individuals because of the reduced bottleneck. Our proposed solution is implemented in Python and is available under the open-source MIT License athttps://github.com/mitunhub/HAWK-i.

Remote monitoring is an important application of intelligent transportation systems (ITSs). The combination of monitoring equipment and tracking algorithms can be used to automatically track moving targets. The tracking algorithm based on the Siamese network is both accurate and efficient, and its development potential is better than that of other algorithms. Its output is a detection map that reflects the probability that any position in the search area is the center of the target’s bounding box, and the maximum value of the detection map is the center of the target’s bounding box predicted by the algorithm. Owing to partial occlusion, target deformation, out-of-view, and background clutter, local maxima in the detection map may also be the center of the target’s bounding box. A tracker’s ability to make accurate judgments is currently limited. Furthermore, previous trackers extracted only the target features in the initial frame as the matching template. Although this matching template is highly reliable, it cannot effectively combine the target features available in the subsequent frames. Therefore, in this study, fuzzy inference is introduced into the tracking process to analyze the reliability of the detection map. When this map is reliable, the target feature of the search area is transformed into a substitute template; otherwise, multiple substitute templates are selected from the template pool for parallel matching as per the set rules. The optimal result is selected from multiple detection results, based on the priority of the detection results when the initial frame is used as the matching template. Experimental results on multiple datasets show that the proposed algorithm is superior to other similar algorithms in terms of multiple assessment metrics and can improve the robustness of remote monitoring tasks in ITSs.

The Internet of Things (IoT), as an important part of the new generation of information technology, connects any object to the Internet according to the agreed protocol through radio frequency identification, global positioning system, and other information sensing equipment for information exchange and communication. With the continuous development of the IoT technology, it has injected new power into its further development and improvement. The Internet of Vehicles (IoV) is the development focus of the IoT, and the improvement of its connection capability enables the application of IoV to be upgraded from vehicle entertainment to unmanned driving, fleet arrangement and management, and traffic intelligent service. With the release of the market potential of IoV, the transportation cost will also drop significantly, and more transformation opportunities will emerge in the traditional intelligent transportation industry. As an important part of intelligent transportation, intelligent city, intelligent village, and intelligent park, the IoT intelligent infrastructure plays an important role in providing high-quality public services, reducing costs, and achieving sustainable development. At present, the IoT intelligent infrastructure has a wide range of demand around the world, and has become an important innovation and industrial development force in this field.

Rapid developments in deep learning (DL) and the Internet-of-Things (IoT) have enabled vision-based systems to efficiently detect fires at their early stage and avoid massive disasters. Implementing such IoT-driven fire detection systems can significantly reduce the corresponding ecological, social, and economic destruction; they can also provide smart monitoring for intelligent transportation systems (ITSs). However, deploying these systems requires lightweight and cost-effective convolutional neural networks (CNNs) for real-time processing on artificial intelligence (AI)-assisted edge devices. Therefore, in this paper, we propose an efficient and lightweight CNN architecture for early fire detection and segmentation, focusing on IoT-enabled ITS environments. We effectively utilize depth-wise separable convolution, point-wise group convolution, and a channel shuffling strategy with an optimal number of convolution kernels per layer, significantly reducing the model size and computation costs. Extensive experiments on our newly developed and other benchmark fire segmentation datasets reveal the effectiveness and robustness of our approach against state-of-the-art fire segmentation methods. Further, the proposed method maintains a balanced trade-off between the model efficiency and accuracy, making our system more suitable for IoT-driven fire disaster management in ITSs.

Industrial Internet of things (IIoT) refers to a network of smart devices, equipped with a variety of sensors connected to the Internet. The devices in IIoT can be shared among multiple public and/or private applications. These applications can simultaneously access the data generated by these devices, necessitating concurrent data-collection. With devices being power-constrained, the chances of device failures are high in shared device infrastructure. This results in partitioned network topology and impacts data collection. Furthermore, the network-topology reconstruction process is also energy-consuming. This paper proposes a fault-tolerant design of concurrent data col-lection process in shared IIoT applications. Via simulation, we show our proposed algorithm handles device failures without affecting the overall time-duration of concurrent data-collection and handles the faults better as compared to an existing algorithm in terms of better overall data collection time.

Wireless Sensor Networks (WSNs) are widely spread to monitor all type of parameters with the aim of improving our every-day lives. Typical WSN are networks on which only one wireless technology is used, so applicability and restrictions are determined by it. In this paper, a WSN of WSNs with the configuration of multiple wireless technologies is proposed. A network discovery system is defined to explain how to build the topology of a network with different standards. In the same way, a routing protocol is defined to communicate devices with different wireless technologies. As part of the network, LoRa technology is intended to cover long distances so it can be used to interconnect WSNs located on the edge to the Core of the network. Low-cost Heltec LoRa WiFi v2 devices are employed to determine the performance of lowcost solutions for one hop. This study is done for both 433 MHz and 868 MHz devices, which are the available frequencies in Europe. The results show that the best coverage results are obtained for SF 7 in all cases. Furthermore, the use of these low-cost devices is not advised for distances above 1 Km.

In this demonstration, we suggest lightweight haptic communications from the context of remote neurorehabilitation. In particular, we collect tactile data from pressure sensors attached to the human hand and design a classifier to determine the objects that an individual holds or grasps. Finally, we have implemented the proposed system on Raspberry Pi and demonstrated a personalized classification while rendering the haptic feedback in a virtual perception.

The IEEE 802.15.4 standard is one of the widely adopted networking specification for realizing different applications of Internet of Things (IoT). It defines several physical layer options and medium access control (MAC) sublayer protocols for low-power devices supporting low-data rates. One such MAC protocol is the deterministic and synchronous multichannel extension (DSME), which addresses the limitation on the maximum number of guaranteed time slots (GTSs) in 802.15.4-2011 MAC, and provides channel diversity to increase network robustness. However, beacon scheduling in peer-to-peer networks suffers from beacon slot collisions when two or more coordinators simultaneously compete for the same vacant beacon slot. In addition, the standard does not explore DSME-GTS scheduling (DGS) across multiple channels. This article addresses the beacon slot collision problem by proposing a nonconflicting beacon scheduling mechanism using association order (AO). Furthermore, a distributed multichannel DSME-GTS schedule is proposed that optimally assigns DSME-GTSs across different channels. The objective is to minimize the number of times-lots used while maximizing the usage of available channels. Through simulations, the proposed mechanisms’ performance is analyzed in terms of energy efficiency, transmission overhead, scheduling efficiency, throughput, and latency and is shown to outperform the other existing schemes.

Internet of Things (IoT) is considered nowadays as the most important and indispensable support to ensure all types of communication, over almost all sectors of activities. The specificity of each area, as well as its own requirements in terms of Quality of Service (QoS), make this communication difficult to ensure and thus, face multiple challenges. One of these challenges is related to the needed autonomy for IoT, not only in terms of available resources (energy and bandwidth for example) but also in terms of self-configuring and self-organizing within the network. It is in this context that we propose a new cross layers approach for better self-configuring and self-organizing of devices and communications within IoT environments. The proposed approach is named 2SAEC-IoT (self-organizing and self-configuring algorithms for efficient communications within IoT) that leads to guarantee an efficient data communication for IoT applications. 2SAEC-IoT is a cross layers solution since it considers important communication parameters related to three levels which are MAC, network, and transport. The proposed approach allows the continuity of services for IoT applications, especially for those with very sensitive data (e-health for example), by tolerating possible communications failures or devices breakdown. The evaluation of the proposed approach shows a clear improvement in terms of QoS, and energy efficiency compared to those obtained by three other IoT networks using different communication algorithms.

Augmetned reality is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs. Enter augmented reality (AR) technology, which can be used to visualize data from hundreds of sensors simultaneously, overlaying relevant and actionable information over your environment through a headset. With the rapid development of 5G, which makes AR technology much faster, with much more data flow. With easier and more accessible use, for a variety of different functions (besides video gaming), widespread adoption seems likely.

The IEEE 802.15.4 standard is one of the widely adopted specifications for realizing different applications of the Internet of Things. It defines several physical layer options and Medium Access Control (MAC) sub-layer for devices with low-power operating at low data rates. As devices implementing this standard are primarily battery-powered, minimizing their power consumption is a significant concern. Duty-cycling is one such power conserving mechanism that allows a device to schedule its active and inactive radio periods effectively, thus preventing energy drain due to idle listening. The standard specifies two parameters, beacon order and superframe order, which define the active and inactive period of a device. However, it does not specify a duty-cycling scheme to adapt these parameters for varying network conditions. Existing works in this direction are either based on superframe occupation ratio or buffer/queue length of devices. In this article, the particular limitations of both the approaches mentioned above are presented. Later, a novel duty-cycling mechanism based on MAC parameters is proposed. Also, we analyze the role of synchronization schemes in achieving efficient duty-cycles in synchronized cluster-tree network topologies. A Markov model has also been developed for the MAC protocol to estimate the delay and energy consumption during frame transmission.

In this demonstration, we present an end-to-end assistive method for human body temperature screening system starting from collecting raw data using a thermal camera to identify the suspected individual for combating communicable infectious diseases. We deploy a lightweight MobileNet v2 in resource-constrained Raspberry Pi 4B to detect the human's head and body from the thermal image and use a classifier to determine the temperature from the raw temperature data. The experiments show that although the detection accuracy is not very high, we can reduce the bottleneck from screening time and reduce the exposure for the individuals because of the reduced bottleneck.

Summary The explosion of cloud services over the Internet has raised new challenges in cloud service selection and ranking. The existence of a great variety of offered cloud services made the users think deeply about the most appropriate services that meet their needs and at the same time are adaptable to their context. Nowadays, online reviews are used for the purpose of enhancing the effectiveness of finding useful product information, having impact on the consumers' decision‐making process. In this context, the current paper suggests a context‐aware cloud service ranking approach using online reviews and based on sentiment analysis (CROSA). Its main objective is to ease the cloud service selection. The CROSA approach analyzes sentiments associated with service measurement index (SMI)–based service properties for each alternative cloud service. Moreover, it enhances the cloud service decision‐making by supporting fuzzy sentiments through the intuitionistic fuzzy set theory and PROMETHEE II. The experimental results presented in this paper show that this approach is efficient and performing.

The IEEE 802.15.4 standard specifies two network topologies: 1) star and 2) cluster tree. A cluster-tree network comprises of multiple clusters that allow the network to scale by connecting devices over multiple wireless hops. The role of a cluster head (CH) is to aggregate data from all devices in the cluster and then transmit it to the overall personal area network (PAN) coordinator. This specific role of CH needs to be rotated among multiple coordinators in the cluster to prevent it from energy drain out. Prior works on CH rotation are either based on threshold energy levels or rely on periodic rotation. Both approaches have their respective limitations and, at times, result in unnecessary CH rotations or nonoptimal selection of CH. To address this, we propose a nonthreshold CH rotation scheme (NCHR), which incurs minimal rotation overhead. It supports topological changes, node heterogeneity, and can also handle CH failures. Through simulations and hardware implementation, the performance of the proposed NCHR scheme is analyzed in terms of network lifetime, CH rotation overhead, and the number of CH rotations. It is shown that the proposed scheme boosts network lifetime, incurs less rotation overhead, and needs fewer CH rotations compared to other related schemes.

Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this article, we focus on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management. We predict future energy consumption for short intervals of time as well as provide an efficient way of communication between energy distributors and consumers. The key contributions include edge devices-based real-time energy management via common cloud-based data supervising server, optimal normalization technique selection, and a novel sequence learning-based energy forecasting mechanism with reduced time complexity and lowest error rates. In the proposed framework, edge devices relate to a common cloud server in an IoT network that communicates with the associated smart grids to effectively continue the energy demand and response phenomenon. We apply several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting and implement it over resource-constrained devices. We perform extensive experiments and witness 0.15 and 3.77 units reduced mean-square error (MSE) and root MSE (RMSE) for residential and commercial datasets, respectively.

Next-generation wireless networks are witnessing an increasing number of clustering applications, and produce a large amount of non-linear and unlabeled data. In some degree, single kernel methods face the challenging problem of kernel choice. To overcome this problem for non-linear data clustering, multiple kernel graph-based clustering (MKGC) has attracted intense attention in recent years. However, existing MKGC methods suffer from two common problems: (1) they mainly aim to learn a consensus kernel from multiple candidate kernels, slight affinity graph learning, such that cannot fully exploit the underlying graph structure of non-linear data; (2) they disregard the high-order correlations between all base kernels, which cannot fully capture the consistent and complementary information of all kernels. In this paper, we propose a novel non-negative matrix factorization (NMF) tailored graph tensor MKGC method for non-linear data clustering, namely TMKGC. Specifically, TMKGC integrates NMF and graph learning together in kernel space so as to learn multiple candidate affinity graphs. Afterwards, the high-order structure information of all candidate graphs is captured in a 3-order tensor kernel space by introducing tensor singular value decomposition based tensor nuclear norm, such that an optimal affinity graph can be obtained subsequently. Based on the alternating direction method of multipliers, the effective local and distributed solvers are elaborated to solve the proposed objective function. Extensive experiments have demonstrated the superiority of TMKGC compared to the state-of-the-art MKGC methods.

In the cognitive computing of intelligent industrial Internet of Things, clustering is a fundamental machine learning problem to exploit the latent data relationships. To overcome the challenge of kernel choice for nonlinear clustering tasks, multiple kernel clustering (MKC) has attracted intensive attention. However, existing graph-based MKC methods mainly aim to learn a consensus kernel as well as an affinity graph from multiple candidate kernels, which cannot fully exploit the latent graph information. In this article, we propose a novel pure graph-based MKC method. Specifically, a new graph model is proposed to preserve the local manifold structure of the data in kernel space so as to learn multiple candidate graphs. Afterward, the latent consistency and selfishness of these candidate graphs are fully considered. Furthermore, a graph connectivity constraint is introduced to avoid requiring any postprocessing clustering step. Comprehensive experimental results demonstrate the superiority of our method.

Internet of Vehicles (IoV) is a large interactive network composed of information such as vehicle location, speed and route. Vehicles can collect their own environment and state information through GPS, RFID, sensors, camera image processing, and other devices. They can transmit their various information to the central processing unit through Internet technology. These large amounts of vehicle information can be analyzed and processed through computer technology to calculate the optimal route for different vehicles, and report road conditions in time and schedule signal light cycles. Internet of Vehicles (IoV) is a large-scale system network for wireless communication and information exchange between vehicles and people, vehicles and roads, vehicles and the Internet, which is based on intra-vehicle network, inter-vehicle network and vehicle mobile Internet, in accordance with the agreed communication protocols and data interaction standards. The system realizes the integrated network of intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control by “filtering and cleaning” massive data and processing data on the platform. Internet of Vehicles (IoV) system utilizes advanced IoT technology, cloud computing, and big data to make the system fully aware of roads and traffic. It enables all vehicles to collect information through their own environment and state, and upload all kinds of information to the Internet big data platform. The central processing unit collects, analyzes, and processes a large amount of uploaded information. The system will control every vehicle involved in the traffic and control every road in real time to provide users with traffic efficiency and safety.

Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.

The hardware realization of Universal Filtered Multi Carrier (UFMC) architecture has attracted significant attention in fifth generation (5G) and beyond. In addition to the flexibility in fast Fourier transform (FFT)-length, a flexible prototype filter in combination with multiplicative complex spectrum shifting co-efficients is required for realizing flexible UFMC architecture. The existing architectures of UFMC transmitter commonly adopted fixed-size FFT-length, number of subbands, subband size, and filter-length. Moreover, the lack of flexible prototype filter and spectrum localization of filter co-efficients to individual subbands limits the flexible UFMC system design. In this paper, we propose VLSI architecture for a flexible length prototype filter that can generate spectrally shifted filter co-efficients to individual subbands in tune with the changing value of FFT-length, number of subbands, subband size, and filter-length. For 16-bit word size architecture, our proposed design produces filter co-efficients and spectrum shifting co-efficients upto length,$2^{15}$. Thus, any desired combination of FFT-length, number of subbands, subband size and filter-length is selected to generate the filter co-efficients for the individual subbands. Moreover, complex multiplication and addition operations are reduced in proposed architecture, quantitatively, about 58.81% reduction in filtering unit is achieved over the state-of-the-art architecture. Finally, hardware implementation output and XILINX post route simulation result matches perfectly with MATLAB simulations.

The last decade has witnessed a steep growth in multimedia traffic due to real‐time content delivery such as in online games and video conferencing. In some contexts, MANETs play a key role in the hyperconnectivity of everything in multimedia services. In this context, this work proposes a new scheduling approach based on context‐aware mobile nodes for their connectivity. The contribution relies on reporting not only the locations of devices in the network but also their movement identified by sensors. In order to illustrate this approach, we have developed a novel agent‐based simulator called MASEMUL for illustrating the proposed approach. The results show that a movement‐aware scheduling strategy defined with the proposed approach has decreased the ratio of channel interruptions over another common strategy in mobile networks.

Spontaneous networks lack an a priori communication infrastructure, the neighbors are unknown right after the deployment, and they are used during a period of time and in a certain location. In this paper, we present a new randomized creation model of a spontaneous wireless ad hoc network based on trusted neighbors. The idea is to manage the neighbor discovery with the exchange of identity cards, and the checking of a signature establishes a relationship based on trust of the neighbors. To asses the performance of our randomized trusted network proposal and compare it against an existing deterministic protocol used as reference, we relied on Castalia 3.2 simulator, regarding 4 metrics: time, energy consumption, throughput, and number of discoveries vs packet sent ratio. We found that our proposal outperforms the reference protocol in terms of time, energy, and discoveries vs packet sent ratio in a one‐hop setting, while it outperforms the reference protocol regarding all 4 metrics in multihop environments. We also evaluated our proposal through simulations varying the transmission probability and proved that it does not require to know the number of nodes if a fixed transmission probability is set, providing reasonable results. Moreover, our proposal is based on collision detection, it knows when to terminate the process, it does not require a transmission schedule, and it follows more realistic assumptions. In addition, a qualitative comparison is carried out, comparing our proposal against existing protocols from the literature.

It is evident that the Internet of Things (IoT) is a technology that is not only already being implemented but it is also going to be part of our everyday life in many aspects regarding our homes, workplace and the cities we live in. As the concept of IoT evolves, new terminologies are being developed to specify certain characteristics of the IoT network. The Internet of Underground Things (IoUT) is an example of these new terms, considering, in this case, the soil medium. IoUT is then separated from IoT in the fact that the requirements of underground communications and the underground nodes are different from those in regular IoT networks and, therefore, IoUT should be studied separately. However, as IoUT is a new concept, there are not many studies available. Therefore, in this paper, an overview of the IoUT concept and the most important conclusions that have been reached on this topic is presented. Furthermore, transmission experiments between underground and above-ground low-cost WiFi nodes deploying them at different depths, heights, and distances to determine the suitability of the currently available low-cost nodes for this new IoT concept have been performed. The results show that the optimal height is 1.5 meters and the optimal depth is 20 cm. Furthermore, the connection was able to reach distances up to 10 m. Lastly, an underground transmission decision-making algorithm for soil monitoring nodes in precision agriculture has been provided.

Fog computing architecture allows the end-user devices of an Internet of Things (IoT) application to meet their latency and computation requirements by offloading tasks to a fog node in proximity. This fog node in turn may offload the task to a neighboring fog node or the cloud-based on an optimal node selection policy. Several such node selection policies have been proposed that facilitate the selection of an optimal node, minimizing delay and energy consumption. However, one crucial assumption of these schemes is that all the networked fog nodes are authorized part of the fog network. This assumption is not valid, especially in a cooperative fog computing environment like a smart city, where fog nodes of multiple applications cooperate to meet their latency and computation requirements. In this paper, we propose a secure task-offloading framework for a distributed fog computing environment based on smart-contracts on the blockchain. The proposed framework allows a fog-node to securely offload tasks to a neighboring fog node, even if no prior trust-relation exists. The security analysis of the proposed framework shows how non-authenticated fog nodes are prevented from taking up offloading tasks.

The Internet of Things integrates a large number of distributed nodes to collect or transmit data. When the network scale increases, individuals use multiple sink nodes to construct the network. This increases the complexity of the network and leads to significant challenges in terms of the existing methods with respect to the aspect of data forwarding and collection. In order to address the issue, this paper proposes a Shortcut Addition strategy based on the Particle Swarm algorithm (SAPS) for multisink network. It constructs a network topology with multiple sinks based on a small-world network. In the SAPS, we create a fitness function by combining the average path length and load of the sink node, to evaluate the quality of a particle. Subsequently, crossover and mutation are used to update the particles to determine the optimal solution. The simulation results indicate that the SAPS is superior both to the greedy model with small world and the load-balanced multigateway aware long link addition strategy in terms of the average path length, load balance, and number of added shortcuts.

The tremendous growth of interconnected things/devices in the whole world advances to the new paradigm i.e. Internet of Things (IoT). This special session focuses on the recent challenges, design and issues for Energy Management, Protocols and Security for the next generation networks and IoT. The aim of this special issue in IEEE Transactions on Industrial Informatics is to bring together global ICT state of the art and research trends with new developments in this area. This theme is expected to provide the primary and major resources necessary for researchers, academicians, IT industries, and scientists to adopt and implement new inventions. Ten papers have been selected for publication in this part of the section, following several rounds of rigorous reviews. These papers cover the range of encouraging milestones: energy management, resource management, blockchain, cryptography and security aspects for satisfying demands, new algorithms and protocols for the IoT, cloud computing, fog-computing, smart grid, wireless data center and next-generation networks.

In the past years, videoconferencing (VC) has become an essential means of communications. VC allows people to communicate face to face regardless of their location, and it can be used for different purposes such as business meetings, medical assistance, commercial meetings, and military operations. There are a lot of factors in real-time video transmission that can affect to the quality of service (QoS) and the quality of experience (QoE). The application that is used (Adobe Connect, Cisco Webex, and Skype), the internet connection, or the network used for the communication can affect to the QoE. Users want communication to be as good as possible in terms of QoE. In this paper, we propose an architecture for videoconferencing that provides better quality of experience than other existing applications such as Adobe Connect, Cisco Webex, and Skype. We will test how these three applications work in terms of bandwidth, packets per second, and delay using WiFi and 3G/4G connections. Finally, these applications are compared to our prototype in the same scenarios as they were tested, and also in an SDN, in order to improve the advantages of the prototype.

In this paper, we address the symbol synchronization issue in molecular communication via diffusion (MCvD). Symbol synchronization among chemical sensors and nanomachines is one of the critical challenges to manage complex tasks in the nanonetworks with molecular communication (MC). As in diffusion-based MC, most of the molecules arrive at the receptor closer to the start of the symbol duration, the wrong estimation of the start of the symbol interval leads to high symbol detection error. By utilizing two types of molecules with different diffusion coefficients we propose a synchronization technique for MCvD. Moreover, we evaluate the symbol-error-rate performance under the proposed symbol synchronization scheme for equal and non-equal symbol duration in MCvD systems.

Today’s vehicles are no longer stand-alone transportation means, due to the advancements on vehicle-tovehicle (V2V) and vehicle-to-infrastructure (V2I) communications enabled to access the Internet via recent technologies in mobile communications, including WiFi, Bluetooth, 4G, and even 5G networks. The Internet of vehicles was aimed toward sustainable developments in transportation by enhancing safety and efficiency. The sensor-enabled intelligent automation of vehicles’ mechanical operations enhances safety in on-road traveling, and cooperative traffic information sharing in vehicular networks improves traveling efficiency.

The IEEE 802.15.4 standard is one of the most widely used link layer technology for building Internet of Things (IoT). It specifies several physical layer options and MAC layer for meeting low-power and low-rate requirements of devices deployed in a network of IoT. The standard also specifies a synchronization scheme for devices connected in a star topology, operating in beacon-enabled (BE) mode using periodic beacons. The BE mode facilitates synchronization among devices for data transmission and is suitable for large networks to establish low duty-cycles. Absence of a such a scheme for a cluster-tree network has confined its application only to nonbeacon mode. The challenge here is to schedule beacon frame transmissions of multiple devices in a nonoverlapping manner to avoid beacon collisions. This paper tackles the problem of synchronization by proposing localized beacon synchronization (LBS) scheme, a distributed technique for beacon scheduling in cluster-tree network topologies. LBS uses 2-hop information and association order to compute beacon transmission offsets that better utilize the available time slots, incur fewer transmissions, and is highly scalable. Further, we analytically show that the schedulability of the proposed scheme is higher compared to other related schemes. In addition, we also address the important issue of resynchronization that has been ignored in all of the prior works. The proposed resynchronization mechanisms consider the interdependencies between synchronization and duty-cycling schemes and are shown to significantly lower the synchronization overhead when synchronization among devices is lost.

The Internet of Vehicles (IoV) provides new opportunities for the coordination of vehicles for enhancing safety and transportation performance. Vehicles can be coordinated for avoiding collisions by communicating their positions when near to each other, in which the information flow is indexed by their geographical positions or the ones in road maps. Vehicles can also be coordinated to ameliorate traffic jams by sharing their locations and destinations. Vehicles can apply optimization algorithms to reduce the overuse of certain streets without excessively enlarging the paths. In this way, traveling time can be reduced. However, IoV also brings security challenges, such as keeping safe from virtual hijacking. In particular, vehicles should detect and isolate the hijacked vehicles ignoring their communications. The current work presents a technique for enhancing security by applying certain prioritization rules, using digital certificates, and applying trust and reputation policies for detecting hijacked vehicles. We tested the proposed approach with a novel agent-based simulator about security in Internet of Things (IoT) for vehicle-to-vehicle communications. The experiments focused on the scenario of avoidance of collisions with hijacked vehicles misinforming other vehicles. The results showed that the current approach increased the average speed of vehicles with a 64.2% when these are giving way to other vehicles in a crossing by means of IoT.

Both limited main memory size and memory interference are considered as the major bottlenecks in virtualization environments. Memory deduplication, detecting pages with same content and being shared into one single copy, reduces memory requirements; memory partition, allocating unique colors for each virtual machine according to page color, reduces memory interference among virtual machines to improve performance. In this paper, we propose a coordinate memory deduplication and partition approach named CMDP to reduce memory requirement and interference simultaneously for improving performance in virtualization. Moreover, CMDP adopts a lightweight page behavior-based memory deduplication approach named BMD to reduce futile page comparison overhead meanwhile to detect page sharing opportunities efficiently. And a virtual machine based memory partition called VMMP is added into CMDP to reduce interference among virtual machines. According to page color, VMMP allocates unique page colors to applications, virtual machines and hypervisor. The experimental results show that CMDP can efficiently improve performance (by about 15.8 percent) meanwhile accommodate more virtual machines concurrently.

The papers in this special section focus on edge computing and the challenges that exist for artificial intelligence applications. Edge computing has the advantages of real-time response and less network demand for computing closer to the edge of the network, while bridging the physical and digital worlds. The core of the edge computing is to provide the edge intelligent service. Therefore, edge artificial intelligence is becoming a popular trend for the future, such as intelligent sound box, and so on. Edge artificial intelligence combines edge computing with artificial intelligence, while taking both advantages. However, there are some problems, which need to be solved for the edge artificial intelligence. Addresses these issues and examines future areas of development in this area.

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the factors impacting transmission delay, such as the bandwidth between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover and mutation operators of the genetic algorithm were adopted to avoid the premature convergence of traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing.

Due to large volume and high variability of editing tools, protecting multimedia contents, and ensuring their privacy and authenticity has become an increasingly important issue in cyber-physical security of industrial environments, especially industrial surveillance. The approaches authenticating images using their principle content emerge as popular authentication techniques in industrial video surveillance applications. But maintaining a good tradeoff between perceptual robustness and discriminations is the key research challenge in image hashing approaches. In this paper, a robust image hashing method is proposed for efficient authentication of keyframes extracted from surveillance video data. A novel feature extraction strategy is employed in the proposed image hashing approach for authentication by extracting two important features: the positions of rich and nonzero low edge blocks and the dominant discrete cosine transform (DCT) coefficients of the corresponding rich edge blocks, keeping the computational cost at minimum. Extensive experiments conducted from different perspectives suggest that the proposed approach provides a trustworthy and secure way of multimedia data transmission over surveillance networks. Further, the results vindicate the suitability of our proposal for real-time authentication and embedded security in smart industrial applications compared to state-of-the-art methods.

The papers in this special section examines the use of interactive virtual environments in the field of neuroscience. Virtual environments is a technology able to establish a relationship between the user and the environment created, enabling real-time integration with controlled virtual objects. A virtual environment can be explored through visual and haptic devices, without real restrictions. The iteration derives from the communication between human actions and the outcome of these actions, processed by the computer generating a response inside the virtual environment. The interaction can be passive, such as watching television, or active, for instance in the case of users manipulating their body movements or a particular object inside a virtual scenario.

Localization is a fundamental function in cooperative control of micro unmanned aerial vehicles (UAVs), but is easily affected by flip ambiguities because of measurement errors and flying motions. This study proposes a localization method that can avoid the occurrence of flip ambiguities in bounded distance measurement errors and constrained flying motions; to demonstrate its efficacy, the method is implemented on bilateration and trilateration. For bilateration, an improved bi-boundary model based on the unit disk graph model is created to compensate for the shortage of distance constraints, and two boundaries are estimated as the communication range constraint. The characteristic of the intersections of the communication range and distance constraints is studied to present a unique localization criterion which can avoid the occurrence of flip ambiguities. Similarly, for trilateration, another unique localization criterion for avoiding flip ambiguities is proposed according to the characteristic of the intersections of three distance constraints. The theoretical proof shows that these proposed criteria are correct. A localization algorithm is constructed based on these two criteria. The algorithm is validated using simulations for different scenarios and parameters, and the proposed method is shown to provide excellent localization performance in terms of average estimated error. Our code can be found at: https://github.com/QingbeiGuo/AFALA.git.

The sensors for monitoring different parameters in smart cities are becoming a hot topic for researchers all around the world. The smart grids are one of the main objectives of the smart city concept. In this paper, we present an inductive sensor for monitoring the water level in tubes for water distribution grids. Different prototypes are proposed and tested. All of the prototypes are composed of two copper coils, which are fed with a sine wave with an amplitude of 3.3V peak-to-peak. The proposed sensor is based on the measurement of the changes in the induced magnetic field when the medium changes. The sensor is placed inside the tube and it is able to detect the amount of water contained in the tube. The prototypes are tested with the tube full of water and the tube empty of water at different frequencies in order to found the best working frequency. Then, more data is gathered with the selected frequencies and different percentages of water amount in the tube. After testing the different prototypes we conclude that the best one is the prototype 4, which presents a voltage variation higher than 1 V. It is formed by two solenoidal coils of 0.4 mm of copper in form of half-circle with 55 spires. This sensor can be used for detecting obstruction in the water distribution network, sewerage, and leaks of water. This sensor can be connected to a node as Arduino Uno in order to transmit the gathered data to the database of the water supplier.

The birth of a premature baby implicitly involves a series of special care focused on the monitoring of the evolution in terms of weight gain, movements of the baby, etc. It is also important to maintain the environmental parameters of the incubator in adequate values to avoid the heat loss of babies. Therefore, this paper presents the proposal of a system for infant incubators based on the use of temperature and humidity sensors, and a set of weight sensors, which allows us to monitor the progress of the baby. Each incubator that has this system is connected to a central network based on Long Range Networks (LoRa) that allows registering the medical data in a database. Finally, the system has a Near Field Communication (NFC) interface that allows the identification of doctors, the view of the patient evolution with tablets and the introduction of new data by the doctor. Finally, the system has been tested in a laboratory environment to ensure the proper functioning of system.

The role of cluster-head in an IEEE 802.15.4 cluster-tree network is to aggregate data from various devices in the cluster and cumulatively transmit to the PANC. This is an energy efficient way of sending data compared to individual reporting of devices independently. Cluster-head coordinators expend more energy compared to other coordinators in the cluster as they have to remain active for longer duration and carry out tasks like aggregation and transmission. Therefore this role of a cluster-head has to be periodically rotated among different coordinators to prevent exhaustion of a particular coordinator's energy and to extend the overall network lifetime. Few of the works done in this direction consider the existence of single hop transmission link to the PANC. Majority of other works designed for wireless sensor networks (WSNs) base the cluster-head rotation decision on threshold of available residual-energy in a coordinator. In this paper, we present a non- threshold based cluster-head rotation scheme that makes a rotation decision based on network- lifetime. It considers the residual energy, transmission cost and aggregation cost from associated coordinators and end-devices in synchronized IEEE 802.15.4 cluster-tree networks. Through simulations, we show that the proposed mechanism extends the overall network lifetime, outperforming other approaches.

Due to the recorded success by Internet of Things (IoT) technology, more and more domains use it as a communications and exchange network such as e- health, smart cities, vehicles, etc. IoT do not stop integrating an important number of components and objects that are characterized by their complexity and heterogeneity. Such constraints make the existing routings protocols unsuitable for IoT communications. To accomplish all the expected tasks and satisfy the user services, it is important to guarantee a quality of communication that answers to the requirements of the various applications in terms of data and processing (availability, integrity, efficiency, etc.). The objective of our work in this paper is to propose a smart and efficient routing algorithm to improve the IoT communications performance. The proposed method is called SERA (Smart and Efficient Routing Algorithm) which is based on self-organization of the communications between devices according to their new parameters. The evaluation of SERA has shown that SERA improves considerably the IoT communications performance in terms of some QoS and energy efficiency parameters.

Fog computing-based radio access networks (F-RANs) aim to extend the computing and storage facilities of the centralized cloud radio access networks (C-RANs) to the network edge. Compared with the centralized baseband unit pool in the C-RAN, F-RAN reduces the burden on fronthaul. Thus, the F-RAN is foreseen as a viable solution towards ultra-low latency service provisioning. However, due to limited computing and storage facilities in fog computing-enabled access points (F-APs), some tasks that cannot be executed on the primary F-APs are transferred to other F-APs. In worst-case, the tasks are sent to the resource-enriched centralized cloud for processing. The transmission latency between F-APs, F-AP-to-end- user, and fronthaul latency strongly depends on interference power from the undesired network element as well as end-users. At the same time, the computational latency increases with the queuing delay. In this paper, we propose a load balancing scheme to address the tradeoff between transmission and computing latencies in F- RANs. Finally, the extensive simulation results show that the proposed scheme outperforms the greedy approach to meet the critical requirements, such as low-latency and minimal task offloading to the cloud in the F-RAN for the low-latency communications.

The use of sensors has increased in the last years. They allow the real-time monitoring and reduce the processing cost because most of wireless sensor networks (WSNs) do not require the intervention of people to perform the measurement. Sensors are useful for industrial processes that can imply some danger for workers. These devices also make easy the development of some tasks as the ones carried out in Wastewater Treatment Plant (WWTP). This paper presents the design, deployment and calibration of a low cost sensor able to detect the concentration of solids in some phases of the water treatment. The sensor is based on 2 coils and a microprocessor module. Several models and combination of coils have been testes. The results show that our sensor is capable of measuring concentrations form 0% to 20% (0 mg/l to 200,000 mg/l) with an error lower than the 3.5%.

Internet of Things (IoT) can be combined with machine learning in order to provide intelligent applications to the network nodes. Furthermore, IoT expands these advantages and technologies to the industry. In this paper, we propose a modification of one of the most popular algorithms for feature selection, fast-based-correlation feature (FCBF). The key idea is to split the feature space in fragments with the same size. By introducing this division, we can improve the correlation and, therefore, the machine learning applications that are operating on each node. This kind of IoT applications for industry allows us to separate and prioritize the sensor data from the multimedia-related traffic. With this separation, the sensors are able to detect efficiently emergency situations and avoid both material and human damage. The results show the performance of the three FCBF-based algorithms for different problems and different classifiers, confirming the improvements achieved by our approach in terms of model accuracy and execution time.

Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for Internet of Things (IoT). Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance (FT) in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering FT and communication delay as two conflicting objectives. An adapted nondominated sorting-based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, FT and delay computation, crossover and mutation, and nondominance-based sorting. Analytical and simulation-based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes FT for virtualization in WSNs.

Location-oriented services in vehicular cyber-physical system (VCPS) have witnessed significant attention due to their potentiality to address traffic safety and efficiency related issues. The multichannel communication aids these services by tuning their overall performance in vehicular environments. Related literature on multichannel communication focuses on interference as channel quality measure. However, uncertain mobility and density of vehicles significantly affect channel quality apart from interference. The static quantification of channel quality is not suitable due to the dynamic characteristics of the channel quality parameters. In this context, this paper proposes fuzzy-based channel selection framework for location-oriented services in multichannel VCPS environments. A system model is presented for deriving channel access delay (CAD) using Markov chain model. The channel quality is estimated using CAD and signal-to-interference ratio (SIR). The fuzzy logic-based channel selection framework is developed considering fuzzification and defuzzification of CAD and SIR. The comparative performance evaluation attests the benefit of the framework as compared to the state-of-the-art techniques in VCPS.

In the context of Industry 4.0, industrial robotics such as automated guided vehicles have drawn increased attention due to their automation capabilities and low cost. With the support of cognitive technologies for industrial Internet of Things (IoT), production processes can be significantly optimized and more intelligent manufacturing can be implemented for smart factories. In this paper, for advanced material handling, a cognitive industrial entity called context-aware cloud robotics (CACR) are introduced and analyzed. Compared with the one-time on-demand delivery, CACR is characterized by two features: (1) context-aware services and (2) effective load balancing. First, the system architecture, advantages, challenges, and applications for CACR are introduced. Then, fundamental functions for material handling are articulated, namely, decisionmaking mechanisms and cloud-enabled simultaneous localization and mapping. Finally, a CACR case study is performed to highlight its energy-efficient and cost-saving material handling capabilities. Simulations indicate the superiority of cognitive industrial IoT and show that using CACR for material handling can significantly improve energy efficiency and save cost.

Exponential growth of multimedia data has been witnessed in recent years from various industries, such as e-commerce, health, transportation, and social networks, etc. Access to desired data in such gigantic datasets require sophisticated and efficient retrieval methods. In the last few years, neuronal activations generated by a pretrained convolutional neural network (CNN) have served as generic descriptors for various tasks including image classification, object detection and segmentation, and image retrieval. They perform incredibly well compared to hand-crafted features. However, these features are usually high dimensional, requiring a lot of memory and computations for indexing and retrieval. For very large datasets, utilization of these high dimensional features in raw form becomes infeasible. In this paper, a highly efficient method is proposed to transform high dimensional deep features into compact binary codes using bidirectional Fourier decomposition. This compact bit code saves memory and eases computations during retrieval. Further, these codes can also serve as hash codes, allowing very efficient access to images in large datasets using approximate nearest neighbor (ANN) search techniques. Our method does not require any training and achieves considerable retrieval accuracy with short length codes. It has been tested on features extracted from fully connected layers of a pretrained CNN. Experiments conducted with several large datasets reveal the effectiveness of our approach for a wide variety of datasets.

This paper proposes a secure surveillance framework for Internet of things (IoT) systems by intelligent integration of video summarization and image encryption. First, an efficient video summarization method is used to extract the informative frames using the processing capabilities of visual sensors. When an event is detected from keyframes, an alert is sent to the concerned authority autonomously. As the final decision about an event mainly depends on the extracted keyframes, their modification during transmission by attackers can result in severe losses. To tackle this issue, we propose a fast probabilistic and lightweight algorithm for the encryption of keyframes prior to transmission, considering the memory and processing requirements of constrained devices that increase its suitability for IoT systems. Our experimental results verify the effectiveness of the proposed method in terms of robustness, execution time, and security compared to other image encryption algorithms. Furthermore, our framework can reduce the bandwidth, storage, transmission cost, and the time required for analysts to browse large volumes of surveillance data and make decisions about abnormal events, such as suspicious activity detection and fire detection in surveillance applications.

Over the past couple of decades, transportation systems have begun to receive widespread attention from the scientific community and emerged toward Intelligent Transportation Systems (ITS). Effective vehicular connectivity techniques can significantly enhance efficiency of travel, reduce traffic incidents and improve safety, and alleviate the impact of congestion; devising the ITS experience. Furthermore, during the past decades, the volume and density of vehicles increased significantly, especially the road traffic; this lead to a dramatic increase in the number of accidents and congestion, with negative impacts on the economy, environment, and quality of people’s lives. In particular, according to the World Health Organization (WHO), road traffic injuries are estimated to be the leading cause of death for young people aged 15–29 and the ninth cause of death worldwide in 2015. The enabling communication technologies are intended to realize the frameworks that will spur an array of applications and use cases in the domain of road safety, traffic efficiency, and driver’s assistance. Although these applications will allow the dissemination and gathering of useful information among vehicles and between transportation infrastructure and vehicles in pursuance of assisting drivers to travel safely and comfortably, much effort is required to implement these practices for the success of these applications.

Sensor networks and Internet of Things (IoT) are useful for many purposes such as military defense, sensing in smart homes, precision agriculture, underwater monitoring in aquaculture, and ambient‐assisted living for healthcare. Efficient and secure data forwarding is essential to maintain seamless communications and to provide fast services. However, IoT devices and sensors usually have low processing capabilities and vulnerabilities. For example, attacks such as the Distributed Denial of Service (DDoS) can easily hinder sensor networks and IoT devices. In this context, the current approach presents an agent‐based simulation solution for exploring strategies for defending from different DDoS attacks. The current work focuses on obtaining low‐consuming defense strategies in terms of processing capabilities, so that these can be applied in sensor networks and IoT devices. The experimental results show that the simulator was useful for (a) defining defense and attack strategies, (b) assessing the effectiveness of defense strategies against attack ones, and (c) defining efficient defense strategies with low response times.

Lloret, J.; Ahmed, SH.; Rawat, DB.; Ejaz, W.; Yu, W. (2018). Editorial on Wireless Networking Technologies for Smart Cities. Wireless Communications and Mobile Computing (Online). 2018. doi:10.1155/2018/1865908

The urban lawns are frequently composed by different grass species to combat some problems of water scarcity and diseases. In order to maintain these lawns, high amount of water is required. Nowadays, smart cities can be understood as a new concept of city that includes, among others, efficient distribution of energy, water, and other resources by using technology. In these cases, the main challenge is to try to estimate the necessary amount of water for irrigation and the phytosanitary uses without wasting water. In this paper, we propose a method to identify the percentage of grass coverage in lawns to deduce the grass productivity and estimate the most accurate quantity of water to ensure a good production of grass. The system is based on a Smart Autonomous Vehicle (SAV) controlled by an Arduino Mega 2560. It also contains an array of 120 colour sensors used to gather the data. The selected colour sensor is a TCS3472. With these sensors, we obtain the RGB histograms of the lawns. For these experiments, we have several lawn parcels of 1.5 x 1 m. From these, a matrix of 150 x 100 RGB values is obtained. After processing the green values of matrix, we have observed a correlation between the level of coverage and these values. The grass coverage is related with values of brightness between 40 and 60 which allow us to classify the lawn as a function of its coverage and the irrigation needs.

Wireless Sensor Networks (WSNs), Internet of things (IoT) and aerial mapping are nowadays being used very much in agriculture. The challenge of joining those technologies requires a new and smart wireless network topology for devices communication. Problems like scalability and manageability are important challenges when there are many devices. This paper presents the design of a smart IoT communication system manager used as a low cost irrigation controller. The proposal is a powerful irrigation tool that uses real time data such as the variable rate irrigation and some parameters taken from the field. The field parameters, the index vegetation (estimated using aerial images) and the irrigation events, such as flow level, pressure level or wind speed, are periodically sampled. Data is processed in a smart cloud service based on the Drools Guvnor (a Business Rules Manager). The developed multimedia platform can be controlled remotely by a mobile phone. Finally, we measured the bandwidth consumed when the system is sending different kinds of commands and data.

More than forty million people in the world are epilepsy sufferers. Epilepsy is a neurological disease. That is characterized by the presence of seizures in which the patient has convulsions, among other symptoms, that can cause damage, and even lead him to death. For this reason, it is important to develop systems for allowing early detections. Additionally, the cost of this kind of devices is usually quite high, so they are often not accessible to everyone. In this paper, a low-cost monitoring system is presented. It is able to detect early symptoms of epilepsy seizures. The system uses several corporal parameters such as temperature, heart rate, and extremities motion. The device transmits the occurrences of these seizures to a mobile phone, where an application notifies the person or institution in charge of the patient. This application also keeps a record of these parameters and events which can be viewed later by the user. Finally, the system is tested in order to check both its functionality and the correct operation of the system.

Nowadays, new technologies are focused on providing ubiquity and the possibility of accessing services from everywhere while reducing power consumption and the CO2emissions in networking. Furthermore, the process of network virtualization is more and more used. However, both concepts are not currently treated as a set. For this reason, this paper presents a practical evaluation where the power consumption in a virtualized network is measured. To do it, we have developed a topology composed by several virtual routers that emulates the core of a network through which we have sent several data flows. Measurements have been performed by using http and video streaming traffic while running Open Shortest Path First (OSPF) and Routing Information Protocol version 2 (RIPv2) as routing protocols. Result shows that both protocols present similar behavior when streaming traffic is sent while RIPv2 demands more CPU usage when http traffic is sent. Finally, the energy consumption in http traffic is always bigger.

Adaptive video streaming providers take advantage of the HTTP protocol to deliver video contents to the applications of the end-users. Although, these applications use bitrate adaptation to adjust video resolution to network user conditions, this mechanism is used to improve fairness. Jain's algorithm has been employed to determine the increase of the Quality of Experience (QoE) of end-users. In wireless networks, performance is highly degraded when there is high number of concurrent users requesting adaptive video streaming. As a result, the QoE of some users is unpredictably degraded. In this paper, we propose an SDN-based TCP throughput management algorithm to provide fairness to competing users over a wireless network. It provides fairness among clients in order to avoid oscillation of the video quality and stable buffer length. Real measurements show an improvement of 39% of fairness among users for two competing clients, 43% for three competing clients and 46% for four.

Abstract Multi‐hop wireless mesh networks (WMNs) are increasingly growing interest as a promising technology for high‐speed network access. WMNs are integrated with other networks such as Internet, sensor networks, and cellular networks through gateways. Therefore gateways in WMNs are prone to various security threads and can be easily exploited by the attacker from any part of the Internet to launch a distributed flooding attack to compromise computer systems, affect the network performance, and destruct the services. Many approaches have been proposed by researchers in this regard but still more efforts are needed in terms of accuracy and efficiency. In this research paper, we will propose an artificial neural network‐based technique for detection of distributed flooding attacks to things such as sensors or actuators in WMNs called the distributed flood attack detector. In our simulation, sample dataset used to train and test the artificial neural network is generated using NS‐2 network simulator. Simulation results and real system implementation proved that the distributed flood attack detector can be used in a real network environment to detect the intermediate and severe distributed flood attacks with low‐false positive and false‐negative rates. Copyright © 2015 John Wiley & Sons, Ltd.

Sensor networks can be used in many sorts of environments. The increase of pollution and carbon footprint are nowadays an important environmental problem. The use of sensors and sensor networks can help to make an early detection in order to mitigate their effect over the medium. The deployment of wireless sensor networks (WSNs) requires high-energy efficiency and secures mechanisms to ensure the data veracity. Moreover, when WSNs are deployed in harsh environments, it is very difficult to recharge or replace the sensor's batteries. For this reason, the increase of network lifetime is highly desired. WSNs also work in unattended environments, which is vulnerable to different sort of attacks. Therefore, both energy efficiency and security must be considered in the development of routing protocols for WSNs. In this paper, we present a novel Secure and Low-energy Zone-based Routing Protocol (SeLeZoR) where the nodes of the WSN are split into zones and each zone is separated into clusters. Each cluster is controlled by a cluster head. Firstly, the information is securely sent to the zone-head using a secret key; then, the zone-head sends the data to the base station using the secure and energy efficient mechanism. This paper demonstrates that SeLeZoR achieves better energy efficiency and security levels than existing routing protocols for WSNs. Copyright © 2016 John Wiley & Sons, Ltd.

Operators are continuously expanding their Internet Protocol Television (IPTV) service due to both the increase of the users subscribed to the service and the increase of the type of devices used by the customers. When a user connects to the IPTV service using multiple devices simultaneously, each connection consumes its corresponding bandwidth. This can create serious problems to the operators such as the lack of enough bandwidth, decrease of the Quality of Service, etc. This paper presents the study carried out using a basic topology on which a video is broadcasted varying several network parameters and the network conditions. The received video quality is assessed by using the Mean Option Score. Taking into account all gathered information, we propose a new protocol and algorithm to improve the Quality of Experience of IPTV service customers.

Currently, one of the main problems facing the video broadcast on the Internet is the limitation of the available bandwidth. The media elements contain a large number of multiplexed data and its encoding is necessary for a smooth issuance through the new Application Programming Interface (API) in HTML5 or through third-party plugins. Internet offers a wide compatibility with many formats and video codecs, but its emission focuses specifically on the three main ones: MPEG-4, Ogg and WEBM. In this paper, we study different encodings of a video depending on the bitrate and the encoding time. This study will allow us to know the characteristics of each one of these three video formats. Then, we use this information to design an algorithm, which will run in a server, in order to send the appropriate video type according to the studied characteristics.

Recently, wireless sensor networks (WSNs) have been widely used in many applications, such as Smart Grid. However, it is generally known that WSNs are energy limited, which makes WSNs vulnerable to malicious attacks. Among these malicious attacks, a sinkhole attack is the most destructive one, since only one sinkhole node can attract surrounding nodes with unfaithful routing information, and it executes severe malicious attacks, e.g. the selective forwarding attack. In addition, a sinkhole node can cause a large amount of energy wastes of surrounding nodes, which results in abnormal energy hole in WSNs. Thus, it is necessary to design an effective mechanism to detect the sinkhole attack. In this paper, we propose a novel Intrusion Detection Algorithm based on neighbor information against Sinkhole Attack (IDASA). Different from traditional intrusion detection algorithms, IDASA takes full advantage of neighbor information of sensor nodes to detect sinkhole nodes. In addition, we evaluate IDASA in terms of malicious node detection accuracy, packet loss rate, energy consumption and network throughput in MATLAB. Simulation results show that the performance of IDASA is better than that of other related algorithms.

The necessity of energy-efficient systems in order to protect our environment, cope with global warming, and facilitate sustainable development is paramount for the researching world because the survival of the planet is at stake. Thus, optimizing the energy efficiency of wireless communications not only reduces environmental impact, but also cuts overall network costs and helps make communication more practical and affordable in a pervasive setting. This paper is focused on a solution to enhance the energy efficiency in outdoor wireless local area networks using the standard IEEE 802.11b/g. So, from a previous study about the weather’s impact on the number of control frame errors and retransmissions, we propose a green cognitive algorithm that adapts wireless transmissions to the channel conditions caused by the weather. The goal is to reduce retransmissions and control errors in order to save energy and to enhance network performance. Our proposal is based on a mathematical analysis in which we see how the frame error rate is related to the power consumption according to the modulation scheme and data rate used by transmitters. Finally, several simulations show that the green cognitive algorithm presented in this paper involves significant energy savings for outdoors WLANs.

In sensor networks, the large amount of data generated by sensors greatly influences the lifetime of the network. To manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.

The energy efficient in medical ad hoc sensors network (MASN) is one of the most important areas of researches to ensure better services for applications using such an environment. The challenge is how to use of energy of the sensors nodes in a fair way to avoid a break of connectivity in the network as long as possible. Our work focus on how to optimize the energy consumption and to increase the performance of the user applications in medical context. Our proposed approach is called M-EE (Medical Energy Efficient) which is based on the routing protocols with adding a new algorithm for energy fairness. M-EE takes into account the medical communication environment to manage efficiency the energy of the sensor nodes. With this mechanism, sensor nodes which are using image and video medical data are allowed more energy than other sensors nodes. The simulation results showed that our proposed M-EE approach allow significant energy consumption of the network, a reduction of the data loss and an increase in average working time of the sensors.

In recordings of naturals environments there is often a predominant color due to the type environment such as a forest, a desert or ocean. Currently, there are a wide range of available video codecs that provide different compression results for different types of videos based on their predominant colors. Any way to save video flows bandwidth becomes a system to save or earn money. In this paper, we propose a decision algorithm which selects the most efficient codec to compress a video as a function of its chroma characteristics. In order to do this, first we created 5 videos where the main colors were white, black, red, green and blue in order to see the compression characteristics of each codec. Then, we used 4 videos of real environments with different predominant colors in order to test the video quality of the result and the compression time. Using these results we propose an algorithm that takes into account the predominant color and the video quality to efficiently select the best codec for each case. Moreover, we also provide the protocol design for the proper operation of our system.

ABSTRACT The next‐generation networks improve people's quality of life with new services and applications. The evolution of Internet technologies also promoted the development of electronic commerce and the evolution of mobile payments. Near field communication (NFC) was proposed as a new short‐range wireless technology and can play an important role in such issues. Money transactions among mobile devices could play an important role in a near future in order to simplify and perform such operations safely. Furthermore, it is important that tasks are easy to perform. Then, this paper presents a mobile solution based on a peer‐to‐peer communication system for money transfer, based on NFC and Bluetooth communications. A hybrid communication protocol combining the security characteristics of NFC and better Bluetooth data transfer rates is proposed. The proposal was deployed in real environments, and it was evaluated, demonstrated, and validated with different NFC tags and the Bluetooth traffic was also analyzed. It was shown that system presents good response times. The solution offers a friendly user's interface and very easy to use, guaranteeing the security of money transfer, and it is ready for use. Copyright © 2013 John Wiley & Sons, Ltd.

Combining evaluations of different kinds, like of user feedback origin, is often a necessary optimization step in communications software and in several applied areas that require personalized or statistical customization. This paper is introducing a fingerprinting fusion approach to identify the optimal operator inside a family of well known and identified as suitable information retrieval data-fusion methods; even if the approach was identified to address an issue related to the identification of the optimal streaming server for differential positional data from satellite systems, the model presented and the methodology followed define a multidisciplinary approach that can be utilized in several experimental areas utilizing information retrieval data-fusion methods. A mobile prototype was developed and simulation data was utilized to experiment and evaluate the proposal, with good results, identifying that for the specific representation a linear operator that outperforms weighted CombSUM and CombMAX.

In order to use intelligent video sensing systems, three main features should be satisfied. HD video transmission to provide enough video quality for image treatment, a wireless ad hoc network to intelligent locate and focus the issue to be sensed and enough available bandwidth to provide HD video quality. This paper presents a communication protocol designed and deployed to provide HD video transfers between several mobile devices working with Android and Linux OS. Experimental results using AR Drones with Wi-Fi forming an ad hoc network demonstrate the big potential of this kind of networks in mobile devices. The use of AR Drones will revolution the field of HD video communications over air for intelligent video sensing because they have many sensors to be controlled autonomously using GPS or manually using remote control.

Vehicular Ad hoc Networks (VANETs) are a killer application of Mobile Ad hoc Networks (MANETs), which exchange data among vehicles and vehicles to roadside infrastructures by routing. To save energy, various routing protocols for VANETs have been proposed in recent years. However, VANETs impose challenging issues to routing. These issues consist of dynamical road topology, various road obstacles, high vehicle movement and the fact that the vehicle movement is constrained on roads and traffic conditions. Moreover, the movement is significantly influenced by driving behaviors and vehicle categories. To this end, we incorporate them into routing and propose energy-efficient routing using movement trends (ERBA) for VANETs—an energy-efficient routing protocol. ERBA classifies vehicles into several categories, and then leverages vehicle movement trends to make routing recommendation. It predicts the movement trends by current directions and next directions after going through the road intersections. With the vehicular category information, the driving behavior patterns, the distance between the current sections and the next intersections, ERBA propagates information among vehicles with less energy consumption. The proposed scheme is validated by real urban scenarios extracted from ShanghaiGrid project. Experimental results show that ERBA outperforms the compared routing protocols with respect to the end-end delay, the packet delivery ratio and the path duration time.

SUMMARY Denial of service (DoS) attacks can be defined as any third‐party action aiming to reduce or eliminate a network's capability to perform its expected functions. Although there are several standard techniques in traditional computing that mitigate the impact of some of the most common DoS attacks, this still remains a very important open problem to the network security community. DoS attacks are even more troublesome in smart object networks because of two main reasons. First, these devices cannot support the computational overhead required to implement many of the typical counterattack strategies. Second, low traffic rates are enough to drain sensors' battery energy making the network inoperable in short times. To realize the Internet of Things vision, it is necessary to integrate the smart objects into the Internet. This integration is considered an exceptional opportunity for Internet growth but, also, a security threat, because more attacks, including DoS, can be conducted. For these reasons, the prevention of DoS attacks is considered a hot topic in the wireless sensor networks scientific community. In this paper, an approach based on 6LowPAN neighbor discovery protocol is proposed to mitigate DoS attacks initiated from the Internet, without adding additional overhead on the 6LoWPAN sensor devices.Copyright © 2012 John Wiley & Sons, Ltd.

Some works on the researchers cooperation's literature provide the key lines for building research networks and propose new protocols and standards for business to business (B2B) data exchange, but none of them explains how researchers should contact and the procedure to select the most appropriate partner of a research enterprise, institute or university. In this paper, we propose a B2B architecture and protocol between research entities, that uses ebXML protocol. The contacts for cooperation are established based on some defined parameters and an information retrieval system. We explain the information retrieval system, the researcher selection procedure, the XML-based protocol and the workflow of our proposal. We also show the information that has to be exchanged to contact other researchers. Several simulations demonstrate that our proposal is a feasible architecture and may be used to promote the research cooperation. The main purpose of this paper is to propose an efficient procedure for searching project partners.

This paper presents a secure protocol for spontaneous wireless ad hoc networks which uses an hybrid symmetric/asymmetric scheme and the trust between users in order to exchange the initial data and to exchange the secret keys that will be used to encrypt the data. Trust is based on the first visual contact between users. Our proposal is a complete self-configured secure protocol that is able to create the network and share secure services without any infrastructure. The network allows sharing resources and offering new services among users in a secure environment. The protocol includes all functions needed to operate without any external support. We have designed and developed it in devices with limited resources. Network creation stages are detailed and the communication, protocol messages, and network management are explained. Our proposal has been implemented in order to test the protocol procedure and performance. Finally, we compare the protocol with other spontaneous ad hoc network protocols in order to highlight its features and we provide a security analysis of the system.

Mobile health (m-Health) includes the use of mobile applications to deliver healthcare services anytime and anywhere. This paradigm benefits with the advent of Web services based systems since they allow health applications available for users with mobility support. This paper presents a novel cooperation strategy for m-Health services and applications. It comprises a reputation-based strategy for Web services oriented architectures that includes a cooperative Web service for managing all the network cooperation and nodes reputation. The cooperation strategy considers two modules for mobile nodes, one for verifying the cooperation status of neighbor nodes and another for classifying the neighbor nodes in function of their reputation. The performance of the proposal is evaluated in a real scenario, using an m-Health application, called SapoFit.

Improving the performance and the behavior of outdoor wireless local area networks (WLAN) is a very important objective for almost all researchers in the telematics field. In this paper, a clear relation between the weather and the performance of an outdoor WLAN is shown. This relation is extracted from a correlation analysis between several meteorological variables and the control and management frames of the data link layer of a WLAN at the Universitat Politècnica de València campus. In order to carry out this study, it was necessary to collect the required data during two months from an outdoor WLAN, as well as the meteorological variables from the weather station located at the same university campus. Then, we processed all this data and concluded that the temperature and the solar radiation affect the RTS frames, CTS frames, acknowledgments, and others.

The appearance of several IEEE 802.11 variants made possible the availability of many wireless local area network access options for end users in the same location. Network requirements for IPTV service become higher when the quality of the delivered video is higher. Moreover, in wireless networks the network performance is highly degraded when there is high number of users. In this paper, we propose a protocol and algorithm that makes to handover IPTV users to the best available WLAN when the QoS parameters received by the end user reaches a threshold. It guarantees anytime the highest QoS for the user. Real measurements show that the vertical handover performed by the user is performed without noticing it. This algorithm is highly appropriate for mobile users.

According to statistics data, the population of many countries is aging. Moreover, there is also a small sector of the population that is affected by disabilities. Researchers should develop systems to improve their quality of life. In recent years, the development of smartphones and reduced size devices, but with high processing capacity, has increased dramatically. We can take profit of the sensors and applications embedded in the smartphones in order to monitor disabled and elderly people. In this paper, we describe a smart collaborative system based on the sensors placed in the mobile devices, which allow us to monitor the status of a person based on what is happening in the group people. The network algorithm and the smart system protocol is described and simulated in order to show the performance of our proposal.

New communication technologies must be provided in order to improve the energy distribution. As far as we know, there is not any network protocol designed exclusively for this purpose. In this paper, we present the design and simulation of a group-based protocol to improve the energy distribution in smart grids. We will show the group-based architecture and the protocol operation. Finally we will show some measurements taken from a test bench in order to test its performance.

In order to have long distance underwater wireless communications, low frequencies must be used. But the main problem when using low frequencies is the lack of bandwidth to transmit high data rate. Sometimes wireless sensors must be placed quite close for taking more accurate measurements from the water, so higher frequency bands could be used in special cases. In this paper we measure the maximum coverage distance of underwater wireless sensors when they are placed at about 15 cm underneath the water surface, without having wireless coverage from the air side. Then, we measured the number of lost packets and round trip time for 1, 2, 5.5 and 11 Mbps at different frequencies for different distances.

One of the main issues in aquaculture is the high cost of the lost food when the fish are fed. Furthermore, this wasted food is deposited in the seabed and generates an environmental impact on the surrounding area. It causes failure to uphold aquaculture legislation. The authors propose a feeding control system based on groups of sensors that take the appropriate decisions when the fish are fed in marine fish farms. This system mixes data obtained from a group of sensors and performs an exhaustive control on the food offered to the fish, thus reducing costs. This will avoid wasting large quantities of food and an economic saving will be achieved. To develop the proposed system, the authors used the behaviour of the movements of the fishes and some parameters from the water. These movements are monitored strategically by a group ofunderwater transducers placed along the length, width and depth of the cage and, through data fusion, they provide the information needed in the system to continue feeding the fish or not.

Many wireless sensor network developers have adopted Bluetooth and Zigbee technologies for sensor communication. However, it has a big problem: the radio coverage is very short; so many sensors are needed to cover wide areas and sensors are expensive. On the other hand, these technologies lack a high bandwidth. IEEE 802.11a/b/g technologies have higher coverage areas and higher bandwidth, but their energy consumption is significantly higher. This paper discusses the energy consumption analytically and gives the considerations that should be taken into account in developing a wireless local area sensor network. Then, we propose a new protocol based on group collaboration to be implemented in these types of networks in order to decrease the energy consumption and save the energy wasted by the whole system. Finally, the protocol proposed is simulated and compared with other protocols available in the Ns2 simulator in order to prove that our solution is better.

A social network site (SNS) is a Web 2.0 software application used by many Internet users. These types of networks have their advantages and disadvantages. The main advantage is the degree of communication that they can bring among users. This can improve the work environment, the productivity and also the educational environment. The negative point of these networks is communication misuse. Sometimes, users can share private information that can lead them to trouble. In order to solve these problems two options could be used: (a) create a closed platform, which only feasible in working and educational environments, or (b) use a Web platform, where the data about users are anonymous. In this latter case, we present a platform to share pictures based on the Web 2.0, where user information is anonymous. In this platform, in addition to sharing photos, we can create groups, introduce comments, etc. The platform is based on PHP and MySQL. We present the evaluation of this SNS in a real environment during a month. The parameters measured show that the SNS is working fine. Nobody has measured the same parameters as the ones studied in this paper.

In contrast with the simple and elegant concept of global computing, also called ubiquitous computing - where daily life objects get now computing capacity and interact among them to provide a wide range of services to users - a major difficulty is arising with the software engineering process to develop applications for these devices. This article discusses the additional parameters that should be taken into account when designing and developing software for ubiquitous devices in ad-hoc networks. In these networks the mobility of the devices could increase the complexity of the requirements to be considered. As a case study, we present a Bluetooth application that has been developed to fulfill a variety of established requirements. The application allows the creation of different types of networks for non-expert users thanks to the context aware capacity of Bluetooth.

Most of actual multimedia tools use RTP/RTCP for inter-stream synchronization, but not for group synchronization. In this paper we describe and evaluate a proposal of modification of RTCP packets to provide a sender-based method for synchronization of a group of receivers

A direct method that uses a random optimization process is proposed for the design of parametric IIR (infinite impulse response) filters for audio equalization. The method uses the loudspeaker response as starting point in order to obtain the desired electro-acoustical target. The algorithm uses a bank of parametric second order filters, known as peak filters in the audio field, to equalize and approximate the filtered response to the desired one. When the recursive process is finished, the parameters that define the filters (frequency, gain and Q) are obtained in correction order of importance; first the ones that provide deep correction in the response. This characteristic allows the implementation of scalable systems with different degrees of complexity and correction.