Multimodal data have emerged as a cornerstone for understanding and analyzing complex human behaviors, particularly in mental health monitoring. In this study, we propose a deep learning-driven behavioral modeling framework for intelligence of social things (IoST)-based mental health monitoring and intervention, designed to integrate and analyze multimodal data—including text, speech, and physiological signals—captured from interconnected IoST devices. The framework incorporates an adaptive attention-based fusion mechanism that dynamically adjusts the contribution of each modality based on contextual relevance, enhancing the robustness of multimodal integration. Additionally, we employ a temporal-aware recurrent neural network with an attention mechanism to capture long-term dependencies and evolving behavioral patterns, ensuring precise mental health state prediction. To validate the framework, extensive experiments were conducted using three publicly available datasets: DAIC-WOZ, SEED, and MELD. Comparative experiments demonstrate the superior performance of the proposed framework, achieving state-of-the-art accuracy of 93.5%, F1-scores of 92.9%, and AUC-ROC of 0.95 values. Ablation studies highlight the critical roles of attention mechanisms and multimodal integration, showcasing significant performance improvements over single-modality and simplified fusion approaches. These findings underscore the framework’s potential as a reliable and efficient tool for real-time mental health monitoring in IoST environments, paving the way for scalable and personalized interventions.

With the rising costs of drugs, medical devices, and diagnostic development, the topic of data-driven cognitive computing is currently an emerging research area in smart healthcare construction. With the support of machine learning and artificial intelligence empowered cognitive computing, the significant insights and knowledge hidden behind medical data can be capitalized for process optimization, anomaly detection, energy management, and so on. The special issue is an effort to provide a platform for researchers to explore healthcare issues supported by data-driven cognitive computing-related technologies from both theoretical and practical perspectives.

Content retrieval in content-centric vehicular networks faces challenges that include high latency, especially when content is stored far from the requesting vehicle. On-path caching feature in the conventional vehicular named data Networks (VNDN) enables content storage that can reduce latency. However, due to the constantly changing dynamic ad hoc nature of the vehicular network, the availability of stored content for the requester vehicle cannot be guaranteed. In addition, without knowing which content will be requested, where it will be requested and when it will be requested, the content caching functionality of VNDN is underutilized. To address this issue, this manuscript proposes a content prefetching scheme for the Content-centric Internet of Vehicles (CIoV) by introducing the content Value of Popularity ($VoP$) matrix. Considering vehicles requesting content of similar interests, we evaluate$VoP$through three value update functions that follow the power law of the time elapsed since the last content requested. By multiple parameters of consumer vehicle similarity, an on-road proactive content retriever vehicle is selected. The simulation results showed that the proposed proactive on-path content prefetching mechanism significantly reduces the content delivery delay while increasing the success delivery ratio by 48% and extends the spread of content within the network by 53%.

In An era where technology advances at an unprecedented pace, the healthcare sector stands at the cusp of a transformative revolution. The confluence of distributed Big Data intelligence with instantaneous e-healthcare services heralds a new paradigm, where the boundaries between medicine, artificial intelligence, and data science are blurred, giving rise to innovative solutions that redefine patient care. The emergence of personalized medicine, bolstered by the power of machine learning, graph-based techniques, and real-time analysis, is not merely a technological triumph but a testament to human ingenuity. It's a response to a world grappling with complex diseases, burgeoning healthcare costs, and an ever-increasing demand for precision and efficiency.

Maritime search and rescue plays an important part in ensuring the safety of life at sea. When using wireless Sensor Network (WSN) technologies in maritime, nevertheless, it endures from situations where the measurement information is inadequate. In computing and networking for maritime applications, Wireless Sensor Network (WSNs) is a rising inflexion because of its amazing features. Simultaneously, there are some challenges faced by WSNs and node localization is one of them. Node localization is an important factor because until the location of reporting node is unknown, the data sensed by that node is totally useless. The main aim of this paper is towards gaining more improvement in localization by using swarm intelligence algorithm. To achieve this aim, a range-free and distributed method by using the application of salp swarm algorithm for moving target node in network for maritime rescue is proposed. The results are compared with existing algorithm Particle Swarm Optimization (PSO) and Butterfly Optimization Algorithm (BOA). The proposed method has approximately 10% less localization error as compared to PSO and BOA. The proposed algorithm is validated in terms of localization accuracy, localized nodes, localization errors and computing time.

introduction Share on Introduction to the Special Issue on Cognitive Computing for Internet of Medical Things in Smart Healthcare Authors: Syed Hassan A. Shah California State University, Fullerton, USA California State University, Fullerton, USA 0000-0002-1381-5095View Profile , Shahid Mumtaz Instituto de Telecomunicações, Portugal Instituto de Telecomunicações, Portugal 0000-0001-6364-6149Search about this author , Wei Wei Xi'an University of Technology, China Xi'an University of Technology, China 0000-0002-8751-9205Search about this author Authors Info & Claims ACM Transactions on Sensor NetworksVolume 19Issue 3Article No.: 48epp 1–3https://doi.org/10.1145/3584742Published:25 April 2023Publication History 0citation69DownloadsMetricsTotal Citations0Total Downloads69Last 12 Months69Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Seamless connectivity between the vehicles and the infrastructures is required for the provisioning of future vehicular applications. However, the infrastructures in vehicular ad hoc networks (VANETs) such as roadside units (RSUs) are insufficient to provide ubiquitous connectivity and coverage in urban areas. Thus the services get interrupted, which results in lower network performance in VANETs. Motivated by this in this paper, we proposed a Proactive UAV Placement (PUP) technique to assist the RSUs in delivering the services in the out-of-coverage region by placing the UAVs in the appropriate positions. We first predicted the distribution of the future vehicles by utilizing the LSTM neural network. Based on the distribution of the future vehicles, the optimization problem for optimal UAV placement is formulated and solved by utilizing the Particle Swarm Optimization (PSO) algorithm. The results demonstrated that our scheme achieves better average coverage compared to two other UAV-assisted schemes.

Medical imaging, also known as diagnostic imaging, is growing fast due to its potential to provide accurate and detailed information regarding medical issues in cost-effective fashion. It can help detect diseases, monitor disease progression and is useful for surgical planning. The major challenge in this area is how to interpret images. Hence, doctors need tools that assist them in analysing the data and presenting them in an understandable manner to take the necessary course of action. The use of artificial intelligence (AI) in medical imaging has the potential to improve patient care, enhance collaboration between radiologists and other clinicians, and even help extend access to health services that would otherwise be impossible. One of the challenges faced in medical imaging is understanding the relationship between anatomy and the pathologic process. Hence, key areas of medical imaging that can benefit from AI are computer-aided diagnosis or interpretation, decision support systems for clinicians, and effective communication for patients. This special issue includes six articles, and each one was accepted after a blinded review process. Their major contributions are highlighted below. The first article is entitled ‘Health care intelligent system: A neural network based method for early diagnosis of Alzheimer's disease using MRI images.’ The authors propose a neural network-based algorithm for early diagnosis of Alzheimer's disease. The prediction model is built using MRI images. This approach provides better classification accuracy with improved performance measures. The second article is entitled ‘BLSNet: Skin lesion detection and classification using broad learning system with incremental learning algorithm.’ The authors focus on skin lesion detection using machine learning algorithms. A broad learning-based algorithm is used for the lesion detection and classification process. This is made with the help of incremental algorithms. The third article is entitled ‘Detecting pulmonary edema in lung resection patient through point-of-care lung ultrasonography.’ Pulmonary edema remains a serious health concern throughout the world. The authors propose an efficient algorithm for earlier detection of pulmonary edema using point-of-care lung ultrasonography. This approach provides better results when compared with traditional methods. The fourth article is entitled ‘3D brain image-based Alzheimer's disease (AD) detection techniques using fish swarm optimizer's deep convolution siamese neural network.’ The authors propose a fish swarm optimizer's deep convolution siamese neural network for earlier detection and diagnosis of Alzheimer's diseases. The prediction accuracy is comparatively better than conventional approaches, and it offers better performance. The fifth article is ‘Detecting breast cancer using novel mask R-CNN techniques.’ Breast cancer remains a serious concern throughout the world. The authors propose an R-CNN technique for efficient detection and diagnosis of breast cancer at an early stage of development. This approach provides better accuracy and precision measures. The sixth article is entitled ‘Feature optimization and identification of ovarian cancer using internet of medical thing.’ The major focus of this work is ovarian cancer. The proposed method is based on feature optimization techniques using machine learning. It provides improved optimization efficiency and better accuracy. With this approach, ovarian cancer can be detected easily at an earlier stage. Artificial intelligence (AI) has come a long way in healthcare. While it may be easy to think of AI as making machines intelligent, the more accurate definition is that it allows computers to complete tasks in ways that currently require human intelligence. At the same time, however, AI can complete many tasks that would be far too time-consuming or not possible for a human to do. This special issue has explored the significance of AI approaches for medical imaging. We hope this special issue will add significant benefits to the research community. We thank all the authors and reviewers for their timely contributions.

The relationship between computing and healthcare has a long history, but adoption of telemedicine is gradual due to political resistance, lack of infrastructure development frameworks, and lack of resources. One of the most rapid technological advancements will be the Internet of Medical Things (IoMT), which is predicted to bring about the greatest technological delivery ever. Edge computing in conjunction with 5G speed is the solution to achieve the requirements of quality of service metrics metrics during the analysis of clinical data. Artificial intelligence with edge computing has made significant contributions to the smart healthcare system's network for ultra-reliable communication in the areas of less delay, widespread device connectivity, and enhanced speed of data transmission. Since the edge-enabled IoMT-based system in the healthcare system offers a number of extraordinary potential, this Special Issue explores those areas of applicability. The aim of Special Issue is to cover the research difficulties associated with the implementation of edge computing-based IoMT systems in the healthcare system and suggests a framework for such a system.

Nowadays, a new intelligence structure known as the Artificial Intelligence of Things (AIoT) comes into play. Broadly speaking, AIoT is a fusion of Artificial Intelligence (AI) and Internet of Things (IoT) in practical applications. It applies AI to the edge and gives devices the ability to understand the data, observe the environment around them, and decide what to do best. However, the link among cloud, edge, blockchain, 5G, and AI poses many challenges that call for collaborative approaches and rethinking of the entire architecture, communication, and processing to meet requirements in latency, reliability, and so on. The purpose of this special section is to provide the academic and industrial communities a venue covering all aspects of the state-of-the-art collaborative approaches and systems at the AI and IoT, to advance their applications in the future.

The papers in this special section focus on artificial intelligence Internet of Things for medical things (AIoMT), with particular emphasis on medical sensors for remote patient monitoring and body area interfacing. Examines issues involving design and implementation, practice use, measurements, and patient monitoring.

Intelligent transportation system (ITS) is a key enabler for future road traffic management systems. The core components of ITS include vehicles, roadside units, and traffic command centers. They generate a large amount of data flow that is made up of both mobility and service-related data. Therefore, some data science methods to handle the transportation data are very necessary for ITS. Although some attempts have been done to explore data science methods for ITS, there exist various scientific and engineering challenges including software and hardware development, computational complexity, data multi-source heterogeneity, and privacy protection. Consequently, to fully explore the benefits of ITS applications like connected and autonomous vehicles, traffic control and prediction, road safety, and accident prediction, advanced data science methodologies and applications are in great need.

In intelligent transportation systems, the key issue of the Ride-Sharing Service (RSS) is to find proper drivers for the passengers by Intelligent Matching (IM) of two or three objects, including the positions of drivers, the travel information of passengers, and the spots where passengers and drivers meet and separate. Unfortunately, the exposure of travel plans of passengers in the IM process due to inference attacks has raised concerns about the privacy violation. To resist the inference attacks, we propose a Differentially Private Tripartite IM (DPTIM) protocol for RSS. DPTIM is based on the tripartite IM process, which intelligently finds the suitable threshold to filter out the matched objects with satisfaction scores below the threshold, so as to provide the high average satisfaction score of matched passengers. Compared to existing relevant mechanisms, DPTIM is distinguished by the feature that it leverages the inference error and differential privacy techniques to prevent the prior-information-based inference attacks and constrain the posterior information leakage, while providing satisfactory matching results. Furthermore, DPTIM meets the personalized demand of location privacy by using the passenger-specific tolerance estimation on inference errors and the personalized privacy budget. Finally, we implement DPTIM on real-world datasets, and demonstrate the satisfactory performance of DPTIM in terms of the average satisfaction score of passengers, the anti-inference-attack capability, and the passenger-specific privacy requirement.

Due to air quality significantly affects human health, it is becoming increasingly important to accurately and timely predict the air quality index (AQI). To this end, this article proposes a new federated learning (FL)-based aerial-ground air quality sensing framework for fine-grained 3-D air quality monitoring and forecasting. Specifically, in the air, this framework leverages a lightweight Dense-MobileNet model to achieve energy-efficient end-to-end learning from haze features of haze images taken by unmanned aerial vehicles (UAVs) for predicting AQI scale distribution. Furthermore, the FL framework not only allows various organizations or institutions to collaboratively learn a well-trained global model to monitor AQI without compromising privacy but also expands the scope of UAV swarms monitoring. For ground sensing systems, we propose a graph convolutional neural network-based long short-term memory (GC-LSTM) model to achieve accurate, real time, and future AQI inference. The GC-LSTM model utilizes the topological structure of the ground monitoring station to capture the spatiotemporal correlation of historical observation data, which helps the aerial-ground sensing system to achieve accurate AQI inference. Through extensive case studies on a real-world data set, numerical results show that the proposed framework can achieve accurate and energy-efficient AQI sensing without compromising the privacy of raw data.

Multimodality sentiment analysis in the social Internet of Things is a developing field, which is basic to empathetic mechanisms, affective computing, and artificial intelligence. Current works in this domain do not explicitly consider the influence of contextual information fusion based on correlation coefficient and memory network with branch structure for sentiment analysis. Unlike present works, this article presents a hierarchical self-attention fusion (H-SATF) model for capturing contextual information better among utterances, a contextual self-attention temporal convolutional network (CSAT-TCN) for sentiment recognition in the social Internet of Things, and a multibranch memory (MBM) network that stores self-speaker and interspeaker sentimental states into global memories. For MOSI data sets, the hybrid H-SATF-CSAT-TCN-MBM model outperforms the state-of-the-art networks and shows 0.31%-9.93% improvement.

Abstract Fog computing is a promising technique to reduce the latency and power consumption issues of the Internet of Things (IoT) ecosystem by enabling storage and computational resource close to the end‐user devices with additional benefits such as improved execution time and processing. However, with an increase in IoT devices, the resource allocation and job scheduling became a complicated and cumbersome task due to limited and heterogeneous resources along with the locality restriction in such computing environment. Therefore, this paper proposes a cache‐based approach for efficient resource allocation in fog computing environment, while maintaining the quality of service. The proposed algorithm is realized using iFogSim simulator and a comprehensive comparison is presented with the traditional First Come First Served and Shortest Job First policies. The performance evaluation revealed that with the proposed scheme the execution time, latency, processing delays and power consumption decreased by 38%, 11.1%, 6%, and 17.8%, respectively, as compared to those of the traditional schemes.

The articles in this special section focus on deep learning algorithms for the Industrial Internet of Things (IIoT). Currently, the industrial Internet of Things (IIoT) has been widely utilized in various fields (e.g., smart transportation, smart home, smart manufacturing). However, there are still some challenges, which hinder the further large-scale application of IIoT. Specifically, the data in IIoT are with a certain redundancy, while transmitting and processing these redundant data consume energy unnecessarily. Therefore, these redundant data should be compressed or removed. Conventionally, machine learning algorithms are used to process these redundant data in IIoT. However, with the growing diversity of IIoT and complexity of mobile network architectures, as well as increasing volume of data with increased dimensions and dynamics, they have made monitoring and managing a multitude of IIoT elements extremely difficult using machine learning algorithms. As we all know, deep learning algorithms can solve more complicated problems, unsolvable by machine learning algorithms, and produce high accurate results. Thus, machine learning algorithms are being replaced by advanced deep learning algorithms in various fields of IIoT. Incorporating advanced deep learning algorithms into IIoT can provide radical innovations in data analysis and pathbreaking industry applications.

The papers in this special section focus on artificial intelligence (AI) and 5G Internet of Medical Things. The recent developments in biomedical sensors, wireless communication systems, and information networks are transforming the conventional healthcare systems. The transformed healthcare systems are enabling distributed healthcare services to patients who may not be co-located with the healthcare providers, providing early diagnoses, and reducing the cost in the healthcare section. The Internet of Medical Things (IoMT), which includes medical devices, wearable devices, sensors and apps, is a critical piece of the digital transformation of healthcare, as it allows new business models to emerge and enables changes in work processes, productivity improvements, cost containment and enhanced customer experiences. IoMT can help monitor, inform and notify not only care-givers, but provide healthcare providers with actual data to identify issues bef

Today, with the worldwide offer and rapid increment in multimedia applications on the web, the demands of users to get them accessed are also increasing prominently. The users in vehicular environment too expect efficient multimedia streaming while travelling on the road. However, the high mobility of vehicles as well as the limited transmission range of infrastructure components in IP based network provides low performance by offering high delay and additional network overhead. To provide better Quality of Experience (QoE) with high performance, Information Centric Networking (ICN) is blended with vehicular environment. Caching the content inside network nodes is inherent feature of ICN with various associated benefits such as low content retrieval delay, less network traffic, path reduction and so on. However, challenges still exists for caching the content due to resource constrained network environment (such as limited cache capacity, node battery) as well as for secure delivery of cached data. To solve these challenges and to enhance network performance, we propose a cooperative caching scheme in hierarchical network architecture that jointly considers cache location as well as combined content popularity and predicted future rating score while making caching decision. The proposed approach uses two layer hierarchical architecture where nodes in edge layer are divided into clusters. The proposed scheme uses modified Weighted Clustering Algorithms (WCA) for selection of cluster heads which are then used to decide cache location. A probability matrix is used to compute content caching probability which considers both popularity and predicted future rating of content. The proposed approach dynamically predict the user's preferences using non-negative matrix factorization (NMF) - a machine learning technique which eventually provides prediction of future rating. Based on the selection of both cache location and content to cache, the proposed scheme can effectively cache the content in the network. Further, to deal with the secure delivery of cached content, this work supports legitimate user authorization at edge nodes. The performance of the proposed scheme is evaluated in MATLAB parallel computing toolkit. The results prove significant caching improvement in terms of cache hit, hop reduction and average delay using our proposed scheme.

Social Internet of Vehicles (SIoV) is an evolving vehicular networking framework integrating the next generation smart devices with vehicular communications. Green computing and communication under disruptive vehicular environment is one of the challenging tasks for enabling SIoV. In this context, green traffic data dissemination in SIoV environments is modelled as an NP-hard problem focusing on heterogeneous traffic data, transmission distance from next generation smart devices and probabilistic delay in transmissions due to disruptive vehicular environment. An adopted meta-heuristic solution namely Two-Way Particle Swarm Optimization (TWPSO) is developed for the green traffic data dissemination problem in SIoV considering software defined vehicular network architecture. Extensive simulation experiments were performed to assess the performance of TWPSO as compared to the state-of-the-art techniques. The critical analysis of the comparative results attest the green computing oriented benefits of TWPSO under real SIoV environments.

In this paper, we aim to give a unified performance limit analysis of the visible light-based positioning (VLP) for a vehicular user equipment (UE), which will help to understand the essence of time-domain localization cooperation and gain insights into how to improve the performance limit of the vehicular VLP system. This is challenging due to the complex system models and the complex dependency between UE location performance and orientation performance. To achieve the above goal, we will first characterize the closed-form error bounds of the UE location and orientation at each time slot, respectively, in terms of Fisher information. Generally, the VLP error will propagate over time as the vehicular UE moves, and hence the VLP error at the current time slot is affected by the VLP performance at the previous time slot, the UE mobility and the channel quality. Based on the obtained VLP error bounds, we then reveal the impact of prior UE location knowledge, UE mobility and signal-to-noise-ratio on the VLP performance. Furthermore, the time-domain evolution of the VLP error is studied, where the convergence of the time-domain VLP error evolution is established and its closed-form stable state is quantified, which will shed light on the long-term performance of the vehicular VLP system.

Cyber-physical system (CPS) provides a well-organized integration betweencommunication,computation, andcontrol(3C) technologies. CPS has been widely used in the vehicular networks and it requires to discover multimodal data from the physical system to make appropriate decisions and actions, for example, congestion warnings, applying brakes, adjusting speed limits, etc. Information discovery and availability at individual network elements is one of the fundamental foundations of CPS. In this paper, we proposed two multimodal network information discovery schemes for vehicular CPS using the Named Data Networking (NDN). One of the proposed schemes simply modifies the pull-based NDN communication mechanism to discover multimodal multi-hop data from the network and the other scheme uses the Interest broadcast suppression (IBS) mechanism. The proposed Interest broadcast suppression scheme adapts the holding time technique to defer the Interest forwarding and its computation involves the hop-count, distance, and other network parameters. Simulation results show that the proposed schemes discover about 172 and 162 percent more multimodal information from approximately 283 and 210 percent more network area by suppressing approximately 50 percent of the Interest broadcast storm in highway and the urban traffic scenarios, respectively.

Fault detection is a fundamental requirement for Industrial Internet of Things (IIoT), such as the process industry. This article first reviews the recent studies focusing on applying the fault detection techniques to the IIoT networks. However, we find that numerous studies focus on the resource utilization and workload allocation. The fault detection toward IIoT facilities is still in its immature stage because the existing approaches are not accurate enough for the stringent fault detection in IIoT networks. To this end, we present a novel algorithm, named Gaussian Bernoulli restricted Boltzmann machines (GBRBMs)-based deep neural network (DNN), to transform the fault detection into a classification problem. The real trace-driven experiments show that the proposed scheme outperforms other baseline machine learning methods. We anticipate that this article can inspire blooming studies on the related topics of smart IIoT networks.

With the rapid explosion of Industrial Internet of Things (IIoT), the need for real-time data processing with enhanced flexibility and scalability has increased manifold. However, the newly evolved containerization technology offers lucrative advantages in comparison to the conventional virtual machines. However, management of these light-weight containers is a tedious task, but Google Kubernetes offers a consolidated container management and scheduling for successful execution of various lightweight containers. Nevertheless, the existing Kubernetes solutions fall short in efficiently handling the “interference” and “energy minimization” challenges in IIoT set-up. Hence, in this article, we present a competent controller, named Kubernetes-based energy and interference driven scheduler (KEIDS), for container management on edge-cloud nodes taking into account the emission of carbon footprints, interference, and energy consumption. The problem of task scheduling has been formulated using integer linear programming based on multiobjective optimization problem. In detail, KEIDS minimizes the energy utilization of edge-cloud nodes in IIoT for optimal green energy utilization. Henceforth, the applications are scheduled on the available nodes in less time with minimum interference from other applications, which in turn guarantees an optimal performance to the end-users. An extensive evaluation of the proposed KEIDS scheduler in comparison to the existing state-of-the-art schemes indicates its superior performance on real-time data acquired from Google compute cluster.

Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this article, we consider the optimization of channel selection that is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

In the explosive growth of time-series data (TSD), the scale of TSD suggests that the scale and capability of many Internet of Things (IoT)-based applications has already been exceeded. Moreover, redundancy persists in TSD due to the correlation between information acquired via different sources. In this article, we propose a cohort of dominant data set selection algorithms for electricity consumption TSD with a focus on discriminating the dominant data set that is a small data set but capable of representing the kernel information carried by TSD with an arbitrarily small error rate less than$\varepsilon $. Furthermore, we prove that the selection problem of the minimum dominant data set is an NP-complete problem. The affine transformation model is introduced to define the linear correlation relationship between TSD objects. Our proposed framework consists of the scanning selection algorithm with$O({n^{3}})$time complexity and the greedy selection algorithm with$O({n^{4}})$time complexity, which are, respectively, proposed to select the dominant data set based on the linear correlation distance between TSD objects. The proposed algorithms are evaluated on the real electricity consumption data of Harbin city in China. The experimental results show that the proposed algorithms not only reduce the size of the extracted kernel data set but also ensure the TSD integrity in terms of accuracy and efficiency.

These redundant data in IoT should be compressed or removed. Furthermore, the unstructured data in IoT plays an important role for analyzing the user behaviors, while transmitting and processing these unstructured data consumption of substantial energy. These unstructured data should be mined or restructured. In addition, the increasing number of users in IoT leads to a fast-growing data in IoT, while the Quality of Service (QoS) of IoT should be maintained regardless of the number of IoT users. Therefore, the data transmission and processing in IoT should be performed in a more intelligent manner. All these observations indicate that the integration of big data and artificial intelligence (AI) for IoT is a good propellant to improve the data transmission and processing in IoT, since big data technology (e.g., data integration, data mining, data prediction) could effectively handle various data while AI technology could further facilitate capturing and structuring big data.

Sharing information among nodes is a key function in Wireless sensor networks (WSNs) applications. Gossip protocols can be employed to facilitate data exchange and ensure data delivery to a sink node. In this paper, we propose two protocols to enhance the traditional gossip protocol by providing lightweight distance-based methods for selecting the next optimal node to retransmit a message. The first proposed protocol, NNGossip, uses the nearest neighbor distance measure, while the second, CBGossip, uses the city block distance measures. The major advantage of using these two techniques is that the computation is simple and fast and hence preserves the energy. We evaluated the performance of the proposed protocols against the traditional gossip protocol, Gossiping, and the Fair Efficient Location-based Gossiping protocol, FELGossiping. The experimental results show that using the proposed protocols maximize the network lifetime and insures efficient bandwidth while maintaining the delay at a low level.

Smart metering infrastructure (SMI) is the core component of the smart grid (SG) which enables two-way communication between consumers and utility companies to control, monitor, and manage the energy consumption data. Despite their salient features, SMIs equipped with information and communication technology are associated with new threats due to their dependency on public communication networks. Therefore, the security of SMI communications raises the need for robust authentication and key agreement primitives that can satisfy the security requirements of the SG. Thus, in order to realize the aforementioned issues, this paper introduces a lightweight and secure authentication protocol, "LiSA", primarily to secure SMIs in SG setups. The protocol employs Elliptic Curve Cryptography at its core to provide various security features such as mutual authentication, anonymity, replay protection, session key security, and resistance against various attacks. Precisely, LiSA exploits the hardness of the Elliptic Curve Qu Vanstone (EVQV) certificate mechanism along with Elliptic Curve Diffie Hellman Problem (ECDHP) and Elliptic Curve Discrete Logarithm Problem (ECDLP). Additionally, LiSA is designed to provide the highest level of security relative to the existing schemes with least computational and communicational overheads. For instance, LiSA incurred barely 11.826 ms and 0.992 ms for executing different passes across the smart meter and the service providers. Further, it required a total of 544 bits for message transmission during each session.

In Underwater Wireless Sensor Networks (UWSN), Autonomous Underwater Vehicles (AUVs), which are responsible to collect(deliver) data from(to) sensors, bridge the network to the rest of the world-wide network to form the Internet of Underwater Things (IoUT). In IoUT acoustic sensors that cause long propagation delay together with slow AUV speed could render the collected data useless if not delivered to the sink in timely manner. In addition, in security, environment monitoring, and emergency applications the age of the data cached by the AUV is very crucial. In this paper, we harnessed the end-to-end data freshness constraint to design AUV path of traversal. The resultant dynamic path of traversal improves the overall data freshness of the sectors of UWSN. In the evaluation, compared to the conventional lawnmower and shortest path traversal algorithms, the proposed scheme improved the overall data freshness at the cost of data collection delay.

The Internet of Vehicles (IoV) has emerged as a new spin-off research theme from traditional vehicular ad hoc networks. It employs vehicular nodes connected to other smart objects equipped with a powerful multisensor platform, communication technologies, and IP-based connectivity to the Internet, thereby creating a possible social network called Social IoV (SIoV). Ensuring the required trustiness among communicating entities is an important task in such heterogeneous networks, especially for safety-related applications. Thus, in addition to securing intervehicle communication, the driver/passengers honesty factor must also be considered, since they could tamper the system in order to provoke unwanted situations. To bridge the gaps between these two paradigms, we envision to connect SIoV and online social networks (OSNs) for the purpose of estimating the drivers and passengers honesty based on their OSN profiles. Furthermore, we compare the current location of the vehicles with their estimated path based on their historical mobility profile. We combine SIoV, path-based and OSN-based trusts to compute the overall trust for different vehicles and their current users. As a result, we propose a trust-aware communication architecture for social IoV (TACASHI). TACASHI offers a trust-aware social in-vehicle and intervehicle communication architecture for SIoV considering also the drivers honesty factor based on OSN. Extensive simulation results evidence the efficiency of our proposal, ensuring high detection ratios >87% and high accuracy with reduced error ratios, clearly outperforming previous proposals, known as RTM and AD-IoV.

Chikungunya is a mosquito instinctive disease that spreads hurriedly in various parts of the country. For the awareness and prevention measure of this disease a new paradigm in Smart Health (S-Health) required to be devised. The auspicious prospective of evolving Internet of Things (IoT) technologies for interconnected heterogeneous devices and objects has played a vital role in the next generation health care systems for eminent patient care to protect the citizens from these types of diseases. Still there is a need for real time health monitoring to analyze the patients for early preventive measures and precautions for healthy life. S-Health care IoT has substantial impending for the cognizance of analogues monitoring. It includes the interconnected apps, objects (devices and people), communication technologies, tracking system, and patients' knowledge base. This paper presents an IoT-enabled model where data collected from the sensors, objects, and people will be gathered at the cloud to take the preventive actions by healthcare professionals. Precautionary measures will be taken by collecting the information about causes of growth of mosquitoes. The suitability of the approach is validated at the base layer of the IoT and data is transmitted to the cloud with the help of edge nodes. From simulations, it is endorsed that the proposed approach is better over ME-CBCCP protocol.

Tactile Internet can combine multiple technologies by enabling intelligence via mobile edge computing and data transmission over a 5G network. Recently, several convolutional neural networks (CNN) based methods via edge intelligence are utilized for fire detection in certain environment with reasonable accuracy and running time. However, these methods fail to detect fire in uncertain Internet of Things (IoT) environment having smoke, fog, and snow. Furthermore, achieving good accuracy with reduced running time and model size is challenging for resource constrained devices. Therefore, in this paper, we propose an efficient CNN based system for fire detection in videos captured in uncertain surveillance scenarios. Our approach uses light-weight deep neural networks with no dense fully connected layers, making it computationally inexpensive. Experiments are conducted on benchmark fire datasets and the results reveal the better performance of our approach compared to state-of-the-art. Considering the accuracy, false alarms, size, and running time of our system, we believe that it is a suitable candidate for fire detection in uncertain IoT environment for mobile and embedded vision applications during surveillance.

The unique characteristics of underwater wireless sensor networks (UWSN) pose many constraints in transmission of data packets and energy efficiency is one of them. The battery powered underwater acoustic nodes require sophisticated protocols to control the nodes' active and sleep periods in order to increase the nodes lifetime. In the terrestrial networks a number of variants of Receiver Initiated MAC (RI-MAC) protocols utilize the nodes active and sleep cycles with the aim to improve the energy efficiency of the overall network. Motivated by the significance of less costly and energy efficient RI-MAC protocol, we present its two variants for the UWSN. In the first protocol, Duty Cycle Scheduling based on Residual Energy (RidE), each sender node to adjust its duty-cycle based on its residual energy. In the second protocol, Duty Cycle scheduling based on Next Wake-up Time (NeWT), each node calculates its duty cycle according to the duty cycle of last awake node, this allows the nodes to avoid any data collision and utilize their sleep modes efficiently to conserve their energy. The simulation results show that, RidE alleviates the need of additional re-transmissions as all the sender nodes overhear the communication with the receiver and plan their communication accordingly at the cost of additional energy consumption. Whereas, in NeWT energy consumption is minimized as the nodes stay awake for shorter period.

Recently, Underwater Wireless Sensor Networks (UWSN) have been proven to provide numerous application including military, environmental, and pollution surveillance, etc. However, once sensors are deployed in the deep sea, it is merely possible to recharge or replace their batteries. Hence, an efficient management of the available resources can extend the network lifetime. For example, clustering the sensor nodes is a potential solution, yet, exchange of multiple packets for cluster head selection, packet collision while sending data to cluster head and the continuous awakening of nodes waste a considerable amount of energy. Utilizing the advanced technology of the Autonomous Unmanned Vehicle(AUV) in UWSN, we present AUV assisted Energy-efficient Clustering(AEC) mechanism that introduces wake-up sleep cycle for the underwater sensor nodes. In the designed scheme, AUV not only collects data but also operates as a central regulator. AUV onus includes cluster creation, the cluster head nomination and creation of a wakeup- sleep schedule for the UWSN that relieves the additional burden from energy limited underwater sensor nodes. Unlike traditional clustering mechanisms, no additional packets exchanged in energy-efficient dynamic cluster head selection. The proposed scheme is evaluated and compared with Clustering with Fixed Cluster head(CFC) scheme. Simulation results show that by applying AEC, the network can remain stable for a long time that allows UWSN to deliver data reliably for the extended time span.

Dual-Structural Network (DSN), pioneered by our group for content sharing, is a networking paradigm with the Internet as primary structure and the broadcast-storage network (BSN) as secondary structure. In order to quantitatively evaluate its content sharing capability, in this paper, we generally adopt a deductive methodology, namely that DSN is formalized as a complex network and then its sharing capability is derived according to graph theory. In specific, according to bipartite graph theory, we first construct a bipartite dual-structural network model to obtain an abstract content sharing graph through top-down projection, and then content sharing hops (CSH) in the graph is capitalized as a metric to evaluate the sharing capability between any two content nodes. Furthermore, we leverage the general random graph theory to generate the sharing graph for deriving quantitative upper bounds on average content sharing hops (ACSH) and maximum content sharing hops (MCSH) of DSN. Lastly, the theoretical derivations are validated by numerical simulation. Moreover, compared with content delivery network (CDN), content centric network (CCN) and information-centric mobile ad hoc networks (ICMANET), DSN is demonstrated to be superior in terms of the sharing capability.

During the past two decades, we have witnessed a tremendous development in Vehicular networks, while exploring emerging communication technologies such as vehicular cyber-physical systems (VCPS). Basically, VCPS requires multimodal data from the physical system to take appropriate decision and actions, for example, the congestion warnings, applying brakes, adjusting speed limits, etc. However, there are multiple systems interconnected in the VCPS with different communication capabilities and data communication between those systems that lead us to a challenging task. In this paper, we consider named data networking (NDN) as a promising solution to enhance the reachability of Data among multi-hop VCPS. NDN offers a simple pull-based content communication in the network with multiple interfaces and also supports heterogeneity in terms of communications technologies. The proposed NDN forwarding scheme enables vehicles to send one Interest (request) to collect multiple instances of the Data from different content sources in the network. Simulation results show that the proposed scheme can collect information from many nodes that are at longer distance from the information requesting nodes.

Vehicular Named Data Network (VNDN) uses NDN as an underlying communication paradigm to realize intelligent transportation system applications. Content communication is the essence of NDN, which is primarily carried out through content naming, forwarding, intrinsic content security, and most importantly the in-network caching. In vehicular networks, vehicles on the road communicate with other vehicles and/or infrastructure network elements to provide passengers a reliable, efficient, and infotainment-rich commute experience. Recently, different aspects of NDN have been investigated in vehicular networks and in vehicular social networks (VSN); however, in this paper, we investigate the in-network caching, realized in NDN through the content store (CS) data structure. As the stale contents in CS do not just occupy cache space, but also decrease the overall performance of NDN-driven VANET and VSN applications, therefore the size of CS and the content lifetime in CS are primary issues in VNDN communications. To solve these issues, we propose a simple yet efficient multi-metric CS management mechanism through cache replacement (M2CRP). We consider the content popularity, relevance, freshness, and distance of a node to devise a set of algorithms for selection of the content to be replaced in CS in the case of replacement requirement. Simulation results show that our multi-metric strategy outperforms the existing cache replacement mechanisms in terms of Hit Ratio.

Information-centric networking (ICN) approaches have been considered as an alternative approach to TCP/IP. Contrary to the traditional IP, the ICN treats content as a first-class citizen of the entire network, where names are given through different naming schemes to contents and are used during the retrieval. Among ICN approaches, content centric networking (CCN) is one of the key protocols being explored for Internet of Things (IoT), names the contents using hierarchical naming. Moreover, CCN follows pull-based strategy and exhibits the communication loop problem because of its broadcasting mode. However, IoT requires both pull and push modes of communication with scalable and secured content names in terms of integrity. In this paper, we propose a hybrid naming scheme that names contents using hierarchical and flat components to support both push and pull communication and to provide both scalability and security, respectively. We consider an IoT-based smart campus scenario and introduce two transmission modes: 1) unicast mode and 2) broadcast mode to address loop problem associated with CCN. Simulation results demonstrate that proposed scheme significantly improves the rate of interest transmissions, number of covered hops, name aggregation, and reliability along with addressing the loop problem.

In this paper, we propose a distributed interest forwarder selection (DIFS) scheme that mitigates the interest broadcast storm in vehicular named data networks. In DIFS, a vehicle sends an interest packet piggybacking its location, distance to the neighbors, and speed. In this case, the immediate neighbors do not have the requested content and rank themselves to be an eligible interest forwarder by using multiple attributes. Additionally, every intermediate vehicle uses a digital map to be selected as forwarders in both (forward and backward) directions of the consumer. Simulations show that DIFS satisfies more interest packets with less delay as compared with the recent forwarding solutions.

In Vehicular Delay Tolerant Networks, a number of Roadside Units (RSUs) are deployed along the road and connected to the infrastructure network to provide various services to the vehicles on the road. However, it is hard to cover the long highways completely, due to the deployment cost. In such uncovered areas between two neighboring RSUs, a connection between a vehicle and an RSU cannot be established. To cope with this, few schemes have been proposed recently, enabling one RSU to select one relay vehicle to provide continuous communications for the vehicle moving in the uncovered area. However, the selection of an appropriate relay vehicle for pre-storing maximum data is an open issue. In this paper, we, therefore, propose an adaptive multiple-relay selection scheme that allows RSU to select relay vehicles while taking most relevant multiple criteria into the account. The relay selection triggers when a vehicle is unable to receive all the requested data from the corresponding RSU. The simulation results show that our scheme enables vehicles to retrieve maximum amount of the requested data in uncovered areas.

High vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, and constraints for routing protocols in vehicular ad hoc networks (VANETs). The routing must avoid forwarding packets through segments with low network density and high scale of network disconnections that may result in packet loss, delays, and increased communication overhead in route recovery. Therefore, both traffic and segment status must be considered. This paper presents real-time intersection-based segment aware routing (RTISAR), an intersection-based segment aware algorithm for geographic routing in VANETs. This routing algorithm provides an optimal route for forwarding the data packets toward their destination by considering the traffic segment status when choosing the next intersection. RTISAR presents a new formula for assessing segment status based on connectivity, density, load segment, and cumulative distance toward the destination. A verity period mechanism is proposed to denote the projected period when a network failure is likely to occur in a particular segment. This mechanism can be calculated for each collector packet to minimize the frequency of RTISAR execution and to control the generation of collector packets. As a result, this mechanism minimizes the communication overhead generated during the segment status computation process. Simulations are performed to evaluate RTISAR, and the results are compared with those of intersection-based connectivity aware routing and traffic flow-oriented routing. The evaluation results provided evidence that RTISAR outperforms in terms of packet delivery ratio, packet delivery delay, and communication overhead.

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

In deep waters, both the natural acoustic systems (such as marine mammals) and artificial acoustic systems (like underwater sensor networks (UWSNs) and sonar users) use acoustic signals for communication, echolocation, sensing, and detection. This makes the channel spectrum, heavily shared by UWSNs posing several salient features such as narrow bandwidth, long propagation delay, and high packet loss caused by acoustic channel. Flooding of data packets in such environment, therefore, is known to be a more appropriate mechanism. Hence, many researchers proposed flooding-based routing protocols for UWSNs such as VBF and HH-VBF. Nevertheless, these known protocols maximize overhead within network due to their dependency on additional parameters such as routing vector. To deviate this overhead and control the flooding, we previously proposed a Directional Flooding Routing (DFR) protocol, which controls the flooding area based on link quality. However, DFR lacks to deal with dynamic changes under the shores due to fixed system parameters throughout the network communications. In this paper, we therefore propose two new DFR protocols, called QA_DFR_AA (QoS-Aware DFR with Angle Adaption) and QA_DFR_TA (QoS-Aware DFR with Threshold Adaption) to reflect QoS dynamically. In addition, we also aid our current DFR with holding time technique to avoid packet collision and redundant packet transmission. Through NS-2 simulations, we found that our new intelligent DFR (iDFR) and two new versions of DFR outperforms the current version of the DFR.

Internet of Things (IoT) plays a key role in connecting the e-health system with the cyber world through new services and seamless interconnection between heterogeneous devices. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Therefore, keeping in view the needs above, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real-time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy, and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.

This paper presents an end-to-end deep learning framework using passive WiFi sensing to classify and estimate human respiration activity. A passive radar test-bed is used with two channels where the first channel provides the reference WiFi signal, whereas the other channel provides a surveillance signal that contains reflections from the human target. Adaptive filtering is performed to make the surveillance signal source-data invariant by eliminating the echoes of the direct transmitted signal. We propose a novel convolutional neural network to classify the complex time series data and determine if it corresponds to a breathing activity, followed by a random forest estimator to determine breathing rate. We collect an extensive dataset to train the learning models and develop reference benchmarks for the later studies in the field. Based on the results, we conclude that deep learning techniques coupled with passive radars offer great potential for end-to-end human activity recognition.

In Vehicular Delay Tolerant Networks (VDTNs), a number of Roadside Units (RSUs) are deployed along the road and connected to the infrastructure network to provide various services to the vehicles on the road. However, it is hard to cover the long highways completely, due to the deployment cost. In such uncovered areas between two neighboring RSUs, a connection between a vehicle and an RSU cannot be established. To cope with this, few schemes have been proposed recently, enabling one RSU to select one relay vehicle to provide continuous communications for the vehicle moving in the uncovered area. However, the selection of an appropriate relay vehicle for pre-storing maximum data is an open issue. In this paper, we, therefore, propose an adaptive multiple-relay selection scheme that allows RSU to select relay vehicles while taking most relevant multiple criteria into the account. The relay selection is initiated when a vehicle is unable to receive all the data that is requested in the coverage of the RSU. The simulation results show that our scheme enables vehicles to retrieve maximum amount of the requested data in uncovered areas.

Load balancing and energy conservation techniques are one of the important constraints in the design of in wireless sensor network (WSN). Usually, clustering technique helps the network in the minimum utilization of energy that results in enhancing network lifetime. Moreover, various nodes in the multihop network that are near to the base station drain their battery very quickly thus result in creating hot spot problem in a network. To overcome such constraints, this paper proposes a multi-layer clustering architecture for selection of forwarding node, rotation of cluster head, and inter and intra-cluster routing communication. The proposed scheme efficiently tackle the rotation of forwarder node by incorporating routing table (table list) at each node. Moreover, the rotation is performed by the consideration of two threshold levels of the residual energy of a node. Also, the exploitation of decision maker node, forwarder node, backup forwarder node, and non-forwarder node enhancing the routing strategy in a network. The performance of the proposed scheme is tested and evaluated by C programming language. The results show that the proposed scheme successful achieve better results than TLPER and EADUC in energy consumption per node, end-to-end communication, hop count in cluster formation.

Aphasia is a common adult language disorder acquired after a stroke, head injury, tumor, etc. Accurate diagnosis influences the prognosis of any speech and language disorder including aphasia. Therefore, in this paper we have proposed a semi-automated Aphasia diagnosis and classification framework employing feature extraction and pattern matching techniques of the digital signal processing (DSP). The proposed scheme evaluates the acoustic properties, time consumed, and speech characteristics for each language component i.e. naming, repetition, and comprehension. The naming and repetition tasks utilize DSP techniques. The proposed solution is highly scalable since it determines the diagnosis based on acoustic properties instead of the language characteristics. Thus, it eases extending into multiple languages. The mathematical relationships calculate the corresponding score for each component. The framework then determines the diagnosis according to the obtained scores. Since it occupies computational analysis of the speech signals, it reduces the subjectivity of the manual diagnosis process, meanwhile increasing the efficiency and accuracy by consistent diagnosis decisions. Finally, it distinguishes two sub types of Aphasia i.e. Anomic Aphasia and Wernicke's Aphasia. The results clearly revealed the efficiency improvement achieved by replacing the live auditory model with pre-recorded auditory model.

The IEEE 1609.4 standard allows a single radio device to utilize multiple channels by alternating control channel (CCH) and service channel (SCH). During the SCH interval, the RTS/CTS/data/ACK handshake can be triggered to transmit large size of data without the hidden node problem. However, it can cause the exposed node problem that hinders concurrent transmissions, which is fatal in highly dynamic VANETs. Even though judicious SCH selection in a multi-channel environment can mitigate the exposed node problem, IEEE 1609.4 does not specify how to select a SCH, which can cause the randomly selected SCHs to be biased. Conforming to the current standards, we therefore propose a novel scheme that enables the exposed vehicles to avoid selecting the same SCH by piggybacking a candidate SCH selection within the optional field of the basic safety message. Through extensive simulations, it is verified that the average throughput can be improved by up to 26%.

the recent advancement of networking technology has enabled the streaming of video content over wired/wireless network to a great extent. Video streaming includes various types of video content, namely, IP television (IPTV), Video on demand (VOD), Peer-to-Peer (P2P) video sharing, Voice (and video) over IP (VoIP) etc. The consumption of the video contents has been increasing a lot these days and promises a huge potential for the network provider, content provider and device manufacturers. However, from the end user's perspective there is no universally accepted existing standard metric, which will ensure the quality of the application/utility to meet the user's desired experience. In order to fulfill this gap, a new metric, called Quality of Experience (QoE), has been proposed in numerous researches recently. Our aim in this paper is to research the evolution of the term QoE, find the influencing factors of QoE metric especially in video streaming and finally QoE modelling and methodologies in practice.

The high popularity of LTE standard amongst the users and the service providers is resulting in an ever-increasing number of devices being included in LTE service architecture. This rate of inclusion with the higher demand of improved QOS/QOE with high mobility is resulting in an increment in consumption of energy by the LTE service architectures. High consumption of energy being critical to the environment due to its impact during generation of energy, consumption along with its effect on energy consuming devices has given rise to the demand of energy efficient algorithms that mitigate this rising energy demand by the architectures. We adopt an innovative approach in form of a Green Cellular Algorithm towards this issue of energy management and propose a testbed design in implementation and experimentation of the solution. We describe the approach towards designing the testbed and features peculiar to the implementation of the adopted green cellular algorithm using the testbed. Further, we also illustrate few basic scenarios to demonstrate the working of the algorithm on the testbed. Finally, we come up with future improvements in the testbed with respect to its ability to scale is size, implement different power efficiency algorithms with existing setup and also with technical improvements in the testbed.

Some of the major challenges while developing a Wireless Body Area Network (WBAN) related to BCI (Brain Computer Interface) applications is to increase the network lifetime and minimize the power consumption. An energy efficient and transmission reliable Medium Access Control (MAC) protocol can be used to address the above problems by modifying certain control parameters. Past study reveals that there has been no accurate model detailing on the control parameters of delay, minimum energy, and throughput for BCI applications. However, there is no mechanism available on the adoption and implementation details of these parameters on multiple transmitters implanted inside the brain. In this paper, we provide the mechanism for implementing an enhanced MAC protocol for implantable passive UHF-RFID transmitters by modifying the super-frame structure. In-depth analysis of energy consumption of multiple implantable transmitters including the delay constraints and reliable transmission is showcased in this paper. Moreover, the performance analysis is provided based on the results obtained using Opnet simulator with CSMA/CA and TDMA based MAC protocols.

The futuristic trend is toward the merging of cyber world with physical world leading to the development of Internet of Things (IoT) framework. Current research is focused on the scalar data-based IoT applications thus leaving the gap between services and benefits of IoT objects and multimedia objects. Multimedia IoT (IoMT) applications require new protocols to be developed to cope up with heterogeneity among the various communicating objects. In this paper, we have presented a cross-layer protocol for IoMT. In proposed methodology, we have considered the cross communication of physical, data link, and routing layers for multimedia applications. Response time should be less, and communication among the devices must be energy efficient in multimedia applications. IoMT has considered both the issues and the comparative simulations in MATLAB have shown that it outperforms over the traditional protocols and presents the optimized solution for IoMT.

Recently, incredible growth in communication technology has given rise to the hot topic, big data. Distributed wireless sensor networks (WSNs) are the key provider of big data and can generate a significant amount of data. Various technical challenges exist in gathering the real-time data. Energy-efficient routing algorithms can overcome these challenges. The signal transmission features have been obtained by analyzing the experiments. According to these experiments, an energy-efficient big data algorithm (big data efficient gathering, BDEG) for a WSN is proposed for real-time data collection. Clustering communication is established on the basis of a received signal strength indicator and residual energy of sensor nodes. Experimental simulations show that BDEG is stable in terms of the network lifetime and the data transmission time because of the load-balancing scheme. The effectiveness of the proposed scheme is verified through numerical results obtained in MATLAB.

Named Data Networking (NDN) has been recently added to the future Internet family. NDN is basically an extension to the Content Centric Network (CCN) and is expected to support various applications. Those applications are to be supported by the future internet architectures. NDN believes in naming the content rather than using end-to-end device names. Recently, NDN has been adapted into Vehicular Ad hoc Networks (VANETs) and hence, we name it Vehicular NDN (VNDN). At it's early stage, VNDN faces several challenges such as consumer/provider mobility, Interest/Data forwarding, content caching and so on. Mostly, VNDN relies on the fact that Data is sent back to the consumer via same path the Interest packet was received from. However, we analyzed that it's not true in a VANET and there is lack of discussion about managing the Data flow back to the consumers in the current literature of VNDN. In this paper, we therefore, pursue to control the data flooding/broadcast storm of the conventional VNDN by proposing our scheme “CONET”. The main idea of CONET is to allow the consumer vehicle to start hop counter in the Interest message and upon receiving that interest by any potential provider, to include Time To Live (TTL) value with data messages. The TTL value includes the number of hops, Data packets should travel on its way back to the consumer. Simulation results show that CONET forwards less Copies of Data Messages Processed (CDMP) while achieving similar Interest Satisfaction Rate (ISR) as the basic VNDN. In addition, CONET also minimizes the overall Interest Satisfaction Delay (ISD), respectively.

The current IEEE 1609.4 standard defines multi-channel operations to alternate control and service channel intervals during a period of 100ms. However, there is no mention of service channel selection for a service provider, which allows hidden service providers to select the same service channel. This limitation can cause the hidden terminal problem during the service channel intervals, leading to significant performance deterioration. Without modifying the existing standards, our proposed scheme enables hidden service providers to avoid selecting the same service channel by delivering their candidate service channel number in the optional field of the basic safety message (BSM). Through extensive simulations, it is verified that the packet reception ratio can be improved by up to 23% in typical broadcast scenarios.

Recently, Vehicular Ad Hoc Networks (VANETs) have been providing a number of services for on-road users, including video content retrieval using 3G and 4G networks. However, owing to the highly dynamic network topology of VANETs, such services is highly susceptible to poor performance. In such a real-time scenario, a requesting vehicle may not be able to guarantee the video quality using its own wireless interface independently. Thus, motivated to provide a quality video stream, we propose a Fleet Based video Retrieval (FBR) scheme that allows the requesting vehicle to download the H.264/SVC encoded video stream in collaboration with its 1-hop neighbors. The collaborators are selected considering the multiple characteristics of each neighboring vehicle, i.e., 1-hop distance, link duration, velocity, and the available cellular bandwidth. The selected vehicles download the video data using their wireless link and then forward it to the requested vehicle through a Dedicated Short-Range Communication (DSRC) protocol. For comparison, we first evaluate our FBR with 3G equipped vehicles and secondly, we designed FBR to work in 4G/LTE environment. Through simulations, we found that FBR has outperformed the recently proposed scheme in terms of receiving video quality and video-flow handling.

Recently, the Delay Tolerant Networks (DTN) have been utilized in various operational communication paradigms. This includes the communication scenarios that are subject to disruption and disconnection as well as the scenarios with high delay and frequent partitioning, i.e., Vehicular Ad hoc Networks (VANETs). Due to several characteristics match, a new research paradigm named as Vehicular Delay Tolerant Network (VDTN) is introduced. Through relays and store-carry-forward mechanisms, messages in VDTNs can be delivered to the destination without an end-to-end connection for delay-tolerant applications. However, the choice of routing algorithms in VDTNs is still under study. Numerous routing protocols have been proposed to meet requirements of many applications. In this paper, we therefore provide some detailed study of recently proposed routing schemes for VDTNs. We also perform comparative analysis on the basis of unique criterion such as forwarding metrics with their implementations. In addition, open challenges and future directions are provided to make room of interest for the research community.