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

Person re-identification (re-ID) is a challenging retrieval task that requires matching a person’s captured images across non-overlapping camera views, with re-ranking being a critical step for improving accuracy. The k-nearest neighbors relationship is commonly used to determine the rank results by selecting only k fixed pedestrian image values for distance calculations. This operation, however, generates additional distance errors due to changes in the appearance of pedestrians. This paper addresses the above issue by proposing a simple but effective Extended Neighborhood Dynamic Selection (ENDS), distance to optimize the performance of ReID reranking. The number of images selected is then distributed over an interval. A limit of upper and lower is placed on the selected number to ensure that it is neither too low nor too high. The automatic selection of adjacent images is achieved using this method. This distance is determined by combining ENDS distances with Jaccard distances. It is the core principle of this method that, instead of using fixed values, the choice of the number of images to be included in each neighborhood should be made automatically. It also allows the removal of images that are dissimilar in favour of those that are more representative. Experimental results demonstrate the novel method of ranking by using Market-1501 and DukeMTMC’s reID dataset. In this paper, we propose a method that increases the Market-1501 mAP/Rank1 by 29.4%/12.9% while DukeMTMC-reID reranking by 35%/20.7%.

As an intermediate task in computer vision, multiple pedestrian tracking (MPT) aiming at tracking the pedestrians from a given video, has attracted attention due to its potential academic and commercial value. However, pedestrians commonly suffer from occlusion due to diverse and complex scenarios, which increases the challenge of this task. This survey provides comprehensive review in terms of occlusion scenarios encountered during MPT, and investigates the model robustness of the existing methods in this scenarios. Firstly, this survey introduces the various and states of occlusion. Secondly, the related occlusion datasets are introduced. Subsequently, we categorize existing occlusion handling methods according to the tracking process and detail their pros and cons. In addition, occlusion handling precision (OHP) metric is proposed to evaluate the ability of a tracker in handling occlusion in this survey. Moreover, comprehensive analyzes and discussions in several public datasets are provided to verify the effectiveness of these methods. Finally, the existing issues and future directions for occlusion handling methods are discussed. In doing so, this work serves as a foundation for future research by providing researchers with information about the occlusion handling method of MPT.

Multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) cellular networks are expected to support large-scale connectivity. This paper proposes a scheme that uses MIMO-NOMA technology combined with edge computing to alleviate the challenge of high latency in traditional VR streaming systems to optimize the rendering delay of virtual reality (VR) streaming in the Industrial Internet of Things (IIoT) environment. By optimizing the channel allocation strategy and accelerating the data transmission rate through MIMO-NOMA, the data transmission latency from HoT devices to edge servers is effectively reduced. In addition, offloading computing tasks to edge servers helps to quickly process and render VR data, further minimizing rendering latency. Experimental verification demonstrates the superiority of this MIMO-NOMA-assisted edge computing scheme in enhancing VR streaming performance, demonstrating its potential for achieving seamless and immersive VR experience in industrial applications.

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

Abstract Within the field of human–computer interaction, data gloves play an essential role in establishing a connection between virtual and physical environments for the realization of digital human. To enhance the credibility of human‐virtual hand interactions, we aim to develop a system incorporating a data glove‐embedded technology. Our proposed system collects a wide range of information (temperature, bending, and pressure of fingers) that arise during natural interactions and afterwards reproduce them within the virtual environment. Furthermore, we implement a novel traversal polling technique to facilitate the streamlined aggregation of multi‐channel sensors. This mitigates the hardware complexity of the embedded system. The experimental results indicate that the data glove demonstrates a high degree of precision in acquiring real‐time hand interaction information, as well as effectively displaying hand posture in real‐time using Unity3D. The data glove's lightweight and compact design facilitates its versatile utilization in virtual reality interactions.

The Industrial Internet of Things (IIoT) refers to the use of interconnected smart devices, sensors, and other technologies to create a network of intelligent systems that can monitor and manage industrial processes. 6TiSCH (IPv6 over the Time Slotted Channel Hopping mode of IEEE 802.15.4e) as an enabling technology facilitates low-power and low-latency communication between IoT devices in industrial environments. The Routing Protocol for Low power and lossy networks (RPL), which is used as the de-facto routing protocol for 6TiSCH networks is observed to suffer from several limitations, especially during congestion in the network. Therefore, there is an immediate need for some modifications to the RPL to deal with this problem. Under traffic load which keeps on changing continuously at different instants of time, the proposed mechanism aims at finding the appropriate parent for a node that can forward the packet to the destination through the least congested path with minimal packet loss. This facilitates congestion management under dynamic traffic loads. For this, a new metric for routing using the concept of exponential weighting has been proposed, which takes the number of packets present in the queue of the node into account when choosing the parent at a particular instance of time. Additionally, the paper proposes a parent selection and swapping mechanism for congested networks. Performance evaluations are carried out in order to validate the proposed work. The results show an improvement in the performance of RPL under heavy and dynamic traffic loads.

Internet of Things (IoT) with edge computing capability enhances efficiency, availability, and improves latency of an industrial automation system. However, to provide dynamic services at the resource-constrained edge device, reconfiguration of services is necessary. This article proposes a programmable edge network to (re)configure different services of industrial IoT, that employs programmable layers at the edge for reconfiguring the sensor/actuator network and application services. The lowermost layer allows reconfiguring the communication-related parameters and the middle layer consists of a software-defined networking (SDN) controller that can dynamically program different modules and handles actuation decisions from the edge. An interfacing protocol between the layers is proposed to provide reliability by considering the required configuration parameters among layers. At the top layer, a priority forwarding mechanism is designed for SDN core (control loop) communication when sensor and actuator are on different edges. The proposed architecture significantly improves the actuation latency and is highly energy efficient compared to the existing state-of-the-art.

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.

Average-consensus protocol is one of the ways to develop distributed time-synchronization algorithms in Internet of Things (IoT) networks. However, the large number of iteration leads to a common time notion issue in nodes. This poses a critical challenge in the convergence of the time-synchronization algorithm and resulting asymptotic convergence in the average consensus protocol. In this article, a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm is proposed. For fast convergence, the proposed PACTS quickly forwards the time information to multihop nodes and employs multihop average-consensus instead of single-hop average consensus. Specifically, a node asynchronously and periodically broadcasts the relative clock offset estimation of neighbors with its local time information. Meanwhile, the relative clock offset estimation of the multihop node is calculated and used to estimate the average value. Consequently, an average consensus among local multihop nodes is obtained. As a result, the iteration number and convergence time are significantly reduced over the network. Finally, the experimental results indicate that the proposed PACTS algorithm has low complexity, high accuracy, and quick convergence.

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

In a fog-computing assisted Internet of Things network, end-devices typically offload computation and storage-intensive tasks to fog devices. It is primarily done to meet the latency requirements of tasks, and QoS requirements of the network. In addition to providing localized computing and storage services, the fog network also needs to support end-device mobility while handling offloaded tasks, especially, to mimic the ubiquitous availability of the cloud. Most of the existing works in this direction either recommend task migration or offloading tasks by predicting the device's location. Both these approaches are shown to have their respective limitations, and, thus a mobility-aware task offloading scheme is crucial to meet end-device task requirements. In this paper, we present an approach to handle the mobility of end-devices for effectively handling offloaded tasks. The proposed mechanism is simple, effective, and is not constrained by a device's location, thereby lowering the costs associated with mobility. Especially, the proposed scheme entirely eliminates the cost induced during migration, since effective task offloading can lessen the necessity to attempt task migrations. The simulation result of the proposed scheme reduces execution latency by 44%, saves upto 68% of network usage and 62% of computational cost at the cloud compared to the state-of - the-art.

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.

Human-computer interaction provides pervasive services advocating several exciting interactive systems, such as remote automation, surgery, and rehabilitation. This paper studies a tight synchronization between human and robot hands to establish near to real-time maneuvering. We leverage the computing resources of the participating units of the master domain to determine the useful data by overserving and predicting the immediate reaction of the human hand movement. Moreover, we consider the unreliable wireless channels that lead to packet error during data transmission from the master domain to the controlled domain. In particular, by bringing the concept of edge computing while utilizing the Raspberry Pis's available yet limited computing resources, we aim to determine the balance between useful data and redundant packets without any significant performance degradation. Finally, we implement the proposed synchronization method of human-robot arm interactions in a real testbed and compare the performance with baselines.

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

Compared to conventional micro-expression analysis, cross-database micro-expression recognition (CDMER) considers a more practical scenario, where the training and testing samples come from different databases. Under this problem setting, most previous micro-expression recognition methods may suffer severe performance degradation due to dataset bias or domain shift. This makes CDMER a challenging but interesting problem. Most existing CDMER methods transfer global micro-expression features without considering the different contributions of facial regions to micro-expression. To tackle this problem, we first confirm the following argument: for samples of the same category from different datasets, their discriminative facial regions are similar and relevant. Therefore, we can transfer the knowledge about discriminative facial regions learned from the source domain to the target domain directly. Based on this argument, we design a novel deep domain adaption method called discriminative region transfer network (DRTN). The DRTN uses an adversarial-based adaptation method to align the source and the target distribution in the learned feature space. Then during transfer, we focus on the discriminative facial regions by the feat of attention mechanism. We conducted extensive experiments on CASME II and SMIC datasets and achieves very competitive results. These evaluations convincingly demonstrate the effectiveness of our proposed method.

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.

In this demonstration, we present a wearable haptic system to realize the perceptual illusion in a virtual environment. We collect the pressure data from sensors attached with the fingertip, and after passing through a classifier, we render the sensation of pressure. We mainly focus on designing a lightweight and wearable haptic interface with fast rendering. At last, we implement the proposed interface on Raspberry Pi and present preliminary results with various test objects.

Password is one of the most well-known authentication methods in accessing many Internet of Things (IoT) devices. The usage of passwords, however, inherits several drawbacks and emerging vulnerabilities in the IoT platform. However, many solutions have been proposed to tackle these limitations. Most of these defense strategies suffer from a lack of computational power and memory capacity and do not have immediate cover in the IoT platform. Motivated by this consideration, the goal of this article is fivefold. First, we analyze the feasibility of implementing a honeyword-based defense strategy to prevent the latest developed server-side threat on the IoT domain’s password. Second, we perform thorough cryptanalysis of a recently developed honeyword-based method to evaluate its advancement in preventing the threat and explore the best possible way to incorporate it in the IoT platform. Third, we verify that we can add a honeyword-based solution to the IoT infrastructure by ensuring specific guidelines. Fourth, we propose a generic attack model, namely,matching attackutilizing the compromised password file to perform the security check of any legacy-UI approach for meeting the all essential flatness security criterion. Last, we compare the matching attack’s performance with the corresponding one of a benchmark technological methods over the legacy-UI model and confirm that our attack has 5%–22% more vulnerable than others.

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.

The Industrial Internet of Things (IoT) typically consists of several thousands of heterogeneous devices, such as sensors, actuators, access points, machinery, end-users' handheld equipment, and supply chain. In such an industrial environment, a multitude of data is generated from massive IoT devices, e.g., sensors for monitoring the environment, reading temperature, and gauging pressure. Most of the data are from delay-sensitive and computation-intensive applications, such as real-time manufacturing and automated diagnostics, which require big data analytics with low latency. Machine learning (ML) has been witnessed as an efficient solution for big data analytics. The majority of such ML algorithms are centralized methods, meaning that they first gather data from different users for use as a training dataset, which is placed on the ML server, and then build a model to classify the new data samples by applying the ML algorithms to this training dataset. However, the access to these datasets in the centralized ML methods raises concerns about data privacy for users. Federated learning (FL) was designed to protect data privacy to address a part of these issues. In FL, each participant uses a global training model without uploading their private data to a third-party server. Compared with the conventional ML, FL can preserve data security, especially in terms of participant data during the learning process. In particular, FL can also help in updating server-side data for the global model, and the participant is not required to provide their data. However, in FL, individual computing units may show abnormal actions, such as faulty software, hardware invasions, unreliable communication channels, and malicious samples deliberately crafting the model. To mitigate these challenges, we require robust policies to control the learning phases in FL. Motivated by the abovementioned issues, this special section solicits original research and practical contributions that advance the security and privacy of the FL solutions for industrial IoT applications as follows.

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

The 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.

Online multiple object tracking (MOT) is a challenging problem in complex scenes due to frequent occlusions. Most of the existing MOT methods tend to focus on addressing an individual type of occlusion, which cannot meet the requirements of real complex scenes. In this paper, we propose a unified MOT framework that combines long- and short-term prediction models for online multiple object tracking. Basically, The short-term prediction model consists of an appearance-based model and a motion-based model, aiming at exploiting the appearance and motion of objects to handle different types of occlusions jointly. Furthermore, we adopt a cubic spline interpolation as a long-term prediction model to estimate the trajectory of the target in occluded frames. To handle different lengths of occlusions, an adaptive weighted fusion model is proposed to combine the short-term prediction model, and the long-term prediction model. Experimental results on several challenging datasets demonstrate that the proposed method outperforms state-of-the-art methods.

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.

Multiple Object Tracking (MOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. Although many researchers have proposed effective method, they failed in crowd scene. The reason is that body detection and tracking is used in existing MOT methods. In crowd scene, many detections are missed detect and many overlap body bounding boxes decrease the quality of data association. To handle this issue, this paper propsed an online novel multiple pedestrians tracking, which is based on head detection. We first fuse the head and body detection to improve the detection result. Then, we use the head detection bounding box to replace the body detection bounding box for tracking. Finally, the experimental results demonstrate the effectiveness of our proposed method and achieve best performance with the state-of-the-art MOT trackers.

Without prediction and prior warning, earthquakes can cause massive damage to human society. The earthquake research has been exploring, and researchers discover that earthquakes happen with many natural phenomena, earthquake precursors. Geo-acoustic signals may contain a good precursor signal to a seismic event. The Acoustic Electromagnetic to AI (AETA) system, a high-density multi-component seismic monitoring system, is deployed to record geo-acoustic signals across 0.1Hz 10kHz. This paper aims to detect the anomalies of geoacoustic signals that may contain earthquake precursors. This study employs the One-Class Support Vector Machine(OCSVM) to detect the anomalies and applies Particle Swarm Optimization (PSO) to optimize the parameters of OCSVM. The experimental results show that the proposed method obtains promising results concerning the abnormal detection in geo-acoustic signals of the AETA system.

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.

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.

Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential in a commercial application. It aims to predict multiple pedestrian trajectories and maintain their identities, given a video sequence. In the past decade, due to the advancement in pedestrian detection algorithms, Tracking-by-Detection (TBD) based algorithms have achieved tremendous successes. TBD has become the most popular MPT framework, and it has been actively studied in the past decade. In this paper, we give a comprehensive survey of recent advances in TBD-based MPT algorithms. We systematically analyze the existing TBD-based algorithms and organize the survey into four major parts. At first, this survey draws a timeline to introduce the milestones of TBD-based works which briefly reviews the development of the existing TBD-based methods. Second, the main procedures of the TBD framework are summarized, and each stage in the procedure is described in detail. Afterward, this survey analyzes the performance of existing TBD-based algorithms on MOT challenge datasets and discusses the factors that affect tracking performance. Finally, open issues and future directions in the TBD framework are discussed.

Classical automated schemes in the industrial Internet of Things (IIoT) are challenged by the problems related to huge record storage and the way they respond. To properly manage the manufacturing settings, cognitive systems aim to find a way to efficiently adapt their actions based on uncertainty management and sensory data. However, due to the lack of existing IT integration, cognitive systems are not fully exploited by organizations. In this article, we provide a novel decision-making process in industrial informatics during information transmission, manufacturing, and storing records through the simple additive weighting and analytic hierarchy process. The proposed mechanism is analyzed and validated rigorously using various sensing and decision-making parameters against a baseline solution in industrial parameter settings. The simulation results suggest that the proposed mechanism leads to 87% efficiency in terms of better detection of the sensor node, decision-making, and alteration of transmitted data during analyses of product manufacturing in the IIoT.

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.

Radio-frequency-energy-powered cognitive radio network (RF-CRN) is being taken seriously in Connected Vehicles, especially in 5G network, which can better address the challenges of energy limitation and spectrum scarcity. However, the energy efficiency (EE) of the RF-CRN wherein multiple secondary users (SUs) share the same channel is rarely presented. In this article, we consider a RF-CRN in which SUs first harvest energy from RF signals originating from a primary network (PN) and then utilize the available energy in the battery to transmit data. Since all SUs can access the authorized spectrum for transmission simultaneously, co-frequency interference (Co-FI) occurs among SUs. Given the quality of service (QoS) requirement, our goal is to achieve the maximum EE of the RF-CRN by jointly optimizing transmission time and power control. To this end, a resource allocation scheme referred to as approximate convex policy for co-frequency interference (CO-ACP) is proposed. Specifically, the EE problem is firstly converted into a convex one by CO-ACP. Then, we utilize Frank-Wolfe (FW) and one-dimensional linear programming to obtain the optimal solution. Simulation results demonstrate that a tight lower-bound optimum solution for the non-convex EE maximization can be achieved by CO-ACP. Moreover, the CO-ACP provides meaningful system features, such as the number of SUs, energy harvesting efficiency, and the battery energy state of the SUs under different RF-CRN scenarios, providing a clear reference for future deployment of RF-CRN.

Intelligent reflecting surface (IRS), which is composed of a large number of low-cost passive elements, has the ability to reflect the incident signal independently with an adjustable phase and amplitude shifts, has been regarded as a key and emerging technology for achieving the cost-effectively spectrum and addressing energy issues in the next generation of wireless networks. In this paper, we propose a general wideband non-stationary channel model for IRS-assisted multiple-input multiple-output (MIMO) communication scenarios, which aims at capturing the underlying propagation characteristics of IRS-assisted communication systems. By properly adjusting the key system parameters, the proposed channel model can be used to describe various IRS-assisted communication scenarios. Furthermore, we separate the channel between the mobile transmitter (MT) and mobile receiver (MR) into the subchannel between the MT and IRS, the subchannel between the IRS and MR, and the subchannel between the MT and MR. The physical properties of each subchannel are investigated accordingly; then, we develop an equivalent channel model to study the key characteristics of the proposed IRS-assisted channel model, such as the time-varying spatial-temporal (ST) cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency CCFs. Finally, numerical results demonstrate that the proposed channel model is practical for describing the IRS-assisted MIMO wireless communication scenarios.

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.

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

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

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.

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

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

Industrial applications, such as real-time manufacturing, fault classification and inference, autonomous cars, etc., are data-driven applications that require machine learning with a wealth of data generated from industrial Internet of Things (IoT) devices. However, conventional approaches of transmitting this rich data to a remote data center to learn may be undesired due to the non-negligible network transmission delay and the sensitiveness of data privacy. By deploying a number of computing-capable devices at the network edge, edge computing supports the implementation of machine learning close to the industrial environment. Considering the heterogeneous computing capability as well as network location of edge devices, there are two types of feasible edge computing based machine learning models, including the centralized learning and federated learning models. In centralized learning, a resource-rich edge server aggregates the data from different IoT devices and performs machine learning. In federated learning, distributed edge devices and a federated server collaborate to perform machine learning. The features that data should be offloaded in centralized learning while it is locally trained in federated learning make centralized learning and federated learning quite different. We study the computation offloading problem for edge computing based machine learning in an industrial environment, considering the abovementioned machine learning models. We formulate a machine learning-based offloading problem with the goal of minimizing the training delay. Then, an energy-constrained delay-greedy (ECDG) algorithm is designed to solve the problem. Finally, simulation studies based on the MNIST dataset have been conducted to illustrate the efficiency of the proposal.

By providing the flexible and shared computing and communication resources along with the cloud services, the fog computing became an attractive paradigm to support delay-sensitive tasks in the Internet of Things (IoT). The existing researches for offloading delay-sensitive tasks in a hierarchical fog-cloud environment mostly focused on minimizing the overall communication delay. However, a fair offloading strategy selects a suitable computing device in terms of fog node or cloud server based on the resource requirements of the task while meeting the deadline. In this article, we design a new delay-dependent priority-aware task offloading (DPTO) strategy for scheduling and processing the tasks, generated from the IoT devices to suitable computing devices. The proposed strategy assigns a priority on each task based on its deadline and assigns it to a suitable multilevel-feedback queue. This schema reduces the waiting time of the delay-sensitive tasks on the queue and minimizes the starvation problem of the low priority tasks. Moreover, the DPTO strategy selects an optimal computing device for each task based on its resource availability and transmission time from the IoT device. This strategy minimizes the overall offloading time of the tasks while meeting the deadlines. Finally, the extensive simulation results with various performance parameters show the effectiveness of the proposed strategy over the existing baseline algorithms.

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

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

In this 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.

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

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

The 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.

This paper presents a comprehensive survey of the existing blockchain protocols for the Internet of Things (IoT) networks. We start by describing the blockchains and summarizing the existing surveys that deal with blockchain technologies. Then, we provide an overview of the application domains of blockchain technologies in IoT, e.g., Internet of Vehicles, Internet of Energy, Internet of Cloud, Edge computing, etc. Moreover, we provide a classification of threat models, which are considered by blockchain protocols in IoT networks, into five main categories, namely identity-based attacks, manipulation-based attacks, cryptanalytic attacks, reputation-based attacks, and service-based attacks. In addition, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving blockchain technologies with respect to the blockchain model, specific security goals, performance, limitations, computation complexity, and communication overhead. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the blockchain technologies for IoT.

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.

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 IEEE 802.15.4 standard is a widely adopted standard for low power wireless personal area networks. It defines several medium access control layer functionalities including channel access, beacon management, guaranteed time slot management, etc. These issues are relatively straight forward in star topology, but the similar tasks pose several challenges in a peer-to-peer cluster tree network. Specifically, beacon synchronization and duty cycling schemes that are influenced by superframe parameters need to operate effectively as they serve as major energy saving avenues. The former that is part of beacon management process allows a device to synchronize its transmissions with a coordinator to facilitate better channel utilization. Further, duty-cycling allows devices to enter low-power mode by scheduling their sleep period. Lack of these schemes in the standard for cluster-tree networks has motivated research in this direction. However, all the related works have aimed to address the problem of duty-cycling and synchronization independently without considering the interdependencies between them. These dependencies arise due to the common superframe parameters. In this paper, we first analyze various works carried out to address beacon synchronization and duty-cycling issue in IEEE 802.15.4 networks. Later, we establish a co-relation between these two mechanisms and show how the former effects the later and vice-versa. The analytical and simulation results allow us to understand the existing schemes better and further assist in the design of aforementioned schemes to maximize energy savings.

The papers in this special section examine the use of fog computing applications in industrial electronics. Due to the increased number of connected things in industrial applications, the growing volume and velocity of Internet of Things (IoTs) data exchange urge for more and more communication resources, leading to the bottleneck in terms of data processing, data latency, and traffic overhead. Fog computing emerges as an alternative for traditional cloud computing to support geographically distributed, latency-sensitive, and QoS-aware IoT applications while reducing the burden of data centers in traditional cloud computing. In particular, fog computing with the features (e.g., low latency, location awareness, and capacity of processing large number of nodes with wireless access) to support heterogeneity and real-time applications is an attractive solution to delay- and resource-constraint large-scale industrial applications. However, with the benefits of fog computing, the research challenges arise regarding fog computing for industrial applications.

The IEEE 802.15.4 standard does not allow to make dynamic adjustments to the inactive portion of the superframe, thus affecting the duty cycles of the coordinator and all the devices attached to it. Prior works in this direction are either based on superframe occupation ratio or buffer occupancy/queue length of the transmitting nodes. In this paper, we present the respective limitations of both these schemes that lead to sub-optimal MAC parameter (BO and SO) settings and later propose a dynamic duty cycling mechanism based on MAC parameters (macMinBE, macMaxCSMABackoffs and macMaxFrameRetries). A Markov model is developed for IEEE 802.15.4 CSMA-CA that is used to analytically estimate the delay and energy consumption during transmission of frames using the MAC parameters.

Duty-cycling schemes allow devices to dynamically adjust their active period to conserve energy. On the other hand, synchronization schemes allow multiple coordinators to schedule their transmissions in order to prevent the overlapping of superframe schedules. In this paper, we analyze the impact of a synchronization mechanism on duty-cycling schemes in an IEEE 802.15.4 cluster tree network. We show the necessity of an operational synchronization mechanism when devices adopt an independent duty-cycling approach so that the later accounts to effective energy savings.

With the advancement in energy harvesting in terms of wireless charging techniques, it provides a novel way to solve traditional energy constraint problems in Wireless Sensor Networks (WSNs). Renewable energy such as solar, wind, and geo-thermal energy is converted to energy-storage and further use via harvest-then-transmit strategy. This article introduces a two-layer sleep scheduling system in energy-harvesting WSNs with an aim to satisfy sustainable throughput by analysis and optimization of network performance. Evaluation results provide a typical demonstration of how to obtain the appropriate value of key parameters according to specific requirement.

With the advancement in energy harvesting, wireless powered communication networks overcome the problem of replacing fixed energy sources, e.g., batteries in difficult access areas of industrial networks. However, the harvested energy is not always enough to support reliable and low end-to-end data transmission in industrial wireless sensor networks (IWSNs). This poster introduces an energy utilization-concerned sleep scheduling in wireless-powered IWSNs with an aim to balance network demand and residual energy.

Group connectivity is one of the major research challenges in large-scale industrial wireless sensor networks (IWSNs). Critical nodes (CNs) are mainly responsible to maintain group-connectivity. This article focuses on prioritize these CNs to sleep more than other nodes in order to save their energy resulting prolong global connectivity in group-based IWSNs.

The IEEE 802.15.4 standard does not support adaptive duty cycles. Prior works in this direction are either based on superframe occupation ratio or buffer occupancy/queue length of the transmitting nodes. In this paper, we find the respective limitations of both these schemes that lead to sub-optimal duty cycle parameter settings. Afterward, a duty cycling algorithm is proposed wherein the channel state is estimated with the help of MAC parameters (macMinBE, macMaxCSMABackoffs, and macMaxFrameRetries) that induces dynamic adaptation of duty-cycle among the nodes. A Markov model is developed for IEEE 802.15.4 carrier sense multiple access with collision avoidance (CSMA-CA) to estimate the delay and energy consumption while transmitting frames using MAC parameters.

Underwater environments may vary gradually even when the occurrence of events is detected. Sensory data may follow a certain trend and are predictable during certain time durations. Taking these into consideration, a simple but practical data prediction mechanism is adopted for estimating sensory data and the geographical location of sensor nodes at sink nodes, and these data are synchronized with those sensed by underwater sensor nodes only when their variation is beyond a prespecified threshold. Leveraging these predicted data, the coverage and sources of potential events are identified by the sink node, and the evolution of these events is determined accordingly. Evaluation results show the applicability and energy-efficiency of this approach, especially when the variation of network environments follows certain and simple patterns.

In this article, we address a sleep scheduling scheme that ensures a coverage degree requirement based on the dangerous levels of the toxic gas leakage area, while maintaining the global network connectivity with minimal awake-nodes compared to other schemes that increase the number of awake- nodes over the entire network.

In this article, we propose a new scheme DeGas to determine the boundary area of the toxic gas with planarization algorithm. This detected boundary area will ensure safe area around the toxic gas and will provide a decision reference for evacuation and rescue of the first-line workers in the large-scale petrochemical plants. Understanding the implications of these observations enable to find out an optimal trade-off between the cost of a number of deployed sensor nodes and the accuracy of the estimated toxic gas boundary area size.

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE.

Universal-filtered multi-carrier (UFMC) technique is considered as a potential candidate for future communication systems due to its robustness against inter-carrier interference (ICI), suitability for non-contiguous fragmented available spectrum resources and low latency scenario in 5G network. In this paper, we present a novel pulse shaping approach in UFMC to reduce the spectral leakage into nearby subbands used for same or other users with low complexity and high throughput. In the new scheme, we apply Bohman filter-based pulse shaping with combination of antipodal symbol-pairs to the edge-subcarriers of the subbands, and consequently reduce the out-of-band radiation. This scheme outperforms the current state-of-the art and offers better signal-to-interference ratio (SIR) to improve the robustness against carrier frequency offset (CFO) for energy saving in loosely synchronized scenario. We further validate the proposed scheme on field programmable gate array (FPGA) hardware prototype.

Dynamic spectrum sharing with minimum spectrum leakage is actively considered to meet the requirements of future generation networks which are expected to support huge amount of data traffic beyond 2020 with limited spectrum. In this paper, we propose a spectral leakage suppression technique for orthogonal frequency division multiplexing (OFDM)-based overlay cognitive radio (CR). We apply the Bohman window-based pulse shaping, which has a high sidelobe fall rate and low highest sidelobe power, to the edge subcarriers rather than the entire waveform of the secondary users (SUs) utilized spectrum. We allocate antipodal symbol pairs to the Bohman-windowed subcarriers to overcome the problem of a slightly large 3-dB bandwidth of the Bohman window. The power spectral density (PSD) of the proposed scheme rolls off asymptotically as of f-8, as compared to the current state-of-the-art where the sidelobe rolls off asymptotically as of f-4. Simulation results show improved bit-error-rate (BER) performances of the primary user (PU) and the SU in the proposed scheme due to improved spectral leakage suppression. The practicality of the proposed scheme is validated by field programmable gate array (FPGA) prototyping.

In this work, we investigate the sensitivity to carrier phase jitter of a phase-locked local oscillator on the performance of variable rate CI/MC-CDMA system over non-dispersive channel. It is shown that for full load, the degradation depends on the jitter variance and non-orthogonality among the spreading codes of the users. When all the subcarriers have same power level and jitter spectrum, the degradation caused by the jitter variance and cross-correlation variance varies from user set to user set in variable rate transmission schemes.

This paper introduces an improved iterative soft decision subcarrier parallel interference cancellation (SDSub-PIC) technique for the carrier interferometry/multicarrier code division multiple access (CI/MC-CDMA) system that significantly reduces the multiple access interference (MAI) for the desired user. Carrier interferometry (CI) codes are used to minimize the cross-correlation between different users. In this paper, the interference cancellation is done by taking the soft decision estimates of transmitted data bit at the subcarrier level. MAI estimates become more reliable with multistage iterative structure of the receiver, which ensures improved bit error rate (BER) performance. Simulation results show that SDSub-PIC provides average BER of 1e-04 at 10 dB SNR which is about 1.5 dB off from single user bound in an AWGN channel at 50% overloading. In slow frequency selective Rayleigh fading channel, SDSub-PIC ensures BER of 4e-05 at 25 dB SNR with four-fold diversity at 100% overloading. We have observed that the new scheme performs considerably better than Block-PIC [14] and Sub-PIC [15] proposed earlier for CI/MC-CDMA system. Complexity of SDSub-PIC is O(N) while for conventional PIC and Block-PIC, it is O(2N-1) and 2O(N) respectively.

This paper proposes a new model of high capacity carrier interferometry/multicarrier code division multiple access (CI/MCCDMA) system through the simultaneous support of high and low data rate transmission using odd and even subcarriers. Reduction in peak-to-average power ratio (PAPR) is achieved through the phase shift of even (odd) CI code using odd (even) sub-carriers by an amount of ¿/2 and odd (even) CI code using odd (even) sub-carriers by -¿/2, all angles measured with respect to the orthogonal codes supporting high data rate transmission. The CI code optimization is done for further reduction in PAPR values. Specifically, assuming a frequency selective Rayleigh-fading channel, bit error rate (BER) at receiver is improved through the cancelation of MAI (multiple access interference) at subcarriers level using PIC (parallel interference cance-lation) at reduced computation cost. Finally, the system is optimized with respect to the number of subcarriers, number of users (capacity) and signal-to-noise ratio (SNR) using GAs (Genetic Algorithms) to achieve an acceptable set of values for BER, PAPR and average data rate (ADR).