The primary objective of fog computing is to minimize the reliance of IoT devices on the cloud by leveraging the resources of fog network. Typically, IoT devices offload computation tasks to fog to meet different task requirements such as latency in task execution, computation costs, etc. So, selecting such a fog node that meets task requirements is a crucial challenge. To choose an optimal fog node, access to each node's resource availability information is essential. Existing approaches often assume state availability or depend on a subset of state information to design mechanisms tailored to different task requirements. In this paper,OptiFog:a cluster-based fog computing architecture for acquiring the state information followed by optimal fog node selection and task offloading mechanism is proposed. Additionally, a continuous time Markov chain based stochastic model for predicting the resource availability on fog nodes is proposed. This model prevents the need to frequently synchronize the resource availability status of fog nodes, and allows to maintain an updated state information. Extensive simulation results show thatOptiFoglowers task execution latency considerably, and schedules almost all the tasks at the fog layer compared to the existing state-of-the-art.

The widespread adoption of Internet of Things (IoT) devices has transformed industries such as healthcare, manufacturing, and smart cities. However, these devices often possess limited resources and weak security mechanisms, making them vulnerable to cyberattacks. Traditional Intrusion Detection Systems(IDS) rely on known attack signatures and are ineffective against novel or unknown threats. Although recent machine learning (ML) approaches aim to detect anomalous activity, many continue to suffer from high false alarm rates and degraded performance under dynamic network conditions. To address these challenges, we propose a two-phase deep learning framework, VAE-BiLSTM-IDS, designed specifically for IoT networks. In the first phase, Variational Autoencoders (VAEs) learn normal traffic patterns and detect anomalies using adaptive thresholds that adjust to changing network behavior. In the second phase, a CNN-BiLSTM model leverages both spatial and temporal features to classify anomalies and identify specific attack types. Evaluated on the Edge-IIoT dataset, which contains realistic IoT traffic and zero-day attacks, our framework yields a detection accuracy of 98.89%. It significantly reduces false positives compared to state-of-the-art methods. This approach offers a robust and adaptive solution for improving IoT security.

In this paper, we propose RedgeX, a meta-learning based approach for generating analytical models in a distributed edge intelligence network. The approach involves training a meta-learning model on a large dataset of edge device information and performance metrics to predict the optimal analytical model for a given task and available resources. An edge controller, which has the status of all the edge devices, can then deploy the optimal model to the most suitable edge devices based on their available resources. The RedgeX improves the efficiency and effectiveness of edge intelligence systems by dynamically generating analytical models based on the specific requirements of each task and the available resources in the edge devices. The performance evaluation of the proposed scheme shows better utilization of resources, improved performance, and reduced latency in edge intelligence systems.

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.

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.

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.

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.

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.

Due to the multi-hop, long-distance, and wireless backbone connectivity, provisioning critical and diverse services face challenges such as low latency and reliability. This paper proposes ioFog, an offline fog architecture for achieving reliability and low latency in a large backbone network. Our solution uses a Markov chain-based task prediction model to offer dynamic service requirements with minimal dependency on the Internet. The proposed architecture considers a central Fog Controller (FC) to (i) provide a global status view and (ii) predict the type of tasks at the Fog Nodes for intelligent offloading decisions. The FC also has the current status of the existing fog nodes in terms of their processing and storage capabilities. Accordingly, it can schedule the possible future offline computations and task allocations. ioFog considers the requirements of individual IoT applications and enables improved fog computing decisions. As compared to the existing offline IoT solutions, ioFog reduces service time significantly and service delivery ratio up to 23%, compared to the existing relevant architectures.

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.

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.

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.

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.

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

The 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 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.

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.

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.