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.

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.

Due to the lack of flexible analytical and traffic measurement modules, the current 6TiSCH network architecture is deficient for use in low-latency and reliable control-loop communications. In this paper, we propose a programmable scheme, PRC6, for use over the 6TiSCH network to support low latency and reliable communications. PRC6 provisions an edge-based programming mechanism, which is capable of (i) programming various application-specific tasks, (ii) reconfiguring the 6TiSCH schedule as per the requirements of tasks, and (iii) scheduling deadline-aware forwarding at the SDN core network. The programmability feature of PRC6 offers flexibility in the execution of decision-making and analytics for improved actuation in the associated control-loops. By reconfiguring 6TiSCH-related parameters, PRC6 establishes low-latency communication between the sensor and actuator nodes. We define a new header type to enable interoperable packet delivery between the Low Power Lossy Networks (LLNs) and the core network.

The advent of Internet of Things (IoT) has resulted in a massive influx of data from remote networks, with big data originators located at the edge of the Internet. Software-Defined Networking (SDN) has recently emerged as an effective tool for centralized network control, including access nodes, to manage and optimize the flow of data generated by a vast number of IoT devices. However, remote and relay-positioned access nodes are often vulnerable to security attacks. In this paper, we propose DL-IPS, a novel intrusion detection mechanism for Software-Defined IoT (SD-IoT) based on Deep Learning (DL) techniques. Our proposed approach employs a Deep Neural Network (DNN) algorithm to monitor the traffic behaviours generated from IoT devices and predict potential intruders in the network. The proposed mechanism identifies malicious nodes and packets by monitoring incoming traffic behaviours at the local access nodes, thereby providing protection to the switch and flow tables from various attacks. Furthermore, our approach efficiently places flow rules to devices by removing malicious flows, reducing space consumption and overhead, and detecting anomalies effectively.

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.

The rapid advancement of the Internet of Things (IoT) in real-world applications has attracted immense research endeavors in the last few years. It has got tremendous potential in industrial automation and various other fields. The use of IoT has changed the perspective of general applications in today’s world. In this article, a next-generation architecture, Nx-IoT, is proposed for conventional 6LoWPAN-based IoT. The proposed Nx-IoT architecture works in two modes: 1) single controller-based (6SSDx) and 2) multicontroller-based (6MSDx). The Nx-IoT architecture uses new algorithms for routing management and load distribution among the SDN controllers. The experimentation is carried out in a prototype testbed environment. The result shows improved performances in terms of round trip and packet drop, compared to the conventional 6LoWPAN and cloud system. The proposed Nx-IoT architecture reduces the latency and shows better throughput performances as compared to the existing state of the art.

With the growing number of green Internet of Things (IoT) applications, the underlining network and decision services need more Machine Learning (ML) models at the same time while reducing latency and utilized energy. In this paper, we propose HI-SDN, a Heterogeneous fog Intelligence enabled architecture for complex Software-Defined Network (SDN)-based IoT network running applications such as a smart city and healthcare. HI-SDN proposes a fog node and ML algorithm selection mechanism with dynamic traffic forwarding for green IoT to reduce delay and energy consumption. It adopts a reconfigurable approach to the ML method to dynamically change the required prediction policy and flow rules to be executed in a selected edge/fog node for critical computation. The results from the performance analysis show that HI-SDN has a substantial reduction in delay by 32% and consumed energy by 25%, in comparison to the existing state-of-the-art, besides having an increase in packet delivery ratio.

The IEEE 802.11ah, also known as WiFi HaLow, is a scalable solution for medium-range communication in Internet of Things (IoT). While provisioning support for the IoT and machine-to-machine (M2M) communication, IEEE 802.11ah leverages various innovative medium access control (MAC) layer concepts, such as restricted access window (RAW), hierarchical association identification (AID), traffic indication map (TIM) segmentation, etc. This article presents a survey on various MAC protocols for IEEE 802.11ah. While discussing the essential features of IEEE 802.11ah, this survey points out various issues and limitations of such MAC protocols. Although there are some surveys available for MAC protocols of IEEE 802.11ah, they do not include a large number of schemes that have been recently proposed to solve different standardization and implementation-based issues. This article individually surveys issues and challenges in the different problem domains of the IEEE 802.11ah MAC protocol and analyzes the recently proposed solutions. Moreover, this article identifies various factors for further improvement of these protocols. Compared to other relevant surveys, this article emphasizes the issues and challenges to enable researchers to easily identify the problem domain.

Due to the lack of unified standards in the Internet of Things (IoT), heterogeneity in terms of protocol and packet types exist. In such a case, accurate traffic identification using port-based and payload-based solutions are not suitable for flow processing in Software-Defined IoT (SD-IoT). In this article, we proposeiAcceSD, an intelligent Access Node (SD-Access) and SDN controller (SD-Controller) collaborated traffic identification mechanism for SD-IoT. Concerning the heterogeneous and unknown traffic flows in IoT, iAcceSD uses machine learning (ML)-based traffic identification mechanisms at the access node. The SD-Controller trains a lightweight ML module for a specific SD-Access node dealing with a similar set of traffic flows. By processing flows at the edge, network latency is reduced in the proposed scheme. An optimization model is developed to program the SD-Access nodes considering the heterogeneous and unknown traffic. Thorough performance analysis of iAcceSD shows improvement in latency by 34% and controller overheads by 23.4%, compared to the existing state of the art, with a simultaneous improvement in energy consumption and packet delivery ratio.

Underwater Internet of Things (IoT) faces challenges, such as low data rate, high node mobility, high error probability, and propagation delay. Furthermore, underwater multimedia communication is more challenging, as it requires a high data rate and reliable delivery. The next-generation networking paradigm—software-defined network (SDN)—improves flexibility and virtualization through programmability. SDN increases resource utilization, simplifies management, and reduces operating costs. This article proposes ProStream, a reconfigurable SDN-based underwater multihop network for improved topology management, association control, and multimedia transmission. The proposed scheme considers a network with multiple access points (APs) and mobile relays and stations. The SDN controller places flow rules in the relays, and APs are placed as per the location of the nodes. Moreover, we present a programmable station for reducing Tx/Rx complexity in underwater networks. The stations or relays are triggered through programmability to notify their sleep time and current relays and AP for association and data transmission. We evaluate the proposed scheme first through simulation-based experimentation. We also develop a small-scale real testbed prototype of the proposed SDN. The performance results show significant improvement of performance in terms of latency, throughput, and packet delivery ratio.

High propagation delay, high error probability, floating node mobility, and low data rates are the key challenges for Underwater Wireless Multimedia Sensor Networks (UMWSNs). In this article, we propose RL-MAC, a Reinforcement Learning (RL)–based Medium Access Control (MAC) protocol for multimedia sensing in an Underwater Acoustic Network (UAN) environment. The proposed scheme uses Transmission Opportunity (TXOP) for relay nodes in a multi-hop network for improved efficiency concerning the mobility of the relays and sensor nodes. The access point (AP) and relay nodes calculate traffic demands from the initial contention of the sensor nodes. Our solution uses Q-learning to enhance the contention mechanism at the initial phase of multimedia transmission. Based on the traffic demands, RL-MAC allocates TXOP duration for the uplink multimedia reception. Further, the Structural Similarity Index Measure (SSIM) and compression techniques are used for calculating the image quality at the receiver end and reducing the image at the destination, respectively. We implement a prototype of the proposed scheme over an off-the-shelf, low-cost hardware setup. Moreover, extensive simulation over NS-3 shows a significant packet delivery ratio and throughput compared with the existing state-of-the-art.

Due to the lack of an integrated communication and computation architecture for Software-Defined Healthcare IoT (SD-HI), provisioning critical services is challenging. In this paper, we propose SD-Health, an edge-based decision making and task allocation (EDT) scheme for SD-HI. The proposed SD-HI network uses Machine Learning (ML)-based approach to predict the criticality of flows and location of mobile devices. Based on the predicted values, the controller delegates the required EDT module to the respective edge node. The controller identifies the future healthcare-related decisions for an edge node and prepares the module accordingly. The ML-based trajectory prediction allows to find the future location of mobile devices in the network. Once the location of the mobile device is predicted, a set of computation tasks is dynamically allocated to the edge node. The results of performance analysis show that SD-Health has a significant improvement in latency by 43.3% and energy consumption by 30%, compared to the existing state-of-the-art, along with a fair improvement in packet delivery ratio.

In this paper, we propose SD-Reco, a Software-Defined Network (SDN)-based centralized AP and relay node re-configuration scheme for link recovery in IEEE 802.11ah networks. The proposed scheme identifies the overlapping regions and congestion of a network and re-configures Relays, APs, and SDN core nodes for reliable data transmission. Taking advantage of redundant links, the SDN controller triggers a node to receive/transmit frames using the best relay/AP for reliable and low latency delivery. The stations use redundant available links to relay/AP for any failure, not meeting the Quality of Service (QoS) requirement and congestion. Our solution use a relay placement scheme to know topology of the network for centralized controlling. An AP node determines the congestion status of each relay and itself and updates the same to the SDN controller. Accordingly, the controller selects an AP/relay and places flow-rules from overlapping regions for forwarding the traffic dynamically. SD-Reco improves packet delivery ratio up to 18.7% and latency up to 33.3%, compared to the existing state-of-the-art.

Due to the large-scale deployment of mobile stations, relays, and Access Points (APs), IEEE 802.11ah brings challenges that cannot be effectively addressed in a distributive manner. In this paper, we propose an uplink traffic-aware dynamic association and channel control mechanism for improving multi-hop throughput and delay performances. The proposed software-defined access network estimates throughput for relays and APs considering the stations’ probability of successful transmission. A centralized controller uses the estimated throughput for better decisions in association and channel selection in relays and APs. For improving the capacity of a relay or an AP, a utility function is dynamically measured from the available channels and bandwidth. The proposed association control mechanism allows for seamless handoff throughout the network. Overall, the proposed protocol improves throughput up to 10% and delay up to 13% as compared to the traditional scheme.

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.

Due to the low data-rate, high propagation delay, floating node mobility, and high error probability, underwater multimedia communication is still challenging. In this paper, we propose an acoustic-based reliable streaming network for resource-constrained underwater communication. The proposed protocol uses a Null Data Packet (NDP)-based contention and acknowledgment mechanism to reduce control overhead and improve reliability and energy efficiency. With the use of a lightweight Traffic Indication Map (TIM) and video compression technique, our system's efficiency for multimedia transmission is further improved. The proposed schemes provide multimedia communication without compromising on the quality of the data transmitted. We experimentally demonstrate the proposed scheme in a hydrodynamic water-tank facility on our campus. The testbed we have built in this facility is capable of running real-time video streaming. The system's performance, which is evaluated using parameters such as coverage, range, latency, and energy consumption, was found to prove the proposed solution's validity.

In this paper, we propose Health-Flow, a criticality-aware traffic forwarding scheme for mobile devices to maximize the efficiency of a software-defined healthcare network. The proposed scheme uses a machine learning-based approach to find the criticality of flows and the location of the mobile device. Concerning the criticality levels in traffic, the proposed protocol dynamically places or removes flow-rules at the edge access points. Consequently, it helps to take adequate actions for the incoming requests adaptively with improved network reconfiguration overhead, latency, and energy consumption. We mathematically formulate Integer Linear Programming for optimally selecting access points. We mathematically formulate the resource reallocation problem in terms of optimization by minimizing the network overhead subject to the packet flows' criticality requirements. The proposed scheme has the potential to reduce latency by 52%, overhead by 19%, and energy consumption by 12% as compared to the existing schemes.

In this paper, we propose a secure programmable fog architecture for future Internet of Things (IoT) applications. The programmable feature in the proposed architecture enables us to achieve additional flexibility and support changes over the fog nodes deployed on a large scale network, where individual fog nodes are managed by the centralized fog controller. Specifically, the exchange of programmable content between the controller and the nodes is secured using blockchain. The performance evaluation of the proposed scheme shows significant improvements in reducing downtime and increasing reliability, as compared to conventional fog computing schemes.

In this paper, we propose a channel access scheme for relay-based IEEE 802.11ah network and analyze the feasibility and efficiency of it for multi-hop and large-scale Internet of Things (IoT). A new Restricted Access Window (RAW) scheme is designed to consider the currently active number of stations in a group for channel access. The proposed protocol facilitates multi-hop communication efficiently by utilizing the available channels. Accordingly, the relay node can maintain the best possible frame size for multi-hop communication. Simulation and analysis results show that the proposed protocol improves throughput performance up to 30.7% and the average frame delay up to 41.6% over the traditional scheme.

IPv6 over low-power wireless personal area network (6LoWPAN) has been widely used for large-scale sensing and actuating purposes in the Internet of Things (IoT). Though promising, many challenges, such as high latency, heterogeneity, and packet loss persist. To mitigate these challenges, the software-defined network (SDN) technique can be hybridized with existing IoT structures that can address many of them. In this article, we propose an approach-edge-based 6LoWPAN-SDN (6LE-SDN) architecture, which can improve the system limitations mentioned. It uses an edge-based computational capability to improve the network performance over 6LoWPAN. To reduce heterogeneity, we develop a hybrid-edge switch that helps to enable communication among 6LoWPAN and SDN entities. For efficient communication between different devices, a new protocol-edge-based 6LoWPAN-SDN protocol (6LE-SDNP) is proposed, which is capable of ensuring optimal routing of the packet for efficient communication among the devices. We use the SDN-based edge controller for reducing the latency of the network apart from improving the interoperability feature. The testbed evaluation of the proposed solution indicates satisfactory performance in terms of reducing latency by 60% and network overhead by 91%. The 6LE-SDN network also succeeded in reducing the average round trip time (RTT) by 31% and the packet loss by 70% as compared to that of the traditional 6LoWPAN-based IoT.

Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT-based agriculture and farming solutions, the proposed solution reduces network latency up to a certain extent. In this, a cross-layer-based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency.