The evolution of Software-Defined Networking (SDN) and network slicing has revolutionized network management in Fifth-Generation (5G) and Sixth-Generation (6G) systems to enable flexible and programmable resource control. However, efficient traffic management and dynamic resource allocation under heterogeneous traffic demands remain challenging. This paper proposes TRON, an adaptive traffic management and resource allocation framework that leverages OpenFlow Group Tables to optimize link utilization across network slices for data and VoIP traffic. Integrated with the Ryu SDN controller, TRON employs real-time flow redirection and intelligent traffic splitting. Experimental evaluations using Mininet and Open vSwitch show that TRON improves link utilization by 22.5%–43.25% and network throughput by 31.64%–56.55% compared to baseline approaches. The simulation results highlight that TRON provides a lightweight and real-time traffic management framework for SDN-enabled 5G/6G networks.

The advent of the sixth-generation (6G) wireless networks promises to revolutionize the telecommunications landscape by offering significantly high data rates, low latency, and enhanced reliability compared to the predecessors, i.e., 5G and 4G. In the existing literature, the problem of heterogeneous bandwidth management using Software-Defined Networking (SDN) is not explored to leverage the 6G technology in the Industrial Internet of Things (IIoT). In this work, we propose a novel framework, FALCON, to dynamically manage bandwidth in softwarized 6G networks while focusing on the sustainability of IIoT applications. We simulate the effectiveness of FALCON framework using the Mininet simulator. In FALCON, the Ryu SDN controller orchestrates bandwidth dynamically across SDN-capable Open vSwitches. The proposed dynamic FALCON scheme improves the average packet drop by 26.32% over data, VoIP, and video traffic than the existing schemes. Through extensive simulation, we observe that FALCON reduces packet loss, enhances throughput, and reduces energy consumption at edge nodes for heterogeneous data traffic in comparison to the existing schemes on the IIoT ecosystem.

This work addresses the problem of ensuring service availability, trust, and profitability in sensor-cloud architecture designed toSensors-as-a-Service(Se-aaS) using IoT generated data. Due to the requirement of geographically distributed wireless sensor networks for Se-aaS, it is not always possible for a single Sensor-cloud Service Provider (SCSP) to meet the end-users requirements. To address this problem, we propose a federated sensor-cloud architecture involving multiple SCSPs for provisioning high-quality Se-aaS. Moreover, for ensuring trust in such a distributed architecture, we propose the use ofconsortium blockchainto keep track of the activities of each SCSP and to automate several functionalities throughSmart Contracts. Additionally, to ensure profitability and end-user satisfaction, we propose a composite scheme, named BRAIN, comprising of two parts. First, we defineminer's scoreto select an optimal subset of SCSPs asminersperiodically. Second, we propose a modifiedmultiple-leaders-multiple-followers Stackelberg game-theoretic approach to decide the association of an optimal subset of SCSPs to each service. Thereafter, we evaluate the performance of BRAIN by comparing with three existing benchmark schemes through simulations. Simulation results depict that BRAIN outperforms existing schemes in terms of profits and resource consumption of SCSPs, and price charged from end-users.

Cognitive Radio (CR)-based solutions have been suitable for efficient spectrum sharing among the unlicensed and licensed Internet of Things (IoT). Consequently, keeping a careful balance among several priority class users ensures that network congestion is avoided. In order to regulate data transmission and interconnectivity among various IoT devices, this paper proposes an event-triggered sliding mode controller for congestion maintenance, abbreviated as ETSMCC. The suggested controller improves the disturbance rejection capabilities and robustness in an IoT protocol stack for cognitive networks. Using this method, unscrupulous spectrum sharing and efficient congestion control is achieved. Our approach reduces energy expenditures and computational difficulties in the IoT protocol stack. ETSMCC with IoT protocol stack achieves an average distortion rate of 0.009%, throughput of 256 Kbps, 23% packet loss, delay with 0.32 ms and packet delivery ratio of 7.21%. ETSMCC performs better than state-of-the-art schemes, such as context-aware congestion control (CACC) and dynamic, driven congestion control (DDCC).

This article proposes coalitional game-based broadcast proxy re-encryption (P-RE) in IoT—a broadcast P-RE for adding new IoT devices. The P-RE is extended to broadcast P-RE to prevent recalculation of the re-encryption key (ReKey). However, the group of recipients needs to be predetermined before the calculation of the ReKey. If any new IoT device requires the same data, an individual ReKey is generated for him/her. Hence, generating individual ReKey is an overhead for the organization. We propose a ReKey updation for the broadcast P-RE method. If excessive IoT devices want to join the group of the existing recipient, then updation needs to be done learnedly as an excessive number of recipients in a group increases the computation cost of the decryption extremely for all the members of the group. Therefore, we use the coalitional game theory to estimate the optimal number of new recipients from all new recipients. We update the ReKey for the optimal number of members and a separate ReKey is calculated for other recipients. We prove the correctness of the CBP. We prove that if any recipient behaves maliciously, s/he cannot get the secret key of the organization.

In this work, the problem of high-quality energy service provisioning in the presence of competitive prosumers and micro-grids in cloud-enabled smart grid is studied. Oligopolistic prosumers behave non-cooperatively and store the excess generated energy for future use, which increases the load on the main grid and degrades the performance of the smart grid. To address this issue, we propose a dynamic cloud-based pricing scheme, named SmartPrice, to enforce cooperation among the prosumers for ensuring high quality of service provided by the micro-grids. In SmartPrice, using cloud infrastructure, each micro-grid calculates a reward factor for each prosumer based on his/her behavior to enforce cooperation among them. We model the interaction between each micro-grid and the prosumers using a single-leader-multiple-followers Stackelberg game, where the micro-grids and the prosumers act as the leaders and the followers, respectively. Each micro-grid determines the unit energy price to be charged/paid and each prosumer determines the quantity of excess energy to be supplied for ensuring high revenue. Thus, SmartPrice enforces cooperation among the micro-grids and prosumers. Additionally, using SmartPrice, the price for unit energy charged from the prosumers reduces by 23.37-$35.63\%$, thereby ensuring high revenue and the number of prosumers served by the micro-grids increases by 38.19-$53.14\%$.

In this article, we study the problem of quality of service (QoS)-aware cost management of sensor-cloud comprising multiple sensor-cloud service providers (SCSPs) and sensor-owners. The rapid adaptation of the wireless sensor network (WSN) and Internet-of-Things (IoT) technology led to the conceptualization of the sensor-cloud infrastructure which primarily aims to reduce the complexities associated with operating WSN-based applications by rendering Sensors-as-a-Service (Se-aaS). However, the oligopolistic market scenario of sensor-cloud involving multiple SCSPs and sensor-owners significantly impacts its profitability and QoS. Thus, there is a need to explore the dynamics of this market competition elaborately in order to maintain the usability of sensor-cloud. The existing works fail to address the aforementioned issues in sensor-cloud. Hence, in this work, we analyze the interactions among the sensor-owners and the SCSPs using a game-theoretic approach. We propose a QoS-aware dynamic cost management scheme, named QUEST, to determine the optimal strategies of the various actors in sensor-cloud market. Through simulations, we observe that, using QUEST, the price paid by the end-users decreases by 10.31-20.43 percent and the revenue of sensor-owners improves by 66.83-89.94 percent. Moreover, QUEST ensures the service satisfaction of the end-users while optimally distributing the services among the SCSPs and the sensor-owners.

In this paper, the problem of ensuring reliable energy distribution in smart grid is studied, while considering that each customer is connected with multiple micro-grids. In the traditional smart grid, each customer is connected with a single micro-grid. Additionally, in the existing literature, some researchers proposed energy distribution schemes considering the presence of multiple micro-grids. However, none of these existing schemes consider that the customers can consume energy from multiple micro-grids simultaneously, which can essentially enhance the quality of service (QoS) in energy distribution, as it aids in reducing the transmission loss and increasing the profit of the micro-grids, while the customers pay less. To address the aforementioned problem, we design a sustainable energy distribution scheme, named SEED, to decide the distributed energy request vector, while ensuring high QoS in terms of energy availability and the price charged by the micro-grids in smart grid. We use an evolutionary game to ensure that the energy load is optimally distributed among the micro-grids and each micro-grid gets an equal opportunity to earn a profit. Through simulation, we observe that using SEED, renewable energy consumption per customer improves by 14.05 percent while reducing the cost by 29.87 percent. In other words, SEED ensures a sustainable environment by reducing the CO$_2$2emission by 14.05 percent, while reducing non-renewable energy consumption from the main grid. Additionally, the profit of each micro-grid increases by 58.32 percent.

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.

Sensor-cloud architecture is a wireless sensor network (WSN)-based Service-Oriented Architecture (SOA), in which a Sensor-Cloud Service Provider (SCSP) obtains WSNs on rental basis from multiple sensor-owners and provides these resources to the users in the form of chargeable units of services, termed as Sensors-as-a-Service (Se-aaS). A fraction of the revenue earned by the SCSP from the users is distributed among the oligopolistic sensor-owners for the usage of their nodes. Due to the inter-dependency among the sensor-owners for Se-aaS provisioning, selfish sensor-owners behave dishonestly to gain higher profits, thereby degrading the overall QoS. Existing works on sensor-cloud fail to address this issue. Hence, in this work, we propose DETER, a dynamic trust enforcing pricing scheme, which enforces trust among the selfish sensor-owners while ensuring profits for the SCSP.

In this paper, the problem of provisioning high quality of Sensors-as-a-Service (Se-aaS) in the presence of competitive sensor-owners, i.e., oligopolistic market, and heterogeneous sensor nodes in service-oriented sensor-cloud is studied. Oligopolistic sensor-owners adopt unfair means to degrade the quality of service provided by other sensor-owners in the sensor-cloud market. In order to address this problem, a dynamic pricing scheme, named DETER, is proposed in this work to enforce trust among the sensor-owners for maintaining the quality of Se-aaS provided by the Sensor-Cloud Service Provider (SCSP). Each sensor node calculates distributed trust opinion for other nodes, while the SCSP calculates centralized trust opinion for each sensor-owner. A Single-Leader-Multiple-Follower Stackelberg Game is formulated in which the SCSP acts as the leader and decides price to be paid to each sensor-owner, while ensuring maximum profit. On the other hand, the sensor-owners act as the followers and decide their strategies for earning maximum profit. Thereby, using DETER, SCSP enforces high trust among the sensor-owners. Additionally, using DETER, energy consumption of sensor nodes in sensor-cloud decreases by 4.69-11.56 percent, and network overhead decreases by 52.6-56.53 percent. The trade-off between price earned by the sensor-owners and profit of the SCSP in service-oriented sensor-cloud is also maintained using DETER.

In this work, we address the problem of efficient utilization of resource-constrained wireless sensor nodes for provisioning Sensors-as-a-Service (Se-aaS) with high quality. In sensor-cloud, the sensor-owners provide their respective sensor nodes to the sensor-cloud service provider (SCSP) on rent. The SCSP utilizes these nodes to create virtual sensors and provisions them as Se-aaS for serving their WSN-dependent applications of the end-users and earns revenue in exchange. To ascertain high quality-of-service (QoS) of Se-aaS while simultaneously ensuring profits for itself and the sensor-owners, the SCSP needs to optimally allocate physical sensor nodes to serve the virtual sensors, while considering their limited capacity and the fair distribution of service load among different sensor-owners. Although a few existing works focused on resource allocation problem in sensor-cloud, none of them considered the possibility of sharing the same physical sensor node among multiple virtual sensors. Hence, in this work, we propose a resource orchestration scheme for sensor-cloud, named SensOrch, which is based on coalition formation game with transferable utility. Using SensOrch, the SCSP ensures the optimal allocation of sensor nodes to form virtual sensors while maintaining high QoS and profitability of Se-aaS. Through simulations, we yield that, using SensOrch, the network lifetime increases by 25.31 - 59.6% along with a simultaneous increase in the profit of the SCSP by 23.64 - 29.49%, compared to the existing schemes. Additionally, SensOrch ensures fair revenue distribution among the sensor-owners.

In this paper, we study the problem of data flow management in the presence of heterogeneous flows — elephant and mice flows — in software-defined networks (SDNs). Most of the researchers considered the homogeneous flows in SDN in the existing literature. The optimal data flow management in the presence of heterogeneous flows is NP-hard. Hence, we propose a game theory-based heterogeneous data flow management scheme, named FlowMan. In FlowMan, initially, we use a generalized Nash bargaining game to obtain a sub-optimal problem, which is NP-complete in nature. By solving it, we get the Pareto optimal solution for data-rate associated with each switch. Thereafter, we use a heuristic method to decide the flow-association with the switches, distributedly, which, in turn, helps to get a Pareto optimal solution. Extensive simulation results depict that FlowMan is capable of ensuring quality-of-service (QoS) for data flow management in the presence of heterogeneous flows. In particular, FlowMan is capable of reducing network delay by 77.8–98.7%, while ensuring 24.6–47.8% increase in network throughput, compared to the existing schemes such as FlowStat and CURE. Additionally, FlowMan ensures that per-flow delay is reduced by 27.7% with balanced load distribution among the SDN switches.

In this paper, the problem of dynamic topology management in underwater wireless multimedia sensor networks (UWMSNs) in the presence of underwater wireless multimedia sensor nodes is studied using cooperation game theory. In the existing literature, researchers focused on the efficient management of underwater sensor networks and terrestrial wireless multimedia sensor networks. However, in the presence of underwater multimedia wireless sensor nodes, the amount of data to be transmitted increases significantly which deteriorates the overall network performance. Hence, there is a need to design a delay-optimal dynamic topology control scheme for UWMSNs, while maximizing the network throughput and lifetime. In this work, we propose a cooperative game theory-based scheme, named DATUM, for dynamic topology control. In DATUM, initially, we explore the feasible data transmission path available from the source node at seabed to the sink node at the surface of the ocean. Thereafter, using cooperation game theory, we identify the set of optimal paths to be selected. Finally, in DATUM, each underwater wireless multimedia sensor node decides its optimum transmission communication range for maximizing the network lifetime, while ensuring the network connectivity. Through simulation, we observed that using DATUM, network delay reduces by 30.74 percent, while the network lifetime increases by 59.61 percent.

In this work, we propose a dynamic cache-based pricing scheme, named CASH, for service-oriented sensor-cloud. In sensor-cloud, the Sensor-Cloud Service Provider (SCSP) provisions Sensors-as-a-Service (Se-aaS) to multiple end-users based on a pay-per-use model. The service-requests of the end-users have heterogeneous data-rate requirements. In the cache-enabled architecture of sensor-cloud, these service-requests are served by the SCSP using either the Internal or the External cache, which incurs different costs to the SCSP. Thereby, the SCSP tries to maximize its own profit by distributing these service-requests, optimally, among the caches. Additionally, the SCSP ensures that the end-users are minimally charged. Existing literature fails to propose any pricing scheme for service-oriented sensor-cloud, while considering the cost incurred for data caching. In CASH, we propose a dynamic pricing model for sensor-cloud using dynamic coalition formation game with transferable utility. Using CASH, based on the preference relation of the partitions, we determine the optimal internal cache refresh rate, while maximizing the coalition value. Through simulation, we observe that the cost incurred by the SCSP reduces by 34.32–51.15% and the price paid by the end-users decreases by 9.60–17.47% as compared to the existing schemes. Additionally, CASH ensures 9.60–21.85% increase in the profit of the SCSP.

In this paper, we address the issue of rule duplication during network updates in software-defined networking (SDN). In SDN, network update involves the controller in sending update packets to desired set of switches, where the update rules are installed. To ensure update consistency, old flow rules are stored until the total update procedure is complete. Higher consumption of ternary content addressable memories (TCAMs) during update increases the cost of network update and decreases the scalability of SDN. In this paper, we propose an approach for consistent update with redundancy reduction, named CURE, which reduces the TCAM usage during update. CURE prioritizes switches according to their usage pattern and schedules updates based on priority zones. The proposed approach guarantees that highly loaded switches are updated first. CURE also maintains packet-level consistency by implementing a multilevel queuing approach. In this framework, each switch in the current update region stores the incoming packets in individual device queues until the switch completes update. Therefore, after the initiation of an update, packets are processed according to new rules only. The results of performance evaluation depict that the average rule space utilization during update using CURE is 29.954% less than using the two-phase update proposed in the existing literature.

In this work, the problem of topology control for self-adaptation in stationary Wireless Sensor Networks (WSNs) is revisited, specifically for the case of networks with a subset of nodes having temporary connection impairment between them. This study focuses on misbehaviors arising due to the presence of\enskip “dumb” nodes [Misra et al. 2014; Roy et al. 2014a, 2014b, 2014c; Kar and Misra 2015], which can sense its surroundings but cannot communicate with its neighbors due to shrinkage in its communication range by the environmental effects attributed to change in temperature, rainfall, and fog. However, a dumb node is expected to behave normally on the onset of favorable environmental conditions. Therefore, the presence of such dumb nodes in the network gives rise to impaired connectivity between a subset of nodes and, consequently, results in change in topology. Such phenomena are dynamic in nature and are thus distinct from the phenomena attributed to traditional isolation problems considered in stationary WSNs. Activation of all the sensor nodes simultaneously is not necessarily energy efficient and cost-effective. In order to maintain self-adaptivity of the network, two algorithms, named Connectivity Re-establishment in the presence of Dumb nodes ( CoRD ) and Connectivity Re-establishment in the presence of Dumb nodes Without Applying Constraints ( CoRDWAC ), are designed. The performance of these algorithms is evaluated through simulation-based experiments. Further, it is also observed that the performance of CoRD is better than the existing topology control protocols—LETC and A1—with respect to the number of nodes activated, overhead, and energy consumption.

In this paper, we propose a localization scheme named Opportunistic Localization by Topology Control (OLTC), specifically for sparse Underwater Sensor Networks (UWSNs). In a UWSN, an unlocalized sensor node finds its location by utilizing the spatio-temporal relation with the reference nodes. Generally, UWSNs are sparsely deployed because of the high implementation cost, and unfortunately, the network topology experiences partitioning due to the effect of passive node mobility. Consequently, most of the underwater sensor nodes lack the required number of reference nodes for localization in underwater environments. The existing literature is deficient in addressing the problem of node localization in the above mentioned scenario. Antagonistically, however, we promote that even in such sparse UWSN context, it is possible to localize the nodes by exploiting their available opportunities. We formulate a game-theoretic model based on theSingle-Leader-Multi-Follower Stackelberg gamefor topology control of the unlocalized and localized nodes. We also prove that both the players choose strategies to reach asocially optimal Stackelberg-Nash-Cournot Equilibrium. NS-3 based simulation results indicate that the localization coverage of the network increases upto 1.5 times compared to the existing state-of-the-art. The energy-efficiency of OLTC has also been established.

In this paper, the problem of energy distribution using virtual energy-cloud to the plug-in hybrid electric vehicles (PHEVs) is studied as a single leader multiple follower non-cooperative Stackelberg game. In this game, the energy-cloud service provider acts as the leader, and decides the price to be paid by each PHEV according to its usage. On the other hand, the PHEVs act as the followers, and need to decide the amount of energy to be consumed based on their requirements. Using variational inequality, it is shown that the proposed scheme, virtual energy cloud topology control (VELD), has a generalized Nash equilibrium, which is also socially optimal. The proposed scheme, VELD, which enables the energy-cloud service provider and the PHEVs to reach the equilibrium state, is evaluated theoretically as well as through simulations. Using the proposed scheme, VELD, the PHEVs consume up to 47.49-52.96% higher amount of energy, while paying 5.52% less per unit energy, which, in turn, increases the utilization of the generated energy by the micro-grids.

In this work, we consider the presence of dumb nodes in the underlying physical networks of Sensor-Cloud infrastructure. The dumb nodes get isolated from the network and degrade the network performance. Consequently, a Cloud Service Provider (CSP) is unable to use Wireless Sensor Networks (WSNs) in an efficient manner, as the dumb sensor nodes cannot use as a virtual sensor. Thus, in this work, we propose a scheme, Connectivity Re-establishment in the Presence of Dumb Nodes in Sensor-Cloud Infrastructure (CoRDS), that facilitates the reestablishment of the connectivity between dumb nodes and the sink. Using the proposed scheme, CoRDS, the CSP is able to provide the services efficiently as per end-users requirements. In order to re-establish connectivity between the dumb nodes and the sink, we use a single leader multiple follower Stackelberg game. Additionally, theoretical characterization of CoRDS has been shown.