The deployment of software-defined wireless local area networks (SDWLANs) in hostile regions requires solar energy harvesting-enabled battery-powered access points (APs). The aforementioned APs replenish batteries and forward data flows by avoiding the problem of frequent energy outages. In this work, we propose a scheme calledSeamFlowto place APs in energy harvesting mode without disrupting the forwarding of flows. In contrast to previous works, we focus not only on the energy replenishment of battery, but also, seamless forwarding of flows without violating flow constraints. Specifically, we formulate SeamFlow as a two-phase optimization problem – a) optimal selection of APs and b) finding forwarding paths. As solving both optimization problems are NP-hard, we propose two greedy-heuristic schemes to solve them in polynomial time. Extensive simulation results show that proposed scheme reduces flow forwarding cost by 24% and 16%, per-flow energy consumption by 27% and 21%, and flow violations by 92% and 86% compared to the benchmarks using random and grid topologies, respectively.

The next-generation cyber–physical systems (CPSs) with heterogeneous applications have diverse Quality-of-Service (QoS) requirements in terms of throughput, end-to-end latency, and packet drop reliability. To meet such diverse QoS requirements, in this article, we propose a QoS-aware traffic forwarding scheme in software-defined CPS. The proposed scheme is presented as a two-stage optimization framework to minimize the associated costs in traffic forwarding. In the first stage, we aim to minimize the required number of “candidate” switches for a given network to minimize network deployment costs. In the second stage, we design a comprehensive cost function considering end-to-end delay, flow-rule utilization, and link utilization in the network. Based on the designed cost function, we formulate another optimization problem for optimal traffic forwarding (OTF). As solving OTF is NP-hard, we propose an efficient greedy-heuristic approach to solve the problem while considering application-specific QoS requirements. Further, we propose a packet-tagging method to assist the controller in mitigating rule congestion at the software-defined networking devices, and hence improve the overall network performance. Extensive results show that the proposed scheme minimizes the network delay and QoS-violated flows by up to 50% and 90%, respectively, compared to the state-of-the-art schemes.

Software-defined IoT (SDIoT) is a promising approach to address the requirements of the Internet of Things (IoT), such as network management, Quality of Service (QoS), and resource utilization. The advantages of SDIoT are facilitated by the separation of the data- and the control-planes usingflow-rules, that allow fine-grained control over individual flows. However, the number of flow-rules that can be placed at the switches is limited, leading to scalability issues in SDIoT. Existing approaches to flow-rule management either do not consider the impact on QoS or are applicable only to a particular topology. In this article, we propose a QoS-aware flow-rule aggregation scheme for generic network topologies, which aims to achieve a satisfactory tradeoff among flow-rule compression and its impact on the QoS of IoT traffic flows. Specifically, the proposed scheme adaptively aggregates flow-rules while considering different QoS requirements of IoT applications in the network, and the flow-rule capacity of the switches. The proposed scheme consists of the following components—1) a path selection heuristic to increase the total number of flow-rules that can be accommodated in the network and 2) a multiarm bandit-based flow-rule aggregation scheme capable of reducing the number of flow-rules, while maintaining adequate performance in terms of QoS. Experimental results using IoT traffic show that, on average, the proposed scheme is capable of reducing the average end-to-end delay and QoS-violated flows in the network by 22% and 30%, respectively, compared to the state-of-the-art schemes.

Edge computing with network slicing enables 5G networks to meet the diverse and stringent requirements of new services and applications. We study the problem of admitting network slice requests and serving currently active slices in the 5G edge network, while avoiding active slice redistributions in the network. We pose it as a constrained optimization problem that seeks to maximize the total reward or revenue to the network operator from serving the new and active slices minus a term that penalizes slice redistributions in the network. We propose a three-phase polynomial-time, greedy, heuristic approach called RESET to solve this NP-hard problem. The first phase employs a cost function that determines the order in which new slice requests and a fraction of the active slices are served. It takes into account the bandwidth, storage, and computing requirements of a slice request. The second phase selects an edge cloud (EC) to assign an admitted request based on the residual bandwidth of its incoming links, and storage and computing resources at the EC. The last phase determines the forwarding path to route the traffic associated with the slice request to the selected EC. Extensive simulation results show that RESET achieves a total reward that is competitive with the optimal solution and is higher than benchmark schemes, while requiring far fewer slice redistributions.

In this paper, we propose a dynamic controller assignment scheme while considering flow-specific requirements, with an aim to minimize controller response time in software-defined networks (SDN). Using the FlowVisor model, we develop a virtualized platform that acts as a manager between the control- and data-planes of SDN architecture. The proposed scheme consists of two phases - adaptive window selection and controller assignment. In the window selection phase, the virtualized manager determines time to wait before incoming flows can be assigned to controllers in adaptive manner. Based on the adaptive window size, the flows are assigned to the controllers in the second phase. We use dynamic stable-matching game to assign flows to controllers, while defining their preference lists to minimize flow-setup delay and associated control overhead. Simulation studies show that the proposed scheme is capable of minimizing controller response time by atleast 31% compared to the existing state-of-the-art. Further, the proposed scheme also reduces the percentage of QoS violated flows in the network.

In this paper, we propose an adaptive flow-rule placement scheme, FlowStat, in a software-defined network (SDN) with an aim to provide per-flow statistics to SDN controller while enhancing overall network performance. The proposed scheme consists of three phases-forwarding path selection, flow-rule placement, and rule redistribution. In the first phase, we formulate a max-flow-min-cost optimization problem to determine optimal forwarding paths while considering multi-commodity flows with heterogeneous requirements. In the second phase, an integer linear programming problem is formulated to decide forwarding rules for paths computed in the first phase, so that the total number of exact-match is minimized. As finding optimal solution to the problems is NP-hard, we propose two greedy heuristic approaches to solve the problems in polynomial time. Finally, we propose a rule redistribution scheme on detecting rule congestion at a switch, in order to accommodate new flows in the network. Extensive experimental results show that the proposed scheme, FlowStat, is capable of providing per-flow statistics to the SDN controller while enhancing the network performance compared to existing schemes-ReWiFlow and ExactMatch. In particular, FlowStat is capable of reducing end-to-end delay and QoS violation by 46% and 75% (approx.), respectively, compared with the ReWiFlow and ExactMatch schemes, while providing 85% accurate per-flow statistics to the SDN controller.

In this paper, we propose a mobility-aware adaptive flow-rule placement scheme, named as Mobi-Flow, with an aim to maximize the overall performance in a software-defined access network (SDAN). The proposed scheme consists of two components - path estimator and flow-manager. The path estimator predicts future locations of end-users present in the network, depending on their history location sets, and delivers the predicted locations to the flow-manager. We use the order-k Markov predictor to predict the next possible locations of the end-users. Based on the predicted locations, the flow-manager determines access points (APs) in the network, which can be associated with the users to fulfill the requirements of the latter. We formulate an integer linear programming (ILP) to determine optimal number of APs, so that overall cost associated with flow-rule placement is minimized. Further, we propose a greedy approach to determine optimal number of APs, as solving the ILP is NP-hard. Consequently, the flow-manager implements the flow-rules at APs, so that adequate actions for the incoming requests can be taken in an adaptive manner, without querying the controller. We consider a practical scenario of an IoT environment, in which both static and mobile users are present. Therefore, the proposed scheme, Mobi-Flow, can be integrated atop the SDN controller to support the emerging concept of SDN-based IoT networking. Through extensive simulations, we show that Mobi-Flow is beneficial for minimizing the delay, number of activated APs, control overhead, energy consumption, and cost in the network, compared to the existing schemes-open shortest path first (OSPF), minimum occupied rule capacity (MRC), distributed (non-SDN), and MoRule. Particularly, the proposed scheme is capable of reducing the cost by 39, 38, 65, and 11 percent, compared to OSPF, MRC, distributed, and MoRule, respectively.

In this paper, we propose a QoS-aware adaptive flow-rule aggregation scheme in software-defined IoT (SDIoT) network with an aim to address flow-table overflow problem in SDN switches. The proposed scheme uses a key-based mechanism that is capable of fast aggregation and provides sufficient reduction in the number of flow rules, while having minimal impact on the QoS of IoT traffic. Further, we observe that it is necessary to adequately select a QoS path from multiple candidate paths, while considering the flow-table utilization at the switches. Accordingly, we present the Best-fit heuristic which takes into account the number of flow-rule insertions along with the bottleneck rule-capacity switch on a path, in order to minimize the total number of flow-rules in the network. Experimental results show that the proposed scheme is capable of reducing the average delay and packet drop by 35% and 12%, respectively, and improving the average throughput by 20% compared to the existing delay-based flow-aggregation scheme, while having comparable performance in terms of rule-aggregation.

Internet of things (IoT) facilitates billions of devices to be enabled with network connectivity to collect and exchange real-time information for providing intelligent services. Thus, IoT allows connected devices to be controlled and accessed remotely in the presence of adequate network infrastructure. Unfortunately, traditional network technologies such as enterprise networks and classic timeout-based transport protocols are not capable of handling such requirements of IoT in an efficient, scalable, seamless, and cost-effective manner. Besides, the advent of software-defined networking (SDN) introduces features that allow the network operators and users to control and access the network devices remotely, while leveraging the global view of the network. In this respect, we provide a comprehensive survey of different SDN-based technologies, which are useful to fulfill the requirements of IoT, from different networking aspects-edge, access, core, and data center networking. In these areas, the utility of SDN-based technologies is discussed, while presenting different challenges and requirements of the same in the context of IoT applications. We present a synthesized overview of the current state of IoT development. We also highlight some of the future research directions and open research issues based on the limitations of the existing SDN-based technologies.

In this paper, we propose a mobility-aware flow-table implementation scheme with an aim to maximize overall network performance in software-defined IoT. The proposed scheme consists of two components - path estimator and flow-manager. The path estimator predicts future locations of end devices present in the network, and delivers info to the flow-manager. Based on predicted locations, the flow-manager implements forwarding rules at access devices (ADs) in the network, so that adequate actions for incoming requests can be taken immediately without asking the controller. We use order-k Markov predictor to predict the next possible locations of the end devices. We consider a practical scenario of an IoT environment, in which both static and mobile devices are present. Extensive simulation results show that the proposed scheme is beneficial for improving network performance in terms of energy consumption and message overhead for flow-table implementation, while predicting the future locations of the devices. We show that the proposed scheme is capable of enhancing the overall network performance approximately by 50%.

In a smart grid, micro-grids can exchange energy among themselves in order to provide reliable energy service to customers. Therefore, the micro-grids need to exchange their real-time energy status with other micro-grids, which, in turn, maximizes the energy consumption and CO2emissions to them. In this paper, we propose a cloud-based energy forecasting scheme to minimize the energy consumption and CO2emission towards enabling a green smart grid communication technology. Additionally, we device an optimal strategy for the proposed cloud-based energy forecasting scheme to minimize the energy consumption furthermore. Numerical results show the effectiveness of the proposed scheme over without cloud-based approach in terms of message overhead, energy consumption, and CO2emissions of the micro-grids. We see that the proposed scheme can minimize the energy consumption and the CO2emissions involved in the forecasting process significantly, which supports the green architecture of the smart grid communication technology. Additionally, the message overhead for energy forecasting can also be minimized.

In this paper, energy trading for the distributed smart grid architecture is projected as an incomplete information game —a viewpoint that contrasts from all the existing pieces of literature available on the broader issue of energy management in smart grid. The incomplete information is considered as the real-time demand and price to grid and customers, respectively, due to the packet loss in the communication network. Therefore, the paper addresses a realistic scenario, in which real-time information to the destination may not be guaranteed to be received adequately, due to the packet loss. In the proposed scheme, we introduce two types of intelligent agents— customer-agents and grid-agent . The customer-agents are deployed at the customers׳ end, and are capable of estimating adequately the real-time price decided by the grid. On the contrary, the grid-agent is deployed at the service provider׳s end, and are also capable of estimating adequate real-time energy demand from the customers. Therefore, one of the key advantage of the proposed agent-based scheme is that the customers and the grid are not involved in complex calculations in order to take real-time decisions for cost-effective energy management, while there is information loss in the communication networks. In the proposed game model, the grid-agent and the customers agents are the players, and estimate real-time demand and price based on the probability of belief to each other. We show the existence of Bayesian Nash Equilibrium in the proposed model, where the utility of the players is maximized. We compare the real-time price with and without packet loss as the price with incomplete and complete information, respectively. We observe that the proposed model is beneficial for the grid, as its utility is maximized. The simulation results show that the utility of the grid increases approximately 40% over that of the existing ones under the scenario of information incompleteness.

The fast-paced development of power systems necessitates smart grids to facilitate real-time control and monitoring with bidirectional communication and electricity flows. Future smart grids are expected to have reliable, efficient, secured, and cost-effective power management with the implementation of distributed architecture. To focus on these requirements, we provide a comprehensive survey on different cloud computing applications for the smart grid architecture, in three different areas-energy management, information management, and security. In these areas, the utility of cloud computing applications is discussed, while giving directions on future opportunities for the development of the smart grid. We also highlight different challenges existing in the conventional smart grid (without cloud application) that can be overcome using cloud. In this survey, we present a synthesized overview of the current state of research on smart grid development. We also identify the current research problems in the areas of cloud-based energy management, information management, and security in smart grid.

Future large-scale deployment of plug-in hybrid electric vehicles (PHEVs) will render massive energy demand on the electric grid during peak-hours. We propose an intelligent distributed dynamic pricing (D2P) mechanism for the charging of PHEVs in a smart grid architecture-an effort towards optimizing the energy consumption profile of PHEVs users. Each micro-grid decides realtime dynamic price as home-price and roaming-price, depending on the supply-demand curve, to optimize its revenue. Consequently, two types of energy services are considered-home micro-grid energy, and foreign micro-grid energy. After designing the PHEVs' mobility and battery models, the pricing policies for the home-price and the roaming-price are presented. A decision making process to implement a cost-effective charging and discharging method for PHEVs is also demonstrated based on the real-time price decided by the micro-grids. We evaluate and compare the results of distributed pricing policy with other existing centralized/distributed ones. Simulation results show that using the proposed architecture, the utility corresponding to the PHEVs increases by approximately 34 percent over that of the existing ones for optimal charging of PHEVs.

In this paper, we propose a dynamic duty scheduling scheme for minimizing the energy consumption of the on-field sensor networks in a sensor-cloud application framework. The conjugation of cloud framework with Wireless Sensor Networks (WSNs) adds enhanced processing and storage capacity to the on-field WSN applications. However, the WSN applications performing periodic information update to the cloud exhibit low network lifetime, low resource utilization, and high cost. In this regard, the advent of the sensor-cloud technology facilitates dynamic duty scheduling of the on-field WSNs. As a result, the on-field WSNs attain improved energy-efficiency and cost-effectiveness. The simulation results show the effectiveness of the proposed scheme over the traditional scenarios.

In a smart grid, smart meters are expected to be the key technology to support bi-directional information exchange between service providers and end-users. The on-growing demand of smart meters (residential customers and plug-in hybrid electric vehicles) would result in huge energy consumption, while communicating with the entities in the smart grid. Therefore, green wireless communication technologies are expected to help in reducing their impact on environment. Therefore, it is important to design energy-efficient schemes that can reduce CO2emissions and cost-effective energy management in the smart grid. In this paper, an energy-efficient smart metering scheme is proposed - an effort towards minimizing the energy consumption by the smart meters for green smart grid communication. We incorporate the use of coalition game to form multiple coalitions among smart meters to communicate with the service provider. We show that there exists a stable condition of the coalitions for which the payoff values of the smart meters are maximized. The simulation results show that using the proposed approach, energy consumption by the smart meters can be reduced, which, in turn, would enable green wireless communication in the smart grid.