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The fifth generation (5G) networks are expected to support diverse business verticals (i.e., manufacturing, health care, etc.) with varying quality of service requirements. While today’s mobile networks are a one size fits all architecture, tomorrow’s 5G mobile networks are envisioned to encourage agility, programmability and elasticity through enabling a software-based architecture promoted by network slicing. Network slicing is a new paradigm consisting of partitioning the underlying network infrastructure into different logical network slices, each dedicated to address the requirements (i.e., ultra-low latency, ultra-reliability, etc.) of a group of services. Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies have been identified as main enablers of network slicing, facilitating the fulfillment of the aforementioned services’ requirements. In this paper, we study the Latency-Aware service scheduling (LASS) problem to solve the network function mapping, the traffic routing and the network service scheduling in the context of an ultra-low latency network slice to consider services with stringent deadlines. We propose the LASS-Game, a novel game-theoretic approach presenting a scalable solution for the LASS problem that accounts for the centralized aspect of the problem while leveraging a decentralized mapping, routing and scheduling decisions.

The transition to electric vehicles (EVs) has prodigious plausibility in reducing green house gas (GHG). But EVs acceptance is, however, hindered by several challenges; among them is their avidity for quicker charging at lower price. This article considers a photovoltaic (PV)-powered station equipped with an energy storage system (ESS), which is assumed to be capable of assigning variable charging rates to different EVs to fulfill their demands inside their declared deadlines at minimum price. To ensure fairness, a charging rate-dependent pricing mechanism is proposed to assure a higher price for enjoying a higher charging rate. The PV generation profile and future load request are forecasted at each time slot, to handle the respective uncertainty. An integer linear programming (ILP)-based centralized system is first proposed to minimize the charging price per EV. Due to the larger computational time, we subsequently present two game theoretic algorithms, i.e., game 1 and game 2. In game 1, players are oblivious of upcoming charging requests, whereas in game 2, players consider the future anticipated load to select their charging strategies. The games are shown to converge to a Nash equilibrium. The average unit price of game 2 is shown to be the same as the one of the optimal solution and takes considerably less computation time than the centralized method.

Today, the evolution of smart grid, electric vehicles (EVs) with voltage to the grid mode, and deployment of renewable energy sources (RESs) are bringing revolutionary changes to the existing electrical grid. Volt-VAR optimization (VVO) is a well-studied problem, for bringing solutions to reduce the losses and demand along the distribution lines. The current VVO, however, does not acknowledge the role of elastic and inelastic loads, EVs, and RESs to reduce the reactive power losses and hence the cost of generation. We propose a mathematical model Volt-VAR and CVR optimization (VVCO)/optimal energy consumption model (OECM) to solve the VVO problem by considering load shifting, EV as the storage and carrier of the energy, and use of RES. The VVCO/OECM not only reduces the reactive load but also flatten the load curve to reduce the uncertainty in the generation and to decrease the cost. The system also considers the efficiency of the electrical equipment to enhance the lifetime of the devices. We develop a non-cooperative game to solve the VVCO/OECM problem. To evaluate the performance, we simulate the VVCO/OECM model and compare with the existing VVO solution. We found that our method took almost a constant time to produce a solution of VVO regardless of the size of the network. The proposed method also outperform the existing VVO solution by reducing the generation cost and flatten the load and minimizes the uncertainty in the power generation. Results have shown that exploiting RES will reduce the voltage drop through reducing the injection of reactive power to the system.

The evolution in microgrid technologies as well as the integration of electric vehicles (EVs), energy storage systems (ESSs), and renewable energy sources will all play a significant role in balancing the planned generation of electricity and its real-time use. We propose a real-time decentralized demand-side management (RDCDSM) to adjust the real-time residential load to follow a preplanned day-ahead energy generation by the microgrid, based on predicted customers’ aggregate load. A deviation from the predicted demand at the time of consumption is assumed to result in additional cost or penalty inflicted on the deviated customers. To develop our system, we formulate a game with mixed strategy which in the first phase (i.e., prediction phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flattened curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another game with mixed strategy to mitigate the deviation between the instantaneous real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and ESSs and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator to better deal with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. We evaluate the performance of our method against a centralized allocation and an existing decentralized EV charge control noncooperative game method both of which rely on a day ahead demand prediction without any refinement. We run simulations with various microgrid configurations, by varying the load and generated power, and compare the outcomes.

The rapid deployment of phasor measurements in smart grid transmission system has opened opportunities to utilize new applications and enhance the grid operations. Thus, the smart grid has become more dependent on communication and information technologies, such as wide area measurement systems (WAMSs). WAMS are used to collect real-time measurement from sensors across widely dispersed areas. Such systems will improve real-time monitoring and control; however, recent studies have pointed out that the use of WAMS introduces significant vulnerabilities to the smart grid that can be leveraged by attackers. Therefore, preventing or reducing the damage of cyber attacks is critical to the security of the smart grid. In this paper, we focus on the relation between cyber-attack propagation and IP multicast routing, which is an essential aspect to the collection of phasor measurement units (PMUs) measurements. To this extent, we formulate the problem as the construction of a multicast tree that minimizes the propagation of cyber-attacks, while satisfying real-time and capacity requirements. The proposed attack propagation multicast tree is evaluated using IEEE 14-bus, IEEE 24-bus, IEEE 39-bus, the New England 39-bus, and IEEE 57-bus test systems.

The evolution of smart microgrid and its demand–response characteristics not only will change the paradigms of the century-old electric grid, but also will shape the electricity market. In this new market scenario, once always energy consumers, now may act as sellers due to the excess of energy generated from newly deployed renewable energy generators. In this paper, we propose an optimization scheme to minimize the electricity price with a framework for optimal trading of energy between sellers and buyers of the microgrid network (MGN). The proposed scheme is capable of solving the optimal power allocation problem for an MGN in a polynomial time without modifying the actual marginal costs of a generator. Initially, we mathematically formulate the problem as nonlinear nonconvex and later decompose the problem to separate the optimal marginal-cost model from the electricity allocation model. Then, we develop a divide-and-conquer method to minimize the electricity price by jointly solving the optimal marginal-cost model and electricity allocation problems. To evaluate the performance of the solution method, we develop and simulate the model with various cost functions and compare it with a first come first serve electricity allocation method and distributed energy trading for multiple microgrids.

The selection of workers for carrying out tasks in mobile crowdsourcing systems that maximizes Quality of Service (QoS) is a challenging problem due to their diverse task completion properties and profit demands. The existing works in the literature are limited either by merely considering the minimization of task completion time only or exploitation of colocated locations of the worker and task. In this paper, we have developed an optimization framework to make a trade-off in between the profit of workers and task completion time. The proposed framework considers locations related to task's service point, task's delivery point and current location of the worker. The results of performance studies depict that the proposed system offers competitive task delivery time as well as workers' profit.

The Fifth Generation (5G) era is bringing tremendous new network capabilities enabling diverse services belonging to different business verticals (i.e., manufacturing, automotive, etc.) and provided with top-notch Quality of Service (QoS) (i.e., ultra-low latency, ultra-reliability, etc.). Empowered by soft-warization technologies such as Network Function Virtualization (NFV), 5G networks are envisioned to be agile, sustainable and self-organized. NFV promotes the automated provisioning of Network Services (NSs) through processing their traffic by a chain of Virtual Network Functions (VNFs). As VNFs are shared between multiple NSs, a clear approach to map and schedule the carried traffic of these services is required. Hence, in this paper, we solve the Latency-Aware Service Schedule Orchestration problem (LASSO) that jointly addresses the mapping and scheduling of services to VNFs. We formulate the problem as a Mixed Integer Linear Program (MILP) and we present ENCHAIN, a novel game-theoretic approach exploiting a scalable solution for the LASSO problem while providing each NS the freedom to decide on its own mapping and scheduling solution.

To reduce greenhouse gas emissions, several incentives are given to use renewable energy sources (RES) and plug-in electrical vehicles (PEVs). By using PEVs for electricity storage and RES as distributed generators, microgrids (MG) become more reliable, stable, and cost-effective. However, the high intermittent nature of energy sources and unpredictable presence of PEV in the MG offers new technological challenges to the smart grid/MG energy management system (EMS). In this paper, we propose a new distributed real-time electricity allocation (DRTA) scheme for the smart grid/MG EMS whose objectives are to reduce the electricity bill of the residential customers, to increase the overall social benefit of smart MG/grid community, and to increase the energy efficiency and reliability of the MG to rely on locally generated electricity. We formulate the problem as a noncooperative game using mechanism design and solve it to optimality. The proposed DRTA scheme converges to an optimal Nash equilibrium and produces a near-optimal solution. We compare the proposed scheme with a centralized optimal electricity consumption scheme and to the solution obtained from unregulated (natural) scheduling framework.

The integration of renewable energy sources and electrical vehicles (EVs) into microgrids is becoming a popular green approach. To reduce greenhouse gas emissions, several incentives are given to use renewable energy sources and EVs. By using EVs as electricity storage and renewable energy sources as distributed generators (DGs), microgrids become more reliable, stable, and cost-effective. In this paper, we propose an optimal centralized scheduling method to jointly control the electricity consumption of home appliances and plug-in EVs as well as to discharge the latter ones when they have excess energy, thereby increasing the reliability and stability of microgrids and giving lower electricity prices to customers. We mathematically formulate the scheduling method as a mixed integer linear programming (MILP) problem and solve it to optimality. We compare the optimal solution to that obtained from a scheduling framework, where EVs do not have discharge capabilities, decentralized charge control using game theory and to a solution obtained from a naive scheduling framework.

The development of smart grid, electric vehicles (EV), and integration of renewable energy sources (RES) changes the existing power grid. Volt-VAR optimization (VVO) is a well-known problem to reduce the losses along the distribution lines. Current VVO does not endorse the role of elastic and inelastic loads, EVs, and RESs to decrease the reactive losses and hence the cost of generation. We propose a mathematical model VVCO/OECM to solve the VVO problem by considering load shifting, EV as storage, and use of RES. The system also admits the efficiency of the electrical equipment to enhance the life of the devices. We develop a non-cooperative game to solve the VVCO/OECM problem. To evaluate the performance, we simulate the VVCO/OECM model and compare with the existing VVO solution (VVCO). We found that our method took nearly a constant time to produce a solution of VVO despite the size of the network. It outperforms the existing VVO solution by reducing the generation cost and flatten the load.