In this paper, a blockchain-based decentralized stochastic energy management scheme is proposed for smart grid-connected households with photovoltaic generation and battery energy storage systems. The proposed scheme autonomously solves the joint optimization problem of maximizing individual household benefits while also minimizing total line loss in the distribution system. Internet-of-Things (IoT) technology is essential for the optimization as continuous sensing of the household power and battery states, coupled with autonomous bidding in voltage regulation auctions, are vital ingredients of this energy management scheme. A double-auction algorithm for coordinated voltage regulation with IoT access control is designed and implemented as a smart contract, which enables proactive voltage issue diagnosis while preserving privacy. The proposed blockchain-based decentralized stochastic energy management scheme consists of two subschemes, corresponding to individual households and the distribution system, respectively. Two decentralized stochastic energy management schemes are proposed for the blockchain platform. Specifically, an individual household energy management scheme is developed to maximize the benefit for each residence, while a collaborative energy management scheme is proposed to optimize the system’s joint benefit by minimizing the total line loss. Both schemes are modeled as decentralized Markov decision processes, thus avoiding the need for centralized computation. To enhance the scalability of the smart contract, a novel pruning-based auction algorithm for voltage regulation is developed. A case study on the IEEE 123-Node Test Feeder indicates that residential voltage regulation can be coordinated effectively through the proposed scheme. The algorithm is computationally efficient, with computation time scaling logarithmically with the number of participants.

Modern power systems are undergoing a paradigm shift from traditional grids towards smart grids. It fundamentally changes traditional power systems into complex cyber-physical systems. On the other hand, new challenges arise in terms of grid resilience, because natural disasters can cause damages to both cyber and physical systems. In this article, we propose a stochastic sequential restoration scheme for cyber-physical power distribution systems considering resilience. The sequential restoration problem is formulated as an uncertain Markov decision process (UMDP) with hurricanes incorporated as natural disasters. Different wind velocities and directions are considered as hurricane scenarios, which are used to obtain the fragility of distribution lines. The fragility functions are further used for the derivation of uncertain state transition functions of the UMDP. The minimax regret optimization considering the sample weights of UMDP is presented. The robust sequential actions are determined, such that the loads can be restored in a timely manner. To improve computational efficiency, a minimax regret policy iteration algorithm is presented based on the regret Bellman equation. Case studies are conducted based on the IEEE 123-Node Test Feeder and historical data of Hurricane Bonnie to demonstrate the effectiveness of the proposed scheme.

Earthquakes, which consist of one intensive main shock and a series of aftershocks, can significantly damage power distribution systems (PDSs). In this article, a data-driven PDS resilience enhancement strategy is proposed against multishocks of earthquakes. In particular, the investment and prepositioning of mobile emergency generators (MEGs) is determined against multishocks of earthquakes. The reallocation of MEG and the repair scheduling are obtained considering aftershocks and postrestoration failures. A resistibility index (RI) is developed based on hierarchical hidden Markov model (HHMM) for stochastic resilience evaluation. The historical earthquake data are incorporated into the HHMM as observed information of multishocks of earthquakes. Based on the RI, the problems of prepositioning and reallocation of MEGs are formulated as mixed-integer programming problems. The problem of repair scheduling is formulated as an adaptive multiperiod two-stage stochastic programming problem, for which a revision period is introduced to allow the decisions to adapt to the uncertainties after the revision. To reduce the computational complexity, an iterative algorithm is presented based on linear programming relaxation. The strategy is verified via case studies on the IEEE 123-Node Test Feeder and historical earthquake data. It shows by considering RI, the resilience of restoration can be optimized against future shocks of earthquakes. Also, the overall consideration of MEG investment, prepositioning, reallocation, and repair scheduling against multishocks of earthquakes can achieve an improved restoration performance.

With the growing adoption of electric vehicles (EVs) and advent of bidirectional chargers, EV aggregators, such as charging stations, will become a major player in electricity markets, providing voltage regulation (VR) or other services. We present a novel and practical VR scheme that takes advantage of the charging flexibility of EVs in charging stations that are connected to buses in a distribution grid. This VR scheme relies on real-time measurements, as well as estimates of the distribution system state and regulation capacity of each charging station. We then propose a novel false data injection attack against the VR capacity estimation process that exploits the uncertainty in EV mobility and network conditions. We show the attack vector with the largest expected adverse impact is the solution of a stochastic optimization problem, subject to a constraint that ensures it bypasses bad data detection. We determine this attack vector by solving a sequence of convex quadratically constrained linear programs. The case studies examined in a cosimulation platform, based on two standard test feeders, reveal the vulnerability of the VR capacity estimation process.

Along with the battery technology advancements and government policy support, the penetration level of electric buses (EBs) in the urban public transportation system has been increasing in recent years. Considering the potential influence of the increasing EB charging demand on power systems, estimating the real-time energy consumption of EBs has become a principal issue. In this work, a data-driven approach for EB energy consumption estimation is proposed. In particular, a detailed physical model of EB is constructed to model its energy consumption considering the randomness in EB operation, including speed, acceleration, and passenger count. In order to improve the estimation accuracy, the conventional Kalman filter (KF) is modified involving EB mass estimation considering stochastic real-time passenger count, motion data dimension deduction based on EB operation route. To estimate the EB acceleration accurately and reduce the noise caused by the unimportant features, we extended the feature discarding algorithm of decision trees to the regression trees. In the case study, an Android application is developed to collect the EB motion data so that any general Android smartphone can be used for data collection. The performance of the proposed approach is evaluated based on real-world EB operation data collected from St. Albert Transit, AB, Canada. According to the results, our APP can track the real-time EB trace, and the proposed modified KF can filter most of the noises caused by the GPS data collection process and stochastic passenger count. Also, with the extended random forest algorithm, the unimportant features can be discarded and the real-time EB acceleration is estimated efficiently with a small sum of square error (SSE). Compared with the existing approaches, the proposed approach achieves a more accurate real-time energy consumption estimation of EBs, which in turn, provides a better characterization of power system loading and voltage variation.

Blockchain is an immutable type of distributed ledger that is capable of storing data without relying on a third party. Blockchain technology has attracted significant interest in research areas, including its application in the smart grid for cybersecurity. Although significant efforts have been devoted to utilizing blockchain in the smart grid for cybersecurity, there is a lack of comprehensive survey on blockchain in the smart grid for cybersecurity in both application and technological perspectives. To fill this gap, we conducted a comprehensive survey on blockchain for smart gird cybersecurity. This conducted survey presents the latest insights of ideas, architectures, and techniques of implementation that are relevant to blockchain's application in the smart grid for cybersecurity. This article aims at providing helpful guidance and reference for future research efforts specific to blockchain for cybersecurity in the smart grid.

In recent years, aiming to reduce the metropolitan air pollution caused by fossil fuel-powered vehicles, the electrification of transportation, such as electric vehicles (EVs) and electric buses (EBs), has attracted great attention from the automobile industry, academia, and public transportation. EBs, driven by decarbonized electricity, can reduce the air pollution and noise level. Besides, they can also recover electricity from regenerative braking. Recent years have witnessed continuous works on the topics of EB energy storage, power management, and charging scheduling. In this review, we have comprehensively surveyed three primary parts: important components; existing research topics; and open issues of EBs. Specifically, we first introduce the important components of EBs, including energy storage systems, powertrains, interleaving elements and electric motors, and driving cycles. Then, we review the existing research topics of EBs, including the energy storage system sizing, power/energy management, range remedy methods, charging design/scheduling, and trial projects. At last, extending from existing literatures, we further propose the future research opportunities and ongoing challenges, such as extending EV related research to EBs, EB charging demand modeling, and EB impact on power systems.

In federated learning, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client in each communication round. Naive weighted aggregation of such models causes objective inconsistency, that is, the global model converges to a stationary point of a mismatched objective function which can be arbitrarily different from the true objective. This paper provides a general framework to analyze the convergence of federated heterogeneous optimization algorithms. It subsumes previously proposed methods such as FedAvg and FedProx and provides the first principled understanding of the solution bias and the convergence slowdown due to objective inconsistency. Using insights from this analysis, we propose FedNova, a normalized averaging method that eliminates objective inconsistency while preserving fast error convergence.

Nowadays, due to the severe environmental concerns caused by the emissions from the conventional transportations which use traditional fossil sources, people want to find an alternative method imperatively. Under this condition, electric buses attract transit service providers' attention. However, since the degradation of the battery and charging load for the local grid, the charging schedule is a critical issue to be addressed. In this paper, in order to obtain the optimal on-route charging schedule, a specific physical model and battery degradation model is built for the calculation of the energy consumption and the cost. Semi-Markov decision process (SMDP) is utilized to simulate the running process of the EBs, and the average reward reinforcement learning (ARRL) is introduced to optimize the on-route charging schedule for the EBs. The charging policy and the performance is compared with the default charging schedule according to the real EB operation data provided by the St. Albert Transit, AB, Canada.

The occurrence of natural disasters imposes potential damage on the power systems. Many methods have been proposed to improve power system resilience by constructing load restoration when the blackout happens. However, disaster like earthquakes may have secondary impacts (i.e., aftershock of earthquake) which would undermine the post-contingency power supply and cause restoration failure. To address such challenge, this paper proposes a Mobile Energy Resource (MER) allocation strategy to restore critical loads by determining the most reliable restoration path against the potential earthquakes. Benefited from the mobility of MER, the restoration paths can always be adjusted according to the real damage of earthquakes as long as there are available paths and capacity. On the other hand, when curtailment is inevitable, critical loads would be optimally shed based on priority. The effectiveness of the proposed strategy is verified by case studies on the modified IEEE 123-node test feeder.

In recent years, the environmental issues caused by using conventional energy resources, such as gasoline and diesel, become more and more serious. One promising solution to these issues is the electrification of public transit by replacing the internal combustion engine buses with electric buses (EBs). However, due to the degradation of EB batteries, the optimization of EB charging schedules during operating time is still challenging for the public transit service providers. This challenge is further complicated by the randomnesses of traffic and road conditions. In this paper, the problem of optimizing EB charging schedules is formulated as a Markov decision process, based on the battery degradation model of EBs and the information available via vehicular communication networks in smart grid. A double Q-leaning algorithm is used to optimize the charging schedules by minimizing the battery degradation cost of EBs. The performance of the proposed algorithm is evaluated by comparing with existing algorithms based on the real data of EB mobility and energy consumption collected from St. Albert Transit, AB, Canada.

The development of alternatives for urban mobility and public transportation has been promoted by the increasing concerns on the environmental issues in the recent years. Along with the evolution of battery technology and charging/discharging control methodology, the diesel buses have been replaced with the electric buses (EBs) gradually. In order to satisfy the increasing charging demand of EBs, the photovoltaic (PV) and energy storage system (ESS) are usually installed on- site, which can not only guarantee sufficient power supply for the transit center, but also decrease the service charge for capacity from the distribution system operator (DSO). Besides, through involving the vehicle- to-grid (V2G) mechanism, the EBs parking in the transit center can also supply power to mitigate the peak load. Considering uncertainty of the office base load, PV generation, and the random availability of EBs, a stochastic energy management approach for such EB transit center is urgently needed. In this study, we focus on minimizing the total cost of the transit center taking into account the service charge for capacity, the energy consumption cost, the battery degradation cost of ESS and EBs, and the power generation revenue. A detailed model is built to investigate the energy management operation in the system as well as the corresponding cost. Then, we decompose the non-linear optimization problem into two stages: In the first stage, we aim to mitigate the target peak load through a modified robust optimization over time (ROOT) approach; Based on the optimal target peak load, the charging/discharging schedule is optimized in the second stage. The corresponding algorithm is developed and the performance of the proposed approach is evaluated in the case study based on the actual data obtained from FortisAlberta and St. Albert Transit, AB, Canada.

In this paper, the impacts of false data injection (FDI) attacks against real-time distribution locational marginal pricing (DLMP) in cyber-physical systems (CPS)-based smart distribution systems are analyzed. Firstly, an explicit expression of real-time DLMP is derived based on radial structure, small bus voltage phase angle changes, and high R/X ratio features of practical smart distribution systems, by leveraging the distribution system state estimation results. This derived explicit expression of real-time DLMP is analyzed based on the KarushKuhn-Tucker conditions, which reveals that the real-time DLMP can be modified by manipulating bus voltage information through FDI attacks. Then, the practical construction of FDI attacks against real-time DLMP is investigated based on an optimization problem under the constraints of minimum sparsity for FDI attacks and voltage regulation and power balance for system operation. Further, based on the features of practical power distribution system, this optimization problem is transformed into a convex-concave fractional programming problem, which is solved by using the Dinkelbach's algorithm at low computational complexity. A case study based on modified IEEE 13-bus test feeder is conducted to illustrate the potential impacts of FDI attacks in CPS-based smart distribution systems.

Collaborative beamforming (CB) has recently emerged as a promising technique for transmission range extension and energy consumption reduction in wireless sensor networks (WSNs). However, due to the constrained energy and limited data processing capabilities of sensor nodes, the performance optimization of CB mainlobe and sidelobe control (SC) encounters challenges in the practical deployment. To address these challenges, we present an architecture of software-defined energy harvesting WSN (SD-EHWSN) for CB communications. Specifically, we first design the mechanism of CB communications based on the software-defined network (SDN) architecture to reduce the communication and computational overhead of sensor nodes. Then, we consider solar energy-harvesting system to achieve long-term operation of WSN and utilize a stationary Markov (SM) chain to model the arrival process of solar energy. Based on the stochastic nature of solar energy, a stochastic game model is developed to formulate the problem of CB optimization in SD-EHWSN, and the existence proof of Nash equilibria is provided. Based on the analytical results, we propose a reinforcement learning algorithm to maximize the long-term signal-to-noise ratio (SNR) performance with SC and prove the convergence of the algorithm. Simulation results are presented to validate the efficiency of the proposed scheme for CB communications in SD-EHWSN.

In this paper, we consider false data injection (FDI) attacks with limited information of transmission-line susceptances and new countermeasures in smart grids. First, we prove that the adversary could launch FDI attacks to modify the state variable on a bus or superbus only if he/she knows the susceptance of every transmission line that is incident to that bus or superbus. Based on this observation, we provide a new countermeasure against FDI attacks, i.e., to make the susceptances of n-1 interconnected transmission lines that cover all buses unknown to the adversary (e.g., by proactively perturbing transmission-line susceptances through distributed flexible AC transmission system (D-FACTS) devices), where n is the total number of buses. This new countermeasure can work alone or in conjunction with traditional ones to reduce the number of meter measurements/state variables that are to be secured against FDI attacks. The implementation of FDI attacks with limited susceptance information and the effectiveness of new countermeasures are demonstrated by using an illustrative 4-bus power system and the IEEE 9-bus, 14-bus, 30-bus, 118-bus, and 300-bus test power systems.

In the future, smart grid charging stations will be critical infrastructures for plug-in electric vehicle (PEV) to replenish their batteries in a convenient way. Due to the ever-increasing penetration rate of PEVs, how to efficiently manage the loads of PEV charging stations to ensure system efficiency and reliability is a major challenge faced by the distribution service providers (DSPs) in the smart grid. This challenge is further complicated by the highly dynamic PEV mobility, which results in random PEV arrivals, departures, and charging demands. In order to address this challenge, a stochastic game approach is proposed in this paper to characterize the interactions among DSP, charging stations, and PEV owners, where the randomness in charging decision making processes of PEV owners is modeled by a Markov decision process. Based on the Nash equilibrium solution of the stochastic game, a real time pricing scheme is proposed for the DSP to minimize power distribution losses while ensuring system reliability. The performance of the proposed approach is evaluated via extensive simulations based on the IEEE 123 bus test feeder with real vehicle mobility data from the 2009 National Household Travel Survey and the 2010 National Travel Survey.

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

Under the dynamic pricing environment, electric vehicle (EV) owners are faced with the EV charging and discharging scheduling problem to minimize the electricity cost. On one hand, in existing literatures that only consider the EV charging scenario, the solution cannot be trivially extended to the EV discharging scenario. On the other hand, existing approaches for solving integer programming either have the exponential computational complexity or cannot guarantee the optimal solution. In this paper, instead of focusing on the action selection at each time slot, we traverse all possible states within the state transition process, since the total number of states is much smaller than that of action schedules. Then, the computational complexity of the problem solving can be reduced from an exponential order to a polynomial one. Algorithms with both certain and uncertain future electricity prices are developed. It is demonstrated with real price data from Commonwealth Edison Company that our proposed algorithms can facilitate the optimization solution.

As one of the potential frequency regulation (FR) service providers for the independent system operator (ISO) or regional transmission organization (RTO), electric vehicles (EVs) can response the FR control signals generated by ISO or RTO by changing their real-time charging or discharging power. The target is to keep the area control error (ACE) at a low level while minimizing the cost. Recently, many of the ISOs and RTOs such as CAISO and PJM have offered performance-based pricing schemes for FR service providers. Yet, how to estimate the total FR capacity of EV owners managed by the EV aggregators (AGGs) and optimize the FR signals accordingly still need extensive research. In this paper, we proposed an optimal strategy for ISO (or RTO) to allocate differentiated FR signals to EV AGGs according to their actual FR capacity. Thereby, an inhomogeneous (or time-variant) hidden Markov model (HMM) is developed to estimate the FR capacity through observing the historical FR responses. In this paper, traditional BaumWelch algorithm is extended to be applied in the inhomogeneous scenario through decoupling the transition matrix. In this way, the computational complexity can be significantly reduced. The performance of the proposed algorithm is evaluated through extensive simulations based on the real FR signals from PJM.

With the proliferation of renewable energy sources and the elevation of environmental concerns, it is expected that microgrids will become one of the major means for residential energy supply. However, the distributed nature of microgrid operation brings new technical challenges to energy management. Endowing wireless communication capability to the distributed generation (DG) units and energy storage (ES) devices in a microgrid is beneficial for their cooperation without a centralized controller. Yet, how to establish distributed energy management without \emph{a priori} statistical information for all the DG units and loads still requires extensive research. In this paper, a reinforcement learning algorithm with cooperative neural fitting iteration is proposed for distributed energy management in microgrids via wireless networks. The reinforcement learning algorithm leverages a distributed actor- critic structure to adopt the continuous states and action spaces of a microgrid. A diffusion strategy is incorporated in the reinforcement learning algorithm to coordinate the actions of DG units and ES devices by exchanging their evaluations and decisions via a wireless network. Simulation results based on realistic renewable power generation and load data are presented to evaluate the performance of the proposed algorithm.

Offloading computational intensive applications to cloud broker is a promising solution for overcoming the limitation of computational resources and energy of mobile devices. The cloud broker sends the jobs to the nearest local cloudlet to guarantee the least delay for the applications. Most of the existing studies consider a fixed amount of virtual machines (VMs) that the local cloudlet would like to rent. In reality, the jobs arriving at the cloud broker and the jobs submitted by the interior users to the cloudlet are both dynamic. In order to address this issue, we use the stochastic game approach in this paper to improve the long-term revenue of cloud broker and local cloudlet. We model the jobs arriving at the queue as a stochastic progress. Then, the cloud broker and the local cloudlet sent a number of jobs to the virtual machine pool in each time slot. Both of them ensure that the submitted jobs are completed, and the cloudlet guarantees that each of the jobs submitted by interior users is executed. We prove the existence of the equilibrium in this game and develop an algorithm to calculate the c-Nash equilibrium. The proposed approach is carefully evaluated based on usage the price information of Amazon EC2. The simulation results indicate that the stochastic game approach can improve the revenue for both cloud broker and local cloudlet.

In smart grid, the frequency regulation can be provided by both the automatic generation control (AGC) and the demand-side regulation, and the regulation errors increase the electricity costs to the utility company. The demand-side regulation adopts a hierarchical communication architecture, and the data aggregator unit (DAU) may suffer from congestions which consequently increase the costs to the utility company for more AGC service except for the demand-side regulation. In this paper, we employed the base stations as relays and formulated the electricity costs-based upon the regulation errors and the packets loss model. Specifically, the utility company decides the relaying bandwidth to minimize its electricity costs, and the relay selects the base price to maximize its profits. The novelty of this paper is twofold. First, we formulate the interactions between the utility company and the relay as a bargaining problem. Second, we utilize the Nash bargaining solution (NBS) and Raiffa-Kalai-Smorodinsky (RBS) bargaining solution to achieve the Pareto-optimal outcome. Furthermore, we extended the results to the case with multiple DAUs and multiple relays. The numerical results demonstrate the cost reduction of the utility company and the profit increase of the relay under the NBS or RBS strategy. In addition, the NBS strategy can bring about more profits for the relay than the RBS strategy, while the RBS strategy can provide a fairer payoff allocation and lower costs to the utility company than the NBS strategy.

The accurately estimated state is of great importance for maintaining a stable running condition of power systems. To maintain the accuracy of the estimated state, bad data detection (BDD) is utilized by power systems to get rid of erroneous measurements due to meter failures or outside attacks. However, false data injection (FDI) attacks, as recently revealed, can circumvent BDD and insert any bias into the value of the estimated state. Continuous works on constructing and/or protecting power systems from such attacks have been done in recent years. This survey comprehensively overviews three major aspects: constructing FDI attacks; impacts of FDI attacks on electricity market; and defending against FDI attacks. Specifically, we first explore the problem of constructing FDI attacks, and further show their associated impacts on electricity market operations, from the adversary's point of view. Then, from the perspective of the system operator, we present countermeasures against FDI attacks. We also outline the future research directions and potential challenges based on the above overview, in the context of FDI attacks, impacts, and defense.

Under the real-time electricity pricing environment in smart grid, building owners are faced with the indoor temperature control problem to minimize the daily electricity cost. Taking the heating scenario as an example, an intuitive strategy is to maintain the building's indoor temperature always at the lower bound of the predetermined comfort range. However, this strategy may not always achieve the lowest electricity bill, especially with the significant fluctuation of electricity prices. On the other hand, the cost minimization problem can be optimally solved one day ahead in a temporally- coupled manner, but the challenge lies in that the building owner needs to acquire the accurate information of electricity prices and outdoor temperatures of the next day, which may not be available. In this paper, we equivalently decouple the cost minimization problem into subproblems at each hour. Each subproblem can be temporally decoupled and optimally solved, only requiring the next-hour electricity price. Besides, the temporally-decoupled algorithm explicitly indicates when to take advantage of pre-heating/cooling for electricity cost reduction. It is demonstrated with the real data that our proposed algorithm could result in considerable economic savings compared with the intuitive strategy, paving the way towards practically applicable cost-effective smart buildings.

Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which prestores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing toward low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a workload allocation problem which suggests the optimal workload allocations between fog and cloud toward the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be, respectively, solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.

In this paper, we investigate the network throughput and energy sustainability of green-energy-powered maritime wireless communication networks. Specifically, we study how to optimize the schedule of data traffic tasks to maximize the network throughput with Worldwide Interoperability for Microwave Access technology. To this end, we formulate it as an optimization problem to maximize the weight of the total delivered data packets, while ensuring that harvested energy can successfully support transmission tasks. The formulated energy and content-aware vessel throughput maximize problem is proved to be NP-complete. We propose a green energy and content-aware data transmission framework that incorporates the energy limitation of both infostations and delay-tolerant network throw boxes. The green energy buffer is modeled as a G/G/1 queue, and two heuristic algorithms are designed to optimize the transmission throughput and energy sustainability. Extensive simulations demonstrate that our proposed algorithms can provide simple yet efficient solutions in a maritime wireless communication network with sustainable energy.

In smart grid, a huge amount of privacy data need to be transmitted securely and efficiently. Compressed sensing can be used to improve the transmission efficiency by exploiting the data sparsity, while this sparsity is usually destroyed by the encryption process, making compressed sensing inapplicable. A possible workaround is to perform compressed sensing first and then encrypt the compressed data. However, this two-step workaround lowers the efficiency. In this paper, we propose a novel data transmission scheme EncryCS. Compressed sensing in EncryCS provides security and simultaneously enhances the transmission efficiency in one step. The prerequisite of compressed sensing is to construct a measurement matrix satisfying the restricted isometry property that ensures the perfect recovery of the signal. To make the matrix secret and feasible, we generate it with a pseudorandom sequence generator. By using this matrix, EncryCS transforms a large amount of plaintext data into a small amount of ciphertext data. EncryCS is proven to possess a high security. Extensive simulations are performed based on the real-world data obtained from the PowerNet project at Stanford University. The results demonstrate that EncryCS can compress data by order of magnitude, and the transmitted data can be almost perfectly recovered.

In this paper, cooperative sensing for multi-channel cognitive radio networks (CRNs) is studied, whereby the secondary users (SUs) cooperate with each other to sense the multiple channels owned by the primary users (PUs). The objective is to better protect the primary system while satisfying the SUs' requirement on the expected access time. A general scenario is considered, where the channels present different usage characteristics and the detection performance of individual SUs varies due to the channel conditions between the PUs and SUs. With the dynamics in the channel usage characteristics and the detection capacities, each SU chooses one channel for sensing to minimize the interference to the PUs. The problem is formulated as a nonlinear integer programming problem which is NP-complete in general. To find the solution efficiently, the original problem is transformed into a variant of convex bipartite matching problem by constructing a complete bipartite graph and defining proper weight vectors. Based on the problem transformation, a channel assignment algorithm is proposed for computing in polynomial time the solution in terms of the number of SUs, the number of channels, and the maximum value of weights. Simulation results are presented to validate the performance of the proposed algorithm.

In this paper, we study distributed generation (DG) integration in smart grid, with a focus on the voltage regulation in smart distribution systems. To ensure the operation of a smart distribution system at an acceptable voltage level, voltage regulators are deployed at some strategic locations for voltage control. The two-way communication functionality of the smart distribution system is leveraged such that the voltage regulators are coordinated by a distribution substation. Based on the measurement reports from remote terminal units (RTUs) deployed at DG unit and load connection points, stochastic information management is performed by the distribution substation to address the randomness in renewable power generation and load demand. In this paper, we formulate a voltage regulation problem in the smart distribution system based on power flow analysis, while taking into account communication delays. We show that the problem can be represented as a partially observed Markov decision process (POMDP). Since voltage regulation is performed at a relatively low frequency to avoid excessive wear and tear on the voltage regulators, a large amount of measurements should be reported by the RTUs and processed by the distribution substation for optimal voltage regulation. In order to reduce the communication and computational overhead, we further investigate the voltage regulation problem and mathematically prove that a relatively small amount of information is sufficient for the distribution substation to make an optimal decision. The theoretical results are evaluated based on a case study of IEEE 13-bus test system with real DG power generation and demand data.

Drive-thru Internet has become a popular solution vehicular Internet access. However, the quality-of-service provisioning for high-data-rate drive-thru Internet services poses significant challenges upon medium access control (MAC) in a large-scale and highly dynamic vehicular environment. In this paper, to achieve high throughput in drive-thru Internet, we exploit the mobility interdependency between neighboring vehicles and propose a density-adaptive MAC protocol by predicting the vehicle-density dynamics. Specifically, to predict the vehicle-density fluctuations in the drive-thru Internet scenario, we leverage Navier-Stokes equations to characterize the mobility interdependence and the dynamically waving vehicle-density which can be observed from the simulated vehicle traces in VISSIM. In tune with the dynamic vehicle-density, we propose an enhanced MAC protocol to dynamically adjust the contention window (CW) setting in order to improve the throughput. Extensive simulations validate the effectiveness of the utilized mobility model, and demonstrate the efficiency of the proposed MAC protocol in improving the overall resultant system throughput.

Coordinated charging can provide efficient charging plans for electric vehicles (EVs) to improve the overall energy utilization while preventing an electric power system from overloading. However, designing an efficient coordinated charging strategy to route mobile EVs to fast-charging stations for globally optimal energy utilization is very challenging. In this paper, we investigate a special smart grid with enhanced communication capabilities, i.e., a VANET-enhanced smart grid. It exploits vehicular ad-hoc networks (VANETs) to support real-time communications among road-side units (RSUs) and highly mobile EVs for collecting real-time vehicle mobility information or dispatching charging decisions. Then, we propose a mobility-aware coordinated charging strategy for EVs, which not only improves the overall energy utilization while avoiding power system overloading, but also addresses the range anxieties of individual EVs by reducing the average travel cost. Specifically, the mobility-incurred travel cost for an EV is considered in two aspects: 1) the travel distance from the current position of the EV to a charging station; and 2) the transmission delay for receiving a charging decision via VANETs. The optimal mobility-aware coordinated EV charging problem is formulated as a time-coupled mixed-integer linear programming problem. By solving this problem based on Lagrange duality and branch-and-bound-based outer approximation techniques, an efficient charging strategy is obtained. To evaluate the performance of the proposed strategy, a realistic suburban scenario is developed in VISSIM to track vehicle mobility through the generated simulation traces, based on which the travel cost of each EV can be accurately calculated. Extensive simulation results demonstrate that the proposed strategy considerably outperforms the traditional EV charging strategy without VANETs on the metrics of the overall energy utilization, the average EV travel cost, and the number of successfully charged EVs.

In this paper, dynamic spectrum access (DSA) in multi-channel cognitive radio networks (CRNs) is studied. The two fundamental issues in DSA, spectrum sensing and spectrum sharing, for a general scenario are revisited, where the channels present different usage characteristics and the detection performance of individual secondary users (SUs) varies. First, spectrum sensing is investigated, where multiple SUs are coordinated to cooperatively sense the channels owned by the primary users (PUs) for different interests. When the PUs' interests are concerned, cooperative spectrum sensing is performed to better protect the PUs while satisfying the SUs' requirement on the expected access time. For the SUs' interests, the objective is to maximize the expected available time while keeping the interference to PUs under a predefined level. With the dynamics in the channel usage characteristics and the detection capacities, the coordination problems for the above two cases are formulated as nonlinear integer programming problems accordingly, which are proved to be NP-complete. To find the solution efficiently, for the former case, the original problem is transformed into a variant of convex bipartite matching problem by constructing a complete bipartite graph and defining proper weight vectors. Based on the problem transformation, a channel selection algorithm is proposed to compute the solution. For the latter case, the deterministic optimization problem is first transformed to an associated stochastic optimization problem, which is then solved by cross-entropy (CE) method of stochastic optimization. Then, the sharing of the available channels by SUs after sensing is modeled by a channel access game, based on the framework of weighted congestion game. An algorithm for SUs to select access channels to achieve Nash equilibrium (NE) is proposed. Simulation results are presented to validate the performance of the proposed algorithms.

In this paper, we investigate uplink resource allocation for wireless local area network and cellular network interworking to provide multi-homing voice and data services. The problem is formulated based on the physical layer and medium access control layer technologies of the two networks to ensure that the resource allocation decisions are feasible and can be executed at the lower layers. Furthermore, to efficiently utilize users' equipment (UEs) battery power, the power distribution among multiple network interfaces of the UEs is included in the problem formulation. The optimal resource allocation problem is a multiple time-scale Markov decision process (MMDP) as the two networks operate at different time-scales and due to voice and data service requirements. We derive decision policies for the upper and the lower levels of the MMDP by decomposing each resource allocation problem over multiple time slots to a set of resource allocation problems for individual time slots and solving the resource allocation problems corresponding to individual time slots using convex optimization techniques. To reduce the time complexity, we further propose a heuristic resource allocation algorithm by deriving the decision policies based on a single system state. The system state consists of average square channel gains for dual variable calculation and instantaneous channel gains for resource allocation based on the calculated dual variables. Simulation results demonstrate the achievable throughput and service quality improvements by employing these two algorithms.

In this paper, we investigate uploading monitoring videos for vessels via a maritime wideband communication network. The Worldwide Interoperability for Microwave Access (WiMAX) technology is utilized to establish a shore-side network infrastructure, and a packet store-carry-and-forward routing mechanism is implemented to address the intermittent network connectivity in maritime communications. A resource allocation problem is formulated to maximize the weights of uploaded video packets, subject to the intermittent network connections and the release time and deadline of each video packet. Time-capacity mapping is applied to transform the original resource allocation problem to a two-machine non-preemptive scheduling problem. As ship routes are relatively stable, the global information in terms of release time, deadline and other time indices of video packets, as well as the schedules of vessels is known a priori. We propose two offline scheduling algorithms, namely Time-capacity mapping based two phase (TMTP) algorithm, and Interval graph theory based job relay selection (IGTJRS) algorithm. Both algorithms achieve a time complexity of O(n2). The performance of proposed algorithms is evaluated through simulation based on actual ship route traces obtained from dedicated Navigation software BLM-Ship.

This work is concerned with enhancement of spectrum utilization by using polarization enabled two-phase cooperation between primary users (PUs) and secondary users (SUs) in cooperative cognitive radio networking (CCRN). The use of orthogonally dual-polarized antennas (ODPAs) enables concurrent transmissions of multiple independent signals of PUs and SUs, and interference suppression via polarization zero-forcing and polarization filtering to obtain significant performance improvement. To maximize a weighted sum throughput of PUs and SUs under energy/power constraints, the problem is formulated and solved based on a multi-timescale Markov decision process, and two modified backward iteration algorithms are devised to attain the optimal policies. Numerical results validate the effectiveness of the proposed CCRN framework, showing that the obtained policy outperforms both greedy and random ones.

For the future smart grid, decentralized inverter control is essential in distributed generation (DG) microgrids where a powerful central controller is unavailable for cost and reliability concerns. However, decentralized inverter control suffers from a limited system stability mainly because of the lack of communications among different inverters. In this paper, we investigate the stability enhancement of the droop based decentralized inverter control in microgrids. Specifically, we propose a power sharing based control strategy which incorporates the information provided by a wireless network to improve system stability. The wireless network is used to acquire the total real and reactive power generation of all DG units in a decentralized manner. Based on the desired power sharing of each DG unit and the acquired information of total generation, additional control terms are added to the traditional droop controller. We evaluate the performance of our proposed control strategy based on small-signal stability analysis. Extensive numerical results are presented to demonstrate the system stability.

A quadrature signaling based two-phase cooperation framework for cooperative cognitive radio networking is proposed. By leveraging the degrees of freedom provided by orthogonal modulation, secondary users are able to relay the traffic of primary users and transmit their own in the same time slot without interference. To evaluate the cooperation performance of the proposed framework, a weighted sum throughput maximization problem is formulated, and closed-form solutions of the optimal power setting/allocation are obtained in the amplify-and-forward and decode-and-forward relaying modes. Simulation results validate the efficiency of the proposed framework.

As an important component of smart grid, the vehicle-to-grid (V2G) system is recently introduced to enable bidirectional energy delivery between the power grid and plug-in electric vehicles. Communication technology is incorporated to facilitate the energy delivery by providing electricity pricing and energy demand information. However, different from the stationary energy storage systems, the energy store-carry-and-deliver mechanism for a V2G system poses new challenges for performance optimization, such as bi-directional energy flow and non-stationary energy demand. How to utilize the statistical information provided by the communication system to achieve efficient energy delivery is critical for a V2G system and is still an open issue. In this paper, we address a specific problem in this new research area, i.e., daily energy cost minimization of vehicle owners under time-of-use (TOU) electricity pricing. We investigate a plug-in hybrid electric vehicle (PHEV) with a realistic battery model, which is general for both battery electric cars and plug-in hybrids. A dynamic programming formulation is established by considering the bidirectional energy flow, non-stationary energy demand, battery characteristics, and TOU electricity price. We prove the optimality of a state-dependent double-threshold (or (S, S')) policy based on the stochastic inventory theory. A modified backward iteration algorithm is devised for practical applications, where an exponentially weighted moving average (EWMA) algorithm is used to estimate the statistics of PHEV mobility and energy demand. The performance of the proposed scheme is demonstrated by simulations based on survey and real data collected from Canadian households. Numerical results indicate that our proposed scheme performs closely to a scheme with a priori knowledge of the PHEV mobility and energy demand information. Compared with the existing approaches, the proposed scheme can achieve energy cost reduction, which increases with the battery capacity.

As essential building blocks of the future smart grid, microgrids can efficiently integrate various types of distributed generation (DG) units to supply the electric loads at the minimum cost based on the economic dispatch. In this paper, we introduce a decentralized economic dispatch approach such that the optimal decision on power generation is made by each DG unit locally without a central controller. The prerequisite power generation and load information for decision making is discovered by each DG unit via a multiagent coordination with guaranteed convergence. To avoid a slow convergence speed which potentially increases the generation cost because of the time-varying nature of DG output, we present a heterogeneous wireless network architecture for microgrids. Low-cost short-range wireless communication devices are used to establish an ad hoc network as a basic information exchange infrastructure, while auxiliary dual-mode devices with cellular communication capabilities are optionally activated to improve the convergence speed. Two multiagent coordination schemes are proposed for the single-stage and hierarchical operation modes, respectively. The optimal number of activated cellular communication devices is obtained based on the tradeoff between communication and generation costs. The performance of the proposed schemes is analyzed and evaluated based on real power generation and load data collected from the Waterloo Region in Canada. Numerical results indicate that our proposed schemes can better utilize the cellular communication links and achieve a desired tradeoff between the communication and generation costs as compared with the existing schemes.

In this paper, we investigate on-demand data services for high-speed trains via a cellular/infostation integrated network. Service requests and acknowledgements are sent through the cellular network to a content server, while data delivery is achieved via trackside infostations. The optimal resource allocation problem is formulated by taking account of the intermittent network connectivity and multi-service demands. In order to achieve efficient resource allocation with low computational complexity, the original problem is transformed into a single-machine preemptive scheduling problem based on a time-capacity mapping. As the service demands are not known a priori, an online resource allocation algorithm based on Smith ratio and exponential capacity is proposed. The performance bound of the online algorithm is characterized based on the theory of sequencing and scheduling. If the link from the backbone network to an infostation is a bottleneck, a service pre-downloading algorithm is also proposed to facilitate the resource allocation. The performance of the proposed algorithms is evaluated based on a real high-speed train schedule. Compared with the existing approaches, our proposed algorithms can significantly improve the quality of on-demand data service provisioning over the trip of a train.

In this paper, we investigate data dissemination in delay tolerant networks (DTNs) via roadside wireless local area networks (RS-WLANs). The data dissemination service is destined to a group of nomadic nodes roaming in a large network region with a low node density. The local nodes within the coverage area of an RS-WLAN can provide packet caching and relaying capabilities. We consider a cooperative data dissemination approach where information packets are first pre-downloaded to the local nodes within the RS-WLAN before the visit of a pedestrian nomadic node, and then opportunistically scheduled to transmit to the nomadic node upon its arrival. In order to resolve the channel contention among multiple direct/relay links and exploit the predictable traffic characteristics as a result of packet pre-downloading, a double-loop receiver-initiated medium access control (DRMAC) scheme is proposed. The MAC scheme can achieve spatial and temporal diversity via the outer-loop and inner-loop MAC, respectively. A receiver initiated mechanism is used to reduce the signalling overhead, where the ACK message is used as an invitation of channel contention. An analytical model is established to evaluate the performance of the proposed MAC scheme. Numerical results demonstrate that the proposed MAC scheme can significantly improve the number of delivered packets from an RS-WLAN to a nomadic node as compared with the existing MAC schemes.

In this paper, we propose a distributed asynchronous clock synchronization (DCS) protocol for Delay Tolerant Networks (DTNs). Different from existing clock synchronization protocols, the proposed DCS protocol can achieve global clock synchronization among mobile nodes within the network over asynchronous and intermittent connections with long delays. Convergence of the clock values can be reached by compensating for clock errors using mutual relative clock information that is propagated in the network by contacted nodes. The level of clock accuracy is depreciated with respect to time in order to account for long delays between contact opportunities. Mathematical analysis and simulation results for various network scenarios are presented to demonstrate the convergence and performance of the DCS protocol. It is shown that the DCS protocol can achieve faster clock convergence speed and, as a result, reduces energy cost by half for neighbor discovery.

In this paper, we investigate the on-demand data delivery to high-speed trains via trackside infostations. The optimal resource allocation problem is formulated by considering the trajectory of a train, quality of service (QoS) requirements, and network resources. The original problem is transformed into a single-machine preemptive scheduling problem based on a time-capacity mapping. As the service demands are not known a priori, an online resource allocation algorithm is proposed based on the Smith ratio and exponential capacity. The performance of the proposed algorithm is evaluated based on a real high-speed train schedule. Compared with the existing approaches, our proposed algorithm can achieve the best performance in terms of the total reward of delivered services over the trip of a train.

In this paper, we consider a DTN/WLAN integrated network where high-mobility nomadic nodes form a delay tolerant network (DTN) and low-mobility local nodes reside in the coverage area of wireless local area networks (WLANs). A data dissemination service facilitated by packet pre-downloading is generated by a server in the Internet and destined to a group of nomadic nodes. In order to achieve efficient data dissemination, a double-loop receiver-initiated medium access control (MAC) scheme is proposed. By implementing both outer-loop and inner-loop MAC, the proposed MAC scheme can achieve spatial and temporal diversity while reducing the MAC overhead. Analytical and simulation results are presented to demonstrate the performance of the proposed MAC scheme.

In this paper, we consider a DTN/WLAN integrated network where nomadic nodes with high mobility comprise a delay tolerant network (DTN) while local nodes with low mobility reside in the coverage area of wireless local area networks (WLANs). A message dissemination service is considered, where data traffic is generated by a server in the Internet and destined to a group of nomadic nodes. In order to facilitate message dissemination, a delay tolerant cooperative communication (DTCoop) scheme is proposed. The messages for dissemination are first pre-downloaded to a group of storage local nodes within a WLAN before the visit of a nomadic node, and then scheduled for transmission when a nomadic node comes into the transmission range. Analysis and simulation results are presented to evaluate the performance of the proposed DTCoop scheme. It is shown that our proposed scheme can significantly improve the message delivery performance from a WLAN to a nomadic node as compared with existing schemes without message pre-downloading or message scheduling.

In this paper, we consider a rural infostation system where power and bandwidth limited infostations are deployed in a large network area with sparsely populated mobile nodes. Direct transmission services are provided to each mobile node, whereas message dissemination services facilitated by relaying are provided to a subgroup of mobile nodes. We investigate radio resource allocation at infostations, and propose a cross-layer resource allocation scheme with tuneable resource allocation parameters at the network layer and link layer. Analytical models are established to characterize the dependence of system performance on the various parameters of the proposed scheme, and the accuracy is verified by simulations. A cross-layer design example is presented to demonstrate that, by tuning resource allocation parameters at the protocol layers, the performance of direct transmission services can be guaranteed for relay nodes, and the quality of message dissemination services can be improved.