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In this paper, we focus on the tradeoff design between communication and radar detection for a joint communication and radar (CommRadar) system by leveraging a low-complexity hardware architecture. To achieve this, we exploit the communication traffic diversity to design an adaptive scheduling policy. Through adjusting the CommRadar mode selection, radar steering direction and communication user scheduling, and time allocation between communication and radar detection, we could better utilize the un-occupied transmission time to improve radar detection performance when the data traffic is light, and balance the time allocation between communication and radar detection when the data traffic is heavy. Moreover, for our adaptive scheduling method, we have provided a quantitative performance bound for data throughput, queueing delay, and radar detection performance of both short-term and long-term. By conducting extensive simulation studies, we have demonstrated that our method could greatly reduce the communication performance loss when integrating the radar functionality, while still guaranteeing radar detection performance. [ABSTRACT FROM AUTHOR]

Cooperative Intelligent Transport Systems (C-ITS) is a promising technology to make transportation safer and more efficient. Ridesharing for long-distance is becoming a key means of transportation in C-ITS. In this paper, we focus on private long-distance ridesharing, which reduces the total cost of vehicle utilization for long-distance journeys. In this context, we investigate journey scheduling problem with shared vehicles to reduce the total cost of vehicle utilization. Most of the existing works directly schedule journeys to vehicles with long scheduling time and only consider the cost of driving travellers instead of the total cost. In contrast, to reduce the total cost and scheduling time, we propose a comprehensive cost model and a two-phase journey scheduling approach, which includes path generation and path scheduling. On this basis, we propose two path generation methods: a simple near optimal method and a reset near optimal method as well as a greedy based path scheduling method. Finally, we present an experimental evaluation with different path generation and path scheduling methods with synthetic data generated based on real-world data. The results reveal that the proposed scheduling approach significantly outperforms baseline methods in terms of total cost (up to 69.8%) and scheduling time (up to 84.0%) and the scheduling time is reasonable (up to 0.16s). The results also show that our approach has higher efficiency (up to 141.7%) than baseline methods. [ABSTRACT FROM AUTHOR]

Link scheduling is an appealing solution for ensuring the reliability and latency requirements of Internet of Things (IoT). Most existing results on the link scheduling problem were based on the graph or SINR (Signal-to-Interference-plus-Noise-Ratio) models, which ignored the impact of the random fading gain of the signals strength. In this paper, we address the link scheduling problem under the Rayleigh fading model. Both Shortest Link Scheduling (SLS) and Maximum Link Scheduling (MLS) problems are studied. In particular, we show that a set of links can be activated simultaneously under Rayleigh fading model if all link SINR constraints are satisfied. Based on the analysis of previous Link Diversity Partition (LDP) algorithm, we propose an Improved LDP (ILDP) algorithm and a centralized algorithm by localizing the global interference (denoted by CLT), building on which we design a distributed CLT algorithm (denoted by RCRDCLT) that converges to a constant approximation factor of the optimum with the time complexity of $O(\ln n)$ , where $n$ is the number of links. Furthermore, executing repeatedly RCRDCLT can solve the SLS with an approximation factor of $\Theta (\ln n)$. Extensive simulations indicate that CLT is more effective than previous six popular link scheduling algorithms, and RCRDCLT has the lowest time complexity while only losses a constant fraction of the optimum schedule. [ABSTRACT FROM AUTHOR]

Proactive caching has emerged as a promising solution to reduce the content access latency in radio access networks (RANs), thereby attracting considerable attention in the era of 6G research. It allows base stations to push popular content items to mobile users’ devices proactively. Therefore, a cached-enabled RAN may serve a user by either on-demand transmission or proactive content placement, which share a common radio spectrum. How to efficiently schedule proactive caching and on-demand transmission then becomes a challenging issue that remains open. In this paper, we present a unified framework for joint scheduling of caching and on-demand transmission. In particular, we formulate a Markovian queueing model to analyze the average delay and power consumption of the proposed scheduling policy, which are then jointly minimized via linear programming (LP). Furthermore, a low-complexity heuristic scheduling policy is conceived to strike a sub-optimal tradeoff between delay and power based on greedy algorithms. Simulation results shall demonstrate that the overall service latency of a RAN can be substantially reduced by judiciously designing joint scheduling of caching and on-demand transmission. [ABSTRACT FROM AUTHOR]

In this paper, we propose low-complexity algorithms for downlink traffic scheduling in green vehicular roadside infrastructure. In multiple roadside unit (RSU) deployments, the energy provisioning of the RSUs may differ, and it is therefore desirable to balance RSU usage from a normalized min-max energy viewpoint. This paper considers both splittable RSU assignment (SRA) and unsplittable RSU asssignment (URA) scheduling. An offline integer linear programming bound is first derived for normalized min-max RSU energy usage. We then show that in the SRA case, there is a polynomial complexity 2-approximation bound for the normalized min-max energy schedule. This paper then proposes several online scheduling algorithms. The first is a greedy online algorithm that makes simple RSU selections, followed by minimum-energy time slot assignments. A normalized min-max algorithm is then proposed [2-approximation online algorithm (TOAA)], which is an online version of the 2-approximation bound. Two algorithms are then introduced based on a potential function scheduling approach. The 1-objective algorithm uses an objective based on normalized min-max energy, and we show that it has an upper bounded worst-case competitive ratio performance. The 2-objective algorithm uses the same approach but incorporates a total-energy secondary objective as well. Results from a variety of experiments show that the proposed scheduling algorithms perform well. In particular, we find that in the SRA case, the TOAA algorithm performs very close to the lower bound but at the expense of having to reassign time slots whenever a new vehicle arrives. In the URA case, our low-complexity 1-objective algorithm performs better than the others over a wide range of traffic conditions. [ABSTRACT FROM PUBLISHER]

This paper investigates the fundamental building blocks of physical-layer network coding (PNC). Most prior work on PNC focused on its application in a simple two-way-relay channel (TWRC) consisting of three nodes only. Studies of the application of PNC in general networks are relatively few. This paper is an attempt to fill this gap. We put forth two ideas: 1) a general network can be decomposed into small building blocks of PNC, referred to as the PNC atoms, for scheduling of PNC transmissions; and 2) we identify nine PNC atoms, with TWRC being one of them. Three major results are as follows. First, using the decomposition framework, the throughput performance of PNC is shown to be significantly better than those of the traditional multi-hop scheme and the conventional network coding scheme. For example, under heavy traffic volume, PNC can achieve 100% throughput gain relative to the traditional multi-hop scheme. Second, PNC decomposition based on a variety of different PNC atoms can yield much better performance than PNC decomposition based on the TWRC atom alone. Third, three out of the nine atoms are most important to good performance. Specifically, the decomposition based on these three atoms is good enough most of the time, and it is not necessary to use the other six atoms. [ABSTRACT FROM PUBLISHER]

In this paper, we develop optimal energy scheduling algorithms for N-user fading multiple-access channels with energy harvesting to maximize the channel sum-rate, assuming that the side information of both the channel states and energy harvesting states for K time slots is known a priori, and the battery capacity and the maximum energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with \cal O (NK) constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver becomes impractically high when the number of constraints is large. This paper gives an efficient energy scheduling algorithm, called the iterative dynamic water-filling algorithm, that has a computational complexity of \cal O(NK^2 ) per iteration. For the single-user case, a dynamic water-filling method is shown to be optimal. Unlike the traditional water-filling algorithm, in dynamic water-filling, the water level is not constant but changes when the battery overflows or depletes. An iterative version of the dynamic water-filling algorithm is shown to be optimal for the case of multiple users. Even though in principle the optimality is achieved under large number of iterations, in practice convergence is reached in only a few iterations. Moreover, a single iteration of the dynamic water-filling algorithm achieves a sum-rate that is within $(N-1)K$ nats of the optimal sum-rate. [ABSTRACT FROM AUTHOR]

In this paper, we consider the Max-Weight protocol for routing and scheduling in wireless networks under an adversarial model. This protocol has received a significant amount of attention dating back to the papers of Tassiulas and Ephremides. In particular, this protocol is known to be throughput-optimal whenever the traffic patterns and propagation conditions are governed by a stationary stochastic process. However, the standard proof of throughput optimality (which is based on the negative drift of a quadratic potential function) does not hold when the traffic patterns and the edge capacity changes over time are governed by an arbitrary adversarial process. Such an environment appears frequently in many practical wireless scenarios when the assumption that channel conditions are governed by a stationary stochastic process does not readily apply. In this paper, we prove that even in the above adversarial setting, the Max-Weight protocol keeps the queues in the network stable (i.e., keeps the queue sizes bounded) whenever this is feasible by some routing and scheduling algorithm. However, the proof is somewhat more complex than the negative potential drift argument that applied in the stationary case. Our proof holds for any arbitrary interference relationships among edges. We also prove the same stability of \varepsilon-approximate Max-Weight under the adversarial model. We conclude the paper with a discussion of queue sizes in the adversarial model as well as a set of simulation results. [ABSTRACT FROM PUBLISHER]

Due to the different behavior of single-arm and dual-arm cluster tools, it is challenging to schedule a hybrid multi-cluster tool containing both of them. This paper aims to find an optimal one-wafer cyclic schedule for such a multi-cluster tool. It is assumed that the bottleneck individual cluster tool in it is process-bound, thereby making it process-dominant. To do so, this paper models a hybrid multi-cluster tool with Petri nets. With this model, it derives the conditions under which individual cluster tools can operate in a paced way. Based on these conditions, this paper shows that for any process-dominant hybrid multi-cluster tool there is always a one-wafer cyclic schedule. Then, it develops the algorithms to find the minimal cycle time and the optimal one-wafer cyclic schedule. It is computationally efficient and easy-to-implement in practice. Examples are given to show the application and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Scheduling a multicluster tool with wafer residency time constraints is highly challenging yet important in ensuring high productivity of wafer fabrication. This paper presents a method to find an optimal one-wafer cyclic schedule for it. A Petri net is developed to model the dynamic behavior of the tool. By this model, a schedule of the system is analytically expressed as a function of robots’ waiting time. Based on this model, this paper presents the necessary and sufficient conditions under which a feasible one-wafer cyclic schedule exists. Then, it gives efficient algorithms to find such a schedule that is optimal. These algorithms require determining the robots’ waiting time via simple calculation and thus are efficient. Examples are given to show the application and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Consider a group of stations connected through a multiple-access channel, with the constraint that if at a time instant exactly one station transmits a message, then the message is successfully received by any other station, whereas if two or more stations simultaneously transmit their messages then a conflict occurs and all messages are lost. Let us assume that $n$ is the number of stations and that an (arbitrary) subset $A$ of them, $|A|\leq k\leq n$ , is active, that is, there are at most $k$ stations that have a message to send over the channel. In the classical conflict resolution problem, the issue is to schedule the transmissions of each station to let every active station use the channel alone (i.e., without conflict) at least once, and this requirement must be satisfied whatever might be the set of active stations $A$ . The parameter to optimize is, usually, the worst case number of transmissions that any station has to attempt before all message transmissions are successful. In this paper, we study the following question: is it possible to obtain a significant improvement on the protocols that solve the classical conflict resolution problem if we allow the protocols to fail over a “small” fraction of all possible subsets of active stations? In other words, is it possible to significantly reduce the number of transmissions that must be attempted if the set of active stations is chosen uniformly at random and the conflict resolution algorithm is only required to work correctly with “high” probability? In this paper, we will show that this is indeed the case. Our main technical tool is a generalization of selectors, a recently introduced combinatorial structure that has found applications in several areas. As it turned out for selectors, we believe that our new combinatorial structures are likely to be useful also outside the present context. [ABSTRACT FROM PUBLISHER]

We consider the serial batching scheduling problem in which a group of machines can process multiple jobs continuously to reduce the processing times of the second and subsequent jobs. The maximum batch size is finite. Since all jobs in the same batch are loaded and unloaded simultaneously, a completed job has to wait for the others. We examine how to schedule all jobs to minimize the total completion time. If the batch size is only one, i.e., a single job per batch, the total processing time will be longer, since no reduction in processing time is possible. As a consequence, the total completion time will also be longer. On the other hand, if the batch size is large, the total completion time can be large. Each job has to wait until all jobs in the same batch are completed. We identify several optimality properties of the optimal batching sequence. These properties are used to develop a dynamic programming algorithm to optimize the batching sequences efficiently. The complexity of the proposed method depends only on the maximum batch size and the number of jobs. The improvement achieved with the proposed method when compared with two other batching rules is illustrated using two practical applications.

Note to Practitioners— Machines with job cascading (or cluster tools) are commonly seen in semiconductor manufacturing processes. While meeting production move targets is a common goal for the nondelayed jobs, catching up with the schedule is an important task for the delayed jobs to achieve higher customer service level. From the viewpoint of scheduling, reducing the delay is equivalent to reducing completion time. Hence, we propose scheduling algorithms to reduce the completion time by taking advantage of the special structure of a cascading machine. In contrast to existing scheduling theory, the proposed algorithm can be efficient due to the small maximum batch size of real cascading machines. In addition to the theoretical contributions, this paper aims at solving practical problems through a rigorous approach. The finding and insight from this paper can be used to enhance shop floor control in a semiconductor fab. [ABSTRACT FROM PUBLISHER]

A time instant is said to be a critical instant for a task, if the task’s arrival at the instant makes the duration between the task’s arrival and completion the longest. Critical instants for a task, once revealed, make it possible to check the task’s schedulability by investigating situations associated with the critical instants. This potentially results in efficient and tight schedulability tests, which is important in real-time systems. For example, existing studies have discovered critical instants under preemptive fixed-priority scheduling (P-FP), which limit interference from carry-in jobs, yielding the state-of-the-art schedulability tests on both uniprocessor and multiprocessor platforms. However, studies on schedulability tests associated with critical instants have not matured yet for non-preemptive scheduling, especially on a multiprocessor platform. In this paper, we find necessary conditions for critical instants for non-preemptive global fixed-priority scheduling (NP-FP) on a multiprocessor platform, and develop a new schedulability test that takes advantage of the finding for reducing carry-in jobs’ interference. Evaluation results show that the proposed schedulability test finds up to 14.3 percent additional task sets schedulable by NP-FP, which are not deemed schedulable by the state-of-the-art NP-FP schedulability test. [ABSTRACT FROM AUTHOR]

Bus rapid transit (BRT) has been proved to be an effective tool to improve mass transit services. However, BRT's adaptive operations like management and scheduling under different scenarios are too complicated to implement using traditional methods. The ACP approach, which is based on holism and complex system theory and consists of artificial systems (A), computational experiments (C) and parallel execution (P), offers an efficient new method to cope with these complex systems, including BRT. In this paper, the parallel transportation management and control system for BRT (PTMS-BRT) is presented, which is designed and implemented using the ACP approach. PTMS-BRT integrates such functions as BRT's monitoring, warning, forecasting, incident management, and real-time scheduling, to provide its operations smoother, safer, more efficient, and reliable. It has been piloted successfully in Guangzhou BRT to demonstrate it as another successful example of parallel transportation systems. [ABSTRACT FROM PUBLISHER]

In this paper, we devise a scheduling algorithm for ordering transmission of synchrophasor data from the substation to the control center in as short a time frame as possible, within the real-time hierarchical communications infrastructure in the electric grid. The problem is cast in the framework of the classic job scheduling with precedence constraints. The optimization setup comprises the number of phasor measurement units (PMUs) to be installed on the grid, a weight associated with each PMU, processing time at the control center for the PMUs, and precedence constraints between the PMUs. The solution to the PMU placement problem yields the optimum number of PMUs to be installed on the grid, while the processing times are picked uniformly at random from a predefined set. The weight associated with each PMU and the precedence constraints are both assumed known. The scheduling problem is provably NP-hard, so we resort to approximation algorithms, which provide solutions that are suboptimal yet possessing polynomial time complexity. A lower bound on the optimal schedule is derived using branch and bound techniques, and its performance evaluated using standard IEEE test bus systems. The scheduling policy is power grid-centric, since it takes into account the electrical properties of the network under consideration. [ABSTRACT FROM PUBLISHER]

Primary care, the backbone of the nation’s healthcare system, is at the risk of collapse. Patients are dissatisfied due to poor access to care, and physicians are unhappy and burning out with an enormous amount of tasks. To improve the primary care access, many healthcare organizations have introduced electronic visits (or e-visits) to provide patient-physician communications through securing messages. In this paper, we introduce an analytical model to study e-visits in primary care clinics. Analytical formulas to evaluate the mean and variance of the patient length of visit in primary care clinics with e-visits are derived. System properties are investigated. In addition, comparisons of different scheduling policies between the office and the e-visits are carried out. The first come first serve, preemptive-resume, and non-preemptive policies are studied and the results show that the first come first serve policy typically leads to the best performance.

Note to Practitioners—The primary care delivery system is under a lot of strain. Due to population growth and aging, and the expanded healthcare insurance coverage, the demand for primary care services has increased substantially in the past years. Patients have difficulty of getting timely access to care, while primary care physicians are facing insurmountable tasks. Electronic visit, or e-visit, as an alternative to the traditional office visit, provides an innovative way of patient-physician communication through securing messages. The successful implementation of e-visit relies on a proper understanding of the impact of e-visit on care access, and an appropriate design and scheduling of workforce and operations. Therefore, the objective of this paper is to develop an analytical model of the primary care delivery with e-visits, using which one can investigate the impact of e-visits on patient accessibility. In particular, the average value and variance of patients’ length of visit for their encounters are evaluated. Different policies for physicians to schedule office and e-visit patients are compared. In addition, physicians’ nondirect care activities, such as billings and documentations, are also considered in the model. [ABSTRACT FROM PUBLISHER]

This paper presents a general discrete-event system (DES)-based hard periodic real-time task model. Based on supervisory control theory (SCT), an optimal priority-free real-time scheduling technique is proposed to process all the tasks running in uniprocessor or multiprocessor real-time systems (RTS). The preemption relation in this paper generalizes priority-based preemption. First, regular languages are utilized to describe the processor behavior related to each task’s execution. Thereafter, the languages are represented by DES generators. Finally, the global processor behavior is generated as the synchronous product of these DES generators. By discarding the priorities, a novel preemption policy, namely conditional-preemption, is developed. Two sets of conditional-preemption specifications are developed, on the processor level and task level, respectively. Moreover, in order to control the system to be nonblocking and also limit the worst-case response time of the tasks, two corresponding sets of specifications are presented. After generating the global specification as the synchronous product, by implementing SCT the calculated supervisor can provide all the safe real-time execution sequences. The supervisor calculation can be sped up by a three-step algorithm. Finally, the real-time scheduling is implemented for real-world examples. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reliability and makespan optimization of hardware task graphs in reconfigurable platforms by applying fault tolerance (FT) techniques to the running tasks based on the exploration of the Pareto set of solutions. In the presented solution, in contrast to the existing approaches in the literature, task graph scheduling, tasks parallelism, reconfiguration delay, and FT requirements are taken into account altogether. This paper first presents a model for hardware task graphs, task prefetch and scheduling, reconfigurable computer, and a fault model for reliability. Then, a mathematical model of an integer nonlinear multi-objective optimization problem is presented for improving the FT of hardware task graphs, scheduled in partially reconfigurable platforms. Experimental results show the positive impacts of choosing the FT techniques selected by the proposed solution, which is named Pareto-based. Thus, in comparison to nonfault-tolerant designs or other state-of-the-art FT approaches, without increasing makespan, about 850% mean time to failure (MTTF) improvement is achieved and, without degrading reliability, makespan is improved by 25%. In addition, experiments in fault-varying environments have demonstrated that the presented approach outperforms the existing state-of-the-art adaptive FT techniques in terms of both MTTF and makespan. [ABSTRACT FROM PUBLISHER]

Recently, as plug-in hybrid electric vehicles (PHEVs) take center stage for the eco-friendly and cost-effective transportation, commercial PHEV charging stations will be widely prevalent in the future. However, previous studies in the fields of the management of PHEV charging stations have not synthetically taken practical charging systems into account. In this paper, we study the profit-optimal management of a PHEV charging station under the realistic environment addressing not only various types of vehicles but waiting time guarantee for PHEV customers as well. This paper is first to jointly take into account pricing for charging services, scheduling of reserved vehicles to PHEV chargers, dropping of reserved vehicles, and management of the energy storage in a unified framework that contains key features of a practical PHEV charging station. Based on this framework, we develop an algorithm to find the parameters required for charging management by invoking the “Lyapunov drift-plus-penalty” technique. Through theoretical analysis, we prove that the proposed algorithm achieves close-to-optimal performance under particular conditions by exploiting opportunism of time-varying arrival of charging vehicles, price of electricity, and renewable energy generation, but it requires no probabilistic future information. Finally, we find several significant messages via trace-driven simulation of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Effective radio frequency spectrum sharing methods are crucial for sustaining growth and development in mobile wireless services. In this paper, we consider a real-world scenario involving spectrum sharing between mobile wireless and meteorological satellite services as motivation for examining the general problem of efficient resource scheduling in a shared spectrum environment. We formulate an optimization framework for maximizing network utility subject to stochastic interference protection constraints. We design and propose a novel solution inspired by analysis of the optimization problem, where the primary contribution is an efficient power allocation algorithm to manage interference between systems. Using theory and simulations, we show that our algorithm significantly outperforms alternative approaches by well approximating the optimal solution with low enough complexity for practical, real-time application to large networks. [ABSTRACT FROM PUBLISHER]

In this paper, we study the resource allocation and user scheduling algorithm for minimizing the energy cost of data transmission in the context of orthogonal frequency-division multiple-access (OFDMA) collaborative mobile clouds (CMCs) with simultaneous wireless information and power transfer receivers. The CMC, which consists of several collaborating mobile terminals, offers one potential solution for downlink content distribution and for energy consumption (EC) reduction. Previous work on the design of the CMC system mainly focused on cloud formulation or energy efficiency (EE) investigation, whereas how to allocate the radio resource and schedule user transmission has not gotten much attention. With the objective of minimizing system EC, an optimization problem that jointly considers subchannel assignment, power allocation, and user scheduling has been presented. We propose different algorithms to address the formulated problem based on the convex optimization technique. Simulation results demonstrate that the proposed user scheduling and resource allocation algorithms can achieve significant EE performance. [ABSTRACT FROM PUBLISHER]

Bulk integrated electricity and gas systems (IEGSs) introduce complex coupling relations and induce synergistic operation challenges. The growing uncertainty arising from the renewable power generation in the IEGS further aggravates the synergistic problems. Considering the availability of historical wind power generation data, this paper adopts a two-stage sample robust optimization (SRO) model, which is equivalent to the two-stage distributionally robust optimization (DRO) model with a ${\text{type}} - \infty $ Wasserstein ambiguity set, to address the wind power penetrated unit commitment optimal energy flow (UC-OEF) problem for the IEGS. Compared to the equivalent DRO model, the two-stage SRO model can be approximately transformed into a computationally efficient form. Specifically, we employ linear decision rules to simplify the proposed UC-OEF model. Moreover, we further enhance the tractability of the simplified model by exploring its structural features and, accordingly, develop a solution method. Simulation results on two IEGSs validate the effectiveness of the proposed model and solution method. [ABSTRACT FROM AUTHOR]

Due to their fast response ability and high ramping rates, pumped-storage hydropower (PSH) plants are playing an increasingly critical role in mitigating power generation fluctuations induced by the growing proliferation of renewable energy resources. However, because they are energy-limited resources, actual reserve deployment from PSHs would cause the deviation of state-of-charge (SOC) from the scheduled value, which, once accumulated over time, could induce potential violations of SOC limits and cause non-dispatchability of PSHs. In recognizing this potential issue, it can become important to securing the reserve schedule of PSHs and other energy limited resources preventively in the day-ahead market clearing. To ensure reserve dispatchability of PSHs despite SOC deviations, this paper proposes the reserve secure constraints, which are cast as a distributionally robust chance-constrained formulation by judiciously introducing random parameters to describe deployment ratios of scheduled regulation reserve from PSHs. These constraints are converted into tractable second-order cone constraints, and further approximated as linear constraints to be compatible with the current ISO day-ahead market clearing model. A modified IEEE 118-bus system with multiple PSHs is adopted to verify the proposed approach. [ABSTRACT FROM AUTHOR]

In Part I of this paper we have introduced the closed-form conditions for guaranteeing regional frequency stability in a power system. Here we propose a methodology to represent these conditions in the form of linear constraints and demonstrate their applicability by implementing them in a generation-scheduling model. This model simultaneously optimises energy production and ancillary services for maintaining frequency stability in the event of a generation outage, by solving a frequency-secured Stochastic Unit Commitment (SUC). We consider the Great Britain system, characterised by two regions that create a non-uniform distribution of inertia: England in the South, where most of the load is located, and Scotland in the North, containing significant wind resources. Through several case studies, it is shown that inertia and frequency response cannot be considered as system-wide magnitudes in power systems that exhibit inter-area oscillations in frequency, as their location in a particular region is key to guarantee stability. In addition, securing against a medium-sized loss in the low-inertia region proves to cause significant wind curtailment, which could be alleviated through reinforced transmission corridors. In this context, the proposed constraints allow to find the optimal volume of ancillary services to be procured in each region. [ABSTRACT FROM AUTHOR]

The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios, but also mitigates congestion during specific hours while respecting EV drivers’ preferences. Moreover, the simulation results revealed the significant impact of EV drivers' preferences on the optimal solutions and cost/revenue of the stakeholders. [ABSTRACT FROM AUTHOR]

This paper investigates schemes for multihop scheduling and power control for vehicle-to-vehicle (V2V) multicast communication, taking into account the effects of both co-channel interference and adjacent channel interference, such that requirements on latency or age of information (AoI) are satisfied. Optimal performance can be achieved by formulating and solving mixed Boolean linear programming (MBLP) optimization problems for various performance metrics, including network throughput and connectivity. Fairness among network nodes (vehicles) is addressed by considering formulations that maximizes the worst-case network node performance. Solving the optimization problem comes at the cost of significant computational complexity for large networks and requires that (slow) channel state information is gathered at a central point. To address these issues, a clustering method is proposed to partition the optimization problem into a set of smaller problems, which reduces the overall computational complexity, and a decentralized algorithm that does not need channel state information is provided. [ABSTRACT FROM AUTHOR]

This paper studies the problem of cHarging tAsk Scheduling for direcTional wireless chargEr networks (HASTE), i.e., given a set of rotatable directional wireless chargers on a 2D area and a series of offline (online) charging tasks, scheduling the orientations of all the chargers with time in a centralized offline (distributed online) fashion to maximize the overall charging utility for all the tasks. We prove that HASTE is NP-hard. Then, we prove that a relaxed version of HASTE falls within the realm of maximizing a submodular function subject to a partition matroid constraint, and propose a centralized offline algorithm that achieves $(1-\rho)(1-\frac{1}{e})$ (1 - ρ) (1 - 1 e) approximation ratio to address HASTE where $\rho$ ρ is the switching delay of chargers. Further, we propose a distributed online algorithm and prove it achieves $\frac{1}{2}(1-\rho)(1-\frac{1}{e})$ 1 2 (1 - ρ) (1 - 1 e) competitive ratio. We conduct simulations and field experiments on a testbed consisting of eight off-the-shelf power transmitters and 8 rechargeable sensor nodes. The results show that our distributed online algorithm achieves 92.97 percent of the optimal charging utility, and outperforms the comparison algorithms by up to 15.28 percent in terms of charging utility. [ABSTRACT FROM AUTHOR]

Flexibility helps accommodate uncertainties from the increasing renewables, such as wind power generation (WPG), but limited to scheduling methods, flexibility potentials of the power system have not been fully realized. This paper proposes the mathematical formulation and novel transformation for flexibility-enhanced continuous-time stochastic scheduling (FE-CTSS) of the power system under wind uncertainties. The Bernstein polynomial (BP) spline-based solution space transformation (SST) for solving continuous-time (CT) scheduling is revisited and the potential capacity and ramping flexibility, which are lost during the SST from function space to algebraic space, are then exposed for the first time. Developed from De Casteljau's algorithm (DCA), the enhancement matrix is derived to tighten the convex hull property and the enhanced SST is proposed to alleviate the feasible region narrowing to activate the potential flexibility in FE-CTSS. Case studies in the modified 6-bus system and the IEEE 118-bus system demonstrate the effectiveness and the promising role of FE-CTSS in the future high-renewable-penetrated and high-flexibility-required power system. [ABSTRACT FROM AUTHOR]

This paper investigates the stochastic on time arrival (SOTA) problem in Gaussian process (GP) regulated environments. We presume that the travel times of the underlying transportation network follow a multi-variate Gaussian distribution, i.e. Gaussian process, and propose a Gaussian process proactive path planner (GP4), which outputs a dynamic routing policy for maximizing the traveller's on-time-arrival probability. When given an instantiated travel time of a certain link, GP is able to analytically represent/predict the posterior travel time distribution over the entire transportation network. With that, we let the ego vehicle ‘proactively’ select the next traversal link, which maximizes the expected stochastic on time arrival probability based on the predicted posterior travel time distribution. Various extensions of GP4 to other forms of travel time distribution assumptions, such as Log-GP and Bi-GP, are proposed and extensive experimental results demonstrate the efficiency and applicability of GP4 to various transportation networks, including a real use case with real traffic data in Chengdu, China. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive study of uplink scheduling in multi-cell OFDMA networks. We first focus on two scenarios for the homogeneous case, one without and one with a Cloud-RAN (C-RAN), and explore how to design efficient practical uplink schedulers for those scenarios. To compute the best achievable performance (BAP) under complete information, we study a centralized multi-cell scheduler. To this end, we formulate an MINLP problem and show how to solve it quasi-optimally. Then, we study the performance of an existing practical local benchmark scheduler (LBM) in terms of goodput and losses. We compare its performance to BAP and show that LBM only yields 44 percent of BAP. To reduce this performance gap, we propose two practical enhancements for LBM, one per scenario. The enhanced scheduler for the first scenario yields 51 percent of BAP (70 percent for the second). To reduce the gap further, we propose a new scheduler inspired by soft-frequency reuse (SFR). Its performance is 69 percent (resp. 83 percent) of BAP. It outperforms LBM by 56 percent for the scenario without C-RAN (84 percent with C-RAN). We finally extend our SFR-based scheduler to heterogeneous networks and show that it outperforms LBM by 53 percent for the scenario without C-RAN (96 percent with C-RAN). [ABSTRACT FROM AUTHOR]

The millimeter wave (mmWave) band offers vast bandwidth and plays a key role for next generation wireless networks. However, the mmWave network raises a great challenge for user association and scheduling, due to the limited power budget and beamformers, diverse user traffic loads, user quality of service requirement, etc. In this paper, we propose a novel framework for user association and scheduling in multi-base station mmWave networks, termed the clustering Based dOwnlink UE assOciation, Scheduling, beamforming with power allocaTion (BOOST). The objective is to reduce the downlink network transmission time, subject to the base station power budget, number of beamformers, user traffic loads, and the quality of service requirement at users. We compare BOOST with three state-of-the-art user scheduling schemes. On average, BOOST reduces the transmission time by 37, 30, and 26 percent, and achieves a sum rate gain of 56, 43, and 34 percent, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we investigate hybrid beamforming, user scheduling, and resource allocation optimization based on spectrum coexisting forward transmission in integrated terrestrial-satellite network (ITSN) with the purpose of improving system sum rate and energy efficiency. Considering the limitation of on-board beamforming, a hybrid analog-digital beamforming scheme is designed under the scenario of millimeter wave (mmWave) coexisting in the ITSN framework. Besides, in order to further mitigate intra-beam and inter-beam interference, we propose an adaptive user scheduling scheme, which first determines the cluster center based on adaptive threshold, and then selects users with less channel correlation into a scheduling cluster. Moreover, we model system sum rate maximization problem that incorporates maximum power constrains and minimum data rate requirements. Combined with the aforementioned hybrid beamforming and user scheduling strategy, we formulate the sum rate maximizing problem to a pure power allocation issue. In view of the non-convexity and high complexity, we propose a feasible optimization method based on the minimum mean square error (MMSE) criterion and logarithmic linearization to optimize the power allocation for each user terminal (UT). Simulation results show that our proposed joint beamforming and resource allocation optimization scheduling scheme can achieve an attractive gain in system sum rate and energy efficiency compared with conservative beamforming and allocations. [ABSTRACT FROM AUTHOR]

Layout synthesis, an important step in quantum computing, processes quantum circuits to satisfy device layout constraints. In this paper, we construct QUEKO benchmarks for this problem, which have known optimal depths and gate counts. We use QUEKO to evaluate the optimality of current layout synthesis tools, including Cirq from Google, Qiskit from IBM, ⟨t|ket〉 from Cambridge Quantum Computing, and a recent academic work. To our surprise, despite over a decade of research and development by academia and industry on compilation and synthesis for quantum circuits, we are still able to demonstrate large optimality gaps: 1.5-12x on average on a smaller device and 5-45x on average on a larger device. This suggests substantial room for improvement of the efficiency of quantum computer by better layout synthesis tools. Finally, we also prove the NP-completeness of the layout synthesis problem for quantum computing. We have made the QUEKO benchmarks open-source. [ABSTRACT FROM AUTHOR]

This paper studies a charging facility for electrical vehicles (EVs) at a parking garage, under the assumption that the infrastructure has a limited power capacity. In situations of overload, scheduling decisions must be made on which EVs to charge, possibly taking into account users’ sojourn times. We propose a fluid model that tracks service and sojourn times for a large population, enabling analytical studies of the distribution of service in steady state, for different policies. These results raise the issue of fairness in the distribution of partial service. We introduce a new policy called least laxity ratio to achieve a suitable notion of proportional fairness. We test our results in simulation, including a real data-set with time-varying load. Our results show that our conclusions remain valid in this scenario, in particular the proposed policy performs well under an objective empirical measure of fairness. [ABSTRACT FROM AUTHOR]

Inter-regional power trading plays a crucial role in optimal resource utilization. However, intra-regional operation constraints are currently not well considered in practical implementation, which may lead to internal congestions and economic loss. In this paper, a non-iterative tie-line scheduling framework taking account of both intra- and inter-regional network constraints is proposed. Based on projection theory, admissible set of tie-line injections (ASTI) is defined as the allowable range of tie-line power injections that can be met by a regional system. Then, the ASTI is implemented in tie-line scheduling to ensure the feasibility of the tie-line schedule result for each region. Requirements for practical algorithms that calculate the ASTI in power system applications are analyzed, and a novel vertex-oriented projection algorithm is proposed to efficiently calculate the ASTI. Case studies based on several IEEE and Polish test systems verify the validity and efficiency of the proposed projection algorithm. Simulations based on a four-region system demonstrate the effectiveness of the proposed tie-line scheduling framework. [ABSTRACT FROM AUTHOR]

This paper studies a new scheduling problem in a steel plant, referring to continuous casting (CC), reheating furnace, and hot rolling (HR) processes, which is meaningful and important to the production efficiency and energy saving. First, the problem is modeled as a combination of two coupled subproblems: one assigns casts to continuous casting (CC) machines, decides sequence and start time for casts and rolling units; and another assigns furnaces and decides start time for rolling slabs in a reheating furnace. The objectives are to maximize the number of slabs processed in a mode of hot charge rolling or direct hot charge rolling so as to reduce the energy requirement and the temperature drop of slabs and minimize the residence time of slabs in a reheating furnace to save energy. Then, based on a Benders decomposition strategy, a hybrid algorithm that combines mixed-integer programming and constraint programming is designed to solve each subproblem. An effective cut-generation scheme based on a priority relationship is developed for resolving resource conflicts and unsatisfied setup time constraints. Finally, extensive experiments are conducted to verify the effectiveness of the proposed approach. Note to Practitioners—This paper deals with a scheduling problem arising from CC to HR process in steel manufacturing. It decomposes the original problem into a CC–HR scheduling problem and a reheating furnace scheduling problem. Previously, such a problem is handled, respectively, which always cause energy waste and mismatching plan. This paper takes complex technology constraints into full account to minimize energy waste and energy requirement and establishes nonlinear mathematical models for studied problems. Then, it designs a hybrid algorithm combined mixed-integer programming and constraint programming. The results demonstrate that the proposed approach can solve them effectively. The obtained solution gives decision makers some desired reference to determine a right schedule when actual production tasks are executed. [ABSTRACT FROM AUTHOR]

Though intercell scheduling problems have been studied in the literature, extant algorithms can hardly come into play in practice. This is because of two reasons: 1) transportation among cells, which is important for practical intercell scheduling, has not been adequately considered and 2) the problem size is large in practice, which may lead to intolerable computation efficiency. The motivation of this paper is to automatically design intercell scheduling heuristics that are suitable for practical application. A genetic programming algorithm with a pretraining strategy (GP-PS) is proposed. Production within cells and transportation among cells are simultaneously considered, and a cooperative coevolutionary framework is designed. To evolve better heuristics, a PS is developed. A speedup strategy is designed to accelerate the evolutionary process. Comparative experiments are conducted with other GP-based algorithms, speedup strategies, and with some state-of-the-art intercell scheduling algorithms. GP-PS is also put into use in a large manufacturing enterprise of China. Computational experiments and application results both verify the effectiveness of GP-PS. Note to Practitioners—Although intercell scheduling problems have been studied in the literature, it is still common to rely on manually generated schedules in industrial environments. This paper does not attempt to seek solutions directly, but try to automatically design good heuristic rules to address the problem, so that the computation efficiency that is required by real-time decision and the optimization performance that is important for a complex problem, can be satisfied at the same time. [ABSTRACT FROM AUTHOR]

Large-scale, data-intensive scientific applications are often expressed as scientific workflows (SWfs). In this paper, we consider the problem of efficient scheduling of a large SWf in a multisite cloud, i.e., a cloud with geo-distributed cloud data centers (sites). The reasons for using multiple cloud sites to run a SWf are that data is already distributed, the necessary resources exceed the limits at a single site, or the monetary cost is lower. In a multisite cloud, metadata management has a critical impact on the efficiency of SWf scheduling as it provides a global view of data location and enables task tracking during execution. Thus, it should be readily available to the system at any given time. While it has been shown that efficient metadata handling plays a key role in performance, little research has targeted this issue in multisite cloud. In this paper, we propose to identify and exploit hot metadata (frequently accessed metadata) for efficient SWf scheduling in a multisite cloud, using a distributed approach. We implemented our approach within a scientific workflow management system, which shows that our approach reduces the execution time of highly parallel jobs up to 64 percent and that of the whole SWfs up to 55 percent. [ABSTRACT FROM AUTHOR]

A coflow is a collection of related parallel flows that occur typically between two stages of a multi-stage computing task in a network, such as shuffle flows in MapReduce. The coflow abstraction allows applications to convey their semantics to the network so that application-level requirements can be better satisfied. In this paper, we study the routing and scheduling of multiple coflows to minimize the total weighted coflow completion time (CCT). We first propose a rounding-based randomized approximation algorithm, called OneCoflow, for single coflow routing and scheduling. The multiple coflow problem is more challenging as coexisting coflows will compete for the same network resources, such as link bandwidth. To minimize the total weighted CCT, we derive an online multiple coflow routing and scheduling algorithm, called OMCoflow. We then derive a competitive ratio bound of our problem and prove that the competitive ratio of OMCoflow is nearly tight. To the best of our knowledge, this is the first online algorithm with theoretical performance guarantees which considers routing and scheduling simultaneously for multi-coflows. Compared with existing methods, OMCoflow runs more efficiently and avoids frequently rerouting the flows. Extensive simulations on a Facebook data trace show that OMCoflow outperforms the state-of-the-art heuristic schemes significantly (e.g., reducing the total weighted CCT by up to 41.8% and the execution time by up to 99.2% against RAPIER). [ABSTRACT FROM AUTHOR]

The significance and need of demand response (DR) programs is realized by the utility as a means to reduce the additional production cost imposed by the accelerating energy demand. With the development in smart information and communication systems, the price-based DR programs can be effectively utilized for controlling the loads of smart residential buildings. Nowadays, the use of stochastic renewable energy sources like photovoltaic (PV) by a small domestic consumer is increasing. In this paper, a generalized model for the residential load scheduling or load commitment problem (LCP) in the presence of renewable sources for any type of tariff is presented. Reinforcement learning (RL) is an efficient tool that has been used to solve the decision making problem under uncertainty. An RL-based approach to solve the LCP is also proposed. The novelty of this paper lies in the introduction of a comprehensive model with implementable solution considering consumer comfort, stochastic renewable power, and tariff. Simulation experiments are conducted to test the efficacy and scalability of the proposed algorithm. The performance of the algorithm is investigated by considering a domestic consumer with schedulable and nonschedulable appliances along with a PV source. Guidelines are given for choosing the parameters of the load. [ABSTRACT FROM AUTHOR]

In order to support the development of 5G technologies, researchers are actively engaged in addressing the challenges accompanying the emerging 5G applications. Unquestionably, an eminent technology gaining significant research attention is edge computing. Vehicular edge computing brings data storage and computing capabilities as well as hosting support applications that comprise emerging vehicular services and applications which demand low-delay processing, to the edge closer to the vehicles, reducing response time and increasing reliability, therefore achieving the holistic vision of the tactile Internet. In this context, this paper considers a vehicular network with edge computing capabilities deployed at road side units, and addresses the problem of workload offloading as well as scheduling of computation tasks on the computing resources available at the edge. The challenge here is the high mobility of the vehicles and hence their short residence time within the coverage range of the road side units hosting the edge computing resources. A joint problem considering the communication and computation resources, as well as the latency requirements of the workload is formulated and the scheduling is shown to be NP-Hard. Subsequently, efficient solutions based on Lagrangian relaxation are derived and presented. We evaluate numerically the proposed methods and show their closeness to the optimal solutions. [ABSTRACT FROM AUTHOR]

Cellular vehicle-to-everything (C-V2X) networks can operate without cellular infrastructure support. Vehicles can autonomously select their radio resources using the sensing-based semi-persistent scheduling (SPS) algorithm specified by the Third Generation Partnership Project. The sensing nature of the SPS scheme makes C-V2X communications prone to the well-known hidden-terminal problem. To address this problem, this paper proposes a novel geo-based scheduling scheme that allows vehicles to autonomously select their radio resources based on the location and ordering of neighboring vehicles on the road. The proposed scheme results in an implicit resource selection coordination between vehicles (even with those outside the sensing range) that reduces packet collisions. This paper evaluates analytically and through simulations the proposed scheduling scheme. The obtained results demonstrate that it reduces packet collisions and significantly increases the C-V2X performance, compared to when using the sensing-based SPS scheme. [ABSTRACT FROM AUTHOR]

Age of Information (AoI) is a performance metric that captures the freshness of the information from the perspective of the destination. The AoI measures the time that elapsed since the generation of the packet that was most recently delivered to the destination. In this paper, we consider a single-hop wireless network with a number of nodes transmitting time-sensitive information to a base station and address the problem of minimizing the expected weighted sum AoI of the network while simultaneously satisfying timely-throughput constraints from the nodes. We develop four low-complexity transmission scheduling policies that attempt to minimize AoI subject to minimum throughput requirements and evaluate their performance against the optimal policy. In particular, we develop a randomized policy, a Max-Weight policy, a Drift-Plus-Penalty policy, and a Whittle’s Index policy, and show that they are guaranteed to be within a factor of two, four, two, and eight, respectively, away from the minimum AoI possible. The simulation results show that Max-Weight and Drift-Plus-Penalty outperform the other policies, both in terms of AoI and throughput, in every network configuration simulated, and achieve near-optimal performance. [ABSTRACT FROM AUTHOR]

In this paper, we investigate scheduling policies that minimize the age of information in single-hop queueing systems. We propose a Last-Generated, First-Serve (LGFS) scheduling policy, in which the packet with the earliest generation time is processed with the highest priority. If the service times are i.i.d. exponentially distributed, the preemptive LGFS policy is proven to be age-optimal in a stochastic ordering sense. If the service times are i.i.d. and satisfy a New-Better-than-Used (NBU) distributional property, the non-preemptive LGFS policy is shown to be within a constant gap from the optimum age performance. These age-optimality results are quite general: (i) they hold for arbitrary packet generation times and arrival times (including out-of-order packet arrivals); (ii) they hold for multi-server packet scheduling with the possibility of replicating a packet over multiple servers; (iii) and they hold for minimizing not only the time-average age and mean peak age, but also for minimizing the age stochastic process and any non-decreasing functional of the age stochastic process. If the packet generation time is equal to the packet arrival time, the LGFS policies reduce to the Last-Come, First-Serve (LCFS) policies. Hence, the age optimality results of LCFS-type policies are also established. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-timescale operational approach for a three-phase unbalanced distribution system containing hourly scheduling at substation level and minutes power flow operation at feeder level. In the substation scheduling model, the objective is to minimize the system cost with variable renewable generations. The given error distribution model of renewable generation is formulated as a chance constraint and derived into a deterministic form by Gaussian mixture model with genetic algorithm-based expectation-maximization. In the feeder scheduling model, the system cost is further reduced with the optimal power flow (OPF) at a timescale of minutes. Considering the nonconvexity of the three-phase unbalanced OPF problem in distribution systems, the semidefinite programming is used to relax the problem into a convex problem and a distributed computation approach is built based on alternating direction method of multiplier. The IEEE 123-bus distribution system, University of Denver campus distribution system, and the IEEE 8500-bus distribution system are used as the test bench for the proposed approach. The numerical results demonstrate the effectiveness and validity of the proposed method. [ABSTRACT FROM AUTHOR]

Connection and service request scheduling affects the performance of a mobile relay node (MRN) in a relay-assisted long-term evolution network in terms of crucial quality-of-service (QoS) metrics such as the blocking probability and the system's response time. In this paper, a novel Deadline-constrained Connection and service Request Scheduling (DCRS) scheme is proposed with the objective of improving an MRN's performance in terms of the above-mentioned metrics. In this paper, a stochastic queueing model is formulated for the purpose of capturing the MRN's behavioral dynamics and evaluating its performance as it operates under DCRS. Simulations are conducted in order to verify the model's results’ validity and accuracy. Moreover, the MRN performance achieved under DCRS is compared to that achieved under the typically adopted first-come-first-service (FCFS) scheme. Results indicate that DCRS remarkably outperforms FCFS. [ABSTRACT FROM AUTHOR]

Information updates in multihop networks such as Internet of Things (IoT) and intelligent transportation systems have received significant recent attention. In this paper, we minimize the age of a single information flow in interference-free multihop networks. When preemption is allowed and the packet transmission times are exponentially distributed, we prove that a preemptive last-generated, first-served (LGFS) policy results in smaller age processes across all nodes in the network than any other causal policy (in a stochastic ordering sense). In addition, for the class of new-better-than-used (NBU) distributions, we show that the non-preemptive LGFS policy is within a constant age gap from the optimum average age. In contrast, our numerical result shows that the preemptive LGFS policy can be very far from the optimum for some NBU transmission time distributions. Finally, when preemption is prohibited and the packet transmission times are arbitrarily distributed, the non-preemptive LGFS policy is shown to minimize the age processes across all nodes in the network among all work-conserving policies (again in a stochastic ordering sense). Interestingly, these results hold under quite general conditions, including 1) arbitrary packet generation and arrival times, and 2) for minimizing both the age processes in stochastic ordering and any non-decreasing functional of the age processes. [ABSTRACT FROM AUTHOR]

Sensor networks are increasingly being used in the development and application of military surveillance systems. Small size battery-powered sensor devices are deployed in unattended and hostile environments, such as battlefields to detect any physical intrusion. Energy efficiency, coverage, and connectivity are the three major quality-of-service requirements of such mission critical applications. Energy-efficient communication is required to prolong the network lifetime. Better coverage is required to detect the physical intrusion attempts of all kinds. Similarly, connectivity is necessary to provide mission critical messages to the base station in a timely manner. A scheme that satisfies energy efficiency, coverage, and connectivity requirements is an N-P complete problem. This paper proposes an energy-efficient node scheduling algorithm called ${{EC}}^{{2}}$ that addresses the problems of energy efficiency, coverage, and connectivity in military surveillance applications using the fuzzy graphs. [ABSTRACT FROM AUTHOR]