Intelligent Priority Research Articles

Enhancing smart city operation management: Integrating energy systems with a subway synergism hub

This paper is centered on establishing a secure framework for the optimal concurrent operation of a smart city, encompassing transportation, water, heat, electrical, and cooling energy systems. The studied smart city includes the microgrid, smart transportation system (STS), energy hub (EH) and smart grid. In this regard, a subway synergism hub (SSH) as a new non-energy system is added to the smart city with the aim of serving the subway's water, heat, electrical and cooling demands as well as diminishing the operation cost of the smart city. The EH within the SSH cooperated with a desalination unit is considered to supply the subway's stations water demand by using the sea water. The investigation of the optimal allocation of the SSH unit for reducing the cost of smart city operation is also conducted by introducing a novel intelligent priority selection (IPS) analytical algorithm. In comparison to common meta-heuristic algorithms for allocation problems, the accurate optimal solution can be found in low runtime by the IPS algorithm. To achieve an accurate model of the smart city, directed acyclic graph (DAG) based blockchain approach is provided which can enhance the data and energy exchanges security within the smart city. This research paper introduces a security framework deployed in a smart city setting to establish a secure platform for energy transactions. The findings validate the effectiveness of this model and highlight the value of the IPS method. The effectiveness of the suggested approach has been assessed using the smart city system is comprised of various sections, including EVs, smart grid, microgrid, and SSH, demonstrating the credibility and accuracy of this study.

The Ukraine war and the shift in Russian intelligence priorities

ABSTRACT The war in Ukraine has transformed Russian intelligence activities. It has drawn the bulk of Russian intelligence collection resources, both inside Ukraine and further afield, to focus on war-related, often low-level operational/tactical targets. Even strategic collection is related to the war, especially directed toward bolstering Russia’s global reputation. However, the war has also led to the dismantling of a large portion of Russia’s intelligence apparatus, including both human and signals intelligence, especially in Europe, just when it is needed the most. It has prompted greater scrutiny and international counterintelligence cooperation against Russian intelligence activities than has been seen since the 1980s. Russia’s own actions have drawn those reactions. Nevertheless, Russian intelligence services are resilient and persistent. They learn from mistakes and adapt to changing circumstances.

Creation of the National Intelligence Priorities Framework (NIPF): A Personal Narrative

Creation of the National Intelligence Priorities Framework (NIPF): A Personal Narrative

WHAT SHOULD INTELLIGENCE PRIORITIES BE GIVEN THE STATE OF HEGEMONIC INSTABILITY?

On November 15, 2023, Dr. Loch K. Johnson, Regents Professor Emeritus of Public and International Affairs at the University of Georgia, presented What Should Intelligence Priorities Be Given The State of Hegemonic Instability? for this year’s West Coast Security Conference. The presentation was followed by a question-and-answer period with questions from the audience and CASIS Vancouver executives. The key points discussed were the history of US intelligence prioritization, the need for increased attention towards universal threats, and the need for open societies to share intelligence tasking and findings with each other.
 
 Received: 01-12-2024
 Revised: 01-26-2024

A Bus Signal Priority Control Method Based on Deep Reinforcement Learning

To investigate the issue of multi-entry bus priority at intersections, an intelligent priority control method based on deep reinforcement learning was constructed in the bus network environment. Firstly, a dimension reduction method for the state vector based on the key lane was proposed, which contains characteristic parameters such as the bus states, the flow states, and the signal timing. Secondly, a control action method that can adjust phase sequence and phase green time at the same time was proposed under the constraints of maximum green and minimum green. Furthermore, a reward function, which can be uniformly converted into the number of standard cars, was established focusing on the indexes such as the busload and maximum waiting time. Finally, through building an experimental environment based on SUMO simulation, a real-time bus signal priority control method based on deep reinforcement learning was constructed. The results show that the algorithm can effectively reduce the waiting time of buses without affecting overall traffic efficiency. The findings can provide a theoretical basis for the signal control method considering bus priority and improve the operation efficiency of public transport.

Intelligent priority based generation control for multi area system

ABSTRACT Increased innovation and automation for higher comfort level of living beings have stressed power sector for more units of power to be generated and supplied. To meet this demand, several generating stations are needed to be connected to supply this enormous load to end consumers. While interconnecting generated power of multi-area system, several problems are being encountered. In the current proposed work, 2-area interconnected power system has been considered and automatic generation control problem for single area loading and multi-area multi-type loading has been solved with the help of intelligent control strategy using TLBO algorithm. The proposed work has been processed and simulated in MATLAB and SIMULINK environment. Three types of loadings are considered in the proposed work. First one is single area fixed loading, second one is both area loading with different fixed load, and third one is increasing type load for fixed duration of time in single area. Based on the nature of severity of disturbances in power network, a fitness function has been designed for these multi-type of loadings to improve its transient response to avoid failure of synchronism and improve resiliency of power network to supply uninterrupted power to end consumers.

An Adoptive Miner-Misuse Based Online Anomaly Detection Approach in the Power System: An Optimum Reinforcement Learning Method

Over the past few years, the Bitcoin-based financial trading system (BFTS) has created new challenges for the power system due to the high-risk consumption of mining devices. Briefly, such a problem would be a compelling incentive for cyber-attackers who intend to inflict significant infections on a power system. Simply put, an effort to phony up the consumption data of mining devices results in the furtherance of messing up the optimal energy management within the power system. Hence, this paper introduces a new cyber-attack named miner-misuse for power systems equipped by transaction tech. To overwhelm this dispute, this article also addresses an online coefficient anomaly detection approach with reliance on the reinforcement learning (RL) concept for the power system. On account of not being sufficiently aware of the system, we fulfilled the Observable Markov Decision Process (OMDP) idea in the RL mechanism in order to barricade the miner attack. The proposed method would be enhanced in an optimal and punctual way if the setting parameters were properly established in the learning procedure. So to speak, a hybrid mechanism of the optimization approach and learning structure will not only guarantee catching in the best and most far-sighted solution but also become the high converging time. To this end, this paper proposes an Intelligent Priority Selection (IPS) algorithm merging with the suggested RL method to become more punctual and optimum in the way of detecting miner attacks. Additionally, to conjure up the proposed detection approach’s effectiveness, mathematical modeling of the energy consumption of the mining devices based on the hashing rate within BFTS is provided. The uncertain fluctuation related to the needed energy of miners makes energy management unpredictable and needs to be dealt with. Hence, the unscented transformation (UT) method can obtain a high chance of precisely modeling the uncertain parameters within the system. All in all, the F-score value and successful probability of attack inferred from results revealed that the proposed anomaly detection method has the ability to identify the miner attacks as real-time-short as possible compared to other approaches.

Novel secured distributed energy management structure for solar based smart grids incorporating miners

Addressing the blockchain technology (BCT) challenges, such as the high energy consumption is unavoidable in smart power systems to take maximum advantage. As an antithetical point, there is a structural duality between using BCT in the electrical grid due to its advantages and energy consumption. To this end, this research work is an inclination toward controlling the miner’s energy consumption based on a realistic and efficacious two-stage energy mechanism toward the distributed energy concept and optimal placement of data mining devices. In this circumstance, such a new structure allows miners to mainly sustain their hash rates as a prominent index for security achievement and optimally decrease their cost. This paper suggests a novel distributed model for miners coincided with the smart electrical grid getting inspired by the Prima-Dual Method of Multipliers (PDMM) based distributed approach to settle down the needed power bartering between miners and energy supply resources. In addition, for a similar purpose, it seems to reach the ideal resolution needs to perform the modified reaffirmation of miners (the mining pool) within the secured grid. To execute this task, this research recommends the Intelligent Priority Selection (IPS) techniques as well. When all is said and done, the result of this approach can aid researchers with performing in the field of improving social advantages by the usage of the suggested method.

IRATS: A DRL-based intelligent priority and deadline-aware online resource allocation and task scheduling algorithm in a vehicular fog network

Cloud computing platforms support the Internet of Vehicles, but the main bottlenecks are high latency and massive data transmission in cloud-based processing. Vehicular fog computing has emerged as a promising paradigm to accommodate the increasing computational needs of vehicles. It provides low latency network services that are most important for latency-sensitive tasks. The dynamic nature of VFC, having vehicles with heterogeneous computing resources, vehicle mobility, and diverse tasks with different priorities are the main challenges in vehicular fog networks. In VFC, vehicles can share their idle compute resources with other task-generating vehicles. So, scheduling the tasks on the idle resources of resource-limited vehicles is very important. Existing solutions use a heuristic approach to solve this issue but lack generalizability and adaptability. In this paper, we describe a PPO-based intelligent, priority and deadline-aware online and distributed resource allocation and task scheduling algorithm, called IRATS, in vehicular fog networks. IRATS formulates the resource allocation problem as a Markov decision process to minimize the waiting time and delay of tasks. For vehicles sharing their idle resources, we design a task scheduler for the orderly execution of received tasks according to their priorities using multi-level queues. We conducted extensive simulations using SUMO, OMNeT++, Veins, and veins-gym to validate the effectiveness of the presented algorithm. The simulation results confirm that the proposed algorithm improves the percentage of in-time completed tasks and decreases the packet loss, waiting time, and end-to-end delay as compared to random, A2C, and DQN algorithms considering the task priority and link duration of vehicles.

Automatic inverse treatment planning of Gamma Knife radiosurgery via deep reinforcement learning.

Several inverse planning algorithms have been developed for Gamma Knife (GK) radiosurgery to determine a large number of plan parameters by solving an optimization problem, which typically consists of multiple objectives. The priorities among these objectives need to be repetitively adjusted to achieve a clinically good plan for each patient. This study aimed to achieve automatic and intelligent priority tuning by developing a deep reinforcement learning (DRL)-based method to model the tuning behaviors of human planners. We built a priority-tuning policy network using deep convolutional neural networks. Its input was a vector composed of multiple plan metrics that were used in our institution for GK plan evaluation. The network can determine which tuning action to take based on the observed quality of the intermediate plan. We trained the network using an end-to-end DRL framework to approximate the optimal action-value function. A scoring function was designed to measure the plan quality to calculate the received reward of a tuning action. Vestibular schwannoma was chosen as the test bed in this study. The number of training, validation and testing cases were 5, 5, and 16, respectively. For these three datasets, the average scores of the initial plans obtained with the same initial priority set were 3.63 ± 1.34, 3.83 ± 0.86 and 4.20 ± 0.78, respectively, while they were improved to 5.28 ± 0.23, 4.97 ± 0.44 and 5.22 ± 0.26 through manual priority tuning by human expert planners. Our network achieved competitive results with 5.42 ± 0.11, 5.10 ± 0. 42, 5.28 ± 0.20, respectively. Our network can generate GK plans of comparable or slightly higher quality than the plans generated by human planners via manual priority tuning for vestibular schwannoma cases. The network can potentially be incorporated into the clinical workflow as planning assistance to improve GK planning efficiency and help to reduce plan quality variation caused by interplanner variability. We also hope that our method can reduce the workload of GK planners and allow them to spend more time on more challenging cases.

Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

During the last few years, attention has overwhelmingly focused on the integrated management of urban services and the demand of customers for locally-based supply. The rapid growth in developing smart measuring devices has made the underlying systems more observable and controllable. This exclusive feature has led the system designers to pursue the implementation of complex protocols to provide faster services based on data exchanges. On the other hand, the demands of consumers for locally-based supply could cause a disjunction and islanding behavior that demands to be dealt with by precise action. At first, keeping a centralization scheme was the main priority. However, the advent of distributed systems opened up new solutions. The operation of distributed systems requires the implementation of strong communication links to boost the existing infrastructure via smart control and supervision, which requires a foundation and effective investigations. Hence, necessary actions need to be taken to frustrate any disruptive penetrations into the system while simultaneously benefiting from the advantages of the proposed smart platform. This research addresses the detection of false data injection attacks (FDIA) in energy hub systems. Initially, a multi-hub system both in the presence of a microgrid (the interconnected smart energy hub-based microgrid system) and without it has been modeled for energy management in a way that allows them to cooperate toward providing energy with each other. Afterward, an FDIA is separately exerted to all three parts of the energy carrier including the thermal, water, and electric systems. In the absence of FDIA detection, the impact of FDIA is thoroughly illustrated on energy management, which considerably contributes to non-optimal operation. In the same vein, the intelligent priority selection based reinforcement learning (IPS-RL) method is proposed for FDIA detection. In order to model the uncertainty effects, the unscented transformation (UT) is applied in a stochastic framework. The results on the IEEE standard test system validate the system’s performance.

A Cost-Efficient-Based Cooperative Allocation of Mining Devices and Renewable Resources Enhancing Blockchain Architecture

The impressive furtherance of communication technologies has exhorted industrial companies to link-up these developments with their own abilities with the target of efficiency enhancement through smart supervision and control. With this in mind, the blockchain platform is a prospective solution for merging communication technologies and industrial infrastructures, but there are several challenges. Such obstacles should be addressed to effectively adopt this technology. One of the most recent challenges relative to adopting blockchain technology is the energy consumption of miners. Thus, providing an accurate approach that addresses the underlying cause of the problem will carry weight in the future. This work addresses managing the energy consumption of miners by using the advantage of distributed generation resources (DGRs). Along the same vein, it appears that achieving the optimal solution requires executing the modified reconfirmation of DGRs and miners (indeed, mining pool systems) in the smart grid. In order to perform this task, this article utilizes the Intelligent Priority Selection (IPS) method since this method is up to snuff for corporative allocation. In order to find practical solutions for this problem, the uncertainty is also modeled as a credible index highly correlated with the load and generation. All in all, it can be said that the outcome of this research study can help researchers in the field of enhancement of social welfare by using the proposed technology.

A Secured Advanced Management Architecture in Peer-to-Peer Energy Trading for Multi-Microgrid in the Stochastic Environment

Careful consideration of grid developments illustrates the fundamental changes in its structure which its developments have taken place gradually for a long time. One of the most important developments is the expansion of the communication infrastructure that brings many advantages in the cyber layer of the system. The actual execution of the peer-to-peer (P2P) energy trading is one core advantage which also may lead to the systematic risks such as cyber-attacks. Consequently, it is necessary to form a useful way to cover such challenges. This paper focuses on the online detection of false data injection attack (FDIA), which tries to disrupt the trend of optimal peer-to-peer energy trading in the stochastic condition. Moreover, this article proposes an effective modified Intelligent Priority Selection based Reinforcement Learning (IPS-RL) method to detect and stop the malicious attacks in the shortest time for effective energy trading based on the peer to peer structure. The presented method is compared with other methods such as support vector machine (SVM), reinforcement learning (RL), particle swarm optimization (PSO)-RL, and genetic algorithm (GA)-RL to validate the functionality of the method. The proposed method is implemented and examined on three interconnected microgrids in the form of peer-to-peer structure wherein each microgrid has various agents such as photovoltaic (PV), wind turbine, fuel cell, tidal system, storage unit, etc. Eventually, the unscented transformation (UT) is applied for uncertainty analysis and making the near-reality simulations.

Optimal Scheduling and Management of a Smart City Within the Safe Framework

This paper proposes an enhanced cyber secure energy and data transaction framework for the optimal operation and management of the smart city. Recently, the concept of smart city within the power systems has taken the center stage. Although the power system full monitoring and accessibility is guaranteed through the smart city concept, the risk of cyber-attacks to the system has been promoted, severely. With this in mind, in this paper an effective smart city model including the smart grid, smart transportation systems (STSs) that consist of the metro and electric vehicles (EVs), microgrid and smart energy hub (EH) is presented. In the proposed model, an improved directed acyclic graph (DAG) approach is represented in order to enhance the security of data transaction within the smart city. In this approach, a security layer is added to the blockchain which will prevent the cyber hackers access to the system information. An effective energy management schedule is also developed in this paper. To do so, an intelligent priority selection (IPS) based on advanced math operators is provided to allocate the metro-owned charging stations (MCSs), optimally. Furthermore, the unscented transform (UT) is utilized to handle the uncertainty of the system parameters. The results showed that the proposed IPS could improve the method CPU time over 75% compared to other well-known meta-heuristic methods in the area. Moreover, the results showed that the proposed framework has remarkably reduced the run time and increased the accuracy of the solution compared to the other meta-heuristic algorithms.

Intelligent prioritization of public transport on intercity roads

Intelligent Transport Systems tools and equipment are built still not very often out of cities. It seems, that simple accommodation on traffic lights is the higher level of ITS there. This situation will evolve in the near future. What is important, dynamic developing of intelligent priority systems does not have to be limited only to cities or urban areas.

AODV with intelligent priority flow scheme for multi-hop ad hoc networks

A mobile ad hoc network is formed by a cluster of mobile stations randomly located within a certain area without the infrastructure of base stations. There have been many researches proposing different models of routing protocols and numerous simulations to enhance their performances under varying conditions and constraints. However, most of them only focus on network layer without considering the influence of media access control (MAC) layer. In this paper, a new strategy of cross layer algorithm is proposed based on the characteristics of reactive routing and multi-hop jumping. In addition, artificial bee colony algorithm is performed to calculate the contention window of the nodes along the routing path over MAC layer. This framework could avoid data collision in MAC layer, and results in getting better performances of data transmission in network layer. Extensive simulations show that the proposed AODV with intelligent priority flow scheme is able to earn a significant improvement over the conventional routing algorithm.

Militarising – and marginalising? – African Studies USA

The militarisation of US–African relations has attracted considerable attention in recent years. Left largely unexplored, however, is the question of how this process has involved US-based scholars. This essay examines this process with particular attention to the rapid expansion of military and intelligence research on and in Africa, and, in particular, military and intelligence funding of US Africanists' research including at the major African Studies centres. While the classification of much federal research limits conclusions, it is apparent that military and intelligence priorities are coming to significantly shape the present and future of much research and training.

An Intelligent Priority Resource Allocation Scheme for LTE-A Downlink Systems

In this paper, we propose an intelligent priority resource allocation (iPRA) scheme for long term evolution-advanced (LTE-A) downlink systems with multimedia traffic. The iPRA scheme intelligently computes priorities for users by using fuzzy inference system so as to enhance system throughput and attain high Jain fairness index while guarantee QoS requirements. Simulation results show that the iPRA scheme can achieve higher system throughput and better QoS requirement guarantee than conventional radio resource allocation schemes in high traffic load, while attains a rather large Jain fairness index.

Reflections on CIA Analysis: Is it Finished?

Despite the CIA's improved analytical tradecraft and increased resources, the future of its analytic mission remains in doubt. Post-9/11 improvements have been coupled with a continued focus on current intelligence priorities that minimize attention to the development of strategic research and deeper knowledge. Simply increasing the number of analysts has not produced deeper expertise. The CIA's traditional recruitment and training methods, as well as its rewards and promotion system, encourage analysts to avoid concentrating on any single area of regional or functional expertise in favor of moving around the agency to build a successful career. A continued reliance on risk-avoidance security practices also restricts analysts' contact with non-government and foreign experts who often have needed political and cultural knowledge of intelligence topics. To rectify these inadequacies, the CIA's analytic directorate needs to develop incentives for analysts who wish to develop more strategic analysis and remove the security barriers to closer collaboration with experts outside the US government. Developing cross-agency analytic collaboration would also maximize expertise and would benefit from intelligence community-wide training programs similar to what the US military does at its senior service colleges.

The “Ball of Confusion” in Federal Budgeting: A Shadow Agenda for Deliberative Reform of the Budget Process

The budget process is seriously flawed, as Irene Rubin suggests, but there is little prospect for its effective reform. Current economic and political conditions could open the window for reform, but the excessive partisanship that helped create these conditions also has reduced the pool of institutionalists who could lead reforms. More important is confusion about which reforms might be most effective. Most proposed reforms would create more rules, but they will not work unless politicians commit to meeting the goals such rules are intended to support. Those commitments could be produced by deliberation over critical issues that have been neglected in recent discussions of budget process reform: how the process could support macroeconomic policy making, how improved budget concepts could accurately measure finances and aid in dealing with upcoming policy challenges, how reorganization could enable intelligent priority setting, and how the process could be better aligned with the constitutional sharing of powers and the electoral system.