Temporal heterogeneous network representation learning is a pivotal approach for encapsulating the diversity of nodes and edges along with their temporal evolution into concise, low‐dimensional node representations. This technique has demonstrated remarkable efficacy in various network analysis and inference tasks. However, existing approaches study network evolution mainly by analyzing snapshots of temporal networks, while neglecting the intrinsic formation mechanisms of temporal heterogeneous networks. Few dynamic models delve into the intrinsic factors propelling network evolution. To fill this research gap, we introduce a novel learning framework for temporal heterogeneous network representation learning with dynamic influence modeling, denoted as THNRD. THNRD pioneers the application of the Hawkes process to temporal heterogeneous networks, utilizing the linking process of dynamic events to emulate the network’s formation mechanism, capturing the intrinsic dynamic progression of temporal heterogeneous networks. Subsequently, THNRD introduces a multilayer spatiotemporal aggregation model under a unified spatiotemporal framework, which is designed to harmoniously integrate the semantic and dynamic attributes of the networks. We also take node influence into consideration to further describe the temporal emergent phenomena. We verify the effectiveness of our proposed method via extensive experimental evaluations on real‐world datasets. The results consistently demonstrate that THNRD outperforms current state‐of‐the‐art methods.

This article introduces a command and control (C2) organizational structure model, which can analyze military C2 organization structure from three perspectives: organizational elements, organizational relationships, and organizational processes. In the organizational processes, a decision-making problem of recommending course of action (COA) is described. Then, a planning method is proposed to solve this decision-making problem, which includes six evaluation metrics of COA effectiveness and a tabu search algorithm implemented within the C2 organization structure model. On this basis, we provide two application examples to demonstrate the usability of the proposed C2 organizational structure model and its COA planning method for operational decision-making.

In this article, we investigate the edge cache deployment for high definition (HD) urban map provisioning, which is an essential building block for future autonomous driving. Given a deployment budget, we first formulate a satisfied downloading request maximization (SDRM) problem to obtain the deployment locations and customized cache sizes. The SDRM problem is unsolvable directly as the urban transportation traffic is highly dynamic and future traffic conditions are unknown in advance. Based on data analytics of the vehicular GPS trace, we propose an architecture named CoDe , built on which we transform and address the SDRM problem. The reference implementation of CoDe is respectively developed based on the insights from two urban-scale carpool GPS traces. The novelty and contributions of CoDe lie in its three-layer design. Particularly, at data feeding layer, we make use of two urban 60-day GPS traces involving respectively 37,801 and 17,517 vehicles, to extract the analytics samples. At mobility characterization layer, we conduct extensive data analytics on traffic mobility in terms of their distribution, correlation, and variation, to mine the crucial traffic mobility patterns for strategy customization. At cache deployment layer, we propose the K -order subgraph for each block to record the moving statistics within its K -order neighborhood, and transform the SDRM problem accordingly. Then, the R oute we I ght ba S ed gr E edy ( RISE ) algorithm is devised for the problem, which can deliver the deployment decisions. Extensive data-driven experiments are carried out to demonstrate the superior performance of CoDe in terms of request hit ratio and caching resource utility.

A large number of emergency humanitarian rescue demands in conflict areas around the world are accompanied by intentional, persistent and unpredictable attacks on rescuers and supplies. Unfortunately, existing work on humanitarian relief planning mostly ignores this challenge in reality resulting a parlous and short-sighted relief distribution plan to a large extent. To address this, we first propose an offline multi-stage optimization problem of emergency relief planning under intentional attacks, in which all parameters in the game between the rescuer and attacker are supposed to be known or predictable. Then, an online version of this problem is introduced to meet the need of online and irrevocable decision making when those parameters are revealed in an online fashion. To achieve a far-sighted emergency relief planning under attacks, we design an online learning approach which is proven to obtain a near-optimal solution of the offline problem when those online reveled parameters are i.i.d. sampled from an unknown distribution. Finally, extensive experiments on a real anti-Ebola relief planning case based on the data of Ebola outbreak and armed attacks in DRC Congo show the scalability and effectiveness of our approach.

Edge computing is a promising computing paradigm by pushing the cloud service to the network edge. To this end, edge infrastructure providers (EIPs) need to bring computation and storage resources to the network edge and allow edge service providers (ESPs) to provision latency-critical services for end users. Currently, EIPs prefer to establish a series of private edge-computing environments to serve specific requirements of users. This kind of resource provisioning mechanism severely limits the development and spread of edge computing in serving diverse user requirements. In this paper, we propose an integrated resource provisioning model, namededge federation, to seamlessly realize the resource cooperation and service provisioning across standalone edge computing providers and clouds. To efficiently schedule and utilize the resources across multiple EIPs, we systematically characterize the provisioning process as a large-scale linear programming (LP) problem and transform it into an easily solved form. Accordingly, we design a dynamic algorithm to tackle the varying service demands from users. We conduct extensive experiments over the base station networks in Toronto. Compared with the fixed contract model and multihoming model, edge federation can reduce the overall costs of EIPs by 23.3% to 24.5%, and 15.5% to 16.3%, respectively.

Stealth malware, a representative tool of advanced persistent threat (APT) attacks, in particular poses an increased threat to cyber-physical systems (CPS). Due to the use of stealthy and evasive techniques (e.g., zero-day exploits, obfuscation techniques), stealth malwares usually render conventional heavyweight countermeasures (e.g., exploits patching, specialized ant-malware program) inapplicable. Light-weight countermeasures (e.g., containment techniques), on the other hand, can help retard the spread of stealth malwares, but the ensuing side effects might violate the primary safety requirement of CPS. Hence, defenders need to find a balance between the gain and loss of deploying light-weight countermeasures. To address this challenge, we model the persistent anti-malware process as a shortest-path tree interdiction (SPTI) Stackelberg game, and safety requirements of CPS are introduced as constraints in the defender's decision model. Specifically, we first propose a static game (SSPTI), and then extend it to a multi-stage dynamic game (DSPTI) to meet the need of real-time decision making. Both games are modelled as bi-level integer programs, and proved to be NP-hard. We then develop a Benders decomposition algorithm to achieve the Stackelberg Equilibrium of SSPTI. Finally, we design a model predictive control strategy to solve DSPTI approximately by sequentially solving an approximation of SSPTI. The extensive simulation results demonstrate that the proposed dynamic defense strategy can achieve a balance between fail-secure ability and fail-safe ability while retarding the stealth malware propagation in CPS.

Graphical models are widely used in cyber security analysis to capture relationships among variables in attack scenarios. However, most models are difficult to build due to the greatly imbalanced data in cyber attacks. To solve this problem, we propose a two-level Bayesian network framework in this paper. We firstly classify the cyber attacks into two levels, one is used to identify general types of attacks and the other is used to classify specific forms. Then we train Bayesian networks for each level. To help administrators cope with threats in time, we propose an analysis method. This method finds the important node's Markov blanket and sorts nodes in it by their influence on each specific form, which could help to understand key threats in cyberspace.

In this paper we propose a novel method for modeling the shape of vessel trajectories in a manner which may facilitate the application of machine learning techniques. This is achieved by transforming the topological feature of vessel trajectories into vectors. More specifically, we calculate scale-invariance indicators for every vessel trajectory as shape characteristics, and other indicators to denote the trajectory area. The proposed method is validated using both synthetic trajectories and real-world AIS datasets. We demonstrate that it can achieve good time efficiency and may support vessel trajectory related analysis.

Key element protection of combat system is a challenging problem in modern combat. Effectively evaluating the key elements would be of great help in force deployment in crisis situations. This paper proposes a new evaluation method that combines expert evaluation, PCA (Principal Component Analysis) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). Specifically, we first use expert evaluation to get attributes' values of each element. Then, we apply PCA to extract principal components from elements' attributes. Next, we use TOPSIS to calculate elements' proximities to ideal solutions under different principal components. Finally, we synthesize each element's proximity under different principal component to get the relative superiority. In addition, we test the proposed method in an experimental combat scenario, and show the plausibility of this method.

To solve a real‐world planning problem with interfering subgoals, it is essential to perform early detection of subgoal dependencies and achieve the subgoals in the correct order. This is also the case for planning problems with forced goal‐ordering (FGO) constraints. In automated planning, forward search with FGO constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. Many existing methods such as goal agenda manager and ordered landmarks cannot detect the FGOs accurately, and thus, the undiscovered ordering relationship may cause the forward search to suffer from deadlocks. In this article, we put forward an approach via an effective search heuristic to constrain a planner to satisfy the FGOs. We make use of an atomic goal‐achievement graph in a look‐ahead search under the FGO constraints. This allows a forward search strategy to plan forward efficiently in multiple steps toward a goal state along a search path. Experimental results illustrate that, by avoiding deadlocks, we can solve more benchmark planning problems more efficiently than previous approaches. We also prove several formal properties for search that are related to FGO detection.

A good task assignment is an important guarantee to achieve great combat effectiveness. This paper investigates the task assignment problem, where the value of the targets is time changing in the battlefield, and presents a solution approach that is a combination of two algorithms: the multidestination route planning algorithm based on dynamic programming and the multisubgroup ant colony algorithm (MSACO). The two algorithms coordinately solve the task assignment problem. The route planning algorithm can obtain available routes between any two targets and provide reasonable routing information for MSACO. Then, the ant colony algorithm is applied to solve the task assignment problem. To solve the task assignment problem in the battlefield environment, several key technologies are introduced to improve the traditional ant colony algorithm, which include the subgroup selection strategy, the dynamic candidate aggregate policy, the state transferring policy, and the information-element updating mechanism. Simulation results show that the proposed approach can produce a reasonable and available plan for all the test cases in short computational time.

In order to improve scalability and to reduce the maintenance overhead for structured peer-to-peer (P2P) networks, researchers have proposed architectures based on several interconnection networks with a fixed-degree and a logarithmical diameter. Among existing fixed-degree interconnection networks, the Kautz digraph has many distinctive topological properties compared to others. It, however, requires that the number of peers have the some given values, determined by peer degree and network diameter. In practice, we cannot guarantee how many peers will join a P2P network at a given time, since a P2P network is typically dynamic with peers frequently entering and leaving. To address such an issue, we propose the balanced Kautz tree and Kautz ring structures. We further design a novel structured P2P system, called BAKE, based on the two structures that has the logarithmical diameter and constant degree, even the number of peers is an arbitrary value. By keeping a total ordering of peers and employing a robust locality-preserved resource placement strategy, resources that are similar in a single or multidimensional attributes space are stored on the same peer or neighboring peers. Through analysis and simulation, we show that BAKE achieves the optimal diameter and as good a connectivity as the Kautz digraph does (almost achieves the Moore bound), and supports the exact as well as the range queries efficiently. Indeed, the structures of balanced Kautz tree and Kautz ring we propose can also be applied to other interconnection networks after minimal modifications, for example, the de Bruijn digraph.

Command and control (C2) organization and its existing research are introduced, and C2 organization efficiency measure is discussed. The C2 network model is built, and the capability of C2 network is analyzed by the method of weighted-node. The average cooperating efficiency is proposed to measure C2 network's cooperating efficiency, and validate measurement's validity by comparing with network efficiency. The optimal C2 network's topology property is analyzed by modulating some parameters. The property has a direction for designing the actual C2 network.

In this paper, we introduce the basic concepts in a Multi-Agent System (MAS), and propose an organization model based upon self-similar entities called holons, whose concept itself integrates both the agent aspect and organizational aspect of the MAS. By discarding the concept of role which is widely used in traditional methods of agent organization design, the model based on holon is more flexible and could facilitate self-adapting and self-organizing of the organization, thus more suited with a dynamic task environment. The allocation and execution of tasks in the organization, and self-organization mechanism of holons are also discussed.

Human-System systems design is a critical part of Command and Control (C2) design, in order to improve the whole effectiveness of C2 design, the human beings and systems must be in harmony in the human-in-loop systems, which can be referred to as Cyber-physical-social systems (CPSS). Based on the coordination theory, self synchronization of a CPSS is both a self-organizing and self-adapting process, of which the end effect will be shown as the emergence of all the dependency relationships among the courses of action (COAs). Human-system systems design for C2 will be modeled in different views: the system view, the human view and the coordination view, being a loop iteration process.

Military mission planning aims to coordinate a set of platforms with different capacities to accomplish a set of tasks under temporal, spatial and resource constraints. However, as military operations are intrinsically dynamic and uncertain, a solution (plan) corresponding to the deterministic circumstance is fragile due to unexpected events. To tackle this problem, this paper proposes a chance constrained programming model, which incorporates many uncertain factors, such as the durations, locations and resource requirements of tasks. The objective of mission planning is to coordinate the platforms to maximize the probability that all of tasks are completed successfully while satisfying the chance constraints. The problem is solved under a computational framework combining GA and Monte Carlo Simulation, the GA is used to solve the platform allocation and task scheduling while Monte Carlo Simulation is used to process the chance constraints. A mission instance is presented which demonstrates the usefulness of the proposed model and algorithm.

Different from the traditional military network model, this paper proposes a new Operation System of Systems (OSoS) model—FINC-E Model (Force, Intelligence, Networking, and C2, Extended Model) which provides the formulization framework for nodes and edges of the OSoS. Secondly, a new index—edge key potential is proposed, which acts as the measurement of the edge's importance, meanwhile the performance metrics for the OSoS are provided, i.e. coordination coefficient, execution capacity coefficient and information support coefficient. In the end, the attack strategy based on edge key potential is put forward, and a practical case, which is aiming at the edge attack rather than node attack, is conducted to demonstrate the effectiveness of our model.

This paper presents the monotonic indices space (MIS) method used for the extended complex system capability indices effectiveness analysis. Based on the assumption that indices are monotonic with respect to the requirement measurements, an algorithm is proposed and applied to attain numerical approximation of monotonic indices requirement locus with hyperboxes. Two algorithms for acquiring intersection of several monotonic indices requirement loci are proposed, and two system analysis models based on MIS, the system evaluation model and the index sensitivity analysis model, are put forward. Finally, the models previously mentioned are used to analyze the capability indices effectiveness of a notional antistealth information system. The results show that the MIS method is promising.

In this paper, we improve the image compression method based on the theory of fuzzy information granulation (TFIG). Granulation structure of image is introduced, and the basic principle of complementary image compression based on TFIG is investigated. A new compression method is proposed based on such granulation structure. It has been testified with better effect than the original method based on TFIG by many experiments.

In Network Centric Warfare, the sensor network's capability is much stronger than ever; the force can get a mass of information about battlespace in real or near-real time. How to utilize the information about battlespace effectively and transform the information superiority into knowledge superiority is a key problem for NCW research. To solve this problem, first we need to establish a suitable knowledge infrastructure. In this article, battlespace ontology is considered as the knowledge infrastructure of NCW, and we propose a battlespace ontology construction approach based on OWL, which consists of two parts: formal ontology construction approach to construct subdomain ontologies of battlespace and formal ontology integration approach to integrate subdomain ontologies of battlespace. Then a concrete application of the approach to an air combat battlespace is given. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1219–1231, 2005.

Summary form only given. This article addresses the impact of capability description on query planning in heterogeneous data integration system. Query planning covers the selection of data sources related to the query and the determination of subgoals' execution orders. In the context of capability description, we propose a framework for data sources description towards generating good feasible query plans. Our approach uses information such as the semantic correspondences between local schemas and mediated schemas and the query capability descriptions to investigate factors that provide a good foundation for query planning. Finally, the proposed approach is compared with the other capability description approaches described in the literature. The obtained results demonstrate that our approach will allow data sources to advertise their capabilities in a flexible way and help to efficiently query planning.

An effective task allocating is important for MAS to complete its missions through efficient cooperation among agents. A new approach is advanced in this paper, which is based on the multidimensional dynamic list scheduling (MDLS) algorithms. And the results from the two algorithms are compared.

This paper presents an efficient decentralized Grid service discovery approach based on service ontology. It uses two techniques to improve efficiency. First, Grid information nodes are organized into community overlays of different service categories defined in service ontology. A distributed hash table (DHT) based upper layer network is constructed to provide efficient navigation between communities. Second, a simple and lightweight greedy search based service location (GSBSL) method is introduced to identify service providers with high QoS efficiently within communities. Simulation results show that, the efficiency is improved compared with existing decentralized Grid service discovery approaches, and the overhead is acceptable and controllable.

Service location based on greedy search is studied. A model is built to analyze the influence of the network topologies on this location method, and different network topologies are constructed to validate the model by way of simulation. The results show that, though the model assumes a uniform degree distribution, it agrees with the simulation result well if the average degree of the network is reasonably big. Hops and relative QoS index of the node found in a service location process are used to evaluate the effectiveness of the location method as well as the probability of locating the first 5% nodes with higher QoS level. Both model and simulation result show that the performance of greedy search based service location improves significantly with the increase of the average degree of the network. If changes of both topology and the QoS level of nodes can be ignored during a location process, it can be guaranteed that the location has high probability of finding the nodes with relatively high QoS level in small number of hops in a big service community. Model extension under arbitrary network degree distribution is further studied. A "two-phase" topology construction and service location based on greedy-search is also put forward.