Achieving fully privacy-preserving private range queries over outsourced cloud data

With the prevalence of cloud computing, data owners are motivated to outsource their databases to the cloud server. However, to preserve data privacy, sensitive private data have to be encrypted before outsourcing, which makes data utilization a very challenging task. Existing work either focus on keyword searches and single-dimensional range query, or suffer from inadequate security guarantees and inefficiency. In this paper, we consider the problem of multidimensional private range queries over encrypted cloud data. To solve the problem, we systematically establish a set of privacy requirements for multidimensional private range queries, and propose a multidimensional private range query (MPRQ) framework based on private block retrieval (PBR), in which data owners keep the query private from the cloud server. To achieve both efficiency and privacy goals, we present an efficient and fully privacy-preserving private range query (PPRQ) protocol by using batch codes and multiplication avoiding technique. To our best knowledge, PPRQ is the first to protect the query, access pattern and single-dimensional privacy simultaneously while achieving efficient range queries. Moreover, PPRQ is secure in the sense of cryptography against semi-honest adversaries. Experiments on real-world datasets show that the computation and communication overhead of PPRQ is modest.     

Hierarchical multi-agent planning for flexible assembly of large-scale lunar facilities

Several national space agencies and commercial aerospace companies plan to set up lunar bases with large-scale facilities that rely on multiple lunar robots’ assembly. Mission planning is necessary to achieve efficient multi-robot cooperation. This paper aims at autonomous multi-robot planning for the flexible assembly of the large-scale lunar facility, considering the harsh lunar environment, mission time optimization, and joint actions. The lunar robots and modules are scattered around the mission area without fixed assembly lines. Thus, the traditional assembly planning methods ignoring the optimal selection of modules are unable to handle this problem. We propose a hierarchical multi-agent planning method based on two-stage two-sided matching (HMAP-TTM) to solve this critical problem. First, the distributed planning framework with multi-replica public agents is introduced, ensuring robot plan knowledge consistency through public agents’ communication. Second, the hierarchical task graph (HTG) divides the mission into task layers based on task dependency knowledge. Third, we develop a novel two-stage two-sided matching algorithm. Time-optimal plans emerge from the matching games among public and private agents in each layer of HTG. Agents make decisions in the game based on action knowledge updated during planning. Finally, an assembly mission is presented to prove the method’s effectiveness. The simulation results show that the HMAP-TTM can generate plans with shorter mission time and require smaller communication costs than the baseline methods.     

A distributed multi-agent production planning and scheduling framework for mobile robots

Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.     

An ontology based approach to organize multi-agent assisted supply chain negotiations

Effective supply chain management (SCM) comprises activities involving the demand and supply of resources and services. Negotiation is an essential approach to solve conflicting transaction and scheduling problems among supply chain members. The multi-agent system (MAS) technology has provided the potential of automating supply chain negotiations to alleviate human interactions. Software agents are supposed to perform on behalf of their human owners only when equipped with sophisticated negotiation knowledge. To better organize the negotiation knowledge utilized by agents and facilitate agents’ adaptive negotiation decision making ability, an ontology-based approach is proposed in this paper. Firstly, the multi-agent assisted supply chain negotiation scheme is presented to configure the general design components of the negotiation system, covering the agent intelligence modules, the knowledge organization method and the negotiation protocol. Then, the ontology-based negotiation knowledge organization method is specified. The negotiation knowledge is separated into shared negotiation ontology and private negotiation ontology to ensure both the agent communicative interoperability and the privacy of strategic knowledge. Inference rules are defined on top of the private negotiation ontology to guide agents’ reasoning ability. Through this method, agents’ negotiation behaviors will be more adaptive to various negotiation environments utilizing corresponding negotiation knowledge.     

An efficient PHR service system supporting fuzzy keyword search and fine-grained access control

Outsourcing of personal health record (PHR) has attracted considerable interest recently. It can not only bring much convenience to patients, it also allows efficient sharing of medical information among researchers. As the medical data in PHR is sensitive, it has to be encrypted before outsourcing. To achieve fine-grained access control over the encrypted PHR data becomes a challenging problem. In this paper, we provide an affirmative solution to this problem. We propose a novel PHR service system which supports efficient searching and fine-grained access control for PHR data in a hybrid cloud environment, where a private cloud is used to assist the user to interact with the public cloud for processing PHR data. In our proposed solution, we make use of attribute-based encryption (ABE) technique to obtain fine-grained access control for PHR data. In order to protect the privacy of PHR owners, our ABE is anonymous. That is, it can hide the access policy information in ciphertexts. Meanwhile, our solution can also allow efficient fuzzy search over PHR data, which can greatly improve the system usability. We also provide security analysis to show that the proposed solution is secure and privacy-preserving. The experimental results demonstrate the efficiency of the proposed scheme.     

Concurrent localization of multiple robots

A concurrent localization method for multiple robots using ultrasonic beacons is proposed. This method provides a high-accuracy solution using only low-price sensors. To measure the distance of a mobile robot from a beacon at a known position, the mobile robot alerts one beacon to send out an ultrasonic signal to measure the traveling time from the beacon to the mobile robot. When multiple robots requiring localization are moving in the same block, it is necessary to have a schedule to choose the measuring sequence in order to overcome constant ultrasonic signal interference among robots. However, the increased time delay needed to estimate the positions of multiple robots degrades the localization accuracy. To solve this problem, we propose an efficient localization algorithm for multiple robots, where the robots are in groups of one master robot and several slave robots. In this method, when a master robot calls a beacon, all the group robots simultaneously receive an identical ultrasonic signal to estimate their positions. The effectiveness of the proposed algorithm has been verified through experiments.     

Extended-state-observer-based adaptive control for synchronisation of multi-agent systems with unknown nonlinearities

This paper studies the problem of synchronisation to a desired trajectory for non-linear multi-agent systems. By introducing extended state observer approach, decentralised adaptive controllers are designed for distributed systems which have non-identical unknown non-linear dynamics. The non-identical unknown non-linear dynamics allows for a tracked command dynamics which is also non-linear and unknown. State variables of agents can be obtained only in the case where leader agent and the network communication topology for multi-agent systems is strongly connected digraph network structures. A Lyapunov-function-based approach is given to show that the tracking error is ultimately bounded. Some simulation results are given to demonstrate the effectiveness of the developed techniques in this paper.     

The capacitated multi-AGV scheduling problem with conflicting products: Model and a decentralized multi-agent approach

Automated guided vehicles (AGVs) are a key technology to facilitate flexible production systems in the context of Industry 4.0. This paper investigates an optimization model and a solution using a decentralized multi-agent approach for a new capacitated multi-AGV scheduling problem with conflicting products (CMASPCP) to take full advantage of AGVs. The novelty of the problem and our model lies in the introduction of AGV capacity constraints and constraints arising from conflicting products, i.e. products that cannot be transported together. As the new I4.0 paradigm tends towards decentralized control, we also present a decentralized multi-agent approach in which AGVs autonomously coordinate to solve the task. The performance of the proposed decentralized approach is compared to a mixed-integer linear programming model on a set of 110 problem instances with different sizes and degrees of complexity. The obtained results show that the proposed decentralized multi-agent approach is effective and competitive in terms of the solution quality and computational time.     

Towards a construction of an intelligent business process based on cloud services and driven by degree of similarity and QoS

Knowledge and competence management in companies focus on resources allocation to business activities, knowledge management approaches promise a positive impact on cost structures. Moreover, with cloud computing and multi-agent systems, we can have a positive impact on business process construction. In fact, cloud computing gives us the ability to perceive resources such as unlimited computer resources. Meanwhile, multi-agent systems provide some degree of independence to achieve specific goals. Nowadays, on the web we can find a significant number of services that provide the same functionality. Hence, business process construction by reusing these services has become a challenging problem. The solution proposed in this paper, can be considerate as an enterprise solution to construct a business process using some competence and knowledge management aspects. Furthermore, the solution is based on cloud computing and agents to discover similar services and classify services according to their similarity degrees and their quality of services.     

Asynchronous decentralised event-triggered control of multi-agent systems

In this paper, the consensus problem of first-order multi-agent systems under linear asynchronous decentralised event-triggered control is investigated. Both undirected and directed topologies are considered. In the analysis, the closed-loop multi-agent systems with the event-triggered control are modelled as switched systems. After proposing the decentralised event-triggered consensus protocols, decentralised state-dependent event conditions are derived, which act as switching signals. The consensus analyses are performed based on graph theory and stability results of switched systems. Under the event-triggered control schemes presented, consensus is reached with enlarged sampling periods and no Zeno behaviour. Simulation examples are given to illustrate the effectiveness of the proposed theoretical results.     

Enhancing peer-to-peer collaboration using trust

Distributed systems generally require their component parts to interact cooperatively, in order for the system as a whole to function effectively. For any given activity, there are typically several alternative components that have the required capabilities. In decentralised systems, where there is no overarching control, individual components are responsible for selecting other components with which to cooperate. However, the candidate components may be unreliable or dishonest, and are typically locally controlled. Such decentralised systems can be viewed as multi-agent systems, comprising autonomous agents that must cooperate for the system to be effective. Peer-to-peer (P2P) systems are a subclass of decentralised distributed systems, in which not only is there no overarching control, but neither is there any hierarchy of control, power, or responsibly among the system components. Selecting appropriate peers to cooperate with is a challenging problem, since the candidate peers are autonomous and may be unreliable or dishonest. Peers need a mechanism for task delegation that takes the uncertainly of interactions into account. In this paper we present a mechanism, called Mdt-r, that enables peers to delegate activities appropriately, using trust and the recommendations of other peers to meet individual preferences, such as minimising risk and maximising quality.     

Enhancing peer-to-peer collaboration using trust

Distributed systems generally require their component parts to interact cooperatively, in order for the system as a whole to function effectively. For any given activity, there are typically several alternative components that have the required capabilities. In decentralised systems, where there is no overarching control, individual components are responsible for selecting other components with which to cooperate. However, the candidate components may be unreliable or dishonest, and are typically locally controlled. Such decentralised systems can be viewed as multi-agent systems, comprising autonomous agents that must cooperate for the system to be effective. Peer-to-peer (P2P) systems are a subclass of decentralised distributed systems, in which not only is there no overarching control, but neither is there any hierarchy of control, power, or responsibly among the system components. Selecting appropriate peers to cooperate with is a challenging problem, since the candidate peers are autonomous and may be unreliable or dishonest. Peers need a mechanism for task delegation that takes the uncertainly of interactions into account. In this paper we present a mechanism, called Mdt-r, that enables peers to delegate activities appropriately, using trust and the recommendations of other peers to meet individual preferences, such as minimising risk and maximising quality.     

Using tabu search with ranking candidate list to solve production planning problems with setups

This study considers production planning problems involving multiple products, multiple resources, multiple periods, setup times, and setup costs. It can be formulated as a mixed integer program (MIP). Solving a realistic MIP production planning problem is NP-hard; therefore, we use tabu search methods to solve such a difficult problem. Furthermore, we improve tabu search by a new candidate list strategy, which sorts the neighbor solutions using post-optimization information provided by the final tableau of the linear programming simplex algorithm. A neighbor solution with higher priority in the ranking sequence has a higher probability of being the best neighbor solution of a current solution. According to our experiments, the proposed candidate list strategy tabu search produces a good solution faster than the traditional simple tabu search. This study also suggests that if the evaluation of the entire neighborhood space in a tabu search algorithm takes too much computation and if an efficient and effective heuristic to rank the neighbor solutions can be developed, the speed of tabu search algorithm could be significantly increased by using the proposed candidate list strategy.     

An algorithm for cooperative task allocation in scalable,constrained multiple robot systems

The current trends in the robotics field have led to the development of large-scale multiple robot systems, and they are deployed for complex missions. The robots in the system can communicate and interact with each other for resource sharing and task processing. Many of such systems fail despite the availability of necessary resources. The major reason for this is their poor coordination mechanism. Task planning, which involves task decomposition and task allocation, is paramount in the design of coordination and cooperation strategies of multiple robot systems. Task allocation mechanism allocates the task in a mission to the robots by maximizing the overall expected performance, and thereby reducing the total allocation cost for the team. In this paper, we formulate a heuristic search-based task allocation algorithm for the task processing in heterogeneous multiple robot system, by maximizing the efficiency in terms of both communication and processing cost. We assume a set of decomposed tasks of a mission, which needs to be allocated to the robots. The near-optimal allocation schemes are found using the proposed peer structure algorithm for the given problem, where the number of the tasks is more than the robots present in the system. The cost function is the summation of static overhead cost of robots, assignment cost, and the communication cost between the dependent tasks, if they are assigned to different robots. Experiments are performed to verify the effectiveness of the algorithm by comparing it with the existing methods in terms of computational time and quality of solution. The experimental results show that the proposed algorithm performs the best under different problem scales. This proves that the algorithm can be scaled for larger system and it can work for dynamic multiple robot system.     

Supervisory control for co-ordination of multiple robots†

This paper deals with a solution of supervisory control problem for co-ordination of multiple robots. The application of two or more robots employed in a co-operating mode can increase the level of flexibility and productivity in an automated manufacturing environment. However, as the number of robots or the intricacy of task to be performed increases, the control requirements also increase substantially. Co-operating robots can be used in typically complex assembly operations where the payload may be too heavy for a single arm to handle; the object to be manipulated may be irregularly shaped and provide only limited access for physical handling, or the two parts to be assembled together may require simultaneous orientation and unique positioning.

The co-ordination control strategies developed were applied to two IBM 7540 SCARA robots. Several control schemes were devised to achieve concurrent robot co-ordination and trajectory tracking. A digital computer program operable on an IBM PS/2 System 80 provided multiple concurrent programming of the two robots. Interactive control strategy and master-slave control strategy were designed, implemented, optimized, and tested. The results showed excellent trajectory adherence and repeatability for both control schemes     

A branch and price approach for deployment of multi-tier software services in clouds

This paper considers a service deployment problem that combines service placement and replication level decisions in a cloud computing context. The services are composed of multiple components that are to be placed on nodes in the private cloud of the service provider or, if the private cloud has limited capacity, partly in a public cloud. In the service delivery, the provider has to take into account the quality of service guarantees offered to his end-users. To solve the problem, we develop a branch and price algorithm, where the subproblems both are formulated as a linear mixed integer program and a shortest path problem with resource constraints (SPPRC) on a network with a special structure. The SPPRC can be solved by an exact label-setting algorithm, but to speed up the solution process, we develop a heuristic label-setting algorithm based on a reduced network and simplified dominance rule. Our results show that using the heuristic subproblem solver is efficient. Furthermore, the branch and price algorithm performs better than a previously developed pre-generation algorithm for the same problem. In addition, we analyze and discuss the differences in solutions that utilize resources in a public cloud to different degrees. By conducting this analysis we are able to identify some essential characteristics of good solutions.     

Bio-insect and artificial robot interaction using cooperative reinforcement learning

In this paper, we propose fuzzy logic-based cooperative reinforcement learning for sharing knowledge among autonomous robots. The ultimate goal of this paper is to entice bio-insects towards desired goal areas using artificial robots without any human aid. To achieve this goal, we found an interaction mechanism using a specific odor source and performed simulations and experiments [1]. For efficient learning without human aid, we employ cooperative reinforcement learning in multi-agent domain. Additionally, we design a fuzzy logic-based expertise measurement system to enhance the learning ability. This structure enables the artificial robots to share knowledge while evaluating and measuring the performance of each robot. Through numerous experiments, the performance of the proposed learning algorithms is evaluated.     

Role of acquaintance models in agent’s private and semi-private knowledge disclosure

The organizational architecture of the multi-agent systems and the structure of social knowledge that the members of the community administer are critical factors for assuring such patterns of information exchange that keep agents private knowledge confidential. In this paper we will introduce the concept of agents’ private and semi-private knowledge and we will explain the difference between the alliance – a semi-permanent organizational structure and a coalition – a goal-oriented, non-permanent organizational structure. We will provide the reader with an analysis on how does the agents’ social knowledge, stored in the tri-base acquaintance model, contributes to permanent confidentiality of agents’ private knowledge, preferences, decision making models, resources, etc. The study has been experimentally verified in the domain of planning for humanitarian relief operations within a high number of hardly collaborating and vaguely linked non-governmental organizations is a challenging problem.     

A hybrid metaheuristic algorithm to optimise a real-world robotic cell

In this paper, a real-world robotic cell is investigated by transforming it into a special job shop with a set of stationary robots for manufacturing the parts of a product (i.e., operations of a job) at multiple operational stages. In addition, this robotic cell contains a particular mobile robot to transport the parts among stationary robots inside the cell as well as a depot (for initialising the production) and a stockpile (for stocking the complete products) outside the cell. Thus, a new scheduling problem called Blocking Job Shop Scheduling problem with Robotic Transportation (BJSSRT) is proposed. A numerical example is presented to illustrate the characteristics and complexity of BJSSRT. According to the problem properties, four types of robotic movements are defined for a mobile robot in an operation’s execution: processing-purpose, depot-purpose, return-purpose and stocking-purpose. By satisfying complex feasibility conditions, an innovative graph-based constructive algorithm is developed to produce a good feasible BJSSRT schedule. Embedded with the constructive algorithm, a hybrid Tabu Search and Threshold Accepting metaheuristic algorithm is developed to find a near-optimal solution in an efficient way. The proposed BJSSRT methodology has practical benefits in modelling the automated production system using stationary and mobile robots, especially in manufacturing and mining industries.     

A Stochastic Multi-agent Optimization Model for Energy Infrastructure Planning under Uncertainty in An Oligopolistic Market

This paper presents a mathematical model for analyzing long-term infrastructure investment decisions in a deregulated electricity market, such as the case in the United States. The interdependence between different decision entities in the system is captured in a network-based stochastic multi-agent optimization model, where new entrants of investors compete among themselves and with existing generators for natural resources, transmission capacities, and demand markets. To overcome computational challenges involved in stochastic multi-agent optimization problems, we have developed a solution method by combining stochastic decomposition and variational inequalities, which converts the original problem to many smaller problems that can be solved more easily.