Internet-of-Things in motion: A UAV coalition model for remote sensing in smart cities

Unmanned aerial vehicles (UAVs) or drones are increasingly used in cities to provide service tasks that are too dangerous, expensive or difficult for human beings. Drones are also used in cases where a task can be performed more economically and or more efficiently than if done by humans. These include remote sensing tasks where drones can be required to form coalitions by pooling their resources to meet the service requirements at different locations of interest in a city. During such coalition formation, finding the shortest path from a source to a location of interest is key to efficient service delivery. For fixed-wing UAVs, Dubins curves can be applied to find the shortest flight path. When a UAV flies to a location of interest, the angle or orientation of the UAV upon its arrival is often not important. In such a case, a simplified version of the Dubins curve consisting of two instead of three parts can be used. This paper proposes a novel model for UAV coalition and an algorithm derived from basic geometry that generates a path derived from the original Dubins curve for application in remote sensing missions of fixed-wing UAVs. The algorithm is tested by incorporating it into three cooperative coalition formation algorithms. The performance of the model is evaluated by varying the number of types of resources and the sensor ranges of the UAVs to reveal the relevance and practicality of the proposed model

Using artificial intelligence to support emerging networks management approaches

In emergent networks such as Internet of Things (IoT) and 5G applications, network traffic estimation is of great importance to forecast impacts on resource allocation that can influence the quality of service. Besides, controlling the network delay caused with route selection is still a notable challenge, owing to the high mobility of the devices. To analyse the trade-off between traffic forecasting accuracy and the complexity of artificial intelligence models used in this scenario, this work first evaluates the behavior of several traffic load forecasting models in a resource sharing environment. Moreover, in order to alleviate the routing problem in highly dynamic ad-hoc networks, this work also proposes a machine-learning-based routing scheme to reduce network delay in the high-mobility scenarios of flying ad-hoc networks, entitled Q-FANET. The performance of this new algorithm is compared with other methods using the WSNet simulator. With the obtained complexity analysis and the performed simulations, on one hand the best traffic load forecast model can be chosen, and on the other, the proposed routing solution presents lower delay, higher packet delivery ratio and lower jitter in highly dynamic networks than existing state-of-art methods

On distributed mobile edge computing

Mobile Cloud Computing (MCC) has been proposed to offload the workloads of mobile applications from mobile devices to the cloud in order to not only reduce energy consumption of mobile devices but also accelerate the execution of mobile applications. Owing to the long End-to-End (E2E) delay between mobile devices and the cloud, offloading the workloads of many interactive mobile applications to the cloud may not be suitable. That is, these mobile applications require a huge amount of computing resources to process their workloads as well as a low E2E delay between mobile devices and computing resources, which cannot be satisfied by the current MCC technology. In order to reduce the E2E delay, a novel cloudlet network architecture is proposed to bring the computing and storage resources from the remote cloud to the mobile edge. In the cloudlet network, each mobile user is associated with a specific Avatar (i.e., a dedicated Virtual Machine (VM) providing computing and storage resources to its mobile user) in the nearby cloudlet via its associated Base Station (BS). Thus, mobile users can offload their workloads to their Avatars with low E2E delay (i.e., one wireless hop). However, mobile users may roam among BSs in the mobile network, and so the E2E delay between mobile users and their Avatars may become worse if the Avatars remain in their original cloudlets. Thus, Avatar handoff is proposed to migrate an Avatar from one cloudlet into another to reduce the E2E delay between the Avatar and its mobile user. The LatEncy aware Avatar handDoff (LEAD) algorithm is designed to determine the location of each mobile user\u27s Avatar in each time slot in order to minimize the average E2E delay among all the mobile users and their Avatars. The performance of LEAD is demonstrated via extensive simulations. The cloudlet network architecture not only facilitates mobile users in offloading their computational tasks but also empowers Internet of Things (IoT). Popular IoT resources are proposed to be cached in nearby brokers, which are considered as application layer middleware nodes hosted by cloudlets in the cloudlet network, to reduce the energy consumption of servers. In addition, an Energy Aware and latency guaranteed dynamic reSourcE caching (EASE) strategy is proposed to enable each broker to cache suitable popular resources such that the energy consumption from the servers is minimized and the average delay of delivering the contents of the resources to the corresponding clients is guaranteed. The performance of EASE is demonstrated via extensive simulations. The future work comprises two parts. First, caching popular IoT resources in nearby brokers may incur unbalanced traffic loads among brokers, thus increasing the average delay of delivering the contents of the resources. Thus, how to balance the traffic loads among brokers to speed up IoT content delivery process requires further investigation. Second, drone assisted mobile access network architecture will be briefly investigated to accelerate communications between mobile users and their Avatars

동시도달을 고려한 복수 무인기 임무할당 기법

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 김유단.무인항공기의 자율비행 기술이 성숙함에 따라 무인항공기에 요구되는 임무의 복잡도와 정밀도가 증가하고 있다. 최근에는 단일 무인항공기에 의한 감시정찰 임무에서 나아가 다수의 무인항공기의 협력적인 임무수행 능력에 관한 연구가 활발히 수행되고 있다. 본 연구에서는 무인항공기의 협업에 의한 잠재력을 최대한으로 활용하기 위하여 다수의 무인항공기가 동시에 수행해야 하는 임무를 고려하였다. 이러한 임무로는 위험도가 높은 방어 시스템을 동시에 공격하는 임무, 넓은 재난지역을 다수의 무인기가 동시에 수색, 물품지원, 구조 등을 수행하는 임무, 그리고 무거운 물체를 다수의 무인항공기가 협력하여 수송하는 임무 등을 고려할 수 있다. 이와 같이 복잡한 임무를 관리하기 위해 지상 조종사는 다수의 무인항공기를 관제하여야 하며, 이 과정에서 과도한 업무부하는 조종사 실수를 유발하여 임무수행 효율저하로 이어질 수 있다. 본 연구에서는 다수 무인항공기의 동시도달을 고려한 협력 임무할당 문제를 정수계획법으로 정식화하고, 중앙집중형 임무할당 방식과 분산형 임무할당 방식을 연구하였다. 무인항공기로부터 수집된 정보를 기반으로 최적에 가까운 임무할당을 결정하는 중앙집중형 임무할당 방식으로는 모든 해 공간을 탐색하여 최적해를 계산하는 방식, 경험적인 법칙을 통해 신속하게 해를 결정하는 방식, 그리고 메타 휴리스틱 기법의 일종인 군집 최적화 기법을 활용하는 방식을 제안하였다. 분산형 임무할당 방식으로는 개별 무인항공기는 모든 무인항공기가 아닌 이웃 무인항공기들과만 정보를 교류하고, 이를 통하여 자율적으로 임무를 할당하는 기법을 제안하였다. 제한된 통신반경에 따른 실시간 네트워크 위상변화 상황을 고려하기 위하여 집결지 개념을 도입하였으며, 연결된 네트워크 상황에 대하여 수렴성과 확장성을 분석하였다. 제안한 기법들의 성능을 검증하기 위하여 적 대공망 제압작전 시나리오에 대한 수치 시뮬레이션을 수행하고, 제안한 기법 간의 성능을 비교 분석하였다.With increasing demand for unmanned aerial vehicles (UAVs) in military and civilian areas, coordination of multiple UAVs is expected to play a key role in complex missions. As the number of agents and tasks increases, however, a greater burden is imposed on ground operators, which may cause safety issues and performance degradation accomplishing the mission. In particular, the operation requiring temporal and spatial cooperation by UAVs is significantly difficult. This dissertation proposes autonomous task allocation algorithms for cooperative timing missions with simultaneous spatial/temporal involvement of multiple agents. After formulating the task allocation problem into integer programming problems in view of UAVs and tasks, centralized and distributed algorithms are proposed. In the centralized approach, an algorithm to find an optimal solution that minimizes the time to complete all the missions is introduced. Since the exact algorithm is time intensive, heuristic algorithms working in a greedy manner are proposed. A metaheuristic approach is also considered to find a near-optimal solution within a feasible duration. In the distributed approach, market-based task allocation algorithms are designed. The mathematical convergence and scalability analyses show that the proposed algorithms have a polynomial time complexity. The baseline algorithms for a connected network are then extended to address time-varying network topology including isolated sub-networks due to a limited communication range. The performance of the proposed algorithms is demonstrated via Monte Carlo simulations for a scenario involving the suppression of enemy air defenses.Chapter 1 Introduction 1 1.1 Motivation and Objective 1 1.2 Literature Survey 3 1.2.1 Vehicle Routing Problem 3 1.2.2 Centralized and Distributed Control 4 1.2.3 Centralized Control: Optimal Coalition Formation Problem 5 1.2.4 Distributed Control 8 1.3 Research Contribution 10 1.3.1 Systematic Problem Formulation 10 1.3.2 Design of a Centralized TA Algorithm for a Cooperative Timing Mission 11 1.3.3 Design of a Distributed TA Algorithm for a Cooperative Timing Mission 11 1.4 Dissertation Organization 12 Chapter 2 Problem Statement 13 2.1 Assumptions 13 2.2 Agent-based Formulation 15 2.3 Task-based Formulation 19 2.4 Simplified Form of Task-based Formulation 21 Chapter 3 Centralized Task Allocation 23 3.1 Assumptions 23 3.2 Exact Algorithm 24 3.3 Agent-based Sequential Greedy Algorithm: A-SGA 26 3.4 Task-based Sequential Greedy Algorithm: T-SGA 28 3.5 Agent-based Particle Swarm Optimization: A-PSO 30 3.5.1 Preliminaries on PSO 30 3.5.2 Particle Encoding 33 3.5.3 Particle Refinement 33 3.5.4 Score Calculation Considering DAG Constraint 34 3.6 Task-based Particle Swarm Optimization: T-PSO 38 3.6.1 Particle Encoding 38 3.6.2 Particle Refinement 39 3.7 Numerical Results 41 Chapter 4 Distributed Task Allocation 49 4.1 Assumptions 50 4.2 Project Manager-oriented Coalition Formation Algorithm : PCFA 51 4.3 Task-oriented Coalition Formation Algorithm: TCFA 63 4.4 Modified Greedy Distributed Allocation Protocol: Modified GDAP 68 4.5 Properties 71 4.5.1 Convergence 71 4.5.2 Scalability 72 4.5.3 Performance 75 4.5.4 Comparison with GDAP 76 4.6 TA Algorithm in Dynamic Environment 79 4.6.1 Challenges in Dynamic Environment 79 4.6.2 Assumptions 79 4.6.3 Distributed TA Architecture in Dynamic Environment 80 4.6.4 Rally Point 85 4.6.5 Convergence 87 4.6.6 Deletion of Duplicated Allocation 87 4.7 Numerical Results 88 4.7.1 Scalability 88 4.7.2 Application: SEAD Scenario 94 4.7.3 Discussion 106 Chapter 5 Conclusions 107 5.1 Concluding Remarks 107 5.1.1 Problem Statement 107 5.1.2 Centralized Task Allocation 107 5.1.3 Distributed Task Allocation 108 5.2 Future Research 110 Abstract (in Korean) 125Docto

An Infrastructure for Robotic Applications as Cloud Computing Services

Robotic applications are becoming ubiquitous. They are widely used in several areas (e.g., healthcare, disaster management, and manufacturing). However, their provisioning still faces several challenges such as cost efficiency. Cloud computing is an emerging paradigm that may aid in tackling these challenges. It has three main facets: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Virtualization is a technique that allows the abstraction of actual physical computing resources into logical units; it enables efficient usage of resources by multiple users. Its role is a key to resource efficiency. Virtualization can be performed at both node and network level. This thesis focuses on the IaaS aspects of robotic applications as cloud computing services. It starts by defining a set of requirements on the infrastructure for cost efficient robotic applications provisioning. It then reviews the state of the art. After pinpointing the shortcoming of the state of the art, it proposes an architecture that enables cost efficiency through virtualization and dynamic task delegation to robots, including robots that might belong to other clouds. Overlays and RESTful Web services are used as cornerstones. The virtualization in the IaaS is achieved by providing a coalition formation algorithm, which is the cooperation between several robots to perform a task that either cannot be solved individually or can be solved more efficiently as a group. Forming the effective coalitions is another big challenge. We adapted heuristic-based Multi Objective- Particle Swarm Optimization (MO-PSO) algorithm to solve this specific problem. As a proof of concept, a prototype is built using LEGO Mindstorms NXT as the robotic platform, and JXTA as the overlay middleware and the prototype architecture is presented along with the implemented scenario (i.e., wildfire suppression). Performance measurements have also been made to evaluate viability. To evaluate the effectiveness of our algorithm, WEBOTS simulation software is used

Constrained Task Assignment and Scheduling on Networks of Arbitrary Topology.

This dissertation develops a framework to address centralized and distributed constrained task assignment and task scheduling problems. This framework is used to prove properties of these problems that can be exploited, develop effective solution algorithms, and to prove important properties such as correctness, completeness and optimality. The centralized task assignment and task scheduling problem treated here is expressed as a vehicle routing problem with the goal of optimizing mission time subject to mission constraints on task precedence and agent capability. The algorithm developed to solve this problem is able to coordinate vehicle (agent) timing for task completion. This class of problems is NP-hard and analytical guarantees on solution quality are often unavailable. This dissertation develops a technique for determining solution quality that can be used on a large class of problems and does not rely on traditional analytical guarantees. For distributed problems several agents must communicate to collectively solve a distributed task assignment and task scheduling problem. The distributed task assignment and task scheduling algorithms developed here allow for the optimization of constrained military missions in situations where the communication network may be incomplete and only locally known. Two problems are developed. The distributed task assignment problem incorporates communication constraints that must be satisfied; this is the Communication-Constrained Distributed Assignment Problem. A novel distributed assignment algorithm, the Stochastic Bidding Algorithm, solves this problem. The algorithm is correct, probabilistically complete, and has linear average-case time complexity. The distributed task scheduling problem addressed here is to minimize mission time subject to arbitrary predicate mission constraints; this is the Minimum-time Arbitrarily-constrained Distributed Scheduling Problem. The Optimal Distributed Non-sequential Backtracking Algorithm solves this problem. The algorithm is correct, complete, outputs time optimal schedules, and has low average-case time complexity. Separation of the task assignment and task scheduling problems is exploited here to ameliorate the effects of an incomplete communication network. The mission-modeling conditions that allow this and the benefits gained are discussed in detail. It is shown that the distributed task assignment and task scheduling algorithms developed here can operate concurrently and maintain their correctness, completeness, and optimality properties.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91527/1/jpjack_1.pd