Abstract-The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urbanscale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g. WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first softwaredefined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g. 67.21% throughput improvement, 72.99% reduced delay, and 69.59% jitter improvements, compared with alternative SDN systems.

Abstract-We present ADDSEN middleware as a holistic solution for Adaptive Data processing and dissemination for Drone swarms in urban SENsing. To efficiently process sensed data in the middleware, we have proposed a cyber-physical sensing framework using partially ordered knowledge sharing for distributed knowledge management in drone swarms. A reinforcement learning dissemination strategy is implemented in the framework. ADDSEN uses online learning techniques to adaptively balance the broadcast rate and knowledge loss rate periodically. The learned broadcast rate is adapted by executing state transitions during the process of online learning. A strategy function guides state transitions, incorporating a set of variables to reflect changes in link status. In addition, we design a cooperative dissemination method for the task of balancing storage and energy allocation in drone swarms. We implemented ADDSEN in our cyber-physical sensing framework, and evaluation results show that it can achieve both maximal adaptive data processing and dissemination performance, presenting better results than other commonly used dissemination protocols such as periodic, uniform and neighbor protocals in both single-swarm and multi-swarm cases.

The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g. WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions (assigning one controller per partition) and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g. 67.21% throughput improvement, 72.99% reduced delay, and 69.59% jitter improvements, compared with alternative SDN systems.

The scarcity of parking spaces in cities leads to a high demand for timely information about their availability. In this paper, we propose a crowdsensed parking system, namely ParkCrowd, to aggregate on-street and roadside parking space information reliably, and to disseminate this information to drivers in a timely manner. Our system not only collects and disseminates basic information, such as parking hours and price, but also provides drivers insights of the real-time and future availability of parking spaces based on aggregated crowd knowledge. To improve the reliability of the information being disseminated, we dynamically evaluate the knowledge of crowd workers based on the veracity of their answers to a series of location-dependent point of interest (POI) control questions. We propose a logistic regression based method to evaluate the reliability of the crowd knowledge for real-time parking spaces information. Besides, a joint probabilistic estimator is employed to make inference of parking spaces' future availability based on crowdsensed knowledge. Moreover, to incentivise wider participation of crowd workers, a reliability based incentivisation method is proposed to reward workers according to their reliability and expertise levels. The efficacy of ParkCrowd for aggregation and dissemination of parking space information has been evaluated in both real-world tests and simulations. Our results show that the ParkCrowd system is able to accurately identify the reliability level of the crowdsensed information, estimate the potential availability of parking spaces with high accuracy, and be successful in encouraging participation of the more reliable crowd workers by offering them higher monetary rewards.

Small to medium sized transportation and logistics companies are usually constrained by limited computing and IT professional resources on implementing an efficient parallel metaheuristic algorithm for planning or management solutions. In this paper we extend the standard meta-description for genetic algorithms (GA) with a simple non-trivial parallel implementation. Our parallel GA framework is chiefly concerned with the development of a straightforward way for engineers to modify existing genetic algorithm implementations for real transportation and logistics problems to make use of commonly available hardware resources without completely reworking complex, useful and usable codes. The framework presented at its parallel base is a modification of the primitive parallelization concept, but if implemented as described it may be gradually extended to fit the qualities of any underlying problem better (via the adaptation of the merging and communications functions).We present our framework and computational results for a classical transportation related combinatorial optimization problem-the traveling salesman problem with a standard sequential genetic algorithm implementation. Our empirical analysis shows that this simple extension can lead to considerable solution improvements. We also tested our assumptions that the framework is easily implemented by an engineer not initially familiar with genetic algorithms to implement the framework for another minimum multiprocessor scheduling problem. These case studies verify that our framework is better than primitive parallelization because it gives empirically better results under equitable conditions. It also outperforms fine grained parallelization as it is easier and faster to implement. INDEX TERMS Parallel metaheuristics, genetic algorithm, transportation planning, logistics management. I. INTRODUCTION Genetic algorithms (GA) are an iterative search method in which new answers are produced by combining two predecessor answers and mimicking the process of natural selection [1]. Its metaheuristic can routinely generate useful solutions to optimization and search problems, therefore GA has been widely used in transportation planning [2]-[5] and logistics operations management applications and software systems [6]-[8]. Some current relevant applications include The associate editor coordinating the review of this manuscript and approving it for publication was Guangdong Tian. pavement lifecycle analysis [9], additive manufacturing [10], accident emergency response [11] and land use planning [12]. Other frameworks for transportation analysis might benefit from the addition of parallel migration implementation in scenarios such as traffic congestion [13]-[15], vehicular sensing [16]-[18], activity planning [19]-[21], mobility management [22]-[24], spatial-temporal modeling [25]-[27] and others [28]-[31]. A. MOTIVATIONS Genetic algorithm is a common tool that has been employed in many transportation and logistics settings. However,