Hailong Feng

Resource allocation in cooperative cognitive radio networks towards secure communications for maritime big data systems

In this paper, an innovative framework labeled as cooperative cognitive maritime big data systems (CCMBDSs) on the sea is developed to provide opportunistic channel access and secure communication. A two-phase frame structure is applied to let Secondary users (SUs) entirely utilize the transmission opportunities for a portion of time as the reward by cooperation with Primary users (PUs). Amplify-and-forward (AF) relaying mode is exploited in SU nodes, and Backward induction method based Stackelberg game is employed to achieve optimal determination of SU, power consumption and time portion of cooperation both for non-secure communication scenario and secure communication. Specifically, a jammer-based secure communications scheme is developed to maximize the secure utility of PU, to confront of the situation that the eavesdropper could overheard the signals from SUi and the jammer. Close-form solutions for the best access time portion as well as the power for SUi and jammer are derived to realize the Nash Equilibrium. Simulation results validate the effectiveness of our proposed strategy.

Cooperative Networking towards Maritime Cyber Physical Systems

An innovative paradigm named Cooperative Cognitive Maritime Cyber Physical Systems (CCMCPSs) is developed to achieve high-speed and low-cost communication services. The analysis of the available white space at sea, as well as the framework, is presented. Specifically, a bilevel game with two stages of PUs-to-SUs (primary users to secondary users) and SUs-to-SUs is proposed, to address the resource allocation issue of Decode-and-Forward (DF) relay mode with maximal-ratio combining (MRC) receiving mode in destination. Stackelberg game with priority is employed between PU and SUs, while a symmetrical system model is considered among SUs-to-SUs. The game theoretic procedure that converges to Nash equilibrium based on the utility and payoff function is illustrated. Simulation results demonstrate that our proposed strategy could effectively increase the throughput as well as the payoffs of the system.

Resource Allocation in Cooperative Cognitive Maritime Wireless Mesh/Ad Hoc Networks: An Game Theory View

In this paper, an innovative paradigm cooperative cognitive maritime wireless mesh/ad hoc networks (CCMWMAN) is proposed to provide high-speed and low-cost communications for maritime environment. The framework of CCMWMAN is exploited firstly, as well as the analysis of available white space at sea as well as the regulation requirement and standards relatively, to efficiently use the limited frequency resource. Specially, game theory method is applied within the cooperative SUs. An symmetrical system model is constructed, and a price game based on a payoff function is proposed. Then we describe the game theory process to converge to Nash equilibrium, which is verified the results effectiveness of proposed scheme. The simulation results show that the strategy can effectively increases the payoffs of the system.

Genetic optimization–based scheduling in maritime cyber physical systems:

In this article, we attempt to solve the issue of optimal scheduling for vessels monitoring video data uploading in maritime cyber physical systems, during the period of sailing from the origin port to destination port. We consider the terrestrial infrastructure-based networking, delivering video packets assisted by the shoreside infostations to the authorities on land. Each video packet has respective elements (i.e. release time, deadline, processing time, and weight) to describe, in which deadline is utilized to demonstrate the time domain limitation before that to upload it successfully. In order to cope with the computation complexity of traditional scheduling algorithms in intermittent infostations scenario, time-capacity mapping method is exploited to transfer it to a continue scenario when classic scheduling algorithms could be utilized with lower time complexity. An ingenious mathematic job-machine scheduling formulation is indicated with the goal of minimizing the total penalties of tardiness of uploaded video packets, taking into account the tardiness and the weights of jobs simultaneously. A genetic based algorithm, as well as an improved genetic algorithm–based optimization scheme, is proposed to target this optimization formulation. Specially, the genetic based algorithm as well as the improved genetic based algorithm are described in detail, including a novel chromosome representation, a heuristic initialization procedure, as well as a modified crossover and mutation process. The effectiveness of the proposed schemes is verified by the simulation results.

Towards Scheduling to Minimize the Total Penalties of Tardiness of Delivered Data in Maritime CPSs (Invited Paper)

This paper focused on addressing the issue of uploading surveillance videos that vessels generate from the origin port to destination port in maritime Cyber Physical Systems (CPSs). During the period of sailing, the videos should be delivered to the infostations offshore to connect to the Internet. Deadlines are defined respectively to restrict the time domain to finally upload the video packets effectively. Time-capacity mapping method is applied to confront the intermittent infostations scenario. An effective mathematic job-machine scheduling (JMS) problem is represented to minimize the total penalties of tardiness of delivered data considering tardiness and weights of jobs, within each job is expressed with a release time, a deadline, a processing time, and a weight. We develop an offline scheduling algorithm depending on a genetic optimization process comprised with a novel chromosome representation, a heuristic initialization procedure as well as a modified crossover and mutation process. Simulation results demonstrate the effectiveness of the proposed algorithm to solve the (JMS) in maritime CPSs.

Towards scheduling to maximize weighted delivered data in maritime CPSs

This paper develops offline schedule and online schedule algorithms for maritime Cyber Physical Systems (CPSs), to address the issue of delivered surveillance videos that vessels generate from the origin port to destination port. During the saiüng, the surveillance videos could be uploaded by the infostations offshore. The videos are splited into packets, which could be delivered successfully before their deadlines. We act the issue into a mathematic job-machine problem. And each job is revealed at a release time, a deadline, a processing time, and a weight. To maximize the weight of jobs, we propose three algorithms, an offline algorithm, an online ADMISSION Algorithm with no bounded processing times, as well as Exponential-Capacity Algorithm with bounded processing times. The approximation ratio and competitive ratios of them are inducted respectively. Simulation results verified the performance of them.

Resource allocation in cooperative cognitive radio networks towards maritime cyber physical systems

In this paper, an innovative framework labeled as cooperative cognitive Cyber Physical Systems (CCCPSs) on the sea is developed to provide opportunistic channel access. Specifically, a two-phase frame structure is applied to let Secondary users (SUs) entirely utilize the transmission opportunities for a portion of time as the reward by cooperation with Primary users (PUs). Amplify-and-forward (AF) relaying mode is exploited in SU nodes, and Backward induction method based Stackelberg game is employed to achieve optimal determination of SU, power consumption and time portion for cooperation. Close-form solutions for the best access time portion βi to SUi as well as the power P2i for SUi are derived to realize the Nash Equilibrium. Simulation results validate the effectiveness of our proposed strategy.

Perceiving who and when to leverage data delivery for maritime networks: An optimal stopping view

The advances in the integration of wireless communication and sensor technologies have stimulated an innovative paradigm named Crowd Sensing Networks, which caters to the exponential growth of service demands on the sea and drives the development of potential maritime wideband networks. This paper investigates the issue of sensed traffic data scheduling between vessels, combining Time Division Long Term Evolution (TD-LTE) and delay-tolerant networks (DTNs) on the sea. Specially, we propose a unique network topology which combines maritime crowd sensing network and delay tolerant networks, i.e., a store-carry-and-forward routing topology is explored to address the intermittent network connectivity in maritime context. Notably, the alternative eco-friendly green energy in maritime environment will make the scheduling issue more challenging. To the best of our knowledge, this is the first work to do such investigation with the goal of minimizing the costs associated with end-to-end delay of data delivery and energy consumption of DTN throw box. On this basis, we design the scheme of data cooperation transmission between vessels that the hosting vessel decides which DTN throw box to store the data, and when a vessel arrives, the DTN throw box determines whether to stop, i.e., let the arriving vessel carry the data, or skip it and continue to wait for other vessels. A Two-step Time and Energy Oriented Optimal-stopping (TTEOO) algorithm leveraging backward induction method is proposed, based on the optimal stopping rules. Simulation results are presented to show the effectiveness of the proposed method, in terms of consumption cost and data delivery ratio.