Bayesian Coalition Game for Contention-Aware Reliable Data Forwarding in Vehicular Mobile Cloud

Kumar, Neeraj; Iqbal, Rahat; Misra, Sudip; Rodrigues, Joel J. P. C.

doi:10.1016/j.future.2014.10.013

Cited by 91 publications

(33 citation statements)

References 42 publications

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“…In [38], adaptive Learning Automata based Contention Aware Data Forwarding (LACADF) is proposed. This solution is evaluated in different network scenarios with respect to multiple parameters (such as throughput and delay) by varying the density and mobility of vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the assumptions mentioned in the previous section, we would like to propose a solution for service selection adaptation which applies supervised learning, executed on-line, on a mobile device, in contrast to [36,38] cited above, in which other optimization techniques are used or [41] in which MCC is not considered. The decision module, located on a mobile device, works as follows: it gets task and context as an input and applies knowledge to make a decision where the task should be executed in the given context.…”

Section: Decision Module: Model Architecture and Algorithmmentioning

confidence: 99%

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Autonomous Context-Based Service Optimization in Mobile Cloud Computing

Nawrocki

Śnieżyński

2017

J Grid Computing

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As the concept of merging the capabilities of mobile devices and cloud computing is becoming increasingly popular, an important question arises: how to optimally schedule services/tasks between the device and the cloud. The main objective of this paper is to investigate the possibilities for using a decision module on mobile devices in order to autonomously optimize the execution of services within the framework of Mobile Cloud Computing while taking context into account. A novel model of the decision module with learning capabilities, service-oriented architecture, and service selection optimization algorithm are proposed to solve this problem. To achieve autonomous, online learning on mobile devices, we apply supervised learning. Information about the context, task description, the decision made and its results such as calculation time or power consumption are stored and form training data for a supervised learning algorithm, which updates the knowledge used by the decision module to determine the optimal place for the execution of a given type of task. To verify the solution proposed, service-oriented mobile processing systems

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Section: Related Workmentioning

confidence: 99%

Section: Decision Module: Model Architecture and Algorithmmentioning

confidence: 99%

Autonomous Context-Based Service Optimization in Mobile Cloud Computing

Nawrocki

Śnieżyński

2017

J Grid Computing

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“…The development of IoT technologies can support a variety of applications, including the intelligent power grid [1][2][3][4], intelligent transportation [5,6], intelligent medicine and healthcare [7][8][9][10], intelligent art [11,12], intelligent logistics [13,14], intelligent environmental monitoring [15,16], smart life [17][18][19][20], etc. In a smart home context, numerous smart objects need to use wireless communications due to the requirements of supporting mobile applications and maintaining neat living space.…”

Section: Open Accessmentioning

confidence: 99%

Design and Implementation of an IoT Access Point for Smart Home

et al. 2015

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Network communication and micro-electro-mechanical embedded technologies have attracted much attention in recent years. Through these technologies, the capabilities of sensing, identification, and communication can be embedded in various smart devices. These smart devices can automatically connect to the Internet and form an intelligent network called Internet of Things (IoT). However, these devices are embedded with different wireless communication interfaces such as Wi-Fi and ZigBee. This paper presents the design and implementation of an IoT access point that supports functionalities of coordination of various wireless transmission protocols. Based on the existing Wi-Fi access point, we have embedded a ZigBee module and implemented ZigBee and UPnP protocols into the designed IoT access point, which supports ZigBee communication capabilities over the Internet.

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“…Vehicle destinations are selected dynamically at run time using DFROUTER function present in SUMO. The routes of the vehicles, their entry and destination points are defined using a function DFROUTER present in SUMO [49]. In order to define time at which vehicles enter or exit from the network, the DFROUTER takes three inputs, starting time, exit time and offset.…”

Section: Simulation Environmentmentioning

confidence: 99%

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

et al. 2015

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In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

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Bayesian Coalition Game for Contention-Aware Reliable Data Forwarding in Vehicular Mobile Cloud

Cited by 91 publications

References 42 publications

Autonomous Context-Based Service Optimization in Mobile Cloud Computing

Autonomous Context-Based Service Optimization in Mobile Cloud Computing

Design and Implementation of an IoT Access Point for Smart Home

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

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