Internet of Vehicles for E-Health Applications in View of EMI on Medical Sensors

Lin, Di; Wu, Xianliang; Labeau, Fabrice; Vasilakos, Athanasios V.

doi:10.1155/2015/315948

Cited by 15 publications

(11 citation statements)

References 22 publications

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“…The traditional BSNs are not suitable for all applications due to using application-dedicated data transmission protocols. Besides, electromagnetic interference (EMI) will arise during the transmission of radio frequency between the medical sensors [49].…”

Section: Ip-enabled Techniquesmentioning

confidence: 99%

“…The aggregator that has more capability acts as an access point used to receive the captured data from the low-power WSN and transmit it to the global Internet when the connection becomes available. As a result to using the BSNs technology, several challenges are bring out such as energy efficiency, scalability, interference mitigation, QoS, and security, which are highlighted in [49]. Various algorithms proposed to achieve energy efficient, security, and routing optimization [50][51][52][53][54][55][56].…”

Section: Ip-enabled Techniquesmentioning

confidence: 99%

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Mobility management for IoT: a survey

Ghaleb

Subramaniam

Zukarnain

et al. 2016

J Wireless Com Network

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Internet of Thing (IoT) or also referred to as IP-enabled wireless sensor network (IP-WSN) has become a rich area of research. This is due to the rapid growth in a wide spectrum of critical application domains. However, the properties within these systems such as memory size, processing capacity, and power supply have led to imposing constraints on IP-WSN applications and its deployment in the real world. Consequently, IP-WSN is constantly faced with issues as the complexity further rises due to IP mobility. IP mobility management is utilized as a mechanism to resolve these issues. The management protocols introduced to support mobility has evolved from host-based to network-based mobility management protocols. The presence of both types of solutions is dominant but depended on the nature of systems being deployed. The mobile node (MN) is involved with the mobility-related signaling in host-based protocols, while network-based protocols shield the host by transferring the mobility-related signaling to the network entities. The features of the IoT are inclined towards the network-based solutions. The wide spectrum of strategies derived to achieve enhanced performance evidently displays superiority in performance and simultaneous issues such as long handover latency, intense signaling, and packet loss which affects the QoS for the real-time applications. This paper extensively reviews and discusses the algorithms developed to address the challenges and the techniques of integrating IP over WSNs, the attributes of mobility management within the IPv4 and IPv6, respectively, and special focus is given on a comprehensive review encompassing mechanisms, advantages, and disadvantages on related work within the IPv6 mobility management. The paper is concluded with the proposition of several pertinent open issues which are of high research value.

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Section: Ip-enabled Techniquesmentioning

confidence: 99%

Section: Ip-enabled Techniquesmentioning

confidence: 99%

Mobility management for IoT: a survey

Ghaleb

Subramaniam

Zukarnain

et al. 2016

J Wireless Com Network

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“…Body-area sensor networks based physiological signal detection and their augmentation with the ADL to help older adults live independently has been proposed in Chen et al (2011) while the data collected using the wearable devices around the users have been encrypted in Zhang et al (2012). Integrating a multitude of ambient and wearable devices and making them interoperable are key research tasks for building wireless sensor networks based health and activity recognition model and tool kit (Lin et al 2015b). While body-area sensor networks help gather meaningful data based on user movements, activities, and contexts, but storing and processing data on the cloud have become inevitable components in activity recognition pipeline (Fortino et al 2014; Almashaqbeh et al 2014).…”

Section: Related Workmentioning

confidence: 99%

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments

Roy

Misra

Cook

2015

J Ambient Intell Human Comput

101

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Activity recognition in smart environments is an evolving research problem due to the advancement and proliferation of sensing, monitoring and actuation technologies to make it possible for large scale and real deployment. While activities in smart home are interleaved, complex and volatile; the number of inhabitants in the environment is also dynamic. A key challenge in designing robust smart home activity recognition approaches is to exploit the users' spatiotemporal behavior and location, focus on the availability of multitude of devices capable of providing different dimensions of information and fulfill the underpinning needs for scaling the system beyond a single user or a home environment. In this paper, we propose a hybrid approach for recognizing complex activities of daily living (ADL), that lie in between the two extremes of intensive use of body-worn sensors and the use of ambient sensors. Our approach harnesses the power of simple ambient sensors (e.g., motion sensors) to provide additional ‘hidden’ context (e.g., room-level location) of an individual, and then combines this context with smartphone-based sensing of micro-level postural/locomotive states. The major novelty is our focus on multi-inhabitant environments, where we show how the use of spatiotemporal constraints along with multitude of data sources can be used to significantly improve the accuracy and computational overhead of traditional activity recognition based approaches such as coupled-hidden Markov models. Experimental results on two separate smart home datasets demonstrate that this approach improves the accuracy of complex ADL classification by over 30 %, compared to pure smartphone-based solutions.

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“…There are lots of practical issues to overcome, such as how to utilize cloud resources and information-centric networking to improve multimedia delivery [4,5], how to circumvent driver distraction when manipulating built-in applications [6], and how to design a smart car with ubiquitous computing and intuitive human-computer interaction [7]. Additionally, smart applications like E-health based on the internet of vehicles are promising future domains [8]. …”

Section: Introductionmentioning

confidence: 99%

Data Acquisition Based on Stable Matching of Bipartite Graph in Cooperative Vehicle–Infrastructure Systems

Tang

Hong

Chen

2017

Sensors

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Existing studies on data acquisition in vehicular networks often take the mobile vehicular nodes as data carriers. However, their autonomous movements, limited resources and security risks impact the quality of services. In this article, we propose a data acquisition model using stable matching of bipartite graph in cooperative vehicle-infrastructure systems, namely, DAS. Contents are distributed to roadside units, while vehicular nodes support supplementary storage. The original distribution problem is formulated as a stable matching problem of bipartite graph, where the data and the storage cells compose two sides of vertices. Regarding the factors relevant with the access ratio and delay, the preference rankings for contents and roadside units are calculated, respectively. With a multi-replica preprocessing algorithm to handle the potential one-to-many mapping, the matching problem is addressed in polynomial time. In addition, vehicular nodes carry and forward assistant contents to deliver the failed packets because of bandwidth competition. Furthermore, an incentive strategy is put forward to boost the vehicle cooperation and to achieve a fair bandwidth allocation at roadside units. Experiments show that DAS achieves a high access ratio and a small storage cost with an acceptable delay.

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Internet of Vehicles for E-Health Applications in View of EMI on Medical Sensors

Cited by 15 publications

References 22 publications

Mobility management for IoT: a survey

Mobility management for IoT: a survey

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments

Data Acquisition Based on Stable Matching of Bipartite Graph in Cooperative Vehicle–Infrastructure Systems

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