Advanced verification on WBAN and cloud computing for u-health environment

Jung, Minwoo; Han, Kabsu; Cho, Jeonghun

doi:10.1007/s11042-014-2095-y

Cited by 8 publications

(9 citation statements)

References 28 publications

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“…However, the work was based on the existing routing schemes (similar to [32,39]) and presented a theoretical analysis. Wireless network protocols such as the Bluetooth device discovery, and those encompassing the wireless sensor and local area networks, have been verified extensively in the literature [43]. However, the verification of opportunistic routing of offloaded data received less attention comparatively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Records the N_IDs of Com-N, N_IDs of Ds of messages to be relayed by CI, ICT-based utilities of Com-N. specification, and verification of the correctness of systems [43]. Model checking is a verification technique, used to verify the properties of a system.…”

Section: Verification Of Hsm Far and Ubsmentioning

confidence: 99%

“…Model checking is a verification technique, used to verify the properties of a system. The process encompasses an exhaustive search of all of the possible states that the system may enter during the execution [43]. The process comprises of the (a) specification of the system properties, (b) system modeling, and (c) verification of the specifications, using tools, such as NuSMV.…”

Section: Verification Of Hsm Far and Ubsmentioning

confidence: 99%

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Data offloading in IoT environments: modeling, analysis, and verification

Ghosh

Khalid

Rais

et al. 2019

J Wireless Com Network

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Recent years have seen a significant growth in Internet of Things (IoT) technology consisting of a large number of devices embedded with sensors and deployed to perform monitoring and actuation tasks. The IoT devices collect large volumes of data that is usually uploaded to cloud to perform analytics and predictions. One of the main challenges in IoT is the transportation of large-scale data collected over a period of time at a remote site. Cellular networks are already facing explosive growth of mobile data traffic due to the proliferation of smart devices and traffic-intensive applications. An alternate solution is to perform the data offloading, where a portion of data can be offloaded from primary links and transferred using opportunistic terminal-to-terminal (T2T) network that relies on direct communication between mobile users, without any need for an infrastructure backbone. However, such approach may lead to data loss and delay if dynamics of time-varying topology and mobility of nodes is not taken care of. To address this challenge, we propose three prediction-based offloading schemes that exploit the mobility patterns and temporal contacts of nodes to predict future data transfer opportunities. We have utilized the High-level Petri Nets to model and formally analyzed the communication processes of the proposed schemes. The new symbolic model verifier (NuSMV) has been employed for the verification of the three schemes against the identified constraints. The verification results affirm the correctness and scalability of the models. The protocols are evaluated with performance metrics, such as the delivery ratio, latency, and overhead. Our results indicate significant improvement in performance compared to existing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Verification Of Hsm Far and Ubsmentioning

confidence: 99%

Section: Verification Of Hsm Far and Ubsmentioning

confidence: 99%

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Data offloading in IoT environments: modeling, analysis, and verification

Ghosh

Khalid

Rais

et al. 2019

J Wireless Com Network

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“…AODVLP can be further improved by limiting overhead of unnecessary control messages To solve the above discussed problems in the table 1, [19][20][21][22][23][24][25], we have proposed a novel approach based on hybrid technique. Like [29][30][31] our work is also multi-objective i.e.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Direction-Based PDORP Routing Protocol for WSN

Brar

Rani

Chopra³

et al. 2016

IEEE Access

124

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Energy consumption is one of the constraints in Wireless Sensor Networks (WSNs). The routing protocols are the hot areas to address quality-of-service (QoS) related issues viz. Energy consumption, network lifetime, network scalability and packet overhead. The key issue in WSN is that these networks suffer from the packet overhead, which is the root cause of more energy consumption and degrade the QoS in sensor networks. In WSN, there are several routing protocols which are used to enhance the performance of the network. Out of those protocols, Dynamic Source Routing (DSR) protocol is more suitable in terms of small energy density, but sometimes when the mode of a node changes from active to sleep, the efficiency decreases as the data packets needs to wait at the initial point where the packet has been sent and this increases the waiting time and end to end delay of the packets which leads to increase in energy consumption. Our problem is to identify the dead nodes and to choose another suitable path so that the data transmission becomes smoother and less energy gets conserved. In order to resolve these issues, we propose directional transmission based energy aware routing protocol named as PDORP. The proposed protocol PDORP has the characteristics of both Power Efficient Gathering Sensor Information System (PEGASIS) and DSR routing protocols.In addition, hybridization of Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) is applied to proposed routing protocol to identify energy efficient optimal paths. The performance analysis, comparison through a hybridization approach of the proposed routing protocol gives better result comprising less bit error rate, less delay, less energy consumption and better throughput which leads to better QoS and prolong the lifetime of the network. Moreover, the Computation Model is adopted to evaluate and compare the performance of the both routing protocols using soft computing techniques.

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“…The IoT can be implemented through various areas of technology such as wireless/broadband connectivity, sensors, cloud services, and data analytics, and the wireless connectivity is an indispensable component for the creation of a large-scale IoT [6][7][8]. Here, we consider the technological features that wireless connectivity technologies should have for the IoT.…”

Section: Introductionmentioning

confidence: 99%

Uplink scheduling of MU-MIMO gateway for massive data acquisition in Internet of things

Kim¹,

Kim

2016

J Supercomput

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Due to the uplink-centric characteristic of diverse Internet of things (IoT) applications, the wireless connectivity for the IoT should provide a sufficient data rate for the delivery of a huge amount of data traffic toward the uplink direction; furthermore, it needs to satisfy the low complexity and high energy efficiency for the deployment of resource-constrained IoT devices. To this end, we propose an IoT uplink multiple-input and multiple-output (MIMO) scheduling scheme (IoT-UL-MIMO) for which a multi-user (MU)-MIMO capability is implemented in the IoT-gateway device to greatly extend the uplink bandwidth and support the massive data delivery from a plurality of devices toward the gateway. In the IoT-UL-MIMO, a simple user-selection process that selects a proper set of transmitters by just counting the number of successful control-frame transmissions from the devices is used without a channel-sensing mechanism. The aim of this design principle is a mitigation of the system complexity that reduces the energy consumption, which is a key consideration for resourceconstrained IoT devices. The simulation results show significant improvements for the throughput and energy efficiency of IoT-UL-MIMO. A duration-length optimization regarding the uplink scheduling phase is also discussed in this paper.

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Advanced verification on WBAN and cloud computing for u-health environment

Cited by 8 publications

References 28 publications

Data offloading in IoT environments: modeling, analysis, and verification

Data offloading in IoT environments: modeling, analysis, and verification

Energy Efficient Direction-Based PDORP Routing Protocol for WSN

Uplink scheduling of MU-MIMO gateway for massive data acquisition in Internet of things

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