An Enhanced Mobility and Temperature Aware Routing Protocol through Multi-Criteria Decision Making Method in Wireless Body Area Networks

Sung

2020

IJERPH

Self Cite

Various simulation studies for wireless body area networks (WBANs) based on the IEEE 802.15.6 standard have recently been carried out. However, most of these studies have applied a simplified model without using any major components specific to IEEE 802.15.6, such as connection-oriented link allocations, inter-WBAN interference mitigation, or a two-hop star topology extension. Thus, such deficiencies can lead to an inaccurate performance analysis. To solve these problems, in this study, we conducted a comprehensive review of the major components of the IEEE 802.15.6 standard and herein present modeling strategies for implementing IEEE 802.15.6 MAC on an NS-3 simulator. In addition, we configured realistic network scenarios for a performance evaluation in terms of throughput, average delay, and power consumption. The simulation results prove that our simulation system provides acceptable levels of performance for various types of medical applications, and can support the latest research topics regarding the dynamic resource allocation, inter-WBAN interference mitigation, and intra-WBAN routing.

Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An NS-3 Implementation and Experimental Performance Analysis of IEEE 802.15.6 Standard under Different Deployment Scenarios

Sung

2020

IJERPH

Self Cite

“…The literature on individual techniques and MCDA in general is vast. Multi-criteria decision models have been applied to study fall protection support for construction sites [44], temperature-aware routing in wireless body area networks [45], performance assessment of credit granting decision systems [46], assessment of player rankings in E-Sports [47], load profiling for power systems [48], identification of ideal business location selection [49], performance of emergency systems under COVID-19, [50] venture investment [51], failure modes analysis [52], group decision making [53], drug trafficking [54], and remote sensing for drought characterization [55]. This article focuses on summarizing common weighting methods, including their advantages and disadvantages, to aid the reader in the determination of an appropriate method for use given the particulars of a given decision to be made.…”

Section: Introductionmentioning

confidence: 99%

Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques

2021

Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions.

“…Mainly there are two major sources of tissue heating: Antenna radiations of biomedical sensor nodes absorbed by surrounding tissues and the heat produced due to the circuit of biomedical sensor nodes as mentioned in [8], [9]. The rate of temperature rise can be calculated by using the Penne's bioheat equation [10] as mentioned in equation 2:…”

Section: Introductionmentioning

confidence: 99%

WETRP: Weight Based Energy & Temperature Aware Routing Protocol for Wireless Body Sensor Networks

et al. 2019

The technological advancements in wireless communication and miniaturization of sensor nodes have resulted in the development of Wireless Medical Sensor Networks (WMSNs) which can be effectively used for remote patient monitoring. Remote patient monitoring is one such application of wireless sensor networks which is becoming increasingly prevalent in healthcare. The healthcare applications of the WMSNs are delay-sensitive and require timely delivery of patient-critical data. However, the frequent exchange of critical data packets results in higher delays, collisions, packet drop, and re-transmissions. Consequently, it brings a detrimental impact on the performance of the WMSNs. In addition, the implanted biomedical sensor nodes produce electromagnetic radiations, pose a serious threat of damaging sensitive tissues in the human body. Protecting tissue damage requires thermal-aware routing protocols. However, most of the thermal-aware routing protocols developed for the WBSNs primarily focused on minimizing temperature, while overlooking the energy conservation goal and optimization of route selection. In this paper, we propose a weighted, QoS-based, energy and temperature-aware routing protocol, referred to as (WETRP), for the WMSNs that utilizes a composite routing metric by keeping in view temperature, remaining node energy, and link-delay estimation during route selection decisions. The simulation results presented in the paper demonstrates the efficacy of the proposed scheme in terms of preventing temperature rise, dealing with hotspot nodes, and maximizing network's lifetime. INDEX TERMS Wireless body sensor network, routing protocols, QoS, energy efficiency, temperature, hotspot nodes.