BANSIM: A new discrete-event simulator for wireless body area networks with deep reinforcement learning in Python

Many studies have investigated machine learning algorithms to improve the performance of wireless body area networks (WBANs). However, it was difficult to evaluate algorithms in a network simulator because of missing interfaces between the simulators and machine learning libraries. To solve the problem of compatibility, some researchers have attempted to interconnect existing network simulators and artificial intelligence (AI) frameworks. For example, ns3-gym is a simple interface between ns-3 (in C++) and the AI model (in Python) based on message queues and sockets. However, the most essential part is the implementation of an integrated event scheduler, which is left to the user. In this study, we aim to develop a new integrated event scheduler. We present BANSIM, a discrete-event network simulator for WBAN in standard Python that supports deep reinforcement learning (DRL). BANSIM provides an intuitive and simple DRL development environment with basic packet communication and BAN-specific components, such as the human mobility model and on-body channel model. Using BANSIM, users can easily build a WBAN environment, design a DRL-based protocol, and evaluate its performance. We experimentally demonstrated that BANSIM captured a wide range of interactions that occurred in the network. Finally, we verified the completeness and applicability of BANSIM by comparing it with an existing network simulator

Adaptive Scheduling and Power Control for Multi-Objective Optimization in IEEE 802.15.6 Based Personalized Wireless Body Area Networks

Multi-objective optimization (MOO) has been a topic of intense interest in providing flexible trade-offs between conflicting optimization criteria in wireless body area networks (WBANs). To solve diverse multi-objective optimization problems (MOPs), conventional resource management schemes have dealt with the classic issues of WBANs, such as traffic heterogeneity, emergency response, and body shadowing. However, existing approaches have difficulty achieving MOO because, despite the personalization of WBANs, they still miss the new constraints or considerations derived from user-specific characteristics. To address this problem, in this paper, we propose an adaptive scheduling and power control scheme for MOO in personalized WBANs. Specifically, we investigate the existing scheduling and power control schemes for solving MOPs in WBANs, clarify their limitations, and present two feasible solutions: priority-based adaptive scheduling and deep reinforcement learning (DRL) power control. By integrating these two mechanisms in compliance with the IEEE 802.15.6 standard, we can jointly improve the optimization criteria, that is, differentiated quality of service (QoS), transmission reliability, and energy efficiency. Through comprehensive simulations, we captured the performance variations under realistic WBAN deployment scenarios and verified that the proposed scheme can achieve a higher throughput and packet delivery ratio, lower power consumption ratio, and shorter delay compared with a conventional approach

Adaptive scheduling for multi-objective resource allocation through multi-criteria decision-making and deep Q-network in wireless body area networks

To provide compelling trade-offs among conflicting optimization criteria, various scheduling techniques employing multi-objective optimization (MOO) algorithms have been proposed in wireless body area networks (WBANs). However, existing MOO algorithms have difficulty solving diverse multi-objective optimization problems (MOPs) in dynamic and heterogeneous WBANs because they require a prior preference of the decision makers or they are unable to solve non-discrete optimization problems, such as time slot scheduling. To overcome this limitation, in this paper, we propose a new adaptive scheduling algorithm that complements existing MOO algorithms. The proposed algorithm consists of two parts: scheduling order optimization and the auto-scaling of relative importance. With the former, we logically integrate the decision criteria using a multi-criteria decision-making (MCDM) method and then optimize the scheduling order. For the latter, we adaptively adjust the scales of the relative importance among the decision criteria based on the network conditions using a deep Q-network (DQN). By tightly integrating these two mechanisms, we can eliminate the intervention of decision makers and optimize non-discrete tasks simultaneously. The simulation results prove that the proposed scheme can provide a flexible trade-off among conflicting optimization criteria, that is, a differentiated QoS, reliability, and energy efficiency/balance compared with a conventional approach

An Enhanced Temperature Aware Routing Protocol in Wireless Body Area Networks

© 2018 IEEE. In this paper, we propose a new enhanced temperature aware routing protocol to assign the temperature of node by considering current temperature and expected rise caused by the packets in the buffer. Also, two hops ahead algorithm is employed to ensure further packet forwarding to the sink. The simulation results are shown to prove that the proposed scheme is able to increase packet delivery ratio and network lifetime

Percolation properties of growing networks under an Achlioptas process

We study the percolation transition in growing networks under an Achlioptas process (AP). At each time step, a node is added in the network and, with the probability $\delta$, a link is formed between two nodes chosen by an AP. We find that there occurs the percolation transition with varying $\delta$ and the critical point $\delta_c=0.5149(1)$ is determined from the power-law behavior of order parameter and the crossing of the fourth-order cumulant at the critical point, also confirmed by the movement of the peak positions of the second largest cluster size to the $\delta_c$. Using the finite-size scaling analysis, we get $\beta/\bar{\nu}=0.20(1)$ and $1/\bar{\nu}=0.40(1)$, which implies $\beta \approx 1/2$ and $\bar{\nu} \approx 5/2$. The Fisher exponent $\tau = 2.24(1)$ for the cluster size distribution is obtained and shown to satisfy the hyperscaling relation.Comment: 4 pages, 5 figures, 1 table, journal submitte