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Wireless Body Sensor Networks (WBSNs) are becoming increasing popular in a number of healthcare applications. A particular requirement of WBSNs in a healthcare system is the transmission of time-sensitive and critical data, captured by heterogeneous biosensors, to a base station while considering the constraints of reliability, throughput, delay and link quality. However, the simultaneous communication among various biosensors also raises the possibility of congestion on nodes or transmission links. Consequently, the likelihood of a number of untoward situations increases, such as disruption (high delays), packet losses, retransmissions, bandwidth exhaustion, and insufficient buffer space. The significant level of interference in the network leads to a higher number of collisions and retransmissions. The selection of an optimized route to cope with these issues and satisfy the QoS requirements of a WBSN has not been well-studied in the relevant literature. In this regard, we propose a multi-constraint, Intra-BAN, QoS-Aware Routing Protocol (referred to as MIQoS-RP) which introduces an improved, multi-facet routing metric to optimize the route selection while satisfying the aforementioned constraints. The performance of the proposed protocol is evaluated in terms of average end-to-end delay, throughput and packet drop ratio. The comparison of MIQoS-RP with the existing routing protocols demonstrates its efficacy in terms of the selected criteria. The results show that the MIQoS-RP achieves improved throughput by 22%, average end-to-end delay by 29% and packet drop ratio performance by 41% as compares to existing schemes.

Wireless Body Sensor Network (WBSN) is deployed in delay-sensitive application scenarios where providing Quality-of-Service (QoS) is utmost important. The QoS-aware routing protocol not only discovers a route from source to destination but also satisfies QoS requirements in heavily loaded wireless networks. Emergency / critical data packets must reach the intended destination without incurring significant delays and fulfill multiconstrained demands (reliability, delay, Packet Delivery Ratio (PDR)) of heterogeneous applications. Congestion occurs in heavy traffic situation when the incoming traffic load exceeds the capacity of transmission link, buffer overflows, packet collision, channel contention. Consequently, it impacts QoS in terms of packet loss, end-to-end delay and PDR. Moreover, the selection of poor links/routes may have detrimental impacts of the performance of WBSN and there can be significant variations in throughput, delay, network lifetime, route stability performance. Majority of the existing priority-aware routing protocols proposed for Medium Access Control (MAC) layer to solve the slot allocation problem by which data packets are classified into different categories. However, less attention has been given to traffic prioritization at network layer for data categorization. Furthermore, optimized traffic prioritization has been overlooked, thereby increases the data redundancy, queue/link delay, data loss and decreases the reliability of the network, and it does not satisfy the QoS requirements of WBSN and affects the critical data to be delivered in a less privileged manner. This work proposes the Low Latency Traffic Prioritization scheme for QoS-aware routing (LLTP-QoS). The LLTP-QoS is designed to enhance the transmission of critical data in a privileged manner (reliability) and avoids the end-to-end delay. The performance of proposed scheme is evaluated in terms of throughput, average end-to-end delay, PDR, normalized routing load, network lifetime through extensive simulations using Network Simulator-2 (NS2). The simulation results verified improved performance of proposed LLTP-QoS scheme as compared to existing routing protocols. INDEX TERMS Wireless body sensor networks, physiological data, QoS, data traffic, priority queue, packet delivery ratio, latency.

A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.

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