Infrastructure-less (sometimes known as ad-hoc) networking paradigm is very appealing and potentially shaping its future into almost all emerging networks (i.e., IoT, wireless sensor networks, vehicular ad-hoc networks, emergency, and tactical radio networks, etc.). However, when conventional networking protocols are used, such networks often perform poorly, mainly because of interference within the network and limited network throughput. Recent advancements in wireless communications have enabled full-duplex (FD) operation by suppressing self-interference, which can theoretically double the network throughput. However, conventional medium access control (MAC) protocols like carrier-sense multiple access with collision avoidance (CSMA/CA) favor half-duplex (HD) operation and fail to benefit from FD transmission opportunities. This article proposes a novel hybrid MAC protocol for full-duplex ad-hoc networks. The proposed MAC combines time division multiple access (TDMA) and IEEE 802.11 distributed coordination function (DCF) strengths in chains of time-slotted contention-based control frames and collision-free data frames. The aim is to fully utilize FD transmission opportunities to increase network throughput. The proposed protocol modifies request-to-send (RTS) and clear-to-send (CTS) frames in IEEE 802.11 DCF MAC to form FD-RTS/CTS frames. These frames are used to enable collision-free FD communications among neighbors. The proposed scheme mitigates conventional MAC issues like hidden-node problem (HNP) and exposed-node problem (ENP). It also allows concurrent FD data transmissions in a collision-free manner. The model is generic and can be applied to any ad-hoc wireless network. In this article, the design is applied to a single channel ad-hoc network with FD transceivers. We compared the proposed design with IEEE 802.11 CSMA/CA protocol with both HD and FD transceivers. The simulation results show a 30% gain in throughput, reduced latency, and fairness among participating wireless nodes.

The paper presents a novel methodology based on machine learning to optimize medical benefits in healthcare settings, i.e., corporate, private, public or statutory. The optimization is applied to design healthcare insurance packages based on the employee healthcare record. Moreover, with the advancement in the insurance industry, it is rapidly adapting mathematical and machine learning models to enhance insurance services like funds prediction, customer management and get better revenue from their businesses. However, conventional computing insurance packages and premium methods are time-consuming, designation specific, and not cost-effective. During the design of insurance packages, an employee’s needs should be given more importance than his/her designation or position in an organization. The design of insurance packages in healthcare is a non-trivial task due to the employees’ changing healthcare needs; therefore, using the proposed technique employees can be moved from their existing package to another depending upon his/her need. This provides the motivation to propose a methodology in which we applied machine learning concepts for designing need-based health insurance packages rather than professional tagging. By the design of need-based packages, medical benefit optimization which is the core goal of our proposed methodology is effectively achieved. Our proposed methodology derives insurance packages that are need-based and optimal based on our defined criteria. We achieved this by first applying the clustering technique to historical medical records. Subsequently, medical benefit optimization is achieved from these packages by applying a probability distribution model on five years employees’ insurance records. The designed technique is validated on real employees’ insurance records from a large enterprise.The proposed design provides 25% optimization on medical benefit amount compared to current medical benefits amount therefore, gives better healthcare to all the employees.

Unlike commercial networks, the tactical networks drive in a critical environment and without a backbone infrastructure. These networks involve mission-critical operations that are dependent on the rapid and reliable transfer of delay-sensitive data to conduct command and control (C2). Owing to the decentralized and dynamic nature, tactical networks need to survive by maintaining seamless and simultaneous time-sensitive communication among software-defined radios (SDRs). Under mobility and dynamic network topology, link status continuously changes that cause substantial packet loss, and degrade network performance. It is challenging to maintain the connectivity between communicating nodes and find a suitable time for sending control messages (e.g., packet forwarding and route discovery), known as control phase time (CPT). Given a maximum transmission range for narrowband (NB) and wideband (WB) communication, the knowledge of link duration between communicating radios are of major concern, particularly for low latency and reliable communication requirements. Many existing techniques focus on topology control by exchanging mobility parameters in control transmissions, increasing the delay in data transmission. Therefore, it is a non-trivial task to calculate the expected time for the control transmissions due to the confrontation of speed and random movement of nodes. This paper presents a novel methodology to estimate a suitable time to execute the control phase based on the lifetime of communication links between SDRs in tactical MANETs. It uses stochastic distribution to make a network capable of effectively figuring out operative connectivity. The proposed methodology evaluates the maximum network connectivity based on the distances between communicating radios and radio transmission ranges for different quality-of-service (QoS) requirements. The simulation results validate that the proposed methodology’s CPT estimations are more appropriate for the timely link-formations in tactical radio MANETs. The proposed technique is generic and can be applied to any MANET environment using different mobility models.

A tactical network mainly consists of software-defined radios (SDRs) integrated with programmable and reconfigurable features that provide the addition and customization of different waveforms for different scenarios, e.g., situational awareness, video, or voice transmission. The network, which is mission-critical, congested, and delay-sensitive, operates in infrastructure-less terrains with self-forming and self-healing capabilities. It demands reliability and the need to survive by seamlessly maintaining continuous network connectivity during mobility and link failures. SDR platforms transfer large amounts of data that must be processed with low latency transmissions. The state-of-the-art solutions lack the capability to provide high data throughput and incorporate overhead in route discovery and resource distribution that is not appropriate for resource-constrained mission-critical networks. A cross-layer design exploits existing resources to react to environment changes efficiently, enable reliability, and escalate network throughput. A solution that integrates SDR benefits and cross-layer optimization can perform all the mentioned operations efficiently. In tactical networks, SDR’s maximum usable bandwidth can be utilized by exploiting radios’ autonomous behavior. This paper presents a novel virtual sub-nets based cross-layer medium access control (VSCL-MAC) protocol for self-forming multihop tactical radio networks. It is a MAC-centric design with cross-layer optimization that enables dynamic routing and autonomous time-slot scheduling in a multichannel network environment among SDRs. The cross-layer coupling uses link-layer information from the hybrid of time division multiple access and frequency division multiple access (TDMA/FDMA) MAC to proactively enable distributed intelligent routing at the network layer. The virtual sub-nets based distributed algorithm exploits spectrum resources and provides call setup with persistently available k-hop route information and simultaneous collision-free transmission of voice and data. The experimental results over extensive simulations show significant performance improvements in terms of minimum control overhead, processing time in relay nodes, a substantial increase in network throughput, and lower data latency (up to 76.98%) compared to conventional time-slotted MAC protocols. The design is useful for mission-critical, time-sensitive networks and exploits multihop simultaneous communication in a distributed manner.