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Mammography is a popular diagnostic imaging procedure for detecting breast cancer at an early stage. Various deep-learning approaches to breast cancer detection incur high costs and are erroneous. Therefore, they are not reliable to be used by medical practitioners. Specifically, these approaches do not exploit complex texture patterns and interactions. These approaches warrant the need for labelled data to enable learning, limiting the scalability of these methods with insufficient labelled datasets. Further, these models lack generalisation capability to new-synthesised patterns/textures. To address these problems, in the first instance, we design a graph model to transform the mammogram images into a highly correlated multigraph that encodes rich structural relations and high-level texture features. Next, we integrate a pre-training self-supervised learning multigraph encoder (SSL-MG) to improve feature presentations, especially under limited labelled data constraints. Then, we design a semi-supervised mammogram multigraph convolution neural network downstream model (MMGCN) to perform multi-classifications of mammogram segments encoded in the multigraph nodes. Our proposed frameworks, SSL-MGCN and MMGCN, reduce the need for annotated data to 40% and 60%, respectively, in contrast to the conventional methods that require more than 80% of data to be labelled. Finally, we evaluate the classification performance of MMGCN independently and with integration with SSL-MG in a model called SSL-MMGCN over multi-training settings. Our evaluation results on DSSM, one of the recent public datasets, demonstrate the efficient learning performance of SSL-MNGCN and MMGCN with 0.97 and 0.98 AUC classification accuracy in contrast to the multitask deep graph (GCN) method Hao Du et al. (2021) with 0.81 AUC accuracy.

The IEEE 802.11ax standard is designed to provide high-efficiency WLAN operating in dense deployment, with a focus on increasing robustness and uplink transmission. IEEE 802.11ax aims to deliver self-configuration and self-adaptation capabilities for interference management techniques in dense deployments, to improve overall network conditions. The IEEE 802.11ax medium strives to intelligently use network allocation vectors (NAV), which block simultaneous transmission of neighboring co-existing access points from transmitting for a set period of time. Given an acceptable collision probability, this collision avoidance strategy tends to degrade network throughput and increase transmission delays since the medium is never completely exploited. In this paper, we propose affinityGNN, a graph neural network approach to actively optimize network co-existence transmission in a dense scenario. We introduce direct-affinityGNN which considers direct communications between IEEE802.11ax-compliant wireless systems and in contrast, skip-affinityGNN, which considers communication between IEEE802.11ax and Legacy devises by aggregating neural messages from direct and skip (two-hop) communicating neighbors in the dense wireless network deployment. Extensive experimental results not only show the superior performance of our proposed model over the state-of-the-art, but also demonstrate its potentially good interpretability and robustness for wireless network structure.

Hybrid FSO/RF system requires an efficient FSO and RF link switching mechanism to improve the system capacity by realizing the complementary benefits of both the links. The dynamics of network conditions, such as fog, dust, and sand storms compound the link switching problem and control complexity. To address this problem, we initiate the study of deep reinforcement learning (DRL) for link switching of hybrid FSO/RF systems. Specifically, in this work, we focus on actor-critic called Actor/Critic-FSO/RF and Deep-Q network (DQN) called DQN-FSO/RF for FSO/RF link switching under atmospheric turbulences. To formulate the problem, we define the state, action, and reward function of a hybrid FSO/RF system. DQN-FSO/RF frequently updates the deployed policy that interacts with the environment in a hybrid FSO/RF system, resulting in high switching costs. To overcome this, we lift this problem to ensemble consensus-based representation learning for deep reinforcement called DQNEnsemble-FSO/RF. The proposed novel DQNEnsemble-FSO/RF DRL approach uses consensus learned features representations based on an ensemble of asynchronous threads to update the deployed policy. Experimental results corroborate that the proposed DQNEnsemble-FSO/RF’s consensus learned features switching achieves better performance than Actor/Critic-FSO/RF, DQN-FSO/RF, and MyOpic for FSO/RF link switching while keeping the switching cost significantly low.

Mammography is a popular diagnostic imaging procedure for detecting breast cancer at an early stage. Various deep learning (DL) approaches to breast cancer detection incur high costs and are prone to classify incorrectly. Therefore, they are not sufficiently reliable to replace existing techniques used by medical practitioners. Specifically, these DL approaches do not exploit the complex texture patterns and interactions in mammograms. These DL approaches need labelled data to enable learning, limiting the scalability of these methods because of sufficient labelled datasets. Further, these DL models lack generalisation capability to newly-synthesised patterns/textures. To address these problems, in the first instance, we design a graph model to transform the mammogram images into highly correlated multi-graphs, encoding rich structural relations and high-level texture features. Then, we consider a self-supervised learning multi-graph encoder (SSL-MG) to improve the features presentation, especially when limited labelled data is available. Finally, we design a semi-supervised mammogram multi graph convolution neural network downstream model (MMGCN) to perform multi classification of mammogram segments encoded in the multi-graph nodes. We evaluate the classification performance of MMGCN independently and with integration with SSL-MG in a model called SSL-MMGCN over several training settings. Our results reveal the efficient learning performance of SSL-MNGCN and MMGCN with 0.97 and 0.98 AUC classification accuracy in contrast to the multitask deep graph (GCN) method of Hao Du et al. (2021) with 0.81 AUC accuracy.

Mammography is a popular diagnostic imaging procedure for detecting breast cancer at an early stage. Variousdeep learning approaches to breast cancer detection incur high costs and are erroneous. Therefore, they are notreliable to be used by medical practitioners. Specifically, these approaches do not exploit the complex texture patternsand interactions. These approaches warrant the need for labelled data to enable learning, limiting the scalabilityof these methods under insufficient labelled datasets. Further, these models lack generalisation capability to newsynthesizedpatterns/textures. To address these problems, in the first instance, we design a graph model to transform themammograms images into highly correlated multigraph encode rich structural relations and high-level texture features.Then, we consider a self-supervised learning multigraph encoder (SSL-MG) to improve the features presentation,especially under limited labelled data constraints. Finally, we design a semi-supervised mammogram multigraphconvolution neural network downstream model (MMGCN) to perform multi classifications of mammogram segmentsencoded in the multigraph nodes. We evaluate the classification performance of MMGCN independently and withintegration with SSL-MG in a model called SSL-MMGCN over multi training settings. Our results reveal the efficientlearning performance of SSL-MNGCN and MMGCN with 0.97 and 0.98 AUC classification accuracy in contrast tothe multitask deep graph (GCN) method Hao Du et al. (2021) with 0.81 AUC accuracy.

Due to high data rates, license-free spectrum, and immunity to electromagnetic interference, free-space optical (FSO) links are being considered as a potential candidate to meet the ever-increasing traffic demands of users. The FSO links remain less explored, as their performance depends on environmental conditions such as dust, fog, and clouds. Such conditions do not affect the radio frequency (RF) links in a similar way; however, RF resources are scarce. As an ultimate solution to this performance/scarcity dilemma, we propose a fuzzy logic-based hybrid architecture of FSO and RF links, which can be used to enhance reliability and resource efficiency. We present an intelligent soft switching mechanism between FSO and RF links using a fuzzy inference system to achieve the maximum link reliability and provide heterogeneous wireless services.

LoRa is a long-range, low power and single-hop wireless technology that has been envisioned for Internet of Things (IoT) applications having battery driven nodes. Nevertheless, increase in number of end devices and varying throughput requirements impair the performance of pure Aloha in LoRaWAN. Considering these limitations, we evaluate the performance of slotted Aloha in LoRaWAN using extensive simulations. We employed packet error rate (PER), throughput, delay, and energy consumption of devices under different payload sizes and varying number of end devices as benchmarks. Moreover, an analytical analysis of backlogged and non-backlogged under slotted Aloha LoRaWAN environment is also performed. The simulation shows promising results in terms of PER and throughput compared to the pure Aloha. However, increase in delay has been observed during experimental evaluation.Finally, we endorse slotted aloha LoRaWAN for Green IoT Environment.

Symmetrical precoding and algorithms play a vital role in the field of wireless communications and cellular networks. This paper proposed a low-complexity hybrid precoding algorithm for mmWave massive multiple-input multiple-output (MIMO) systems. The traditional orthogonal matching pursuit (OMP) has a large complexity, as it requires matrix inversion and known candidate matrices. Therefore, we propose a bird swarm algorithm (BSA) based matrix-inversion bypass (MIB) OMP (BSAMIBOMP) algorithm which has the feature to quickly search the BSA global optimum value. It only directly finds the array response vector multiplied by the residual inner product, so it does not require the candidate&rsquo, s matrices. Moreover, it deploys the Banachiewicz&ndash, Schur generalized inverse of the partitioned matrix to decompose the high-dimensional matrix into low-dimensional in order to avoid the need for a matrix inversion operation. The simulation results show that the proposed algorithm effectively improves the bit error rate (BER), spectral efficiency (SE), complexity, and energy efficiency of the mmWave massive MIMO system as compared with the existing OMP hybrid and SDRAltMin algorithm without any matrix inversion and known candidate matrix information requirement.

The use of multiple channels for transmissions has raised several challenges, for example, multi-channel hidden terminal problem, channel switching delay, and control channel saturation problem. Dedicated control channel techniques simplify channel coordination by eliminating the need for synchronization, however the control channel may become the bottleneck for the performance of the network. A better trade off which can solve the coordination problem, and can mitigate the control channel bottleneck is desirable. Least Channel Variant Multi-channel MAC (LCV-MMAC) is a multi-channel MAC based on IEEE 802.11 MAC. The novel part of this protocol is the channel assignment technique, where a mechanism to avoid unnecessary channel assignment and thus channel switching is used. Further, LCV-MMAC avoids channel contention when the control channel is highly saturated. In this paper, we explore the properties of LCV-MMAC through extensive simulations with the help of ns-2, and compare it with popular existing multi-channel MAC protocols in different mobile and static random topologies. Experimental results validate that LCV-MMAC achieves significantly better aggregated throughput, and fairness index than other multi-channel MAC protocols in different random network scenarios.

Energy conversation is one of the primary objectives of duty cycle MAC protocols. Different synchronous and asynchronous duty cycle MAC protocols have been proposed in recent years. These protocols perform well under low traffic loads, but efficiency of these protocols degrade under high traffic loads. We present an asynchronous hybrid duty cycle MAC protocol called Hybrid MAC (H-MAC), which uses both sender and receiver initiated mechanisms to combat the packet delivery latency. In H-MAC each node schedules its sleep and wake up time based on cross layer routing information on the receiver initiated part and on the sender initiated part each sender chooses its wake up time based on the receiver's wake up information. We have evaluated HMAC in diverse network under dynamic traffic loads. Experiments reveal that, H-MAC significantly reduces packet delivery latency and energy consumption compared to RIMAC.

Vehicular Ad hoc Networks (VANETs) provide road safety, commercial, entertainment, and convenience services. Users have access to real time traffic updates, road hazard notifications, post crashed notification, remote vehicle diagnostics, audio and video streaming, and route suggestions to enhance the travel experience. However service delivery in VANETs suffers as nodes in VANETs move at high speed causing the network topology to change rapidly. A node becomes unreachable from its current point of attachment as it moves out of its range. Mobility management ensures that a mobile node can continuously access the services while it roams around by re-attaching itself via another point of attachment. Various solutions for mobility management have been proposed. Nonetheless handoff delay, packet loss and signaling overhead, and are still a concern in VANETs. Internet Engineering Task Force (IETF) has chartered a Distributed Mobility Management (DMM) group in year 2014. The purpose of DMM is to provide an alternate solution to the mobility management problem in wireless networks. In this research, the effectiveness of distributed mobility management schemes in VANETs is compared on the basis of different parameters.

In this paper, we present a MAC protocol called Hop Extended Pipelined Routing MAC (HE-PRMAC) that enables a node to send a data packet for multiple hops in a single duty cycle similar to PR-MAC and has the ability to handle traffic overhead in an efficient way. HE-PRMAC addresses delivery latency and throughput degradation in wireless sensor networks (WSNs) using hop extended mechanism and a new message, i.e., RTR message. The main goal of this research is to reduce the overhead and latency compared to R-MAC and PR-MAC. We evaluate the performance of our protocol using ns-2 simulations. As compared to R-MAC and PR-MAC, HE-PRMAC shows 22.1% less power consumption and 13.6% less packet latency on average for 250 nodes in chain, cross, and real scenario.