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One of the major applications of the Heterogeneous Network (HetNet) is in the healthcare system. Deploying HetNet in healthcare systems enables patients, physicians, and other stakeholders to communicate easily with each other. Due to the large growth in the network's subscribers, the security of the stored health data became one of the major concerns because unauthorized access to this data may lead to very serious complications, and unreliable transmission of data may lead to fatal risks to the patient's life. Therefore, taking data integrity into consideration, user authentication has become one of the main factors. However, significant research work has been performed at HetNet's physical layer to secure communication, but the result of this leads to an increase in hardware components. The increasing hardware components not only costs money but also power consumption. Therefore, this paper presents an alternate way of securing communication in HetNet at the network layer. However, resolving security problems at the network layer increases computational complexity. Nevertheless, earlier, some encryption techniques like identity-based encryption (IBE), symmetric key encryption (SKE), and public-key encryption (PKE) have been utilized for securing data. Due to their own disadvantages, this paper utilizes an attribute-based encryption (ABE) authentication scheme for securing health data in medical applications. With the help of this method, access to the intruders is denied which results in reduced communication overhead. This authentication scheme helps protect the essential information against attacks by the intruders. It includes a third party server that helps to authenticate and store patient's information. The whole security technique has been written in the form of HLPSL (high-level protocol specification language) codes, and the results are then validated with the help of AVISPA (automated validation of Internet security protocols and applications) tool. [ABSTRACT FROM AUTHOR]

• The research employs a transformer-based network framework for object detection (TOD − Net) for object detection. • The predictor model bridges the connectivity between the encoder and the transformer module and offers better insight into the transformer module's local measures. • The feature extractor and the attention modules help to obtain the multi-scale features, and interpolation helps in fusing the features and enhancing the prediction accuracy. • The proposed model is experimented with MS COCO dataset (Microsoft Common Objects in Context). Various object detection approaches using a learning model intends to learn the semantic and multi-scaling information to attain superior object saliency. This research employs a transformer-based network framework for object detection (TOD − Net) for object detection. It is composed of encoders, decoders, and transformer and predictor module. The predictor model bridges the connectivity between the encoder and the transformer module and offers better insight into the transformer module's local measures. Here, feature extraction is performed to measure the local features and establishes dense modeling by analyzing local features. The model gives broader knowledge of local and global features. Python programming was used to experiment with the MS COCO dataset (Microsoft Common Objects in Context) where the experimentation gives better results over existing models. In contrast to existing methods, the proposed method achieves 68.7% precision and 4% accuracy. The proposed model outperforms different prevailing approaches and establishes a better trade-off. [Display omitted] [ABSTRACT FROM AUTHOR]

Recently, transformer has been applied to the image caption model, in which the convolutional neural network and the transformer encoder act as the image encoder of the model, and the transformer decoder acts as the decoder of the model. However, transformer may suffer from the interference of non-critical objects of a scene and meet with difficulty to fully capture image information due to its self-attention mechanism's dense characteristics. In this Letter, in order to address this issue, the authors propose a novel transformer model with decreasing attention gates and attention fusion module. Specifically, they firstly use attention gate to force transformer to overcome the interference of non-critical objects and capture objects information more efficiently via truncating all the attention weights that smaller than gate threshold. Secondly, through inheriting attentional matrix from the previous layer of each network layer, the attention fusion module enables each network layer to consider other objects without losing the most critical ones. Their method is evaluated using the benchmark Microsoft COCO dataset and achieves better performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Spectrum scarcity is the principal motivation behind the development of cognitive radio. This technology introduces new functionalities at the physical, medium access control, and network layers of the TCP/IP protocol stack. These functionalities can be subject to new security threats. Most of the existing works in cognitive radio focused on the security issues in both the physical and medium access control layers. However, threats related to the network layer have not been studied despite its importance in establishing communication between different users in cognitive radio networks. In this paper, we classify and give an overview of attacks that target the network layer functionalities of cognitive radio networks. We discuss the existing detection techniques and countermeasures and highlight the main security challenges for such networks. [ABSTRACT FROM AUTHOR]

At present, free-to-move node self-deployment algorithms aim at event coverage and cannot improve network coverage under the premise of considering network connectivity, network reliability and network deployment energy consumption. Thus, this study proposes pigeon-based self-deployment algorithm (PSA) for underwater wireless sensor networks to overcome the limitations of these existing algorithms. In PSA, the sink node first finds its one-hop nodes and maximizes the network coverage in its one-hop region. The one-hop nodes subsequently divide the network into layers and cluster in each layer. Each cluster head node constructs a connected path to the sink node to guarantee network connectivity. Finally, the cluster head node regards the ratio of the movement distance of the node to the change in the coverage redundancy ratio as the target function and employs pigeon swarm optimization to determine the positions of the nodes. Simulation results show that PSA improves both network connectivity and network reliability, decreases network deployment energy consumption, and increases network coverage. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc networks (VANETs) display many characteristics that are different from the traditional wireless networks, e.g. the rapidly changing topology and short-lived link. The vehicle communication stack is thus redesigned differently from the traditional TCP/IP architecture in order to accommodate these diversities. The network layer bears the function of the reliable routing and delivery of packets from sources to destinations over a highly dynamic path. We investigate the network layer within the vehicle communication stack and present an abstraction approach, with aims at filling the gap between the simplified and the very detailed models by incorporating more realistic routing protocols and network topologies. We study how the network dynamics, as monitored by multiple network performance indicators (e.g. transmission delay, path length, throughput, and loss rate), is affected by various combinations of routing algorithms, packet sizes, moving speeds, communication ranges, processing rates, boundary conditions, and movement patterns. The proposed suite can be modified and expanded easily to incorporate more features of the real VANET, e.g. traffic light control. [ABSTRACT FROM AUTHOR]

Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the performance of machine-learning-based NIDS in IoT networks. Specifically, we study six supervised learning methods that belong to three different classes: (1) ensemble methods, (2) neural network methods, and (3) kernel methods. To evaluate the developed NIDSs, we use the distilled-Kitsune-2018 and NSL-KDD datasets, both consisting of a contemporary real-world IoT network traffic subjected to different network attacks. Standard performance evaluation metrics from the machine-learning literature are used to evaluate the identification accuracy, error rates, and inference speed. Our empirical analysis indicates that ensemble methods provide better accuracy and lower error rates compared with neural network and kernel methods. On the other hand, neural network methods provide the highest inference speed which proves their suitability for high-bandwidth networks. We also provide a comparison with state-of-the-art solutions and show that our best results are better than any prior art by 1~20%. [ABSTRACT FROM AUTHOR]

New network architectures and routing technologies are being proposed to reflect the changes in the current trend of Internet communication that are becoming more and more resource-centric; the demand for the hierarchically structured resource in the network is emphasized, not the location of the resource. Therefore, routing by the resource rather than routing by the conventional Internet protocol address is more suitable for the future Internet with some attractive advantages such as reducing the burden of resolving identifier to location and achieving higher scalability by using a provider-independent addressing structure. In this paper, we propose resource name-based routing within the routers in the network layer since te ardware architecture should also be able to support the paradigm shift from host-centric to resource-centric communication. Through evaluating the required network architecture and memory size in routers, we show the feasibility of resource name-based routing by using fully qualified domain names (FQDN) as an example of describing structured information. Using approximately 700 million existing FQDNs, the evaluation result shows that resource name-based routing is feasible even when considering the limitations of ternary content addressable memory size in routers. [ABSTRACT FROM AUTHOR]

Research in Mobile IP has proposed that IP should take support from the underlying wireless network architecture to achieve good performance for handover and paging protocols. The IETF work defines requirements for layer 2 (the data link layer of the OSI model) to support optimized layer 3 (the network layer of the OSI model) handover and paging protocols. Layer 2 can send notification to layer 3 that a certain event has happened or is about to happen. The notification is sent using a trigger. We list the triggers needed by IP handover and paging protocols. We map the triggers to their corresponding events in CDMA2000 [ABSTRACT FROM PUBLISHER]

Summary: Technologies are advancing at a rapid rate in the current era. People are advancing with the advancement of technologies, be it in education industry, health industry, and providing luxury. With the advent of autonomous luxury‐in‐motion car that provides in‐car‐entertainment (ICE), vehicular communication is the prerequisite towards achieving this goal. Dedicated short range communication (DSRC) is a matured vehicular communication standard, and Long‐Term Evolution (LTE) is the most competing technologies in the cellular communication. In this paper, we focus on the uplink performance of the LTE‐DSRC hybrid infrastructure. The basic transmission scheme for uplink direction is based on single carrier transmission in the form of discrete Fourier transform (DFT)‐spread Orthogonal Frequency Division Multiplexing (OFDM) with a minimum mean square error (MMSE) receiver at the LTE Evolved Node B (eNodeB) and a DSRC OFDM transmitter. A comprehensive bit error rate (BER) performance simulative study has been made on a color image transmission in uplink hybrid LTE‐DSRC system, and the results obtained are encouraging that the proposed convergence is possible, as it provides a substantial decrease in the BER with a gradual increase in the signal ‐to‐ noise ratio Signal to Noise Ratio (SNR). [ABSTRACT FROM AUTHOR]

Planning and deploying a functional large scale Wireless Sensor Network (WSN) or a Network of Internet of Things (IoTs) is a challenging task, especially in complex urban environments. A main network design bottleneck is the existence and/or correct usage of appropriate cross layer simulators that can generate realistic results for the scenario of interest. Existing network simulators tend to overlook the complexity of the physical radio propagation layer and consequently do not realistically simulate the main radio propagation conditions that take place in urban or suburban environments, thus passing inaccurate results between Open Systems Interconnection (OSI) layers. This work demonstrates through simulations and measurements that, by correctly passing physical information to higher layers, the overall simulation process produces more accurate results at the network layer. It is demonstrated that the resulting simulation methodology can be utilized to accomplish realistic wireless planning and performance analysis of the deployed nodes, with results that are very close to those of real test-beds, or actual WSN deployments. [ABSTRACT FROM AUTHOR]

Wireless sensor networks with mobile collectors or sinks face some challenges regarding the data collection process and the continuous connectivity and delivering of data while the mobile sink is moving throughout the network. These challenges increase as the network grows. For this aim, we propose in this paper a cross-layer routing protocol which supports mobility for large-scale wireless sensor networks, which we name CLR-MSPH. We adapt CLR-MSPH for the hierarchical architecture of the network, and it performs on cluster-based wireless sensor networks where the network is organized in clusters. Our proposed protocol deals with the problem of handover data after the mobile sink leaves the radio range of cluster head without sending all data stored in the cluster head's buffer. We also introduce a mobility model for the mobile sink for a better data collection process. CLR-MSPH is considered as an extending implementation of BMAC protocol with handover mechanism (BMAC-H). In order to prove the efficiency of the proposed protocol, we compare CLR-MSPH to BMAC-H, where we adapted BMAC-H to perform in cluster-based wireless sensor networks. The simulation results show that CLR-MSPH performs better than BMAC-H in terms of packets reception rate, energy, and latency. [ABSTRACT FROM AUTHOR]