The Transmission Control Protocol.

Power Aware Routing in Mobile Ad Hoc Networks

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

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Federated Learning for Internet of Things: A Comprehensive Survey

The rapid evolutions in microcomputing, mini-hardware manufacturing, and machine-to-machine (M2M) communications have enabled novel Internet-of-Things (IoT) solutions to reshape many networking applications. Healthcare systems are among these applications that have been revolutionized with IoT, introducing an IoT branch known as the Internet-of-Medical Things (IoMT) systems. IoMT systems allow remote monitoring of patients with chronic diseases. Thus, it can provide timely patients’ diagnostic that can save their life in case of emergencies. However, security in these critical systems is a major challenge facing their wide utilization. In this article, we present state-of-the-art techniques to secure IoMT systems’ data during collection, transmission, and storage. We comprehensively overview IoMT systems’ potential attacks, including physical and network attacks. Our findings reveal that most security techniques do not consider various types of attacks. Hence, we propose a security framework that combines several security techniques. The framework covers IoMT security requirements and can mitigate most of its known attacks.

Recent Advances in the Internet-of-Medical-Things (IoMT) Systems Security

In duty cycled MAC protocols, multi-packet, multi-flow and multi-hop traffic patterns experience significant latencies, which are partially due to duty cycling. Several cross-layer routing/MAC schemes have been proposed to mitigate this latency. However, they utilize routing information from a single flow and/or a single packet perspective, thus limiting their adaptation to varying traffic loads and patterns. In this paper, we propose a novel Cross-Layer MAC protocol (CL-MAC) for WSNs, to efficiently handle multi-packet, multi-hop and multi-flow traffic patterns while adapting to a wide range of traffic loads. CL-MAC's scheduling is based on a unique structure of flow setup packets that efficiently utilize routing information to transmit multiple data packets over multiple multi-hop flows. Unlike other MAC protocols, supporting construction of multi-hop flows, CL-MAC considers all pending packets in the routing layer buffer and all flow setup requests from neighbors, when setting up a flow. This allows CL-MAC to make more informed scheduling decisions, reflecting the current network status, and dynamically optimize its scheduling mechanism accordingly. We evaluate CL-MAC through extensive ns-2 simulations and compare its performance to the state of the art, over various networks and for a wide variety of traffic loads and patterns. In all our experiments, CL-MAC substantially reduces end-to-end latency, increases delivery ratio while reducing the average energy consumed per packet delivered.

CL-MAC: A Cross-Layer MAC protocol for heterogeneous Wireless Sensor Networks

Pneumonia is a high mortality disease that kills 50, 000 people in the United States each year. Children under the age of 5 and older population over the age of 65 are susceptible to serious cases of pneumonia. The United States spend billions of dollars fighting pneumonia-related infections every year. Early detection and intervention are crucial in treating pneumonia related infections. Since chest x-ray is one of the simplest and cheapest methods to diagnose pneumonia, we propose a deep learning algorithm based on convolutional neural networks to identify and classify pneumonia cases from these images. For all three models implemented, we obtained varying classification results and accuracy. Based on the results, we obtained better prediction with average accuracy of (68%) and average specificity of (69%) in contrast to the current state-of-the-art accuracy that is (51%) using the Visual Geometry Group (VGG16 also called OxfordNet), which is a convolutional neural network architecture developed by the Visual Geometry Group of Oxford. By implementing more novel lung segmentation techniques, reducing over fitting, and adding more learning layers, the proposed model has the potential to predict at higher accuracy than human specialists and will help subsidies and reduce the cost of diagnosis across the globe.

Optimized CNN-based Diagnosis System to Detect the Pneumonia from Chest Radiographs

In the age of Big Genomics Data, institutions such as the National Human Genome Research Institute (NHGRI) are challenged in their efforts to share volumes of data between researchers, a process that has been plagued by unreliable transfers and slow speeds. These occur due to throughput bottlenecks of traditional transfer technologies. Two factors that affect the efficiency of data transmission are the channel bandwidth and the amount of data. Increasing the bandwidth is one way to transmit data efficiently, but might not always be possible due to resource limitations. Another way to maximize channel utilization is by decreasing the bits needed for transmission of a dataset. Traditionally, transmission of big genomic data between two geographical locations is done using general-purpose protocols, such as hypertext transfer protocol (HTTP) and file transfer protocol (FTP) secure. In this paper, we present a novel deep learning-based data minimization algorithm that 1) minimizes the datasets during transfer over the carrier channels; 2) protects the data from the man-in-the-middle (MITM) and other attacks by changing the binary representation (content-encoding) several times for the same dataset: we assign different codewords to the same character in different parts of the dataset. Our data minimization strategy exploits the alphabet limitation of DNA sequences and modifies the binary representation (codeword) of dataset characters using deep learning-based convolutional neural network (CNN) to ensure a minimum of code word uses to the high frequency characters at different time slots during the transfer time. This algorithm ensures transmission of big genomic DNA datasets with minimal bits and latency and yields an efficient and expedient process. Our tested heuristic model, simulation, and real implementation results indicate that the proposed data minimization algorithm is up to 99 times faster and more secure than the currently used content-encoding scheme used in HTTP of the HTTP content-encoding scheme and 96 times faster than FTP on tested datasets. The developed protocol in C# will be available to the wider genomics community and domain scientists.

A Deep Learning-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets

This paper presents a duty cycling based cross layer MAC protocol for wireless sensor networks (WSNs), referred to as BulkMAC, to support the transmission of multihop multiple packet flows during a single sleep period. We show that without the proposed cross-layered approach, the sensor nodes will spend significant energy and induce longer delays. The proposed protocol cleverly schedules the channel allocation using the upper routing layer information. We implement our protocol in ns2.29 and compare it against RMAC (Routing Enhanced MAC Protocol) and PRMAC (Pipelined-RMAC). On the average, BulkMAC improves data delivery by a factor of 2.26 and 1.67 compared to RMAC and PRMAC, respectively, for data collection in random networks.

BulkMAC: a cross-layer based MAC protocol for wireless sensor networks

Wireless communication became an essential tool for information exchange between modern Implantable Medical Devices (IMDs) and hospital servers. In spite of the many advantages of wireless technology, it puts the patients' health and data privacy in serious danger if no proper security mechanism is imployed. We aim to secure these devices while taking into consideration the limitations of these small devices. The IMDs have resources that are relatively simple and sometimes, once implemented in the body, require surgery to be altered. Consequently, common security mechanisms cannot be simply implemented in fear of consuming all the resources dedicated to healthcare needs. A certain balance between security and efficiency must thus be sought in each IMD architecture. In this work, we propose an encryption scheme for IMDs that stores its monitored data for future use. For privacy issues, not all the stored data should be accessed by any device that has access to the IMD. Certain privileges need to be allocated to different people to protect the privacy of the patient. Hence, we propose a new role-based encryption scheme, that both guarantees hierarchical access to personal data based on their role and still satisfies the computational limitations of IMDs. This scheme employs the Chinese Remainder properties to achieve the desired encryption hierarchy. The IMD uses keys form the same key pool for any encryption, and depending on the access rights of the users, the latter will only be able to decrypt the data he is allowed to. This work resulted in a secure scheme that we have proven it can formally protect the stored data. This scheme performs well under statistical analysis and is characterized by a relatively low complexity. Also, this work led to encrypted data with a lossless compression rate that saves on the communication cost.

Mohamed Hefeida

Papers

CL-MAC: A Cross-Layer MAC protocol for heterogeneous Wireless Sensor Networks

Optimized CNN-based Diagnosis System to Detect the Pneumonia from Chest Radiographs

A Deep Learning-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets

BulkMAC: a cross-layer based MAC protocol for wireless sensor networks

Role-Based Hierarchical Medical Data Encryption for Implantable Medical Devices