In Delay Tolerant Networks (DTN) disruptions may happen frequently as end to end path is not available all the time. Thus, delays can also be extended due to its environment nature like deep space, underwater, ocean sensor networks. In manage to achieve message delivery probability in such demanding networking situations , researchers have proposed the design of store-carry-and-forward routing protocols, here a node might accumulate a message in its buffer and carry it next to for unlimited time , awaiting till a suitable forwarding opportunity acquire .Moreover, multiple message duplication into the network to increase delivery probability. This arrangement of long-standing storage and replication force a high storage overhead on network. Therefore, efficient buffer drop policies are required to resolve on buffer, which decides messages must be dropped, while node buffers are overflow.In this paper, we propose effective buffer management drop policy E-DROP for delay tolerant networks. We illustrate that conventional buffer management policy like MOFO be unsuccessful to consider all relevant information in this framework. E-DROP policy can be adjust to minimize the metrics of relayed, dropped , average latency ,overhead ratio ,hop count and to maximize the average delivery probability and buffer time. Using simulations support on an imitation mobility models shortest path map based and Map route movements, we show that our buffer management E-DROP with random message sizes drop policy performs better as the existing MOFO.

Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

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DTN mobile nodes depend on their mobility to carry the message to destination. Therefore it is important to understand the effect of buffer management policies on the performance of DTN routing protocols under different mobility models.In our previous work of DLA we examine that epidemic router was not showing good delivery probability in case of SPMBM. This paper is the performance of DLA (drop largest) and DOA (Drop oldest) buffer management policy with impact of varying mobility models under epidemic routing protocol. We show that how combination of mobility models and queuing mechanism can optimize the performance of epidemic routing protocol in term of delivery probability, message dropped, buffer time average, overhead ratio and hop count averages. General TermsRouting, Mobility Models, Epidemic Router.

We have witnessed the impact of ML in disease diagnosis, image recognition and classification, and many more related fields. Healthcare is a sensitive field related to people’s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Furthermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed.

Software-defined network (SDN) is a new paradigm that decouples the control plane and data plane. This offered a more flexible way to efficiently manage the network. However, the increasing number of traffics due to the proliferation of the Internet of Things (IoT) devices also increase the number of flow arrival which in turn causes flow rules to change more often, and similarly, path setup requests increased. These events required route path computation activities to take place immediately to cope with the new network changes. Searching for an optimal route might be costly in terms of the time required to calculate a new path and update the corresponding switches. However, the current path selection schemes considered only single routing metrics either link or switch operation. Incorporating link quality and switch’s role during path selection decisions have not been considered. This paper proposed Route Path Selection Optimization (RPSO) with multi-constraint. RPSO introduced joint parameters based on link and switches such as Link Latency (LL), Link Delivery Ratio (LDR), and Critical Switch Frequency Score (CWFscore). These metrics encourage path selection with better link quality and a minimal number of critical switches. The experimental results show that the proposed scheme reduced path stretch by 37%, path setup latency by 73% thereby improving throughput by 55.73%, and packet delivery ratio by 12.5% compared to the baseline work.

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.

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