An Introduction to MultiAgent Systems.

http://wpage.unina.it/valerio.persico/pubs/CloudIoT_FGCS.pdf

Integration of Cloud computing and Internet of Things

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

IoT security: Review, blockchain solutions, and open challenges

Review: Intrusion detection system: A comprehensive review

Nurse Scheduling Problem is traditionally studied as a multi objective problem which aims at an optimum scheduling of nurse assignment to patients in a hospital. Remote healthcare is involved with providing quality care and medical assistance to patients based on remote monitoring. This paper presents an in-depth study of nurse scheduling algorithms. This study is followed with a description of the existing logics and the open issues. It also presents a heuristic framework with new features and a variable wait time based multi-parametric cost function for managing the dynamic NSP associated to remote healthcare.

A Heuristic Framework for Priority Based Nurse Scheduling.

The progress and improvements of distributed networks have played a decisive role in enabling researchers to consider new solutions for various VANET applications such as transportation, road safety, driving assistance, disaster management system, and lots more. Disaster response and management has enabled transport and communications to play an imperative role in dipping loss of life, economic cost, and disruption. VANETs can be measured as a rift of distributed networks. Vehicular ad hoc networks have been implemented as an automated system, and communication has been addressed through vehicular nodes. Since the last two decades, the number of disasters on the side of the road has evidently augmented due to the hasty augment in the number of road vehicles. The damage caused to the human race by disasters has been a severe crisis both national and international arena. In this proposal, we have proposed a scheme disaster management as an application of vehicular ad hoc networks. This proposal would help those affected with an automated system using the functionality of smart vehicles, which would correspond to each other through communication from vehicle to vehicle and could communicate with the drive side of the road through the vehicle–infrastructure communications. Our proposed disaster management system is developed as an application of existing communication system.

Disaster Management System Using Vehicular Ad Hoc Networks.

In a MANET, existing techniques for secure routing involves key generation, encryption and decryption logics besides secret sharing. The dynamic nature of MANETs leads to insecurities and inconsistencies in secret sharing. The existing logics for securing the packet delivery seem to involve too many control signal interchanges. This often leads to congestion in the network, thus causing degraded performance. This paper proposes the use of multiple agents to detect secure routes with minimum load on the network. The Chinese remainder theorem is used in generating the secure key by the source node. Source node shares the secure key among all probable routes using multiple agents. These agents reduce control packet overheads and thus reduce congestion in the network. The final route is selected based on the highest weighted trust value of the routes. The three phased approach is aimed to increase the overall performance in the network and also control congestion in the network using agent based approach. Simulation results have been included to justify our claim.

AESCRT: Agent enabled secure CRT based routing topology for MANET

Segmentation of brain tumor is a very crucial task from the medical points of view, such as in surgery and treatment planning. The tumor can be noticeable at any region of the brain with various size and shape due to its nature, that makes the segmentation task more difficult. In this present work, we propose a patch-based automated segmentation of brain tumor using a deep convolutional neural network with small convolutional kernels and leaky rectifier linear units (LReLU) as an activation function. Present work efficiently segments multi-modalities magnetic resonance (MR) brain images into normal and tumor tissues. The presence of small convolutional kernels allow more layers to form a deeper architecture and less number of the kernel weights in each layer during training. Leaky rectifier linear unit (LReLU) solves the problem of rectifier linear unit (ReLU) and increases the speed of the training process. The present work can deal with both high- and low-grade tumor regions on MR images. BraTS 2015 dataset has been used in the present work as a standard benchmark dataset. The presented network takes T1, T2, T1c, and FLAIR MR images from each subject as inputs and produces the segmented labels as outputs. It is experimentally observed that the present work has obtained promising results than the existing algorithms depending on the ground truth.

A Multi-class Image Classifier for Assisting in Tumor Detection of Brain Using Deep Convolutional Neural Network

The evolution of Internet of Things over the years has led to all time connectivity among us. However, the heterogeneity of the constituent layers of IoT makes it vulnerable to multiple security threats. One of the typical vulnerability of IoT involves the endpoint, i.e. the apps that are used by end users for enabling IoT services. Generally, the users have to authorize the app, during installation time, to perform certain tasks. Often the apps ask for permissions to access information which are not related to the IoT services provided by them. These over-privileged apps have the chance to turn malicious at any moment and use such information for their benefit. Sometimes, the users are naive enough to trust the apps and grant permissions without caution, thus leading to unintended exposure of personal information to malicious apps. It is important to analyze the app description for understanding the exact meaning of a stated functionality in the app description. In this paper, we have focused on the use of NLP in securing user data from malicious IoT apps by analysing their privacy policies and user reviews. This is followed by a novel proposal that supports cautious decision making of users based on a careful analysis of app behaviour.

Rituparna Chaki

Papers

A Heuristic Framework for Priority Based Nurse Scheduling.

Disaster Management System Using Vehicular Ad Hoc Networks.

AESCRT: Agent enabled secure CRT based routing topology for MANET

A Multi-class Image Classifier for Assisting in Tumor Detection of Brain Using Deep Convolutional Neural Network

A Novel Proposal of Using NLP to Analyze IoT Apps Towards Securing User Data.