Showing papers by "Rituparna Chaki published in 2022"
TL;DR: The proposal employs to mitigate congestion while messages are being forwarded via an alternate route to distribute the traffic and increase the throughput, and efficiently relieves congestion while preserving the network's performance for attaining QoS in wireless sensor networks.
Abstract: Congestion is a significant issue for event-based applications due to the continuous data collection and transmission by the sensors constituting the network. The congestion control technique monitors the process of adjusting the data and intends to manage the network traffic level to the threshold value. The information gathered from an intensive study is required to strengthen the knowledge base for devising a QoS based congestion evasion clustering framework of wireless sensor networks. In this scheme, the cluster heads are optimally determined and dispersed over the network. The data aggregation approach has been applied in a clustered network and set out a crucial paradigm for WSN routing. The proposal employs to mitigate congestion while messages are being forwarded via an alternate route to distribute the traffic and increase the throughput. This technique aims to balance the energy ingestion among the sensor nodes, reduce energy consumption, improve network lifetime, and achieve the quality of services. The result analysis revealed that the proposed scheme recommends 22.5% better throughput, 21% lesser end-to-end delay, 25.5% better delivery ratio, and efficiently relieves congestion while preserving the network's performance for attaining QoS in wireless sensor networks.
2 citations
01 Jan 2022
TL;DR: In this article, the authors proposed a new approach to the well-studied nurse scheduling problem, which considers patient recovery as the ultimate objective of nurse scheduling and considers patient needs and priorities besides the nurse skills, and environment (e.g., logistic) parameters as basic constraints.
Abstract: This paper introduces a new approach to the well-studied nurse scheduling problem. Nurse scheduling problem involves multiple inter-related parameters concerning nurse and patients, which makes the problem too complex. As a result, many of the traditional NSPs are forced to consider only the nurse-respective parameters for generating the schedules. In this paper, we have considered patient recovery as the ultimate objective of nurse scheduling. To achieve this, we have considered patient’s needs and priorities besides the nurse skills, and environment (e.g. logistic) parameters as basic constraints. This paper aims to minimize soft constraints as well as to improve patient’s satisfaction and quality of service of nurse assignment. We have defined a number of hard and soft constraints based on patient’s requirements, ailments, preferences for a particular nurse, nurse’s skill parameters, penalty, demands on duties, matching quotient with patient’s requirements, location, etc. The assignment of nurses to patients for a particular shift depend on the relation between patient’s need and the skill factor of the nurse, besides, of course, the availability factor of the nurse. This helps in achieving efficiency of the overall solution, besides properly supporting qualitative issues. In this regards, two objective functions are devised here to maximize the nurse’s rewards and minimize the scheduling computational cost. The resulting algorithm has been tested on real-case scenarios of a nursing centre, providing evidence of the actual advantages of the proposed solution.
1 citations
04 Feb 2022
TL;DR: In this paper , the authors consider the use of natural language processing (NLP) in securing user data from malicious IoT apps by analyzing their privacy policies and user reviews, and describe a technique to aid in decision-making of users based on a careful analysis of app behavior.
Abstract: The evolution of Internet of Things (IoT) 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 vulnerabilities of IoT involves the end point, 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 overprivileged apps have the chance to turn malicious at any moment and use the information against the user's interest. 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. This chapter considers the use of natural language processing (NLP) in securing user data from malicious IoT apps by analyzing their privacy policies and user reviews. This is followed by the description of a technique to aid in decision-making of users based on a careful analysis of app behavior.
01 Jan 2022
04 Feb 2022
TL;DR: In this paper , the authors proposed a mitigation of denial-of-service (DoS) attacks at the base layer of an IoT network, i.e., the wireless sensor networks (WSN).
Abstract: Internet of Things (IoT) consists of a complex interconnection of different types of networks. There is no single solution to the security threats faced by each layer of the IoT network. In this chapter, we deal with the mitigation of denial-of-service (DoS) attacks at the base layer of an IoT network, i.e., the wireless sensor networks (WSN). The individual task load on each node of the network is reduced by using the mobile agents for detecting and reporting the occurrence of a DoS attack. In order to improve scalability of the solution, we have modeled the underlying network using the de Bruijn graph. We have added a case study and result analysis that establish the effectiveness of the proposed logic.
TL;DR: The main intent of this article is to frame the dynamic NSP in the cloud using a multi‐objective optimization strategy called sea lion attacking‐based deer hunting optimization algorithm to generate a feasible and near‐optimal schedule at the end of the horizon.
Abstract: The main intent of this article is to frame the dynamic NSP in the cloud using a multi‐objective optimization strategy. The benefit of the dynamic scheduling over static scheduling is that the scheduling will be done week by week based on the available number of nurses. For solving this problem, several constraints like single assignment per day, under‐staffing, shift type successions, consecutive assignments, consecutive resting days, and complete week‐end are considered. With reference to these constraints, a minimized objective function is used based on the nurse demand in week by week. Each constraint is having some conditions for solving this problem. These schedules are stored in cloud for efficient decision making regarding NSP and it helps in assisting the future scheduling purposes. It also offers secure storage when compared to the other storage devices. As a novelty, this article tries to employ the hybrid meta‐heuristic algorithm called sea lion attacking‐based deer hunting optimization algorithm. Hybrid optimization algorithms have been reported to be promising for certain search problems with a higher convergence rate. Hence, the developed hybrid optimization algorithm hardly helps to generate a feasible and near‐optimal schedule at the end of the horizon.