Distributed and Load-Adaptive Self Configuration in Sensor Networks

Proactive self-configuration is crucial for MANETs such as sensor networks, as these are often deployed in hostile environments and are ad hoc in nature. The dynamic architecture of the network is monitored by exchanging so-called Network State Beacons (NSBs) between key network nodes. The Beacon Exchange rate and the network state define both the time and nature of a proactive action to combat network performance degradation at a time of crisis. It is thus essential to optimize these parameters for the dynamic load profile of the network. This paper presents a novel distributed adaptive optimization Beacon Exchange selection model which considers distributed network load for energy efficient monitoring and proactive reconfiguration of the network. The results show an improvement of 70% in throughput, while maintaining a guaranteed quality-of- service for a small control-traffic overhead

On certain equations of arbitrary length over torsion-free groups

Let $G$ be a non-trivial torsion free group and $t$ be an unknown. In this paper we consider three equations (over $G$) of arbitrary length and show that they have a solution (over $G$) provided two relations among their coefficients hold. Such equations appear for all lengths greater than or equal to eight and the results presented in this article can substantially simplify their solution.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0650

Spherically Symmetric Gravitational Collapse

In this paper, we discuss gravitational collapse of spherically symmetric spacetimes. We derive a general formalism by taking two arbitrary spherically symmetric spacetimes with $g_{00}=1$. Israel's junction conditions are used to develop this formalism. The formulae for extrinsic curvature tensor are obtained. The general form of the surface energy-momentum tensor depending on extrinsic curvature tensor components is derived. This leads us to the surface energy density and the tangential pressure. The formalism is applied to two known spherically symmetric spacetimes. The results obtained show the regions for the collapse and expansion of the shell.Comment: 12 pages, 4 figures, accepted for publication in Mod. Phys. Lett.

Performance tradeoffs in static and dynamic load balancing strategies

The problem of uniformly distributing the load of a parallel program over a multiprocessor system was considered. A program was analyzed whose structure permits the computation of the optimal static solution. Then four strategies for load balancing were described and their performance compared. The strategies are: (1) the optimal static assignment algorithm which is guaranteed to yield the best static solution, (2) the static binary dissection method which is very fast but sub-optimal, (3) the greedy algorithm, a static fully polynomial time approximation scheme, which estimates the optimal solution to arbitrary accuracy, and (4) the predictive dynamic load balancing heuristic which uses information on the precedence relationships within the program and outperforms any of the static methods. It is also shown that the overhead incurred by the dynamic heuristic is reduced considerably if it is started off with a static assignment provided by either of the other three strategies

A Novel 6G Conversational Orchestration Framework for Enhancing Performance and Resource Utilization in Autonomous Vehicle Networks

A vision of 6G aims to automate versatile services by eliminating the complexity of human effort for Industry 5.0 applications. This results in an intelligent environment with cognitive and collaborative capabilities of AI conversational orchestration that enable a variety of applications across smart Autonomous Vehicle (AV) networks. In this article, an innovative framework for AI conversational orchestration is proposed by enabling on-the-fly virtual infrastructure service orchestration for Anything-as-a-Service (XaaS) to automate a network service paradigm. The proposed framework will potentially contribute to the growth of 6G conversational orchestration by enabling on-the-fly automation of cloud and network services. The orchestration aspect of the 6G vision is not limited to cognitive collaborative communications, but also extends to context-aware personalized infrastructure for 6G automation. The experimental results of the implemented proof-of-concept framework are presented. These experiments not only affirm the technical capabilities of this framework, but also push into several Industry 5.0 applications

Dynamic symmetrical topology models for pervasive sensor networks

The success of pervasive computing environments using ubiquitous loco-dynamic sensing devices is very dependent upon the sensor deployment topology (DT) employed. This paper presents a systematic mathematical model for efficient sensor deployment and provides a comparison with other popular topologies. The model focuses upon blanket coverage of a surveillance area using a minimum number of sensing devices, with minimal infra-sensor overlapping to reduce collisions and co-existence problems. Simulation results are presented for the hexagonal, triangular and square grid topologies for various dimensions of surveillance area. The results confirm that the hexagonal model gives optimal performance in terms of requiring the minimal number of sensors. The paper also highlights the improved performance of ubiquitous wireless sensor networks when a hexagonal topology (HT) is used

SkillBot: Towards Data Augmentation using Transformer language model and linguistic evaluation

Creating accurate, closed-domain, and machine learning-based chatbots that perform language understanding (intent prediction/detection) and language generation (response generation) requires significant datasets derived from specific knowledge domains. The common challenge in developing a closed-domain chatbot application is the lack of a comprehensive dataset. Such scarcity of the dataset can be complemented by augmenting the dataset with the use of state- of-the-art technologies existing in the field of Natural Language Processing, called ‘Transformer Models’. Our applied computing project experimented with a ‘Generative Pre-trained Transformer’ model, a unidirectional transformer decoder model for augmenting an original dataset limited in size and manually authored. This model uses unidirectional contextual representation i.e., text input is processed from left to right while computing embeddings corresponding to the input sentences. The primary goal of the project was to leverage the potential of a pre-trained transformer-based language model in augmenting an existing, but limited dataset. Additionally, the idea for using the model for text generation and appending the generated embedding to the input embedding supplied was to preserve the intent for the augmented utterances as well as to find a different form of expressions for the same intent which could be expressed by the potential users in the future. Our experiment showed improved performance for understanding language and generation for the chatbot model trained on the augmented dataset indicating that a pre-trained language model can be beneficial for the effective working of natural language-based applications such as a chatbot model trained on the augmented dataset indicating that a pre-trained language model can be beneficial for the effective working of natural language-based applications such as a chatbo

The Refined Topological Vertex

We define a refined topological vertex which depends in addition on a parameter, which physically corresponds to extending the self-dual graviphoton field strength to a more general configuration. Using this refined topological vertex we compute, using geometric engineering, a two-parameter (equivariant) instanton expansion of gauge theories which reproduce the results of Nekrasov. The refined vertex is also expected to be related to Khovanov knot invariants.Comment: 70 Pages, 23 Figure