Present MLL (Multimodal Large Language) models do exceptionally well in computer vision tasks, such as answering virtual questions and captioning images. However, they are not up to par in important perceptual tasks like object detection. To overcome this constraint, an entirely novel research work is presented for contextual object detection, which aims to comprehend observable items in various human-AI interaction scenarios. This study investigates two widely used scenarios: captioning the images and virtual question answering. For human-machine interaction, a brand-new CODNet (Context-based Object Detection Network) is presented that can locate, recognize, and connect visual objects with spoken inputs. The proposed network is capable of end-to-end modeling of visual language contexts. This network unifies language and vision functions by interpreting images in a foreign language and coordinating language functions with vision-centric jobs that may be managed and created flexibly using language instructions. The network comprises three sub-modules: an encoder that extracts the required features from the context, a pre-trained large language model meta AI (Llama) for decoding the context, and a decoder for predicting anchor boxes around the contextually recognized objects in images. The features are extracted with the help of a convolutional neural network merged with multiplicative LSTM. A fine-tuned YOLOv6 model detects and locates the required objects in the image provided. The network is trained on the MS-COCO dataset, evaluated on real-time images, and achieved over 95% average weighted accuracy. • Context-based object detection refers to identifying objects within an image or a sequence of images by considering the surrounding context in which these objects appear. For example, a keyboard is usually found near a computer. Some objects are more likely to appear together. For example, a fork and a knife are often found together on a dining table. • This study presents a new context-based object detection network that defines a new direction in visual object detection and improves LLMs. • A novel CODNet network is proposed that helps in first generating the object words and then classifying the objects in the images. The network comprises three sub-modules: encoder, Llama, and decoder. The network combines CNN-mLSTM (Convolutional Neural Network merged with multiplicative LSTM) to help extract features from provided images. Llama is a pre-trained large language model for decoding the context and finetuned YOLOv6 for object detection. • A new set of object words, namely, ERCODE (Easy Readable Context-Based Object DEtection), is also proposed in this work to help classify required objects in images.

rough wireless sensor networks (WSN), we can acquire the various interesting event information around sensor nodes through multihop communications.In WSN, there are two types of applications, that is, event or query based.Commonly, in these application, the value on each sensor node is very sensitive to delay or latency.So, it is strongly required to deliver data to sink node within the deadline since data received aer the deadline is not acceptable at all in WSN. e good example of application demanding real-time communication in WSN includes tracking of moving object and intrusion detection.However, compared to typical networks, it is very difficult to achieve real-time communication in WSN.Severe constraints such as limited computing power and narrow bandwidth are not suitable to provide real-time communication accordingly.So, a number of important issues and research challenges have to be addressed to provide realtime communication in WSN.Based on this demand, this special issue is planned to contribute to advances in real-time communications in WSN.While considering our objective, editors believe that this special issue provides collection of articles on networking technique in real-time communications.We have selected valuable papers by evaluating several aspects such as relevance to special issue and novelty of solution.e topic of these papers is roughly categorized into the following areas: multichannel transmission, MAC protocol, testbed for routing protocol, and comprehensive survey for real-time in WSN.

Wireless Sensor Networks (WSNs) are formed of various nodes that gather parameters in a monitored environment. These nodes interact with each other or can be arranged into clusters controlled by a cluster head that has the task of rerouting the acquired data to a base station. Both the Quality of Service (QoS) and low data quality are common issues in WSNs, mainly prompted by the data fusion mechanism, where a certain amount of low-quality data may affect the overall fusion result negatively. In this paper, a fuzzy-based solution for data fusion in WSNs is presented to provide a better QoS and to reduce the energy consumption. The suggested approach can aggregate only true value rather than process the full data. This purpose is accomplished thanks to a Fuzzy Logic Controller (FLC) implemented within nodes. Besides, the data, which have been separated, are aggregated by a cluster head which also has the responsibility of determining the probability that an event has happened in the monitored environment. Finally, the base station estimates whether an event has occurred and, eventually, raises an appropriate alarm. The results of a real testbed scenario reveal that the proposed method achieves encouraging performance.

Smart grids are an evolution of the existing electric distribution systems due to the growing demand of energy, the expansion in the use of renewable energy sources, and the development of novel and innovative information and communication technologies (ICT). The installation of systems based on wireless networks can play a key role in the extension of the smart grid toward smart homes, that can be deemed as one of the most important components of smart grids. In fact, monitoring and control applications, energy harvesting, and innovative metering methodologies through smart wireless devices are becoming increasingly important. This paper proposes a novel energy management approach for smart homes that combines a wireless network, based on bluetooth low energy (BLE), for communication among home appliances, with a home energy management (HEM) scheme. The proposed approach addresses the impact of standby appliances and high-power rating loads in peak hours to the energy consumption charges of consumers. Simulation results show that the proposed approach is efficient in terms of reducing peak load demand and electricity consumption charges with an increase in the comfort level of consumers.

The IEEE 802.15.4 standard protocol for Wireless Sensor Networks (WSNs) offers several advantages, such as the low power consumption, which can also be applied in industrial environments. However, many researchers highlighted some limitations also. For example, the CSMA/CA algorithm, that manages the access to the medium, is a contention-based approach and, as a consequence, it is non deterministic. This paper proposes an approach that aspires to an efficient use of the available bandwidth reducing the number of collisions using a fuzzy logic controller that dynamically adjusts the Contention Window (CW) and the Backoff Exponent (BE) parameters of the CSMA/CA, taking into account the Throughput (TH) and the Workload (WL) of an industrial IEEE 802.15.4 WSN.

This paper shows an innovative Wireless Sensor Network (WSN) architecture based on a flexible Quality of Service (QoS) approach for road traffic management. The aim of this work is to analyse an algorithm that dynamically enables/disables some cameras according to the real need to monitor a given area, It is based on traffic volume measured values. This approach has been developed in order to manage both network topology and workload conditions, using a fuzzy logic controller for a flexible QoS management. Performances of this approach have been evaluated using True Time and Simulink/Matlab.

Advantages provided by the use of wireless technologies in industrial scenarios, like reduced costs of installation, better flexibility, the ability to easily realize temporary deployments (e.g. for monitoring purposes), etc. are discussed by researchers in literature. In these contexts, time-critical transmissions have to be supported by automation networks. In IEEE 802.11 standard protocol, one of most popular technologies for process control networks, Access Points (APs) are responsible for nodes connection. Timeliness of communications can be compromised by transmission of wireless nodes through overloaded APs. This paper shows a new dynamic Load Balancing approach that distributes network load in order to ensure, for each station, best possible performances in terms of soft real-time constraints, in an industrial process control scenario.

In industrial Wireless Sensor Networks (WSNs), traffic flows are characterized by regular arrival times and real-time constraints. Sampling times management is a key aspect in terms of timeliness in information retrieval and real-time management of critical events in factory automation. This paper shows an algorithm that dynamically update sampling times in an industrial monitoring system based on WSNs. Sampling times are determined using gathered information and network efficiency parameters.