In today’s world, the safety of children is of utmost importance due to numerous compelling factors, for example, accidents and injuries. In this fast-paced lifestyle of the new age parents, they might not always be able to accompany their children everywhere therefore the need for the tracking of the child including safety considerations and also the parent’s desire to stay connected with their child and in the absence of the parents the guardian of the child can look up for the safety of the child. This paper presents an implementation of the smart bag for toddlers which is built using blockchain technology and language solidity that ensures the tracking facility of the child. blockchain is the decentralized ledger technology that provides transparency and security between the networks. Therefore we have created a digital contract known as the smart contract on blockchain technology named a smart bag for toddlers. A smart contract is a digital agreement that is signed and stored on the blockchain and executes automatically when its terms and conditions are met.

Unmanned Aerial Vehicles (UAV) are highly versatile systems with applications expanding in various fields, for instance, Surveillance, Reconnaissance, Disaster Response, Agriculture, and many more. Although UAVs have many important advantages over conventional manned aircraft, such as cheaper operating costs and a lower risk for human pilots, their vulnerability to Radio Frequency (RF)-jamming poses a substantial risk to navigation and communication systems by disrupting signals. UAVs depend on remote control and autonomous operations. These attacks are concerning in many application areas. Communication and navigation play crucial roles in these autonomous systems. This paper aims to explore different deep-learning algorithms for the detection of RF-jamming attacks in UAVs. A comparison analysis is conducted to evaluate five distinct architectures using conventional evaluation metrics criteria comprising accuracy, precision, recall, confusion matrices, and the area under the ROC curve. These comparative analyses helped in selecting an accurate architecture for the definition, RNN architecture gave an impressive accuracy of 93% and demonstrated a superior performance than other architectures. This paper aims to strengthen RF-jamming detection systems in UAVs to enhance their safety and operational reliability in several scenarios.

The quick progress of technology has greatly influenced multiple industries, with Artificial Intelligence (AI) and Robotic Process Automation (RPA) leading the way in this transformation. When combined, these technologies can improve business procedures, increase productivity, and lower operational expenses in various sectors. The primary goal of this task is to examine how AI technologies can be incorporated into current RPA frameworks to promote the development of intelligent automation systems. These improved systems are created to not just complete specific tasks but also to autonomously make decisions and flexibly optimize operations. This research aims to link AI theoretical models with their practical uses in RPA, highlighting recent advancements and predicting future trends in AI-driven RPA solutions.

In dynamic defense operations, satellite imagery has a major role in offering crucial insights into geographical landscapes and possible dangers. Satellite imagery provides an aerial view of the battlefield environment, which helps strategically improve decision-making. The proposed framework makes use of deep learning techniques to segment satellite images into particular regions. The image is segmented into different classes, such as land, buildings, roads, vegetation, and water. The combination of the segmentation model with a comprehensive situational awareness framework allows military personnel in analyzing terrain, identification of infrastructure, and detection of anomalies. This helps improve operational effectiveness and rapid response in dynamic battlefield environments.

Software Defined Networks (SDN) utilize the network devices as virtual devices to analyze the traffic and transmit the relevant packets. It is quite beneficial for the internet of Things (IoT) where the huge number of devices share the packets which cannot be handled by the conventional networks. SDN is also essential to manage congestion scenarios during massive communication especially in video streaming. The main problem arises when the residual queues on the network path are left underutilized whereas the bandwidth is reduced as per the minimum bandwidth link between any two nodes in the path. It occurs based on the feedback based congestion avoidance mechanism. This work presents a proposed mechanism to utilize the residual queue in the communication path to enhance the minimum bandwidth. It maintains the average queue length to analyze the percentage of queue utilized and then decide about the packet dropping probability. In proposed scheme, the congestion can be managed earlier whereas the base scheme applies a constraint of double the size of previous threshold which may result in identifying the congestion at a later stage where the retreat is hardly possible. According to the findings in results after simulations, the proposed IAGRED dominates the counterparts in terms of queue delay, queue length, link utilization and throughput.

Dementia patients living alone in the communities without much support from near and dear ones find it chal-lenging to receive instant help when needed including during emergencies. This work focuses on designing an end - to-end response system primarily to support early-stage Alzheimer's dementia (AD) patients living alone in their homes, in case of emergencies, including medical emergencies. The system aided with pervasive technologies can integrate AD patients, informal caregivers, and formal caregivers to support AD patients in need. Informal caregivers act as first responders to attend to patients and selecting appropriate informal caregivers based on certain predefined parameters is an important component of this system. This work has used single and ensemble Machine Learning (ML) algorithms for binary (to check if informal caregiver is available) and multiclass (to select the most suitable informal caregiver) classification.

Globally, an increasing number of elderly people live alone at home either by choice or out of compulsion, and many have health issues. During a medical emergency involving an elderly person living alone, getting prompt access to formal medical aids like calling an ambulance or paramedics can be critical. Moreover, an ambulance can also arrive late adding to the patient's suffering. In such situations, informal caregivers can act as first responders to attend to the patients even before an ambulance arrives. The selection of appropriate informal caregivers to attend a medical emergency incident remains a key issue and this paper proposes an informal caregiver selection process based on certain pre-defined characteristics of informal caregivers. Single and ensemble multiclass classification algorithms are considered in this work to rank informal caregivers and preliminary simulation results have shown that ensemble models perform better than single models.

Natural disasters, such as earthquakes, can cause severe destruction and create havoc in the society. Buildings and other structures may collapse during disaster incidents causing injuries and deaths to victims trapped under debris and rubble. Immediately after a natural disaster incident, it becomes extremely difficult for first responders and rescuers to find and save trapped victims. Often searches are carried out blindly in random locations, which delay the rescue of the victims. This paper introduces a Smartphone Assisted Disaster Recovery (SmartDR) method for post-disaster communication using Smartphones. SmartDR utilizes the device-to-device (D2D) communication technology in Fifth Generation (5G) networks, which enables direct communication between proximate devices without the need of relaying through a network infrastructure, such as mobile access points or mobile base stations. We examine a scenario of multi-hop D2D communication where smartphones carried by trapped victims and other people in disaster affected areas can self-detect the occurrence of a disaster incident by monitoring the radio environment and then can self-switch to a disaster mode to transmit emergency help messages with their location coordinates to other nearby smartphones. To locate other nearby smartphones also operating in the disaster mode and in the same channel, each smartphone runs a rendezvous process. The emergency messages are thus relayed to the functional base station or rescue centre. To facilitate routing of the emergency messages, we propose a path selection algorithm, which considers both delay and the leftover energy of a device (a smartphone in this case). Thus, the SmartDR method includes: (i) a multi-channel channel hopping rendezvous protocol to improve the victim localization or neighbour discovery, and (ii) an energy-aware multi-path routing (Energy-aware ad-hoc on-demand distance vector or E-AODV) protocol to overcome the higher energy depletion rate at devices associated with single shortest path routing. The SmartDR method can guide search and rescue operations and increase the possibility of saving lives immediately aftermath a disaster incident. A simulation-based performance study is conducted to evaluate the protocol performance in post-disaster scenario. Simulation results show that a significant performance gain is achievable when a device utilizes the channel information for the rendezvous process and the leftover energy for routing path selection. Our results show that peer discovery in multi-channel D2D environment can be significantly improved when channel quality information is considered in CH sequence design. Moreover, selecting a routing path based on LoE of a device and the standard deviation of residual energy of a path, can not only enhance the network lifetime, but also reduce the chance of network being partitioned.

Technology and policy are coming together to enable a paradigmatic change to the most widely used mechanism, exclusive rights, which allows mobile telecommunications operators to use the radio spectrum. Although spectrum sharing is not a new idea, the limited supply of spectrum and the enormous demand for mobile broadband services are forcing spectrum authorities to look more closely into a range of tools that might accelerate its adoption. This paper seeks to understand how co-existence and co-operation of Wi-Fi and cellular networks in the unlicensed spectrum can increase the overall capacity of heterogeneous wireless networks. It also reveals the challenges posed by new uses, such as machine-to-machine communications and the Internet of Things. It also brings together two major proposed regulatory approaches, such as those by the U.K.'s Ofcom and the European Commission, which currently represent leading efforts to provide spectrum authorities with robust spectrum sharing frameworks, to discuss policy tools likely to be implemented.

We propose a fast and reliable target Evolved Node B (TeNB) selection scheme for Long Term Evolution-Advanced (LTE-A) handover (HO). Selection of the TeNB in LTE-A HO procedure is based on signal strength requirements and thus may not be ideal. Also, during each measurement period the user equipment (UE) needs to perform scanning of multiple neighboring eNB (NeNB) frequencies resulting in delays too large for high speed services. In our scheme, the TeNB is chosen based on three different parameters: the orientation match, the current load (CL) of NeNBs and their received signal strengths (RSS). This selection based on three independent parameters makes it more reliable, enhancing HO performance. Moreover, as a UE only measures the NeNBs shortlisted based on these parameters, the measurement time is also reduced, which leads to a reduction in the overall HO time.

Present Internet architecture is facing a number of challenges related to security, scalability, ubiquitous computing, management etc. Many initiatives are being carried out globally on designing new architecture for Future Internet which will address these issues. Large scale testbeds are required to validate new architectural designs. In this regard, two major initiatives being carried out are the European Union's (EU) Future Internet Research and Experimentation (FIRE) and USA's Global Environment for Network Innovations (GENI). Apart from these, other countries like Japan, China and Korea are also working towards their own testbeds. This paper gives an overview of large scale testbed initiatives across the world and discusses the major challenges to the development of the global Future Internet testbed. It also compares two major regional testbed architectures and suggests development of formal architecture for worldwide global testbed which will eliminate improvident investments of time, resources and duplicate efforts.

This paper proposes a mobile station (MS)-controlled fast MAC-layer handover (HO) scheme to reduce the HO latency in Mobile WiMAX. Based on the received signal strength (RSS) from any Base Station (BS), the MS can estimate its present distance from that BS. This capability allows the MS, through utilization of the broadcast messages by its Serving BS (SBS) and through few scannings of the Neighbouring BSs (NBS), to self-track its own direction of motion relative to the SBS and the NBSs and thus look ahead to perform an intelligent choice of the Target BS (TBS) for the impending HO. Simulation studies have shown a considerable reduction in the HO latency in the proposed method.