Heterogeneous network is a dedicated approach to leveraging the potential benefits of all existing wireless networks by integrating diverse radio access technologies with varying specifications to fulfill the demands of mobile users. Two primary factors that influence a mobile user to switch network connections are their mobility and changing network conditions. An efficient vertical handover solution can enable the seamless transfer of an ongoing user's connection to a better-suited network. This paper proposes an intelligent, optimized vertical handover decision algorithm with a two-phase approach. First, a feasible network category (i.e., small cell or macro cell) is determined based on user speed. This phase ensures fast-moving users do not experience handover abnormalities during communication. Then, an optimal target node is selected from this feasible set using a hybrid multi-criteria decision-making approach. Second, a dynamic$\epsilon$-greedy-based Q-learning technique is employed to learn the near-optimal handover triggering point, ensuring timely and successful handover execution. Crucially, the$\epsilon$value is not rigidly fixed; instead, it adapts dynamically, driven by rewards from the environment. The conclusions drawn from network simulations illustrate that the proposed algorithm outperforms other existing algorithms by minimizing handover delays, ping-pong effects, failure rates, and packet loss rates.

Vehicular Ad hoc Networks (VANETs) provide an extensive number of services through a collection of applications for intelligent transportation. It mainly reduces the difficulties for the drivers of vehicles by generating alerts for any unforeseen road accidents, warnings and road congestions in real time. Wireless Body Area Network (WBAN) facilitates remote health monitoring for patients and elderly people in healthcare sector. This paper proposes an architecture which combines WBAN and VANET — WBAN is used for patient monitoring while travelling and VANET takes the charge of delivering the alert message generated from abnormal physiological data of the patient to its destination. A Road Side Unit (RSU) allocates distinct channels to the On Board Units (OBU) of vehicles that ask for it. However, allocation scheme taking the priority of the OBUs into account allocates the channels. An OBU will send the emergency messages using the assigned channel. In this work, we develop a novel technique to compute the priority of vehicles using three important criteria such as severity of the message, vehicle speed, and channel occupancy time. The technique utilized two multi-criteria based decision making schemes — Analytic Hierarchy Process (AHP) for finding a weight for individual criterion, and Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) for computing normalized priority values of vehicles. The main aim of this work is to minimize the End-to-End delay of alert messages. Our simulation study shows that our proposed scheme provides better results as compared to the standard scheme.

In this paper, we envisage the architecture of Green Wireless Body Area Nanonetwork (GBAN) as a collection of nanodevices, in which each device is capable of communicating in both the molecular and wireless electromagnetic communication modes. The term green refers to the fact that the nanodevices in such a network can harvest energy from their surrounding environment, so that no nanodevice gets old solely due to the reasons attributed to energy depletion. However, the residual energy of a nanodevice can deplete substantially with the lapse of time, if the rate of energy consumption is not comparable with the rate of energy harvesting. It is observed that the rate of energy harvesting is nonlinear and sporadic in nature. So, the management of energy of the nanodevices is fundamentally important. We specifically address this problem in a ubiquitous healthcare monitoring scenario and formulate it as a cooperative Nash Bargaining game. The optimal strategy obtained from the Nash equilibrium solution provides improved network performance in terms of throughput and delay.

A Wireless Bio-nanosensor Network (WB2N) is a collection of bio-nanodevices having applications in e-health. In this paper, we address the issue of catastrophic collision - a phenomenon which exhibits recurrent collisions of femtosecond-long pulse symbols emanating from the nanodevices in a WB2N. Such type of collision is very serious in these networks due to unique properties of the terahertz band (0.1-10 THz). The existing sate-of-the-art on the issue of coordination for medium access by nano-devices is based on asynchronous exchange of pulses by assigning different symbol rates. The existing method of choosing the symbol rate does not completely avoid catastrophic collision. The severity of collision is further pronounced when molecular absorption noise gets compounded. In essence, a few number of collisions, in turn, invites huge number of such events for the subsequent transmission and eventually degrades the whole network performance. So, it is important to handle such collisions for successful execution of protocols of the higher layers. In present work, we analyze the catastrophic collision in detail and model the collision. The preliminary results exhibit the severity of such collisions in the network. It requires immediate attention in order to accept WB2Ns to be successful e-health system.