In the healthcare research community, Internet of Medical Things (IoMT) is transforming the healthcare system into the world of the future internet. In IoMT enabled Computer aided diagnosis (CAD) system, the Health-related information is stored via the internet, and supportive data is provided to the patients. The development of various smart devices is interconnected via the internet, which helps the patient to communicate with a medical expert using IoMT based remote healthcare system for various life threatening diseases, e.g., brain tumors. The brain tumor is one of the most dreadful diseases ever known to human beings. Often, the tumors are predecessors to cancers. The survival rates for these diseases are very low. So, early detection and classification of tumors can save a lot of lives. IoMT enabled CAD system plays a vital role in solving these problems. Deep learning, a new domain in Machine Learning, has attracted a lot of attention in the last few years. The concept of Convolutional Neural Networks (CNNs) has been widely used in this field. In this paper, we have classified brain tumors into three classes, namely glioma, meningioma and pituitary, using transfer learning model. The features of the brain MRI images are extracted using a pre-trained CNN, i.e. GoogLeNet. The features are then classified using classifiers such as softmax, Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN). The proposed model is trained and tested on CE-MRI Figshare dataset. Further, Harvard medical repository dataset images are also considered for the experimental purpose to classify four types of tumors, and the results are compared with the other state-of-the-art models. Performance measures such as accuracy, precision, recall, specificity, and F1 score are examined to evaluate the performances of the proposed model.

The future advancement of technology in Internet of Things (IoT) paradigm, Wireless Sensor Networks (WSNs) provide sensing services to connect all the devices. In the upper layer of OSI model designing an energy efficient routing protocol in WSN is a challenge, which can ease the work of Multi-access edge computing (MEC) in IoT applications. The advent of 6G is also playing key role for reliable communication between the sensing elements for IoT applications. These two phenomena are significantly influencing for the progress of next generation Intelligent Transportation System (ITS). Therefore, the proposed work presents a novel method of implementing Distributed Artificial Intelligence (DAI) with neural networks for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS. Although there exist several works on the inter-cluster energy-efficient network, our work proposes a new way of implementing the hybrid approach of DAI and Self Organizing Map (SOM). The proposed approach proves to be a better solution in terms of overall energy consumption by the network, along with the computational challenges. Further, the work presents mathematical analysis, simulation results and comparison with the conventional techniques for justification.

The Device-to-device (D2D) networks bear a close resemblance to future Internet of Things (IoT) networks. Resource management is an important aspect for realization of D2D communication in upcoming IoT networks. In this paper, we select underlay in-band D2D communication as it is more beneficial in terms of spectral efficiency even though it comes at the cost of interference with cellular communication. To deal with this difficulty, we propose a novel downlink resource sharing strategy, where multiple D2D users (D2Ds) share the resource of a single cellular user (CU), and each D2D pair reuses multiple channels from different cellular users (CUs). The proposed scheme adopts a channel selection technique, wherein multiple channels of CU can be shared by each D2D user. Furthermore, optimal power for each D2D user is determined through the Lagrangian dual optimization method. The formulated power control maximization scheme nicely balances the total transmission power of D2D and D2D sum-rate. The proposed channel and power allocation problem aims at maximizing the D2D sum-rate by increasing the number of active D2D links while preserving the quality of service (QoS) of CU. Finally, a relationship between energy efficiency (EE) and transmit power of D2D is investigated through an EE maximization problem. The overall system performance is evaluated in terms of the D2D shared ratio, throughput gain of the network, and computational complexity of the proposed optimal strategy. Further, the merits of using the proposed resource sharing scheme over the existing schemes are also verified through numerical results.

Industrial Internet of Things (IIoT) paves way into Industry 5.0, which incorporates human–machine collaboration, thereby making manufacturing industry efficient. As 5G architecture supports massive IoT connectivity and has higher spectrum efficiency, device-to-device (D2D) communication is favorable at 28 GHz. While transmitting data from sensors to end user through IoT network, interference affects the system. Thus, an efficient resource allocation scheme is needed for minimizing interference and increasing data rate. Here, formulated problem is divided into two subproblems, channel assignment and power optimization in order to lower computational complexity. A partial resource multiplexing scheme is proposed that will allocate channels to available D2D users. Later, power optimization problem is formulated which is determined through Lagrangian dual optimization technique. Dynamic sectorization overcomes issue of increase in user traffic. Stability factor, fairness index (FI), and energy efficiency depict the performance superiority of proposed scheme over existing schemes. Simulation results prove efficacy of proposed system.

Intelligent transportation systems (ITS) in smart cities offer extensive network coverage and simultaneous connections, even in high-mobility scenarios within the IoT network. This way, 5G technology will become a key enabler for IoT and ITS by allowing massive vehicular connections, high throughput, low latency, and wide coverage. Thus, resource sharing becomes very important in dealing with massive IoT connections in ITS and providing efficient communication among vehicles for smart cities. We propose a resource-sharing scheme for Cellular-vehicle-to-everything (C-V2X) communication in 5G cellular networks for smart cities, having two groups of users: Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). Our primary goal is to maximise the throughput of V2V pairs and V2I users while preserving the QoS for both users. The maximisation problem formulated is non-convex and is solved using the Lagrange multiplier method by optimizing the transmit power of both V2V and V2I users. The overall system performance is evaluated in terms of throughput of V2V and V2I users for different SINR, number of vehicles, velocities, and transmit power of vehicular users. Furthermore, the EE of V2V communication is analyzed in terms of the transmit power of VUs. Also, the proposed resource-sharing scheme is compared with the existing schemes, demonstrated through simulations.

A novel interference management paradigm has been proposed for overlay mode of Device-to-Device (D2D) communication in mm-Wave 5G networks. The theory proposes alternate offer bargaining game (AOBG) for the problem eradication which is a non-symmetric Nash bargaining solution. In this game, players considered are D2D users in the cell. The payoffs received by the players are transmit power. Neighboring D2D users compete with each other for higher signal to interference plus noise ratio (SINR). The proposed algorithm proves that equilibrium can be achieved wherein the system throughput can be increased without compromising the SINR. The numerical results show the effectiveness of the algorithm. Furthermore, simulation results of the proposed scheme have been compared with the existing scheme.