This paper examines the resource block and power allocation in the power domain non-orthogonal multiple access (PD-NOMA) based cellular device-to-device (D2D) systems. To improve the energy efficiency of the D2D systems and to manage the mutual interference level as well as the quality of service (QoS) requirement of cellular users, different power level is applied to the D2D users sharing the same resource blocks (RBs) to the legacy users. It is essential to design an efficient resource block and power allocation method for PD-NOMA based cellular D2D systems which guarantee the successive interference cancellation (SIC) order in the power allocation solution. In this paper, we propose an iterative algorithm of resource block and power allocation for cellular D2D system which incorporates the SIC aware geometric water filling (GWF) method in the power allocation solution. It is shown that the proposed SIC aware geometric water filling achieves higher energy efficiency compared to iterative water-filling (IWF) power allocation and the GWF based orthogonal multiple access (OMA) method.

Routing or forwarding information, such as the location of incidents and victims in a disaster, is significantly important for quick and accurate incident response. However, forwarding such information in disaster areas has been a challenging task for the Wireless Sensor Network as existing networks are affected (destroyed or overused) the disaster. Opportunistic information forwarding can play a vital role in such circumstances. Existing opportunistic routing protocols require huge message transmissions for cluster restoration, which is not energy efficient and results in packet loss. Hence, this paper introduces an energy efficient and reliable opportunistic density cluster-based routing protocol that opportunistically transmits data using a density-clustering protocol for emergency and disaster situations. Simulation results show that the proposed protocol outperforms some existing and well-known routing protocols in terms of network energy consumption, throughput and successful data transmissions.

Sensors are being used in thousands of applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control. As these applications collect zettabytes of data everyday sensors play an integral role into big data. However, most of these data are redundant, and useless. Thus, efficient data aggregation and processing are significantly important in reducing redundant and useless data in sensor-based big data frameworks. Current studies on big data analytics do not focus on aggregating and filtering data at multiple layers of big data frameworks especially at the lower level at data collecting nodes (sensors) that reduce the processing overhead at the upper layer, i.e., big data server. Thus, this paper introduces a multi-tier data aggregation technique for sensor-based big data frameworks. While this work focuses more on data aggregation at sensor networks. To achieve energy efficiency it also demonstrates that efficient data processing at lower layers (sensor) significantly reduces overall energy consumption of the network and data transmission latency.

This paper presents an intelligent, sensor-driven framework that integrates emerging technologies to deliver smart, reliable, and accessible transit assistance for visually impaired people (VIP). The proposed system leverages Internet of Things (IoT), Internet of Devices (IoD), GPS, and crowdsensing to collect multimodal data, comprising audio, video, and environmental signals, used to characterize and respond to users’ real-time mobility needs. A cloud-based architecture performs centralized data fusion and decision-making using artificial intelligence (AI) and machine learning (ML) algorithms, enabling rapid interpretation of sensor inputs and generation of personalized navigation guidance. The framework is implemented through a mobile application that coordinates data exchange between edge devices and cloud services, providing context-aware navigation, obstacle alerts, and two-way communication with transit operators. Unlike existing assistive mobility solutions, which rely primarily on static location services and lack cross-sensor integration, the proposed system introduces a unified AI-enabled sensing layer that supports dynamic adaptation to complex urban environments. The results demonstrate the framework’s potential to enhance autonomy, safety, and situational awareness for VIPs, offering a scalable foundation for inclusive smart transportation systems.