In an integrated sensing and communication (ISAC) system, improving the accuracy of delay and Doppler shift sensing parameter estimation is a critical task that sustains the performance of the communication system. To tackle this task, we introduce a two-stage estimation algorithm named matched filter-Fibonacci (MF-F), which employs the orthogonal time frequency space (OTFS) waveform characteristics in the delay-Doppler (DD) domain. In the first step (MF), the sensing parameters are approximated on an integer grid using the relationship between the input and output signals of the DD domain ISAC model. This approximation is executed on an integer grid using the cyclic shift property of the matrix. In the following step (F), we implement a two-dimensional (2D) searching technique based on the Fibonacci sequence, called the Fibonacci search method, to achieve sensing parameter estimation with fractional accuracy. This method decreases the number of comparisons needed and enhances the search process speed. Finally, the proposed method in this study was tested using numerical simulations and hardware experiments. The results demonstrate that the MF-F method can precisely estimate the speed and distance with millimeter-level accuracy and has robustness and low complexity in numerical simulation. Moreover, the estimated Doppler and delay parameters in the hardware experiment can respectively reach the centimeter and meter level.

The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots.

The rapid development of the Internet of Things (IoT) has opened the way for transformative advances in numerous fields, including healthcare. IoT-based healthcare systems provide unprecedented opportunities to gather patients’ real-time data and make appropriate decisions at the right time. Yet, the deployed sensors generate normal readings most of the time, which are transmitted to Cluster Heads (CHs). Handling these voluminous duplicated data is quite challenging. The existing techniques have high energy consumption, storage costs, and communication costs. To overcome these problems, in this paper, an innovative Energy-Efficient Fuzzy Data Aggregation System (EE-FDAS) has been presented. In it, at the first level, it is checked that sensors either generate normal or critical readings. In the first case, readings are converted to Boolean digit 0. This reduced data size takes only 1 digit which considerably reduces energy consumption. In the second scenario, sensors generating irregular readings are transmitted in their original 16 or 32-bit form. Then, data are aggregated and transmitted to respective CHs. Afterwards, these data are further transmitted to Fog servers, from where doctors have access. Lastly, for later usage, data are stored in the cloud server. For checking the proficiency of the proposed EE-FDAS scheme, extensive simulations are performed using NS-2.35. The results showed that EE-FDAS has performed well in terms of aggregation factor, energy consumption, packet drop rate, communication, and storage cost.

An intra-band pattern-corrected decoupling vertical conducting wall is realized by dielectric substrate with conductor cladding on both side wall between two tightly spaced H-plane microstrip patches with λ₀/20 edge-to-edge spacing. The wall is grounded and two symmetrical slots are etched on the vertical substrate. The measured results agree with the simulations, showing that the slotted vertical wall reduces the mutual coupling within the bandwidth to −30 dB and corrects the radiation beam tilt to be within −4.5° to 3° from the broadside direction. A gain reduction of 0.6 dB is observed compared to the gain without the slotted decoupling wall.