Recent advancements in Bluetooth Low Energy (BLE) have made it a promising solution for delay-sensitive and energy-constrained IoT applications, such as robotic automation in industrial settings. However, existing BLE service architectures, namely the BLE beacon-to-user and beacon-gateway-server-user models, either suffer from high delays, unreliable performance, or a reliance on internet connectivity, which is often limited in environments such as underground parking areas, airports, and supermarkets. Additionally, these architectures use BLE legacy advertising, which offers limited throughput and thus contributes to further delays. To address these limitations, this paper proposes a novel BLE-centric enhanced service architecture that minimizes the delay experienced by users and enhances the energy efficiency of BLE beacons. Based on the proposed service architecture, we first develop an analytical model to evaluate delay and then derive simplified closed-form expressions for selecting optimal transmission parameters. These parameters minimize the delay experienced by users and improve the energy efficiency of BLE beacons. The proposed architecture also leverages BLE extended and periodic advertising modes, which offer higher throughput, thereby further reducing delay and improving overall performance. Additionally, lightweight algorithms are introduced to adapt these parameters dynamically based on network conditions. The proposed model is validated through simulations, showing strong agreement with the analysis and confirming its practical effectiveness.

The network connectivity while traveling in a vehicle is an important issue, which needs to be addressed by mobile vehicular networks. This paper proposes a novel scheme to improve the connectivity of mobile vehicular networks. In particular, the paper proposes a medium access control (MAC) layer hybrid mmWave and microwave scheme for vehicular networks, and leverage their capabilities to improve the vehicle’s connectivity. The novel computational model is derived to evaluate the connectivity for the proposed scheme. The model is used for performance analysis of vehicles moving on a multi-lane highway road and getting connectivity with road-side-units (RSUs) deployed along the roads. Our analysis considers that reference VN has perfect channel state information. It is assumed that the RSU radiates ubiquitously on the road surface using microwave radio access technology (RAT), while it radiates directionally towards reference VN using mmWave RAT. For mathematical analysis, the directionality in mmWave RAT is well approximated by a sectored antenna model. The analysis for the proposed scheme is compared with the existing mmWave network and packet data convergence protocol (PDCP) layer hybrid scheme. The analysis claims that the proposed hybrid scheme significantly improves the connectivity performance in mobile vehicular networks over the existing schemes. The computation results are validated with the simulation results. Also, the paper offers parametric analysis for connectivity probability with vehicle speed and slot duration to enable its practical implementation in 5G/6G technologies.

Internet-of-Things (IoT) applications require a network that covers a large geographic area, consumes less power, is low-cost, and is scalable with an increasing number of connected devices. Low-power wide-area networks (LPWANs) have recently received significant attention to meet these requirements of IoT applications. Long-range wide-area network (LoRaWAN) with long range (LoRa) (the physical layer design for LoRaWAN) has emerged as a leading LPWAN solution for IoT. However, LoRa networks suffer from the scalability issue when supporting a large number of end devices that access the shared channels randomly. The scalability of LoRa networks greatly depends on the spreading factor (SF) allocation schemes. In this article, we propose an exponential windowing scheme (EWS) for LoRa networks to improve the scalability of LoRa networks. EWS is a distance-based SF allocation scheme. It assigns a distance parameter to each SF to maximize the success probability of the overall LoRa network. Using stochastic geometry, expressions for success probability are derived under co-SF interference. The impact of exponential windowing and packet size is analyzed on packet success probability. In addition, the proposed scheme is compared with the existing distance-based SF allocation schemes: equal-interval-based and equal-area-based schemes, and it is shown that the proposed scheme performs better than the other two schemes.

A decade back, emergency voice communication was the only target to support the patient in an ambulance. It is now evolved from emergency voice communication to vital signal monitoring and operating the machines from the remote place. This evolution requires support from technology to meet the high data rates along with reliability for the specified applications. The millimeter-wave (mmWave) communication support high data rate requirements of vehicular communication. However, in the case of mmWave, the radio signals vary fast. It poses the implementation challenge to the mmWave system in this scenario. The other implementations challenges of mmWave are high path loss, severe blockage and frequent beam updates which inhibit seamless connectivity (reliability) to vehicular nodes. However, the reliability is always a prime concern for any vehicular communication system. This paper addresses these challenges by implementing a novel energy-efficient strategy based on RSUs deployment and radio access technology (RAT). The strategy is to deploy RSUs on either side of the road and use an optimal combination of mmWave and microwave RAT. The essential analysis of such a hybrid system involves the evaluation of parameters based on the analytic model. Hence, this paper analytically obtains the expression for seamless coverage and connectivity. The analysis is also extended to rate and energy efficiency calculations. The analysis is supported by probabilistic models-based simulations that agree closely with computation results. The results claim that the proposed model leads to improved performance in terms of coverage and rate while maintaining the cost and energy efficiency within the limits.

Autonomous Vehicles (AVs) rely on a set of radar sensors operated on frequency modulated waveforms. Due to the large bandwidth requirement of frequency modulated radars, only a limited number of proximate vehicles can be allowed for a concurrent transmission within the available spectrum. However, with the explosive growth of AVs, the available spectrum may soon reach its capacity. As a results, the coexistence of multiple AVs working on same resource, may lead to the problem of Radar-to-Radar (R2R) interference, also known as radar blindness. In this paper, we propose the notion of coded waveforms to minimize the R2R interference among the vehicles operating on the same resource. Specifically, the spread spectrum codes have been used as another degree of freedom (i.e., along with time-frequency resources) to spread the inter-vehicular radar interference over the wider spectrum, which enable to orthogonalize more number of AVs over the available range of spectrum. In addition, we have formulated a Spread Spectrum-based Radar Transmission Scheme (SS-RTS), and described the transmission and reception through SS-RTS. Also, the SS-RTS has been compared with the existing Graph-based Resource Allocation (GRA) scheme. Further, simulation results verified that SS-RTS significantly reduces the inter-vehicular R2R interference and outperforms GRA in terms of blind probability.

Autonomous Vehicles (AVs) rely on a set of radar sensors used to map surrounding environment. Most commonly used radar sensors for AVs use Frequency Modulated Continuous Wave (FMCW) ramps for object parameter (i.e., range and relative velocity) estimation. Due to large bandwidth requirement of FMCW radar, only a limited number of AVs can be operated in the available spectrum. Consequently, the co-existence of large number of AVs may lead to the problem of radar-to-radar interference, also referred to as radar blindness. Moreover, the problem becomes more severe in the higher traffic scenarios. In this work, we propose a Traffic-based Adaptive Ramp Packing (TRAP) scheme, which adapts radar range and assigns FMCW ramp parameters on the basis of inter-vehicular distance among AVs. Specifically, TRAP scheme allows to make effective use of the available time-frequency resource, and enables to pack more ramps in the dense traffic scenarios. Further, it is shown that adaptive radar range adoption may provide significantly more number of ramps in the given bandwidth. Furthermore, through simulation results, it is shown that TRAP significantly reduces the blind probability against state-of-the-art fixed range schemes.

Abstract Coordinated Multi‐Point (CoMP) transmission in Cloud Radio Access Network (CRAN) requires a large amount of data transmission and processing within a coherence time window. Hence, CoMP transmission puts a lot of burden on the central processor and back‐haul unit. Also, establishing CoMP for high‐mobility users is challenging due to small coherence window and large beamforming overhead over mm‐Wave transmission. This paper proposes a two‐layer CRAN architecture with intelligent mm‐Wave and micro‐Wave allocation. A dual connectivity framework has been introduced to increase the coverage of high‐mobility users. Further, it is shown that the proposed scenario reduces the load on the central processor and central back‐haul. To avoid unnecessary handoffs, a mobility management algorithm is also proposed, which can provide seamless connectivity to the users irrespective of their velocity. Further, through simulation results, it is shown that the proposed network outperforms the existing CRAN framework.

Over the past few years, we have witnessed an explosive increase in the number of long range wide area network (LoRaWAN) devices, primarily because LoRaWAN offers attractive features such as long-range, low-power, and low-cost communications. However, the scalability of LoRaWAN is a major concern, which in particular depends on spreading factor (SF) allocation schemes. Primarily, SFs are assigned based on distance from the gateway, using equal-interval-based (EIB) and equal-area-based (EAB) SF allocation schemes. In this article, we have proposed an SNR-based SF allocation scheme to improve the scalability of LoRaWAN. We have introduced two different algorithms for the proposed scheme. Using stochastic-geometry, an analytical framework is developed for both the algorithms, and the expressions are derived for the packet success probability (PSP) under the co-SF interference scenario. In addition, the impact of an inter-SF interference on the PSP performance is analyzed by simulations. The proposed algorithms are compared with the EIB and EAB SF allocation schemes, and it is shown that the proposed algorithms perform better than the other two schemes. We have also analyzed the impact of end device density, packet size, and cell-radius on the LoRaWAN scalability. Moreover, we have performed real-time experiments to prove the applicability of the presented work in practical scenarios.

Interference among self driving vehicles (SDVs) has become a serious problem due to the extensive growth of radar based connected vehicles (CVs). Since the driving resources (i.e., frequency and/or time slots) for these radar sensors are limited, a large value of radar range in a high traffic density may interfere large number of surrounding vehicles, while a small radar range in the low traffic density may lose the opportunity to detect the surrounding vehicles. In this paper, we have proposed traffic-based dynamic range approach (TDA) for SDVs equipped with radar sensors. TDA ensures that the range of radar vary in such a way that SDVs must be able to detect the objects in its vicinity as well as the vehicle may not interfere the surrounding vehicles. Moreover, we have compared the performance of TDA with the fixed range approach (that is considered as baseline approach for this paper). Simulation results show that TDA outperforms the fixed range baseline approach.