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.

A new class of sensing paradigm known as lab-on-skin where stretchable and flexible smart sensor devices are integrated into the skin, provides direct monitoring and diagnostic interfaces to the body. Distributed lab-on-skin wireless sensors have the ability to provide continuous long term assessment of the skin health. This paper proposes a distributed skin health monitoring system using a wireless body area network. The system is responsive to the dynamic changes in the skin health, and remotely reports on the same. The proposed algorithm detects the abnormal skin and creates an energy efficient data aggregation tree covering the affected area while putting the unnecessary sensors to sleep mode. The algorithm responds to the changing conditions of the skin by dynamically adapting the size and shape of the monitoring trees to that of the abnormal skin areas thus providing a comprehensive monitoring. Simulation results demonstrate the application and utility of the proposed algorithm for changing wound shapes and sizes.

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.

Exploiting aerial base stations (A‐BSs) in conjunction with terrestrial base stations (T‐BSs) is an effective solution to provide uniform coverage everywhere, and anytime. However, the addition of A‐BSs in an existing terrestrial network would intensify the inter‐cell interference to user equipment, leads to a degraded signal‐to‐interference‐ratio. There is a need to exploit inter‐cell‐interference‐coordination (ICIC) scheme in such aerial‐terrestrial network. Further, the aerial‐terrestrial network requires a multi‐layer environment to be modelled in many scenarios. In this study, the authors propose fractional frequency reuse (FFR) scheme as an ICIC scheme for two‐layer aerial‐terrestrial network. The analytical expression of coverage probability and average rate are derived for the proposed scheme, and compare it with the scheme, where no ICIC is considered in aerial‐terrestrial network, and conventional scheme, where only T‐BSs are considered. It is shown that the proposed scheme outperforms the no ICIC scheme and conventional scheme. Simulation results are shown to support their analytical results.

In this work, the authors have analysed the impact of correlation among interferers, and between signal and interferers. In particular, the impact of correlation on average rate performance in an interference‐limited scenario has been studied. The user's desired channel link and interfering channel links are assumed to experience Nakagami‐ m fading. Under this assumption, they compared the average rate for the following cases: (a) interferers are independent and the user's signal‐and‐interferers are also independent, (b) interferers are correlated and the user's signal‐and‐interferers are independent, and (c) interferers are correlated and the user's signal and‐interferers are also correlated. It has been analytically shown that the average rate in case (b) is higher than the average rate in case (a) when the interferers are positively correlated. In other words, the positive correlation among the interferers increases the average rate. Furthermore, the average rate in case (c) is lower than the average rate in case (b). In other words, the positive correlation between the signal and interferers decreases the average rate. Moreover, the average rate in case (c) can be lower or higher than the average rate in case (a), depending upon the amount of correlation. Simulation results have been shown to support analytical observations.

Pregnancy is a special condition in which women go through various health complications throughout the period of gestation. Conventional health monitoring systems are either too specific or too general, therefore not flexible enough to be suited for pregnant women. In this paper, requirements and challenges in health monitoring of pregnant women are summarized. First, the specific requirements for health monitoring are derived, including adaptive monitoring, need for big data, and real time monitoring. Then a first ever reference architecture is proposed specifically for health monitoring of pregnant women. Mobile devices, body sensors, cloud and thin client of health care professionals are integrated together through the proposed architecture for an end to end self adaptive health monitoring solution design. Apart from self adaptation based on system dynamics, other features of the proposed architecture are mobile device as a gateway for body and ambient sensors, risk factor evaluation and possible action identification, and prolonged monitoring. The proposed architecture facilitates big data analytic and real time monitoring by remote thin client of health professionals through the assistance of cloud infrastructure.

We propose an area segmentation algorithm which is completely distributed, highly responsive, and exhibits a wide range of application scenarios. The proposed algorithm segments the area based on similarity of local sensor data and therefore, it requires a similarity measure parametrized with selected system indicators. In addition, algorithm creates an energy efficient data aggregation tree with a local highest energy node as a root. The resulting segmented sub-areas represents a level of spatial diversity and an abstraction of the sensor field which has a wide range of large scale distributed applications. Through simulation, the application and working of our scheme is demonstrated.

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.

In this paper, we derive the data-aided error vector magnitude (EVM) in an interference limited system when both the desired channel and interferers experience independent and nonidentically distributed κ-μ shadowed fading. Then, it is analytically shown that the EVM is equal to the square root of number of interferers when the desired channel and interferers do not experience fading. Furthermore, the EVM is derived in the presence of interference and noise, when the desired channel experiences κ-μ shadowed fading and the interferers experience independent and identical Nakagami fading. Moreover, using the properties of the special functions, the derived EVM expressions are also simplified for various special cases.

World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of traffic data has been produced by people, Internet of Vehicles and Internet of Things (IoT). Also, thanks to advances in cloud computing and proliferation of mobile communication technologies, it is now possible to perform analysis on vast amount of generated data (crowd sourced) and deliver the result back to users in real time. This paper proposes SafeRNet, a safe route computation framework which takes advantage of these technologies to analyze streaming traffic data and historical data to effectively infer safe routes and deliver them back to users in real time. SafeRNet utilizes Bayesian network to formulate safe route model. Furthermore, a case study is presented to demonstrate the effectiveness of our approach using real traffic data. SafeRNet intends to improve drivers' safety in a modern technology rich transportation system.

Device-to-device (D2D) communication underlying cellular networks, allows direct transmission between two devices in each other's proximity that reuse the cellular resource blocks in an effort to increase the network capacity and spectrum efficiency. However, this imposes severe interference that degrades the system's performance. This problem may be circumvented by incorporating fractional frequency reuse (FFR) or soft frequency reuse (SFR) in OFDMA cellular networks. By carefully considering the downlink resource reuse of the D2D links, we propose beneficial frequency allocation schemes, when the macrocell has employed FFR or SFR as its frequency reuse technique. The performance of these schemes is quantified using both the analytical and simulation results for characterizing both the coverage probability and the capacity of D2D links under the proposed schemes that are benchmarked against the radical unity frequency reuse scheme. The impact of the D2D links on the coverage probability of macrocellular users (CUs) is also quantified, revealing that the CUs performance is only modestly affected under the proposed frequency allocation schemes. Finally, we provide insights concerning the power control design in order to strike a beneficial tradeoff between the energy consumption and the performance of D2D links.

We analytically characterize the data-aided error vector magnitude (EVM) performance of a single-input multiple-output (SIMO) communication system relying on maximal ratio combining (MRC) having either independent or correlated branches that are nonidentically distributed. In particular, exact closed form expressions are derived for the EVM in$\eta\text{-}\mu$fading and$\kappa W\mu$shadowed fading channels and these expressions arevalidatedby simulations. The derived expressions are expressed in terms of Lauricella’s function of the fourth kind$F_D^{(N)}(.)$, which can be easily computed. Furthermore, we have simplified the derived expressions for various special cases such as independent and identically distributed branches, Rayleigh fading, Nakagami-mfading, and$\kappa\text{-}\mu$fading. Additionally, a parametric study of the EVM performance of the wireless system is presented.

Expressions are derived for the coverage probability and average rate of both multi-user multiple input multiple output (MU-MIMO) and single input multiple output (SIMO) systems in the context of a fractional frequency reuse (FFR) scheme. In particular, given a reuse region of 1/3 (FR3) and a reuse region of 1 (FR1) as well as a signal-to-interference-plus-noise-ratio (SINR) threshold Sth, which decides the user assignment to either the FR1 or FR3 regions, we theoretically show that: 1) the optimal choice of Sthwhich maximizes the coverage probability is Sth= T, where T is the target SINR required for ensuring adequate coverage, and 2) the optimal choice of Sthwhich maximizes the average rate is given by Sth= T', where T' is a function of the path loss exponent, the number of antennas and of the fading parameters. The impact of frequency domain correlation amongst the OFDM sub-bands allocated to the FR1 and FR3 cell-regions is analysed and it is shown that the presence of correlation reduces both the coverage probability and the average throughput of the FFR network. Furthermore, the performance of our FFR-aided MU-MIMO and SIMO systems is compared. Our analysis shows that the (2 × 2) MU-MIMO system achieves 22.5% higher rate than the (1 × 3) SIMO system and for lower target SINRs, the coverage probability of a (2 × 2) MU-MIMO system is comparable to a (1 × 3) SIMO system. Hence the former one may be preferred over the latter. Our simulation results closely match the analytical results.

The κ - μ and η - μ are general fading distributions, which model line-of-sight and non-line-of-sight propagation effects, respectively. In this work, expressions for the coverage probability and average rate when the user experiences κ - μ fading with arbitrary values of κ and μ, and interferers experience η - μ fading with arbitrary values of η and μ are derived for downlink of a cellular network. Both expressions can be expressed in terms of sum of Lauricella's function of the fourth kind. Further, using the properties of the special functions, the average rate expression is simplified for various special cases. Finally, simulation results are provided and these match our analytical results.