Tactile Internet (TI) facilitates virtual immersion in a remote environment by transferring the sense of touch, thereby posing stringent latency requirements. It is challenging to meet those requirements, especially for large data volumes and in the uplink. This work presents a latency reduction scheme for uplink data transmission while ensuring high reliability based on considerations of the timing contribution from both compression and communication. The approach is termed Parallel Compression and Transmission (PCT) and the key idea is to send part of the data uncompressed while simultaneously compressing the remaining data. This is formulated into a non-convex optimization problem that minimizes the latency under a statistical constraint on reliability. The results show that the proposed approach can lead to latency reduction of more than 30% under a reliability level of 99% in the considered setup, outlining the promise of the concept of parallel compression and computation towards latency reduction.

Collision-free trajectory planning of UAVs is a challenging task. In this paper, we address the UAV collision avoidance problem in the presence of wind, which cannot be ignored in practical aerial-aided communication infrastructure deployment scenarios. In order to avoid a collision with an obstacle, the UAV needs to decide to make a turn from a sufficient distance at an appropriate angle, and this phenomenon is characterized by the two parameters termed as turning radius and semiapex angle. First, the effect of wind speed on the UAV movement is modeled, which is used to estimate the turning radius and semiapex angle in the presence of wind. The wind data is random in nature, so the collision avoidance parameters are also random. To this end, a discrete-time Markov chain model is used to analyze the collision avoidance parameters in a statistical sense. Finally, the probability of collision avoidance is estimated. For performance evaluation, the wind data of the last four years with five minutes resolution from three cities of the USA situated at diverse geographical locations is used. Through system simulations, the severity of collision performance in the presence of random wind is captured. Further, as a measure of collision avoidance, the turning radius and simplex angle are estimated as a function of wind velocity.

The stringent timing and reliability requirements in mission-critical applications require a detailed statistical characterization of end-to-end latency. Teleoperation is a representative use case, in which a human operator (HO) remotely controls a robot by exchanging command and feedback signals. We present a framework to analyze the latency of a closed-loop teleoperation system consisting of three entities: an HO, a robot located in remote environment, and a Base Station (BS) with Mobile edge Computing (MEC) capabilities. A model of each component is used to analyze the closed-loop latency and optimize the compression strategy. The closed-form expression of the distribution of the closed-loop latency is difficult to estimate, such that suitable upper and lower bounds are obtained. We formulate a non-convex optimization problem to minimize the closed-loop latency. Using the obtained upper and lower bound on the closed-loop latency, a computationally efficient procedure to optimize the closed-loop latency is presented. The simulation results reveal that compression of sensing data is not always beneficial, while system design based on average performance leads to under-provisioning and may cause performance degradation. The applicability of the proposed analysis is much wider than teleoperation, including a large class of systems whose latency budget consists of many components.

We present a framework to analyse the latency budget in wireless systems with Mobile Edge Computing (MEC). Our focus is on teleoperation and telerobotics, as use cases that are representative of mission-critical uplink-intensive IoT systems with requirements on low latency and high reliability. The study is motivated by a general question: What is the optimal compression strategy in reliability and latency constrained systems? We address this question by studying the latency of an uplink connection from a multi-sensor IoT device to the base station. This is a critical link tasked with a timely and reliable transfer of potentially significant amount of data from the multitude of sensors. We introduce a comprehensive model for the latency budget, incorporating data compression and data transmission. The uplink latency is a random variable whose distribution depends on the computational capabilities of the device and on the properties of the wireless link. We formulate two optimization problems corresponding to two transmission strategies: (1) Outage-constrained, and (2) Latency-constrained. We derive the optimal system parameters under a reliability criterion. We show that the obtained results are superior compared to the ones based on the optimization of the expected latency.

Hovering inaccuracy of unmanned aerial vehicle (UAV) degrades the performance of UAV-aided radio frequency energy transfer (RFET). Such inaccuracy arises due to positioning error and rotational motion of UAV, which lead to localization mismatch (LM) and orientation mismatch (OM). In this paper, antenna array beam steering based UAV hovering inaccuracy mitigation strategy is presented. The antenna beam does not accurately point towards the field sensor node due to rotational motion of the UAV along with pitch, roll, and yaw, which leads to deviation in the elevation angle. An analytical framework is developed to model this deviation, and its variation is estimated using the data collected through an experimental setup. Closed-form expressions of received power at the field node are obtained for the four cases arising from LM and OM. An optimization problem to estimate the optimal system parameters (transmit power, UAV hovering altitude, and antenna steering parameter) is formulated. The problem is proven to be nonconvex. Therefore, an algorithm is proposed to solve this problem. Simulation results demonstrate that the proposed framework significantly mitigates the hovering inaccuracy; compared to reported state-of-the-art the same performance can be achieved with substantially less transmit power.

Unmanned aerial vehicle (UAV)-enabled cellular architecture over the millimeter-wave (mmWave) frequency band is likely to be the best solution for on-demand high data rate service provisioning in the next generation communication networks. The beam formed by the mmWave antenna array is highly directional and requires multiple-beam scans to cover the entire area. This work presents a novel sectoring approach to ensure coverage of the whole area. The side lobe gain of the antenna array is taken into consideration, which generates substantial interference in other sectors. The expression for the probability distribution of the signal-to-interference-plus-noise ratio due to simultaneous transmissions in different sectors is derived in the downlink communication scenario. To limit interference in the concurrent transmission strategy, a threshold on power spillage from adjacent sectors is placed. For this topology, a resource allocation problem is formulated aiming to maximize the sum rate while ensuring a minimum rate guarantee to each user. It is observed that sum-rate variation with height is unimodal. The sum power and backhaul capacity constraints are accounted. This optimization problem is mixed-integer nonconvex programming. Hence, it is solved using the Lagrangian dual decomposition method, which provides an asymptotic global optimal solution. Since this method is computationally intensive, a suboptimal solution is proposed. Simulation results demonstrate convergence to an optimal solution, and it is observed that backhaul link capacity restricts the sum rate. Numerical results are presented for multiple representative field environments consisting of different types of built-up areas. It is observed that the transmitter antenna array sidelobe has a strong impact on the performance as compared to the ideal scenario without sidelobe, which overestimates the total sum rate by a factor of 3.

In this paper, performance of unmanned aerial vehicle (UAV)-aided RF energy transfer (RFET) in presence of hovering inaccuracy is investigated. Hovering inaccuracy of UAV comprises of two types of mismatches: Localization mismatch (LM) and Orientation mismatch (OM). Thus, a total of four combinations arise. Their impact on received power at ground deployed sensor node is characterized. For this purpose, a generalized radiation pattern of UAV-mounted transmitter antenna is considered. A closed-form expression of received power at the sensor node is obtained for each of these four cases. An optimization problem is formulated with the objective of optimizing the system parameters, such as transmit power, hovering altitude, and antenna exponent. This problem contains mixed nature of variables, i.e., continuous as well as discrete. To solve this problem, an algorithm, called Hovering Inaccuracy-aware Optimal Charging System Design (HI-OCSD), is proposed to find the optimal system parameters. Through system simulations it is demonstrated that, hovering inaccuracy has notable impact on the performance, as received power at the sensor node reduces significantly in presence of hovering inaccuracy compared to ideal scenario. The effect of LM is more severe than that of OM. Further, a scenario with different level of hovering inaccuracy accounting for different deployment scenarios is considered, and the optimal system parameters are also evaluated. This study reveals that, UAV needs to hover at a relatively higher altitude to overcome the severity of hovering inaccuracy.

Solar-enabled systems are becoming popular for provisioning pollution-free and cost-effective energy solution. Dimensioning of a solar-enabled system requires estimation of appropriate size of photovoltaic (PV) panel as well as storage capacity while satisfying a given energy outage constraint. Dimensioning has strong impact on the user's quality of experience and network operator's interest in terms of energy outage and revenue. In this paper, dimensioning problem of solar-enabled communication nodes is analyzed in order to reduce the computation overhead, where stand-alone solar-enabled base station (SS-BS) is considered as a case study. For this purpose, hourly solar data of last 10 years has been taken into consideration for analysis. First, the power consumption model of BS is revised to save energy and increase revenue. Using the hourly solar data and power consumption profile, the lower bounds on panel size and storage capacity are obtained using the Gaussian mixture model, which provides a reduced search space for cost-optimal system dimensioning. Then, the cost function and energy outage probability are modeled as functions of panel size and number of battery units using curve fitting technique. The cost function is proven to be quasiconvex, whereas energy outage probability is proven to be convex function of panel size and number of battery units. These properties transform the cost-optimal dimensioning problem into a convex optimization framework, which ensures a global optimal solution. Finally, a Computationally-efficient Energy outage aware Cost-optimal Dimensioning Algorithm (CECoDA) is proposed to estimate the system dimension without requiring exhaustive search. The proposed framework is tested and validated on solar data of several cities; for illustration purpose, four cities, New Delhi, Itanagar, Las Vegas, and Kansas, located at diverse geographical regions, are considered. It is demonstrated that, the presented optimization framework determines the system dimension accurately, while reducing the computational overhead up to 94 percent and the associated energy requirement for computation with respect to the exhaustive search method used in the existing approaches. The proposed framework CECoDA takes advantage of the location-dependent unique solar profile, thereby achieving cost-efficient solar-enabled system design in significantly less time.

Limited battery capacity is one of the major hurdles towards perpetual operation of wireless sensor networks (WSNs). In this work, a framework for Unmanned Aerial Vehicle (UAV) based wireless charging of sensor nodes using radio frequency energy transfer (RFET) is presented. First, RFET zone is conceptualized where energy transfer is possible such that the received power is above a sensitivity level of power harvester. Next, two novel strategies Static Charging Time Allocation (SCTA) and Optimal Charging Time Allocation (OCTA) are proposed for charging the sensors. The UAV remains static in SCTA, whereas in OCTA it hovers above each sensor node and replenishes depleted energy of the sensors that are within its RFET zone. Further, a model for evaluating the energy consumption of UAV is presented in order to determine the number of rounds required by the UAV for charging. Different gas sensors are planted emulating a practical deployment scenario, and with this arrangement charging time with the proposed strategies are evaluated. Our numerical results justify applicability of the UAV-assisted RFET.

Solar-enabled cellular base stations are getting significant attention because they avoid greenhouse gas emission as well as easily available. Dimensioning of base station is a very important issue where the photo-voltaic (PV) panel size and storage capacity are determined to operate the system for a long time with minimum energy black out. In this work we look into energy outage aware system cost as well as utility of solar-enabled base stations. Hourly harvested energy and traffic dependent hourly consumed energy are considered for analysis. First, the quantized profile of harvested and consumed energy are obtained. Subsequently, the lower bound on PV panel and storage capacity is determined which provides dimensioning information a priori and at reduced complexity. To calculate the outage probability, a discrete-time Markov chain is formed with different energy levels of the battery as the system states. The state transition probabilities depend on the harvested and consumed energy profile. It is observed that, with more stringent outage constraint, the harvested energy which is neither utilized nor stored increases significantly. To this end, we suggest the system other than cost optimal where the excess energy is sold to the intelligent smart power grid to generate revenue. Harvested and consumed energies of the three cities situated at different geographical locations are considered to verify the results.

This paper presents a novel single limb body balance analysis system which will aid medical practitioners to analyze crucial factor for fall risk minimization, injury prevention, fitness and rehabilitation programs. We use skeleton data obtained from Microsoft Kinect which captures full human body as well as ensures user's privacy. A new eigen vector based curvature analysis algorithm is developed to compute single limb stance (SLS) duration on the skeleton data. Two parameters vibration-jitter and force per unit mass (FPUM) are derived for each body part to assess postural stability during SLS. Experimental results show the efficacy of our system to apply it in medical domain.