Dr. Manoj Kumar Panda

From past decades, India is an agriculture-based country where the majority of the population is heavily dependent on farming. However, energy management and resource conservation continues to be the major issues in agricultural domain. The main motivations of this research is to conserve water which is a fast depleting source, and also automates the process of watering in order to reduce human workload in remote areas. In this paper, a smart irrigation alert system is developed using NodeMCU boards, a soil moisture sensor and a servo motor. The sensor measures the volumetric content of water in the soil and data is uploaded to the cloud. The main challenge addressed in this research is to seamlessly connect multiple Node MCUs to a sink (or, gateway) node such that the data can be uploaded to the cloud. Two topologies are considered. The first one is the star topology which is efficient for a kitchen garden where each of the plants is in close proximity of the sink. The second one is the multi-hop topology which is necessary in a big agriculture field. The soil moisture is sensed using multiple FC-28 sensors and uploaded to the cloud. When the moisture content drops below the threshold value, an alert notification is sent to the subscriber by e-mail and automatic watering follows by actuating the servo motor. The user can monitor and control the status of watering anywhere through the Internet. The integration of sensors, cloud and the servo motor through multiple Node MCUs is the main novelty of this work.

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Wireless Sensor Networks (WSNs) are one of the most important technologies today due to their numerous applications in the fields of environment monitoring, defense and agriculture, to name a few. The paper proposes some architectural alternatives to the traditional WSNs with static sinks by including mobile sinks mounted on Unmanned Aerial Vehicles (UAVs). The main performance measures of interest, energy consumption per node, throughput evolution over time, and delay per packet are studied and compared for the different architectures. The simulation results exhibit how better performance is achieved by using a mobile sink than with a static sink and how clustered topologies perform better than flat topologies.

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Dynamic Traffic Assignment (DTA) provides an approach to determine the optimal path and/or departure time based on the transportation network characteristics and user behavior (e.g., selfish or social). In the literature, most of the contributions study DTA problems without including traffic signal control in the framework. The few contributions that report signal control models are either mixed-integer or nonlinear formulations and computationally intractable. The only continuous linear signal control method presented in the literature is the Cycle-length Same as Discrete Time-interval (CSDT) control scheme. This model entails a trade-off between cycle-length and cell-length. Furthermore, this approach compromises accuracy and usability of the solutions.
In this study, we propose a novel signal control model namely, Signal Control with Realistic Cycle length (SCRC) which overcomes the trade-off between cycle-length and cell-length and strikes a balance between complexity and accuracy. The underlying idea of this model is to use a different time scale for the cycle-length. This time scale can be set to any multiple of the time slot of the Dynamic Network Loading (DNL) model (e.g. CTM, TTM, and LTM) and enables us to set realistic lengths for the signal control cycles. Results show, the SCRC model not only attains accuracy comparable to the CSDT model but also more resilient against extreme traffic conditions. Furthermore, the presented approach substantially reduces computational complexity and can attain solution faster.

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This paper devises locally optimal traffic Signal Control (SC) settings in a Non-Holding-Back Dynamic Traffic Assignment with SC (NHB DTA-SC) formulation for single destination (i.e. one source to one destination and many sources to one destination) networks. To this end, we apply temporal–spatial dual decomposition method and decompose the NHB DTA-SC problem into intersection cells and non-intersection cells. Then we further decompose the intersection cells into different subproblems, i.e. Occupancy Minimization (OM), Flow Maximization (FM), and SC. To study the optimal SC structures, we examine the Karush–Kuhn–Tucker (KKT) optimality conditions of the decomposed SC subproblem. Finally, we obtain the locally optimal SC structures under different network conditions that include over-saturated, under-saturated, and queue spillback traffic scenarios. We also present several numerical results to verify the optimality structures found by our theoretical derivations.

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Traffic state estimation plays an important role in Intelligent Transportation Systems (ITS). It provides the latest traffic information to travelers and feedback to signal control systems. The Interactive Multiple Model (IMM) filtering provides a powerful estimation method to deal with the non-differentiable nonlinearity caused by the phase transitions between the under-critical and above-critical traffic density regimes. The IMM filtering also accounts for the uncertainty in the current ‘mode of operation’. In this paper, we develop an enhanced IMM filtering approach to traffic state estimation, with an underlying Cell Transmission Model (CTM) for traffic flow propagation. We improve the IMM filtering with CTM in two ways: (1) We apply two simplifying assumptions that are highly likely to hold in urban roads in incident-free conditions, which makes the computational complexity to grow with the number of cells only polynomially, rather than exponentially as reported in prior work. (2) We apply a novel approach to noise modeling wherein the process noise is explicitly obtained in terms of the randomness in more fundamental quantities (e.g., free-flow speed, maximum flow capacity, etc.), which not only makes noise calibration using real data convenient but also makes the computation of the cross-correlation between the process and measurement noises transparent. However, it leads to ‘process dynamic’ and ‘measurement’ equations that involve multiplier matrices whose elements are random variables rather than deterministic scalars, and hence, standard filtering equations cannot be applied. We derive the appropriate filtering equations from first principles. We calibrate the traffic parameters and the total inflow and outflow on the links using the SCATS loop detector data collected in Melbourne and report significant improvements in accuracy, which is due to the accurate computation of the cross-covariance of process and measurement noises. © 2019 Elsevier Ltd More »»

Dynamic Traffic Assignment (DTA) is a mathematical framework that with a System Optimal (SO) objective is often used for long-term transport planning, design, and traffic management. However, the conventional SO-DTA formulation gives optimal solutions having an unrealistic vehicle Holding–Back (HB) property. Existing approaches in the literature aiming to resolve the HB problem are either computationally intractable or suffer from recursive parameter selection problem. In addition, most of the existing Signal Control (SC) models considered in the DTA formulation are mixed-integer or nonlinear in nature that are not scalable for large networks. With an exception, there exists a linear signal control model that can only set signal control cycle-length equal to DTA time-slot duration, and thus trades the accuracy of the SO-DTA solution for a more realistic cycle-length. In this article, we address the above issues by proposing a linear Non-Holding-Back SO-DTA with SC (NHB DTA-SC) formulation for single destination networks. The embedded signal control in the proposed framework enables us to set realistic cycle-length using any DTA time-slot (i.e., flexible time-scale). We find that the time-scale has a significant impact on traffic density which affects vehicle-discharged emissions. To this end, we develop a novel linear Emission-Based DTA with SC (EB DTA-SC) formulation that obtains NHB flows as well as lowest possible emission. Our results show that there is a 32% difference between emission estimated by 60-second and 5-second time-scales.

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We develop a comprehensive analytical model for multiple long-lived multipath Transmission Control Protocol (TCP) connections downloading content from a remote server in the Internet using parallel paths with Wi-Fi and cellular last-miles. This is the first analytical model developed in the literature that captures the coupling between the paths through heterogeneous wireless networks where the coupling arises due to the multipath TCP coupled congestion control protocol. The model also takes into account the impact of the shared nature of the wireless medium and the finite access point (AP) buffer in the Wi-Fi last-mile. The accuracy of the proposed model is demonstrated via extensive ns-2 simulations. Furthermore, we discover a new type of throughput unfairness among the competing regular and multipath TCP connections going through the same AP with a droptail buffer; the regular TCP connections essentially steal almost all the Wi-Fi bandwidth away from the multipath TCP connections. To tackle this problem, we present two simple solutions utilizing our analytical model and achieve fairness.

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