Showing papers on "Routing (electronic design automation) published in 2022"

Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS

Abstract: Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system’s efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms.

Vehicle routing problem with drones considering time windows

Abstract: The cooperation of trucks and unmanned aerial vehicles (UAV) has become a new delivery method in the area of logistics and transportation. In this form of cooperation, the trucks are not only able to provide services to the customers, but also serve as a ‘launch pad’ for the drones, in which the drones can be launched to service a customer and then recovered at the rendezvous node. This study intends to explore this cooperation by developing a model for the vehicle routing problem with drones that considers the presence of customer time windows (VRPTWD). A mixed-integer programming (MIP) model is presented to minimize the total travelling costs. Then, a simple yet effective variable neighborhood search (VNS) procedure with a novel solution representation is proposed as a solver. The numerical results indicate the ability of the proposed VNS to solve the VRPTWD, as well as the improvement of delivery performance using drones.

Learning Improvement Heuristics for Solving Routing Problems

Abstract: Recent studies in using deep learning (DL) to solve routing problems focus on construction heuristics, whose solutions are still far from optimality. Improvement heuristics have great potential to narrow this gap by iteratively refining a solution. However, classic improvement heuristics are all guided by handcrafted rules that may limit their performance. In this article, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems. We design a self-attention-based deep architecture as the policy network to guide the selection of the next solution. We apply our method to two important routing problems, i.e., the traveling salesman problem (TSP) and the capacitated vehicle routing problem (CVRP). Experiments show that our method outperforms state-of-the-art DL-based approaches. The learned policies are more effective than the traditional handcrafted ones and can be further enhanced by simple diversifying strategies. Moreover, the policies generalize well to different problem sizes, initial solutions, and even real-world data set.

Entanglement phase structure of a holographic BCFT in a black hole background

Abstract: A bstract We compute holographic entanglement entropy for subregions of a BCFT thermal state living on a nongravitating black hole background. The system we consider is doubly holographic and dual to an eternal black string with an embedded Karch-Randall brane that is parameterized by its angle. Entanglement islands are conventionally expected to emerge at late times to preserve unitarity at finite temperature, but recent calculations at zero temperature have shown such islands do not exist when the brane lies below a critical angle. When working at finite temperature in the context of a black string, we find that islands exist even when the brane lies below the critical angle. We note that although these islands exist when they are needed to preserve unitarity, they are restricted to a finite connected region on the brane which we call the atoll. Depending on two parameters — the size of the subregion and the brane angle — the entanglement entropy either remains constant in time or follows a Page curve. We discuss this rich phase structure in the context of bulk reconstruction.

Design and Simulation of Ring Network-on-Chip for Different Configured Nodes

Abstract: : The network-on-chip (NoC) technology is frequently referred to as a front-end solution to a back-end problem. The physical substructure that transfers data on the chip and ensures the quality of service begins to collapse when the size of semiconductor transistor dimensions shrinks and growing numbers of intellectual property (IP) blocks working together are integrated into a chip. The system on chip (SoC) architecture of today is so complex that not utilizing the crossbar and traditional hierarchical bus architecture. NoC connectivity reduces the amount of hardware required for routing and functions, allowing SoCs with NoC interconnect fabrics to operate at higher frequencies. Ring (Octagons) is a direct NoC that is specifically used to solve the scalability problem by expanding each node in the shape of an octagon. This paper discusses the ring NoC design concept and its simulation in Xilinx ISE 14.7, as well as the communication of functional nodes. For the field-programmable gate array (FPGA) synthesis, the performance of NoC is evaluated in terms of hardware and timing parameters. The design allows 64 to 256 node communication in a single chip with ‘N’ bit data transfer in the ring NoC. The performance of the NoC is evaluated with variable nodes from 2 to 256 in Digilent manufactured Virtex-5 FPGA hardware.

Dynamic stochastic electric vehicle routing with safe reinforcement learning

Abstract: Dynamic routing of electric commercial vehicles can be a challenging problem since besides the uncertainty of energy consumption there are also random customer requests. This paper introduces the Dynamic Stochastic Electric Vehicle Routing Problem (DS-EVRP). A Safe Reinforcement Learning method is proposed for solving the problem. The objective is to minimize expected energy consumption in a safe way, which means also minimizing the risk of battery depletion while en route by planning charging whenever necessary. The key idea is to learn offline about the stochastic customer requests and energy consumption using Monte Carlo simulations, to be able to plan the route predictively and safely online. The method is evaluated using simulations based on energy consumption data from a realistic traffic model for the city of Luxembourg and a high-fidelity vehicle model. The results indicate that it is possible to save energy at the same time maintaining reliability by planning the routes and charging in an anticipative way. The proposed method has the potential to improve transport operations with electric commercial vehicles capitalizing on their environmental benefits.

Cooperative routing for improving the lifetime of wireless ad-hoc networks

Abstract: In Wireless Ad-hoc Networks (WANET), route detection is the main issue. In the usual route detection method, the sender itself discovers the route to the receiver based on the shortest path. In this path, the sender node does not require knowledge of the in-between nodes, and the sender node transmits the information to the in-between nodes. The in-between nodes transmit the data to the near node that receives it. This procedure will be maintained till the information reaches the receiver node. The main disadvantage of usual route detection is that the node is highly moved; thus, the transmitted data packet will be dropped. A Cooperative Routing for Improving Lifetime (CRIL) in WANET is introduced to solve these issues. This approach aims to enhance the WANET lifetime and minimize the cost of route discovery. This approach uses the fresher encounter algorithm with energy-efficient routing to improve network lifetime. It is a simple algorithm to efficiently discover the routes in WANET.

Shadows and optical appearance of a power-Yang-Mills black hole surrounded by different accretion disk profiles

Abstract: In the present work, we investigate the image of a power-Yang-Mills black hole and its luminosity under distinctive accretion models. Meanwhile, a special emphasis is put on the different image characteristics of the distant observer when the related physical quantity of the black hole varies. Concretely, after showing the power Yang-Mills black hole's relevance with the Event Horizon Telescope data, we unveil the impact of the power parameter $\ensuremath{\gamma}$ on the photon trajectories, the observed intensity, and consequently on the image formation of such black hole. Furthermore, we establish that for the thin disk accretion model that the shadow, lens ring, and photon ring appear near the black hole following some parameter impact ranges. We also show that due to the extreme demagnetization, the remote observer cannot obtain the observation flux provided by the photon ring. Hence, the intensity of observation is provided by the direct image from accretion, and the lens ring also contributes, but only a small part. In addition, different emission profiles of the accretion will also directly affect the observed specific intensity. Such investigations have been realized within a variety of $\ensuremath{\gamma}$'s values. Later, the spherical infalling accretion model is considered and discussed within the $\ensuremath{\gamma}$ variation and by paying attention to the conformally invariant case associated with $\ensuremath{\gamma}=\frac{3}{4}$. Lastly, one concludes that the black hole presents a larger intensity as the power parameter $\ensuremath{\gamma}$ increases. In a word, observational appearances of the power Yang-Mills black hole surrounded by various accretions present some nice features that can be used to distinguish black holes from different gravity theories.

A Fuzzy Logic-Based Intelligent Multiattribute Routing Scheme for Two-Layered SDVNs

Abstract: Due to the complicated and changing urban traffic conditions and the dynamic mobility of vehicles, the network topology can rapidly change which causes the communication links between vehicles disconnected frequently, and further affects the performance of vehicular networking. To overcome this problem, we propose a intelligent multi-attribute routing scheme (MARS) for two-layered software-defined vehicle networks (SDVNs). The proposed scheme is divided into two phases, the routing path calculation and the multi-attribute vehicle autonomous routing decision-making. In this paper, we construct the topology diagram in SDVNs for finding the efficient routing paths. To increase the packet arrival rate and reduce the end-to-end delay, an intelligent multi-attribute routing scheme is proposed by employing fuzzy logic and design a technique of order preference by similarity to ideal solution (TOPSIS) algorithm to find the next-hop forwarder. To solve the uncertainty problem of multiple attributes, we apply the fuzzy logic to identify the weight of each attribute in TOPSIS algorithm. Simulation results demonstrate that MARS can effectively improve packet delivery ratio and reduce average end-to-end delay in urban environments compared with its counterparts.

Thermal imaging of dust hiding the black hole in NGC 1068

Abstract: In the widely accepted 'unified model'1 solution of the classification puzzle of active galactic nuclei, the orientation of a dusty accretion torus around the central black hole dominates their appearance. In 'type-1' systems, the bright nucleus is visible at the centre of a face-on torus. In 'type-2' systems the thick, nearly edge-on torus hides the central engine. Later studies suggested evolutionary effects2 and added dusty clumps and polar winds3 but left the basic picture intact. However, recent high-resolution images4 of the archetypal type-2 galaxy NGC 10685,6, suggested a more radical revision. The images displayed a ring-like emission feature that was proposed to be hot dust surrounding the black hole at the radius where the radiation from the central engine evaporates the dust. That ring is too thin and too far tilted from edge-on to hide the central engine, and ad hoc foreground extinction is needed to explain the type-2 classification. These images quickly generated reinterpretations of the dichotomy between types 1 and 27,8. Here we present new multi-band mid-infrared images of NGC 1068 that detail the dust temperature distribution and reaffirm the original model. Combined with radio data (J.F.G. and C.M.V.I., manuscript in preparation), our maps locate the central engine that is below the previously reported ring and obscured by a thick, nearly edge-on disk, as predicted by the unified model. We also identify emission from polar flows and absorbing dust that is mineralogically distinct from that towards the Milky Way centre.

Forecasting the Detection Capabilities of Third-generation Gravitational-wave Detectors Using GWFAST

Abstract: We introduce GWFAST, a novel Fisher-matrix code for gravitational-wave studies, tuned toward third-generation gravitational-wave detectors such as Einstein Telescope (ET) and Cosmic Explorer (CE). We use it to perform a comprehensive study of the capabilities of ET alone, and of a network made by ET and two CE detectors, as well as to provide forecasts for the forthcoming O4 run of the LIGO-Virgo-KAGRA (LVK) collaboration. We consider binary neutron stars, binary black holes, and neutron star–black hole binaries, and compute basic metrics such as the distribution of signal-to-noise ratio (S/N), the accuracy in the reconstruction of various parameters (including distance, sky localization, masses, spins, and, for neutron stars, tidal deformabilities), and the redshift distribution of the detections for different thresholds in S/N and different levels of accuracy in localization and distance measurement. We examine the expected distribution and properties of golden events, with especially large values of the S/N. We also pay special attention to the dependence of the results on astrophysical uncertainties and on various technical details (such as choice of waveforms, or the threshold in S/N), and we compare with other Fisher codes in the literature. In the companion paper Iacovelli et al., we discuss the technical aspects of the code. Together with this paper, we publicly release the code GWFAST, (https://github.com/CosmoStatGW/gwfast) and the library WF4Py (https://github.com/CosmoStatGW/WF4Py) implementing state-of-the-art gravitational-wave waveforms in pure Python.

Collaborative truck multi-drone routing and scheduling problem: Package delivery with flexible launch and recovery sites

Abstract: This paper deals with the problem of coordinating a truck and multiple heterogeneous unmanned aerial vehicles (UAVs or drones) for last-mile package deliveries. Existing literature on truck–drone tandems predominantly restricts the UAV launch and recovery operations (LARO) to customer locations. Such a constrained setting may not be able to fully exploit the capability of drones. Moreover, this assumption may not accurately reflect the actual delivery operations. In this research, we address these gaps and introduce a new variant of truck–drone tandem that allows the truck to stop at non-customer locations (referred to as flexible sites) for drone LARO. The proposed variant also accounts for three key decisions — (i) assignment of each customer location to a vehicle, (ii) routing of truck and UAVs, and (iii) scheduling drone LARO and truck operator activities at each stop, which are always not simultaneously considered in the literature. A mixed integer linear programming model is formulated to jointly optimize the three decisions with the objective of minimizing the delivery completion time (or makespan). To handle large problem instances, we develop an optimization-enabled two-phase search algorithm by hybridizing simulated annealing and variable neighborhood search. Numerical analysis demonstrates substantial improvement in delivery efficiency of using flexible sites for LARO as opposed to the existing approach of restricting truck stop locations. Finally, several insights on drone utilization and flexible site selection are provided based on our findings.

A multi-agent approach to the truck multi-drone routing problem

Abstract: • Truck-n-drone routing problem coded as a grid in which locations-agents can move. • Agent-based approach in which locations to be visited are the agents instead of orders or vehicles. • Battery capacity constraints and multiple customer’s visits per flight allowed. • Better performance of agent-based approach than that of other meta-heuristics in large problem instances. In this work, we address the Truck-multi-Drone Team Logistics Problem (TmDTL), devoted to visit a set of points with a truck helped by a team of unmanned aerial vehicles (UAVs) or drones in the minimum time, starting at a certain location and ending at a different one. It is an enhanced version of the multiple Flying Sidekicks Traveling Salesman Problem (mFSTSP) presented in Murray and Raj (2020) wherein drones are allowed to visit several customers per trip. In order to cope with large instances of the complex TmDTL, we have developed a novel agent-based method where agents represent the points that are going to be visited by vehicles. Agents evolve by means of movement inside a grid (locations vs. vehicles) according to a set of rules in the seek of better objective function values. Each agent needs to explore only a fraction of the complete problem, sharing its progress with the rest of the agents which are coordinated by one central agent which helps to maintain an asynchronous memory of solutions – e.g. on the control of the mechanism to escape from local minima. Our agent-based approach is firstly tested using the largest instances of the single TDTL problem reported in the literature, which additionally serves as upper bounds to the TmDTL problem. Secondly, we have solved instances up to 500 locations with up to 6 drones in the fleet. Thirdly, we have tested the behavior of our approach in 500 locations problems with up to 8 drones in order to test the fleet size sensitivity. Our experiments demonstrate the ability of the proposed agent-based system to obtain good quality solutions for complex optimization problems that arise. Further, the abstraction in solutions coding applied makes the agent-based approach scalable and flexible enough to be applied to a wide range of other optimization problems.

Timing-Aware Layer Assignment for Advanced Process Technologies Considering via Pillars

Abstract: Interconnect delay is a key factor that affects the chip performance in layer assignment. Particularly in the advanced process technologies of 5 nm and beyond, interconnect delay has grown significantly due to the increase of circuit scale. Moreover, coupling effect existed in wires reduces the accuracy of delay evaluation. On the other hand, the size of vias is often ignored in layer assignment, which enlarges the mismatch between global routing and detailed routing. To solve these problems, we propose VPT , a timing-aware layer assignment algorithm considering via pillars, which includes the following five key techniques: 1) via pillar structure combined with nondefault-rule (NDR) wires is adopted to form a net delay optimization system for advanced process technologies; 2) a synthetical model that can adapt to varying types and sizes of both vias and wires is designed to evaluate overflow effectively; 3) a sorting strategy is devised to reduce uncertainty of layer assignment flow and improve stability of the proposed algorithm; 4) an awareness strategy based on multiaspect congestion assessment is designed to reduce overflow significantly; and 5) a net scalpel algorithm is devised to minimize the maximum delay of nets, so that the timing behaviors can be improved systematically. The experimental results on multiple benchmarks confirm that the proposed algorithm leads to lower delay and less overflow, while achieving the best solution quality among the existing algorithms with the shortest runtime.

Energy Efficiency and Reliability Considerations in Wireless Body Area Networks: A Survey

Abstract: In this paper, we have reviewed and presented a critical overview of “energy-efficient and reliable routing solutions” in the field of wireless body area networks (WBANs). In addition, we have theoretically analysed the importance of energy efficiency and reliability and how it affects the stability and lifetime of WBANs. WBAN is a type of wireless sensor network (WSN) that is unique, wherever energy-efficient operations are one of the prime challenges, because each sensor node operates on battery, and where an excessive amount of communication consumes more energy than perceiving. Moreover, timely and reliable data delivery is essential in all WBAN applications. Moreover, the most frequent types of energy-efficient routing protocols include crosslayer, thermal-aware, cluster-based, quality-of-service, and postural movement-based routing protocols. According to the literature review, clustering-based routing algorithms are the best choice for WBAhinwidth, and low memory WBAN, in terms of more computational overhead and complexity. Thus, the routing techniques used in WBAN should be capable of energy-efficient communication at desired reliability to ensure the improved stability period and network lifetime. Therefore, we have highlighted and critically analysed various performance issues of the existing “energy-efficient and reliable routing solutions” for WBANs. Furthermore, we identified and compiled a tabular representation of the reviewed solutions based on the most appropriate strategy and performance parameters for WBAN. Finally, concerning to reliability and energy efficiency in WBANs, we outlined a number of issues and challenges that needs further consideration while devising new solutions for clustered-based WBANs.

A timing engine inspired graph neural network model for pre-routing slack prediction

Abstract: Fast and accurate pre-routing timing prediction is essential for timing-driven placement since repetitive routing and static timing analysis (STA) iterations are expensive and unacceptable. Prior work on timing prediction aims at estimating net delay and slew, lacking the ability to model global timing metrics. In this work, we present a timing engine inspired graph neural network (GNN) to predict arrival time and slack at timing endpoints. We further leverage edge delays as local auxiliary tasks to facilitate model training with increased model performance. Experimental results on real-world open-source designs demonstrate improved model accuracy and explainability when compared with vanilla deep GNN models.

The Electric Vehicle Routing Problem with Capacitated Charging Stations

Abstract: Electric vehicle routing problems (E-VRPs) deal with routing a fleet of electric vehicles (EVs) to serve a set of customers while minimizing an operational criterion, for example, cost or time. The feasibility of the routes is constrained by the autonomy of the EVs, which may be recharged along the route. Much of the E-VRP research neglects the capacity of charging stations (CSs) and thus implicitly assumes that an unlimited number of EVs can be simultaneously charged at a CS. In this paper, we model and solve E-VRPs considering these capacity restrictions. In particular, we study an E-VRP with nonlinear charging functions, multiple charging technologies, en route charging, and variable charging quantities while explicitly accounting for the number of chargers available at privately managed CSs. We refer to this problem as the E-VRP with nonlinear charging functions and capacitated stations (E-VRP-NL-C). We introduce a continuous-time model formulation for the problem. We then introduce an algorithmic framework that iterates between two main components: (1) the route generator, which uses an iterated local search algorithm to build a pool of high-quality routes, and (2) the solution assembler, which applies a branch-and-cut algorithm to combine a subset of routes from the pool into a solution satisfying the capacity constraints. We compare four assembly strategies on a set of instances. We show that our algorithm effectively deals with the E-VRP-NL-C. Furthermore, considering the uncapacitated version of the E-VRP-NL-C, our solution method identifies new best-known solutions for 80 of 120 instances.

Testing Horndeski Gravity from EHT Observational Results for Rotating Black Holes

Abstract: The Event Horizon Telescope (EHT) collaboration recently unveiled the first image of the supermassive black hole M87*, which exhibited a ring of angular diameter $\theta_{d}=42 \pm 3 \mu as$, a circularity deviation $\Delta C \leq 0.1$, and also inferred a black hole mass of $M=(6.5 \pm 0.7) \times 10^9 M_\odot $. This provides a new window onto tests of theories of gravity in the strong-field regime, including probes of violations of the no-hair theorem. It is widely believed that the Kerr metric describes the astrophysical black holes, as encapsulated in the critical but untested no-hair theorem. Modeling Horndeski gravity black holes -- with additional hair parameter $h$ besides the mass $M$ and spin $a$ of the Kerr black hole -- as the supermassive black hole M87*, we observe that to be a viable astrophysical black hole candidate, the EHT result constrains ($a$, $h$) parameter space. However, a systematic bias analysis indicates rotating Horndeski black hole shadows may or may not capture Kerr black hole shadows, depending on the parameter values; the latter is the case over a substantial part of the constrained parameter space, allowing Horndeski gravity and general relativity to be distinguishable in the said space, and opening up the possibility of potential modifications to the Kerr metric.

Learning Based Massive Data Offloading in the IoV: Routing Based on Pre-RLGA

Abstract: With the increasing demand for bulk data transmission, offloading techniques to transmit data via mobile physical media is becoming common increasingly. The development of the Internet of Vehicles (IoV) provides new solutions for data offloading, as the IoV brings massive storage and transmission capabilities. However, the impact caused by the mobility of physical media brings a challenge to delay optimization and route selection of data offloading. In this paper, we consider a data transmission network architecture based on the Manhattan mobility model. The vehicle carries data on a fixed route in this scenario, enabling data transmission between geographically distant data centers. In order to reduce the total transmission time and reduce the impact of retransmission, we consider the temporal convolutional network (TCN) model to predict the allocation of the weight of delay. Next, we solve the optimal routing problem using a genetic algorithm based on a reinforcement learning mechanism (RLGA) to pre-allocate resources for offloading requests. The experimental results show that the proposed data offloading method can reduce the load on the cellular network and decrease the data transmission time, average transmission hops, and retransmission times compared with existing methods.

The healthcare supply location-inventory-routing problem: A robust approach

Abstract: Motivated by a real-world healthcare supply case of a medical implant company, this paper studies a supply network configuration problem that integrates warehouse selections for vendor managed inventory (VMI), inventory policy, and delivery routing optimization together. The problem is a variant of the classic location-inventory-routing problem (LIRP) with both deterministic demand and uncertain demand, where multi-product, multi-period, multi-type delivery, delivery time limit and VMI are considered. Two types of delivery are used: one is the scheduled bulk delivery to the VMI warehouses and the other is direct shipping for hospitals. To address the problem, first, a deterministic MILP model is presented for the integrated LIRP. Then, to deal with the uncertainty in demand, we propose a robust optimization model and transform it into a tractable linear equivalent formulation. Further, considering the effect of COVID-19 pandemic on the demand and delivery time, a new robust model is proposed to account for this special situation. Numerical experiments are conducted to verify the advantage of the proposed robust optimization models. The sensitivity analysis provides some interesting managerial insights, and a real-world case of medical implant supply configuration with 78 hospitals is solved.

Deep Policy Dynamic Programming for Vehicle Routing Problems

Abstract: AbstractRouting problems are a class of combinatorial problems with many practical applications. Recently, end-to-end deep learning methods have been proposed to learn approximate solution heuristics for such problems. In contrast, classical dynamic programming (DP) algorithms guarantee optimal solutions, but scale badly with the problem size. We propose Deep Policy Dynamic Programming (DPDP), which aims to combine the strengths of learned neural heuristics with those of DP algorithms. DPDP prioritizes and restricts the DP state space using a policy derived from a deep neural network, which is trained to predict edges from example solutions. We evaluate our framework on the travelling salesman problem (TSP), the vehicle routing problem (VRP) and TSP with time windows (TSPTW) and show that the neural policy improves the performance of (restricted) DP algorithms, making them competitive to strong alternatives such as LKH, while also outperforming most other ‘neural approaches’ for solving TSPs, VRPs and TSPTWs with 100 nodes.KeywordsDynamic ProgrammingDeep LearningVehicle Routing

An analysis of architecture, framework, security and challenging aspects for data aggregation and routing techniques in IoT WSNs

Abstract: Internet has evolved into a promising technology after going through various transitional phases in the past decades. It was invented in the early nineties, with the web being static, public and shared. The final transitional phase is the internet of services, where the content, web services, Extensible Markup Language (XML), productivity and commerce tools were created by the user bringing improved websites and services. Later on, with affordable mobile broadband, Android phones and tablets, people could access the internet and be in touch with the world through social media platforms and mobile applications. IoT is a network of physical components, vehicles, household appliances and other items embedded with devices, software, sensor systems, actuators and connectivity, connecting and transferring data. The process of gathering data from heterogeneous sensors for preventing recurrent transmissions simultaneously offering quality aggregated information at the sink node is collectively called as data aggregation and routing process. There is a transmission of only most complex information to the sink node. The continuous use of data aggregation and routing technique increases the energy, bandwidth and memory requirements. The challenging aspects in IoT WSN are energy consumption, bandwidth and memory utilized for data aggregation and routing process. So, it is very much important to focus on these parameters so that the network has a greater lifetime and Quality of service. The tradeoff between energy saving, precision of data and latency can be achieved only by utilizing data redundancy, data similarity, data aggregation and routing algorithms. The aim of the research is to do the analysis of the IoT WSNs on the basis of the architecture, framework, and challenges related to security, data aggregation and routing techniques. This research work motivates the researchers to know about the challenges in data aggregation and routing in IoT WSNs. An efficient data aggregation and routing technique in IoT WSNs help to improve the QoS parameters namely, throughput, end to end delay, routing overhead, packet delivery ratio and energy consumption. Data aggregation technique in IoT WSNs help in saving the energy of the nodes in the network. Thus makes the network efficient in terms of energy and other QoS parameters. Because of the high density of nodes in IoT WSNs, same data is sensed by a lot of nodes, which results in redundancy. Using data aggregation technique, the redundancy can be eliminated while routing packets from source nodes to base station. • The transitional phase is the internet of services, where the content, web services, and commerce tools were created. • The data aggregation is the process of gathering data from heterogeneous sensors for preventing recurrent transmissions. • The analysis of the IoT WSNs on the basis of the architecture, framework, and challenges related to security. • This research work motivates the researches to know about the challenges in data aggregation and routing in IoT WSNs.

Proportional Fairness Based Energy Efficient Routing in Wireless Sensor Network

Abstract: Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions, device monitoring, and collection of information. Due to limited energy resources available at SN, the primary issue is to present an energy-efficient framework and conserve the energy while constructing a route path along with each sensor node. However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routing with minimal energy consumption in WSN. This paper presents an energy-efficient routing system called Energy-aware Proportional Fairness Multi-user Routing (EPFMR) framework in WSN. EPFMR is deployed in the WSN environment using the instance time. The request time sent for the route discovery is the foremost step designed in the EPFMR framework to reduce the energy consumption rate. The proportional fairness routing in WSN selects the best route path for the packet flow based on the relationship between the periods of requests between different SN. Route path discovered for packet flow also measure energy on multi-user route path using the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR develops node dependent energy-efficient localized route path, improving the throughput. The energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing, Throughput, RST, node density and average energy per packet in WSN. The Route Searching Time (RST) is reduced using the Boltzmann Distribution (BD), and as a result, the energy is minimized on multi-user WSN. Finally, GIFM applies an instance time difference-based route searching on WSN to attain an optimal energy minimization system. Experimental analysis shows that the EPFMR framework can reduce the RST by 23.47% and improve the throughput by 6.79% compared with the state-of-the-art works.