Rendezvous Point Research Articles

Optimal rendezvous points selection to reliably acquire data from wireless sensor networks using mobile sink

In rendezvous points (RPs) based data collection, the mobile sink (MS) visits a set of sensor nodes known as RPs for data gathering from wireless sensor networks to minimize the trajectory of MS. Existing RPs based methods are suitable for the scenarios where sensor nodes have uniform data generation rates along with having limited buffer capacity to store the forwarded data. However, in some situations, the sensing rate increases due to the occurrence of unusual events in the surrounding, and the RPs receive more data packets than their capacity. This creates data loss due to buffer overflow. Therefore, the selection of optimal RPs for reliable data gathering, while minimizing the trajectory of MS, is a challenging task. This paper proposes a squirrel search algorithm-based rendezvous points selection (SSA-RPS) method that chooses a set of optimal RPs for reliable data collection. The objective of the SSA-RPS is to minimize the trajectory of MS while visiting a set of optimal RPs under non-uniform data generation and limited buffer capacity constraints of sensor nodes for reliable data acquisition. The SSA-RPS applies an efficient encoding scheme to generate variable dimension squirrels that represent each possible trajectory of MS, and the dimension of squirrel presents the number of RPs. The SSA-RPS also adopts the reselection of RPs to provide a fair energy share among sensor nodes. The simulation results demonstrate that the SSA-RPS outperforms the existing state-of-the-art methods in terms of the number of dropped packets, data gathering ratio, energy consumption, and network lifetime.

Fuzzy Logic-Based Path Planning for Data Gathering Mobile Sinks in WSNs

Mobile sinks (MSs) are capable of collecting data along specified paths in wireless sensor networks (WSNs). They are deployed as a popular alternative for data loggers, to which all nodes have to send sensory data. If MSs' paths (or cycles) are not well determined, it might take a relatively long time for the MSs to make a round trip. Recent research works have proposed methods to determine rendezvous points (RPs) that the MSs must pass by to collect data, with the aim of reducing the data-collection time. Determination of the number of RPs is important, and it is challenging to make ensure that there are sufficient RPs widely located throughout a sensor network, forming a circle along which the MSs can spend limited time traveling. This research presents a method for designing paths and pinpointing RPs for MSs to collect data, as well as determining the next hop to relay data for each sensor node. Instead of reducing the MSs' travel time, the focus of this research is to preserve the energy of all sensor nodes in WSNs. Our method determines the maximum number of RPs such that the MSs can run through each RP's communication range (within a time constraint) without depleting their own energy. The method comprises three main steps. First, we calculate the number of RPs and design the MS path. Second, the exact data-collection points are determined. The last step is to specify the path along which sensory data are relayed to the MS. In our experiments, we simulate two WSNs of different sizes. The results show that our method outperforms the others by 70%-80% in terms of the sensor node uptime, power consumption, MS traveling time and the number of RPs.

A Fuzzy Guidance System for Rendezvous and Pursuit of Moving Targets

This article presents the development of a fuzzy guidance system (FGS) for unmanned aerial vehicles capable of pursuing and performing rendezvous with static and mobile targets. The system is designed to allow the vehicle to approach a maneuvering target from a desired direction of arrival and to terminate the rendezvous at a constant distance from the target. In order to perform a rendezvous with a maneuvering target, the desired direction of arrival is adjusted over time to always approach the target from behind, so that the aircraft and target velocity vectors become aligned. The proposed guidance system assumes the presence of an autopilot and uses a set of Takagi–Sugeno fuzzy controllers to generate the orientation and speed references for the velocity and heading control loops, given the relative position and velocity between the aircraft and the target. The FGS treats the target as a mobile waypoint in a 4-D space (position in 2-dimensions, desired crossing heading and speed) and guides the aircraft on suitable trajectories towards the target. Only when the vehicle is close enough to the rendezvous point, the guidance law is complemented with an additional linear controller to manage the terminal formation keeping phase. The capabilities of the proposed rendezvous-FGS are verified in simulation on both maneuvering and non-maneuvering targets. Finally, experimental results using a multi-rotor aerial system are presented for both fixed and accelerating targets.

Fractional rider optimization algorithm for the optimal placement of the mobile sinks in wireless sensor networks

SummaryWireless sensor networks (WSNs) employ the mobile sinks to gather the information from the sensors deployed in the environment periodically, in such a way to avoid the energy‐crisis and hotspot issues. Delay in visiting all the nodes is addressed through the rendezvous points that collect the data from other nodes such that the nodes collect the data from this point rather than visiting all the nodes, which saves energy. However, the optimal placement of the mobile sinks to visit those rendezvous points is a need, which is addressed optimally using the proposed fractional rider optimization algorithm (FROA). The FROA is the modification of the rider optimization algorithm (ROA) with the fractional theory, and the ultimate goal of the proposed algorithm is to optimally place the mobile sink for which initially, the wireless sensor environment is split as uniform‐sized cells with the Voronoi partitions and clusters are formed using sparse fuzzy c‐means (sparse‐FCM) algorithm. The constraints for enabling the optimal sink placement are distance, delay, and energy of the nodes. The simulation analysis reveals that the proposed method outperformed the existing algorithms, with minimal distance of 132.2069 m, maximal network energy, alive nodes, and throughput of 21.4605, 56, and 62.3702, respectively.

One-Hop Data Collection by Four Quadrants Moving Model for Mobile Sink Wireless Sensor Networks

In wireless sensor networks, the energy of sensors close to the sink within one-hop is prone to be exhausted since these sensors need to forward the data from other sensors to the sink and the energy of each sensor is limited. Once the energy of these sensors close to the sink within one-hop is exhausted, the data thus could not be sent to the sink. To address this issue, the general solution is to ensure that each sensor could consume energy to forward the data from other sensors to by using the mobile sink because the location of the mobile sink is dynamic. In this situation, each sensor may be close to the sink within one-hop. The energy consumption among all sensors thus could be balanced and the network lifespan also could be prolonged. To reduce more energy consumption of sensors by forwarding data to the sink, the one-hop data collection by mobile sink was proposed to claim the sink moved to the source sensor within one-hop to collect the data in this paper. Hence, only the source sensors send the data to the sink within one-hop and other sensors could not forward the data to the sink to save energy. In the one-hop data collection by mobile sink, the rendezvous point moving model (RPMM) and the minimum moving model (MMM) were common proposed to be used. However, the time and energy consumption for collecting data in RPMM and MMM may not be minimized with the large number of source sensors. While the source sensors increase, the mobile sink needs more time and energy to collect data. To address these issues, we proposed a one-hop data collection by four quadrants moving model, FQMM, to collect data in this paper. The implementation tool, such as simulator, was used by Java language with Java SDK to evaluate performance under our proposal and the comparing proposal. For the numerical results, the maximal moving hop count, MHC, in FQMM was 30% less than the maximal MHC in RPMM. The maximal MHC in FQMM was 36% less than the maximal MHC in MMM. The minimum MHC in FQMM was 22% less than the minimum MHC in RPMM. The minimum MHC in FQMM was 28% less than the minimum MHC in MMM. The average MHC in FQMM was 25% less than the average MHC in RPMM. The average MHC in FQMM was 31% less than the average MHC in MMM. The total energy consumption in FQMM was 55% less than that in RPMM. The total energy consumption in FQMM was 59% less than that in MMM. Since the number of source sensors is often large in the real condition, FQMM could be applied for the regarding applications of WSN with mobile sink.

Tour planning for multiple mobile sinks in wireless sensor networks: A shark smell optimization approach

Sink mobility has been regarded as a widely accepted method for data collection in wireless sensor networks (WSNs) as it significantly improves network performance. Particularly, data collection using mobile sink based on rendezvous points (RPs) is a hot research topic which has been paid enormous attention to WSN community. However, efficient tour planning for the mobile sink (MS) is a challenging problem, especially for delay-harsh applications that require shorter paths of the MS. Existing literature finds this problem as NP-hard in nature, and thus nature-inspired algorithms are in demand as they can provide a near-optimal solution within acceptable time and space constraints. There are many schemes on MS tour planning that exist in the form of heuristics or nature-inspired algorithms; nevertheless, they leave out the scope for further research as most of them have not considered disjoint networks. Moreover, they fail to jointly optimize both the number of RPs and the number of MSs. To this end, this paper presents a novel scheme comprising of two algorithms based on the shark smell optimization (SSO) technique that solves the MS tour planning problem. The first algorithm is used to determine an optimal number of RPs and their locations. Based on this, the second algorithm optimizes the number of MSs so as to minimize the overall tour length. Each of the algorithms is developed with an efficient and novel particle encoding scheme along with the derivation of a fitness function. The tour planning is formulated as an Integer Linear Programming problem for the first algorithm and a Non-linear Programming problem for the second algorithm. Simulation results of our scheme confirm the improvement over the state-of-the-art algorithms. The results are also statistically validated through hypothesis testing using ANOVA and post hoc analysis.

An extended ACO-based mobile sink path determination in wireless sensor networks

In wireless sensor networks (WSNs), a mobile sink accumulate the data instead of routing directly to the sink to avoid the hotspot problem. In this process, it traverses a predetermined path by visiting a set of nodes called the rendezvous point (RP), and all the non-rendezvous points can transmit their data to the closest RP. Identifying the best collection of RPs and determining the mobile sink traveling path will decrease data loss and improve network performance. However, choosing a set of RPs and the route between them is a challenging task. It is more complicated in the event-driven applications due to the uneven data rate of SNs. In this context, we propose an extended ant colony optimization (ACO)-based mobile sink path construction for event-driven WSNs. In this, the best set of the RPs and the efficient mobile sink traveling path between them is determined. In addition to this, the RPs re-selection mechanism also adopted for balancing the energy between the nodes. After that, the virtual RPs are introduced to minimize the data transmissions between the sensor nodes and RPs. This process will improve WSNs’ performance in terms of reducing data losses while increasing network lifetime. The improved performance of the extended ACO-MSPD over existing is confirmed through simulation tests.

New approach based on Hilbert curve for energy efficient data collection in WSN with mobile sink

Energy is the most important resource in the design of wireless sensor networks (WSNs); as a result, most of the research work was done on this topic. It has been proven that the mobility in this kind of network plays an important role in energy conservation. The movement of the Sink is the most interesting type of mobility. In this work, the authors optimise the Sink travel in sensor field using Hilbert curve as an efficient data collection trajectory. The sensor field is organised in clusters and a virtual rendezvous point (VRP) is selected at each cluster for the sojourn of the Sink. The optimal position of the VRP is obtained using their integer linear program which takes into account the optimal transmission range between the Sink and the sensor node. The obtained results showed the supremacy of the Hilbert curve compared to other curves generated by other optimisation approaches and confirm that the solution based on clustering and Hilbert curve has performed well in terms of energy gain.

Future development of helicopter emergency medical services in Korea

This paper discusses the future development of air ambulance operations in Korea. Helicopter emergency medical services are a useful means of transporting critically ill patients to the right hospital, at the right time. It is an important element of the emergency medical system to treat acute diseases at the scene, or in transit. For more efficient operations of the emergency medical helicopters (air ambulances or the so-called ‘Doctor Helgi’ in Korea), various challenges are faced. These include, the expansion of air ambulance bases, proper placement of rendezvous points (landing point), increase of field transport requests, and the operation of night missions. First, it is necessary to reduce the disparity in the benefits of emergency medical care for critically ill patients through the expansion of helicopter bases. Second, through the advancement of joint operations of the pan-ministerial emergency medical helicopters, the time from dispatch to helicopter take-off should be minimized, and a quality improvement program for air transportation should be carried out. Third, it is necessary to increase the number of insufficient rendezvous points and ensure the safety of the helicopters during takeoff and landing, to activate field transportation. Finally, the safety of patients and medical staff should be secured through the systematic preparation of the air transport system for future night missions. To solve these tasks, an appropriate legal system for helicopter emergency medical service is required. Based on the improved system, it is expected that everyone will enjoy equal rights for health, regardless of the regions.

Load-Balancing Rendezvous Approach for Mobility-Enabled Adaptive Energy-Efficient Data Collection in WSNs

The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data trans-mission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Di-minishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy effi-ciency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collabo-rations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs.

Truck-drone team logistics: A heuristic approach to multi-drop route planning

Recently there have been significant developments and applications in the field of unmanned aerial vehicles (UAVs). In a few years, these applications will be fully integrated into our lives. The practical application and use of UAVs presents several problems that are of a different nature to the specific technology of the components involved. Among them, the most relevant problem deriving from the use of UAVs in logistics distribution tasks is the so-called “last mile” delivery.In the present work, we focus on the resolution of the truck-drone team logistics problem. The problems of tandem routing have a complex structure and have only been partially addressed in the scientific literature. The use of UAVs raises a series of restrictions and considerations that did not appear previously in routing problems; most notably, aspects such as the limited power-life of batteries used by the UAVs and the determination of rendezvous points where they are replaced by fully-charged new batteries. These difficulties have until now limited the mathematical formulation of truck-drone routing problems and their resolution to mainly small-size cases.To overcome these limitations we propose an iterated greedy heuristic based on the iterative process of destruction and reconstruction of solutions. This process is orchestrated by a global optimization scheme using a simulated annealing (SA) algorithm. We test our approach in a large set of instances of different sizes taken from literature. The obtained results are quite promising, even for large-size scenarios.

Virtual grid-based rendezvous point and sojourn location selection for energy and delay efficient data acquisition in wireless sensor networks with mobile sink

Rendezvous points (RPs) based data acquisition methods are widely accepted as the solution for data acquisition delay/latency problem. In these methods, RPs are a subset of sensor nodes, that store the data of other sensor nodes and forward them to the mobile sink (MS). The locations where MS must arrive to acquire data from RPs are called as sojourn locations. RPs are prone to exhaust energy due to the additional activity of forwarding the data and create the energy-hole problem. Although re-selection of RPs mitigates the energy-hole problem, however, it increases the control overhead due to topology reconstruction. Among the various type of RPs selection methods, the grid-based RP selection is a straight-forward and one-time topology construction method. In the existing grid-based RP selection, grid cell coordinators serve as RPs, which are also considered as sojourn locations of MS, and it increases data acquisition latency. This paper proposes a new virtual grid-based rendezvous point and sojourn location selection (VGRSS) method for energy and delay efficient data acquisition that exploits the virtual-grid and constructs an energy efficient search region inside each grid cell. Afterward, it adopts fuzzy interference system to select/re-select RPs from this region. Additionally, the distributed re-selection of RPs for each grid cell reduces between 10 and 30.84% reconstruction overhead of the entire topology. Along with this, the selection of intersection points of four grid cells as sojourn locations of MS decreases between 14 and 31.25% the data acquisition latency in VGRSS. Through simulation results, this paper demonstrates that the VGRSS is efficient over state-of-the-art in terms of energy consumption, control overhead, network lifetime and data acquisition latency.

A DGA domain names detection modeling method based on integrating an attention mechanism and deep neural network

Command and control (C2) servers are used by attackers to operate communications. To perform attacks, attackers usually employee the Domain Generation Algorithm (DGA), with which to confirm rendezvous points to their C2 servers by generating various network locations. The detection of DGA domain names is one of the important technologies for command and control communication detection. Considering the randomness of the DGA domain names, recent research in DGA detection applyed machine learning methods based on features extracting and deep learning architectures to classify domain names. However, these methods are insufficient to handle wordlist-based DGA threats, which generate domain names by randomly concatenating dictionary words according to a special set of rules. In this paper, we proposed a a deep learning framework ATT-CNN-BiLSTM for identifying and detecting DGA domains to alleviate the threat. Firstly, the Convolutional Neural Network (CNN) and bidirectional Long Short-Term Memory (BiLSTM) neural network layer was used to extract the features of the domain sequences information; secondly, the attention layer was used to allocate the corresponding weight of the extracted deep information from the domain names. Finally, the different weights of features in domain names were put into the output layer to complete the tasks of detection and classification. Our extensive experimental results demonstrate the effectiveness of the proposed model, both on regular DGA domains and DGA that hard to detect such as wordlist-based and part-wordlist-based ones. To be precise,we got a F1 score of 98.79% for the detection and macro average precision and recall of 83% for the classification task of DGA domain names.

Data Collection and Path Determination Strategies for Mobile Sink in 3D WSNs

Wireless sensor networks (WSNs) are composed with a set of sensor nodes, and its primary task is to sense and relay the data to the base station or sink. Sensor nodes are equipped with a limited battery, and more energy is consumed due to the relay of the data. While relaying on more data, nodes near to the base station will die soon and result in energy-hole or hotspot problem. A mobile sink has been introduced to collect the data from the nodes to avoid the hotspot problem. Several data collection algorithms have been proposed for 2D WSNs; however, these algorithms do not meet the requirements of the 3D WSNs due to its variations in topology, connectivity, coverage, scalability, etc. Therefore, the selection of mobile sink visiting points called rendezvous points (RPs), and path determination between then in 3D WSNs is a challenging task. In this paper, we determine the optimal RPs in 3D WSNs using unsupervised learning-based hierarchical agglomerative clustering. Further, we propose an algorithm for path determination between the RPs with minimal computational overhead. The simulation runs show that the proposed method finds the near-optimal set of RPs and path of the mobile sink while enhancing the network lifetime.

Three dimensional distributed rendezvous in spherical underwater robots considering power consumption

This paper introduces distributed control laws to make all spherical underwater robots rendezvous in a three dimensional environment with many obstacles. Assume that every spherical robot is located in 3-D environments where GPS is not available. Also, each robot moves with a constant speed and is given different available power level initially. The maneuver of every robot consumes its power, which implies that all robots must rendezvous in such a way that they do not run out of power. This paper proposes how to select the rendezvous points considering the power consumption. Then, distributed control laws are developed to enable rendezvous of multiple spherical robots with limited power. Using MATLAB simulations, we verify the effectiveness of our rendezvous approach in 3-D environments with many obstacles.

Scheme for tour planning of mobile sink in wireless sensor networks

Exploiting mobile sink (MS) for data gathering in the wireless sensor networks has been extensively studied in the recent researches to address energy-hole issues, thereby facilitating balanced energy consumption among nodes and so prolonging network lifetime. However, such approaches suffer from an extended data collection delay causing buffer overflow problem. In this regard, finding the optimal number of locations (i.e. rendezvous points (RPs) where the MS sojourns for data collection), is not only of utmost importance, but also a challenging task. A novel scheme for trajectory design of MS for data collection is presented in this study. The authors' primary goal is to optimise the number of RPs and their locations to minimise the travelling length of the MS. First, they reduced the problem size by using a combination of breadth-first search and Tarjan's algorithm and then applied spectral clustering to find the optimal set of RPs to plan the tour for the MS. They have performed extensive simulations, and the results are compared with relevant existing schemes. The comparative results confirm the effectiveness of their approach in terms of the number of RPs, path length, the variance of RPs, and energy consumption per round.

M‐RPSS: A modified RPSS for path scheduling of mobile sink in wireless sensor network

SummaryIn wireless sensor networks (WSNs), data gathering is the main concern, since it directly affects the network lifetime and data latency. Rendezvous Point Selection Scheme (RPSS) is a mobile sink node approach; it offers superior performance than its preceding mobile sink schemes like Rendezvous Design for Variable Track (RD‐VT), RD‐VT with Steiner Minimum Tree (RD‐VT‐SMT), and Weight Rendezvous Planning with Steiner Minimum Tree (WRP‐SMT). However, a more uniform distribution of the rendezvous node leads to less energy consumption in WSNs. The more optimum path offers less data latency. In the proposed approach, we use particle swarm optimization (PSO) to find the optimum rendezvous point and adaptive PSO (APSO) to find an optimum path by solving the travelling salesman problem. By rigorous simulation, we prove that modified RPSS (M‐RPSS) increases the network lifetime by more than 10% and decreases the data latency.

Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization

Optimal performance and improved lifetime are the most desirable design benchmarks for WSNs and the mechanism for data gathering is a major constituent influencing these standards. Several researchers have provided significant evidence on the advantage of mobile sink (MS) in performing effective gathering of relevant data. However, determining the trajectory for MS is an NP-hard-problem. Especially in delay-inevitable applications, it is challenging to select the best-stops or rendezvous points (RPs) for MS and also to design an efficient route for MS to gather data. To provide a suitable solution to these challenges, we propose in this paper, a game theory and enhanced ant colony based MS route selection and data gathering (GTAC-DG) technique. This is a distributed method of data gathering using MS, combining the optimal decision making skill of game theory in selecting the best RPs and computational swarm intelligence of enhanced ant colony optimization in choosing the best path for MS. GTAC-DG helps to reduce data transfer and management, energy consumption and delay in data delivery. The MS moves in a reliable and intelligent trajectory, extending the lifetime and conserving the energy of WSN. The simulation results prove the effectiveness of GTAC-DG in terms of metrics such as energy and network lifetime.

Communication-Delay-Dependent Rendezvous With Possible Negative Controller Gain in Cyclic Pursuit

In this article, we address the multiagent rendezvous problem for a group of single-integrator agents in the presence of a constant arbitrarily large bounded homogeneous communication delay. The agents are required to meet at a tunable desired point, which is unknown to all agents. To achieve this objective, we choose the cyclic pursuit strategy, which requires the minimum number of communication links. It is shown that the rendezvous can be achieved with at most one zero or negative gain in the presence of delay. A negative controller gain implies an antagonistic interaction of the agent with its neighbor, whereby it moves away from the neighbor. We find the bound on the negative gain such that rendezvous among all agents is possible. The margin on the negative gain decreases due to the presence of delay. An analytical expression has been obtained for the reachable rendezvous points in terms of an initial configuration of the agents, controller gains, and the delay. For fixed controller gains and increasing delay, the rendezvous point lies along the line joining the weighted centroid and the centroid of the initial configuration with the asymptotic nature toward the centroid when all gains are positive, while away from it when one gain is negative. For finite controller gains, the reachable set remains unchanged even in the presence of any finite delay. Numerical examples illustrate the presented results.

Rendezvous: Opportunistic Data Delivery to Mobile Users by UAVs Through Target Trajectory Prediction

Reliable and timely delivery of data to mobile targets is a challenging problem in mobile ad-hoc networks, because of the opportunistic and unpredictable nature of the problem. Finding a complete series of mobile-to-mobile contacts and forwarding data toward targets within a designated deadline is even more difficult. We leverage unmanned aerial vehicles (UAVs) as message ferries to travel over highly probable rendezvous points for over-the-air delivery to target users. We propose Rendezvous , an opportunistic yet disciplined data delivery scheme based on trajectory prediction of users and UAV path planning. During the offline learning phase, we identify both temporal and spatial regularities of mobile users from real-world trajectories using sequence-wise clustering, and construct a compact yet well-summarized cluster signature that enables efficient search. In the data delivery phase, given recent movement history of users, UAVs perform their own distributed path planning collaboratively over time. Each UAV finds a chronological sequence of future visiting points where it can make consecutive data delivery efforts to users to overcome the uncertainty in mobility and ensure timely delivery. Real-world trace-driven simulation experiments demonstrate that Rendezvous achieves reliable and punctual data delivery using only few UAVs compared with existing algorithms.