Neighbor Discovery Research Articles

Evolutionary neighborhood discovery algorithm for agricultural routing planning in multiple fields

In recent years, operations research in agriculture has improved the harvested yield, reduced the cost and time required for field operations, and maintained economic and environmental sustainability. The heuristics method, named Evolutionary neighborhood discovery algorithm (ENDA), is applied to minimize the inter-field and intra-field distance of the routing planning of machines in multiple agricultural fields. The problem is an extended version of the Agricultural Routing Planning (ARP) that takes into consideration the different capacity of the machines and multiple agricultural fields. This research also describes the mathematical model to represent the proposed problem formulated as an integer program. The experimental results show that ENDA successfully solves ARP instances, giving the best results and the fastest running time compared to those obtained by Genetic Algorithms and Tabu Search. The results also show that ENDA can save an average of 11.72% of the distance traveled by the machines outside the working path (when making maneuvers, going to or from the entrances and going from and returning to the Depot).

Optimizing BLE-Like Neighbor Discovery

Neighbor discovery (ND) protocols are used for establishing a first contact between multiple wireless devices. The energy consumption and discovery latency of this procedure are determined by the parametrization of the protocol. In most existing protocols, reception and transmission are temporally coupled. Such schemes are referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">slotted</i> , for which the problem of finding optimized parametrizations has been studied thoroughly in the literature. However, slotted approaches are not efficient in applications in which new devices join the network gradually and only the joining devices and a master node need to run the ND protocol simultaneously. For example, this is typically the case in IoT scenarios or bluetooth low energy (BLE) piconets. Here, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">slotless</i> protocols that decouple reception and transmission can achieve significantly lower worst-case latencies than slotted ones. In this paper, we study slotless, BLE-like protocols, which schedule receptions and transmissions independently using periodic intervals (PI). For this class of protocols, optimal parameter values remain unknown. To address this, we propose an optimization framework for PI-based protocols, which translates any specified duty-cycle (and therefore energy budget) into a set of optimized parameter values. We show that the parametrizations resulting from one variant of our proposed scheme are optimal when one receiver discovers one transmitter, and no other parametrization or ND protocol – neither slotted nor slotless – can guarantee lower discovery latencies for a given duty-cycle in this scenario. Since the resulting protocol utilizes the channel more aggressively than other ND protocols, beacons will collide more frequently. Hence, due to collisions, the rate of successful discoveries gracefully decreases for larger numbers of devices discovering each other simultaneously. We also propose a scheme for configuring the BLE protocol (and not just BLE- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">like</i> protocols). Though it is not clear whether the resulting parametrizations minimize the latencies of BLE, reasonably low worst-case latencies can be guaranteed.

Vehicle Speed Estimation and Traffic Tracking System Using Machine Learning

In this work, we deploy a real-time method for classifying vehicles and estimating their speeds using footage captured by traffic cameras along motorways. Basic techniques in traffic analysis include the forecasting of traffic flows, the discovery of anomalies, the re-identification of vehicles, and the tracking of moving vehicles. One of the most actively studied areas of these applications is traffic flow prediction, often known as vehicle speed estimation. In this work, we estimate vehicle speeds within classes using feature tracking and neighbor discovery techniques.

Hybrid Block-Based Lightweight Machine Learning-Based Predictive Models for Quality Preserving in the Internet of Things- (IoT-) Based Medical Images with Diagnostic Applications.

In the contemporary era of unprecedented innovations such as the Internet of Things (IoT), modern applications cannot be imagined without the presence of a wireless sensor network (WSN). Nodes in WSN use neighbor discovery (ND) protocols to have necessary communication among the nodes. The neighbor discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximum percentage of neighbors discovered. The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots. This work develops a lightweight intrusion detection system (IDS) based on two machine learning approaches, namely, feature selection and feature classification, in order to improve the security of the Internet of Things (IoT) while transferring medical data through a cloud platform. In order to take advantage of the comparatively cheap processing cost of the filter-based technique, the feature selection was carried out. The two methods are found to have certain drawbacks. The first category disturbs the original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbors. When the second category is followed, it may have inefficient slots in the wake-up schedules leading to performance degradation. Therefore, the motivation behind the work in this paper is that by combining the two categories, it is possible to reap the benefits of both and get rid of the limitations of both. Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring the original integrity of wake-up schedules and adding efficient active slots. Thus, a Hybrid Approach to Neighbor Discovery (HAND) protocol is realized in WSN. The simulation study revealed that HAND outperforms the existing indirect ND models.

Game Theory based Resource Identification Scheme for Wireless Sensor Networks

In modern world of sensing and distributive systems, traditional Wireless Sensor Networks (WSN) has to deal with new challenges, such as multiple application requirements, dynamic and heterogeneous networks. Senor nodes in WSN are resource constrained in terms of energy, communication range, bandwidth, processing delay and memory. Numerous solutions are proposed to optimize the performance and to increase the lifetime of WSN by introducing new resource management principles. Effective and intelligent resource management in WSN involves in resource identification, resource scheduling, and resource utilization. This paper proposes a Bayesian Game Model (BGM) approach to efficiently identify the best node with the maximum resource in WSN for data transmission, considering energy, bandwidth, and computational delay. The scheme operates as follows: (1) Sensor nodes information such as residual energy, available bandwidth, and node ID, etc., is gathered (2) Energy and bandwidth of each node are used to generate the payoff matrix (3) Implementation of node identification scheme is based on payoff matrix, utilities assigned, strategies and reputation of each node (4) Find Bayesian Nash Equilibrium condition using Starring algorithm (5) Solving the Bayesian Nash Equilibrium using Law of Total Probability and identifying the best node with maximum resources (6) Adding/Subtracting reward (reputation factor) to winner/looser node. Simulation results show that the performance of the proposed Bayesian game model approach for resource identification in WSN is better as compared with the Efficient Neighbour Discovery Scheme for Mobile WSN (ENDWSN). The results indicate that the proposed scheme has up to 12% more resource identification accuracy rate, 10% increase in the average number of efficient resources discovered and 8% less computational delay as compared to ENDWSN.

Internet of Things Network Topology Discovery Algorithm Based on Wireless Sensors

Remote sensor network is a multijump self-coordinating organization framework shaped by countless energy-restricted miniature sensor hubs sent in the observing region. Be that as it may, the correspondence conventions of customary organization configuration cannot be straightforwardly applied to remote sensor organizations. The improvement of committed correspondence conventions and estimation techniques has turned into a pressing exploration subject in the field of remote sensor organizations. The research purpose of this paper is to research the Internet of Things network topology discovery algorithm based on wireless sensors. This paper analyzes and mines an adaptive neighbor discovery scheduling algorithm with lower latency, acquires the information of potential neighbor nodes based on existing neighbor nodes, discovers potential neighbor nodes by proactively waking up, and studies the information recommendation mechanism between neighbor nodes and compares. The intimacy between neighbor nodes (such as common neighbor rate) is used to selectively receive recommended information from neighbor nodes, thereby filtering redundant data information, lessening hub energy utilization, and accomplishing the motivation behind broadening the organization life cycle. A geography disclosure calculation in light of versatile specialists is proposed to tackle the geography revelation issue under the various leveled geography structure in remote sensor organizations. The calculation joins the qualities of portable specialist innovation and various leveled Internet of Things network geography and simultaneously further develops the relocation methodology of versatile specialists for information assortment, intermittent undertaking assignment, and organization status observing. Research shows that in the investigation of the effect of hub obligation cycle on framework execution, the energy utilization of the DDC-Group calculation is marginally expanded contrasted and the gathering calculation, and the increase is not more than 1%, but compared to the CNR-Group increase more.

Delay-Driven Opportunistic Routing with Multichannel Cooperative Neighbor Discovery for Industry 4.0 Wireless Networks Based on Power and Load Awareness

During data transmission, a decentralised Mobile Ad Hoc Network (MANET) might result in high Energy Consumption (EC) and a short Network Lifetime (NLife). To address these difficulties, an on-demand Power and Load-Aware multipath node-disjoint source routing is presented based on the Enhanced Opportunistic Routing (PLAEOR) protocol. This unique protocol aims at using power, load, and latency to manage routing costs depending on control packet flooding from the destination node. However, the exchange of control packets from the target to all nodes may impact network efficiency. As a result, the PLAEOR is designed with a Multichannel Cooperative Neighbor Discovery (MCND) protocol to locate the nearby cooperative nodes for each node in the routing path during control packet transmission. Furthermore, when the packet rate of CBR is 20 packets/sec, the simulated results show that the PLAEOR-MCND achieves 120 sec of NLife and 20 J of EC than the state-of-the-art protocols.

Agile neighbor discovery in MEMS-based free space optical networks: a computer vision approach

Highly directional laser-based wireless optical radios are able to spatially reuse several THz of spectrum but suffer from link setup delays. In delay averse and high-throughput self-configuring networks, agile neighbor discovery is of critical importance. Given an optical wireless network with microelectromechanical systems based receivers, we explore the use of adaptive combinatorial group testing and contour tracing to achieve very low neighbor discovery latency. We propose a pattern-based algorithm that leverages group testing and contour tracing techniques to significantly reduce latency. Evaluating our algorithms, we compare hierarchical group testing algorithms to state-of-the-art raster and Lissajous pattern-based scanning and report 99.92% and 87% reduction in latency, respectively, for an array of one million micromirrors. The proposed pattern-based algorithm, when compared to hierarchical group testing and the Moore-neighbor contour tracing algorithms, achieves 63.4% and 4.91% reduction in latency, respectively.

Optimization of IPv6 Neighbor Discovery Protocol

In IPv6, the DAD (Duplicate Address Detection) protocol detects duplicate addresses configured on the local link. Once the IPv6 address is auto configured on an IPv6 enabled host, the host verifies that its address is unique using the DAD procedure. This protocol works when hosts can communicate. If the DAD protocol fails to detect duplication, both the hosts assign the same link-local address. The neighbor discovery protocol verifies the generated address is unique or already exists on the local link. This process is known as Duplicate Address Detection (DAD). This process has critical security vulnerability and is susceptible to many attacks, especially allowing hackers to perform denial of service attacks (DOS). With that, the new devices will not be able to join the network. Researchers have developed various techniques to address DAD vulnerabilities, such as NDPMon, SEND, and Software-defined networking, SAVA, and extension headers. These techniques appear to be neither robust nor performance-oriented with DAD’s DOS detection and mitigation techniques. We have proposed a novel approach that detects and mitigates DOS attacks consuming low bandwidth and overhead.

Effective neighbor node discovery using improved Bayesian clock synchronization‐gossip routing protocol in mobile low duty cycle wireless sensor network

AbstractIn wireless application, mobile low duty cycle wireless sensor network (MLDC‐WSN) gained more attention among the researchers, because it includes advantages like growing node life, saving node power, and network reliability. However, the sleeping features and mobility of MLDC‐WSN results in topology change that restricts the speed of neighbor node discovery. To highlight this concern, a novel routing protocol named as improved Bayesian clock synchronization‐gossip routing protocol is proposed for an effective neighbor node discovery in MLDC‐WSN. The proposed routing protocol reduces end‐to‐end delay, and clock drifts problems. Also, the proposed routing protocol improves the clock synchronization and captures the neighbor discovery nodes by identifying the next neighbor set in the next moment, due to the prediction of the distance and movement speed of neighbors. Simulation and analytical results showed that the proposed routing protocol superiorly decreases the end‐to‐end delay and energy consumption compared to the existing algorithms.

A Talk-Listen-Ack Beaconing Strategy for Neighbor Discovery Protocols in Wireless Sensor Networks.

Neighbor discovery is a fundamental function for sensor networking. Sensor nodes discover each other by sending and receiving beacons. Although many time-slotted neighbor discovery protocols (NDPs) have been proposed, the theoretical discovery latency is measured by the number of time slots rather than the unit of time. Generally, the actual discovery latency of a NDP is proportional to its theoretical discovery latency and slot length, and inversely proportional to the discovery probability. Therefore, it is desired to increase discovery probability while reducing slot length. This task, however, is challenging because the slot length and the discovery probability are two conflicting factors, and they mainly depend on the beaconing strategy used. In this paper, we propose a new beaconing strategy, called talk-listen-ack beaconing (TLA). We analyze the discovery probability of TLA by using a fine-grained slot model. Further, we also analyze the discovery probability of TLA that uses random backoff mechanism to avoid persistent collisions. Simulation and experimental results show that, compared with the 2-Beacon approach that has been widely used in time-slotted NDPs, TLA can achieve a high discovery probability even in a short time slot. TLA is a generic beaconing strategy that can be applied to different slotted NDPs to reduce their discovery latency.

A Bayesian Inference Model for Dynamic Neighbor Discovery in Tactical Networks

This paper introduces a Bayesian Inference model for dynamically changing the neighbor discovery parameters in order to minimize the time to recover from long radio link disconnections in tactical networks. We start with the hypothesis that given a multi-layer tactical system, we can use the in/out chains to learn the distribution of changes in the radio data link with an increasing confidence as time goes by. Moreover, we propose that a static neighbor discovery configuration (e.g., hello time, aggregation time, and so on) in dynamic networks can never be optimal and that it is possible to calculate the close to optimal neighbor discovery configuration dynamically. Thus, we introduce a Bayesian Inference model composed of a Markov-Chain-Monte-Carlo (MCMC) and a Long Short-Term Memory (LSTM) to compute a dynamic configuration by learning the probability distribution of the link conditions (computed using the in/out model) and adapting the configuration parameters dynamically according to this distribution. Our hypothesis was verified with numerical calculations using different patterns of link data rate change. The quantitative analysis suggests that at the tactical edge dynamic control signaling can improve connectivity in ever-changing network scenarios.

Intelligent Hello Dissemination Model for FANET Routing Protocols

The routing mechanisms in flying ad-hoc networks (FANETs) using unmanned aerial vehicles (UAVs) have been a challenging issue for many reasons, such as its high speed and different directions of use. In FANETs, the routing protocols send hello messages periodically for the maintenance of routes. However, the hello messages that are sent in the network increase the bandwidth wastage on some occasions and the excessive number of hello messages can also cause the problem of energy loss. Scarce works deal with the problem of excessive hello messages in dynamic UAVs scenarios, and treat several other problems, such as bandwidth and energy wastage simultaneously. Generally, the existing solutions configure the hello interval to an excessive long or short time period originating delay in neighbors discovery. Thus, a self-acting approach is necessary for calculating the exact number of hello messages with the aim to reduce the bandwidth wastage of the network and the energy loss; this approach needs to be low complex in terms of computational resource consumption. In order to solve this problem, an intelligent Hello dissemination model, AI-Hello, based on reinforcement learning algorithms, that adapts the hello message interval scheme is proposed to produce a dense reward structure, and facilitating the network learning. Experimental results, considering FANET dynamic scenarios of high speed range with 40 UAVs, show that the proposed method implemented in two widely adopted routing protocols (AODV and OLSR) saved 30.86% and 27.57% of the energy consumption in comparison to the original AODV and OLSR protocols, respectively. Furthermore, our proposal reached better network performance results in relation to the state-of-the-art methods that are implemented in the same protocols, considering parameters, such as routing overhead, packet delivery ratio, throughput and delay.

Neighbour Discovery Algorithm for Wireless Cooperative Unmanned Aerial Vehicle Formation

Neighbour Discovery Algorithm for Wireless Cooperative Unmanned Aerial Vehicle Formation

A Vehicular Mobility Management Scheme for a Shared-Prefix Model Over IEEE WAVE IPv6 Networks

The IETF IPWAVE Working Group is developing an IPv6-based solution to establish connectivity for V2V and V2I communication. It proposes a vehicular mobility management scheme based on PMIPv6. In this scheme, a shared-prefix model is proposed for its vehicular link model. The shared-prefix model is to share a common prefix among several RSUs. When using it, a vehicle in the same prefix domain does not need to change its IPv6 address even if its serving RSU is changed. So its CNs do not notice the movement of the vehicle. This concept is very similar with PMIPv6, because PMIPv6 also hides the vehicle&#x2019;s movement to its CNs if PMIPv6 does not use the route optimization. However, current IPWAVE draft of vehicular mobility management has several problems. Firstly, it requires a vehicular neighbor discovery, which is not compatible with standard IPv6 NDP. Secondly, PMIPv6 assumes that it acquires a MN-ID and a profile of a mobile node during the network attachment, but there is no authentication in IEEE WAVE networks. So, such information cannot be acquired in IEEEWAVE networks.We propose a vehicular mobility management scheme for a shared-prefix model over IEEE WAVE IPv6 networks. The proposed scheme introduces VMM-NDP module. It handles the path changes due to the serving RSU changes within a shared prefix domain. It also hides the IPv6 address change to CNs by maintaining the previous IPv6 address when the vehicle&#x2019;s current IPv6 address is changed due to the movement of the vehicle into new shared prefix domain. In this scheme, a vehicle can know its movement. So, there is no need to acquire a MN-ID. And also, since this scheme uses a shared-prefix model, there is no need to obtain a profile.

How reliable is smartphone-based electronic contact tracing for COVID-19?

A look through the lens of neighbor discovery protocols reveals significant potential to improve electronic contact-tracing accuracy.

An adaptive multi-hop routing with IoT abstraction for minimizing delay-node capacity trade-offs in mobile ad-hoc network

&lt;span&gt;Delay and node capacity are incompatible mobile ad hoc constraints because of the network's versatility and self-disciplined design. &lt;/span&gt;&lt;span&gt;It is a challenging problem to maximize the trade-off between the above mobility correlation factors. &lt;/span&gt;&lt;span&gt;This manuscript proposes an adaptive multi-hop routing (A.M.R.) for mobile ad-hoc network (MANET) to minimize the trade-off by integrating the internet of things (IoT). IoT nodes' smart computing and offloading abilities are extended to ad-hoc nodes to improve routing and transmission. Dor MANET nodes in route exploration, neighbor selection, and data transmission, the beneficial features of IoT include enhanced decision making. The traditional routing protocols use IoT at the time of the neighbor discovery process in updating the routing table and localization. &lt;/span&gt;&lt;span&gt;The harmonizing technologies with their extended support improve the performance of MANETs has been estimated. The proposed method achieves better throughput (14.16 Mbps), delay (0.118), packet drop (126), and overhead (36 packets) when compared to existing methods.&lt;/span&gt;

On the Probability of Discovery for Proximity Services and Vehicular Communications

In this letter, we study neighbor discovery in the 5G network exploiting reference signals on which a correlation-based metric is applied. Statistical analysis is first performed to determine the distribution of the discovery metric. Then, the discovery probability is derived in its closed-form for a single-user scenario. The comparison of the theoretical result with simulation shows a perfect match. Furthermore, a multiuser scenario is considered to assess the impact of interference on the discovery probability in the case of the vehicular channel model through simulations. The complexity of the approach is also studied to optimize its load computational load.

Analytical models for randomized neighbor discovery protocols based on collision detection in wireless ad hoc networks

Neighbor discovery is a crucial first step after the deployment of wireless ad hoc networks, which do not have a communications infrastructure. In this paper we present analytical models of randomized neighbor discovery protocols for static one-hop environments: CDPRR (Collision Detection Probabilistic Round Robin) and CDH (Collision Detection Hello). For CDPRR we assumed a geometric distribution and a uniform distribution for CDH. For comparison purposes, we chose two protocols from the literature, i.e., Hello and PRR, to be used as reference. To assess the performance of the proposals we carried out a mathematical study regarding six metrics, i.e., neighbor discovery time, energy consumption, overhead (number of packets sent), packet delivery ratio, the CDF of discoveries, and percentage of idle slots, and presented graphical results obtained from the equations. According to the analytical results, CDH protocol outperforms the other solutions regarding the neighbor discovery time, energy consumption, number of packets sent, packet delivery ratio and CDF of discoveries, while CDPRR achieves good results and it is better than Hello and PRR in terms of neighbor discovery time, energy consumption, CDF of discoveries and the overhead (number of packets sent). Moreover, we found that CDPRR presents more percentage of idle slots than PRR, which is a clear advantage in terms of energy consumed and number of packets sent. In addition, as novelty compared to the reference protocols, we found that both CDH and CDPRR protocols manage to discover all the neighbors, know when to terminate the discovery process, and achieve to operate under more realistic assumptions. We also focused the study on the CDH protocol varying the slot width, and demonstrated that the number of nodes in the network can be unknown, still providing reasonable results.

Joint Communication, Sensing, and Computation Enabled 6G Intelligent Machine System

With the rapid development of the smart city, high-level autonomous driving, intelligent manufacturing, and so on, the stringent industrial-level requirements of extremely low latency and high reliability for communication and new trends in sub-centimeter sensing have transcended the abilities of 5G and call for the development of 6G. Based on analyzing the function design of the communication, sensing, and emerging intelligent computation systems, we propose the joint communication, sensing, and computation (JCSC) framework for the 6G intelligent machine-type communication (IMTC) network to realize low latency and high reliability of communication, highly accurate sensing, and fast environment adaption. In the proposed JCSC framework, the communication, sensing, and computation abilities cooperate to benefit each other by utilizing the unified hardware, resource, and protocol design. Sensing information is exploited as a priori information to enhance the reliability and latency performance of wireless communication and to optimize the resource utilization of the communication network, which further improves the distributed computation and cooperative sensing ability. We propose promising enabling technologies such as joint communication and sensing (JCS), JCSC wireless networking techniques, and intelligent computation techniques. We also summarize the challenges to achieve the JCSC framework. Then we introduce intelligent flexible manufacturing as a typical use case of the IMTC with JCSC framework, where the enabling technologies are deployed. Finally, we present the simulation results to prove the feasibility of the JCSC framework by evaluating the JCSC waveform, and the JCSC-enabled neighbor discovery and medium access control.