Neighbor Discovery Research Articles

Using citizen science to explore barriers and facilitators for healthy and sustainable lifestyles in office environments

The socio ecological model states that individual behaviors at work are shaped by the interactions between individual employees and their work environments. This study used citizen science to gain insights into which elements of the built, social, and organizational environment in an office and surrounding neighborhoods in two Swedish cities were perceived as barriers to or facilitators of healthy and sustainable behaviors at work. Participants in the eight-week Sustainable Office Intervention pilot study (SOFIA) (n = 33) were cluster-randomized into an experimental arm (sustainable lifestyle) or a control intervention arm (healthy lifestyle). They used the Our Voice Healthy Neighborhood Discovery Tool mobile app to document contextual elements at their workplaces. In total, 114 photos and text-based narratives were recorded, and four themes emerged: built environment, building design, office ergonomics, and food and beverages. Eighteen percent of the photos were linked to pro-environmental behaviors, all exclusively captured by the experimental arm. Twelve barriers and solutions for improving the workplace environment were identified during discussions with participants in both arms. The findings provide insights for designing or renovating office spaces and urban planning to promote healthier and more sustainable lifestyles for office workers.

A new methodology for evaluating the neighbor discovery time in schedule-based asynchronous duty-cycling wireless sensor networks

Duty cycling is a fundamental mechanism for battery-operated wireless networks, such as wireless sensor networks. Due to its importance, it is an integral part of several Medium Access Protocols and related wireless technologies. In Schedule-based Asynchronous Duty Cycle, nodes activate and deactivate their radio interfaces according to a pre-designed schedule of slots, which guarantees overlapping uptime between two neighbors, independent of the offset between their internal clocks, making communication between them possible. This paper presents a new methodology for evaluating the Neighbor Discovery Time (NDT) of Schedule-based Asynchronous Duty Cycle. Differently from previous methodologies, it accounts for the possibility of the slots in the schedules of the two neighbors not being perfectly border-aligned — an unrealistic assumption in practice. By means of simulation, we show that not taking this under consideration can lead to an overestimate of the NDT by a factor of 2 depending on the particular scenario, thus justifying the importance of our work.•We propose a new subslot-based methodology for computing the NDT of a wakeup schedule used for asynchronous duty cycling.•It replaces the traditional slot-based methodology, by dividing slots into subslots, allowing for the analysis of non-integer clock offsets between nodes, and further allowing mathematical models to consider the more realistic continuous-time case.•Our validation data shows that the slot-based methodology may overestimate NDT by a factor of up to 2, making the proposed subslot-based methodology much more precise.

Enhanced reinforcement learning-based two-way transmit-receive directional antennas neighbor discovery in wireless ad hoc networks

The utilization of directional antennas for neighbor discovery in wireless ad hoc networks brings notable benefits, such as extended transmission range, reduced transmission interference, and enhanced antenna gain. However, when nodes use directional antennas for neighbor discovery, the communication range is limited, resulting in a lack of knowledge of potential neighbors. Hence, it is necessary to design a special antenna direction switching strategy for neighbor discovery based on directional antennas. Traditional methods of switching antenna directions are often random or follow predefined sequences, overlooking the historical knowledge of sector exploration for antenna directions. In contrast, existing machine learning approaches aim to leverage observed historical knowledge to adjust antenna directions for faster neighbor discovery. Nonetheless, the latency of neighbor discovery is still high because the node cannot fully utilize the observed historical knowledge (i.e., only using the knowledge observed by the node in transmission mode, ignoring the knowledge observed by the node in reception mode). Meanwhile, the corresponding reward and penalty mechanisms are still not detailed enough (i.e., these reward and penalty mechanisms only consider the sectors of discovered and undiscovered neighboring nodes, ignoring the scenario of sectors that have been rewarded). In this paper, the neighbor discovery process is modeled as a reinforcement learning-based learning automaton. We propose an enhanced reinforcement learning-based two-way transmit-receive directional antennas neighbor discovery algorithm, called ERTTND. The algorithm consists of a two-way transmit-receive reinforcement learning mechanism (TTRL) and an enhanced reward-and-penalty mechanism (ERAP). This algorithm leverages insights from nodes in transmission and reception modes to refine their tactical decisions. Then, through an enriched reward-and-penalty framework, nodes optimize their strategies, thus expediting neighbor discovery based on directional antennas in wireless ad hoc networks. Simulation results demonstrate that compared to existing representative algorithms, the proposed ERTTND algorithm can achieve over 30% savings in terms of average discovery delay and energy consumption.

Secure Neighbor Discovery as a Service in the Internet of Ships Environment

Secure Neighbor Discovery as a Service in the Internet of Ships Environment

An Adaptive 3D Neighbor Discovery and Tracking Algorithm in Battlefield Flying Ad Hoc Networks with Directional Antennas.

Neighbor discovery and tracking with directional antennas in flying ad hoc networks (FANETs) is a challenging issue because of dispersed node distribution and irregular maneuvers in three-dimensional (3D) space. In this paper, we propose an adaptive 3D neighbor discovery and tracking algorithm in battlefield FANETs with directional antennas. With time synchronization, a flying node transmits/receives the neighbor discovery packets sequentially in each beam around it to execute a two-way handshake for neighbor discovery. The transmitting or receiving status of each discovery slot depends on the binary code corresponding to the identification of the node. Discovered neighbor nodes exchange their 3D positions in tracking slots periodically for node tracking, and the maximum tracking period is determined by node velocity, beamwidth, and the minimum distance between nodes. By configuring the relevant parameters, the proposed algorithm can also apply to two-dimensional planar ad hoc networks. The simulation results suggest that the proposed algorithm can achieve shorter neighbor discovery time and longer link survival time in comparison with the random scanning algorithm in scenarios with narrow beamwidth and wide moving area. When the frame length increases, the protocol overhead decreases but the average neighbor discovery time increases. The suitable frame length should be determined based on the network range, node count, beamwidth, and node mobility characteristics.

Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing

Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing

Hybridizing flower pollination algorithm with particle swarm optimization for enhancing the performance of IPv6 intrusion detection system

Internet Protocol version 6 (IPv6) is the most recent iteration of IP, designed to accommodate hundreds of thousands of devices with unique IP addresses. IPv6 introduces new features, such as the Neighbor Discovery Protocol (NDP) and Address Auto-configuration Scheme. For its effective operation, IPv6 relies on several protocols, including ICMPv6, which carries significant responsibilities. Similar to IPv4, IPv6 is susceptible to various attacks, including newer types like DDoS attacks executed via ICMPv6 messages, posing serious security and financial threats. Consequently, an Intrusion Detection System (IDS) is essential to safeguard against these attacks. IDS continuously evolve to incorporate features that can accurately detect such threats. However, feature selection strategies, particularly bio-inspired algorithms, often yield incorrect subsets of features. During machine learning processes, these inaccuracies impede the detection accuracy of DDoS attacks using ICMPv6. Many Optimization Search Algorithms become trapped in local minima and fail to consider multi-objective approaches, resulting in suboptimal feature selection. To address this, optimizing a bio-inspired algorithm within an IPv6 network has been proposed. Specifically, hybridizing the MFPA algorithm with the PSO algorithm is suggested to enhance detection accuracy. The selected features are used to train the dataset with a Support Vector Machine (SVM) classifier. The proposed approach is evaluated using the ICMPv6 dataset on various attacks, demonstrating superior classification accuracy of 97.99 %. It also reduced the number of features from 19 to 8, showcasing its efficiency.

A novel Q-learning-based secure routing scheme with a robust defensive system against wormhole attacks in flying ad hoc networks

Nowadays, unmanned aerial vehicles (UAVs) organized in a flying ad hoc network (FANET) can successfully carry out complex missions. Due to the limitations of these networks, including the lack of infrastructure, wireless communication channels, dynamic topology, and unreliable communication between UAVs, cyberattacks, especially wormholes, weaken the performance of routing schemes. Therefore, maintaining communication security and guaranteeing the quality of service (QoS) are very challenging. In this paper, a novel Q-learning-based secure routing scheme (QSR) is presented for FANETs. QSR seeks to provide a robust defensive system against wormhole attacks, especially wormhole through encapsulation and wormhole through packet relay. QSR includes a secure neighbor discovery process and a Q-learning-based secure routing process. Firstly, each UAV gets information about its neighboring UAVs securely. To secure communication in this process, a local monitoring system is designed to counteract the wormhole attack through packet relay. This system checks data packets exchanged between neighboring UAVs and defines three rules according to the behavior of wormholes. In the second process, UAVs perform a distributed Q-learning-based routing process to counteract the wormhole attack through encapsulation. To reward the safest paths, a reward function is introduced based on five factors, the average one-hop delay, hop count, data loss ratio, packet transmission frequency (PTF), and packet reception frequency (PRF). Finally, the NS2 simulator is applied for implementing QSR and executing different scenarios. The evaluation results show that QSR works better than TOPCM, MNRiRIP, and MNDA in terms of accuracy, malicious node detection rate, data delivery ratio, and data loss ratio. However, it has more delay than TOPCM.

A multi-token sector antenna neighbor discovery protocol for directional ad hoc networks

A multi-token sector antenna neighbor discovery protocol for directional ad hoc networks

Distributed neighbor discovery with mobility adaptive probing for beyond 5G D2D communications

Representing a paradigm shift in cellular communication, Device-to-Device (D2D) Communication is an emerging technology that enables direct communication between two User Equipments(UEs) without going through the Base Station. It enables decentralization of cellular networks, enhances network capacity, increases spectral efficiency, and offloads network traffic. In order to proceed with direct D2D communication, the devices must have knowledge of the proximal devices and should work in all coverage scenarios such as in-coverage, partial-coverage and out-of-coverage. Most of the existing works on Device Discovery are based on 3GPP Model A and Model B. However, these schemes suffer from overhead of discovery message exchanges with the increase in the number of devices in a mobile environment. To address these issues, we propose a distributed and adaptive device discovery mechanism that can discover the proximal UEs in all possible scenarios. To achieve this, we propose a Hybrid Discovery Model ‘H’, which uses the concept of ‘Who are there?’. In Model ‘H’, the discoverer sends a single request message to discover all proximal devices. The responses of the proximal UEs are staggered based on the estimated number of neighbors to avoid collisions. In addition to that, we adapt the discovery mechanism to the relative mobility of the neighborhood using our proposed Adaptive Probing Algorithm named ‘ReMAP’. The relative mobility is determined based on the current and previous neighbor lists of each UE. The key advantage of our proposed mechanism is that we do not require the knowledge of position and mobility pattern of the UEs. We implement and evaluate our proposed work in D2D Module of the NS3 simulator. From the simulation results, we observe that our proposed model shows better performance in terms of the number of UEs discovered and reduced number of discovery message exchanges compared to the state-of-the-art. We also compare the performance of ReMAP with a constant probing mechanism and show the improvement in terms of adaptability to the mobility of the neighborhood.

Ultraviolet collaborative networking method based on a novel neighbor discovery algorithm

Ultraviolet (UV) optical communication presents several advantages, such as non-line-of-sight propagation, high confidentiality, reliability, and the absence of precise alignment requirements. UV optical communication networks find application in various scenarios in complex electromagnetic environments. In this study, we propose a UV collaborative networking method based on a neighbor discovery algorithm (UVCN–NDA), which establishes neighbor tables within nodes and selects the shortest transmission path between the nodes for frame transmission. During data transmission, nodes dynamically adjust the transmission time of each bit to enhance throughput and use the distance information in the neighbor table for path selection. This strategy prevents ineffective data forwarding and subsequently reduces network power consumption. Simulation results demonstrate that the neighbor discovery algorithm implemented in this protocol exhibits higher efficiency in neighbor discovery and lower message load than existing algorithms. Furthermore, when compared to UV networks based on bit–simultaneous–transmission, the UVCN–NDA–based UV networks achieve an approximate 86.2% reduction in average power consumption and approximately 52% increase in throughput, thereby providing a considerable performance advantage. Finally, the conducted network experiments prove the shortest path selection capability of UVCN–NDA.

Optimal Node Routing Layouts in Mobile Adhoc Network

Mobile Adhoc Network (MANET) is a self-organizing network of various network devices responsible for communication. In MANET, new layouts are adopted for energy optimization, security implementation, traffic minimization and overhead reduction. MANET are aggressive in nature. This requires modification in network routing protocols for performance enhancement. This paper presents the detailed survey of different layouts involved in the implementation of network routing protocols in MANET. The survey is classified into four phases involved in MANET routing applications namely- routing protocols, neighborhood discovery, distributed or centralized hash tables, and bloom filter. Firstly, the classification of routing protocols based on various traffic models presents the MANET configuration. Secondly, a neighborhood discovery mechanism conveys the enhancement in the routing configurations. Thirdly, application of hash table operation (distributed/ centralized) to MANET to improve the matching efficiencies and computational speed. Finally, the application of bloom filter concept to improve service discovery rate and reduce the fake identities. Traditionally, the unpredictability occurs during the prediction of optimal node routing due to the dynamic nature of the MANET. Moreover, the presence of ejected malicious node in another cluster disgraced the link stability. This survey conveys the problems such as storage overhead, mobility nature, malicious nodes, and false positive rates in the traditional methods. The detailed survey leads to the stone of integration of Distributed Hash Table (DHT) with a bloom filter to reduce the problems in traditional research works. Index Terms – Performance Optimization, Bloom Filter, Centralized Hash Table (CHT), Distributed Hash Table (DHT), Energy Consumption, Mobile Ad- hoc Network (MANET), Quality of Service (QoS), Routing.

Asymmetric wake-up scheduling based on block designs for Internet of Things

In general, wireless sensors operate with a limited energy source, and energy efficiency is a critical design issue. In order to extend the operation time of wireless sensors, there have been many energy efficiency neighbor discovery protocols designed for wireless sensor networks (WSNs) such as Quorum-based, prime-number-based, and block-design-based protocols. Among them, the block-design-based approach was known to be more effective solutions for neighbor discovery in terms of the worst-case discovery latency for a given duty cycle. However, the original block- design-based approach is only applicable to a sensor network where all sensors have the same duty cycle. In order to expand a block-design-based neighbor discovery solution to asymmetric WSNs, we introduce a new neighbor discovery protocol (NDP) that combines two block-design-based schedules to produce a new set of discovery schedules for asymmetric WSNs. Furthermore, by using the Kronecker product method, we prove that any pair of neighboring sensors in the proposed protocol has at least one common active slot within a length of their discovery cycle. Furthermore, the results of the simulation study show that the proposed method is better than representative NDPs (such as Quorum, U-Connect, Disco, SearchLight, Hedis, and Todis) in terms of discovery latency and energy efficiency.

A neighbor discovery protocol with adaptive collision alleviation for wireless robotic networks

Recently, several movement control algorithms have been proposed to move robots to the desired locations and complete various collaborative tasks. These algorithms usually require information exchange among robots, which in turn relies on efficient neighbor discovery. However, neighbor discovery of robots is a challenging problem due to limited communication range and beacon collision. Here, how to set the time slot length and contention window to alleviate the beacon collisions is a major issue: too large or too small slot length and contention window values cannot achieve good performance in the robotic network. Therefore, we propose a neighbor discovery protocol with an adaptive collision alleviation mechanism, i.e. ND-ACA. ND-ACA adopts a simple mechanism to detect and avoid potential collisions before sending beacon, and adaptively adjusts both slot length and backoff window according to the number of historical beacon collections and beacon collisions. Extensive simulation results show the efficiency of our proposed approach. To the best of our knowledge, this is the first work in the literature that explore the joint implementation effects that simultaneously consider the robot mobility model and neighbor discovery.

Assisted Slotless Neighbor Discovery Based on Additional Scan Windows

Assisted Slotless Neighbor Discovery Based on Additional Scan Windows

Adaptive HELLO protocol for vehicular networks

In vehicular networks, the update of car Firmware Over The Air (FOTA) is becoming a challenging issue and it mainly relies on topology discovery of neighbouring nodes. Topology discovery in mobile wireless networks is usually done by using HELLO messages. Due to mobility, topology changes occur frequently and must be quickly discovered to avoid routing failures. Since the optimal HELLO frequency depends on parameters that are subject to changes (e.g., speed of nodes, density of nodes), it must be dynamically adjusted to obtain the best trade-off between the network load and the freshness of routing tables. Existing solutions assume random mobility, constant node density and average speed, which do not hold in vehicular networks because vehicles follow specific trajectory patterns (the roads) and density and speed evolve as a function of time (rush hour vs non-rush hour) and area (urban, rural, highway). In this paper, we first draw the specific features of a vehicular network at different times and spaces by analysing real datasets and then propose a dynamic neighbour discovery protocol, Vehicular Adaptive Neighbour discovery Protocol (VANP). VANP is a fully-distributed protocol that sends beacons at an optimal frequency without knowing it a priori. The objective is to reduce the frequency at which HELLO messages are sent to save bandwidth and energy while still preserving the quality of the neighbour discovery. Through extensive simulations run on real datasets, we show that the optimal HELLO frequency can be reached by maintaining a constant optimal turnover, independent from the speed of the nodes and by aiming at this turnover, nodes automatically use the optimal HELLO frequency. Results show that VANP allows the discovery of relevant neighbours by missing at most two neighbours over all scenarios and reducing the number of HELLO messages up to twice, hence saving bandwidth and energy.

Mobile Handoff with 6LoWPAN Neighbour Discovery Auxiliary Communication

The importance of the topic under investigation stems from the burgeoning prominence of the Internet of Things (IoT) paradigm and the consequent imperative to devise and institute an efficacious methodology for neighbour detection that facilitates supplementary connections to mobile nodes. This research primarily aims to elucidate a mobile handover technique aligning with the neighbour discovery paradigm inherent in 6LoWPAN. The methodological framework employed herein amalgamates a systematic analysis of the foundational tenets governing 6LoWPAN communication standards over the IPv6 protocol in the context of IEEE 802.15.4aa personal wireless networks, supplemented with empirical evaluation of message propagation dynamics among discrete nodes of a designated personal wireless network. The findings of this investigation underscore the pre-eminence of the 6LoWPAN networking standard as a prime vehicle for bolstering the dependable communication of IoT's remote entities, contingent upon the utilization of the IPv6 communication protocol. This iteration of the Internet protocol is superior, particularly in surveillance contexts, given its expansive address capacity (128 bits) juxtaposed against its antecedent, the IPv4 protocol (32 bits). The inherent adaptability of this network connection augments the quality of mobile handover, especially when one considers the real-world application of 6LoWPAN's neighbour discovery auxiliary communication standard. The tangible implications of these findings beckon their potential utility in satiating the demands of IoT system users and actualizing the network collaboration ethos intrinsic to the 6LoWPAN communication standard. 

Editorial

Dear Readers,  It gives me great pleasure to announce the second regular issue of 2024. I would like to thank all the authors for their sound research and the editorial board for the extremely valuable reviews and suggestions for improvement. These contributions together with the generous support of the consortium members enable us to run our journal and maintain its quality.  I would still like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and emerging trends. And finally, we are still looking for some financial support for 2024 to cover all our expenses. We would be very grateful if your library or institution can support us. We would then be happy to add it to our consortium list.  In this regular issue, I am very pleased to present 6 accepted papers by 19 authors from 9 countries: Canada, China, Croatia, India, Kazakhstan, México, Sri Lanka, Ukraine, and Vietnam.  Petra Grd, Igor Tomičić, and Ena Barčić from Croatia address in their article a multi-step methodology for face shape classification that is based on the potential of transfer learning and a pretrained EfficientNetV2S neural network.  Lizbeth Alejandra Hernández-González, Ulises Juárez-Martínez, Jezreel Mejía, and Alberto Aguilar-Laserre from México focus their research on applying the naturalistic programming paradigm within a software development process using a naturalistic software development method.  In a joint research, Shanshan Jia from China, Gaukhar A. Kamalova from the Republic of Kazakhstan, and Dmytro Mykhalevskiy from Ukraine report on a mobile handover technique aligning with the neighbour discovery paradigm in 6LoWPAN.  Ajay Kumar from India is investigating a mechanism to assess machine learning approaches for software effort estimation (SEE) modeling in the context of accuracy measures, specifically exploring machine learning techniques for SEE modeling as a multi-criteria decision making (MCDM) problem.  Anne Perera and Amitha Caldera from Sri Lanka conduct a comprehensive review on sentiment analysis in the context of mix of languages, phonetic typing and lexical borrowing in web communication.  And last but not least, in a collaboration between researchers from Vietnam and Canada, Tien Quang Dam, Nghia Thinh Nguyen, Trung Viet Le, Tran Duc Le, Sylvestre Uwizeyemungu, and Thang Le-Dinh look into malware detection methods, specifically leveraging machine learning to encode critical information from portable executable (PE) headers into visual representations of ransomware samples.  Enjoy Reading!  Cordially,  Christian Gütl, Managing Editor-in-Chief Graz University of Technology, Graz, Austria

Neighbor discovery protocol anomaly-based detection system using neural network algorithm

Neighbor discovery protocol anomaly-based detection system using neural network algorithm

Handshake mechanism selection in MANETs with directional antennas: A new evaluation view

AbstractHandshake mechanism plays a significant role in neighbour discovery (ND) algorithms in mobile ad hoc networks (MANETs) with directional antennas. In this letter, aiming at 1) the MANET scenario of uniformly‐distributed nodes and 2) the random beam‐scanning manner, the upper and lower bounds of ND time of the network are theoretically derived for both 1‐way and 2‐way. This analysis indicates that the ND time is approximately linear growth with NDT‐Mutual, where the NDT‐Mutual is defined as the expected time for a node to complete mutual discovery with its neighbours. Then the theoretical expression for NDT‐mutual is derived, which is a function of the number of nodes. The asymptotic analysis reveals that when the number of nodes is very small, the NDT‐mutual of 2‐way is lower than 1‐way, while 1‐way performs better when the number of nodes approaches infinity. The number of nodes when 1‐way and 2‐way perform as well is further presented, which can be utilized to guide the selection of handshake mechanism in MANETs with directional antennas. Simulation results demonstrate the accuracy of our modelling for ND and the effectiveness of using NDT‐mutual to approximate who performs better with 1‐way and 2‐way.