Search Results (110)

Opportunistic Networks (OppNets) lack the capability to maintain consistent end-to-end paths between source and destination nodes, unlike Mobile Ad Hoc Networks (MANETs). This absence of stable routing presents substantial challenges for data transmission in OppNets. Due to node mobility, routing paths are inherently dynamic, requiring the selection of neighboring nodes as intermediate hops to forward data toward the destination. However, frequent node movement can cause considerable delays for senders attempting to identify appropriate next hops, consequently degrading the quality of service (QoS) in OppNets. To mitigate this challenge, this paper proposes an alternative approach for scenarios where senders cannot locate suitable next hops. Specifically, we propose utilizing direct communication via line of sight (LoS) between sender and receiver nodes to satisfy QoS requirements. The proposed scheme is experimented with using the ONE simulator, which is widely used for OppNet experiments and study, and compared against existing schemes such as the history-based routing protocol (HBRP) and AEProphet routing protocol. Full article

The rapid development of sixth-generation mobile communication systems has brought about significant advancements in both Quality of Service (QoS) and Quality of Experience (QoE) for users, largely due to the extremely high data rates and a diverse range of service offerings. However, these advancements have also introduced challenges, especially concerning the growing demand for a wireless spectrum and the limited availability of resources. Various efforts have been made and research has attempted to tackle this issue such as the use of Cognitive Radio Networks (CRNs), which allows opportunistic spectrum access and intelligent resource management. This work demonstrate a new method in the optimization of allocation resource in CRNs based on the Snake Optimizer (SO) along with reinforcement learning (RL), which is an effective meta-heuristic algorithm that simulates snake cloning behavior. SO is tested over three different scenarios with varying numbers of secondary users (SUs), primary users (PUs), and frequency bands available. The obtained results reveal that the proposed approach is able to largely satisfy the aforementioned requirements and ensures high spectrum utilization efficiency and low collision rates, which eventually lead to the maximum possible spectral capacity. The study also demonstrates that SO is versatile and resilient and thus indicates its capability of serving as an effective method for augmenting resource management in next-generation wireless communication systems. Full article

The Social Opportunistic Internet of Things (SO-IoT) is a rapidly emerging paradigm that enables mobile, ad-hoc device communication based on both physical and social interactions. In such networks, routing decisions heavily depend on the selection of intermediate nodes to ensure secure and efficient data dissemination. Traditional approaches relying solely on reliability or social interest fail to capture the multifaceted trustworthiness of nodes in dynamic SO-IoT environments. This paper proposes a trust-based route optimization framework that integrates social interest and behavioral reliability using Bayesian inference and Jeffrey’s conditioning. A composite trust level is computed for each intermediate node to determine its suitability for data forwarding. To validate the framework, we conduct a two-phase simulation-based analysis: a scenario-driven evaluation that demonstrates the model’s behavior in controlled settings, and a large-scale NS-3-based simulation comparing our method with benchmark routing schemes, including random, greedy, and AI-based protocols. Results confirm that our proposed model achieves up to an $88.9\%$ delivery ratio with minimal energy consumption and the highest trust accuracy ($86.5\%$), demonstrating its robustness and scalability in real-world-inspired IoT environments. Full article

Antibiotic resistance represents a global health crisis with far-reaching implications, impacting multiple domains concurrently, including human health, animal health, and the natural environment. Wild birds were identified as carriers and disseminators of antibiotic-resistant bacteria (ARB) and their associated antibiotic resistance genes (ARGs). A majority of studies in this area have concentrated on migratory birds as carriers for the spread of antibiotic resistance over long distances. However, there has been scant research on the resistome of non-migratory Corvidae species that heavily overlap with human activities, which limits our understanding of antibiotic resistance in these birds and hinders the development of effective management strategies. This study employed a metagenomics approach to examine the characteristics of ARGs and mobile genetic elements (MGEs) in five common Corvidae species inhabiting the Qinghai–Tibetan Plateau. The ARGs were classified into 20 major types and 567 subtypes. Notably, ARGs associated with multidrug resistance, including to macrolide–lincosamide–streptogramins, tetracyclines, beta-lactam, and bacitracin, were particularly abundant, with the subtypes acrB, bacA, macB, class C beta-lactamase, and tetA being especially prevalent. A total of 5 types of MGEs (166 subtypes) were identified across five groups of crows, and transposase genes, which indicated the presence of transposons, were identified as the most abundant type of MGEs. Moreover, some common opportunistic pathogens were identified as potential hosts for these ARGs and MGEs. Procrustes analysis and co-occurrence network analysis showed that the composition of the gut microbiota shaped the ARGs and MGEs, indicating a substantial association between these factors. The primary resistance mechanisms of ARGs in crows were identified as multidrug efflux pumps, alteration of antibiotic targets, and enzymatic inactivation. High-risk ARGs which were found to potentially pose significant risks to public health were also analyzed and resulted in the identification of 81 Rank I and 47 Rank II ARGs. Overall, our study offers a comprehensive characterization of the resistome in wild Corvidae species, enhancing our understanding of the potential public health risks associated with these birds. Full article

The proliferation of smart devices such as sensors and communication devices has necessitated the development of networks that can adopt device-to-device communication for delay-tolerant data transfer and energy efficiency. Therefore, there is a need to develop opportunistic networks to enhance energy efficiency through improved data routing. A sensor device equipped with computing, communication, and mobility capabilities can opportunistically transfer data to another device, either as a direct recipient or as an intermediary forwarding data to a third device. Routing algorithms designed for such opportunistic networks aim to increase the probability of successful message transmission by leveraging area information derived from historical data to forecast potential encounters. However, accurately determining the precise locations of mobile devices remains highly challenging and necessitates a robust prediction mechanism to provide reliable insights into mobility encounters. In this study, we propose incorporating a random forest regressor (RFR) to predict the future location of mobile users, thereby enhancing message routing efficiency. The RFR utilizes mobility traces from diverse users and is equipped with sensors for computing and communication purposes. These predictions improve message routing performance and reduce energy and bandwidth resource utilization during routine data transmissions. To evaluate the proposed approach, we compared the predictive performance of the RFR against existing benchmark schemes, including the Gaussian process, using real-world mobility data traces. The mobility traces from the University of Southern California (USC) were employed to underpin the simulations. Our findings demonstrate that the RFR significantly outperformed both the Gaussian process and existing methods in predicting mobility encounters. Furthermore, the integration of mobility predictions into device-to-device (D2D) communication and traditional internet networks showed potential energy consumption reductions of up to one-third, highlighting the practical benefits of the proposed approach. The contribution of this research is that it highlights the limitations of existing mobility prediction models and develops new resource optimization and energy-efficient opportunistic networks that overcome these limitations. Full article

The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue. Full article

Recent advancements in Unmanned Aerial Vehicle (UAV) technology have made them effective platforms for data capture in applications like environmental monitoring. UAVs, acting as mobile data ferries, can significantly improve ground network performance by involving ground network representatives in data collection. These representatives communicate opportunistically with accessible UAVs. Emerging technologies such as Software Defined Wireless Sensor Networks (SDWSN), wherein the role/function of sensor nodes is defined via software, can offer a flexible operation for UAV data-gathering approaches. In this paper, we introduce the “UAV Fuzzy Travel Path”, a novel approach that utilizes Deep Reinforcement Learning (DRL) algorithms, which is a subfield of machine learning, for optimal UAV trajectory planning. The approach also involves the integration between UAV and SDWSN wherein nodes acting as gateways (GWs) receive data from the flexibly formulated group members via software definition. A UAV is then dispatched to capture data from GWs along a planned trajectory within a fuzzy span. Our dual objectives are to minimize the total energy consumption of the UAV system during each data collection round and to enhance the communication bit rate on the UAV-Ground connectivity. We formulate this problem as a constrained combinatorial optimization problem, jointly planning the UAV path with improved communication performance. To tackle the NP-hard nature of this problem, we propose a novel DRL technique based on Deep Q-Learning. By learning from UAV path policy experiences, our approach efficiently reduces energy consumption while maximizing packet delivery. Full article

In Vehicular Ad Hoc Networks (VANETs), high mobility of vehicles issues a huge challenge to the reliability and security of transmitting packets. Therefore, a Secure and Reliable Opportunistic Routing (SROR) is proposed in this paper. During construction of Candidate Forwarding Nodes (CFNs) set, the relative velocity, connectivity probability, and packet forwarding ratio are taken into consideration. The aim of SROR is to maximally improve the packet delivery ratio as well as reduce the end-to-end delay. The selection of a relay node from CFNs is formalized as a Markov Decision Process (MDP) optimization. The SROR algorithm extracts useful knowledge from historical behavior of nodes by interacting with the environment. This useful knowledge are utilized to select the relay node as well as to prevent the malicious nodes from forwarding packets. In addition, the influence of different learning rate and exploratory factor policy on rewards of agents are analyzed. The experimental results show that the performance of SROR outperforms the benchmarks in terms of the packet delivery ratio, end-to-end delay, and attack success ratio. As vehicle density ranges from 10 to 50 and percentage of malicious vehicles is fixed at 10%, the average of packet delivery ratio, end-to-end delay, and attack success ratio are 0.82, 0.26s, and 0.37, respectively, outperforming benchmark protocols. Full article

Crowdsensing has gained popularity across various domains such as urban transportation, environmental monitoring, and public safety. Unmanned aerial vehicle (UAV) crowdsensing is a novel approach that collects extensive data from targeted environments using UAVs equipped with built-in sensors. Unlike conventional methods that rely on fixed sensor networks or the mobility of humans, UAV crowdsensing offers high flexibility and scalability. With the rapid advancement of artificial intelligence techniques, UAV crowdsensing is becoming increasingly intelligent and autonomous. Previous studies on UAV crowdsensing have predominantly focused on algorithmic sensing strategies without considering the impact of different sensing environments. Thus, there is a research gap regarding the influence of environmental factors and sensing strategies in this field. To this end, we designed a $4\times 3$ empirical study, classifying sensing environments into four major categories: open, urban, natural, and indoor. We conducted experiments to understand how these environments influence three typical crowdsensing strategies: opportunistic, algorithmic, and collaborative. The statistical results reveal significant differences in both environments and sensing strategies. We found that an algorithmic strategy (machine-only) is suitable for open and natural environments, while a collaborative strategy (human and machine) is ideal for urban and indoor environments. This study has crucial implications for adopting appropriate sensing strategies for different environments of UAV crowdsensing tasks. Full article

Opportunistic networks, an evolution of mobile Ad Hoc networks (MANETs), offer decentralized communication without relying on preinstalled infrastructure, enabling nodes to route packets through different mobile nodes dynamically. However, due to the absence of complete paths and rapidly changing connectivity, routing in opportunistic networks presents unique challenges. This paper proposes a novel probabilistic routing model for opportunistic networks, leveraging nodes’ meeting probabilities to route packets towards their destinations. Thismodel dynamically builds routes based on the likelihood of encountering the destination node, considering factors such as the last meeting time and acknowledgment tables to manage network overload. Additionally, an efficient message detection scheme is introduced to alleviate high overhead by selectively deleting messages from buffers during congestion. Furthermore, the proposed model incorporates cross-layer optimization techniques, integrating optimization strategies across multiple protocol layers to maximize energy efficiency, adaptability, and message delivery reliability. Through extensive simulations, the effectiveness of the proposed model is demonstrated, showing improved message delivery probability while maintaining reasonable overhead and latency. This research contributes to the advancement of opportunistic networks, particularly in enhancing connectivity and efficiency for Internet of Things (IoT) applications deployed in challenging environments. Full article

With the innovation of wireless communication technology and the surge of data in mobile networks, traditional routing strategies need to be improved. Given the shortcomings of existing opportunistic routing strategies in transmission performance and security, this paper proposes a community opportunistic routing decision-making method based on the trust model. This algorithm calculates the node’s trust value through the node’s historical forwarding behavior and then calculates the node’s trust value based on the trust model. Thresholds and trust attenuation divide dynamic security communities. For message forwarding, nodes in the security community are prioritized as next-hop relay nodes, thus ensuring that message delivery is always in a safe and reliable environment. On this basis, better relay nodes are further selected for message forwarding based on the node centrality, remaining cache space, and remaining energy, effectively improving the message forwarding efficiency. Through node trust value and community cooperation, safe and efficient data transmission is achieved, thereby improving the transmission performance and security of the network. Through comparison of simulation and opportunistic network routing algorithms, compared with traditional methods, this strategy has the highest transmission success rate of 81% with slightly increased routing overhead, and this algorithm has the lowest average transmission delay. Full article

Opportunistic mobile networks, as an important supplement to the traditional communication methods in unique environments, are composed of mobile communication devices. It is a network form that realizes message transmission by using the opportune encounter of these mobile communication devices. Consequently, mobile communication devices necessitate periodic contact detection in order to identify potential communication opportunities, thereby leading to a substantial reduction in the already limited battery life of such devices. Previous studies on opportunistic networks have often utilized geographic information in routing design to enhance message delivery rate. However, the significance of geographic information in energy conservation has been overlooked. Furthermore, previous research on energy-efficient routing has lacked diversification in terms of the methods employed. Therefore, this paper proposes a dynamic co-operative energy-efficient routing algorithm based on geographic information perception (DCEE-GIP) to leverage geographic information to facilitate dynamic co-operation among nodes and optimize node sleep time through probabilistic analysis. The DCEE-GIP routing and other existing algorithms were simulated using opportunistic network environment (ONE) simulation. The results demonstrate that DCEE-GIP effectively extends network service time and successfully delivers the most messages. The service time of DCEE-GIP increased by 8.05∼31.11%, and more messages were delivered by 14.82∼115.9%. Full article

Opportunistic mobile social networks (OMSNs) have become increasingly popular in recent years due to the rise of social media and smartphones. However, message forwarding and sharing social information through intermediary nodes on OMSNs raises privacy concerns as personal data and activities become more exposed. Therefore, maintaining privacy without limiting efficient social interaction is a challenging task. This paper addresses this specific problem of safeguarding user privacy during message forwarding by integrating a privacy layer on the state-of-the-art OMSN routing decision models that empowers users to control their message dissemination. Mainly, we present three user-centric privacy-aware forwarding modes guiding the selection of the next hop in the forwarding path based on social metrics such as common friends and exchanged messages between OMSN nodes. More specifically, we define different social relationship strengths approximating real-world scenarios (familiar, weak tie, stranger) and trust thresholds to give users choices on trust levels for different social contexts and guide the routing decisions. We evaluate the privacy enhancement and network performance through extensive simulations using ONE simulator for several routing schemes (Epidemic, Prophet, and Spray and Wait) and different movement models (random way, bus, and working day). We demonstrate that our modes can enhance privacy by up to 45% in various network scenarios, as measured by the reduction in the likelihood of unintended message propagation, while keeping the message-delivery process effective and efficient. Full article

The development of network functionalities in the urban environment is accompanied by the emergence of new publicly available data sources. They are the basis of the introduced research architecture and environment which are used to investigate the new multicast algorithms proposed in this paper. These message-oriented algorithms are primarily intended to meet the needs of opportunistic routing in heterogeneous urban sensor networks. Although, due to their generalized and protocol-agnostic design, they can be of use in other network applications and research areas. Uncontrolled mobile relay devices are the key elements of the presented delay-tolerant multicast framework. Multicast structures are modeled in four Polish cities based on open data on the location of public transportation vehicles and elements of urban infrastructure. Over 16,000 graphs were built and analyzed. It has been shown that the use of uncontrolled mobile relay enables the construction of time-spanning time-changing multicast structures. Their features are determined by the topology of a given city area, the distribution of destination nodes, as well as the number and the routes of mobile relay nodes. The efficacy and efficiency of the algorithms depend on the radio range of the nodes, maximum time span of forwarded messages, and network structure knowledge availability. Full article

Transmembrane carriers of the Slc11 family catalyze proton (H⁺)-dependent uptake of divalent metal ions (Me²⁺) such as manganese and iron—vital elements coveted during infection. The Slc11 mechanism of high-affinity Me²⁺ cell import is selective and conserved between prokaryotic (MntH) and eukaryotic (Nramp) homologs, though processes coupling the use of the proton motive force to Me²⁺ uptake evolved repeatedly. Adding bacterial piracy of Nramp genes spread in distinct environmental niches suggests selective gain of function that may benefit opportunistic pathogens. To better understand Slc11 evolution, Alphafold (AF2)/Colabfold (CF) 3D predictions for bacterial sequences from sister clades of eukaryotic descent (MCb and MCg) were compared using both native and mutant templates. AF2/CF model an array of native MCb intermediates spanning the transition from outwardly open (OO) to inwardly open (IO) carriers. In silico mutagenesis targeting (i) a set of (evolutionarily coupled) sites that may define Slc11 function (putative synapomorphy) and (ii) residues from networked communities evolving during MCb transition indicates that Slc11 synapomorphy primarily instructs a Me²⁺-selective conformation switch which unlocks carrier inner gate and contributes to Me²⁺ binding site occlusion and outer gate locking. Inner gate opening apparently proceeds from interaction between transmembrane helix (h) h5, h8 and h1a. MCg1 xenologs revealed marked differences in carrier shape and plasticity, owing partly to an altered intramolecular H⁺ network. Yet, targeting Slc11 synapomorphy also converted MCg1 IO models to an OO state, apparently mobilizing the same residues to control gates. But MCg1 response to mutagenesis differed, with extensive divergence within this clade correlating with MCb-like modeling properties. Notably, MCg1 divergent epistasis marks the emergence of the genus Bordetella-Achromobacter. Slc11 synapomorphy localizes to the 3D areas that deviate least among MCb and MCg1 models (either IO or OO) implying that it constitutes a 3D network of residues articulating a Me²⁺-selective carrier conformation switch which is maintained in fast-evolving clades at the cost of divergent epistatic interactions impacting carrier shape and dynamics. Full article