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Delay tolerant networks (DTNs) is a network evolved from mobile networks. Differing from the traditional network, which has a stable end‐to‐end transmission path, DTNs are sparse and intermittently connected mobile ad hoc network, which are widely used in harsh environments, such as battlefields, seabed, space communication networks, and so on. In DTNs, intermittent connectivity, partitioned network, long delays and node mobility characteristics make the network fail to communicate frequently, therefore, how to successfully forward the message is of extreme importance. Up to now, almost all the traditional models in DTNs use the store‐carry‐forward method. This paper proposes a novel clustered DTN routing model based on sensing node relationship strength. The routing mechanism takes advantage of the number of other nodes encountered by the nodes in the process of movement and the changes in the number of nodes to calculate the strength of the relationship between nodes, and clusters DTN routing according to the strength of the relationship between nodes. Moreover, the relationship between nodes in a cluster and other clusters is used to transmit messages between clusters, and messages are transmitted within clusters according to the strength of the relationship between nodes. Simulation results show that the routing mechanism not only increases the success rate of message transmission, but also reduces the transmission delay of messages and improves network performance. [ABSTRACT FROM AUTHOR]

The Korea Pathfinder Lunar Orbiter (KPLO) was launched on 5 August 2022, equipped on the SpaceX Falcon 9 launch vehicle. At present, the KPLO is effectively carrying out its scientific mission in lunar orbit. The KPLO serves as a cornerstone for the development and validation of Korean space science and deep space technology. Among its payloads is the DTNPL, enabling the first-ever test of delay-tolerant network (DTN) technology for satellites in lunar orbit. DTN technology represents a significant advancement in space communication, offering stable communication capabilities characterized by high delay tolerance, reliability, and asymmetric communication speeds—a necessity for existing satellite and space communication systems to evolve. In this paper, we briefly give an overview of the Korea Lunar Exploration Program (KLEP) and present scientific data gathered through the KPLO mission. Specifically, we focus on the operational tests for DTN-ION conducted for message and file transfer, as well as real-time video streaming, during the initial operations of the KPLO. Lastly, this study offers insights and lessons learned from KPLO DTNPL operations, with the goal of providing valuable guidance for future advancements in space communication. [ABSTRACT FROM AUTHOR]

In socially aware networking, nodes typically behave selfishly due to resource constraints and social correlations, resulting in low network performance. To incentivize selfish nodes to actively participate in message forwarding, this paper proposes a resource-based dynamic pricing and forced forwarding incentive algorithm (DFIA). Firstly, the algorithm introduces virtual currency as a transaction medium and then designs a pricing function based on factors such as the node's resource status, participation contribution, location relevance, and social connectivity. It ensures that the forwarding service is transacted at a reasonable price through bargaining rules. Secondly, a forced forwarding strategy is implemented to compel selfish nodes, which are unwilling to participate in other nodes' message forwarding, to forward a certain number of non-local messages. Meanwhile, in order to prevent nodes from discarding messages and to ensure successful forwarding to the destination, specific rules are used to allocate contribution values to nodes that successfully participate in message forwarding. Lastly, to avoid false quotation behavior, blockchain technology is employed. Transaction information is packaged into blocks and added to the blockchain after consensus validation by other nodes in the network, ensuring the transparency and immutability of transaction data. Simulation results indicate that compared with the existing incentive algorithms, this algorithm not only enhances message delivery probability but also effectively reduces average latency. [ABSTRACT FROM AUTHOR]

Path hopping serves as an active defense mechanism in network security, yet it encounters challenges like a restricted path switching space, the recurrent use of similar paths and vital nodes, a singular triggering mechanism for path switching, and fixed hopping intervals. This paper introduces an active defense method employing multiple constraints and strategies for path hopping. A depth-first search (DFS) traversal is utilized to compute all possible paths between nodes, thereby broadening the path switching space while simplifying path generation complexity. Subsequently, constraints are imposed on residual bandwidth, selection periods, path similitude, and critical nodes to reduce the likelihood of reusing similar paths and crucial nodes. Moreover, two path switching strategies are formulated based on the weights of residual bandwidth and critical nodes, along with the calculation of path switching periods. This facilitates adaptive switching of path hopping paths and intervals, contingent on the network's residual bandwidth threshold, in response to diverse attack scenarios. Simulation outcomes illustrate that this method, while maintaining normal communication performance, expands the path switching space effectively, safeguards against eavesdropping and link-flooding attacks, enhances path switching diversity and unpredictability, and fortifies the network's resilience against malicious attacks. [ABSTRACT FROM AUTHOR]

The intersection of cybersecurity and opportunistic networks has ushered in a new era of innovation in the realm of wireless communications. In an increasingly interconnected world, where seamless data exchange is pivotal for both individual users and organizations, the need for efficient, reliable, and sustainable networking solutions has never been more pressing. Opportunistic networks, characterized by intermittent connectivity and dynamic network conditions, present unique challenges that necessitate innovative approaches for optimal performance and sustainability. This paper introduces a groundbreaking paradigm that integrates the principles of cybersecurity with opportunistic networks. At its core, this study presents a novel routing protocol meticulously designed to significantly outperform existing solutions concerning key metrics such as delivery probability, overhead ratio, and communication delay. Leveraging cybersecurity's inherent strengths, our protocol not only fortifies the network's security posture but also provides a foundation for enhancing efficiency and sustainability in opportunistic networks. The overarching goal of this paper is to address the inherent limitations of conventional opportunistic network protocols. By proposing an innovative routing protocol, we aim to optimize data delivery, minimize overhead, and reduce communication latency. These objectives are crucial for ensuring seamless and timely information exchange, especially in scenarios where traditional networking infrastructures fall short. By large-scale simulations, the new model proves its effectiveness in the different scenarios, especially in terms of message delivery probability, while ensuring reasonable overhead and latency. [ABSTRACT FROM AUTHOR]

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP. [ABSTRACT FROM AUTHOR]

The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics. [ABSTRACT FROM AUTHOR]

Although several years have passed since its first introduction, the significance of Delay-Tolerant Networking (DTN) remains evident, particularly in challenging environments where traditional networks face operational limitations such as disrupted communication or high latency. This survey paper aims to explore the diverse array of applications where DTN technologies have proven successful, with a focus on emerging and novel application paradigms. In particular, we focus on the contributions of DTN in the Future Internet, including its contribution to space applications, smart cities and the Internet of Things, but also to underwater communications. We also discuss its potential to be used jointly with information-centric networks to change the internet communication paradigm in the future. [ABSTRACT FROM AUTHOR]

Smart cities today can utilize vehicular delay tolerant networks (VDTN) to collect data from connected-objects in the environment for various delay-tolerant applications. They can take advantage of the available intelligent transportation systems (ITS) infrastructures to deliver data to the central server. The system can also exploit multiple and diverse mobility patterns found in cities, such as privately owned cars, taxis, public buses, and trams, along with their vehicle-toeverything (V2X) communications capabilities. In the envisioned convergence between the ITS and V2X, we believe that a simple and efficient routing protocol can be deployed for the delay-tolerant data delivery, contrary to the implementation of optimized solutions that might be resource-demanding and difficult to standardize. In this paper, we analyzed the performances of four baseline VDTN routing protocols, namely: direct delivery, first contact, epidemic, and spray and wait, to understand their strengths and weaknesses. Our simulation results highlighted the trade-off between distinct approaches used by those protocols and pointed out some gaps that can be refined. This study provides new interesting ideas and arguments towards developing a simple, efficient, and high-performing routing protocol for data collection in smart cities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) implies a new model and concept in the world of information and communication technology that the non-cooperation of some nodes in providing services to other nodes may result in the disconnection of some things with each other. Consequently, the proximity of such nodes is considerably reduced the network performance. In this paper, a multiphase method based on game theory and direct and indirect reputation has been designed to stimulate selfish and malicious nodes to cooperate in the Internet of Things, which starts with setting up nodes in the IoT network. In the first phase, nodes are grouped in clusters with cluster heads to collect data. Then, they play a dynamic and multi-person game when they promote their own data packet or others' data packet in the second phase (multi-person game phase and data packet sending). The result of the game is applied to affect between nodes in the next phase. Nodes can pick their strategy when data packet forwarding in the third phase (direct and indirect reputation update). Nodes will update the neighboring node and the reputation table if they receive a confirmation message from the destination declaring that the data packet has a direct reputation. The results show that the accuracy of selfish node detection has increased by average of 8% and the false positive rate is 7% in comparison to comparative methods. [ABSTRACT FROM AUTHOR]

The Internet Engineering Task Force (IETF) has defined routing protocols for low power and lossy networks (RPL) for constrained devices. RPL constructs DODAGs (destination oriented directed acyclic graphs), to optimize routing. RPL ensures acyclic topology with the DODAG version number. However, the control message's DODAG version number is not authenticated. So, RPL is vulnerable to network resource attack known as DODAG Version Number (DVN) attack. DVN attack creates a packet delay, packet loss, cyclic topology, etc., in the network. This paper proposes a method for detecting DODAG version number attacks. Several existing schemes to defend against the DVN, such as cryptographic techniques, trust-based, threshold-based and mitigation are computationally intensive or require protocol modification. DVN does not change the packet format or sequence of packets, but can still perform attacks and hence fall under the category of stealthy attacks, which are difficult to detect using traditional intrusion detection system's (IDS). Discrete-event system (DES) based IDS have been applied in the literature for stealthy attacks that achieve low overhead, low false alarm rate, etc. However, the construction of DES-based IDS for network protocol may lead to errors, as modelling is manual. The resulting IDS, therefore, is unable to guarantee its correctness. This paper proposes linear temporal logic (LTL) based DES paradigm to detect DVN. LTL-based paradigm facilitates formal verification of the DES-based IDS, and hence the correctness of the scheme is ascertained. The proposed technique is simulated using the Contiki cooja simulator. When the percentage of spiteful nodes in the network increases, the true positive rate, and packet delivery rate drops, while the false positive rate and control message overhead increase. The memory requirement for sending the packets and verifying the nodes is minimal. The LTL-based IDS has been formally verified using NuSMV to ensure the correctness of the framework. [ABSTRACT FROM AUTHOR]

As multi/many-core processors are widely deployed in high-performance computing systems, efficient intra-node communication becomes more important. Intra-node communication involves data copy operations to move messages from source to destination buffer. Researchers have tried to reduce the overhead of this copy operation, but the copy operation performed by CPU still wastes the CPU resources and even hinders overlapping between computation and communication. The copy engine is a hardware component that can move data between intra-node buffers without intervention of CPU. Thus, we can offload the copy operation performed by CPU onto the copy engine. In this paper, we aim at exploiting copy engines for MPI blocking collective communication, such as broadcast and gather operations. MPI is a messaging-based parallel programming model and provides point-to-point, collective, and one-sided communications. Research has been conducted to utilize the copy engine for MPI, but the support for collective communication has not yet been studied. We propose the asynchronism in blocking collective communication and the CE-CPU hybrid approach to utilize both copy engine and CPU for intra-node collective communication. The measurement results show that the proposed approach can reduce the overall execution time of a microbenchmark and a synthetic application that perform collective communication and computation up to 72% and 57%, respectively. [ABSTRACT FROM AUTHOR]

Delay-tolerant networks face challenges in efficiently utilizing network resources and real-time sensing of node and message statuses due to the dynamic changes in their topology. In this paper, we propose a Multi-Decision Dynamic Intelligent (MDDI) routing protocol based on double Q-learning, node relationships, and message attributes to achieve efficient message transmission. In the proposed protocol, the entire network is considered a reinforcement learning environment, with all mobile nodes treated as intelligent agents. Each node maintains two Q-tables, which store the Q-values corresponding to when a node forwards a message to a neighboring node. These Q-values are also related to the network's average latency and average hop count. Additionally, we introduce node relationships to further optimize route selection. Nodes are categorized into three types of relationships: friends, colleagues, and strangers, based on historical interaction information, and message forwarding counts and remaining time are incorporated into the decision-making process. This protocol comprehensively takes into account the attributes of various resources in the network, enabling the dynamic adjustment of message-forwarding decisions as the network evolves. Simulation results show that the proposed multi-decision dynamic intelligent routing protocol achieves the highest message delivery rate as well as the lowest latency and overhead in all states of the network compared with other related routing protocols for DTNs. [ABSTRACT FROM AUTHOR]

Delay tolerant network is a boon in emergency fields like flood and war zones. The data gathered by the sensor nodes is transmitted whenever any aggregator node comes in contact with those stationary sensor nodes. However, few nodes can behave differently and don't transmit the information. These nodes which don't take part in communication to preserve it's battery or are compromised are selfish nodes and have to be identified to avoid communication disruption. This article discusses the selfish node's detection schemes and proposes a novel hybrid scheme. Most of the recent schemes work with on node's reputation or incentives if it takes part in communication. This paper has proposed an incentivized reputation scheme that first clusters the nodes using their social features, calculates the weighted social tie as their social connection strength and updates the weighted social tie by applying reward or penalty. The incentive is offered if residual energy and packet delay has a tradeoff or are penalized. A new modularized deep nonnegative matrix deep autoencoder is developed to calculate the reputation of nodes using social features and named IRU-mDANMF (incentivized reputation update by modularized DANMF). The scheme has been experimented with in several scenarios and is performing significantly better than state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

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The lack of sufficient contact opportunities in sparse vehicular delay tolerant networks results in poor performance. The network performance can be enhanced by deploying additional small wireless devices called throwbox. Nonetheless, the deployment of throwboxes with distinct placement strategies results in diverse outcomes in terms of metrics such as delivery ratio (DR), delay, and overhead cost (OC). In this paper, the throwbox deployment problem has been presented as an optimization problem. A customized binary differential evolution (CBDE)-based iterative optimization method is employed to improve the DR. Furthermore, depending upon a specific application, it might be desired to have a balanced performance that optimizes the trade-off between different metrics. Therefore, the employment of a multi-objective CBDE (MOCBDE) technique is proposed to optimize the trade-off between DR and OC metrics. The proposed approaches are implemented using Matlab and opportunistic network environment simulator. The experimental results presented in the paper validates the superiority of the proposed CBDE approach over existing methods of throwbox deployment. In addition, the MOCBDE approach provides a set of non-dominated (also called pareto-optimal) solutions with an exhaustive view of desirable results. Consequently, depending on the application requirements, the method allows selecting suitable locations for throwbox deployment. [ABSTRACT FROM AUTHOR]

In recent years, WSNs are acquiring popularity due to small-sized and flexible implementation; many applications require quick data transfer with minimal energy consumption of nodes in the midst of the ubiquitous use of WSNs. These sensor nodes cover large regions according to application needs and choose the best optimal path. The main issue with WSN is how to cover the neighborhood correctly and send data to sink without falling into the trap of a single node and single route. Therefore, a recently researched approach namely the swarm-based dragonfly, which has been effectively used in miscellany applications is exploited for this work. The dragonfly method is based on the exploration phase using global search and exploitation phase using local search. The implicit swarming behaviors are thought to be the fundamental drive for routing algorithms. This paper introduce a Meta-heuristic based Optimized Opportunistic Routing Protocol for WSNs (MOORP) based upon the best optimal forwarder node selection and dragonfly route optimization. The forwarder node selection is optimized by residual energy and eucledian distance of the node. The path between forwarder and destination is identified by using the Dragon-fly algorithm. MOORP employs a route searching algorithm (RSA) and a Energy Level Matrix (ELM) update is used to enhancing the routing decision. The RSA finds an optimal path and selects the optimal forwarder node with the help of a heuristic update or ELM. MOORP performance is compared with other opportunistic routing protocols on important parameters such as the number of alive nodes, throughput, packet delivery ratio, message success rate, and average energy consumption,and also compare with pre-existing cluster based routing protocol. The simulation results show that the MOORP considerably outperforms its competitive techniques in terms of energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, network energy assesses the capacity of a node to convey messages to others. In most cases, network energy is created when two nodes interact with one another. If a node is part of the same network as the node it is connecting with, then it will be able to make an inter-network energy connection with the node it is meeting. In the case that this does not take place, there will be an accumulation of energy within the part of the network that is facing the node. A node with higher inter-network energy is considered suitable for forwarding. The energy optimisation is achieved using efficient identification of source and destination pairs. This work has considered two scenarios, i.e. lossless transmission and lossy transmission, for our experiments and evaluated the detection probability. The performance of the proposed PPM is evaluated in terms of delivery ratio, overhead and hops count performance measures. When the buffer size is set to 100 MB, PPM delivers 59% of messages with message overhead of 750 and it has a hop count of 2, which is comparable to the state-of-the-art methods. With lengthy lifetimes of IOT networks, PPM is capable of giving higher performance while maintaining the privacy of network. The detection probability for the lossy observations model is applied to a 10-node, 20-node, 30-node and 40-node IoT network. [ABSTRACT FROM AUTHOR]

Delay tolerant networks (DTNs), are characterized by their difficulty in establishing end-to-end paths and and large message propagation delays. To control network overhead costs, reduce message delays, and improve delivery rates in DTNs, it is essential to not only delete messages that have reached their destination but also to more precisely determine appropriate relay nodes. Based on the above goals, this paper constructs a multi-copy relay node selection router algorithm based on Q-lambda reinforcement learning with reference to the idea of community division (QLCR). In community division, if a node has the highestdegree, it is considered the core node, and nodes with similar interests and structural properties are divided into a community. Node degree refers to the number of nodes associated with the node, indicating its importance in the network. Structural similarity determines the distance between nodes. The selection of relay nodes considers node degree, interests, and structural similarity. The Q-lambda reinforcement learning algorithm enables each node to learn from the entire network, setting corresponding reward values based on encountered nodes meeting the specified conditions. Through iterative processes, the node with the most cumulative reward value is chosen as the final relay node. Experimental results demonstrate that the proposed algorithm achieves a high delivery rate while maintaining low network overhead and delay. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to investigate the potential advantages and also the limits of source routing when applied to delay‐/disruption‐tolerant networking (DTN) space networks. To this end, it uses a variant of contact graph routing (CGR) called moderate source routing (MSR), recently proposed by the authors and fully compatible with interplanetary overlay network (ION), the DTN suite developed by NASA‐JPL. MSR differs from standard CGR as the route to destination is not recalculated from scratch at each node, but possibly reused, if still valid, by next nodes. For this purpose, the route is saved in a dedicated extension block of the forwarded bundle (the data unit of the bundle protocol, used in DTN). Performance of MSR versus CGR is assessed by considering a simple but very challenging space layout. Numerical results, obtained on a GNU/Linux testbed, show that MSR is effective at reducing the chances of loops, in particular when the source has full knowledge of the state of the network; otherwise, network instabilities are still possible. In this case, they can be neutralized by means of the combined use of source routing and anti‐loop tools, as shown in the paper. A further advantage of MSR is that it is compatible with standard CGR, which would facilitate a gradual or partial deployment. [ABSTRACT FROM AUTHOR]

This paper proposes a Software-Defined Delay Tolerant Networking (SDDTN) architecture as a solution to managing large Delay Tolerant Networking (DTN) networks in a scalable manner. This work is motivated by the planned deployments of large DTN networks on the Moon and beyond in deep space. Current space communication involves relatively few nodes and is heavily deterministic and scheduled, which will not be true in the future. It is unclear how these large space DTN networks, consisting of inherently intermittent links, will be able to adapt to dynamically changing network conditions. In addition to the proposed SDDTN architecture, this paper explores data plane programming and the Programming Protocol-Independent Packet Processors (P4) language as a possible method of implementing this SDDTN architecture, enumerates the challenges of this approach, and presents intermediate results. [ABSTRACT FROM AUTHOR]

Mobile Ad-hoc Network (MANET) is one of the most important self-configuring and independent wireless network. Numerous intermediate nodes are used among MANET to interchange the information without the requirement of any centralized infrastructure. But some nodes act selfishly and utilize the resources only for their own purposes and do not share with the neighbors. This selfish nodes might delay or drop the packet and do not perform routing. Though watchdog is a well-known selfish node detection technique, it causes false negatives and false positives that can affect the performance in terms of precision and speed. To eliminate the drawbacks of existing approaches in selfish node detection, this paper integrates both Ad-hoc On-Demand Distance Vector (AODV) protocol incorporated with chimp optimization algorithm and Collaborative Contact based Watchdog to propose a novel technique called Chimp-CoCoWa-AODV in order to improve the performance of MANET. The main role of chimp optimization algorithm in AODV is to undergo optimal route selection process. The performance of the proposed Chimp-CoCoWa-AODV approach is compared with existing approaches in terms of average routing load, Average Packet Delivery Fraction (PDF), Average End-to-end Delay (EED), Average Throughput, Total packet drop in the application layer, and maliciously dropped packet in the routing layer. The simulation results shows that the proposed approach is effective with 82% PDF and 7.4 ms EED at 50 nodes in detection and isolation of selfish nodes in MANET even in the presence of malicious node. [ABSTRACT FROM AUTHOR]

Summary: Vehicular Ad‐hoc Network (VANET) is a vehicle‐to‐vehicle communication technology, which is highly promising due to its continuous demand, applicability, and suitability for public safety. The vehicular nodes establish communication with each other for exchanging traffic data and road maps, especially when warning messages need to be broadcasted on the network during emergency situations. Therefore, accurate vehicle information is necessary in such circumstances to improve the accuracy of Non‐Line‐of‐Sight (NLOS) node localization. In this paper, a Driving Training‐Based Optimization for NLOS Node Localization (DTO‐NL) Scheme is proposed for facilitating better warning message delivery during emergency situations in vehicular networks through a dynamic increase and decrease in the search based on the position of unknown NLOS nodes from the considered anchor nodes employed for localization. The performance of the suggested technique has been analyzed based on parameters like warning message delivery rate, latency, localization error, channel utilization rate, energy consumption, and packet delivery ratio. The proposed DTO‐NL scheme enhances the warning message delivery rate by 9.81%, 11.28%, 14.38%, and 17.64% compared to WIDVBA , ROA‐APS , ILL‐WOA‐NLOS , and WDHPBDS techniques. [ABSTRACT FROM AUTHOR]

Vehicle terminals in the mobile internet of vehicles are faced with difficulty in the requirements for computation-intensive and delay-sensitive tasks, and vehicle mobility also causes dynamic changes in vehicle-to-vehicle (V2V) communication links, which results in a lower task offloading quality. To solve the above problems, a new task offloading strategy based on cloud–fog collaborative computing is proposed. Firstly, the V2V-assisted task forwarding mechanism is introduced under cloud–fog collaborative computing, and a forwarding vehicles predicting algorithm based on environmental information is designed; then, considering the parallel computing relationship of tasks in each computing node, a task offloading cost model is constructed with the goal of minimizing delay and energy consumption; finally, a multi-strategy improved genetic algorithm (MSI-GA) is proposed to solve the above task offloading optimization problem, which adapts the chaotic sequence to initialize the population, comprehensively considers the influence factors to optimize the adaptive operator, and introduces Gaussian perturbation to enhance the local optimization ability of the algorithm. The simulation experiments show that compared with the existing strategies, the proposed task offloading strategy has the lower task offloading cost for a different number of tasks and fog nodes; additionally, the introduced V2V auxiliary task forwarding mechanism can reduce the forwarding load of fog nodes by cooperative vehicles to forward tasks. [ABSTRACT FROM AUTHOR]

In Portugal, more than 98% of domestic cooking oil is disposed of improperly every day. This avoids recycling/reconverting into another energy. Is also may become a potential harmful contaminant of soil and water. Driven by the utility of recycled cooking oil, and leveraging the exponential growth of ubiquitous computing approaches, we propose an IoT smart solution for domestic used cooking oil (UCO) collection bins. We call this approach SWAN, which stands for Smart Waste Accumulation Network. It is deployed and evaluated in Portugal. It consists of a countrywide network of collection bin units, available in public areas. Two metrics are considered to evaluate the system's success: (i) user engagement, and (ii) used cooking oil collection efficiency. The presented system should (i) perform under scenarios of temporary communication network failures, and (ii) be scalable to accommodate an ever-growing number of installed collection units. Thus, we choose a disruptive approach from the traditional cloud computing paradigm. It relies on edge node infrastructure to process, store, and act upon the locally collected data. The communication appears as a delay-tolerant task, i.e., an edge computing solution. We conduct a comparative analysis revealing the benefits of the edge computing enabled collection bin vs. a cloud computing solution. The studied period considers four years of collected data. An exponential increase in the amount of used cooking oil collected is identified, with the developed solution being responsible for surpassing the national collection totals of previous years. During the same period, we also improved the collection process as we were able to more accurately estimate the optimal collection and system's maintenance intervals. [ABSTRACT FROM AUTHOR]

In the last decade, user demand has been increasing exponentially based on modern communication systems. One of these new technologies is known as mobile ad-hoc networking (MANET). One part of MANET is called a vehicular ad-hoc network (VANET). It has different types such as vehicle-tovehicle (V2V), vehicular delay-tolerant networks, and vehicle-toinfrastructure (V2I). To provide sufficient quality of communication service in the Vehicular Delay-Tolerant Network (VDTN), it is important to present a comprehensive survey that shows the challenges and limitations of VANET. In this paper, we focus on one type of VANET, which is known as VDTNs. To investigate realistic communication systems based on VANET, we considered intelligent transportation systems (ITSs) and the possibility of replacing the roadside unit with VDTN. Many factors can affect the message propagation delay. When road-side units (RSUs) are present, which leads to an increase in the message delivery efficiency since RSUs can collaborate with vehicles on the road to increase the throughput of the network, we propose new methods based on environment and vehicle traffic and present a comprehensive evaluation of the newly suggested VDTN routing method. Furthermore, challenges and prospects are presented to stimulate interest in the scientific community. [ABSTRACT FROM AUTHOR]

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) network assisted by two reconfigurable intelligent surfaces (RISs) over Rician fading channels, in which each user communicates with the base station by the virtue of a RIS to enhance the reliability of the received signal. To evaluate the system performance of our proposed RIS-NOMA network, we first derive the exact and asymptotic expressions for the outage probability and ergodic rate of two users. Then, we derive the exact and asymptotic upper bound expressions for the ergodic rate of the nearby user. Based on asymptotic analytical results, the diversity orders for the outage probability and the high signal-to-noise ratio (SNR) slopes for the ergodic rate of the two users are obtained in the high SNR regime. Moreover, we derive the system throughputs of the proposed RIS-NOMA network in delay-limited and delay-tolerant transmission modes. Numerical results confirm our analysis and demonstrate that: 1) The outage probability and ergodic rate of RIS-NOMA networks are superior to that of RIS-assisted orthogonal multiple access (OMA) networks; 2) The RIS-NOMA networks have ability to achieve a larger system throughput compared to RIS-OMA networks; and 3) The system performance of RIS-NOMA networks can be significantly improved as the number of reflecting elements and Rician factor increases. [ABSTRACT FROM AUTHOR]

Delay Tolerant Network offers communication architecture for scenarios where attaining uninterrupted connectivity between source and destination is challenging due to dynamic topology, short transmission range, and mobility of nodes. The messages are delivered by a store, carry, and forward mechanism in which the node store the messages in its buffer, and carries them while moving and forward to its opportunistically connected peers. The buffer space is limited, and the stored messages are dropped to overcome congestion. The existing buffer management policies have utilized the message header data fields to compute heuristic metrics, or probability measures and have not focused on incorporating community characteristics in designing algorithms. In this paper, we have proposed a Social Community Buffer Management Policy for Delay Tolerant Networks in which the community metric consisting of Global Rank Value (GRV) and Local Rank Value (LRV) has been introduced. The GRV is the ability of any individual node to encounter the community in which the message destination resides. Similarly, LRV is the ability of a node to directly connect to the message destination. The congested node does not drop messages for which it has higher GRV and LRV values than predefined thresholds. Furthermore, we have integrated a message-locking mechanism in which the combination of lock variables such as Source Drop Lock, Must Accommodate Lock, Local Rank Lock, and Global Rank Lock are introduced within the message header. We have performed the simulation of existing BMSS, DOA, LIFO, MOFO, Ndrop, SHLI, and DLA with SCBM under real-time mobility scenarios. The simulation result proves the proposed SCBM performs better in terms of increasing delivery rate, buffer time average, and reducing overhead, message transmissions, and message drop. [ABSTRACT FROM AUTHOR]

As an important area for mobile wireless networks, Intermittently Connected Networks (ICNs) have received increasing attention in practice and research in recent years. The main goals of the ICN routing protocol are to maximize the delivery probability and to minimize the delivery delay. These challenges have been investigated in previously proposed protocols with an assumption of fully cooperating nodes. Node cooperation directly impacts ICN performance in terms of delivery probability and delay. However, node cooperation is not guaranteed because a cooperating node consumes its buffer size and energy, thereby highlighting the challenges of memory, computation, and energy efficiencies. This paper addresses the challenge of ensuring node cooperation by proposing a novel routing protocol called Mail Man Ferry (MMF). The new protocol includes a node credit mechanism that ensures the cooperation of the network nodes. Extensive simulations were executed to evaluate the proposed protocol. Varying values of node cooperation in ICNs were employed in the simulation tests. The evaluation shows exciting results in which Mail Man Ferry outperforms previously proposed ICN protocols. [ABSTRACT FROM AUTHOR]

Nowadays, Delay Tolerant Network plays an important role in improving the communication between the network nodes. Applications of Delay Tolerant Network are disaster recovery, vehicular communication, sensor networks, interplanetary networks, and communication in remote and rural areas. Routing is one of the important tasks for enhancing the energy effectiveness of data transmission among the mobile nodes under network congestion and dynamic topology. Machine Learning-based routing algorithms are used for improving network communication in Delay Tolerant Networks. Its objective is to reduce the delay, minimize the overhead, reduce energy consumption, improve throughput, minimize packet loss, and efficient data transmission. This paper presents a comprehensive review of routing algorithms using machine learning for Delay Tolerant Networks. [ABSTRACT FROM AUTHOR]

Mobile network is a collection of devices with dynamic behavior where devices keep moving, which may lead to the network track to be connected or disconnected. This type of network is called Intermittently Connected Mobile Network (ICMN). The ICMN network is designed by splitting the region into 'n' regions, ensuring it is a disconnected network. This network holds the same topological structure with mobile devices in it. This type of network routing is a challenging task. Though research keeps deriving techniques to achieve efficient routing in ICMN such as Epidemic, Flooding, Spray, copy case, Probabilistic, andWait, these derived techniques for routing in ICMN are wise with higher packet delivery ratio, minimum latency, lesser overhead, and so on. A new routing schedule has been enacted comprising three optimization techniques such as Privacy-Preserving Ant Routing Protocol (PPARP), Privacy-Preserving Routing Protocol (PPRP), and Privacy-Preserving Bee Routing Protocol (PPBRP). In this paper, the enacted technique gives an optimal result following various network characteristics. Algorithms embedded with productive routing provide maximum security. Results are pointed out by analysis taken from spreading false devices into the network and its effectiveness at worst case. This paper also aids with the comparative results of enacted algorithms for secure routing in ICMN. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) are increasingly playing a fundamental role in improving driving safety. However, VANETs in a sparse environment may add risk to driving safety. The probability of a low density of vehicles in a rural area at midnight is very high. Consequently, the packet will be lost due to the lack of other vehicles, and the arrival of the following vehicles in the accident area is unavoidable. To overcome this problem, VANET is integrated with Wireless Sensor Network (WSN). The most challenging features of VANETs are their high mobility. This high mobility causes sensor nodes to consume most of their energy during communication with other nodes, leading to frequent network disconnectivity. With the evolution of VANET and WSN, the Store/Carry-Forward (SCF) paradigm has emerged as an exciting research area in the Delay Tolerant Networks (DTNs) to solve network disconnectivity. This paper proposes the Energy-Mobility-Connectivity aware routing protocol (EMCR) for a hybrid network of VANET-WSN. A comprehensive performance analysis that considers realistic propagation models and real city scenario traffic is performed in NS3. The simulation results show that the SCF mechanism is essential in the EMCR protocol to maximize the delivery ratio and minimize energy consumption and overhead. [ABSTRACT FROM AUTHOR]

Vehicular delay-tolerant networks follow a store–carry–forward mechanism to overcome the state of broken links between source and destination, which is crucial for autonomous vehicle networks. These networks depend upon the mobility of vehicular-relay nodes, which can adopt the store–carry–forward arrangement and provide consent to utilize their resources for packet forwarding. In reality, vehicular-relay nodes drop packets because of a lack of resources and selfish behavior, leading to potential failures in networks. The reason for this failure in performance is the absence of an efficient relay node selection strategy. Typically, vehicular delay networks can provide communication solutions in challenging conditions when other traditional networks fail to perform due to disconnection. However, if the routing strategy does not consider nodes' selfish nature and relay nodes' inefficiency, packet drops continue, and the approach will fail to perform. This paper proposes a routing strategy, known as the best-choice vehicular-relay selection strategy for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) transmission, to overcome this breakdown situation. The proposed strategy selects the best-performing vehicular-relay nodes and restricts the packet replication to low-performing vehicular nodes. The proposed routing strategy selects the best-choice relay nodes based on their past performance. Numerical analysis and our simulation results show that the proposed approach outperforms some of the leading routing strategies in its class by increasing the delivery probability, decreasing the overhead ratio, average latency, and the number of dropped packets in the vehicular network. [ABSTRACT FROM AUTHOR]

Delay Tolerant Network (DTN) establishes the communication framework in environments where source and destination cannot establish the end-to-end path due to the limited transmission range, frequent disconnections, and network partitioning. The messages are delivered to their destinations by adopting a store, carry and forward paradigm in which the node stores the arriving message in its buffer, carries it while moving, and forwards on connecting to other nodes. The DTN multi-copy protocols diffuse the several message copies on all connected nodes and deplete the network resources such as buffer space, bandwidth, and energy. This resource consumption is controlled by incorporating a social community message forwarding technique. The node employs the global rank forwarding metric while moving outside the community and shifts to the local rank forwarding metric while moving within the community. This method produces congestion in scenarios where the node continues message replications on higher global and local rank peers. As a result, more messages are dropped, and message delivery is reduced. This paper presented the Message Locking Routing Protocol for Delay Tolerant Network (CTML). The CTML controls the message forwarding process by employing the Local Rank Lock (LRL) and Global Rank Lock (GRL). The Local Rank Lock (LRL) controls the message transmission during node mobility within the community whereas Global Rank Lock (GRL) refrains the message forwarding during node mobility outside the community. Thus, the small number of transmissions reduces congestion. Similarly, CTML control message drop by using Message Drop Lock (MDL) where a node cannot drop a message with higher global rank or local rank values. Finally, the delivery ratio has been increased by using Deliver to Destine Lock (DDL) lock where a node with higher threshold values to meet the destination is enforced to forward the message directly to its destination. The simulation results prove that the proposed CTML has performed better in terms of reducing message transmissions , message drop, hop-count average, overhead, and raising delivery ratio. [ABSTRACT FROM AUTHOR]

With the recent endeavour of smart city mission, it is being planned to install sophisticated infrastructure for providing various smart services. But the planning to handle unwanted events like disaster has not received much attention as part of this endeavour. In a post-disaster scenario, one of the most important services is to disseminate crucial situational information among different stakeholders when the traditional communication infrastructure becomes non-functional. The smart phone based delay tolerant network (DTN) shows its useful applicability in such a scenario. Further, it is well established that due to the movement pattern of the stakeholders with handheld device, it typically forms clusters based on their work areas. However, the existing DTN routing protocols are not designed for cluster architecture. This motivates us to propose a cluster routing considering a utility based optimal dropbox (DB) deployment that assists in providing improved network performance. In this paper, we primarily consider that DBs are deployed at strategic locations of the disaster affected area using an existing utility based dropbox deployment. Then we propose a mixed integer linear programming based optimization model, to enumerate the optimal number of dropboxes to be deployed at high utility locations. We finally propose a cluster based routing with a target to improve network performance. We evaluate the comparative performance of our proposed optimal DB deployment based cluster routing through both theoretical analysis and simulation. Exhaustive simulation is carried out in ONE simulator using real disaster data. Results show our scheme's dominance over the competing schemes. [ABSTRACT FROM AUTHOR]

With the development and maturity of 5G communication technology, the intelligent terminals and applications in the Internet of Things have exploded. The large-capacity data generated by the Internet of Things has higher transmission requirements on the network. Opportunistic social networks use the "store-carry-forward" method to complete message delivery, so it is particularly important to select reliable relay users to ensure the integrity of data transmission. However, users have limited memory resources. A large number of redundant message copies in the network will lead to increased node energy consumption, which will further cause network congestion. In addition, the user's movement is random and dynamic. Frequent changes in the network topology may lead to transmission link interruption and data loss. Based on the characteristics of social networks, this paper proposes a clustered dataset transmission strategy in opportunistic social networks based on the probability of encounters between users and the characteristics of user motion (PC-OSN). First, the method divides users into different clusters according to the strength of the relationship between users, which effectively reduces the occupation of network resources when users are clustered. Then PC-OSN adopts different message forwarding judgment indicators within and between clusters. PC-OSN forwards messages in a cluster by using binary injection to ensure that messages are delivered to users with a high probability of encountering. The user's activity level and motion characteristics are considered comprehensively among the clusters to determine the user's priority so as to deliver the message to the reliable user. PC-OSN fully considers the characteristics of users and the correlation between users, reducing the blindness of message forwarding and waste of ineffective resources. Experimental results show that PC-OSN achieves a message delivery rate of 0.94, which is about 14% higher than other methods. Its average latency and network overhead are reduced by about 24% and 16%, respectively. [ABSTRACT FROM AUTHOR]

Because the operation of space networks is carefully planned, it is possible to predict future contact opportunities from link budget analysis using the anticipated positions of the nodes over time. In the standard approach to space delay-tolerant networking (DTN), such knowledge is used by contact graph routing (CGR) to decide the paths for data bundles. However, the computation assumes nearly ideal channel conditions, disregarding the impact of the convergence layer retransmissions (e.g., as implemented by the Licklider transmission protocol (LTP)). In this paper, the effect of the bundle forwarding time estimation (i.e., the link service time) to routing optimality is analyzed, and an accurate expression for lossy channels is discussed. The analysis is performed first from a general and protocol-agnostic perspective, assuming knowledge of the statistical properties and general features of the contact opportunities. Then, a practical case is studied using the standard space DTN protocol, evaluating the performance improvement of CGR under the proposed forwarding time estimation. The results of this study provide insight into the optimal routing problem for a space DTN and a suggested improvement to the current routing standard. [ABSTRACT FROM AUTHOR]

In a delay tolerant network (DTN) nodes are scattered and intermittently connected. In this network end-to-end path cannot be assumed to be available. Routing protocols in DTNs usually replicate message in the network to transmit the message toward its destination. Managing message replication and distribution, and finding appropriate relay nodes due to the lack of timely and precise information is a major challenge. Existing multi-copy routing protocols when replicate and distribute copies of message don't consider some capabilities of nodes as the power to take and carry copies, this may lead to delays in delivering the message. In this paper, we proposed a multi-step routing protocol for DTNs. In each routing step, our protocol first dynamically determines the needed number of copies and also share of nodes from these copies. It makes decisions with the help of an attribute named Replication Capability Rate (RCR). The fuzzy system provides RCR based on the capability of nodes as power and property of message as time to live. Then, protocol sprays copies until remaining single copy in the buffer of the node. Finally, the single copy of the message is exchanged between nodes according to the encounter frequency. Experiment results show that our protocol significantly decreases the average delay and keeps delivery probability high, in comparison with well-known protocols. [ABSTRACT FROM AUTHOR]

Delay-tolerant networks (DTNs) are networks where there is no immediate connection between the source and the destination. Instead, nodes in these networks use a store–carry–forward method to route traffic. However, approaches that rely on flooding the network with unlimited copies of messages may not be effective if network resources are limited. On the other hand, quota-based approaches are more resource-efficient but can have low delivery rates and high delivery delays. This paper introduces the Enhanced Message Replication Technique (EMRT), which dynamically adjusts the number of message replicas based on a node's ability to quickly disseminate the message. This decision is based on factors such as current connections, encounter history, buffer size history, time-to-live values, and energy. The EMRT is applied to three different quota-based protocols: Spray and Wait, Encounter-Based Routing (EBR), and the Destination-Based Routing Protocol (DBRP). The simulation results show that applying the EMRT to these protocols improves the delivery ratio, overhead ratio, and latency average. For example, when combined with Spray and Wait, EBR, and DBRP, the delivery probability is improved by 13%, 8%, and 10%, respectively, while the latency average is reduced by 51%, 14%, and 13%, respectively. [ABSTRACT FROM AUTHOR]

Opportunistic Delay Tolerant Networks also referred to as Opportunistic Networks (OppNets) are a subset of wireless networks having mobile nodes with discontinuous opportunistic connections. As such, developing a performant routing protocol in such an environment remains a challenge. Most research in the literature have shown that reinforcement learning-based routing algorithms can achieve a good routing performance, but these algorithms suffer from under-estimations and/or over-estimations. Toward addressing these shortcomings, in this paper, a Double Q-learning based routing protocol for Opportunistic Networks framework named Off-Policy Reinforcement-based Adaptive Learning (ORAL) is proposed, which selects the most suitable next-hop node to transmit the message toward its destination without any bias by using a weighted double Q-estimator. In the next-hop selection process, a probability-based reward mechanism is involved, which considers the node's delivery probability and the frequency of encounters among the nodes to boost the protocol's efficiency. Simulation results convey that the proposed ORAL protocol improves the message delivery ratio by maintaining a trade-off between underestimation and overestimation. Simulations are conducted using the HAGGLE INFOCOM 2006 real mobility data trace and synthetic model, showing that when time-to-live is varied, (1) the proposed ORAL scheme outperforms DQLR by 14.05%, 9.4%, 5.81% respectively in terms of delivery probability, overhead ratio and average delay; (2) it also outperforms RLPRoPHET by 16.17%, 9.2%, 6.85%, respectively in terms of delivery ratio, overhead ratio and average delay. [ABSTRACT FROM AUTHOR]

Delay-tolerant networks allow communication using store-carry-forward technique between source and destination nodes where no end-to-end connectivity exists. In case of network disconnection, messages get stored in a node and wait till the connection is restored, which could be forwarded to another node. To explore the possibility of improvement, many researchers have applied protocol based on fuzzy in DTN, but none of them proposed any solution for the situation where the node density is very low. This situation may result in low buffer availability. Hence, the nodes become unavailable for forwarding messages due to low buffer availability. In general, the remote area does not have a dense network thus, required buffer size might not be available at all the nodes for enabling communication. In this paper, we proposed a solution for the scenario where nodes cannot accept any message due to the unavailability of storage. To make communication possible, we formulated a methodology to search for the best node available in the communication range for relaying the message. The selected node is tested for buffer availability. As per negative result, it is subjected to buffer cleaning and then used for sending a message to that node. If the result is positive, the message will be sent directly to the relay node. A Fuzzy logic system is used for relay node selection and buffer cleaning process. ONE simulator has been adopted for simulating the environment and the simulation results indicate that the proposed technique enhanced the network's performance. [ABSTRACT FROM AUTHOR]

Summary: In delay‐tolerant networks (DTN), node connection time and message transmission time are two important influencing factors that can improve the delivery rate. In this paper, we first define a new concept called communication capability (CC) and then apply this concept to the delivery predictability formulation in Prophet and improve it. Then, in Prophet, the selection of relay nodes relies only on the delivery predictability and ignores the caching and forwarding capability of the node. Therefore, we combine delivery predictability, buffering, and forwarding capability to develop a new adaptive relay node selection strategy. Subsequently, we define two metrics called message priority (MP) and message strength (MS). The node forwards messages sequentially based on message priority and discards messages based on message strength. Finally, we present a probabilistic routing algorithm based on node communication capability and message strength (CAMS). The simulation results show that compared with traditional routing algorithms, the CAMS can effectively improve the message delivery rate, reduce the overhead ratio, and keep average hop counts low. [ABSTRACT FROM AUTHOR]

The study of Delay Tolerant Networks (DTNs) has considerably grown in recent years as communication contexts have emerged with needs that go beyond what the Internet could offer. For example, in case of a natural disaster that damages the classic communication network, a delay tolerant network can be implemented ad hoc, to face the challenges imposed by this context. The delay tolerant network is special because there are no permanent end-to-end path between nodes and links characteristics are time-varying. This paper aims to test the performance of the DTN MaxDelivery algorithm in establishing an efficient communication in the context of a post-earthquake situation, that affected the classical communication network. The goal of the algorithm is to maximize the number of high priority messages that manage to reach their destination. A series of simulations are presented to verify the optimal parameters that the network must comply with in order to maximize the message transfer rate. [ABSTRACT FROM AUTHOR]

In predictable delay tolerant networks (PDTNs), the network topology is known a priori or can be predicted over time, such as space planet networks and vehicular networks based on public buses or trains. Due to the intermittent connectivity, network partitioning, and long delays in PDTNs, most of the researchers mainly focuses on routing and data access research. However, topology control can improve energy effectiveness and increase the communication capacity, thus how to maintain the dynamic topology of PDTNs becomes crucial. In this paper, a contact ability based topology control method for PDTNs is proposed. First, the contact ability is calculated using our contact ability calculation model, and then the PDTNs is modeled as an undirected weighted contact graph which includes spatial and contact ability information. The topology control problem is defined as constructing a minimum spanning tree (MST) that the contact ability of the MST is maximized. We propose two algorithms based on undirected weighted contact graph to solve the defined problem, and compare them with the latest method in terms of energy cost and contact ability. Extensive simulation experiments demonstrate that the proposed algorithms can guarantee data transmission effectively, and reduce the network energy consumption significantly. [ABSTRACT FROM AUTHOR]

Mobile Opportunistic Networks (MONs) are characterized by intermittent connectivity with long isolation period, and nodes following redundant transmissions for reliable message delivery. This often leads to unnecessary buffer occupancy, preventing new messages from getting replicated due to small contact duration and low bandwidth, or leading to packet drop under constrained buffer. Although attempts have been made to mitigate buffer congestion, the existing schemes are localized, are slow to react, or are specific to a routing scheme. Moreover, they rely on message exchanges to obtain buffer state/occupancy, thereby incurring additional overhead. In this paper, we first develop a generalized probabilistic forwarding model where the forwarding probability denotes the likelihood of a message to get forwarded to the encountered node. Based on the forwarding probability, we develop a congestion indicator and predict the point of congestion using the Kalman filter. Using this, a node can decide the optimal number and the exact set of messages to replicate, which leads to an optimal performance with minimal packet drop and overhead. Simulation results using a synthetic mobility model and a real-life mobility trace show that the proposed scheme outperforms the existing schemes. [ABSTRACT FROM AUTHOR]

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In Opportunistic Networks, portable devices such as smartphones, tablets, and wearables carried by individuals, can communicate and save-carry-forward their messages. The message transmission is often in the short range supported by communication protocols, such as Bluetooth, Bluetooth Low Energy, and Zigbee. These devices carried by individuals along with a city's taxis and buses represent network nodes. The mobility, buffer size, message interval, number of nodes, and number of messages copied in such a network influence the network's performance. Extending these factors can improve the delivery of the messages and, consequently, network performance; however, due to the limited network resources, it increases the cost and appends the network overhead. The network delivers the maximized performance when supported by the optimal factors. In this paper, we measured, predicted, and analyzed the impact of these factors on network performance using the Opportunistic Network Environment simulator and machine learning techniques. We calculated the optimal factors depending on the network features. We have used three datasets, each with features and characteristics reflecting different network structures. We collected the real-time GPS coordinates of 500 taxis in San Francisco, 320 taxis in Rome, and 196 public transportation buses in Münster, Germany, within 48 h. We also compared the network performance without selfish nodes and with 5 % , 10 % , 20 % , and 50 % selfish nodes. We suggested the optimized configuration under real-world conditions when resources are limited. In addition, we compared the performance of Epidemic, Prophet, and PPHB++ routing algorithms fed with the optimized factors. The results show how to consider the best settings for the network according to the needs and how self-sustaining nodes will affect network performance. [ABSTRACT FROM AUTHOR]

One of the major concerns in any emergency relief operation is appropriate allocation of scarce emergency relief materials to the affected community. Due to several reasons ranging from lack of mechanism to accurately assess demand and utility of relief materials to malicious participation of some of the stakeholders, such allocation may become ad-hoc. Thus, it becomes imperative to have an unchallengeable and globally accessible record of relief requirement vis-à-vis allocation for efficient relief management. Emergency response organizations (e.g. UNICEF) have recommended the adoption of blockchain technology to create such immutable records. However, the usage of blockchain is restricted by the availability of end-to-end internet connection which may not be available in a post-disaster scenario. This paper proposes ReliefChain, a blockchain leveraged post disaster relief allocation system over delay tolerant network that works in such environments. We validate relief requirements to mitigate resource diversion, forecasting the exact demand and enumerating precise utilities of relief items. We design smart contracts for creating new transactions to upload relief requirements and allocations in the blockchain network. The proposed system executes these smart contracts to create an immutable and globally accessible record of relief requirement and allocation. Effectiveness of the proposed system is evaluated through extensive simulation in Ethereum platform. Results substantiate the efficiency of the system over a system using baseline methodologies, in terms of design parameters like shelter specific deficit and average resource deficit while not compromising the blockchain performance in terms of processing time and gas consumption even in presence of malicious forwarders. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimal multi-hop transmission scheme to maximize the end-to-end data rate from the source to the destination node in a wireless powered communication network. The frame structure for multi-hop transmission is presented to transmit multi-hop data while harvesting energy. Then, the transmission time of each node that maximizes the end-to-end transmission rate is determined through mathematical analysis in consideration of different harvested energy and link quality among nodes. We derive an optimization problem through system modeling of the considered wireless powered multi-hop transmission, and prove that there is a global optimal solution by verifying the convexity of this optimization problem. This analysis facilitates to find the optimal solution of the considered optimization problem. The proposed optimal multi-hop transmission scheme maximizes the end-to-end rate by allocating the transmission time for each node that equalizes the transmission rates of all links. [ABSTRACT FROM AUTHOR]