Opportunistic Network Environment Research Articles

VADIA-Verkle Tree-based Approach for Dealing Data Integrity Attacks in Opportunistic Mobile Social Networks

Opportunistic Mobile Social Networks (OMSN) are prone to data integrity attacks that jeopardize the integrity of the routing data inside the network. Among the several techniques that cope with these attacks in OMSN, tree-based approaches have proven to be the most effective due to its ease of data verification and ensurance in data integrity. This paper evaluates two tree-based data structures, Merkle tree and Verkle tree in terms of their effectiveness in detecting and preventing such attacks. The evaluation considers tree-generation time and proof-checking time, and the results demonstrate that the Verkle tree is a bandwidth-efficient solution and have lower proof-checking time, with a reduction of 98.33% than Merkle tree. This makes Verkle tree a good choice for handling data integrity attacks in OMSN. A Verkle tree-based approach, named VADIA, is proposed to handle data integrity attacks such as packet dropping, packet modification and pollution attack in OMSN. The proposed approach is implemented in the Opportunistic Network Environment (ONE) simulator and is shown to be effective in detecting malicious nodes and paths, reducing false negative rates, and improving accuracy in detecting malicious activities. The results demonstrate a 47%, 84% and 69% improvement in malicious node, malicious path and malicious activity detection over a period of time. Furthermore, the approach achieves an 80% reduction in false negative rates.

Dynamic Co-Operative Energy-Efficient Routing Algorithm Based on Geographic Information Perception in Opportunistic Mobile Networks

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%.

Impact of Renewable Energy Resources on the Performance of DTN Networks in the Context of Hierarchical Routing Tree Topology (HRTT)

Delay-Tolerant Networks (DTN) are mobile networks specifically designed to operate in challenging wireless environments where connectivity is not guaranteed, and various issues such as frequent disconnections, long delivery delays, low delivery rates, and high error rates are common. One of the major problems of DTN networks is limited energy resources which impose strict constraints on the operational lifespan of DTN nodes. To solve these problems, we propose the adoption of a long-term strategy for energy management, based on the exploitation of renewable energies and the improvement of the HRTT topological architecture through the direct integration of renewable energy sources within DTN nodes. Indeed, DTN nodes powered by renewable energy resources, such as solar energy, have the potential to recharge themselves, extending their operational lifespan, increasing their storage and transmission capabilities, and reducing dependence on non-renewable energy resources. In this article, we assess the impact of limited energy resources and renewable energy resources like solar panels on the energy performance of DTN networks in the context of the Hierarchical Routing Tree Topology (HRTT), taking into account the average remaining energy as a performance metric. The evaluation of energy performance and the implementation of the HRTT topology are carried out using the ONE (Opportunistic Network Environment) simulator. According to the simulation results, the average remaining energy of nodes using renewable resources is significantly higher compared to the initial energy levels of nodes for all DTN protocols adopted by the nodes in the context of the HRTT topology. However, the average remaining energy of nodes using limited resources is lower compared to their initial energy levels for all DTN protocols adopted by the nodes in the context of the HRTT topology.

A social relationship-based energy efficient routing scheme for Opportunistic Internet of Things

Opportunistic Internet of Things (OppIoT) combines Opportunistic Networks and the Internet of Things to provide opportunistic communication between IoT devices and human-created communities in order to improve network connectivity, reliability, and longevity. This is significant because it introduces a new approach to connect and communicate smart devices in situations where there is no end-to-end connectivity. Due to the non-stable existence of connectivity between nodes, building a scalable, flexible, interoperable, and energy efficient routing protocol is a difficult issue. This study presents a new routing protocol for the Opportunistic Internet of Things called Social relationship based Energy Efficient Routing (SEER), which bases routing decisions on the forwarding feasibility degree, residual energy, and buffer capacity of nodes. SEER surpasses EBR and EDR in terms of delivery probability, hop count, and overhead ratio, according to simulation results utilizing the Opportunistic Network Environment (The ONE) simulator.

Markov Chain-based Mobility Prediction and Relay-node Selection for QoS Provisioned Routing in Opportunistic Wireless Network

In the fields of networking and communication, wireless sensor networks are a commonly used technology. Although Mobility in Wireless Networks or Mobile Ad hoc Networks (MANETs) were developed to solve most critical problems in a disaster scenario. However, data transmission in a disaster area is a difficult operation due to irregular connections between sensing devices and the sink. Opportunistic network-based MANETs are utilized to overcome this problem. There are many routing issues such as communication overhead ratio, packet loss, and packet delivery ratio at mobile nodes. There are several traditional approaches used in opportunistic networks for node localization and routing. The drawback of these traditional routing methods is their poor reliability. A Markov chain-based opportunistic routing protocol is proposed in this paper. A Markov chain model is used to predict the node mobility for the random mobility model, and a neighbor table-based relay node selection procedure is proposed. A Markov prediction model uses the history of a node’s movement without requiring additional information. By analyzing simulation metrics including delay, overhead rate, packet drop, and packet delivery rate, the proposed routing protocol is evaluated and verified based on the Packet Delivery Ratio (PDR) in an opportunistic network environment (ONE) simulator. In addition, existing routing protocols such as Spray and Wait (SNW), Epidemic, Prophet, and MaxPro differ from the proposed routing protocol in terms of buffer size, node count, and message generation rate. Corresponding result analysis demonstrates the superior performance of the proposed routing protocol over existing protocols.

Improving the delivery ratio in vehicular delay‐tolerant network using customized binary particle swarm optimization‐based optimized throwbox deployment

SummaryVehicular delay tolerant networks (VDTNs) suffer due to insufficient connection opportunities in sparse infrastructure. The sparsity of the network results in poor delivery ratio (DR). Deployment of additional small nodes named throwbox increases the contact opportunities, resulting in improved performance in terms of DR. However, randomly deployed throwboxes do not provide optimum results. Therefore, this article proposes the throwbox deployment as an optimization problem. Customized binary particle swarm optimization‐based optimization method is employed to solve the problem. With the objective of maximizing the DR, the method optimize the placement of ‐throwboxes in the network. The performance of the proposed methods is tested and compared by simulating a VDTN model in opportunistic network environment simulator. The optimization framework was implemented in Matlab. Exhaustive experiments were carried out by varying the number of throwboxes (). A statistical comparison of the results with the existing throwbox deployment methods validates the effectiveness of the proposed optimization‐based approach. The article also presents an analysis for choice of value of for a desired network performance.

Improving the Performance of Opportunistic Networks in Real-World Applications Using Machine Learning Techniques

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.

NRTAS: Non-redundant traffic authentication scheme for strengthening privacy in 5 G communication networks

End-to-end authentication in 5G communication networks is a prominent requirement due to the growing application demands and autonomously shared user data. The lack of data-related attributes and the communicating platform serves as a challenging issue in securing the shared content. Besides, administering security for the generated data is less feasible due to un-traceability and handoff experienced in the network. A Non-Redundant Traffic Authentication Scheme (NRTAS) is presented and the main objective of this proposed work provide a reliable authentication based on classifying the traffic. A traffic classification model is developed to categorize the traffic generated by the user equipment. A tree-based process is employed for linear and discrete authentication to enhance network performances. To succeed high connectivity secure 5G communication and information sharing in a heterogeneous platform is presented. The effectiveness of proposed NRTAS-5G communication network approach is executed in Opportunistic network environment (ONE) simulator version: 1.2. The NRTAS achieves 8.09% less access time, reduces the traffic load by 13.69%, and improves the success ratio by 5.36% for 150 UEs (User Equipment’s).

Improving Traffic Load Distribution Fairness in Mobile Social Networks

Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node’s sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node’s periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance.

Enhanced UAV-aided vehicular delay tolerant network (VDTN) routing for urban environment using a bio-inspired approach

The evolution of VDTN routing came to solve the problem of routing in sparse Vehicular Ad-hoc Networks (VANETs). Recently, the implication of Unmanned Aerial Vehicles (UAVs) in VANET communication has gathered quicker information dissemination than Roadside Units (RSUs) in urban VANETs, where ground vehicles’ mobility is restricted by the topology of city roads. Gradually, UAVs are emerging for VDTNs: UAV-aided VDTN (UVDTN) routing field has been introduced for sparse areas in order to reduce the network gaps between Vehicle-to-Vehicle (V2V) connections. Meanwhile, the conventional UVDTN routing protocols expose the short contact opportunity time in the case of Vehicle-to-UAV (V2U) and UAV-to-Vehicle (U2V) connections. This constraint entails more congested bundles’ flooding rates and a higher number of lost bundle copies compared to V2V-based forwarding. In this paper, a bio-inspired UVDTN routing protocol is conceived to improve the bundles’ forwarding between ground vehicles and UAVs in terms of flooding optimization and Quality-of-Service (QoS) performances for urban VANET environments. For this purpose, a swarm-inspired approach called the Social Spider Optimization (SSO) is proposed. SSO seeks to find a better selection of next Store-Carry-and-Forward (SCF) relay nodes specifically for V2U and U2V connections. In order to control the bundle replication rate and enhance bundles’ delivery ratio and delay, the proposed bio-inspired UVDTN protocol adjusts the SCF selection decision. The latter is based on predefined historical knowledge-based forwarding parameters and urban geographic topology information. The proposed bio-inspired routing protocol is simulated using the Opportunistic Network Environment (ONE) simulator and compared to a few VDTN and UVDTN routing protocols.

An adaptive energy aware DTN-based communication layer for cyber-physical systems

Cyber-physical systems (CPS) can significantly transform our daily lives as it is an emerging area for future innovative services. CPS have the inherent features and benefits of using available networks such as wireless sensor networks, following generation networks, and the Internet. However, network disconnection is a concern in traditional networks. Delay tolerant networks can solve the problem of network disconnection. Mobile wireless networks are often characterized by frequent segmentation and potentially long message delivery delays. Modern applications in these networks have shown that energy is an essential concern as energy sources are limited. Nodes in a delay tolerant network are characterized by sparse connectivity, which provides an opportunity to save energy by periodically disabling the node radio (i.e., putting the node into sleep mode). Another problem is the limited energy of nodes, so an effective energy-saving model is necessary to allow these networks to operate for long periods. We have proposed an adaptive energy-aware DTN-based communication layer for CPS. We have designed an energy-saving system for CPS based on a delay tolerant network in the proposed model. We have proposed an energy-saving model that allows energy savings by managing the wake-up period at regular intervals while keeping the node inactive for the rest of the time. We evaluate our model implementing the Opportunistic Network Environment Simulator (ONE). The achieved results show that our energy that our energy-saving model has increased battery life five times as compared to constant access.

An Opportunistic Network Routing Protocol for Maritime Voluntary Observation Ships

In order to solve the issue of data transmission for maritime voluntary observation ships, an opportunistic network routing protocol (MVOS-RP) is proposed for the complicated maritime environment. The proposed protocol transmits data through the encounter between ships. A connected graph is constructed with the sending node as the vertex, and then the selection of the relay node is divided into three scenes by traversing the graph through the Breadth First Search (BFS) algorithm. We also use the ship’s Automatic identification System (AIS) information, such as the estimated time of arrival and the route of ships, to select the appropriate relay node. MVOS-RP protocol solves the transmission issue of opportunistic network in the environment of sparse nodes and poor communication link quality. Simulation is performed on Opportunistic Network Environment (ONE) platform. The results show that, compared with other protocols, the proposed protocol has a better balance in terms of data delivery rate, average latency and routing overhead. It is a reliable and efficient opportunistic network routing protocol.

A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks

Recently, Opportunistic Networks (OppNets) are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent devices. OppNets are characterized by a rough and dynamic topology as well as unpredictable contacts and contact times. Data is forwarded and stored in intermediate nodes until the next opportunity occurs. Therefore, achieving a high delivery ratio in OppNets is a challenging issue. It is imperative that any routing protocol use network resources, as far as they are available, in order to achieve higher network performance. In this article, we introduce the Resource-Aware Routing (ReAR) protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained, stateless and non-social OppNets. The ReAR protocol invokes our recently introduced mutual information-based weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real time. The proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment simulator. Experiments show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR, Epidemic MaxProp, energy-aware Spray and Wait and energy-aware PRoPHET in terms of message delivery ratio and overhead ratio.

Routing in Opportunistic Networks: Implementation and Research Challenges

Opportunistic Networks can be defined as Delay Tolerant Network, which are formed dynamically with participating nodes’ help. Opportunistic Networks follows Store-Carry-Forward principle to deliver/route the data in the network. Routing in Opportunistic Network starts with the Seed Node (Source Node) which delivers the data with the help of Intermediate nodes. Intermediate nodes store the data while roaming in the network until it comes in contact with appropriate forwarding node (relay node) or destination node itself. An extensive literature survey is performed to analyse various routing protocols defined for Opportunistic Network. With mobility induced routing, establishing and maintaining the routing path is a major challenge. Further, Store-Carry-Forward routing paradigm imposes various challenges while implementing and executing the network. Due to the unavailability of the suitable relay node, data needs to be stored within the Node’s Memory, imposes buffer storage issues at the node level. Also, uncontrolled flooding may impose link-level Congestion and treated as overhead to maintain the network. Another major challenge can be maintaining the energy level of the nodes in the network. Recently developed ONE (Opportunistic Network Environment) Simulator is used to simulate and emulate the environment required by Opportunistic Network. Along with the extensive literature survey of the protocols, few of the existing protocols viz. Direct Delivery, ProPHET, Epidemic and Spray & Wait Routing are implemented using ONE Simulator to analyse their performance while in execution. Results are being compared, and the researchers’ future direction is identified to address the open problems and challenges in Opportunistic Network.

Analysis of Epidemic, PROPHET and Spray and Wait Routing Protocols in the Mobile Opportunistic Networks

Background: The Opportunistic Mobile Networks (OMNs) are a type of Mobile Adhoc Networks (MANETs) with Delay-Tolerant Network (DTN) features, where the sender to receiver connectivity never exists most of the time, due to dynamic nature of the nodes and the network partition. The real use of OMNs is to provide connectivity in challenged environments. Methods: The paper presents the detailed analysis of three routing protocols, namely Epidemic, PROPHET and Spray and Wait, against variable size of the messages and the Time To Live (TTL) in the networks. The key contribution of the paper is to explore routing protocols with mobility models for the dissemination of data to the destination. Routing uses the store-carry-forward mechanism for message transfer and network has to keep compromise between message delivery ratio and delivery delay. Results: The results are generated from the experiments with Opportunistic Network Environment (ONE) simulator. The performance is evaluated based on three metrics, the delivery ratio, overhead ratio and the average latency. The results show that the minimum message size (256 KB) offers better performance in the delivery than the larger message size (1 MB). It has also been observed that with the epidemic routing, since there are more message replicas, which in turn increase the cost of delivery, so with a smaller message, the protocol can reduce the overhead ratio with a high proportion. Conclusion: The average latency observed increases with the increase of the TTL of the message in three protocols with variation of the message size from 256KB to 1 MB.

Performance Evaluation of Routing Protocols for Opportunistic Networks: An Energy Efficiency Perspective

In most computer networks, a path between a sending node and the destination node is requiredto transfer data. There are instances where such a path for communication may not exist, suchas in space communications, and the standard message routing techniques will not work. OpportunisticNetworks were developed to solve this problem. Infrastructure-less Opportunistic Networks(OppNets) are a type of Delay Tolerant Networks where nodes in the network are responsible forforwarding messages to the destination nodes under the constraints of intermittent connectivity, dynamictopology changes, and non-guarantee of an end-to-end path. Various routing protocols weredeveloped for OppNets; however, these routing protocols are energy-inefficient, and this is a problembecause devices that are used in OppNets are usually battery-powered. In this project, newenergy-efficient routing protocols for OppNets, namely, E FirstContactRouter, E WaveRouter, EFloatingContentRouter and E LifeRouter were developed from their energy-inefficient base routingprotocols, namely FirstContactRouter, WaveRouter, FloatingContentRouter and LifeRouter. Theenergy-efficient routing protocols were compared against their energy-inefficient base protocols andother developed energy-efficient routing protocols, namely E-Prophet, E-Epidemic, E-MaxProp andE-Spray&Wait routing protocols. The energy metrics that we used to do the tests were the numberof dead nodes and the average remaining energy. Simulations were carried out using the OpportunisticNetwork Environment (ONE) simulator and it was discovered that the proposed energy-awarerouting protocols outperform their non-energy-aware counterparts in terms of the above-mentionedperformance metrics.

SS-Drop: A Novel Message Drop Policy to Enhance Buffer Management in Delay Tolerant Networks

A challenged network is one where traditional hypotheses such as reduced data transfer error rates, end-to-end connectivity, or short transmissions have not gained much significance. A wide range of application scenarios are associated with such networks. Delay tolerant networking (DTN) is an approach that pursues to report the problems which reduce communication in disrupted networks. DTN works on store-carry and forward mechanism in such a way that a message may be stored by a node for a comparatively large amount of time and carry it until a proper forwarding opportunity appears. To store a message for long delays, a proper buffer management scheme is required to select a message for dropping upon buffer overflow. Every time dropping messages lead towards the wastage of valuable resources which the message has already consumed. The proposed solution is a size-based policy which determines an inception size for the selection of message for deletion as buffer becomes overflow. The basic theme behind this scheme is that by determining the exact buffer space requirement, one can easily select a message of an appropriate size to be discarded. By doing so, it can overcome unnecessary message drop and ignores biasness just before selection of specific sized message. The proposed scheme Spontaneous Size Drop (SS-Drop) implies a simple but intelligent mechanism to determine the inception size to drop a message upon overflow of the buffer. After simulation in ONE (Opportunistic Network Environment) simulator, the SS-Drop outperforms the opponent drop policies in terms of high delivery ratio by giving 66.3% delivery probability value and minimizes the overhead ratio up to 41.25%. SS-Drop also showed a prominent reduction in dropping of messages and buffer time average.

Optimizing the performance of vehicular delay tolerant networks using multi-objective PSO and artificial intelligence

Vehicular delay tolerant network (VDTN) technology uses vehicles on the road as moving nodes to deliver data from source to destination using several intermediate nodes. Efficient data dissemination in VDTN is a difficult problem due to existing trade-offs between several metric variables such as delivery ratio, delay, and overhead ratio. Inclusion of important social network parameters (like community, social strength, trust, friendship, and selfishness) in computation of forwarding probability may help to improve the performance of a routing algorithm. However, different tuning of these parameters results in different outcomes for the metric variables. A proper balancing of these parameters may result in an optimized outcome solving the trade-off between the metric variables. Nonetheless, dependency of the variables on a number of social network parameters and their mutual trade-offs makes this a non-trivial optimization problem. In this paper, we propose a novel approach to optimize these trade-offs using multi-objective particle swarm optimization (MOPSO). The proposed approach provides a set of pareto-optimal solutions also known as non-dominating solutions. Further, based on the requirements of a target application scenario, a specific optimal solution out of the pareto-optimal solution set is delivered using artificial intelligence (AI) technique. The proposed methodology is simulated in a VDTN scenario using the opportunistic network environment (ONE) simulator and Matlab. Furthermore, based on the experimental results, an exhaustive analysis is provided about how the metric variables are affected by the involvement of the social-based parameters. The capabilities of the proposed approach are validated using a statistical comparative analysis of the results. In future, the outcome of the study may play a helpful role to decide the priorities of the network parameters while designing new data dissemination algorithms for VDTN.

Review of opportunistic network: Assessing past, present, and future

SummaryOpportunistic networks aim to deal with the unpredictable environment for data communication. Messages in opportunistic networks need to be stored with the node in the absence of any suitable forwarder. With such a store‐carry‐forward paradigm, it aims to eliminate challenges of no pre‐determined/fixed connectivity, absence of direct/in‐direct routes from source to destination, and high error rate and delay in the network. Node carrying message roams in the network to find a suitable forwarding node. In this study, the survey for various routing protocols and mobility models for opportunistic networks is discussed along with tools available for simulations of opportunistic networks. Further, various challenges in opportunistic networks such as storage management, congestion management, and energy constraints are discussed. Many applications for the opportunistic network, along with real‐time case study, are presented in the paper. The objective of the study is to provide future direction to the researchers, along with the systematic review. Hence, future objectives are discussed to mitigate the challenges faced in the current scenario. Also, based upon the study, it is observed that most researchers have taken epidemic, ProPHET, and spray and wait routing protocols as their base of study. Performance analysis for these protocols is done using opportunistic network environment (ONE) simulator.

Binary Spray and wait routing Protocol with controlled replication for DTN based Multi-Layer UAV Ad-hoc network Assisting VANET

Unmanned Arial Vehicle (UAV) Network is an autonomous wireless communication network, mainly used for natural disaster recovery operations to forward the captured images or video information to the ground vehicle. The UAV network can be applied in various applications essentially, search and rescue operations. This work concentrates on Delay Tolerant Network DTN based Decentralized Multi-layer UAV ad-hoc network assisting VANET for post-disaster scenarios. Because of unbalanced links in highly dynamic UAV networks, packet forwarding is a challenge. This work proposes a Binary Spray and wait routing protocol (BSnW) with controlled replication for DTN based Decentralized Multi-layer UAV networks. The Next Layer UAVs are presented to empower a well-organized Store and carry forward (SCF), in Multi-Layer UAV ad-hoc networks. Next Layer UAVs expands the accessibility of connecting tracks among the all DTN UAVs and the ground vehicle, hence reducing end-to-end delays and growing the packet delivery ratio and drop the packet drop ratio. The proposed BSnW with controlled replication protocol enables a high packet delivery ratio, which enables, and guarantees single-copy data forwarding to evade duplication of every message. Proposed protocol has been experimented using Opportunistic Network Environment (ONE) Simulator. Our simulated experiment show that the proposed protocol outstrips the epidemic routing protocols reported in the literature in terms of the packet delivery ratio, average delay, throughput and the routing overhead.