Delay Tolerant Networks Nodes Research Articles

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.

Enhancing the Communication of IoT Using African Buffalo Delay Tolerant and Risk Packet Jump Approach

The Internet of Things is used in many sectors for different applications in an efficient way. However, sharing huge data to distant destinations is a difficult task such as sending large data from remote area for healthcare application. So that the Delay Tolerant Network is used but, in some cases, the presence of selfish nodes in Delay Tolerant Network may drop all the packets. To overcome this problem the current research affords a novel African Buffalo Delay Tolerant Network with Risk Packet Jump (ABDTN –RPJ) mechanism to improve the communication channel by predicting the selfish and misbehavior node in an earlier stage. Besides, RPJ is introduced to split the huge data to the other neighboring nodes to reduce packet load on the node if the load is greater than the capacity of the node. Finally, the ABDTN –RPJ is implemented and evaluated using the NS2 simulator. The comparison results proved the efficiency of the proposed model by reducing the delay and drop rate.

A novel social deep autoencoder NMF incentive scheme to detect a selfish node in delay tolerant network

A novel social deep autoencoder NMF incentive scheme to detect a selfish node in delay tolerant network

A Review on Detection of Malicious Node in Delay Tolerance Network

TODAY, The use of a wireless network enables enterprises to avoid the costly process of introducing cables into buildings or as a connection between different equipment locations. The basis of wireless systems are radio waves, an implementation that takes place at the physical level of network structure. Wireless network is installations a method by which homes, telecommunications networks and enterprise (business) avoid the costly process of introducing cables into a building, or as a connection between various equipment locations. Delay tolerant networks (DTNs), such as sensor networks with scheduled intermittent connectivity, vehicular DTNs that disseminate location-dependent information, and pocket-switched networks that allow humans to communicate without network infrastructure, are highly partitioned networks that may suffer from frequent disconnectivity. In DTNs, the in-transit messages, also named bundles, can be sent over an existing link and buffered at the next hop until the next link in the path appears. This message propagation process is usually referred to as the “store-carry-and-forward” strategy, and the routing is decided in an “opportunistic” fashion. We aim to evaluate the added effect of the presence of malicious nodes on ad hoc network performance, and determine appropriate measures to detect malicious nodes. A malicious node advertising itself as having a valid route to the destination. With this intension the attacker consumes or intercepts the packet without any forwarding. An attacker can completely modify the packet and generate fake information, this cause the network traffic diverted or dropped. Let H be a malicious node. When H receives a Route Request, it sends back a Route Reply immediately, which constructs the data and can be transmitted by itself with the shortest path. So S receives Route Reply and it is replaced by H->S. then H receives all the data from S. In this research we propose a new assesment based scheme for detection of Malicious Nodes in DTN. And examine different strategies for prevention to malicious nodes as well as Compare out come proposed scheme with the earliest established schemes.

Malicious Node Detection in Delay Tolerance Network

Delay tolerant networks (DTNs), such as sensor networks with scheduled intermittent connectivity, vehicular DTNs that disseminate location-dependent information, and pocket-switched networks that allow humans to communicate without network infrastructure, are highly partitioned networks that may suffer from frequent disconnectivity. In DTNs, the in-transit messages, also named bundles, can be sent over an existing link and buffered at the next hop until the next link in the path appears. This message propagation process is usually referred to as the “store-carry-and-forward” strategy, and the routing is decided in an “opportunistic” fashion. We aim to evaluate the added effect of the presence of malicious nodes on ad hoc network performance, and determine appropriate measures to detect malicious nodes. A malicious node advertising itself as having a valid route to the destination. With this intension the attacker consumes or intercepts the packet without any forwarding. An attacker can completely modify the packet and generate fake information, this cause the network traffic diverted or dropped. Let H be a malicious node. When H receives a Route Request, it sends back a Route Reply immediately, which constructs the data and can be transmitted by itself with the shortest path. So S receives Route Reply and it is replaced by H->S. then H receives all the data from S. In this research we propose a new assesment based scheme for detection of Malicious Nodes in DTN. And examine different strategies for prevention to malicious nodes as well as Compare out come proposed scheme with the earliest established schemes.

An Adaptive Routing Algorithm Based on Relation Tree in DTN.

It is found that nodes in Delay Tolerant Networks (DTN) exhibit stable social attributes similar to those of people. In this paper, an adaptive routing algorithm based on Relation Tree (AR-RT) for DTN is proposed. Each node constructs its own Relation Tree based on the historical encounter frequency, and will adopt different forwarding strategies based on the Relation Tree in the forwarding phase, so as to achieve more targeted forwarding. To further improve the scalability of the algorithm, the source node dynamically controls the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to network environment changes. Simulation results show that the AR-RT algorithm proposed in this paper has significant advantages over existing routing algorithms in terms of average delay, average hop count, and message delivery rate.

Prioritized Content Determination and Dissemination Using Reinforcement Learning in DTNs

In a battlefield, several groups of soldiers are deployed with different missions by the command and control center (CC). To continue the missions appropriately and get a better understanding of the situation, the soldiers as well as the CC need to collect information of interest generated in different battle zones using Delay-tolerant Networks (DTN). It is a challenge to determine the topics of interest associated with the events and missions, and efficiently forward the associated content to the CC in this extreme situation. We design a scheme to forward contents generated by the nodes to the CC using Reinforcement Learning (RL) while maximizing the number of interesting data in the respective nodes buffer, and avoiding congestion. In this forwarding process, we focus on identifying the trending topics/keywords among changing missions and their related data at the node level, and the changes of interest of the nodes based on their mobility and connectivity patterns of nodes in DTN. Experiments are conducted using real datasets and ONE simulator to show the effectiveness of Reinforcement Learning (RL) on the prioritized content dissemination in DTN networks.

Autonomous Delay Tolerant Network Management Using Reinforcement Learning

Delay tolerant networks (DTNs) offer a set of standardized protocols to enable Internet-like connectivity across the solar system. Unlike other protocols such as the Transmission Control Protocol (TCP) and the Internet Protocol (IP), DTN protocols are robust to end-to-end connection disruptions and long delays. Although the behavior of DTN core protocols is well understood, management of DTNs is still an area of active research. This paper uses reinforcement learning (RL) to automate the management of a DTN node consisting of an orbital relay between the moon and Earth. More specifically, the RL agent is in charge of deciding when to drop packets, when to change the data rate of the neighbor node links, when to reroute bundles to crosslinks, or when not to change any network parameter. The agent’s goal is to maximize the bits received by the Deep Space Network while minimizing the capacity allocated to all controlled links, and control the buffer utilization to avoid memory overflows. To assess the potential of using RL in DTN management, the performance of the trained RL agent is benchmarked against other non-RL-based policies in a realistic lunar scenario. Results show that the RL agent provides the highest reward, outperforming all non-RL policies in this scenario.

Efficient forwarding strategy in HDRP protocol based Internet of Things

The Internet of Things (IoT) includes a variety of heterogeneous wireless network technologies such as Delay Tolerant Networks (DTN) and all kinds of smart devices connected over a wireless network. The strong size and cost constraints of DTN nodes lead to corresponding constraints on resources such as energy, memory, communication speed and bandwidth, which in turn limits the contact between DTN nodes. DTN has emerged as a promising new network paradigm that aims to cope with the increasing number of heterogeneous networks and the need for efficient and robust data dissemination to be more stable. But one of the hindering bottlenecks the full applicability of such a DTN-based approach to the IoT, is that forwarding data in an environment could lose when the connection between nodes is intermittent. In this paper, we propose an effective bundle forwarding strategy for the IoT environment in which we control the number of bundle replications to balance energy consumption on the network. Simulation results demonstrate that the proposed solution performs better than other methods in terms of delivery delay and delivery rate (roughly 18% improvement).

A Fuzzy‐Logic‐Based Double Q‐Learning Routing in Delay‐Tolerant Networks

Delay‐tolerant networks (DTNs) are wireless mobile networks, which suffer from frequent disruption, high latency, and lack of a complete path from source to destination. The intermittent connectivity in DTNs makes it difficult to efficiently deliver messages. Research results have shown that the routing protocol based on reinforcement learning can achieve a reasonable balance between routing performance and cost. However, due to the complexity, dynamics, and uncertainty of the characteristics of nodes in DTNs, providing a reliable multihop routing in DTNs is still a particular challenge. In this paper, we propose a Fuzzy‐logic‐based Double Q‐Learning Routing (FDQLR) protocol that can learn the optimal route by combining fuzzy logic with the Double Q‐Learning algorithm. In this protocol, a fuzzy dynamic reward mechanism is proposed, and it uses fuzzy logic to comprehensively evaluate the characteristics of nodes including node activity, contact interval, and movement speed. Furthermore, a hot zone drop mechanism and a drop mechanism are proposed, which can improve the efficiency of message forwarding and buffer management of the node. The simulation results show that the fuzzy logic can improve the performance of the FDQLR protocol in terms of delivery ratio, delivery delay, and overhead. In particular, compared with other related routing protocols of DTNs, the FDQLR protocol can achieve the highest delivery ratio and the lowest overhead.

Probabilistic routing protocol with firefly particle swarm optimisation for delay tolerant networks enhanced with chaos theory

In this paper, we propose a proactive routing paradigm for delay tolerant networks (DTN) using probabilistic routing implemented via firefly particle swarm optimisation and optimised by chaos maps. DTN is a specific class of networks where there is absence of an end to end connectivity among nodes and are characterised by long or variable delays, asymmetric data rates and high error rates. The routing scheme considers the constrained environment of DTN nodes and implements a meta-heuristic paradigm to facilitate data transfer. The chaos maps further enhance the probabilistic routing parameters controlling brightness and ageing by randomising them. The protocol is implemented on ONE simulator and its performance is compared with traditional DTN routing schemes across delivery probability, average latency, overhead ratio and buffer utilisation. The simulation results show that our proposed protocol performs better than its contemporary ant PRoPHET protocol across the various performance metrics.

Performance optimization in delay tolerant networks using backtracking algorithm for fully credits distribution to contrast selfish nodes

Delay Tolerant Network (DTN) can be characterized as a heterogeneous network of nodes in which nodes mobility is high and resources are limited to transmit messages. In DTN, nodes use store, carry, and forward principle for delivering messages to the destination node. To forward messages through intermediate nodes may cause security issues in the network since there may exist a few selfish nodes. These nodes can affect the network's performance as they may drop messages due to their limited resources such as energy and storage capacity. To handle this kind of hazard, it is necessary to propose a mechanism that may decrease the degree of the selfishness of nodes and improve the network's delivery ratio by fully distributing credits to nodes. In this article, a credit-based mechanism has been proposed based on Combined Trust Value (CTV) of nodes in DTN. In the proposed mechanism, an agent is used to compute each node's trust value grounded of the number of messages relayed by sensor nodes. This trust value is used to distribute credits to the nodes in a distributed manner without any partiality with nodes. Backtracking approach is used to distribute credits to boundary nodes deserving credits but didn’t get credits by agent node. The proposed mechanism is implemented using ONE simulator, and the performance of the projected scheme is analyzed in comparison to existing techniques Dynamic Trust, SMART (Secure Multilayer credit based incentive Technique) and Credit-based. The results exhibit that the suggested mechanism is superior than existing techniques with reference to various performance metrics like 25% higher delivery ratio, 41% less overhead, 21% less average message delay, and 28% less packets dropped. The proposed mechanism might be helpful in scenarios where the degree of selfishness is high, and the distribution of credit follows a fully distributed approach rather than an existing partial distribution used in existing techniques.

Impact of Node Density and Buffer Size on DTN Routing Protocols with Energy Efficiency

Delay Tolerant Network (DTN) architecture comprises of portable devices known as nodes, considered a resource-limited networking system. These nodes in DTN utilize the ‘Store Carry and Forward’ approach to route data since the end to end connections are absent here due to a large number of constant intermittent connectivity. The energy quantity of nodes is restricted because limited-lifetime rechargeable batteries drive them. Accordingly, energy is an essential resource in DTN scenarios. For efficient network performance, including proper energy usage, nodes need to expense a minimum amount of energy. For this reason, it is essential to select an energy-efficient forwarding strategy and exhibit excellent performance among existent forwarding approaches in the DTN environment for routing messages effectively. In this paper, we have studied the energy efficiency of conventional DTN routing protocols: Epidemic, Spray and Wait, Spray and Focus, MaxProp, and PRoPHET on the impact of varying both buffer size and node density. We analyzed their energy consumption and compared their performance based on five performance metrics: average remaining energy, delivery ratio, average delay, transmission cost, and average hop count, respectively. Using ONE simulator, we performed a simulation with varying node density (while buffer size is fixed) and varying buffer size (while node density is fixed). From the outcomes of simulation, we found that Spray and Wait are the most energy-efficient DTN routing protocols. On the contrary, Spray and Focus possessed as the best performer in terms of average hop count, average delay, delivery ratio and transmission cost among conventional DTN routing protocols.

The Impact of Node Density and Buffer Size on DTN Routing Protocols with Energy Efficiency

Delay Tolerant Network (DTN) architecture comprises of portable devices known as nodes, considered a resource-limited networking system. These nodes in DTN utilize the ‘Store Carry and Forward’ approach to route data since the end to end connections are absent here due to a large number of constant intermittent connectivity. The energy quantity of nodes is restricted because limited-lifetime rechargeable batteries drive them. Accordingly, energy is an essential resource in DTN scenarios. For efficient network performance, including proper energy usage, nodes need to expense a minimum amount of energy. For this reason, it is essential to select an energy-efficient forwarding strategy and exhibit excellent performance among existent forwarding approaches in the DTN environment for routing messages effectively. In this paper, we have studied the energy efficiency of conventional DTN routing protocols: Epidemic, Spray and Wait, Spray and Focus, MaxProp, and PRoPHET on the impact of varying both buffer size and node density. We analyzed their energy consumption and compared their performance based on five performance metrics: average remaining energy, delivery ratio, average delay, transmission cost, and average hop count, respectively. Using ONE simulator, we performed a simulation with varying node density (while buffer size is fixed) and varying buffer size (while node density is fixed). From the outcomes of simulation, we found that Spray and Wait are the most energy-efficient DTN routing protocols. On the contrary, Spray and Focus possessed as the best performer in terms of average hop count, average delay, delivery ratio and transmission cost among conventional DTN routing protocols.

Performance of Delay Tolerant Network Protocol in Smart City Scenario

The smart city is being developed in many big cities to support local governments for resources and infrastructure optimization in terms of enhancing services for the citizen. Large investment costs are required to implement a communication network for the smart city infrastructure (i.e. Internet of Things (IoT) devices) using 4G or Wi-Fi networks. This study aims to implement Delay Tolerant Network (DTN) as a low-cost alternative network for the smart city. We use Surabaya smart city plan scenario in which smart mobility services are provided in city transportation and infrastructure. The IoT devices are installed in numerous locations and continuously transmit messages to the monitoring server located in the city hall, bus and car employed as a DTN node. In this paper, as a preliminary research, we investigate the performance of well-known DTN routing protocols (Epidemic, Maxprop, and ProphetV2) with our proposed protocol called Spray and Hop Distance (SNHD). The evaluation is performed using the ONE simulator in terms of the number of delivered messages, the average latency, and the overhead ratio. The results show the performance of SNHD is better than the well-known DTN protocols in various conditions when the total size of delivered messages is large. Unfortunately, none of those routing protocols has a good performance in all of the scenario conditions. In the future, we desire to implement this system in real smart city system in Surabaya and do simulation with more realistic scenario such as real traffic-trace.

Efficient photo crowdsourcing with evolving POIs under delay-tolerant network environment

In a disaster or battlefield zone, rescue workers, soldiers, and other survivors (referred to as nodes) may need to survey damages and send images to the command and control center (the server) in a hop-by-hop fashion in the absence of any communication infrastructure. The server considers some area/landmark as the point of interest (POI) and distributes a request to the nodes to collect more information about them. Nodes take photos of POIs and share them with each other using the store and forward paradigm, also called Delay-tolerant Networks (DTNs), to send the photos to the server. Due to the highly intermittent contact characteristics of nodes in a DTN network and bandwidth and storage limitations, redundant photos need to be removed in this forwarding technique, whereas photos that cover different angles and views of the POIs need to be shared. Another challenge is that, over time, some server-listed POIs may not be of importance anymore, whereas some new POIs might be of more interest. In this work, we propose a scheme that is able to dynamically update the list of POIs based on the current photo metadata, with reduced consumption of the bandwidth, energy, and storage at DTN nodes by sending only important photos of POIs. In the performance evaluation, we show the scalability of our approach, and also show that it provides the same level of photo coverage but consumes much less energy and bandwidth than other related schemes.

RPRTD: Routing protocol based on remaining time to encounter nodes with destination node in delay tolerant network using artificial neural network

Delay Tolerant Network (DTN) is a kind of network that there is no continuous network connectivity among nodes. There are no end-to-end and constant connection paths from source nodes to destination nodes due to the mobility nature of nodes. In such networks there exists an inevitable long delivery delay. DTN is designed to perform properly over intermittent connections among nodes. Because of mobile nature of nodes, routing in DTN is based on store-carry-forward patterns to forward messages. Store-carry-forward means when a node receives a message from one of its contacts, it stores the message in its buffer and carries the message until it encounters another Contact node with Better conditions to Take the message to the Destination node (CBTD). Recognizing one of visited nodes as CBTD is a challenging problem. After recognizing CBTD, forwarding messages to CBTDs is required and necessary for routing efficiency and performance, because the messages will be delivered to the destination faster through CBTDs. In this paper, routing is performed by recognizing CBTDs and forwarding messages to them using RPRTD algorithm. RPRTD algorithm is based on Remaining Time to encounter nodes with Destination node (RTD). A node with smaller RTD, encounters the destination node in less time than the others. RPRTD algorithm tries to take message somehow to the node with the smallest RTD value among all nodes. RTD is calculated with the help of predicting future coordinates of nodes based on artificial neural networks (ANN). Simulation results show that this algorithm makes the best decision and efficiently determines the most appropriate and the best route to deliver messages to the destination node, increases routing performance, improves number of delivered messages, and decreases delivery delay compared to the state of the art.

On Exploiting Dynamic Trusted Routing Scheme in Delay Tolerant Networks

Delay tolerant network routing is characterized by sparsely or densely populated nodes. In such network routing with limited resource like buffer space is a difficult task. In the past work this characteristic of delay tolerant network node is ignored. To overcome the network routing issues, in this paper we propose dynamic trusted routing scheme for densely populated delay tolerant network. The work proposes routing scheme that works with traditional routing protocol when it encounters a sparse delay tolerant network based on the threshold value. The proposed scheme works in three phases, firstly by identifying trusted nodes based on logistic activation function. In the second phase the dynamic trusted cluster is formed based on threshold value. This eliminates non-cooperative nodes. And, in the third phase the messages are forwarded using dynamic routing scheme. Further, the performance of the dynamic trusted routing is analysed. It reduces high delivery cost and improves network performance. The simulation result states up to 19% reduction in the message abortion rate. When compared with the other routing protocols the delivery probability is updated up to 62%.

Analyzing and detecting faulty nodes in delay tolerant network

For our situation Delay Tolerant Networks (DTN) specifically, the uncommon gathering occasions necessitate that hubs are effective in spreading just right data. For that reason, instruments to quickly distinguish conceivable flawed hubs ought to be created. Disseminated broken hub location has been tended to in the writing with regards to sensor and vehicular systems, however as of now proposed arrangements experience the ill effects of long deferrals in recognizing and disconnecting hubs creating flawed information. This is unacceptable to DTNs where hubs meet just once in a while. This paper proposes a completely appropriated and effectively implementable way to deal with enable each DTN hub to quickly recognize whether its sensors are creating broken information. The dynamical conduct of the proposed calculation is approximated by some constant time state conditions, whose balance is described. The nearness of acting mischievously hubs, attempting to bother the flawed hub discovery process, is additionally considered. Recognition and false alert rates are assessed by looking at both hypothetical and reenactment results. Numerical outcomes evaluate the viability of the proposed arrangement and can be utilized to give rules for the calculation plan.

Resource Optimization Protocol Based on Multicommunity Model for Intermittently Connected Mobile Networks

Intermittently connected mobile networks (ICMNs) are kinds of delay-tolerant networks (DTNs) in which the topology of mobile network changes frequently, the network is sparse, and end-to-end connection is hard to establish. However, as nodes in DTNs may often have limited energy, the performance of the network will be inevitably affected. Particularly, the message delivery ratio will be decreased when some of the nodes are out of energy and unable to participate in relay process. In order to make effective use of resources, we take this into consideration and propose the social similarity and optimized resource (SSAOR) protocol. According to the position relationship between the source and the destination nodes, SSAOR uses two different strategies to determine the order of forwarding messages. If they belong to the same community, the source node forwards message, copies to the relay node with the shorter encounter interval and the higher social friendship rank similarity with destination nodes. Otherwise, the source node forwards message copies to the relay node with the higher weight “betweenness” similarity and the shorter waiting encounter time similarity with destination nodes. In addition, we propose a buffer management method for dynamically distributing message copies to reduce network overhead, due to the fact that only a small number of copies can be successfully delivered. Finally, the simulation results show that SSAOR achieves a higher delivery ratio and lower overhead ratio, compared to other protocols within resource-constrained network situations.