Routing Protocol For Opportunistic Networks Research Articles

Cross-Layer Optimization for Enhanced IoT Connectivity: A Novel Routing Protocol for Opportunistic Networks

Cross-Layer Optimization for Enhanced IoT Connectivity: A Novel Routing Protocol for Opportunistic Networks

Encounter Count and Interaction Time-Based Routing Protocol for Opportunistic Networks

Encounter Count and Interaction Time-Based Routing Protocol for Opportunistic Networks

Double Q-learning based routing protocol for opportunistic networks

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.

Resource-aware routing in opportunistic networks: existing protocols and open research issues

Opportunistic networks provide an effective communication in challenging scenarios such as interplanetary communication, post-disaster situation, etc. As the nodes have limited resources, thus designing an efficient routing protocol in opportunistic networks always remains a challenging task. Several review papers presenting features and applications of opportunistic networks, mobility models, challenges, taxonomies of routing, and incentive schemes exist, but a comprehensive review on resource-aware routing appears to be missing. Therefore, we propose a novel taxonomy for categorising existing resource-aware routing protocols proposed in this field. These are mainly categorised into energy-aware, buffer aware, bandwidth aware, and hybrid routing that considers the energy of nodes, buffer space, bandwidth, and multiple parameters respectively to make forwarding or routing decision. Further, we discuss various existing routing protocols with their strengths and weaknesses, leading to open research issues. This survey aims to help future researchers devise a novel routing protocol for opportunistic networks.

A Privacy-Preserving Exchange-Based Routing Protocol for Opportunistic Networks

In opportunistic networks, nodes forward messages according to routing utility deduced from encounter information. When nodes simply communicate with real IDs, private information such as encounter time and location will be leaked. The private information can be preserved by anonymizing node real IDs, but ID anonymity will also prevent nodes from collecting real ID-based encounter information to compute routing utility. To address this problem, a privacy-preserving exchange-based routing protocol (PPERP) for opportunistic networks is proposed in this paper. In PPERP, nodes are classified into delivery nodes and nondelivery nodes. Two encountering nondelivery nodes exchange approximately equivalent anonymous encounter records. Then, when the exchanged encounter records are sent to a delivery node, it will compute the routing utilities to nondelivery nodes without knowing their real IDs. In PPERP, a node can directly compute its routing utility without relying on any trusted third party, so PPERP is suitable for opportunistic networks in which nodes usually are sparsely distributed and encounter opportunistically. Bilinear mapping technique is adopted to ensure the confidentiality, integrity, and nonrepudiation of encounter records. The security analysis and performance evaluation show that the proposed protocol is secure, efficient, and practical.

ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning

Opportunistic Networks (OppNets) is gaining popularity day-by-day due to their various applications in the real-life world. The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication. Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks (Manets) and Wireless Sensor Networks (WSNs), therefore, it inherits most of the traits from both mentioned networking techniques. Apart from its popularity, Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of data to be communicated and blind forwarding of data among participating nodes in the network. These issues lower the overall performance of the network. Keeping this thing in mind, ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the OppNet using machine learning techniques viz. pattern prediction, decision tree prediction, adamic-adar method for complex networks. Apart from this, ML-Fresh also uses the history of successful encounters between a pair of communicating nodes for route prediction in the background. Simulation results prove that the ML-Fresh outperforms the existing framework of Opportunistic Networks on the grounds of standard Quality-of-Service (QoS) parameters.

MrFbP: Markov random field‐driven spatial entropy‐based routing protocol in opportunistic networks

SummaryRecently, research towards technologies associated with the 5G communication is in full operation. Amongst these enabling technologies, device‐to‐device(D2D) communication is one of the critical factors for scaling up the network efficiency in 5G communication. Intermittent connections due to higher mobility lead to frequent path breaks, and hence a mobility‐based opportunistic routing is suitable enough to control the forwarding process. Opportunistic networks (OppNet) use the pairwise opportunistic contacts and higher mobility to rely on the store‐carry and forward mechanism for routing purpose. In this work, a novel mobility‐induced context‐based routing process has been designed to support D2D communication. The designed Markov random field‐induced protocol (MrFbP) is based on spatial entropy for capturing the coverage span of the forwarding node in the network. The work relies on the monitored historic mobility of a node and is then used to capture the utility metric for taking forwarding decision. MrFbP is compared against the established Direct Delivery (DD), Epidemic (EP), Spray & Wait (SW), and PropHet (PR) on parameters like throughput, delay, hops, overheads, and energy consumption. Simulation has been carried out using ONE simulator to validate an improvement in the design of a designed protocol against the baseline protocols.

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.

RETRACTED ARTICLE: Evaluation of energy consumption in routing protocols for opportunistic networks

Assessments on routing protocols for delay tolerant network (DTN) or opportunistic network considered various metrics such as delivery ratio, overhead and delay, but energy consumption as a metric is not well analyzed. Since the energy aspect is now an issue, routing protocols should be considered from an energy consumption point of view. In this article, some basic routing protocols of the opportunistic network analyzed from the energy aspect alongside other standard metrics. Afterward, the performance of each routing protocol for different situations assessed through network effective factors. Finally, the simulation results show that Spray and Wait algorithm has better energy performance compared to Epidemic and PRoPHET. Analyzing a protocol with a single-relay selection that minimizes the transmission energy and increase the energy efficiency of all devices in a wireless network. This technique is analyze the opportunistic relaying in the paradigm of D2D communication and study the opportunistic schemes with a single relay selection to minimize energy consumption of the devices in a wireless network. The fact is that The D2D communication ensures that the energy consumed for the data relaying is negligible and The D2D communication ensures that the selection of the relaying device exploits the diversity from neighboring devices in order to minimize the energy consumed by the devices for forwarding the data to the BS.

A fuzzy-based check-and-spray geocast routing protocol for opportunistic networks

Unlike communication networks which are traditionally assumed to be connected, Opportunistic networks (OppNets) are a type of wireless ad hoc networks with no guarantee of end-to-end path for data routing, which is due to node mobility, volatile links, and frequent disconnections. As such, data transmission among the nodes relies on their cooperation and this is realized in a store-and-carry fashion. To this end, several opportunistic routing techniques have been proposed in the literature, some of which using geocasting, a technique that consists of scheduling the message to a specific region toward its destination. This paper proposes a Fuzzy-based Check-and-Spray Geocast (FCSG) routing protocol for OppNets, in which a Check-and-Spray mechanism is used to control the message flooding within the destination cast and a fuzzy controller is used for selecting the suitable relay nodes to carry the message toward the destination, with the aim to improve the delivery ratio. Using simulations, the proposed FCSG protocol is shown to outperform the F-GSAF, GeoEpidemic and EECSG routing protocols in terms of overhead ratio, average latency, and delivery ratio, under varying number of nodes, buffer size, and Time-to-Live.

A blockchain-based secure routing protocol for opportunistic networks

Opportunistic Delay Tolerant Networks also referred to as Opportunistic Networks (OppNets) are composed of wireless hosts opportunistically communicating with each other. These networks are designed to operate without the guaranteed existence of a path between the sender and the receiver. Inadequate data security in OppNets has been seriously detrimental to its adoption. This paper explores a secure and decentralized approach that uses the Proof-of-Work (PoW) consensus mechanism and propose a blockchain-based secure routing protocol (called BDRP). In this work, we design an integrated routing protocol that uses Proof-of-Work (PoW) for routing and demonstrates how immutability can be applied to the OppNet paradigm. Blockchain promises transparent, distributed, and tamper-resistant ledger and secure systems that may provide secure routing solutions for OppNets. We aim to study how effectively can Proof-of-Work (PoW) scale-up in OppNets as a consensus mechanism is the most resource and time-intensive component of a blockchain. The work even displays the ways this routing method prevents against attacks such as eavesdropping, masquerading, wormhole, blackhole, and fabrication. The simulation results, along with the theoretical justification, show how the proposed BDRP fares among the existing security policies are used in opportunistic networks.

Reinforcement learning-based fuzzy geocast routing protocol for opportunistic networks

In a communication environment like opportunistic networks (OppNets) where there is no stable path, the message routing is a challenge. In this sense, using a deep learning approach that focuses on utilizing the agents in the environment to fulfill the message routing task has proven to be effective. This paper proposes a novel routing scheme for OppNets called Reinforcement Learning-based Fuzzy Geocast Routing Protocol (RLFGRP) for OppNets, in which a fuzzy controller makes use of the node’s Q-value, reward value and remaining buffer space as input parameters to determine the likelihood of that node to be selected as suitable forwarder of a message from source towards its destination. Through simulations using real mobility traces, the proposed RLFGRP protocol is shown to outperform the established Geocast Fuzzy-Based Check-and-Spray routing (FCSG) [4] and the Fuzzy logic-based Q-learning routing (FQLRP) [9] protocols in terms of overhead ratio, delivery ratio and average latency.

LoSeRO: A Locality Sensitive Routing Protocol in Opportunistic Networks with Contact Profiles

Mobility trajectories of users contain personal information that when analyzed may reveal relevant data usable as message-sharing condition, e.g., interests in common or similar mobility patterns. In particular, leveraging on mobility patterns, in [12] we designed and presented a Geo-casting routing protocol called LoSeRO for opportunistic networks, which uses knowledge of the locations most frequently visited by a user to route messages. LoSeRO forwards messages—in a multi-casting way—to all users who have a mobility profile that intersects the packet's destination zone. LoSeRO presented a relative good performance value, however, to improve its performances, in this paper our contribution is to propose an upgraded version of our earlier proposed protocol, termed as LoSeRO v2 . In particular, it upgrades the traditional working fashion of LoSeRO by extending the knowledge of the most frequented locations to those users not only directly met, i.e., two-hops away. With this purpose, through simulations, we compare the performance of LoSeRO v2, with LoSeRO and other existing routing geo-casting protocols, and we illustrate how LoSeRO v2 achieves enhanced performances comparing precision, coverage and additional metrics.

Impact of Node Density on the QoS Parameters of Routing Protocols in Opportunistic Networks for Smart Spaces

The need and importance of Smart Spaces have been potentially realized by the researchers due to its applicability in the current lifestyle. Opportunistic network, a successor of mobile ad hoc networks and a budding technology of network, is a best-suited technology for implementing Smart Spaces due to its wide range of applications in real-life scenarios ranging from building smart cities to interplanetary communication. There are numerous routing protocols which are available in opportunistic network, each having their pros and cons; however, no research till the time of listing has been done which can quantitatively demonstrate the maximum performance of these protocols and standardize the comparison of opportunistic routing protocols which has been a major cause of ambiguous performance evaluation studies. The work here presents a categorical view of the opportunistic routing protocol family and thereby compares and contrasts the various simulators suited for their simulation. Thereafter, the most popular protocols (selecting at least one protocol from each category) are compared based on node density on as many as 8 standard performance metrics using ONE simulator to observe their scalability, realism, and comparability. The work concludes by presenting the merits and demerits of each of the protocols discussed as well as specifying the best routing protocol among all the available protocols for Smart Spaces with maximum output. It is believed that the results achieved by the implemented methodology will help future researchers to choose appropriate routing protocol to delve into their research under different scenarios.

Neural network‐based routing protocol for opportunistic networks with intelligent water drop optimization

SummaryOpportunistic Networks (OppNets) is a system of wirelessly connected nodes in a varying network topology. Routing in OppNets is a challenge. To overcome the problem of routing, an intelligent dynamic strategy to select next best node for forwarding a message is required. This paper proposes an intelligent routing mechanism based on Intelligent Water Drop (IWD) Algorithm which is used in tandem with Neural Networks (NNs) as an optimization technique to solve the problem of routing in such networks. The nature–inspired IWD algorithm provides robustness, whereas the neural network base of the algorithm helps it to make intelligent routing decisions. The weights in the Neural Network model are calculated by IWD Algorithm using training data consisting of inputs that are characteristic parameters of nodes, such as buffer space, number of successful deliveries and energy levels along with transitive parameters such as delivery probabilities. The proposed protocol Intelligent Water Drop Neural Network (IWDNN) is compared with other protocols that use similar ideologies such as MLProph, K‐nearest neighbour classification based routing protocol (KNNR), Cognitive Routing Protocol for Opportunistic Network (CRPO), and Inheritance Inspired Context Aware Routing Protocol (IICAR), as well as the standard protocol Prophet. IWDNN is shown to outperform all other protocols with an average message delivery ratio of 60%, which is a significant improvement of over 10% in comparison to other similarly conceived algorithms. It has one of the lowest latency among the protocols studied, in a range of 3000 to 4000 s, and incurs comparably low overhead costs in the range of 15 to 30. The drop ratios are one of the lowest, staying near six and approaching zero as buffer size is increased. Average amount of time a message stayed in the buffer was the lowest, with a mean of 1600 s.

Message trust‐based secure multipath routing protocol for opportunistic networks

SummaryOpportunistic networks (OppNets) are composed of wireless nodes opportunistically communicating with each other. These networks are designed to operate in a challenging environment characterized by high delay, intermittent connectivity, and no guarantee of fixed path between the sender and the destination nodes. One of the most vital issues in designing and maintaining practical networks over a time period is the security of the messages flowing in OppNets. This paper proposes a new method called message trust‐based secure multipath routing protocol (MT‐SMRP) for opportunistic networks. Various routing protocols such as ProPHet, Epidemic, and HiBOp, to name a few, have been proposed for OppNets, but none of these have applied a secure multipath routing technique. The proposed MT‐SMRP scheme relays the message to the destination through the disjoint paths, each applying a soft‐encryption technique to prevent message fabrication attacks. Simulations are conducted using the Haggle Infocom'06 real mobility data traces, showing that when time‐to‐live is varied, (1) the proposed MT‐SMRP scheme outperforms D‐MUST by 18.10%, 7.55%, 3.275%, respectively, in terms of delivery probability, messages dropped, and average latency; (2) it also outperforms SHBPR by 21.30%, 7.44%, and 4.85%, respectively, in terms of delivery probability, messages dropped, and average latency. Under similar performance metrics, the performance of MT‐SMRP is also shown to be better than that of D‐MUST and SHBPR when the buffer size (respondents. the message generation interval) is varied.

Energy aware routing for efficient green communication in opportunistic networks

Opportunistic networks are characterised by intermittent connectivity, unstable topology, and no assurance of a fixed end-to-end path to transfer the messages because of nodes mobility. Due to this, the store-carry and forward mechanism is used for routing the data in such networks. Considering that the energy of relaying nodes during the routing operation get depleted, there is a clear demand for power-aware routing schemes for opportunistic networks. This article proposes an energy-efficient altruism-based trust-dependent message forwarding routing protocol for opportunistic networks (called E-ATDTN), where social matrices are exploited to determine the trustworthiness of a node in participating in the message forwarding procedure. Extensive simulations are conducted to assess the performance of the newly proposed E-ATDTN protocol against that of the power-aware PRoPHET, energy efficient PRoWait, and power-aware EDR protocols showing that E-ATDTN outperforms E-PRoPHET, E-ProWait, and E-EDR in terms of average residual energy, overhead ratio, dropped messages, and number of dead nodes under varying time-to-live, and message generation interval.

A framework for the evaluation of routing protocols in opportunistic networks

The evaluation of routing protocols for opportunistic networks can be seen as a multidimensional problem because it involves several performance aspects. To capture these aspects various evaluation metrics are used, such as the number of delivered packets, the delivery delay and the number of transmissions. Unfortunately, in the context of opportunistic networks, these metrics are often highly correlated and usually conflicting. To make things worse, the characteristics of the network affect the importance of each metric as well as the levels of its correlation with other metrics. In this work, we first propose a set of performance evaluation metrics that are normalized with respect to the optimal performance. This approach tackles several of the above-mentioned shortcomings of traditional evaluation metrics. We then formulate the evaluation of routing protocols as a Multiple-Criteria Decision-Making (MCDM) problem where each routing protocol is an alternative and the performance metrics correspond to a set of criteria. We use this formulation to develop an evaluation framework that objectively ranks the performance of opportunistic routing protocols. To this end, we reshape well-known concepts and algorithms from the MCDM field to address the special requirements that are specific to the opportunistic context. We present detailed simulation results of well-known routing protocols in various opportunistic environments and rank their performance according to the proposed framework. In conclusion, no algorithm was able to achieve the best performance in all or the majority of the network topologies that we studied. This demonstrates the diversity of challenges that routing mechanisms face in such networks.

Collaborative Service Discovery in Mobile Social Networks

Mobile social networking is a recent paradigm arisen from the wide spread of mobile and wearable devices. Based on the short-range communication interfaces of these devices it is possible to establish opportunistic communications among them and build networks independent to the global one. Challenges introduced by this new type of networks are related to the sharing of resources and services and to the exploitation of the communication opportunities among devices. Limit of existing algorithms, that have sought to fill these shortages, is the lack of attention on the main actor of this service-oriented chain, the user. To this purpose, we introduce the COllaborative seRvice DIscovery ALgorithm (CORDIAL) that leverages both mobility and sociality of the users. We evaluate the performance of CORDIAL combined with different routing protocols for opportunistic networks, and we compare it with a benchmark algorithm (S-Flood) based on flooding and another service discovery algorithm designed to leverage mobile social network features, namely, ServIce DiscovEry in Mobile sociAl Networks (SIDEMAN). Our results show that the performance of CORDIAL remains stable with the different routing algorithms and that, in function of the query forwarding strategy triggered, CORDIAL matches the performance of S-Flood in terms of Query Response Time, achieving a better proactivity score with respect S-Flood and SIDEMAN as well.

KROp: k-Means clustering based routing protocol for opportunistic networks

Routing and forwarding of messages is a challenging task in Opportunistic networks (Oppnets) due to no fixed architecture and the intermittent connectivity of the nodes. This paper proposes a context-aware routing protocol named kROp, which uses a variety of network features generated dynamically for making routing decisions. Four network features have been defined in this work which is vital for identifying potentially good message forwarders. kROp utilizes unsupervised machine learning in the form of an optimized k-Means clustering algorithm to train on these features and make next hop selection decisions. The performance of kROp is evaluated and compared with HBPR, ProWait and PRoPHET routing protocols in terms of hop count, dropped messages, delivery probability, and overhead ratio. Index terms: kROp, Opportunistic Routing, Unsupervised Learning, k-Means Clustering, Context-Aware Routing, Delivery Probability, Average Hop Count, Wireless Networking.