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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]

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]

Internet of Things (IoT) is characterized by the large volumes of data collection. Since IoT devices are themselves resource-constrained, this data is transferred to cloud-based systems for further processing. This data collected over a period of time possesses high utility as it is useful for multiple analytical, predictive and prescriptive tasks. Therefore, it is crucial that IoT devices transfer the collected data to network gateways before exhausting their storage to prevent loss of data; this issue is referred to as the "data offloading problem". This paper proposes a technique for fault tolerant offloading of data by IoT devices such that the data collected by them is transferred to the cloud with a minimal loss. The proposed technique employs opportunistic contacts between IoT and mobile fog nodes to provide a fault tolerant enhancement to the IoT architecture. The effectiveness of the proposed method is verified through simulation experiments to assess the reduction in data loss by use of proposed data offloading scheme. It is demonstrated that the method outperforms a state-of-art method. [ABSTRACT FROM AUTHOR]

A new paradigm has been introduced by researchers in the field of data accessing at several mobile positions through implementation of WAN instead of cellular telephony. Based on range/coverage and network topology WAN taxonomy has been categorized as fixed network, mobile access network and ad – hoc network/wireless mesh networks respectively. Several interesting challenges has been posed by researchers in wireless networks such as routing, bandwidth, congestion, security, quality of service (QoS) and data rate enhancement. Of late, researchers around the world have started working in the domain of SHiP (Sparse, Hybrid, Intermittent and Partitioned) Network, which are plagued by several characteristics features such as: sporadic connections, intermittent routing, store – carry – forward type of message forwarding mechanism, dynamic network topology, transitive closure of message transmission, fluctuating network link, bottleneck probability, trust, and security in social networks. In this paper an outline of Oppnet (Opportunistic Network), part of SHiP Network has been presented with as in-depth coverage of routing problems and research challenges in it. Additionally, taxonomy of Oppnet routing protocols is illustrated with the features on basis of which categorization has been done. An exhaustive investigation through implementation of well-known routing schemes like: Epidemic, Spray and Wait, PROPHET and Direct delivery against mobility models such as: Random Walk, Random Waypoint, Shortest path-based movement and Cluster Movement has been accomplished in open-source software: ONE simulator with real world mobility dataset and traces of city of New Delhi, capital of India. Results achieved have been good and exceed our expectations. [ABSTRACT FROM AUTHOR]

Opportunistic IoT networks operate in an intermittent, mobile communication topology, employing peer-to-peer transmission hops on a store-carry-forward basis. Such a network suffers from intermittent connectivity, lack of end-to-end route definition, resource constraints and uncertainties arising from a dynamic topology, given the mobility of participating nodes. Machine learning is an instrumental tool for learning and many histories-based machine learning paradigms like MLPROPH, KNNR and GMMR have been proposed for digital transformations in the field with varying degrees of success. This paper explores the dynamic topology with a plethora of characteristics guiding the node interactions, and consequently, the routing decisions. Further, the study ascertains the need for better representation of the versatility of node characteristics that guide their behavior. The proposed scheme Opportunistic Fuzzy Clustering Routing (OFCR) protocol employs a three-tiered intelligent fuzzy clustering-based paradigm that allows representation of multiple properties of a single entity and the degree of association of the entity with each property group that it is represented by. Such quantification of the extent of association allows OFCR a proper representation of multiple node characteristics, allowing a better judgement for message routing decisions based on these characteristics. OFCR performed 33.77%, 6.07%, 3.69%, 6.88% and 78.14% better than KNNR, GMMR, CAML, MLPRoPH and HBPR respectively across Message Delivery probability. OFCR, not only shows improved performance from the compared protocols but also shows relatively more consistency across the change in simulation time, message TTL and message generation interval across performance metrics. [ABSTRACT FROM AUTHOR]

Delay Tolerant Networks enable data transmission in different applications, mainly in environments where communication infrastructure is missing. The existence of links is casual and even when links are created, they are short in time and unstable/unreliable in terms of connectivity. Thus, underwater network applications tend to be delay tolerant. In this paper, we propose a mobility-aware routing protocol for underwater wireless sensor networks. The protocol is based on Focused Beam Routing (FBR) protocol and considers nodes' next destination location when making forwarding decisions. The routing protocol is called Destination-aware Focused Beam Routing (D-FBR) protocol and we compare its performance with FBR protocol for different FBR angles and different applications. We use Delivery Probability (DP), Average Number of Hops (ANH), Overhead Ratio (OR) and simulate our scenarios by The ONE simulator. Simulation results show that, for D-FBR protocol, when FBR angle is 30 ∘ , OR and ANH is improved by 34.5 % and 23.1 % , respectively, while DP is 6.5 % lower, compared to FBR protocol. Both D-FBR and FBR outperform Epidemic Routing Protocol. [ABSTRACT FROM AUTHOR]

As a high density and mobility area of population, urban areas need monitoring of communicable diseases desperately. In additional, there are some smart bracelets equipped with diverse sensors, which can provide the continuously individual health analysis of physiological parameters. Therefore, some individual abnormal signal, such as body temperature, heart rate, private pulse and other signals can be detected after a short-period time. Delay Tolerant Networks (DTNs) becoming mature enough to be used for the outbreak of communicable diseases observation. This paper presents a monitoring system architecture for sudden public health incidents in urban environments, and a routing algorithm named Historical Probability based Relay Selection (HPRS) is introduced, which is more suitable than other existing routing algorithms such as Epidemic and Prophet algorithms. Simulation results suggest that HPRS outperforms Direct Delivery, Epidemic, and Prophet routing for communicable diseases monitoring system in urban environments. [ABSTRACT FROM AUTHOR]

WiFi-enabled buses of the Public Transportation System (PTS) may be employed as the backbone of a metropolitan delay tolerant network that exploits ad hoc connectivity and predictable bus mobility to deliver non-real time information. In this paper, we discuss and study the feasibility of a Mobile Delay Tolerant Networking (MDTN) solution deployed over an actual PTS to provide service opportunistic connectivity. Our solution follows an opportunistic carrier-based approach where buses act as data collectors for users’ requests involving Internet access. Obtained results demonstrate that MDTN represents a viable approach able to provide delay-tolerant service access. To support our claim, we have carried out a set of simulations considering MDTN coupled with state of the art routing protocols deployed over a near-to-real scenario: the PTS of Milan, Italy. [ABSTRACT FROM AUTHOR]

The advances in next generation network and IoT have provided a promising opportunity to resolve the challenges caused by the increasing transportation issues. In this paper, we investigate the performance of different routing protocols in a Vehicular Delay Tolerant Network (VDTN) crossroad scenario. The mobility patterns of vehicles are generated by means of SUMO (Simulation of Urban MObility) and as communication protocol simulator is used ONE (Opportunistic Network Environment). We use Packet Delivery Ratio (PDR), Relay Delivery Ratio (RDR), hop count and delay as evaluation metrics. We compared the performance of six protocols and the simulation results show that for PDR the Epidemic protocol has better performance than other protocols. Considering RDR and hop count, the performance of Direct Delivery protocol is better than other protocols. While for delay, the performance of Epidemic protocol is better than other protocols. [ABSTRACT FROM AUTHOR]

This article is devoted to the search for a generating function for network delay simulation. We justify the choice of the measurement system used to collect delay data with microsecond accuracy. Experimental data are employed in order to form a hypothesis regarding the exponential type of delay. The paper is restricted to the study of the simplest two cases: the linear and quadratic dependence of the delay distribution function. The application of the Pearson criterion led to the conclusion that an exponential distribution can be used to describe network delay, but is valid only for small periods from 10 to 30 minutes. We also justify the expression for the distribution function and generating function for further theoretical and experimental calculations. [ABSTRACT FROM AUTHOR]

Delay Tolerant Network (DTN) is an array of protocols which work together to enable a standardized way to communicate between nodes is store-carry-and-forward approach. In DTN, nodes help to convey message even with connectivity problem. Instead of using the own resource to pass a message to other nodes, a relay node is preferred which discovers the sink node with a greater probability of transferring messages on its own. A condition named node's selfishness is thus raised and it surely degrades network performance, which is more obvious in urban environment. To handle such problem, an incentive-based routing algorithm is generated by using the concept Selfishness-Enhanced Reliable Forwarding (SERF) to remove node selfishness and Direct Diffusion to omit duplicate messages present in relay node. This algorithm finds the probability of messages received by the relay node with reference to the usage of resources of sender node. Concurrently, buffer management policy is maintained, by setting the threshold of message copies according to the resource consumption of the source node when it generates a message. The result reveals that the proposed algorithm is better to the existing algorithms with regard to the performance metrics such as, delivery ratio, the average delay, and network overhead. [ABSTRACT FROM AUTHOR]

Vehicular networking has gained considerable interest within the research community and industry. The automotive industry is supporting the notion of pervasive connectivity by agreeing to equip vehicles with devices required for vehicular ad hoc networking. Equipped with these devices, mobile nodes in vehicular ad hoc networks (VANETs) are capable of hosting many types of applications as services for other nodes in the network. This research focuses on addressing the challenges of location-dependence, intermittent network connectivity and irregular network traffic flows in unplanned areas for VANETs to host and operate non-safety-critical VANETs services. We assume unplanned areas as the one that lack communication infrastructure and planning. Such areas observe irregular vehicular traffic on the roads as well as on the networks. This research investigates the shortcomings of location-dependence, intermittent network connectivity and irregular network traffic flows and addresses them by exploiting location-dependent service migration over an integrated network in an efficient and cost-effective manner. [ABSTRACT FROM AUTHOR]

We propose a general notion of lower k-order representative capacity that is totally identified by the smallest k-order coefficients in one or another equivalent representation, and the k-additive, k-maxitive and k-tolerant capacities are just its special cases. We further construct its dual notion, the upper k-order representative capacity, which is accordingly determined by n - 1 to n - k-order representation coefficients and takes k-minitive and k-intolerant capacities as its concrete instances. Some particular and important cases of the upper and lower k-order representative capacities based on nonadditivity index as well as simultaneous interaction indices are introduced and studied. The identification scheme of lower/upper k-order capacity is also given and illustrated. This general framework allows one to tailor and simplify capacities while matching and representing the decision makers preferences in terms of interaction index or other desired representation of capacity. [ABSTRACT FROM AUTHOR]