Satya Jyoti Borah

Probability-based controlled flooding in opportunistic networks

Opportunistic networks (Oppnet) are challenged networks in present wireless communication scenario. These networks are mainly applied to situations where a persistent end-to-end path between the source and the destination does not exist. Delay/Disruption Tolerant Networking (DTN) is mostly used to solve this end-toend path problem in such networks. Many routing protocols have been proposed in literature that consider various performance metrics such as delivery delay, packet delivery rate, hop count, among others. In this paper, a new routing protocol named as PRoWait has been designed which can overcome the shortcomings of the already existing protocols in Oppnets. The proposed protocol also incorporates the merits of existing protocol so that it can be reliable and efficient for the communication of pedestrians with handheld devices. Simulation results obtained for the proposed scheme show better performance as compared to the Porphet, Spray and Wait and Epidemic routing protocols in terms of packets delivery probability, overhead ratio, and hop count performance metrics.

EDR: An Encounter and Distance Based Routing Protocol for Opportunistic Networks

In an Opportunistic Network (Oppnet), the transmission of messages between mobile devices is achieved in a store-carry-and-forward fashion since nodes store the incoming messages in their buffer and wait until a suitable next hop node is encountered that can carry the message closer to the destination. In such environment, due to the delay-tolerant nature of the network, designing a routing protocol is a challenge. This paper proposes a novel routing protocol called Encounter and Distance based Routing (EDR), which utilizes the so-called forward parameter to determine the next hop selection. This parameter is calculated by taking into account the number of encounters and the distance of each node in the network with respect to a particular destination. Simulation results are provided, showing the superiority of EDR over the History based Prediction for Routing (HBPR) protocol and the ProWait protocol, chosen as benchmark schemes, in terms of hop count, messages dropped, and average latency.

A multi-objectives based technique for optimized routing in opportunistic networks

In the design of routing protocols for opportunistic networks (OppNets), the node’s context information such as number of encounters to destination (EN), distance to destination (DI), delivery probability (PR), to name a few, is used as routing decision patterns when selecting the best next hop candidate to forward the data packets to their destination. Most routing protocols thus far proposed for OppNets have considered either of these patterns or a combination of few of them as design objectives in their next hop selection processes. But none of these work have ever addressed their optimization for the same. In this regard, this paper proposes a novel multi-objectives based technique for optimized routing (MOTOR) in OppNets. This technique involves the use of a weighted function to decide on the next hop selection of a node based on a combination of objectives, namely, maximizing the number of encounters (EN), maximizing the delivery probability (PR), and minimizing the distance to destination (DI). A non-dominated set of solutions is proposed using a Naive and Slow algorithm for forwarding the data packets towards their destination. Simulation results are provided to assess the performance of the MOTOR scheme when the next hop selection process relies on a single objective (EN, PR, or DI), double objectives (EN–DI, EN–PR, or DI–PR), and triple objectives (EN–DI–PR). It is shown that the performance of the proposed routing scheme under the triple objectives option is better than that obtained under all the three double objectives in terms of delivery probability, average latency and number of messages dropped. For instance there is 6%, 41.84% and

EPR: An Encounter and Probability-Based Routing Protocol for Opportunistic Networks

29.26\%

A* search based next hop selection for routing in opportunistic networks

29.26% increase in delivery probability, average latency and number of messages dropped respectively in triple objectives with respect to the double objectives DI–PR.

A Probabilistic Encounter and Distance-based Routing Protocol for Opportunistic Networks

Abstract In an Opportunistic Internet of Things (here denoted oppIoT), a novel paradigm and system that is expected to be an integral part of 5G wireless networks, information dissemination and sharing among opportunistic communities formed by opportunistic contacts using short- range communication and sensing technologies, as well as human mobility, will prevail. In such systems, designing a routing protocol is a major challenge due to the inherent difficulty in dealing with the design constraints associated with such protocols such as node mobility, characterization of the users social behavior, and characterization of the contacts between the opportunistic communities, to name a few. Nonetheless, from a data forwarding perspective, opportunistic networks (oppNets) are a subclass of oppIoT. As such, any novel routing protocol for infrastructure-less oppNet can also find application in an OppIoT system. In this regard, this paper proposes a novel optimized routing protocol (called Game Theoretic Approach for Context Based Routing (GT-ACR)) for oppNets that uses game theory for selecting the best possible next-hop to forward data packets efficiently. In this protocol, the best strategy for the selection of the next hop node is dependent upon a non-zero sum cooperative game of two players considering the context information, encounter index, and distance of the corresponding node from the destination as vital attributes in framing the game. Simulation results are provided, showing that out of the existing four protocols namely Epidemic, Prophet, ProWait and HBPR, the ProWait protocol performed better than the remaining three protocols. However the proposed GT-ACR outperforms even the ProWait in terms of average latency by 15.74%, hop count by 5.3%, number of messages dropped by 14.2% and overhead ratio by 13.6% respectively when number of nodes are varied.

An Energy-Efficient Location Prediction-Based Forwarding Scheme for Opportunistic Networks

In recent years, one of the most promising networks in the field of wireless communication is opportunistic network (OppNet). To transmit a data packet from the source to the destination in such a network is a challenging task. This is due to the absence of path, no network topology, and frequent network partitioning to name a few. So, by exploiting the node’s informative context informations, the researchers are trying to design routing protocols in OppNet. In this work the authors also try to design a routing protocol (so-called EPR): an encounter and probability-based routing protocol for OppNet based on the node’s context information such as number of times the carrier node encounters with the destination and its message delivery probability toward the destination. Further the obtained results are compared with the traditional routing protocols Prophet, HBPR, and EDR. From the obtained results, it has been observed that the proposed protocol EPR outperformed 25.24%, 19.74%, and 11.38% over EDR, HBPR, and Prophet, respectively, in terms of message delivery probability when number of nodes are being varied.

DEEP: Distance and encounter based energy-efficient protocol for opportunistic networks

Abstract Opportunistic Internet of Things (OppIoT) is a paradigm, technology, and system that promotes the opportunistic exploitation of interactions between IoT devices to achieve increased connectivity, reliability, network capacity, and overall network lifetime. The increased demand for identifying such opportunistic exploitation is illustrated by IoT scenarios, where the goal is to recognize when an opportunity for communication is possible, thereby allowing for data forwarding and routing. In an OppIoT system, devising a routing scheme is a challenging task due to the difficulty in guaranteeing the existence of connectivity between devices (nodes) and in identifying an intermediate node as a packet forwarder towards its destination. Considering that opportunistic networks (oppNets) are a subclass of OppIoT and considering IoT scenarios where the opportunistic exploitation of IoT devices is possible even in case the device’s presence is uncertain or may change over time, this paper proposes a novel routing scheme for OppNets (called Location Prediction-based Forwarding for Routing using Markov Chain (LPFR-MC)) that can also be used in IoT scenarios. The proposed LPFR-MC scheme considers the node’s present location and the angle formed by it and the corresponding source (resp. destination) to predict the node’s next location or region using a Markov chain and to determine the probability of a node moving towards the destination. Simulation results are provided, showing that the proposed LPFR-MC outperforms the existing traditional protocols in terms of message delivery probability, hop count, number of messages dropped, message overhead ratio, and average buffer time.