A location Prediction-based routing scheme for opportunistic networks in an IoT scenario
Introduction
The proliferation of devices, which are able to directly connect to the Internet, is leading to a new computing and communication paradigm known as Internet of Things (IoT). In IoT [1], the objective is to expand the reach of the current Internet towards any possible thing (also referred to as object, IoT device or opportunistic community) that needs to be connected and monitored. As a consequence of this pervasive monitoring, things may not always be statically deployed, i.e. they may move freely around while being carried by people, vehicles, or other things, and they may still interact with the static IoT infrastructure. As an example, in a vehicular network-based IoT scenario, statically deployed devices (such as road side sensors) may need to discover mobile devices (such as actuators or sensors) and exploit these devices for gathering and relaying their data towards the intended destinations without relying on a deployed static IoT infrastructure.
In an oppIoT [6], the collection, dissemination, and sharing of data among things are typically formed by opportunistic contacts using mobility, short-range sensing communication and technologies, to name a few. In such systems, designing a routing protocol is a challenging task due to the inherent difficulty in guaranteeing the existence of connectivity between devices (things) and in identifying a suitable data packet forwarder that can carry the packet towards its intended destination.
Considering that OppNets [11] 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 be also be used in IoT scenarios.
In most routing protocols thus far proposed for OppNets, parameters such as message dissemination pattern, node context information (i.e. inter contact time, present location, present direction, delivery probability, encounter, distance to destination, node’s energy, past and present history of nodes), past interaction time, to name a few, are considered as individual or combined decision parameters for selecting the best next forwarder of a data packet to its intended destination. The design of the proposed LPFR-MC routing protocol is fundamentally different from that of the above-discussed protocols in the sense that LPFR-MC 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.
The rest of the paper is organized as follows. In Section 2, some routing protocols for OppNets are discussed. In Section 3, the system model is described. In Section 4, simulation results are presented in-depth. Section 5 concludes the paper.
Section snippets
Related work
In the literature, many routing protocols have been designed which are based on node’s context information and message dissemination. Representative ones are summarized as follows.
In [13], the Epidemic routing protocol is proposed, in which messages are spray and flooded among the nodes that are in the range of the source-intermediate nodes. A table (so-called summary vector), created and maintained at each node, contains a unique ID of each message. Whenever two nodes meet, they exchange their
Terminology
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: Source/carrier node.
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: Neighbour node of S.
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: Destination node.
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: Region in the quadrant with respect to node S, where , 2, 3, and 4
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: State of the node n in the quadrant with respect to node S, where , 2, 3 and 4.
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: Area in a region, where , 2, 3 and 4.
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: Angle in
Simulation results
In this section, our proposed LPFR-MC protocol is evaluated and compared against the Epidemic [13], Prophet [9], HBPR [5], EDR [4], and ProWait [3] protocols, chosen as benchmarks, in terms of delivery probability, hop count, overhead ratio, number of messages dropped, average buffer time (in seconds), and average latency (in seconds), chosen as performance metrics, under varying number of Time-to-Live (TTL), number of nodes, buffer size, and message generation interval respectively. The
Conclusion
In this paper, we have proposed a novel routing protocol for OppNets called Location Prediction-based Forwarding for Routing using Markov Chain (LPFR-MC), which can be used in a IoT scenario. Initially, the LPFR-MC scheme identifies the node’s present region/location and the angle formed by it with the source and destination, then predicts its next region/location towards the intended destination. Simulations are conducted, comparing LPFR-MC against various popular existing benchmark routing
Sanjay K. Dhurandher received his M.Tech. and Ph.D. Degrees in Computer Science from the Jawaharlal Nehru University, New Delhi, India. He is presently working as Professor and Head in the Division of Information Technology, Netaji Subhas Institute of Technology (NSIT),University of Delhi, India. He is also the Head of the Advanced Centre CAITFS, Division of Information Technology, NSIT, University of Delhi. Prior to this, from 1995 to 2000 he worked as a Scientist/Engineer at the Institute for
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Sanjay K. Dhurandher received his M.Tech. and Ph.D. Degrees in Computer Science from the Jawaharlal Nehru University, New Delhi, India. He is presently working as Professor and Head in the Division of Information Technology, Netaji Subhas Institute of Technology (NSIT),University of Delhi, India. He is also the Head of the Advanced Centre CAITFS, Division of Information Technology, NSIT, University of Delhi. Prior to this, from 1995 to 2000 he worked as a Scientist/Engineer at the Institute for Plasma Research, Gujarat, India which is under the Department of Atomic Energy, India. His current research interests include wireless ad-hoc networks, sensor networks, computer networks, opportunistic networks, network security and underwater sensor networks. He is serving as the Associate Editor of Wiley’s International Journal of CommunicationSystems.
Satya J. Borah received his B.Tech. in Computer Science and Engineering and M-Tech in Information Technology from North Eastern Regional Institute of Science and Technology (NERIST) affiliated to North East Hill University(NEHU). Presently he is pursuing his Ph.D. in the research area of Opportunistic Networks at NSIT, under University of Delhi, New Delhi. He is also holding the post of Associate Professor in the Department of Computer Science and Engineering, NERIST, Nirjuli, Arunachal Pradesh. His current research interests include opportunistic networks, wireless ad hoc networks, MANET and sensor networks.
I. Woungang received his Ph.D degree in Mathematics from Universite du South, Toulon & Var, France, in 1994. From 1999 to 2002, he worked as Software Engineer at Nortel Networks, Ottawa, Canada. Since 2002, he has been with Ryerson University, where he is now a Professor of Computer Science & Director of the DABNEL Lab. His current research interests include radio resource management in wireless networks, cloud security, and routing in opportunistic networks. He has published 8 edited & 1 authored books, and over 80 refereed journals and conference papers. He serves as Editor in Chief of the International Journal of Communication Networks and Distributed Systems (IJCNDS) Inderscience, UK, and as Chair of the Computer Chapter, IEEE Toronto Section.
Aman Bansal is an undergraduate student in the Division of Information Technology at Netaji Subhas Institute of Technology (NSIT), University of Delhi. His current research interests lie in the fields of Artificial Intelligence, Mobile Ad-Hoc Networks and Soft Computing.
Apoorv Gupta is an undergraduate student in the Division of Information Technology at Netaji Subhas Institute of Technology (NSIT), University of Delhi. His current research interests lie in the fields of Artificial Intelligence, Opportunistic Networks and Soft Computing.