Optimal relay node selection in time-varying IoT networks using apriori contact pattern information
Introduction
Along with static devices, mobile devices are also becoming an important part of IoT networks as mobile phones and vehicles are embedded with advanced sensors [1], [2]. The presence of mobile devices leads to time-varying connectivity in IoT networks. The connectivity of devices changes very rapidly in time-varying IoT networks. Crowdsensing and vehicle-based sensing are good examples of time-varying connectivity in IoT [3]. Also, the large number of sensing devices generates huge data. Big data and the mobility of devices in IoT networks demand the development of fast and reliable data transmission protocols [4]. In this context, multi-hop data transmission plays a significant role. In multi-hop communication networks, the devices other than sender and receiver act as relay nodes for a data packet. However, the selection of optimal relay nodes in time-varying networks is very challenging [5], [6], [7]. Moreover, the data transmission protocols generally optimize a single network parameter which is data latency in relay node selection [8]. However, the link reliability is also a crucial parameter for relay node selection in time-varying networks. Thus, the joint optimization of data latency and link reliability is essential for optimal relay node selection in time-varying IoT networks. However, no protocols have been standardized to promote the connectivity between heterogeneous devices in IoT networks. In this paper, vehicular ad hoc networks and mobile ad hoc networks are considered for the hypothesis to time-varying IoT networks.
With the utility of smartphones, health monitoring devices and smart vehicles, people leave their digital traces behind [9], [10], [11]. These traces can be precisely recorded with the help of various positioning tools. Thus, the connectivity information of computing devices also called contact pattern information can be extracted from their digital footprints. Further, machine learning and deep learning methods can be utilized to learn the mobility patterns of devices [12]. Moreover, the information about contact patterns can also be predicted using various learning methods. These contact patterns of mobile devices play a vital role in the perfect modelling of time-varying networks. Also, it brings opportunities in developing robust data forwarding protocols in time-varying IoT networks.
In this work, data latency and reliability of a data forwarding path are jointly optimized by utilizing the network contact pattern information of IoT devices. Due to mobility of IoT devices, connectivity between the devices changes over the time. As discussed above, the knowledge of network contact patterns is generally obtained by various machine learning and prediction methods. However, the complete apriori network contact patterns information can not be obtained due to randomness in mobility behaviour of IoT devices and inaccuracy of prediction methods. Hence, in this work, we assume that only partial network contact pattern information is available. The partial knowledge of apriori contact pattern information is utilized to establish a robust data forwarding path over time-varying IoT networks.
The main contributions of this work are enumerated below.
- 1.
An optimal relay node selection method is proposed for time varying IoT networks by jointly optimizing data latency and link reliability using apriori knowledge of network contact patterns.
- 2.
A new modelling technique is proposed for time-varying IoT networks using apriori network contact pattern information.
- 3.
A novel scheme is proposed for jointly optimising the data latency and link reliability for relay node selection.
- 4.
A heuristic cost function is developed which when optimized provides an optimal relay node at every instant in time in a time varying IoT network.
- 5.
Extensive experiments and comparisons are performed on time-varying IoT networks to illustrate the significance of the proposed relay node selection method under various network scenarios.
The proposed policy for cost function formulation is updated at every hop to address time-varying connectivity. Thus, the selection of relay nodes becomes an online process. The online relay node selection establishes a robust data forwarding path for a packet. The process of relay node selection for a packet is continued until that packet reaches the destination. In most of the data forwarding protocols, there is an upper bound on Time-To-Live (TTL) of a packet. For a packet, based on the remaining TTL and link reliability of all the available paths, an optimal relay node is chosen. For high-reliability of links, the variation in contact period (the time for which two devices will keep connected) should be minimum. Therefore, the reliability of links is formulated based on their connectivity history. Contact period of all the links is updated continuously. A heuristic cost function is formulated utilizing the apriori contact pattern information which considers both data latency and link reliability of available data forwarding paths. Based on the apriori contact information, more weightage is given to the favourable links. Thus, the contact patterns (apriori contact information) of links are utilized to improve the optimal relay node selection process. The uncertain links (the links which are not apriori) are accounted based on their connectivity variation. Thus, a robust data forwarding path is established with high-reliability and low-latency. Network performance is analysed by varying the apriori contact patterns in data forwarding over time-varying IoT networks. The effect of transmission range, nodes’ mobility variation, connectivity information delay, nodes’ density, and the connectivity history length on data latency and link reliability is also analysed.
The remainder of this work is organized as follows. Section 2 describes the related work. Modelling of time-varying IoT networks using network contact pattern information is described in Section 3. Basic definitions and preliminaries used in the proposed work are described in Section 4. The proposed optimal relay node selection method utilizing apriori contact pattern information for robust data forwarding in time-varying IoT network is presented in Section 5. Performance analysis of the proposed method is evaluated in Section 6. Finally, the conclusion and future scope of the work are discussed in Section 7.
Section snippets
Related work
The proposed method utilizes the apriori information about the contact patterns of IoT devices. The apriori contact pattern information can be obtained using several machine learning methods. A number of approaches have been proposed in literature to predict the mobility models of humans and computing devices [13], [14], [15], [16]. In [13], a method for predicting immediate future, typically within the next hour in the context of mobility behaviour is proposed. The evaluation of the proposed
Modelling time-varying IoT networks using apriori contact pattern information
An IoT network can be represented as a graph where each node represents a computing device. Edges over the IoT graph illustrate connectivity between the devices. A set of N sensor nodes of an IoT network is given as . ni represents an ith node in the network. A node pair (ni, nj) is considered to be connected when the Euclidean distance is lesser than the transmission range TR. An edge between a node pair (ni, nj) of the graph is modelled as
Basic definitions and preliminaries
This section describes definitions and the preliminary details about data latency and link reliability in static and time-varying IoT network which are further required in this work.
Optimal relay node selection using apriori contact pattern information
This Section explains the proposed method of optimal relay node selection utilizing knowledge of apriori network contact patterns for establishing the robust data forwarding paths over time-varying IoT networks. Firstly, a policy is proposed for formulating the heuristic cost function. The utilization of apriori contact pattern information is described in formulating the cost function which considers joint minimization of data latency and link reliability. Next, the optimal relay node selection
Performance evaluation
In this section, performance of the proposed relay node selection method utilizing apriori network contact patterns is presented. Analysis on data latency and the reliability are conducted for different time-varying IoT networks. Network performance is analysed by varying the contact patterns. Effect of node density, node’s transmission range and connectivity history length on data latency and link reliability is studied. The comparative analysis in terms of packet replication cost is also
Conclusion and future scope
In this work, an optimal relay node selection method is proposed by utilizing the network contact patterns of IoT devices. Digital traces of computing devices provide apriori connect pattern information, which helped in improving the performance of data forwarding scheme. The proposed method utilized this knowledge in jointly optimizing the data latency and link reliability for robust data forwarding over time-varying IoT networks. Performance of the proposed method is evaluated under various
CRediT authorship contribution statement
Surender Redhu: Conceptualization, Formal analysis, Data curation, Writing - original draft, Writing - review & editing. Rajesh M. Hegde: Conceptualization, Formal analysis, Data curation, Writing - original draft, Writing - review & editing.
Declaration of Competing Interest
The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.
Surender Redhu received the B.Tech. degree in electronics and communication engineering from Kurukshetra University, Haryana, India, in 2012 and the M.E. degree in electronics and communications from Thapar University, Patiala, India, in 2015. He is currently working toward the Ph.D. degree in the Department of Electrical Engineering, Indian Institute of Technology Kanpur (IITK), Kanpur, India. He is also a TCS research fellow affiliated to the Wireless Sensor Networks lab at IIT Kanpur. His
References (37)
- et al.
Internet of things (iot): a vision, architectural elements, and future directions
Fut. Gener. Comput. Syst.
(2013) - et al.
Mupf: multiple unicast path forwarding in content-centric vanets
Ad Hoc Netw.
(2018) - et al.
An integrated vanet-based data dissemination and collection protocol for complex urban scenarios
Ad Hoc Netw.
(2016) - et al.
WSNs-assisted opportunistic network for low-latency message forwarding in sparse settings
Fut. Gener. Comput. Syst.
(2019) - et al.
Impact of human behavior on social opportunistic forwarding
Ad Hoc Netw.
(2015) - et al.
Distributed learning of human mobility patterns from cellular network data
Information Sciences and Systems (CISS), 2017 51st Annual Conference on
(2017) Mobility prediction in mobile ad hoc networks using neural learning machines
Simul. Model. Pract. Theory
(2016)- et al.
Learning automata-based opportunistic data aggregation and forwarding scheme for alert generation in vehicular ad hoc networks
Comput. Commun.
(2014) - et al.
A stability-considered density-adaptive routing protocol in manets
J. Syst. Archit.
(2013) - et al.
A reliable QOS aware routing protocol with slot assignment for mobile ad hoc networks
J. Netw. Comput. Appl.
(2009)
A survey of routing protocols based on link-stability in mobile ad hoc networks
J. Netw. Comput. Appl.
Internet of things for smart cities
IEEE Internet Things J.
Vehicular social networks: enabling smart mobility.
IEEE Commun. Mag.
Big data: a survey
Mob. Netw. Appl.
Minimizing end-to-end delay in multi-hop wireless networks with optimized transmission scheduling
Ad Hoc Netw.
Group mobility detection and user connectivity models for evaluation of mobile network functions
IEEE Trans. Netw. Serv. Manag.
Activity-based human mobility patterns inferred from mobile phone data: a case study of singapore
IEEE Trans. Big Data
Far out: predicting long-term human mobility
Twenty-Sixth AAAI Conference on Artificial Intelligence
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Surender Redhu received the B.Tech. degree in electronics and communication engineering from Kurukshetra University, Haryana, India, in 2012 and the M.E. degree in electronics and communications from Thapar University, Patiala, India, in 2015. He is currently working toward the Ph.D. degree in the Department of Electrical Engineering, Indian Institute of Technology Kanpur (IITK), Kanpur, India. He is also a TCS research fellow affiliated to the Wireless Sensor Networks lab at IIT Kanpur. His research interest include the development of cognitive network models for wireless networks for improving the quality of services over wireless sensor and IoT networks. His other areas of research interest are multi-sensor data fusion, data aggregation, and autonomous nodes scheduling in wireless sensor networks and vehicular networks. Additional research information can be found at the URL: https://sites.google.com/view/surender.
Rajesh M. Hegde joined IIT Kanpur as an Assistant Professor of EE in May 2008 after obtaining a Ph.D degree in Computer Science and Engineering from the Indian Institute of Technology Madras in 2005. He became an Associate Professor in 2012 and a Full Professor of EE in July 2016. He currently holds the Umang Gupta Chair position at IIT Kanpur. He was also awarded the P.K. Kelkar Research Fellowship between 2009-13. Between 2005 to 2008, He worked as a Researcher at the California Institute of Telecommunication and Information Technology (CALIT2) and concurrently as a lecturer (2007) at the Department of Electrical Engineering at the University of California San Diego, USA. Prior to 2005, He was involved with teaching undergraduate engineering courses at various levels in India. He was elected as a Senior Member of IEEE in 2017 and is an affiliate member of IEEE-AASP technical committee. He has established two research Laboratories at IIT Kanpur namely, Multimodal Information Systems Lab and Wireless Sensor Networks Lab with funding obtained from BSNL, DST, MietY, LG Soft, Samsung Research and Indian space research organization. He has published prolifically at several international conferences and journals in the area of signal processing, communication and networks. He is also a member of the National working groups of ITU-T (NWG-16 and NSG-6) on developing multimedia applications. He actively teaches both undergraduate and post graduate courses related to electronics, digital signal processing, statistical signal processing, array signal processing, wireless sensor networks, and digital speech processing. Additional biographic information can be found at the URL: http://home.iitk.ac.in/~rhegde.