Cluster-based Rendezvous Points Selection for Reducing Tour Length of Mobile Element in WSN J Siva Prashanth 1,2 1 Academy of Scientific & Innovative Res., CSIR-IICT campus 2 CSIR-Indian Institute Chemical Technology Hyderabad, India. sivaprashanth.j@gmail.com Satyanarayana V Nandury 1,2 1 Academy of Scientific & Innovative Res., CSIR-IICT campus 2 CSIR-Indian Institute Chemical Technology Hyderabad, India. nvs@iict.res.in Abstract— In this paper we develop two new algorithms viz., CCP (Cluster-based Collection Point) and CRP (Cluster-based Rendezvous collection Points) that focus on i) reducing the number of data collection points to be visited by the Mobile Element (ME), and ii) determining an optimal path for ME. The algorithms follow a clustering approach, where a Cluster Head (CH) aggregates data from its cluster nodes and keeps this information ready for onward transmission to the ME. Due to this approach, the ME need only visit the CH instead of visiting each cluster node individually. The CCP algorithm determines an optimal path for ME by connecting all CH/Collection Points (CP). Taking advantage of the transmission range of CH/CPs, the CRP algorithm determines optimum number of Rendezvous Points that cover all CPs, which further reduces the tour length. Both algorithms were subjected to extensive simulations to study their efficacy. The algorithms have outperformed the best known algorithms in terms of tour length and latency. Keywords—collection point; WSN; path optimization; mobile element; Rendezvous Points; I. INTRODUCTION Wireless Sensor Networks (WSNs) have wide range of applications that include, environment monitoring, security & surveillance, process monitoring, tracking vehicular movement, disaster management, etc. Depending on the nature of application, WSNs send their sensed data either directly to a stationary Base Station (BS) or Mobile Element (ME). The ME takes a tour across the WSN and collects data transmitted to it by the sensor nodes at various Collection Points (CP) and submits the data to the BS. The choice of location of the BS is usually fixed, based on various considerations like a) proximity to nodes in WSN, b) availability of resources, c) transmission range of sensor nodes, etc. For large networks, where the wireless sensors nodes are deployed far apart from the BS, the nodes transmit their data to a nearest neighbor, which tries to relay the data in a shortest path to the BS. Whilst sensor nodes closer to the BS can transmit in one-hop, the one’s far away need multiple hops. This imposes challenges related to communication, latency and energy consumption. This also leads to traffic congestion, as nodes closer to BS need to find enough time slots to handle in- coming traffic from their neighbors. As the energy expended for transmission is quite significant compared to processing load, these nodes consume much more energy compared to other nodes. Such scenario might lead to the collapse of WSN, as no data would reach BS if these nodes drain out all their battery life. To overcome these challenges, large WSNs often employ a ME that moves along a predefined path in the WSN space collecting information from sensor nodes. The information collected by the ME, is then transferred to BS [2]. The use of ME minimizes energy consumption and network traffic, but poses newer challenges as the ME needs to find an optimal path to ensure timely collection of data from all sensor nodes in the WSN without resulting in delays due to latency. Several approaches have been proposed, which try to reduce the number of CPs that a ME must necessarily visit to collect data from all sensors in the WSN. The paper discusses a clustering approach to reduce latency where each sensor node in a cluster is within a single hop distance from its Cluster Head (CH) [3], [4]. In this approach, the CH aggregates the data of its cluster nodes and assumes the role of a CP. The tour of the ME would then primarily consists of a visit to all CPs. A Cluster-based Collection Point (CCP) algorithm has been developed based on this approach to reduce the number of CPs to be visited by the ME. In order to further reduce tour length obtained by CCP algorithm, we have developed another algorithm Cluster- based Rendezvous collection Points (CRP). CRP identifies Rendezvous Points (RP), which are collection points that are optimally located at a distance equal to the transmission range of one or more CHs along the ME path. The two algorithms have been subjected to extensive simulation studies to assess their efficacy in optimizing the number of CPs and the tour length. A comparison of the results of the simulation with similar works has shown that both CCP and CRP outperform the best known approach. Rest of the paper is organized as follows: Section II presents an overview of related work on clustering and path- optimization of ME. The proposed cluster based path optimization approach is presented in Section III. Section IV describes CCP and CRP algorithms. The simulations carried out and the performance results are presented in Section V. II. RELATED WORK In applications where BS is stationary, the CHs nearer to it are overloaded with incoming data from other CHs that are far away. This results in early energy drain out of these near- CHs. To conserve their energy, an approach to strategically 1230 978-1-4799-8047-5/15/$31.00 c 2015 IEEE Authorized licensed use limited to: Indian Institute of Chemical Technology. Downloaded on October 28,2022 at 04:10:09 UTC from IEEE Xplore. Restrictions apply.