IJCNC 03

VHFRP: VIRTUAL HEXAGONAL FRAME ROUTING
PROTOCOL FOR WIRELESS SENSOR NETWORK

Savita Jadhav¹ and Sangeeta Jadhav²

¹ Department of Electronics and Telecommunication Engineering, Dr. D. Y. Patil Institute of Technology Pimpri, Pune, India

²Department of IT Engineering, Army Institute of Technology, Dhighi, Pune, India

ABSTRACT

As physical and digital worlds become increasingly intertwined, wireless sensor networks are becoming an indispensable technology. A mobile sink may be required for some applications in the sensor field, where incomplete and/or delayed data delivery can lead to inappropriate conclusions. Therefore, latency and packet delivery ratios must be of high quality. In most existing schemes, mobile sinks are used to extend network lifetimes. By partitioning the sensor field into k equal sized frames, the proposed scheme creates a virtual hexagonal structure. Each frame header (FH) is linked together through the creation of a virtual back bone network. Frame headers are assigned to nodes near the centre of each frame. The virtual backbone network enables data collection from members of the frame and delivers it to the mobile sink.The proposed Virtual Hexagonal Frame Routing Protocol (VHFRP) improves throughput by 25%, energy consumption by 30% and delay by 9% as compared with static sink scenario

KEYWORDS

Hexagonal; Congestion; Dynamic; Routing.

1 INTRODUCTION

As an intelligent home, transportation network, precise agriculture, environmental and habitat monitoring, smart industries, and structures, WSNs are adept at managing critical military missions as well as disaster management [1]. Sensing physical parameters of an environment enables the sensor network to supervise and track it. Wireless sensor networks face various challenges such as clustering, node deployment, localization, topology changes, congestion control, power distribution, and data aggregation. An overflow of packet appearance rate results in congestion [2]. A sensor network experiences congestion due to deteriorating radio links, multiple data transmissions over the links, unpredictable traffic densities, and biased data rates. As a result, it is essential to accurately analyze congestion and local contention on the network to maximize link utilization, extend network lifetime, ensure fairness among flows, reduce data loss due to buffer overflows, and reduce network overhead. Network performance is impacted by router node failures.

Wireless sensor networks face the problem of efficient energy consumption. Static sinks are often used more than other nodes in the network since packets are frequently forwarded between nodes near the sink. Frequently forwarding data causes nodes close to sink to die rapidly and lose communication. Dynamic sinks ensure uniform energy distribution throughout the network, extending its lifespan. As the energy of the FH reduces beyond the operating limit, the proposed approach selects the next best alternative FH based on its distance from the frame centre and residual energy, thereby avoiding communication failures. Waiting times in the data

transmission process increase when there is a heavy load on the node. Dynamic paths are established among nodes with less traffic when data is being delivered and when the sink is moved. This work aims to 1) update the sink position periodically to the corresponding nodes in order to transmit data, 2) select the best dynamic path to transmit data when a node is experiencing high traffic, and 3) use the mobile sink to increase network throughput.

The organization of this paper is as follows: The second section gives highlights of literature reviewed, the main contribution of this work is presented in section three, the section four presents Virtual Hexagonal Frame Routing Protocol (VHFRP), the fifth section provides results of the work done and section six concludes the paper.

2 LITERATURE SURVEY

Energy efficiency is a challenge for wireless sensor networks. A static sink promotes frequent packet forwarding, which causes the nodes closest to sinks are more likely to be used than others in the network. Due to this, sink communication is disrupted when the node closest to it dies very quickly. The network’s lifetime is increased due to the spreading of its energy consumption among its nodes resulting from the use of dynamic or mobile sinks. Using Dynamic Hexagonal Grid Routing Protocol, [1] determines the current position of the sink. First, the network is divided into hexagonal grids so that each node knows where the sink is. Moving the mobile sink in the second phase selects the dynamic path or if data transmission is congested. This technique covers large coverage area but it reduces network lifetime as the number of nodes increase

Energy holes form in the network whenever the node near the sink depletes energy. The mobility of the sink creates a major challenge in reliable and energy efficient data communication towards the sink. The use of mobile sinks requires a new routing protocol that is energy efficient. Increasing packet delivery to mobile sinks in the network is the primary objective [2]. Cluster heads are chosen based on residual energy, distance, and data overhead in Energy Efficient
Clustering Scheme (EECS). A finite state machine represents the sensor node in the mobility model, and a Markov model represents the state transition. The EECS algorithm is outer perform by 1.78 times in terms of lifetime and 1.103 times in terms of throughput. EECS algorithm promotes unequal clustering due to its avoidance of energy holes and hot spots. EECS does not support monitoring with multiple sinks and large ROI

Mobile sinks have become increasingly popular as a way of delivering sensed data because they conserve sensor resources. Due to the need to know the latest location of each node, mobile sinks pose a challenge to data delivery. Flooding the sink’s latest location erodes energy conservation. GCRP minimizes the overhead of updating the mobile sink’s location by utilizing Grid-Cycle Routing Protocol (GCRP) [4]. Grid cell heads (GCHs) are elected for each cell in GCRP, which partitions the sensor field into grid cells. Cycles of four GCHs is formed. Cycles involving border GCHs are referred to as exterior cycles. In addition, there is an interior cycle that involves nonboundary GCHs that connects GCHs of different regions. Through exterior and interior cycles, sinks update the nearest GCH when they stay at one location. It updates the mobile sink’s location with minimum number of message exchange which results in increased data delivery delay.

A novel delay aware energy efficient reliable routing (DA-EERR) [11] technique for datatransmission in heterogeneous sensor environment. In order to achieve energy delay balance between sources and sinks, the DA-EERR is developed. Through data aggregation and load balancing, it improves the percentage of successfully received data packets to sink in a large dense network. Due to the small size of a network, the protocol will introduce overhead in the control packets, and also the ring nodes (RNs) will only perform the ring role for a short period of

time, leading to degraded network performance. Sparse networks make it impossible to store the latest location of sinks in a close ring. This method achieves balance between energy consumption and end-to-end delay but it is not applicable for large scale dense network having multiple mobile sinks.

An optimal rendezvous points are selected using particle swarm optimization based selection (PSOBS) [16].This can effectively manage the network resources. A Sensor node also receives data packets from other sensors that are used for calculating their weights. PSOBS results in reducing the number of hops, the tour length. Other advanced optimization algorithms can further improve the performance.

Sensor networks are challenged by sink mobility. Throughout the network area, sink positions are continuously propagated to keep all sensor nodes informed of which direction to forward data to. In the network, frequent sink position updates can result in higher energy consumption as well as more collisions. To advertise the position of a mobile sink, the network uses a virtual multi-ring infrastructure [18]. Router node failure may affect the algorithm performance.

There are too many constraints on network operation imposed by existing query-driven data collection schemes. An improved mobile data delivery scheme is presented using QDVGDD [19]. Utilizing a virtual infrastructure, it provides high quality service to mobile sinks with minimal network control overhead but increases the data delivery delay

The hybrid optimization algorithm is used for fast congestion control [24]. In the first phase, a multi-input time-on-task optimization algorithm is used to select appropriate next hop nodes. Three factors are taken into account: 1) event waiting delay, 2) received signal strength (RSS value), and 3) mobility during different time periods. In the second phase, an altered gravitational search algorithm is applied to make energy effective path discovery from source to sink.

With mobile base stations, the study [3] proposes an energy-balancing cluster routing protocol that prolongs the life of the network and balances energy consumption. [5] Proposes two data collection schemes based on multiple mobile sinks, Direct Send and Via Static Gateway. An application with query-driven logic [6] uses the sink to disseminate queries across the network. Inter-cluster communication is driven by the energy balance algorithm [7]. The [8] mitigates the issue of hot spots among sensors, thus extending the lifetime of networks. Using in-node data aggregation, [9] propose a method of reducing correlation intensity without wasting energy by eliminating redundant sensed data. The [10] can reduce data transmission but the total number of transmissions for data collection is high. [12] Proposes a differential evolution and mobile sinkbased energy-efficient clustering protocol. [13] Formulate a shortest path (SP) problem in an interval-valued Pythagorean fuzzy environment. The [14] considers rechargeable sensors to be deployed in the sensing region and employs Maximum Capacity Path (MCP), a dynamic loadbalanced routing scheme for load balancing and prolonging the network’s lifetime. The data collection is done by the sink in a Polling based M/G/1 server model [15]. The hexagon beehives feature where sensor nodes are distributed across a hexagon randomly. This hexagonal is divided into equal clusters based on the radius of the hexagonal in [17]. So it provides a pre-determined path for mobile sinks and covers each cluster with sinks from two different directions. A mobile sink [20] was suggested for dealing with the problem of load balancing and energy consumption. A tree-based clustering approach [21] is proposed using an enhanced flower pollination algorithm to extend the operational lifetime of the network. The [22] provides two efficient algorithms to improve the data-gathering process. In [23], an integration of geographic and hierarchical methods with mobile sink is proposed to decrease energy consumption and increase the network lifetime. WBANs are used in [25] to provide remote monitoring of patient’s health status using

International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.2, March 2023

congestion detection and control. Some of the techniques are further compared along with advantages and disadvantages as shown in table (1).

Table 1. Specifications of Several SBCs