Figure 1. Illustration of a Wireless Sensor Network (WSN)
In this paper, the author has proposed a new route next-hop selection algorithm for Wireless Sensor Networks (WSNs) that is aimed towards reducing packet loss, end-to-end delay, and energy consumption. In this proposed algorithm, the forwarding nodes are selected through hop-by-hop estimation of QoS, and residual energy. The efficiency of the proposed routing algorithm is evaluated by using the NS-2 simulator. The simulation results show that the proposed algorithm provides a significant improvement in terms of energy consumption, the number of packets forwarded, an end to end delay, and packet delivery ratio compared to the existing routing protocol.
Wireless sensor is used to measure and record various environmental properties, such as temperature, pressure, stress, and vibration in the form of electrical signals. Wireless Sensor Networks (WSNs) are a collection of such sensors deployed to sense variations in the physical environmental properties, and transmit data through, wireless networks as shown in Figure 1. These sensors and WSN are an integral part of consumer electronics used for the development of smart cities, smart structures, smart transportation systems, and smart healthcare [6, 27, 31].
Figure 1. Illustration of a Wireless Sensor Network (WSN)
All nodes in WSN communicates with each other via wireless links to share data and information. A sensor node is composed of the sensor, processor, transceiver, and power units. In addition to these functionalities, the WSN sensor nodes have the capability of routing. The routing protocol is the main design goal of any wireless sensor networks. In WSNs, routing protocol depends on according to the selection of network application, WSN structure, and routing strategy. Many more routing protocols are available for WSNs out of that Ad hoc On- Demand Distance Vector (AODV) routing protocol, Destination-Sequenced Distance-Vector (DSDV) routing protocol, and Optimized Link State Routing (OLSR) protocol [5, 9, 11, 19, 23, 26, 29, 30, 33, 34].
Most of WSNs applications require online measurements, sending reliable information, regularly and on time is very important. For that, optimal energy consumption is a crucial problem in WSNs [3, 16, 22, 25]. Delay and link reliability are QoS requirements in WSNs. QoS refers to the capability to provide an assurance that the service requirements of applications can be satisfied. The quality of service parameters (delay and link reliability) are used to measure the WSNs performance [5, 10, 12, 24, 32]. Wireless sensor network is characterized by multi-hop wireless connectivity and frequently changing network topology, therefore required efficient and optimal dynamic routing protocols. AODV routing protocol is a commonly used routing matrix for path discovery in wireless sensor network. The AODV should improve to find optimal shortest path. The proposed routing protocol is extracted from AODV.
The objective of this work is to propose an energy aware routing protocol to detect and avoid high cost metric routing bath. It should help wireless sensor network nodes making intelligent routing and forwarding decisions while extending the lifetime of WSNs.
In this paper, an Energy-Efficient Next Hop Selection (EENHS) algorithm is proposed for multi-hop WSNs. The proposed routing algorithm is extracted from AODV. In EENHS routing protocol, the QoS requirements such as delay and reliability are also considered. The EENHS routing protocol uses hop counts and link cost of the neighboring nodes to select the best next hop node for packets forwarding. So, the proposed routing algorithm is considered as the best one in finding the optimal shortest path from source node to destination node. In the proposed protocol (EENHS), each node uses residual energy, available queue size, and link reliability of a neighboring node to calculate its link cost function. Each node selects an appropriate next node among its neighboring nodes and depends on the calculated link cost function. Therefore, the EENHS routing algorithm considers not only the QoS requirements, but also the energy consumption of the nodes to improve the network performance and link reliability. The EENHS algorithm is evaluated by using the NS-2 simulator. The results show that the proposed routing algorithm outperforms the existing routing algorithms in terms of average energy consumption, end-to-end delay, packet delivery ratio, and the number of packets forwarded.
Wireless Sensor Networks is a new technology that received much attention from researchers in recent years. WSNs provide the possibility to monitor a different kind of applications such as environment by sensing physical phenomenon [27, 31]. However, the consumption of energy by WSNs devices is considered as an important issue that need to be aware due to the batteries lifetime [1, 5, 8, 18]. Also, the QoS is considered as an important factor to increase the batteries lifetime.
Routing protocols in WSNs determine a path between the sensor source node and the sensor sink (destination) node upon request of data transmission. In multi-hop WSNs, the source node cannot reach the destination node directly. So, the intermediate sensor nodes have to relay their packets. Routing tables gives the solution. The routing tables contain the lists of node option for any given packet destination [2-4, 13, 14, 16, 21, 22, 25, 36].
WSNs Routing Protocols can be classified into five types,
Figure 2 shows the different types of WSN routing protocols. Most of them focus on maximizing network lifetime. Many more routing protocols are extended versions of DSR and AODV [5, 9, 14, 23, 33].
Figure 2. WSN Routing Protocols
Destination Sequenced Distance Vector routing (DSDV) is a hop-by-hop distance vector routing protocol. In DSDV routing protocol, it requires each node to periodically broadcast routing updates based on the idea of classical Bellman-Ford Routing algorithm [7, 20, 28]. The DSDV routing protocol is adapted from the conventional Routing Information Protocol (RIP). Each node maintains a routing table. Each node contains information about how old the route is the shortest distance as well as the first node on the shortest path to every other node in the WSNs. In order to prevent loops and counter the count to infinity problem, the routing table recording the “next hop” for each reachable destination, number of hops to reach the destination, and the sequence number assigned by the destination node. The sequence number is used to distinguish old routes from new ones. The stations routing tables periodically transmit to their immediate neighbors. Also, the station transmits its routing table if a significant change has occurred in its table from the last update sent. So, the update is both time-driven and event-driven. The routing table updates can be sent in two ways: a full dump or an incremental update.
Ad hoc On-Demand Distance Vector (AODV) routing protocol is an improvement on the DSDV algorithm. The AODV routing protocol is the combination of Dynamic Source Routing (DSR) and DSDV, by absorbing their advantages and getting rid of their deficiencies [7, 15, 20, 28]. It is a reactive routing protocol that uses an ondemand approach to find and establish routes. AODV algorithm maintains routes as long as they are needed by the source nodes and it is considered as one of the better routing protocols in terms of power consumption and establishing the shortest path. However, it is principally used for ad-hoc networks, mobile ad hoc networks, and wireless mesh networks, but nowadays it is widely used in WSNs as well [15]. In AODV, every node periodically broadcasts HELLO messages to its neighboring nodes and then uses these neighbors to establish routing and send messages. Figure 3 shows the structure of the HELLO message.
Figure 3. Hello Message Structure
So, to select next hop we use the following equation (1).
where Cost metric function depends on the residual energy, free buffer, and link performance. After initialization stage, each sensor node has enough information to compute the Cost metric for its neighboring nodes. By using the link cost metric, the sink node locally computes its preferred next hop node and sends out a route request (RREQ) message to its the most preferred next hop. Figure 4 shows the structure of the RREQ message. TR field in RREQ message shows the received time of the packet. Delay field in RREQ message shows the transmission delay of the packet. Similarly, each intermediate node in the network forwards the route request (RREQ) message until it reaches the destination node.
Figure 4. The RREQ Message Structure
The destination node responds to the route request message by transmitting the route reply (RREP) message. Accordingly, the RREP flows through the network; it determines the route from source to destination node. The sequence number is increased by each originating node. It is used to determine whether the received message is the most recent one. The older routing table entries are replaced by the newer ones. Active nodes in the WSNs are determined by broadcasting a “Hello” message periodically in the network. If a node fails to reply, a link break is detected and a route error (RERR) message is transmitted. It is used to invalidate the route as it flows through the network. A node also generates a RERR message if it gets message destined to a node for which a route is unavailable.
The proposed Energy-Efficient Next Hop Selection (EENHS) routing protocol for multi-hop WSNs is extracted from the Ad hoc On-Demand Distance Vector (AODV) routing protocol. Next hop selection depends on the cost function value. The EENHS proposed protocol uses hop counts and link cost of the neighboring nodes to select the best next hop node for packets forwarding. Every node uses available queue size, residual energy, and link reliability of a neighboring node to calculate the cost function. Every node selects an appropriate next node amongst its neighboring nodes. The hop selection depends on its link cost function. The next hop node has the minimum hop counts to the WSN sink node and the maximum link cost. Therefore, the proposed routing algorithm considers not only the quality of services requirements, but also the energy consumption of the nodes to improve the network performance and link reliability. The proposed routing protocol achieves the significant improvement in terms of network life span and provides enhanced energy performance for wireless sensor networks.
Network reliability of WSNs is hard to be evaluated exactly because of its complexity, multi-states, and dynamic characteristics. A high level of reliability is a significant requirement for using WSNs in industrial environments. The Link reliability between two nodes affect the QoS requirements and the energy consumption because the low link reliability causes the high retransmitted packets whereby energy consumption is increased. The link reliability between two nodes depend on the ratio of the number of packets that are failed to deliver to the destination to the total number of packets that are sent by the source as shown in equation (2).
where, LinkR (ij, t) is the link reliability between node i and node j at time (t), Tx is the number of packets failed failed,ij transmitted through the link, and Tx is the total number Total,ij of transmission and retransmission attempts simulation.
Energy balancing and energy efficiency are two major factors for prolonging the network lifetime. The balance energy consumption between sensor nodes and the residual energy of the nodes are taken into account. Residual energy of node (E ) is given by very simple res equations. It depends on both transmitting and receiving data from one node to another node and are given below in the following equations [38-39].
where, E is the residual energy of wireless sensor node, E res init is the initial energy of the node, and E is the consumed cons energy inside the node. The energy consumption in a node is calculated by a total amount of transmission and reception energy in a node.
where, E is the transmission energy defined by the tx following equation.
where, L is the number of bits, and d is distance between two nodes (i and j). E is the reception energy. The rec reception energy is defined by the following equation.
where E and E are the energy which the radio tx,elec rec,elec model needs for the transmitter and the receiver respectively, E is the energy for the transmit amplifier.
The link cost function is applied in each node to find the next hop node. This function contains residual energy of node j, free queue size of node j, and link reliability between nodes I and j. This is defined as follows,
where, E and E are residual energy and initial energy res,j init,j of node j, respectively. Q and Q are available empty,j total,j queue size and total queue size of node j, respectively. LinkR is the link reliability between two nodes i and j. C , C ij E Q and C are three constant coefficients.
In the proposed EENHS routing algorithm, the cost link function value Cost is equal 0 in following cases: - E , ij res,j Q , and LinkR values are equal zero or one of them is empty,j ij equal zero. In link cost value equal 0, the sensor node is died. Three factors (residual energy, available queue size, and link reliability) are considered to satisfy QoS requirements in the proposed algorithm. Residual energy of the nodes is utilized to balance the energy between the nodes. Queue size of the WSN nodes is considered in the cost function because the queuing delay has a significant contribution in the end-to-end delay. Also, the link reliability is used to increase the reliability of the WSNs. In the proposed routing algorithm, the next node is selected by using equation (8).
Hop count of node i is calculated by equation (9).
where, No. of Hop is the minimum hop count of node i min,i to the destination node, and No. of Hop is the minimum min,j hop count of node j to the destination node.
After the node receives a HELLO packet from its neighboring node, the node updates its neighbor table as explained before. The next hop selection updates periodically. Algorithm 1 shows the next hop selection algorithm. The routing algorithm satisfies QoS requirements since it uses the link cost of neighboring nodes (residual energy, queue length, and link reliability) and the minimum hop count to the sink.
Algorithm 1: The pseudo code for next hop selection INPUT: The neighbor set of node i, No. of Hop SN min,i i
OUTPUT: Next hop of node i
Step 1: for(All nodes in the neighbor set of node i) do
Compute Cost , jÎ Neighbor set
End for
Step 2: j = first element of the neighbor set
Step 3: while(Not end of neighbor set) do
If ( No. of Hop = No. of Hop - 1) then
Add j to the select set of node i
End if
j = next element of neighbor set
End while
Step 4: Sort the select set (in descending order of Cost )
Step 5: next hop = First element of the list the select set.
The main objective of the EENHS proposed routing algorithm is to establish the cost field through routing, and transmit the data through the minimum cost path.
In order to evaluate the objective of this work, the discrete event simulator NS-2 is used, which is a powerful tool for simulating wireless sensor networks in Linux Red Hat operating system. But to simulate WSNs in NS-2 needs to have an additional module to represent the protocols specific to WSNs.
To evaluate the performance of the proposed routing algorithm, three different routing algorithms (DSDV, AODV, and EENHS) are simulated. The later one is the proposed routing algorithm. Simulations have been done on the WSN dimensions of 100 × 100 m area with 100 sensor nodes randomly distributed. The primary energy of each Node is 10 joule. In this WSN, propagation model is a free space and the path loss of the propagation model is set to 2 dB. The link layer maximum transmission rate is 250 Kbps and its frequency band is 2.4 Ghz. Table 1 shows the simulation parameters used in this work.
Three performances (energy consumption, end to end delay, and packet delivery ratio) are used to evaluate the proposed routing algorithm.
Table 1. WSN Simulator Parameters
Energy consumption is defined as the average energy consumed by all the nodes due to transmission and reception of data packets. About the energy consumed, the proposed routing protocol reduces this energy as shown in Figure 5. Comparing with other algorithms, maximum Energy Consumption is considerably low in this proposed routing method.
Figure 5. Average Energy Consumption
End-to-end delay (ETED) is defined as the average of latency time for successfully transmitted packets via a route from end to end. Also, throughput is the average time taken by a data packet to arrive at its destination. It also includes the delays caused by route discovery process and the queue in data packet transmission. The proposed Energy-Efficient Next Hop Selection (EENHS) routing protocol for multi-hop WSNs is compared with AOVD and DSDV routing protocols. The algorithm shows a minimal throughput when compared with other two algorithms. It is observed that the throughput decreases with the increase in the number of nodes as shown in Figure 6.
Figure 6. End-to-End Delay
Packet Delivery Ratio (PDR) is defined as the ratio between the number of data packets successfully received and those generated as shown in equation (10).
The result is shown in Figure 7. The proposed Energy- Efficient Next Hop Selection (EENHS) routing protocol for multi-hop WSNs is compared with AODV and DSDV routing protocols.
Figure 7. Packet Delivery Ratio
In this proposed algorithm, the destination receives almost all packets send by source. Re-routing is less in the EENHS routing protocol so its packet delivery ratio is better than DSDV and AODV.
In wireless sensor networks, the energy consumption, and QoS are important issues for the research of the route protocol. In this work, an Energy-Efficient Next Hop Selection (EENHS) routing protocol is proposed for multihop WSNs routing algorithm. The proposed routing algorithm is extracted from AODV. The EENHS routing protocol uses the hop counts and the link cost of the neighboring nodes to select the best next hop node for packets forwarding. Every node uses residual energy, link reliability, and available queue size of a neighboring node to calculate its link cost function. Every node selects an appropriate next node amongst its neighboring nodes. The next hop selection depends on the calculated cost function. So, the proposed routing selects the optimal shortest path from WSN source node to WSN destination node. Also, the EENHS routing algorithm considers not only the QoS requirements, but also the energy consumption of the nodes to improve the network performance and link reliability.
The simulation results show that the proposed routing algorithm reduced the packet loss, end-to-end delay, and energy consumption when compared with the others routing protocols.