In the future network with Internet of Things (IoT), each of the things communicates with the others and acquires information by itself. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose energyefficient probabilistic routing (EEPR) algorithm, which controls the transmission of the routing request packets stochastically in order to increase the network lifetime and decrease the packet loss under the flooding algorithm. The proposed EEPR algorithm adopts energyefficient probabilistic control by simultaneously using the residual energy of each node and ETX metric in the context of the typical AODV protocol. In the simulations, we verify that the proposed algorithm has longer network lifetime and consumes the residual energy of each node more evenly when compared with the typical AODV protocol.
Internet of Things (IoT) is a network that enables new forms of communication between people and things and between things themselves. Each of the things or objects in IoT communicates with the others and plays a defined role [
In such networks for IoT, nodes are distributed in a certain region for specific purpose and gather the required information, for example, the information about the temperature, motion, and physical changes [
In addition, relaying information from a source to a destination is one of the most important tasks to be carried out in a large scale and dynamic IoT environment. The typical reactive routing protocols such as ad hoc ondemand distance vector (AODV) and dynamic source routing (DSR) are designed to find just the shortest path [
Algorithms to enhance the efficiency of the energy consumption have been widely proposed. In [
On the other hand, most of the current routing protocols use hop count as their route selection metric to find the shortest path between source and destination nodes. However, using only hop count as the routing metric is not appropriate in IoT with dynamic network topology, since it is insensitive to packet loss, data rates, link capacity, link quality, channel diversity, interference, or various other routing requirements. Expected transmission count (ETX) [
In this paper, we propose the energyefficient probabilistic routing (EEPR) algorithm, which employs both the ETX metric and the residual energy of each node as the routing metrics at the same time. By using the ETX metric, the EEPR algorithm composes the routing path with good link quality. Using the residual energy of each node as a routing metric makes it possible for all the nodes in the network to use their residual energy more evenly. In addition, the EEPR algorithm controls the flooding of RREQ packets in an opportunistic way, so reduces the overhead in the routing process, and finds the energyefficient routing path more efficiently compared to the typical protocols.
The proposed EEPR algorithm controls the request packet forwarding process in order to reduce the packet loss and network congestion in the context of the AODV protocol. A source node that has data packets to transmit forwards the RREQ packets to its onehop neighbor nodes. In the typical AODV protocol, each node that receives a RREQ packet forwards it to all their onehop neighbor nodes. On the other hand, a node does not forward the RREQ packet all the time but calculates the forwarding probability via the proposed forwarding probability formula and decides stochastically whether to forward or discard it.
In this paper, we employ two different routing metrics. The first one is the ETX metric which presents the link quality between nodes. In general, probe packets are used to heuristically obtain the ETX value of a link [
The denominator of (
In this paper, we induce the ETX value metric not by using the heuristic method but by using the bit error rate (BER) based on the pathloss model. The received signal strength (RSS), the signal strength that the receiving node senses, is calculated as
We calculate the ETX of each link by counting the number of probe packets that a node receives when the total number of probe packets is 10. The result of the ETX metric via distance is shown in Figure
ETX metric via distance.
In this paper, we define
The second routing metric to be used in the proposed EEPR algorithm is the residual energy of a node which shows efficiency of the energy consumption in the network. We define the residual energy of node
Then, the forwarding probability
Forwarding probability via ETX and residual energy.
When forwarder node
Example of the EEPR algorithm.
According to (
To solve the above problem, we propose the advanced EEPR algorithm considering both the residual energy of its onehop neighbor nodes and the average value of residual energy of all nodes in the network. To describe the advanced EEPR algorithm, we should assume two factors. First, it is assumed that each node knows the average value of residual energy of all nodes in the network,
The operational procedure of the advanced EERP algorithm is as follows. When source node needs a routing path, source node broadcasts the RREQ packet to its onehop neighbor nodes. Then, a forwarder node that receives the RREQ packet calculates forwarding probability
An example of the advanced EEPR algorithm is shown in Figure
Example of the advanced EEPR algorithm.
In the case of using the advanced EEPR algorithm,
Flow chart of the advanced EEPR algorithm.
In this paper, we evaluate the performance of the proposed EEPR algorithm and compared it with the typical AODV protocol.
Simulations have been performed by the NS2 simulator version 2.35 on the Linux Fedora 13 [
Factors used in the simulation.
Simulation factor  Value 

Topology  1000 m by 1000 m grid random 
Number of nodes  50 
Path loss model 

Noise power  10^{−11} W 
Transmission range  300 m 
Packet size  1,000 bytes 
Initial node energy  10 J~100 J, uniform distribution 
Transmission power  0.1 mW 
Power consumption for transmission  1.65 W 
Power consumption for reception  1.1 W 

100 

40 

45 

0.7 

1 
Generally the network lifetime is defined as the time difference between the time when the simulation starts and the time when a node having zero residual energy happens. In our work, we extend the concept of the network lifetime and measure the time between the simulation starting time and the time when
Figure
Time as a function of the number of nodes having zero residual energy.
We measure the residual energy of all the nodes and calculate the variance of the residual energy when the simulation ends. The smaller the variance is, the more evenly the algorithm uses the residual energy of the nodes. For performance comparison, the configuration of residual energy distribution is not changed but fixed regardless of the method used.
The result for the variance of the residual energy of all the nodes in the network is shown in Figure
Variance of the residual energy.
Since the EEPR algorithm stochastically controls the number of request packets, the forwarder nodes do not forward the request packets so frequently. This can result in greater routing setup delay compared with the typical AODV protocol. In this paper, we define the routing setup delay as the time difference between the time when a source node forwards the RREQ packets and the time when a destination node receives the first RREQ packet.
The result of the routing setup delay is shown in Figure
Routing setup delay.
The EEPR algorithm stochastically controls the number of the RREQ packets. Therefore, as shown in Section
The result for the routing success probability in Figure
Routing success probability.
In this paper, we proposed EEPR algorithm which employs both the residual energy of a node and the ETX value as the routing metrics, at the same time. The proposed EEPR algorithm stochastically controls the number of the RREQ packets using the residual energy and ETX value of a link on the path and thus facilitates energyefficient route setup. Simulation results show that the proposed EEPR algorithm has longer network lifetime and consumes the residual energy of each node more evenly when compared with the typical AODV protocol while the routing setup delay is slightly increased and the routing success probability is slightly decreased.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research was supported by the ChungAng University