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International Journal of Computer Trends and Technology (IJCTT) – Volume 41 Number 3 – November 2016

ISSN: 2348 – 8387 www.internationaljournalssrg.org 19

An efficient Routing Protocol: ACORP for Wireless Sensor Networks

Chaganti B N Lakshmi1, Dr.S.K.Mohan Rao2 1Associate Professor, Mahaveer Institute of Science and Technology, Hyderabad, Telangana, India

2Principal, Gandhi Institute for Technology, Bhuvaneswar, Odissha, India

Abstract — The most interesting and challenging research areas in WSNs is maximizing the network lifetime using energy efficient routing. Energy conservation is the primary challenge for WSNs and utilizing the energy efficiently during routing is an essential requirement and is a demanding task for all other research areas in WSNs. Minimizing the energy consumption and enhancing the lifetime of the network depending on routing protocols are the main objectives in designing WSNs since the sensor nodes are battery operated and cannot be replenished or recharged frequently. Here A Routing Protocol (ACORP) is proposed for increasing the Wireless Sensor Network lifetime using Ant Colony Optimization meta heuristics. The performance of the protocol is simulated and observed in terms of packet drop, delay, Delivery Ratio and Energy consumption and compared with existing Ad hoc on Demand Distance Vector routing protocol and self healing routing protocol for Wireless Sensor Networks. Keywords — Ant Colony Optimization, Energy conservation, Network Lifetime, Routing Protocol, Wireless Sensor Networks.

I. INTRODUCTION A Computer Network is a collection of computers

and hardware devices that are interconnected together via wired or wireless links. A Wireless Network use radio signals to transmit the data instead of using cables such as coaxial cable, Ethernet etc. Wireless Networks can be further classified into Infrastructure based and infrastructure less ad hoc wireless networks depending on the network architecture. A Wireless Sensor Network (WSN) is a special type of Ad hoc network consisting of large number of Sensor Nodes (SNs) which are mainly used in unattended environments to observe some physical phenomenon. WSNs have many applications in various fields including environment monitoring, battlefield surveillance, habitat monitoring, disaster management etc [1]. The SNs are small in size and are battery operated. These sensor nodes can sense, compute and communicate the observed phenomenon to a remote sink in a multi hop fashion. Since these SNs are generally deployed in harsh and hostile environments they cannot be replaced or replenished frequently. Hence,

minimizing the energy conservation is treated as a major challenging issue in the design of WSNs.

Even though energy can be utilized efficiently at each and every layer of WSN protocol stack, here minimization of the energy conservation is considered at network layer as communication cost is much higher than any other costs in WSNs [2]. Routing protocols designed for traditional networks are not suitable for WSNs due to the unique characteristics of WSNs [3]. These unique characteristics include: large deployment of nodes, dynamic topology, resource constrained nodes etc. Consequently, designing of a routing protocol is difficult for WSNs. Many routing protocols are developed for WSNs to satisfy the diverse requirements of the applications and the unpredictable nature of WSNs [4]. Each proposed routing protocol claims that the proposed method gives an improvement over the other methods considered in the literature for a given network scenario such as increasing the node density and traffic. The remainder of this paper is organized as follows: Section 2 gives related work and Section 3 describes the taxonomy of routing protocols for WSNs. In section 4 ACORP is described. ACORP is compared with Adhoc On demand and Distance Vector (AODV) and Self Healing Routing Protocol (SHRP) in section 5. Finally, conclusion is presented in the last section.

II. RELATED WORK Energy conservation can be minimized by using any efficient communication paradigm. Many issues in communication are framed as multidimensional optimization problems. Swarm Intelligence (SI) have become increasingly popular during the last decade [5]. Swarm intelligence, which is an artificial intelligence (AI) discipline, is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviour of social insects such as ants, termites, bees, and wasps, as well as from other animal societies such as flocks of birds or schools of fish. The advantage of using swarm intelligence approach over traditional techniques is it’s robustness and flexibility [6]. These properties make swarm intelligence a successful design paradigm for algorithms that deal with increasingly complex problems. Many aspects


ISSN: 2231-2803 http://www.ijcttjournal.org Page 120

of the collective activities of social insects are self-organized and work without a central control. Most commonly studied under Swarm Intelligence are the applications of Ant Colony Optimization and Particle Swarm Optimization algorithms [7]. ACO is inspired by the ants foraging behaviour. Ants can communicate indirectly by means of chemical pheromone trails and they find short paths between their nest and food sources. This property of real ant colonies is exploited in ACO algorithms to solve many optimization problems such as scheduling problems, graph colouring, assignment problems or vehicular routing problems [8]. The ant agents can be divided into Forward ANTs (FANT) and Backward ANTs (BANT). The main purpose of this division is to let the BANTs to use the collected information gathered by FANTs on their trip time from Source to Destination. In [9] the energy consumption between nodes is balanced by combining the energy of the node and pheromone and delay of the node in finding the routing path. But this protocol is not scalable and the performance is observed by considering a simple topology. In [10] an Improved Energy-Efficient Ant Based Routing (IEEABR) Algorithm is proposed for Visual Sensor Networks. Compared to the state-of-the-art Ant-Based routing protocols: Basic Ant-Based Routing(BABR) Algorithm, Sensor-driven and Cost-aware ant routing (SC), Flooded Forward ant routing (FF), Flooded Piggybacked ant routing (FP), and Energy Efficient Ant-Based Routing (EEABR), the proposed IEEABR approach have advantages of reduced energy usage, delivering events packets at high success rate with low latency, increases the network lifetime, and actively performing its set target without performance degradation. An adaptive secure routing protocol which is based on bio inspired mechanism is proposed in [11]. It used distributed ant-based methodology to select two optimal paths keeping in view route security. A novel meta heuristic on-demand routing protocol Ant-E, using the Blocking Expanding Ring Search (Blocking-ERS) to control the overhead and local retransmission to improve the reliability in term of packet delivery ratio (PDR) is proposed in [12] . This method enhances the efficiency of MANET routing protocol. Ant-E was inspired by the ant-colony optimization (ACO) used to solve complex optimization problems and utilized a collection of mobile agents as “ants” to perform optimal routing activities. Exhaustive simulations are carried out and it is observed that, Ant-E performs better than other two on demand routing protocols like AODV and DSR.

Ant-based Dynamic Zone Routing Protocol (AD-ZRP), a self-configuring reactive routing protocol for Wireless Sensor Networks (WSNs) is proposed in [13]. Their approach was based on HOPNET, a multi-hop and self-configuring hybrid routing

protocol based on Ant Colony Optimization (ACO) and Zone Routing Protocol (ZRP) for Mobile Ad Hoc Networks (MANETs). There was many challenges in designing routing protocols for WSNs, and topology change was a factor that affects the network lifetime of WSNs.

A quality of service enabled ant colony based multipath routing (QAMR) algorithm based on the foraging behaviour of ant colony for selecting path and transmitting data is proposed in [14]. In this approach, the path was selected based on the stability of the nodes and the path preference probability. The authors have considered bandwidth, delay and hop count as the QoS parameters along with the stability of node, the number of hops and path preference probability factors. Simulations performed with network simulator 2 shows that the proposed algorithm was scalable and performs better at higher traffic load compared to the existing algorithms.

III. ROUTING PROTOCOLS FOR WSNS

WSN Routing Protocols can be classified in four ways according to the way of routing paths are established, according to the network structure, according to the protocol operation and according to the initiator of communications. Routing paths can be established in one of three ways, namely proactive, reactive or hybrid [15]. Proactive protocols compute all the routes before they are really needed and then store these routes in a routing table in each node. When a route changes, the change has to be propagated throughout the network. Since a WSN could consist of thousands of nodes, the routing table that each node would have to keep could be huge and therefore proactive protocols are not suited to WSNs. Reactive protocols compute routes only when they are needed. Hybrid protocols use a combination of these two ideas. But in general, routing in WSNs can be divided into three categories named as flat-based routing, hierarchical-based routing and location based routing depending on the network structure[16]. In flat-based routing, all nodes play the same role. In hierarchical-based routing, however, nodes will play different roles in the network. In location-based routing, sensor nodes positions are exploited to route data in the network. Furthermore, these protocols can be classified into multipath-based, query-based, negotiation-based, QoS -based, or coherent-based routing techniques depending on the protocol operation. Flat Routing (Data Centric Routing protocols): It is not feasible to assign global identifiers to each node due to the sheer number of nodes deployed in many applications of sensor networks. Such lack of global identification along with random deployment of sensor nodes makes it hard to select a specific set of



sensor nodes to be queried. Therefore, data are usually transmitted from every sensor node within the deployment region with significant redundancy. This consideration has led to data-centric routing. In data-centric routing, the sink sends queries to certain regions and waits for data from the sensors located in the selected regions. Hierarchical protocols: The major design attributes of sensor networks are scalable. Since the sensors are not capable of long-haul communication, single gateway architecture is not scalable for a larger set of sensors. Clustering has been pursued in some routing approaches to cope with additional load and to be able to cover a large area of interest without degrading the service. Hierarchical routing works in two layers, first layer is used to choose cluster heads and the other layer is used for routing. To make the WSN more energy efficient, clusters are created and special tasks (data aggregation, fusion) are assigned to them. It increases the overall system scalability, lifetime, and energy efficiency. Location-based protocols: In most cases location information is needed in order to calculate the distance between two particular nodes so that energy consumption can be estimated. Generally two techniques are used to find location, one is to find the coordinates of the neighboring node and the other is to use GPS(Global Positioning System). Since, there is no addressing scheme for sensor networks like IP-addresses and they are spatially deployed on a region, location information can be utilized in routing data in an energy efficient way. Multipath routing protocols: Multiple paths are used to enhance the network performance. When the primary path fails between the source and the destination an alternate path exists that measured the fault tolerance (resilience) of a protocol. This can be increased, by maintaining multiple paths between the source and the destination. This increases the cost of energy consumption and traffic generation. The alternate paths are kept alive by sending periodic messages. Due to this, network reliability can be increased. Also the overhead of maintaining the alternate paths increases. Query based routing protocol: The destination nodes propagate a query for data (sensing task) from a node through the network and a node having this data sends back the data to the node that matches the query to the query that initiates. Usually these queries are described in natural language, or in high-level query languages. Negotiation based routing protocol: In order to eliminate redundant data transmissions, these use high level data descriptors through negotiation. Based on the resources that are available to them, communication decisions are taken. The motivation is that the use of flooding to disseminate data will produce implosion and overlap between the sent data; hence nodes will receive duplicate copies of the same data. This consumes more energy and more

processing by sending the same data to different sensor nodes. So, the main idea of negotiation based routing in WSNs is to suppress duplicate information and prevent redundant data from being sent to the next sensor node or the base-station by conducting a series of negotiation messages before the real data transmission begins. QoS-based routing protocols: In order to satisfy certain QoS (Quality of Service) metrics, e.g., delay, energy, bandwidth, etc. when delivering data to the Base Station, the network has to balance between energy consumption and data quality.

Routing protocols can also be classified based on swarm intelligence and they are namely: Ant based, bee based and slim based. Ant based routing protocols: Some of the ant based algorithms are a. Ant-AODV (Ant Ad hoc on demand distance vector) uses fixed number of ants going around the network in a more or less random manner, keeping track of the last n visited nodes and when they arrive at a node they proactively update its routing table. b. ARA (Ant-colony based routing algorithm) is a purely reactive scheme which uses forward ants and backward ants to create fresh routes from a node to a destination. c. PERA (Probabilistic emergent routing algorithm) reactively establishes route to the destination using delay as the metric. Multiple paths are set up, but only the one with the highest pheromone value is used by data. d. ANSI (Ant hoc networking with swarm intelligence) deployed two types of ants namely; Local proactive ants and global reactive ants. e. Ant Hoc Net (ant-based hybrid routing algorithm) is congestion-aware protocol which only finds routes on- demand, but once a route is established, the route is proactively maintained. f. Ant Net algorithm tries to manage both delay and energy concerns using the concept of ant pheromone to produce two prioritized queues, which are used to send differentiated traffic. But such approach can be infeasible in current sensor nodes due to the memory required to save both queues. So it becomes necessary to build energy efficient protocol to eliminate these disadvantages .

IV. ANT COLONY OPTIMIZATION BASED ROUTING PROTOCOL(ACORP)

There are two types of ants applied in the algorithm, forward ants and backward ants. Forward ants, whose main task is exploring the path and collecting the information from the source nodes to a destination node, have the same number as the source nodes. There are two key factors that conduct the movement of the forward ants: one is pheromone trails that are deposited along the edges, and the other is the nodes potential which provides an



estimate of how far an ant will have to travel from any node to either reach the destination d or to aggregate data with another node. While the backward ants, travelling back from the destination node to source nodes contrary to the forward ants, perform their uppermost function of updating the information of their pass-by nodes. ACO algorithms are a class of constructive meta-heuristic algorithms that mimic the cooperative behaviour of real ants to achieve complex computations and have been proven to be very efficient to many different discrete optimization problems. Many theoretical analyses related to ACO show that this optimization can converge to the global optimal with non-zero probability in the solution space and their performance have greatly matched many well-studied stochastic optimization algorithms, for example, genetic algorithm, pattern search, GPASP, and annealing simulations. Source node broadcasts a FANT to the neighbouring nodes along the destination which collects the hop distance and residual energy of each node. On reaching the destination, a BANT is transmitted through the reverse path. The collected hop distance and residual energy of each node by the BANT is analysed at the source. At the source, the path with shortest hop distance and high residual energy is chosen as primary path for data transmission.

A. Energy Model The advantages and performance of the protocol is effected by the energy model using by the protocol. To facilitate comparison and to make it simple, here the energy model considered includes transmission and receiving units and is represented in the Fig 1.

Eelec X m Eamp X m X d2

Eelec X m

Fig. 1 Energy Consumption Model

The energy required to transmit a unit amount of

data depends on the size and distance between the nodes but the energy consumed for receiving a unit amount of data depends on the size of the data only. Thus when a node sends m bit data to another node which is at a distance d, then the energy consumption is

When a node receives m bit data the energy

consumption will be

B. Network Model

1. The SNs and sink are immobile and are homogeneous. i.e, the SNs can not move and are having same capabilities in terms of processing, transmission and power supplies. However, the Sensor Network is dynamic in nature. 2. The sensor network is composed of a small number of base stations or sinks and a numerous number of wireless sensors randomly distributed in an interesting area. These sensor nodes have limited processing power, storage, bandwidth, and energy, 3. All the wireless communication links are symmetric. 4. All the SNs know the distance between each other. The relative distance between the SNs is obtained using triangulation algorithm based on the received signal strength. 5. Hidden or exposed terminal problem due to big obstacles between source and destination is not encountering. 6. No conflicts with underlying MAC protocols. 7. The SNs can adjust their transmission power based on the distance. 8. The number of nodes in the network is large 9. The initial energy of all the nodes in the network are same. C.ACORP Routing Algorithm

In order to simulate the behaviour of real ants, the following notations are used here.

1. N : Number of nodes in the network 2. m : Number of ants in the ant colony 3. (t): Pheromone intensity on the link i,j at

time t. 4. Ni

k : Feasible neighborhood of ant k at node i.

5. Visibility of an ant at node i to choose the node j.

6. Δt : Time gap between the generation of ants

Packet of size

Electronic Circutry for

Transmission

Packet of size m

Electronic Circuitry for Receiving



7. α :The relative importance degree of pheromone

8. β : The relative importance degree of visibility

9. : The transition probability of ant k from node i to node j

10. : The amount of pheromone to be added

11. r: reinforcement factor D.Rules for the ants behaviour 1. Ant selects the next path according to the

pheromone concentration and residual energy.

2. With each iteration, the pheromone concentration of the whole path is updated.

3. Loops are to be avoided. E. Description In the initial stage, all the nodes are initialized with an amount of pheromone 0.5. An ant k finds the direction according to the amount of pheromone and energy remained in the node. Ant k (k=1,2,…m) determines the direction according to the amount of pheromone on each path. The nodes that are selected or visited are memorized in the ant’s memory. In the search process, state transition probability is calculated according to the pheromone and heuristic information of each path. At time t, the transition probability (t ) is

calculated by the following formula[17]

Where is a heuristic value to find the visibility of a node and is calculated by the following formula

Once FANT selects the node, its pheromone is updated according to the following formula

Where is

F. Path selection based on Hop count and residual energy:

FANT stops its movement when the current node ID matches with the destination node id specified in it. Finding the destination, the BANTs travel back over the recorded path. The routing table is updated with the BANT records. The route table is constructed at the source using the information collected from BANTs, is used in the path selection. The routing table entries are neighbour node id, Destination node ID, Hop count and Residual Energy. Each and every FANT, BANT has the records of the same form. The algorithm for selecting the path is represented in Fig.2

1. Start 2. Define S = source, D = destination, N1, N2,

N3, N4… = intermediate neighbour nodes between the source and destination

3. H = hop count 4. R = Residual energy 5. Ni = {N1, N2, N3, N4……….} 6. For (each path j) 7. While (Ni! =D) 8. { 9. If (R (Ni) > Minvalue) 10. { 11. If ( (H(Ni,j ) < H(Ni,j+1 )) and

(R(Ni,j )> R(Ni,j+1))) 12. Select the node for routing 13. Else 14. Goto 4 15. } 16. Else 17. { 18. Ni sends a warning message to

the source 19. Source tries to find an alternate path 20. If (there is no reachable node) 21. Send data by increasing

transmitting power 22. } 23. } 24. Else 25. Exit 26. End

Fig 2.Algorithm for path selection



V. PERFORMANCE EVALUATION In order to evaluate the performance of ACORP,

it is simulated using NS-2. It is compared with the existing routing protocols AODV and SHRP. The usage of IEEE 802.11 physical and MAC layer protocols which are fully simulated by NS-2 simulator does not affect the evaluation of the proposed protocol ACORP, as it is related to network layer. To observe the performance, the simulated area considered is 500*500 sqm. The number of nodes placed are varied as 50,100,150 and 200 using uniform distribution. The power level of the nodes are adjusted such that the transmission range of the nodes is fixed to 250m. The values of , and r are fixed to 0.8, 0.2 and 0.7. In this simulation, the channel capacity of mobile hosts is set to 2 Mbps. The distributed coordination function (DCF) of IEEE 802.11 for Wireless LANs is used as the MAC layer protocol. The simulated traffic is Constant Bit Rate. The simulation time is 50s.

The following Table-1 summarizes the simulation parameters used.

Table-I

Simulation Settings No. of Nodes 50,100,150,200 Area Size 500 X 500 Mac 802.11 Simulation Time 50 sec Traffic Source CBR Packet Size 512b Transmit Power 0.660 w Receiving Power 0.395 w Idle Power 0.035 w Initial Energy 15.3 J Transmission Range 250 Routing Protocol ACORP A. Performance Metrics

Delay: The end-to-end-delay is averaged over all surviving data packets from the sources to the destinations. It is observed from Fig 3. that the delay of ACORP is smaller than existing AODV and SHRP.

Fig. 3. Performance of Nodes Vs Delay

Packet Delivery Ratio: It is the total number of packets received by the receiver during the transmission. The delivery ratio of our proposed ACORP is higher than the existing AODV and SHRP technique as shown in Fig 4.

Fig. 4. Performance of Nodes Vs Delivery Ratio Drop: It is the number of packets dropped during the data transmission. From Fig 5, it can be understood that the packet drop of our proposed ACORP is less than the existing AODV and SHRP technique.

Fig. 5. Performance of Nodes Vs Drop Average Energy Consumption: The average energy consumed by the nodes in receiving and sending the packets. The energy consumption of the proposed ACORP is less than the existing AODV and SHRP technique as depicted in Fig 6.

Fig. 6. Performance of Nodes Vs Energy Consumption



VI. CONCULSIONS This paper has demonstrated that Ant Colony

Optimization based Routing Protocol can collect the data efficiently across multiple number of nodes in a multi hop fashion in Wireless Sensor Networks while maintaining a constant amount of local state for making local decisions. Since the information required on each node is very low and independent of both network size and network density, ACORP is highly scalable. ACORP gives optimum results to improve the network lifetime with the consideration of hop count and residual energy combindly. It also supports the dynamic characteristics of WSNs when the nodes are joining or leaving. Further, the high traffic load causing by periodic updates and flooding is eliminated here. ACORP is not requiring any geographical information also. The efficiency of ACORP is examined here in terms of packet drop, packet delivery ratio, energy consumption and packet delay through NS-2 simulator. The results shown that ACORP is transmitting the data using an optimal routing path and achieving high packet delivery ratio and low delay.

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Chaganti B N Lakshmi received her B.Tech and M.Tech in Computer Science and Engineering from Jawaharlal Nehru Technology University,Hyderabad. She is pursuing her Ph.D from Rayalasema University, Kurnool, Andhrapradesh. She is a Member of IETE, ISTE, Sensor Research Society (SRS) and Member of Computer Society of India (CSI).

Her research interests include Advanced Computer Networks, MANETs and Sensor Networks.

Dr. S Krishna Mohan Rao received B.Tech degree from JNTU, Hyderabad, M.Tech in Power Systems from JNTU, Ananthapur, M.Tech in Computer Science from IETE, and Ph.d in Computer Science and Engineering from Osmania University in 2009. He has 19 years of Industrial Experience, both in India and abroad and 8 years of teaching and research experience. He has published 6 papers in International Journals and 8 in National Journals. His research areas are Wireless Networks, Mobile Adhoc Networks, Industrial Management and Data ware House and Data Mining. He is Fellow of IETE, IE(I) and Life member of ISTE.

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