in wireless sensor networks

IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS’15

SECURE AND ADAPTIVE LOAD BALANCED RAINBOW ALGORITHM (S-ALBA-R) IN WIRELESS SENSOR NETWORKS Shantha Kumari Shanmugam,

Megalan Leo, Lecturer

Sathyabama University, Chennai, India [email protected]

Sathyabama University, Chennai, India megalanleo @gmail.com

Abstract—Wireless Sensor networks face security challenges due to their inherent limitations in computing, communication and the nature of deployment. S-ALBA-R features secure communication with cross-layer integration of MAC, enhanced greedy geographic routing, load balancing (ALBA) and routing around connectivity holes (Rainbow). The mechanism to manage location aware keys dynamically using bivariate polynomial key pre-distribution ensures improved quality of service with routing despite handling security requirements in wireless sensor networks as they are vulnerable to security threats. Extensive NS2-based simulations demonstrates S-ALBA-R outperforms with other converge casting protocols dealing critical traffic conditions and connectivity holes. Further the NS2 simulation results demonstrate performance improvement achieved in S-ALBAR protocol in terms of throughput, packet delivery ratio and End to End latency with reduced energy consumption. Index Terms—Wireless sensor networks, cross-layer routing, connectivity holes, geographic routing, localization errors, data security, polynomial bivariate key generation.

I. INTRODUCTION Wireless sensor networks (WSNs) with sensors distributed autonomously utilizes distributed sensing and flawless data gathering as the key properties in monitoring parameters such as temperature, rainfall, humidity etc., Data in the form of data packets which is collected through these sensor nodes and transmitted to the sink through multihop wireless routes known as WSN routing or converge casting utilizing. WSNs offering persistent services fulfilling privacy and security requirements with appropriate architecture are required for user acceptance. Research based on protocol design for WSNs is mainly focused on the routing solutions and MAC. The locationbased routing schemes otherwise known as Geographic routing deploys relay greedily chosen based on the data advancement toward sink. Being localized, distributed and almost stateless less storage with least computation is

required at the nodes with geographic routing which makes it most suitable for WSN applications. The important design challenges with geographic routing schemes in WSN are 1) Routing around dead ends 2) Flexibility to localization errors 3) Efficient relay selection and 4) Secure communication. Connectivity holes are inherently related to the way greedy forwarding works. Even in a fully connected topology, there may exist dead ends i.e. nodes without neighbors that could not provide packet advancement toward the sink. These dead ends are unable to forward the packets which they generate or receive, so these data packets will never reach their destination and will eventually be discarded. To alleviate the impact of dead ends many solutions that offer packet delivery guarantees are usually based on making the network topology graph planar, and on the use of face routing [1]. In the presence of realistic radio propagation effect [2] and localization error, planarization does not work well as it depends on unrealistic representations of the network, such as a unit disk graph (UDG [3]. Though With efficient path is established with routing data around dead ends secure data communication may not happen as the data being send from source to destination node may be hacked due to the deployment nature of WSN and resulting in data leakage or data loss. The paper includes following work related to secure communication in WSN research: Data security along with packet advancement and data congestion is considered together while making routing decisions thereby enhancing the greedy geographic forwarding. Routing functions, MAC along with secure communication is implemented in a cross-layer fashion in this relay selection scheme to achieve superior performance when compared with existing protocol ALBA-R [26] in terms of throughput, end to end latency, energy efficiency and packet delivery ratio (PDR). Efficient routing of data packets in and around dead end is achieved using Rainbow mechanism [26]. Rainbow which is resilient to channel propagation impairments and localization errors ensures packet delivery in realistic deployments of WSN. Unlike other protocols, Rainbow does not require planar network topology

International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.20 (2015) © Research India Publications; http://www.ripublication.com/ijaer.htm 15562

IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS’15 Due to the deployment nature and inherent limitations in wireless sensor networks, security becomes a crucial issue. Though load balancing and quality of service is assured in existing ALBA-R[26], data security during the routing operations is not included. So we propose a simple key exchange mechanism to incorporate cryptographic technique with the existing ALBA-R[26] mechanism to make the protocol good enough to consider for experimental and real time implementations. In this proposed work, polynomial bivariate key generation [27] used as a key exchange mechanism and one way hash chain algorithm for data security. Thus S-ALBA-R protocol ensures secure communication along with increase in throughput, packet delivery ratio, energy efficiency and end to end latency. Unique features of S-ALBA-R and its overall performance improvement with respect to previous exemplary solutions for geographic-based and topologybased convergecasting such as ALBA-R is demonstrated using extensive ns2-based simulation. The demonstration shows Rainbow[26] is an effective distributed scheme for routing packets around connectivity holes along with achieving remarkable delivery ratio and latency performance. The critical metrics of end to- end (E2E) latency and packet delivery ratio are evaluated through experiments using test bed with sensor TinyOS based 50 sensor nodes besides validating our simulation model. The results obtained confirm the effectiveness of S-ALBA-R in supporting reliable wireless sensor networking practice. II. RELATED WORK The need for designing power efficient and scalable networks provides justification for applying position-based routing methods in ad hoc networks. Routing protocols have two modes: greedy mode (when the forwarding node is able to advance the message toward the destination) and recovery mode (applied until return to greedy mode is possible)[21] Without location errors, geographic routing - using a combination of greedy forwarding and face routing has been shown to work correctly and efficiently. At microlevel behavioral analysis the possible protocol error scenarios are identified along with their conditions and bounds. This extensive simulation study of GPSR and GHT revealed that even small location errors (of 10% of the radio range or less) can in fact lead to incorrect (nonrecoverable) geographic routing with noticeable performance degradation. Thus a simple modification for face routing is made to eliminate probable errors and leads to near perfect performance [22] For mobile ad hoc networks, Restricted Delaunay graph (RDG) combined with a node clustering algorithm can be used as an underlying graph for geographic routing protocols is proposed. The graph has the following attractive properties: such as planar, between any two graph nodes there exists a path whose length, measure topological or Euclidean distance, and graph can be maintained efficiently. Furthermore, each node only needs constant time to make routing decisions.

Location-based routing used to achieve scalability in large mobile ad hoc networks, is difficult when there are holes in the network topology or when nodes are mobile or frequently disconnected to save battery. Terminode routing using a combination of location-based routing (Terminode Remote Routing, TRR) is used when the destination is far, and link state routing (Terminode Local Routing, TLR)[16], used when the destination is close. Simulation results demonstrate, terminode routing out performs in different size network, whereas in smaller networks, performance comparable with MANET routing protocols. Larger networks with uneven distribution of nodes, terminode routing proves better performance compared with existing location-based or MANET routing protocols.[24] FAR, a Face-Aware Routing protocol for mobicast— features face-routing and timed-forwarding for delivering a message to a mobile delivery zone. The analytical as well as statistical results proves FAR achieves reliable spatial and just-in-time message delivery with only moderate communication and memory overhead [25] LEACH (Low-Energy Adaptive Clustering Hierarchy) a clustering-based protocol uses randomized rotation of local cluster base stations (cluster-heads) to distribute energy load evenly among all sensors in the connected network. Localized coordination enables scalability and robustness for dynamic networks and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. LEACH capable of achieving energy dissipation reduced by a factor of 8 compared with conventional routing protocols. LEACH is capable to distribute energy dissipation evenly across sensors and thereby doubling the useful system lifetime for the simulated network [6]. Load balance vs energy efficiency [8] is two important concepts. Nash bargaining framework treats the objectives as two virtual players in the game theoretic model, negotiation starts on how traffic should be routed in order to obtain optimized range for achieving both the objectives. Throughout the negotiation each of them announces its performance threat value in order to reduce their cost. This model is regarded as a threat value game. The analysis also shows that no agreement can be achieved if any player sets threat value selfishly. It is avoid negotiation break-down and generate a fair solution. According to its initial and simplest formulation, geographic routing concern forwarding a packet in the direction of its intended destination by providing maximum per-hop advancement [9], [10]. Heuristic rules help to define next-hop relays in the greedy forwarding. Examples of this approach include [13], [14], [15], [16], [17] and [18]. Planar routing may then require the exploration of large spanners before being able to switch back to the more efficient greedy forwarding, thus imposing higher latencies [19]. To make planarization work on real networks, a form of periodic signalling must be implemented to check that no links cross, as performed by the Cross-Link Detection Protocol (CLDP) [20].


IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS’15 III. PROPOSED WORK A. Adaptive Load Balancing Algorithm The ALBA protocol is a cross layer solution for converge casting in WSNs and integrates MAC, awake/asleep schedules, routing, back-to back packet transmissions and traffic load balancing. Nodes alternate between awake/asleep modes according to fixed duty cycle and independent wake-up schedules. The sender node broadcasts request-to-send (RTS) and performs polling to identify the availability of its awakened neighbor. The RTS communicates relevant routing information along with channel access sensing (crosslayer approach). The neighboring nodes which is awake, responds with clear-to-send (CTS) packet carrying routing information via which the sender can choose the best relay. Neighbors offering ―good performance‖ with respect to data forwarding is preferred and thus selected to be part of the relay. Nodes with same forwarding performance are differentiated using positive geographic advancement toward the sink (the main relay selection criterion in many previous solutions). Every prospective relay is characterized by two parameters, primarily queue priority index (QPI) and then geographic priority index (GPI). RTS packet includes location of the sender, sink and the length of the data burst NB for the nodes to calculate their GPI and QPI Queue priority index (QPI) The queue priority index is calculated based on the burst size of the packet transmission along with the moving average and the number of packets in an eligible relay queue. QPI

The basic idea for avoiding connectivity holes is that of allowing the nodes to forward packets away from the sink when a relay offering advancement toward the sink cannot be found. Among the colors in an ordered list, each node is labeled by a color. To remember whether to seek for relays in the opposite direction or in the direction of sink, each node searches for neighbors with its own color or with color immediately before in the ordered list. So a feasible route to the sink is always found by using color assigned to each node with the Rainbow mechanism. The transmission region is basically partitioned into two regions called F and FC that include all neighbors of source offering a positive or a negative advancement toward the sink. Based on the reply from the region the source finds the path to forwarding the data.

QPI =0 QPI =1

S

QPI =2 QPI =3

D

(1)

where Q is the number of packets in the queue of eligible relay, moving average of relay is M, NB is the packets requested to be transmitted in back to back transmission and the maximum allowed QPI is Nq.

Figure 1: Geographic Priority Index regions

C. Sleep & Awake schedule Geographic priority index (GPI) The geographic priority index is assigned by the range of distance of each node with respect to sink. Each node computes its GPI using the sink’s position information along with its own location information provided either by GPS or computed through some localization. The GPI is basically a number representing the geographic region of the forwarding area of the sender where a potential relay is located ranging from 0 to Nr – 1. Numbers are assigned such that, the higher the number of the region, the further the sink is the nodes it contains, as shown in the figure 1.

B. Rainbow Mechanism Rainbow is a mechanism used to establish routing around connectivity holes and eliminate losing data packets. Each node in the Here labeling color to the nodes.

Based on independent wake-up schedules with fixed duty cycle d each node alternate between awake/asleep modes. When the sender nodes broadcast request-tosend (RTS) packet to identify its neighbor the available neighboring nodes in awake mode responds with clearto-send (CTS) packet carrying information. The sender chooses the neighbor with better performance in terms of QPI and GPI to be part of the relay. During their handshake nodes realize that they will not be selected as relays go to sleep immediately Similarly nodes also get to understand from the header that they have not been selected as relays and they lost the contention go back to sleep.

D. Data Transmission Once the efficient path is established data being sent from source to destination may be hacked resulting in


IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS’15 losing data or alteration of data. So in the proposed system we ensured secure communication by the use of dynamic key management scheme with public encryption technique. Basic polynomial concept the ―Polynomial key pre distribution schemes‖ to generate keys dynamically and then assign the key to nodes involved. Polynomial shares of degree k are distributed off-line to the set of nodes through a key distribution server (KDS). Using this data now all the k users were able to calculate a common key and communicate with each other. Each node can determine a common key, by evaluating its own stored polynomials with the identifiers (ID) of the other (k − 1) parties which is been independently shared with the other nodes. To compute the key, Blundo [7] proposed a bivariate polynomial f ( x, y) where parameters ( x, y) were defined as the unique ID between the sensors x and y respectively. The polynomial [27] is defined as: k i

f ( x, y )

aij x y

from a finite Galois field GF(Q), coefficients aij (0 ≤ i , j ≤ k) are randomly chosen. To accommodate a cryptographic key Q should be chosen as a large prime number. The symmetric property of bivariate polynomial is. (3) f ( x, y) f ( y, x) As the first step of network deployment every sensor p with unique ID in WSN is initialized by KDS with polynomial share g p y which is obtained by evaluating f ( x, y) , with x

p , gp y

f p, y

(4)

Every sensor node p stores a number of k coefficients g j , 0 j k in its memory, k

i

aij p , 0

j

Parameter

Value

Channel Type

Wireless Channel

Radio Propagation model

TwoRayGround

Network interface type

WirelessPhy

MAC Type

IEEE 802.11

Interface Queue Type

PriQueue

Link Layer Type

LL

Antenna Model

Omni Antenna

Simulation Time

100 ms

Number of Nodes

50

Topology Area

1000 x 1000 m

Routing Protocol

DSDV

(2)

j

i, j 0

gj

TABLE I. Simulation Parameters

k

(5)

i 0

Where p is the node ID of the sensor, and g j is the

A. Packet Received Ratio Fraction of total number of packets successfully delivered to the total packets sent known as packet received ratio is an important metric used to analyze the performance of the proposed protocol S-ALBA-R. Figure.2 show the S-ALBA-R packet received ratio is higher than the existing ALBA-R with x-axis represents simulation time period and y-axis representing number of data packets received. This graph shows the routing system efficiency and the ability of the mobile sink to collect data at a particular time period.

coefficient of y j in the polynomial f ( p, y ) .

IV. SIMULATION ANALYSIS Simulation and analysis of the proposed S-ALBA-R scheme was performed using NS2 simulator. Network simulator is an event driven discrete simulator where all Network Simulator (NS2) is used for analyzing the efficiency of the proposed energy based reliable routing scheme. It is an extensively exercised discrete event simulator used for various test scenario executions using both object oriented tool command language (OTCL) and C++. To test our proposed work, the simulation parameters tabulated in Table 1 with test bed having 50 sensor nodes were used.

Figure 2: Packet Received Ratio International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.20 (2015) © Research India Publications; http://www.ripublication.com/ijaer.htm 15565

IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS’15 simulation time (millisecond) and Y-axis represents delay time (nanosecond) depicts overall network delay. Network delay using S-LABA-R protocol is reduced to a considerable level by the use of cross layer relay selection approach. Also the graph in Figure 4 shows S-ALBA-R end to end delay is lower than the existing ALBA-R

B. Throughput

D. Remaining Energy Sum of energy spent in receiving and transmitting data packets subtracted from initial energy of the node gives remaining energy. Energy efficiency is achieved by the use of sleep-awake schedule. The graph in Figure 5 shows the energy consumption of the network where X-axis represents simulation time and Y-axis represents remaining energy of the nodes in the network. The graph also shows S-ALBA-R energy consumption is lesser when compared with the existing ALBA-R

Figure 3: Throughput

The number of data packets successfully delivered to sink denotes throughput and is an important metric to evaluate the network performance. The Figure 3 shows the throughput of a network with 50 sensor nodes. In the graph, X-axis represents simulation time and Yaxis represents the data packets successfully delivered. The graph shows the S-ALBA-R quality of system is higher than the existing ALBA-R. C. Delay

Figure 5: Residual Energy

Figure 4: Delay

Packet delay defines the time taken or interval between the data forwarding from one system to another system. In the Figure 4, X-axis represents

VI. CONCLUSION The proposed paper deals with working and performance of S-ALBA-R featuring cross-layer integration of geographic routing based on greedy forwarding, MAC, load balancing (ALBA), Rainbow mechanism to route around connectivity holes as well as polynomial bivariate key generation as a key exchange mechanism and one way hash chain algorithm for secure communication. As wireless sensor networks are vulnerable to security threats, S-ALBA-R implements data security during routing operation. A simple key exchange mechanism with public encryption cryptographic technique incorporated to make the protocol most suitable for experimental and real time implementations. In this research work, we use polynomial bivariate key generation as a key exchange


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