On-Demand Customizable Sensor Based ...

Asian Research Consortium Asian Journal of Research in Social Sciences and Humanities Vol. 7, No. 3, March 2017, pp. 169-189.

Asian Journal of Research in Social Sciences and Humanities

ISSN 2249-7315 A Journal Indexed in Indian Citation Index

www.aijsh.com

DOI NUMBER:10.5958/2249-7315.2017.00164.2 Category:Science and Technology

On-Demand Customizable Sensor Based Opportunistic Multipath Secure Routing Using Coalition Game Theory K. Stella*; E. N. Ganesh** *Associate Professor, Department of Electronic and Communications Engineering, Sree Sastha Institute of Engineering and Technology, Chennai, India. [email protected] **Principal, Saveetha Engineering College, Chennai, India.

Abstract Restricted by the energy storage capability of sensor nodes, it is crucial to jointly consider security and reliability in data collection of Wireless Sensor Networks (WSNs). Challenging deployment environments with severe resource constraint sensors pose intricacies in reliable and secure data transmission for these networks. Under the more practical assumption, we propose an “Opportunistic Multipath Secure Routing Protocol (OMSRP)”, a novel security mechanism to support secure data transmission at the same time respecting the network restrictions in terms of energy. In this approach, data packet is split into several shares and transmitted along independent node-disjoint paths. Since the shares follow the multiple paths, even if one node is compromised, remaining data shares will follow other random paths, so the entire data cannot be hacked by the adversaries. During route discovery process, OMSRP employs Coalition Game Theory for angular dispersive node selection. A “best” angular dispersive node (candidate to forward the share) is chosen for each regional node which helped to create private tunnels (secured paths) for multiple shares to reach the destination. In order to realize more flexible reconfiguration and highperformance processing, the proposed method supports on-demand reprogrammable sensor nodes (behavior or functionality of sensor nodes changes as on-demand basis) to enable greater advantages in performing complicated tasks effectively. Simulation results demonstrate that our proposed OMSRP scheme using reprogrammable sensor is highly efficient in enhancing data

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Stella & Ganesh (2017). Asian Journal of Research in Social Sciences and Humanities, Vol. 7, No.3, pp. 169-189.

transmission in WSNs. Further to which the experimental analysis proved that we were able to control energy depletion and prolong network lifetime using the proposed scheme.

Keywords: Multipath Routing, Network Security, Coalition Game Theory, Regional Node, Angular Dispersive Node, Reprogrammable Node.

1. Introduction Wireless Sensor Networks (WSNs) has become a key for ubiquitous living. Advancement in WSNs has emerged as the next wave of wireless technology enabling wide range of application across many fields. Large number of low-cost sensor nodes deployed around one or more base station collectively forms a WSN. Nodes when deployed in hostile ground are subject to security compromise. Making sensor nodes tamper-proof is generally economically not feasible, which means the secrets stored in a node including cryptographic keys, may be compromised once a node is compromised, causing vulnerability to the information relayed by that node. Reliable and efficient communications in wireless sensor networks are challenging, especially when the sensors are deployed in hostile environments. Additionally, limited by the energy resources and processing capabilities of the sensor nodes, it is essential to jointly consider security and energy efficiency in WSNs. Plenty of solutions trying to alleviate this challenge have been proposed by researchers. Among them, mechanism to transmit message securely using deterministic multipath routing strategy [1,2,3,4], prone to network attacks if a node is compromised as the route is not changed under the same topology. Various other schemes [5,6,7,8] that were proposed to dispersively deliver shares such that the probability of packet interception can be guaranteed. However, most of these previous works ignored to consider the most crucial issue of WSN - the network lifetime. Security and Energy-efficient Disjoint Route (SEDR)[9] scheme randomly and dispersively delivers shares all over the network and then transmits these shares to the sink node. It employs random selection of intermediate nodes as forwarders based on hop length. Though this scheme has significant improvement in network security and network lifetime, the complexity (ie., when node density increases causing high routing overhead) in this scheme increases while establishing new routes between regional node and angular dispersive (intermediate) nodes depleting energy of forwarders, thus degrading the network lifetime. Under a more practical assumption, we propose an “Opportunistic Multipath Secure Routing Protocol (OMSRP)” to provide reliable routing and secure data transmission in an energy-efficient way for WSNs. OMSRP employs traffic dispersion in multiple disjoint routes[10,11] to improve reliability caused by frequent topological changes and unreliable links. Rather than using single shortest path to route data from one node to the other, proposed approach transforms a message into several shares using secret sharing scheme and then delivers the shares to the destination via multiple paths. A node in network misbehaves by dropping packets of others in order to save its battery life while sending its own packets (forwarded) by other nodes. This selfish activity of free riders limits connectivity of the network degrading individual fairness and affecting overall network-wide performance. Thus the network will end up with each entity in isolation if all nodes behave selfishly in this manner. To cope up with these behaviors, OMSRP enforces cooperation among the nodes for the stability of overall system. By viewing this behavior from game theoretic perspective [12,13] (where each entity or node is self interested in the network resources or service), main objective of which is to find out the best 170


actions for individual decision makers and organizing stable outcomes where no player can get any benefit by unilaterally deviating from its strategy. Aim of the proposed OMSRP system is to improve the reliability and efficiency of time critical communications using game theoretic approach to achieve the following, 1.

2.

Source nodes are customized with reprogrammable codes to add intelligence, a.

to transform each packet into shares and then forwarding shares to regional nodes

b.

to make it resistant to node-compromised attacks

Regional node forms coalition groups to select a smart angular dispersive node (forwarder) to maximize its groups utility for energy efficient successful data transmission.

Simulation was conducted to prove the efficacy of the proposed OMSRP technique. Performance of the proposed system was evaluated by developing a model using self written script in MATLAB. Simulation results show that our OMSRP scheme can achieve significantly better reliability and security[14,15] with minimal or low overhead. Testbed evaluation also shows that the proposed scheme exploits coalition game theoretic approach in selecting energy efficient [16] forwarders which enrich the network connectivity for reliable data transmission and improves routing efficiency making it extremely suitable for hostile environments. The remainder of the paper is organized as follows. In Section 2, we discuss the proposed OMSRP network architecture and its current technology research towards the realization of this model. In Section 3, simulation and experimental results of the proposed model is discussed. The paper concludes with future research directions in Section 4.

2. System Architecture and Operations In OMSRP, we assume the wireless sensor network contains large number of randomly deployed sensor nodes (N) surrounding a sink (SK ). All the sensors are static nodes with heterogeneous capability. As the sensor nodes are low power devices, the coverage area of sensor nodes during its transmission may increase or decrease dynamically depending on its location, residual energy and transmission capability. The System model of OMSRP system is depicted in Figure 1.

Figure.1. System Model of OMSRP Approach 171


The proposed OMSRP system architecture includes the following main components: Senor Nodes: The sensor nodes (Sni ) senses the environment, collects sensory information and communicates to the sink. To realize more flexible dynamic reconfiguration and high performance processing, the role of sensor node has been extended to provide additional service with its reprogrammable capability bringing intelligence by interpretation techniques. The node is configured for detecting events and handles such events whenever detected. This reprogrammable capability of sensor nodes is used to make it resistant to node-compromised attacks and to perform share delivery, i.e., transforming each packet into shares and then forwarding shares to regional nodes. The node can sleep until an event occurs, providing significant power savings when compared to polling. This provides a good solution in terms of computational and communication efficiency significantly reducing efforts on the transmission overhead in WSN. Sink: It is the back-end centralized control system. It continuously synchronizes data received from sensor nodes over time to the server. The collected information represents a vital source of big data for the statistical and research activity. This centralized control system is crucial and is therefore also an essential part of preventive maintenance. Summary of notations used is OMSRP model is referred in table 1.

Table 1.Summary of Notations 𝐑𝐠 𝐜𝐨𝐮𝐧𝐭 𝐑 𝐜𝐨𝐮𝐧𝐭 𝐒𝐨𝐢 N 𝐍𝐦𝐬𝐠 𝐑 𝐢𝐍 M 𝐀𝐢𝐧 𝐄𝐚𝐢 𝐄𝐫𝐞𝐬 𝐐𝐛𝐢𝐚 𝐓𝐦𝐚𝐱 𝐑𝐢𝐤 K P 𝐀𝐜𝐨𝐮𝐧𝐭 𝐓𝐫𝐮𝐬𝐭𝐜𝐨𝐮𝐧𝐭 𝐓𝐧 𝐖𝐧 𝐄𝐦𝐚𝐱 𝐃𝐨 𝐎𝐦𝐢𝐧 𝐎𝐚𝐝𝐧

Count value for regional node selection Count value forith regional node satisfying threshold value ith Source node Number of sensor nodes in the network Number of splitted fragments of message ith Regional node Message ith Angular dispersive node ith Angular dispersive node‟s energy value Residual energy of angular dispersive node ith Angular dispersive node‟s queuing buffer value for considering overloading factor maximum threshold limit fixed for the queue buffer at the angular dispersive nodes ith Regional node within coalition group Number of regional nodes under coalition formation set of nodes within the coalition group Count value forith angular dispersive node Count value for optimal angular dispersive node Set of trustworthy nodes within coalition group Set of weak (OR) cheat nodes within coalition group Set of angular dispersive that has maximum energy among the trustworthy nodes Set of angular dispersive nodes that has optimal distance to reach the sink among the trustworthy nodes Set of angular dispersive nodes that has minimum overload among the trustworthy nodes Optimal angular dispersive node

172


The OMSRP architecture is categorized into the following phases: 1.

Regional Node Discovery using Reprogrammable Sensor Nodes

2.

Smart Angular Dispersive Node Selection through Coalition Game Theory

3.

Opportunistic Multipath Secure Route Discovery

2.1 Regional Node Discovery using Reprogrammable Sensor Nodes As shown in the figure. 1, a WSN consists of a large number of resource-constrained sensor nodes and a single sink. Reprogrammable Sensor Nodes (RSN): In OMSRP WSN, nodes are customized with reprogrammable codes (creating logic that optimizes the node‟s behavior for specific network requirements) to add intelligence that makes it resistant to node compromised attacks. The fact that the functionality or behavior of sensor nodes can be reprogrammed or customized dynamically even after the nodes are placed in the medium provides greater advantages. Reprogramming all sensor nodes is performed via broadcast mechanism. Only authorized network users have predefined privileges to reprogram the sensor nodes (deploy new image over the air to the node through a wireless network). Hence, for deployed systems, a technician is no longer needed to go into the field to manually load new program image, which reduces wireless system maintenance costs. The sensor node receives and verifies the packet, if the packet verification passes, then the nodes accept the program image (as only the public parameters “params” of the system are preloaded on every sensor node). No matter how many sensor nodes are compromised, only the params are obtained by the adversary. Obviously, the adversary thus cannot impersonate any authorized network user by compromising sensor nodes. In summary, if an adversary injects a forged modified program image, the receiving node can discover it easily because of the authentication of reprogramming packets. First, resource constraints on sensor nodes often make it undesirable to implement expensive algorithm. We design the algorithm very carefully so that it is efficient for resource-constrained sensor nodes to operate energy effectively. Each sensor node is pre-equipped with multiple reprogramming capabilities, each of which corresponds to a particular event. For instance, the node‟s behavior is customized to detect event when the battery voltage falls below a critical level, under such occasions, the node responds by notifying the operator or sink regarding its criticality. By using its reprogramming capability it decreases its sampling (and transmission) rate to conserve the remaining battery life. By default, an un-programmed node transmits every sample acquired back to the sink at a fixed sample rate, so the battery life of a node is directly linked to its sample rate. Regional Node Discovery using RSN: We assume that every node in the network knows its own geographic location. During the neighbour (regional node) discovery phase, the sensor node ( ready to transmit data to sink) broadcasts “hello” message. A neighbour node upon receiving a “hello” message, it records the node as its neighbour in its neighbour list. Receiver node after recoding the information broadcasts the “ack_hello” message back to the later. When a sender node receives “ack_hello” message from the other node, the reprogrammable capability of sensor node, verifies authenticity of the “ack_hello” packet (using system params loaded in nodes during deployment), upon successful authentication, it identifies the latter as its one-hop neighbour (here-in-after 173


referred as regional node). Thus, sensor node identifies its set of regional nodes. During the network initialization phase, the source broadcasts message and identifies its one-hop neighbors as shown in figure 2.

Figure.2. Source Broadcasts Shares to Regional Nodes Next, the source splits the message „m‟ into multiple shares, S1, S2, · · · Sk or fragments using a secret sharing scheme. Number of shares (k) is dependent on the number of regional nodes identified by the source. Each regional node forwards the fragment to its angular dispersive node (ADN). The most suitable ADN is selection process is discussed in detail in the following section. 2.2 Smart Angular Dispersive Node Selection using Coalition Game Theory In an bandwidth constrained (caused due to the shared wireless medium, interference, error rates and retransmissions) wireless networks, nodes exhibit selfish behavior to maximize their own benefit by deviating from the defined protocol, which leads to system-wide performance degradation, instability and individual unfairness. In WSN, each node acts as a source as well as a relay data for others. A selfish non-cooperative node misbehaves by dropping packets of other nodes to save its battery life while sending its own packets to be forwarded by other nodes. This selfish behavior of free riders leads to limited connectivity of the network affecting individual as well as network-wide performance. The network will end up with each entity in isolation if all nodes behave selfishly in the same manner. To cope up with these behaviors and enforce cooperation among the nodes for the stability of overall system, OMSRP technique formulates a coalition game theoretic model to analyze the behavior of the sensor nodes for cooperative packet delivery. By considering selfish nodes (regional nodes) as players of the game, a smart angular dispersive node is selected (based on its maximum utility function) as forwarders to transmit data successfully to the sink. Coalitional game theory allows a reduction of power consumption in WSN by forming coalitions. Regional node is responsible to receive the fragments from the source and deliver it to the angular dispersive node. The regional node applies “Coalition Game Theory” mechanism for selecting the most appropriate angular dispersive node as shown in figure 3. 174


Figure.3. Coalition Game Phase Split Up Process To prolong lifetime of sensor, in our scenario, coalition groups are formed by regional node and it choose strategies to maximize their own group‟s utility. Coalition Formation: In order to minimize the interference and reduce the probability of data fragment loss, the regional node forms coalition group as shown in figure 4. Each regional node (designated as “coalition head”) forms its coalition group towards its angular (θ = 180 o) path to select an optimal angular dispersive node. The node nx is located at a district surrounded by sector radii and arcs, which consists of sensor nodes with identical hop length to the sink node. With the least-hop routing, the nodes in the district relay all the traffic for the nodes with ( l + r), (l + 2r), . . . ,(l + zr) from the sink node in the sector, where „l‟ indicates the distance from sensor node to sink node and ‟r‟ indicates the transmission range. Based on the number of transmitted shares in this sector, we can obtain the number of transmitted shares relayed by sensor node n x with the least-hop routing. Coalition head is assigned with the task, which processes the information of the newly entered angular dispersive node and decides who will be their possible group member in a group based on the following criteria: nodes that are located i) at an identical hop length to the sink node, ii) in a district surrounded by angular path (θ denote the angle of this sector and d x denote the height of the district), iii) node which has the energy above threshold (Ethres ) and performing less number of tasks are allocated as member of the group. 175


Figure.4.Coalitional Formation Among the members in coalition group formed, the coalition head (regional node) select its most appropriate angular dispersive node based on the following three criteria: Probabilistic consideration based on Energy ( Eprob ): The probabilistic consideration for the selection of an optimal angular dispersive node based on energy is, ER

Eprob = max ANprob ∗

E max

(1)

, ETmin

i ) to be elected among „n‟ where, ANprob is the probability of angular dispersive nodes ( Nad programmable sensor nodes, where n varies from 200 to 500 programmable sensor nodes. ER is the residual energy of the angular dispersive nodes. Emax is the maximum energy present in the angular dispersive nodes. ETmin is the minimum threshold energy present in the angular dispersive nodes at that instance of time, the value of ANprob is not allowed to fall below the ETmin since the angular dispersive node which is less than the ETmin cannot be selected.

Probabilistic consideration based on optimal Distance (Dprob ): The angular dispersive node located nearer to the sink node is selected. Angular dispersive nodes are normally distributed in the network along the angular path of the regional node with zero mean and variance σ2D1 . The distance between angular dispersive nodes located at transmitting end (Regional node) and the receiving end (Sink) is the random variable with average mean Davg and variance σ2D1 + σ2D2 . The Dprob is given by, Dprob =

1 2 2π σ2D 1 +σD 2

e

(D prob −D avg )2 2 2 σ2 D 1 +σD 2

−

(2)

Probabilistic consideration based on Overloading Factor (Oprob ): The angular dispersive node with maximum buffer size is considered as the overloaded angular dispersive node. The Oprob is given by, (3)

Oprob = Q s < Tmax

176


Where Q s is the size of the queue buffer at the angular dispersive node present at that instance of time Tmax is the maximum threshold limit fixed for the queue buffer at the angular dispersive nodes. The angular dispersive node which satisfies the above three criteria, Eprob , Dprob , Oprob is selected as the optimal angular dispersive node. Each stage of coalition formation is divided into three phases: Discovery, adaptive coalition formation and coalition sensing. Discovery: In this stage, using the information required to form coalition, each regional node (coalition head) discovers its members (angular dispersive nodes). Adaptive Coalition Formation: Following the discovery stage, adaptive coalition phase begins and the regional nodes cooperate by sharing their sensing results with the discovered coalitions. For this purpose, merge and split rules occur in the network as shown in figure 5, where each coalition decides to merge (or split) depending on the utility improvement. In this phase, regional nodes can autonomously self-organize and adapt the network‟s structure through new merge-and-split iterations with each coalition taking the decision to merge (or split) subject to satisfying the merge (or split) rule through Pareto order. Coalition Sensing: In the final coalition sensing stage, the trustworthy nodes are identified using merge and split operation. The coalition head subsequently makes a final decision (selecting an optimal angular dispersive node) to transmit the data to the sink. Within each coalition S∊ N (number of sensor nodes), the sensor nodes are classified as trustworthy nodes and cheating nodes. Trustworthy nodes are the ones that choose to cooperate with other nodes directly in order to complete data transmission. These nodes are also called as honest nodes. Cheating nodes are the ones that choose to cheat or do not co-operate with other nodes for data transmission. The coalition head selects an optimal angular dispersive node, from the trustworthy nodes to transmit the data fragment to the sink.

Figure.5. Merge and Split Operation A coalition formation algorithm based on merge and split can be formulated for wireless networks. The merge operation is performed by the coalition head to identify the trustworthy nodes in its coalitional group. The trustworthy nodes are merged to form a group. The split operation is performed by coalition head to identify cheating node in its coalition group. The cheating nodes are splitted away from coalition group. Using merge and split rule in the coalition game, nodes within 177


the coalition group that performs the least task (minimal load) and maximum energy is identified as the most appropriate angular dispersive node (forwarder) to perform data transmission. Thus energy of sensor node within the coalition group is effectively utilized. During split operation, the cheating nodes which do not co-operate during data transmission are splitted into individual groups, therefore data fragments from the regional node (coalition head) is successfully transmitted to the sink reducing routing overhead in the network.. Merge and Split Procedure: A collection S is the set, S= {S1 … . Sl } of mutually disjoint coalitions, Si єN. If the collection spans all the players of N i.e., where lj=1 Sj = N, the collection is a partition of N. Various criteria referred as orders can be used as comparison relation ( ) between collection or partitions. Using the Pareto order as the comparison relation, we perform coalition formation phase based on the two rules called “Merge” and “Split”. Merge Rule: Merge any set of coalitions {S1 … . Sl } , where {

l j=1 Sj }

> {S1 … . Sl } , therefore,

l j=1 Sj }

{S1 … . Sl } → { Merge rule is a binding agreement among the regional nodes, to operate together in the sensing phase, and this agreement is partially reversible i.e., it can be reversed by an agreement to split. Split Rule: Split any coalition lj=1 Sj , where → {S1 … . Sl } > { lj=1 Sj }, thus { lj=1 Sj } → {S1 … . Sl }. During the merge procedure, each regional node, engages in (one-on-one) negotiation, i.e., each sensor node performs multitasking, sensor node can either act as a source nor a regional node, nor a relay node. The algorithmic steps involved and flow model of angular dispersive node selection using coalition game theoretic model is summarized in algorithm 1 and figure 6. Algorithm 1: Angular Dispersive Node selection using Coalition Game Theory Input: The regional nodes (k) takes the list of angular dispersive node as input for selection process. Process 1.

Regional node (Rik ) collects data fragments (Nmsg ) from the respective source (Soi ) and transmit to angular dispersive node

2.

Applying Coalition game theory to set of k nodes,

3.

Each regional node (Rik ) discovers its angular dispersive nodes (Ain ) to perform coalition formation. for set of „k‟ nodes /*Criteria for Coalition formation-checks the energy, distance and overloading factor */

Tmax )

if (Ain nodes energyEai ≥ Eres )&&(Ain nodes hopcount Hcai ≤ sink SK )|| (Ain nodes Qbia ≤ Coalition group is formed; 178


end end 4.

After coalition formation, the regional nodes identifies trustworthy and weak nodes within coalition group (p)

5.

Applying Merge and Split rules to the set of „p‟ nodes within the coalition group, in order to select best angular dispersive node to transmit data to the sink.

6.

Get the count of angular dispersive nodes (Acount ) Initialize Trustcount = 0; j=1; for set of „p‟ nodes ifAcount > 1then Repeat for each angular dispersive nodeAin /* Merge and Split rules to find best angular dispersive node among trustworthy nodes*/ if (sensor nodes Sn performs at most one relay process) then Merge the trustworthy nodes (Tn ) in the coalition group (p); then

else if (sensor nodes Sn performs atleast one relay process && acts as source) Split the weak nodes (Wn ) in the coalition group (p); End /* Dynamic selection of angular dispersive nodes among trustworthy nodes (Tn ) for set ofTn nodes if (sensor nodes Sn has Emax )&& (sensor nodes Sn has Do )|| (sensor nodes Sn has Omin then /*Store the optimal angular dispersive node */ Store optimal angular dispersive node (Oadn ); Trustcount = Trustcount + 1; end j=j+1; 179


end Until j ≤ Acount

/*Continue until one optimal angular dispersive node is selected*/

end 7.

Repeat from step (3) using step (6) until Sink is identified.

8.

Thus, angular dispersive nodes (Ain ) collects the splitted data fragments from the regional node and finally transmit to the sink

Figure.6. Coalition Game Theory Process Flow for Optimal Angular dispersive Node Selection 2.3 Opportunistic Multipath Secure Route Discovery Our focus is on designing OMSRP to maximize the lifetime of WSNs while guarantee the security of the whole network. In the opportunistic multipath secure route discovery phase as shown in figure 7 flow model, when a source has data to transmit to the sink, it starts route discovery phase by broadcasting route request (RREQ) message. The RREQ message is broadcasted to its regional nodes (one-hop neighbor) selected as referred in section 2.1. Each regional node selects one 180


intermediate node (angular dispersive node) as referred in section 2.1, as its next hop neighbor and forwards the RREQ message. Each node updates its local information table with information of its neighbors (hop length, energy, load, etc) and modifies the path information in RREQ message before forwarding the message to next selected neighbor. The process continues until sink is reached. From the number of RREQ message received, the sink knows the number of all available disjoint paths to the source. The sink sends route reply (RREP) message back to the source through the selected paths. Source node which is ready to transmit the data message to sink, splits the data message into multiple packets. Ie., for example, the original data message of size „S‟ is split into j packets each of „M‟ fragments of size „b‟ bits. The number of packets depends on the number of node disjoint paths identified. In case if the size of the last packet is less than „M‟ fragments, then zero padding is applied to meet the length of fragment. Number of fragments are encrypted using Reed-Solomon(RS) code as per the requested security level (depends on application). Source then allocates each fragment on each path to enhance security and data reliability during transmission. ie., encrypted fragments are transmitted on different paths to enhance security. For a low security requirement SL=1, source node only encrypts any „N encrypt = F + 1‟ of „M+F‟ fragments from the codeword. To restore each codeword, an adversary or attacker, must receive at least „M‟ of „M+F‟ fragment and should successfully decrypt it. Whereas, if the security level is high, to compromise the data packet, an adversary must receive all the „M‟ fragments and should be able to decrypt all „M‟ fragments successfully to restore the codeword. Our proposed scheme dispersively distributes the fragments all over the WSNs and then forwards these fragments to the sink node along the randomized multipath routes. Diversity of disjoint routes increases security significantly reducing probability of packet interception by adversaries. At the sink side, the encrypted fragments are decrypted first and then all the fragments are decoded to reconstruct the original data packet. Algorithm 2 illustrates the steps for route discovery using OMSRP mechanism. Algorithm 2: Route Discovery using OMSRP Mechanism Input: The number of sensor nodes (N) that are randomly distributed in the heterogeneous network. 1.

The sensor nodes (N) are randomly distributed in heterogeneous network. Sink is located at the centre.

2.

Source (Soi ) which has data wants to send it to sink, so it broadcast “hello” packet and identifies its regional nodes.

3.

Source (Soi ) gets the count of regional node (Rgcount ) and maintains a threshold value for electing regional node Initialize R count = 0; i=1; for set of „N‟ nodes if Rgcount > 1then Repeat for each regional node ( R iN )

181


/* Checks for regional node and splits message(M) into fragments (Nmsg ) based on threshold value*/ if(Nmsg < 4) && (Nmsg > 6)then Broadcast the splitted fragments to R iN ; else Splits original message (M) into fragments; /*Store the list of regional node (R iN ) separately in a list and increment the counter*/ Store R iN data; Set R count = R count + 1; end i=i+1; Until i ≤ Rgcount ; are processed */

/* continue for all regional nodes, i.e., all regional nodes (R iN )

end end /* End of for loop*/ 4.

Regional node (R iN ) collects data fragments (Nmsg ) from source (Soi ) and transmits to angular dispersive node.

5.

The angular dispersive node selection process is initiated.

6.

Regional node selects its angular dispersive nodes (Ain )based on the following three criteria, if (Ain nodes energyEai ≥ Eres )&&(Ain nodes hopcount Hcai ≤sink SK )|| (Ain nodes Qbia ≤ Tmax ) Select Angular dipersive node; (Refer Algorithm 1: Angular dispersive selection process) which then forwards the data to the sink. end

7.

Regional node is responsible to receive data fragments from source (Soi ) and delivers to best angular dispersive nodes Once all the data fragments are collected by the angular dispersive

182


node from [step 8 in Algorithm 1], Sink receives splitted data fragments, categorizes and reframes the original information in the packet. 8.

Thus, route has been established by the source and data is transmitted to the sink via best angular dispersive node.

Output: Route identified by the source (Soi ) and the message is transmitted to the sink. Sink can receive multiple data at a time. Sink has an update of information from all sensor nodes. Sink receives splitted fragments, categorizes and reframes the original information in the packet. While categorizing the data fragment, sink looks for the missing data fragment. In order to retrieve the original information, sink waits for the particular timeslot „T‟ until the missing data fragment is received. If the missing data fragment exceeds timeslot‟ T‟, sink considers the incomplete sequence of data fragments as error data and finally sink discards the error data.

Figure.7. Opportunistic Multipath Route Discovery Flow Model Our analysis shows the probability that the adversary can decode that the packet is close to 0 when there exists one black hole in the WSNs. 183


3. Simulation and Experimental Analysis In this section, the performance of OMSRP scheme is evaluated using a Self written script in MATLAB. Our experiment considers a WSN of 500 m X 500 m area, were nodes (varying from 50 to 250) are randomly deployed. Experiment was conducted against scenarios such sparsely (50 nodes) and densely (250 nodes) deployed set up as shown in the figure 8(a) and 8(b).

Figure. 8(a). Network Model Simulated in MATLAB for OMSRP (b). Secure Data Transmission using OMSRP Scheme The sensor nodes at initial stage have sufficient amount of energy in order to transmit data to sink. Simulation was triggered considering randomly chosen sensor node to act as source that tends to generate message and let it send message to sink on varied time slot. Each intermediate node used a buffer to cache packets from other nodes. For analyzing the packet delivery rate in diversified set up, link loss was considered and the link loss rates is set be between 0% and 10%.The impact of node failure (once a node fails it cannot be used to forward packets) and compromised node problem (if a node is compromised, all the shares/packets relayed by that node are considered compromised) were observed during the simulation and results were captured. The simulation time was set to 1000 time slots. During the experiments, different deployments of sensor networks were generated by varying the number of nodes from 50 to 250 with a step of 50 and the simulation results were averaged. The overall performance of the proposed “Opportunistic Multipath Secure Routing Protocol” was evaluated, namely packet delivery ratio, packet drop, delay, throughput, security and network lifetime. The results are also compared with the schemes [17] such as PPR, NRRP, MDRON, MDRWON etc. Packet Delivery and Packet Loss/Drop Rate: Packet Delivery is the ratio of the number of packets successfully delivered to the sink against the total number of packets generated. The result captured during simulation is shown in figure 9(a). From the results, we can observe that OMSRP can guarantee better delivery rate compared to the other existing schemes such as PPR, NRRP, MDRON, I-Walk[9] and SEDR. Though the packet delivery ratio decreases when the number of compromised nodes increases, the proposed OMSRP scheme is able to maintain pretty good 184


message delivery ratio performance. In OMSRP, the probability that a message might be compromised decreases even with the increase on the number of compromised nodes is due to the unique random paths used to spread the information from the source to the sink. With the increase of network density as well as the compromised nodes, reprogrammable capability of sensor nodes prevents from node-compromised attacks and the selection of the most appropriate energy efficient angular dispersive node using coalition game approach increases the chance for each share to be delivered successfully to the sink during its first transmission. In fact, in less challenging situations (with no or minimal number of compromised nodes), the delivery performance is guaranteed. 4000

23

3000

21

PRP

2000

Packet Drop

Packet Delivery

22

MDRON NRRP I-Walk

1000

0 10

SEDR MDRWON

M DRON NRRP I-Walk

20

SEDR M DRWON

19

OM SRP

18 17 16

OMSRP

20 30 40 50 Compromised Nodes(%)

PRP

15 10

60


60

Figure.9 (a). Compromised Nodes Vs Packet Delivery (b). Compromised Nodes Vs Packet Drop

Packet Loss Rate is the ratio of the number of packets dropped before arriving at the sink node to the total number of packets generated at all source nodes ie., it includes packets violating the delay requirement. Comparison of various scheme and OMSRP with the variation of packet drop for different number of compromised nodes in the network is shown in figure 9(b). It can be seen from the figure that other existing schemes have higher loss rates compared to OMSRP. Besides the other existing schemes, the packet loss traces of PRP and NRRP are high since routing using these protocols is done without special nodes. The proposed OMSRP scheme is able to maintain low packet loss ratio in the face of both link and node failures. i.e., when certain nodes and links become over-utilized and cause congestion, proposed OMSRP can spread traffic over alternate paths (the number of shares corresponds to the number of outgoing paths) to balance the load using coalition game methodology making it more robust to node or link failure problems. In Figure 9(b), when the percentage of the number of compromised nodes increases, the packet drop increases gradually. Additionally in OMSRP, overlapping of nodes is avoided hence the packet drop reduces by 2% in OMSRP as compared with MDRWON which has higher chance of overlapping of nodes. Average Delay and Throughput: The average end-to-end delay is the ratio of sum of the delays of each packet received and number of packets received. Here, the delay of the packet refers to the difference between the time at which the packet reached the final destination minus the origination time of the packet. Here, the end-to-end delay is averaged over all surviving data packets from the 185


sources to the destinations. It shows that in PRP, MDRON and other existing schemes when the number of compromised nodes increases, delay increases abruptly. But in case of OMSRP scheme when the number of compromised nodes increases, delay retains a stable sustainable state because OMSRP employs coalition game theoretical approach in selecting the best forwarder and thereby discovers the shortest multipath disjoint paths for each share to reach the sink on time. Overall, when compared to existing protocol, OMSRP reduces the average end-to-end delay upto 8%. This result shows that OMSRP has an ability to sustain performance even for large node densities as it utilizes multiple paths between the source and sink to provide a solution satisfying the delay requirements. Even if the network density is large, so is the number of compromised nodes in the network, optimization in delay is feasible in OMSRP because numerous paths exist between a source node and the sink. 2.5

0.8 0.7

2 Average Delay

Throughput

0.6 0.5 0.4 0.3 0.2 0.1 0 10

PRP MDRON NRRP I-Walk SEDR MDRWON OMSRP 20 30 40 50 Compromised Nodes(%)

1.5

PRP MDRON NRRP I-Walk SEDR MDRWON OMSRP

1

0.5

0 10

60

Figure.10.(a). Compromised Nodes Vs Throughput


60

(b). Compromised Nodes Vs Average Delay

Throughput is the ratio of successfully received data packets (Mbps) to the sink against the total packets being sent from the source nodes. From the results shown in figure 10(a), the throughput of the network reduces gradually with increase in the percentage of compromised nodes. Throughput degradation noticed in existing scheme is because the probability of the packets being routed along a compromised path does not reach the sink which means the average delay per packet is infinite. Average Delay of various schemes are shown in figure 10(b). In other words, without routing diversity, routing attacks can lead to a zero throughput and infinite delay. From the results increase in throughput is observed in OMSRP indicates that packets are transmitted through reliable paths. When routing path selection is utilized, data is transmitted on the uncompromised paths. Security and Network Lifetime: Security signifies the total number of messages compromised over the total number of messages initiated by all the sensor nodes ie. the probability that a message is compromised. A message is said to be compromised by the compromised nodes when at least „T‟ shares are compromised collectively. Figure 11(a) shows the results of message being compromised against number of paths used, provided the shares (T) are 10, 9 and 8. In the proposed scheme, each share is transmitted through unique random disjoint paths making it difficult for the attacker to 186


compromise the node. Even if the attacker is successful in compromising the node, as the other path selected by the node is random for the other shares, the complete packet data cannot be explored making the proposed scheme effective. The result confirms the effectiveness of the proposed scheme - it is more resistant to the collusive attacks of compromised nodes. In fact, in less challenging situations (i.e., less number of compromised nodes), the improvement is more significant (i.e., curves dropping more steeply). Security is very sensitive to the redundancy, less the redundancy then more secure is the scheme. Security achieved when no retransmission is performed, ie., all the nodes and links are reliable. Retransmission weakens security because of possible overlapping of the paths. Figure 11(a) shows security as a function of the number of paths used respectively. Network Lifetime: The fact that the number of shares transmitted has lower probability to be intercepted by the adversary, OMSRP has higher probability of successful transmission to the sink. This leads to a lower required number of retransmission of shares and releases the burden of the sensor node with maximal energy consumption caused due to retransmission. When retransmission is relatively small, energy consumed is less, whereas when retransmission is relatively large, energy consumed is large due to utilizing the redundant energy to forward the shares. 0.7 T=10 T=9 T=8

0.65 0.6 0.55 0.5 0.45

0.5 0.4 0.3 0.2 0.1

0.4 0.35

OMSRP MDRWON SEDR

0.6 Network Lifetime

Probability that message is compromised

0.7

0 0

2

4 6 Number of Paths

8

Figure.11.(a). Security performance

0

1000

2000 3000 4000 Number of Rounds

5000

(b). Network Lifetime

The network outage highly depends on the number of shares. It can be seen from the figure 11(b), that the network lifetime of the proposed OMSRP scheme is better compared to other SEDR and MDRWON.

Conclusion In this paper we propose a novel security mechanism to support secure data transmission while respecting the network restrictions in terms of energy. After conducting a thorough study by theoretical, mathematical and experimental analysis, we found that secure routing mechanism had higher challenges in terms of energy optimization ie. Whenever higher security logics are imposed 187


there was a higher depletion in energy resources. Our OMSRP scheme was postulated using coalition game theory – an evolutionary approach, keeping this challenge in mind. Coalition game theory was applied in selecting the best angular dispersive node for each regional node which helped to create private tunnels (secured paths) for multiple shares to reach the destination. Further to which a thorough research and the experimental analysis proved that we were able to control energy depletion and prolong network lifetime using the proposed scheme. Our future work would involve an application centric heterogeneous network like IOT with semi-mobility of angular dispersive nodes in a multiple sink network architecture.

References E. Stavrou and A. Pitsillides, “A survey on secure multipath routing protocols in WSNs”,Computer Networks, vol. 54, no. 13, pp. 2215–2238, 2010. L. Chen and J. Leneutre, “On multipath routing in multihop wireless networks: security, performance and their Tradeoff”, Journal of Wireless Communication and Networking, EURASIP, vol. 2009, Article ID 946493, 2009. D. Ganesan, R. Govindan, S. Shenker, and D. Estrin, “Highly-resilient, energy-efficient multipath routing in wireless sensor networks”,ACM SIGMOBILE Mobile Computing and Comm Review, vol. 5, no. 4, pp. 11–25, 2001. Ben-Othman and L. Mokdad, "Enhancing data security in ad hoc networks based on multipath routing”, Journal of Parallel and Distributed Computing, vol. 70, no. 3, pp. 309–316, 2010. A. Perrig, R. Szewczyk, J. D. Tygar, V. Wen, and D. E. Culler, “Spins: Security protocols for sensor networks”, Wireless Netw., vol. 8, no. 5,pp. 521–534, 2002. H. Shokrzadeh, A. T. Haghighat, and A. Nayebi, “New routing framework base on rumor routing in wireless sensor networks”, Comput. Commun., vol. 32, no. 1, pp. 86–93, Jan. 2009. H. Shokrzadeh, M. Mashaiekhi, and A. Nayebi, “Improving directional rumor routing in wireless sensor networks," in Proc. IEEE IIT, Dubai, UAE, 2007, pp. 108–112 T. Banka, G. Tandon, and A. P. Jayasumana, “Zonal rumor routing for wireless sensor networks”, in Proc. IEEE ITCC, Las Vegas, NV, 2005, pp. 562–567. Anfeng Liu, Chao Zhang, Zhigang Chen, and Xuemin (Sherman) Shen, “SEDR: Secure and Energy-Efficient Disjoint Multipath Routing for WSNs”, IEEE Transcations on Vehicular Technology, Vol . 61, No. 7, 2012 Y. Challal, A. Ouadjaout, N. Lasla, M. Bagaa, and A. Hadjidj, “Secure and efficient disjoint multipath construction for fault tolerant routing in wireless sensor networks”, Journal of Network and Computer Applications, vol. 34, no. 4, pp. 1380–1397, 2011. N. Nasser and Y. Chen, “SEEM: secure and energy-efficient multipath routing protocol for wireless sensor networks”,Computer Communications, vol. 30, no. 11-12, pp. 2401–2412, 2007. 188


Z. Han, D. Niyato, W. Saad, T. Baar, and A. Hjrungnes, “Game Theory in Wireless and Communication Networks: Theory”, Models, and Applications, 1st ed. New York, NY, USA: Cambridge University Press, 2012. T. AlSkaif, M. G. Zapata, and B. Bellalta, “Game theory for energy efficiency in wireless sensor networks: Latest trends”,Journal of Networkand Computer Applications, vol. 54, pp. 33– 61, 2015. C. H. Shih, Y. Y. Xu, and Y. T. Wang, “Secure and reliable IPTV multimedia transmission using forward error correction”,International Journal of Digital Multimedia Broadcasting, vol. 2012, Article ID 720791, 8 pages, 2012. S . K. Singh, M. P. Singh, and D. K. Singh, “A survey on network security and attack defense mechanism for wireless sensor networks”,International Journal of Computer Trends and Technology, vol. 1, no. 2, pp. 9–17, 2011. D. Kandris, M. Tsagkaropoulos, I. Politis, A. Tzes, and S. Kotsopoulos, “Energy efficient and perceived QoS aware video routing over wireless multimedia sensor networks”, Ad Hoc Networks, vol. 9, no. 4, pp. 591–607, 2011. K. R. Venugopal and L. M. Patnaik , “Secure Multiple Domain Routing for Wireless Sensor Networks”, Elsevier Publications 2013(Eds.) ICCN 2013, pp. 40–49.

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