Regression based critical information aggregation and dissemination in VANETs: A cognitive agent approach

Vehicular Communications 1 (2014) 168–180

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Vehicular Communications www.elsevier.com/locate/vehcom

Regression based critical information aggregation and dissemination in VANETs: A cognitive agent approach M.S. Kakkasageri a,∗ , S.S. Manvi b a b

Department of Electronics and Communication Engineering, Basaveshwar Engineering College, Bagalkot 587102, India Department of Electronics and Communication Engineering, Reva Institute of Technology and Management, Bangalore 560064, India

a r t i c l e

i n f o

Article history: Received 2 April 2014 Received in revised form 10 June 2014 Accepted 15 July 2014 Available online 18 July 2014 Keywords: VANETs Cognitive agents Regression Data aggregation

a b s t r a c t Data aggregation in Vehicular Ad hoc NETworks (VANETs) is an efficient technique for effective usage of communication resources. In dense VANET traffic scenarios, data aggregation is needed to represent several numbers of almost similar critical information into one refined critical information to reduce bandwidth requirements. This paper proposes a cognitive agent based critical information aggregation and dissemination in VANETs by using regression mechanism. Regression based cognitive agent approach efficiently aggregates the collected critical information and minimizes redundant data dissemination. The proposed scheme works over clustered vehicles by using a set of static and mobile agents. The scheme operates in the following steps: (1) validation and filtering of collected critical information; (2) generation of beliefs based on valid and filtered critical information; (3) aggregating the beliefs to develop desire using a regression technique; (4) revision of desire for better quality of aggregation; (5) finalizing the intention based on revised desire; (6) disseminating aggregated information to neighboring clusters. We validate the proposed scheme by simulation. The scheme performs better as compared to ESSMD (Event Suppression for Safety Message Dissemination) scheme in terms of critical information acquisition delay, aggregation delay, end-to-end delay, dissemination delay and bandwidth utilization. © 2014 Elsevier Inc. All rights reserved.

1. Introduction Vehicular Ad hoc NETwork (VANET) is an example of Mobile Ad hoc NETwork (MANET), where mobile nodes are vehicles. Moving vehicles equipped with communication devices form exactly an instance of long envisioned MANETs. Communication is possible between vehicles within each other’s radio range as well as with fixed road side infrastructure components. VANET is an integral part of the intelligent transportation system (ITS) architecture which helps to improve the road safety and optimize the traffic. Traffic accidents resulting in injury and death as well as traffic congestion are caused by ever increasing number of vehicles on the roads. This is mainly due to lack of safety services information. Traffic congestion results in heavy delays for the travelers and creates high emission of substances harmful to the environment. Hence critical information related issues are given highest priority in VANETs [1–5]. Creating high speed, highly scalable and secure VANETs presents an extraordinary task due to a combination of highly dynamic mo-

*

Corresponding author. E-mail addresses: [email protected] (M.S. Kakkasageri), [email protected] (S.S. Manvi). http://dx.doi.org/10.1016/j.vehcom.2014.07.001 2214-2096/© 2014 Elsevier Inc. All rights reserved.

bility patterns and network topologies. VANET raises several interesting issues with regard to media access control (MAC), information gathering, information dissemination, information aggregation, information validation, routing, network congestion, performance analysis, privacy and security [6]. Critical information data aggregation and dissemination with minimum delay is a challenging issue [7]. Limitations of existing aggregation schemes are as follows: lack of intelligence in aggregation, less flexible for dynamic varying critical information parameters, lack of combination of aggregation and dissemination mechanisms, low scalability and moderate data aggregation time. This paper proposes a cognitive agent based regression model for critical information aggregation and dissemination using clustering concept presented in [8] to overcome the limitations of existing work. Cognitive agents are a class of software agents which are intelligent autonomous programs activated on an agent platform of either a host or network. These agents use their own symbolic representation knowledge and mentalistic notions based belief base to achieve specified goals without disturbing activities of a host. Mobile software agents are flexible modular entities which can be created, migrated, deployed and deleted in real-time. Mobile code should be platform independent, so that, it can execute at

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any remote host in a heterogeneous network environment [11–13]. Usage of mobile agents for development of new applications in vehicular networks is discussed in [14]. The properties of cognitive agent are as follows: carry out activities in a flexible and intelligent manner, rapid response to environment changes, learn from experience, communicative and co-operative with other agents and proactive, i.e., exhibits opportunistic, goal-oriented behavior and takes the initiative appropriately. A framework of reflective–cognitive agent architecture which enables agent to alter its own code in run time according to the changes in environment is proposed in [15]. Some real implementation of cognitive agents are as follows. Cognitive agent implementation in the Blender Game Engine (BGE) environment is proposed in [16]. A cognitive multi-agents architecture for designing intelligent and adaptive learning systems is discussed in [17]. Belief–Desire–Intention (BDI) model of cognitive agents has been widely used in dynamic and complex scenarios where incomplete information about the environment and other agents is available. The BDI model provides an explicit declarative representation of three key mental structures of an agent: informational attitudes about the world (beliefs), motivational attitudes on what to do (desires) and deliberative commitments to act (intentions). BDI agent reaction for incoming event sequences for time-sensitive applications like the Close-In weapon system in air-carriers is described in [18]. Estimation of average response time using the average attributes of a sequence of events based on probability and queuing theory is performed. BDI model has become predominant architecture for the design of cognitive agents [19]. BDI modeling of pedestrian behavior in a real-world environment is discussed in [20]. A BDI-based framework for a cognitive agent that acts as an assistant to a human user by performing tasks on user behalf is given in [21]. Rapport–Belief–Desire–Intention–Adaptation (RBDIA), a method to support progress from individual autonomous agent concept towards a collaborative multiple agents is discussed in [22]. Some of the benefits of BDI architecture for aggregation in VANETs are as follows: (1) BDI model has the capability of quick adaptivity and learning in VANET environment. (2) As critical information in vehicle environment is continuously changing, beliefs about environment can be updated regularly in BDI architecture. (3) Autonomous decision on aggregation can be taken on available beliefs related to critical information. (4) BDI model creates commitments and performs action on the basis of intentions with full commitment, and (5) BDI model provides an explicit model of teamwork which is critically required in VANETs. The clustering concept used in the proposed work is as follows. Multiagent driven clusters of vehicles are formed in VANETs at lane intersections by considering vehicle speed, direction, connectivity degree to other vehicles and mobility pattern. Cluster members are identified based on vehicle’s relative speed and direction. Cluster head is selected based on stability metric derived from connectivity degree, average speed, and time to leave the road intersection. Cluster head predicts future association of cluster members based on mobility patterns. The announcement of cluster mobility pattern to all cluster members is made by cluster head. The cluster members with similar mobility pattern can reconnect with cluster head after passing an intersection of the lane. Each vehicle is equipped with a cognitive agency that comprises of static and mobile agents. The agents used in the proposed model are Vehicle Manager Agent (VMA), Critical Information Collecting Agent (CICA) and Aggregated Information Dissemination Agent (AIDA). The agents are located in cognitive agency of a vehicle. VMA is a static cognitive agent whereas CICA and AIDA are mobile agents. VMA uses Belief–Desire–Intention (BDI) model to employ regression based cognition for critical information aggregation.

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The proposed scheme operates in the following steps after cluster formation. (1) VMA of a cluster head periodically triggers CICA to collect critical information in a cluster. (2) VMA performs the validation process by using validation scheme given in [9], and eliminates duplicates in collected critical information. (4) VMA generates beliefs based on the finalized critical information. (5) Beliefs are aggregated to generate desire using regression technique. (6) Desire is revised to achieve quality of aggregation. (7) VMA finalizes intention about the revised aggregated critical information, and (8) VMA triggers AIDA to relay vehicle of cluster to disseminate aggregated information. Our contributions in comparison to existing works are as follows. (1) Design of cognitive agent based dynamic critical information aggregation and dissemination scheme to optimize overall delay. (2) Mobile agents usage for critical information collection reduces the network load, and (3) mobile agent based data dissemination approach helps to adapt in varying VANET topologies. The proposed scheme has been compared for its performance with Event Suppression for Safety Message Dissemination (ESSMD) scheme [10]. Rest of the paper is organized as follows. Related works are discussed in Section 2. Regression based critical information aggregation and dissemination using cognitive agents is given in Section 3. Simulation model for proposed scheme is presented in Section 4. Result analysis is given in Section 5. Finally, Section 6 concludes the work. 2. Related works Data aggregation is an important mechanism for maintaining the performance of vehicular networks and ensuring information dissemination [23–27]. In [28], Vehicular Event Sharing with a mobile P2P Architecture (VESPA) is presented. The main contribution of VESPA is to process (aggregate) and disseminate any type of event (e.g., available parking spaces, accidents, emergency braking, information relative to the coordination of vehicles in emergency situations, etc.). VESPA is an efficient data aggregation and dissemination scheme if the collected data is limited. An algorithm for hierarchical aggregation of observations in dissemination based, distributed traffic information systems is presented in [29]. Hierarchical aggregation is based on modified Flajolet–Martin sketch as a probabilistic approximation. The main contribution of this approach is to merge two aggregates while avoiding the occurrence of duplicates. The limitation of hierarchical aggregation scheme is that the data map must be predefined. A role-differentiated cooperative deceptive data-detection mechanism, i.e., RD4 , to detect and filter false data in VANETs is given in [30]. In RD4 , when a sensor is deployed, it picks up a role from the role set based on several sensing features. RD4 focuses only on the data quality without security features. Aggregated Emergency Message Authentication (AEMA) scheme to validate an emergency event is presented in [31]. The emergency messages opportunistic data forwarding process allows a vehicle to hold multiple messages which can be aggregated into a single one before the vehicle launches an aggregated message in the air. AEMA reduces both computation and transmission overhead in achieving efficient authentication on emergency messages. AEMA considers aggregated authentication scheme and data forwarding algorithm separately resulting in lack of efficient aggregation effect. An aggregation scheme for traffic flow prediction based on the moving average (MA), exponential smoothing (ES), auto-regressive MA (ARIMA), and neural network (NN) model is described in [32]. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions that result from the different models are used as the basis of the NN in the aggregation

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Table 1 Summary of data aggregation schemes in VANET. Sl. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Aggregation schemes

VANET scenario

Type of message

Aggregation accuracy

Processing time

ESSMD [10] VESPA [28] Probabilistic and hierarchical [29] Role differentiated [30] AEMA [31] Aggregation for traffic flow [32] SLMA [33] CASCADE [34] Secure Aggregation [35] Data aggregation [36] Fuzzy based [37] Adaptive based [40]

Hybrid V2V Hybrid Hybrid V2V Hybrid Hybrid V2V V2V Hybrid Hybrid Hybrid Hybrid

Safety Non-safety Non-safety Safety Safety Safety and non-safety Safety Safety Safety Non-safety Non-safety Non-safety Safety

Medium High Medium High Medium High Medium High High Medium Medium Low High

Medium Low Medium Low Medium High Medium Medium Medium High Medium High High

stage. The output of the trained NN serves as the final prediction. The aggregation strategy offers improvement in operational forecasting. Structure-less message aggregation (SLMA) for safetyrelevant VANET applications is presented in [33]. SLMA eliminates packet exchanges for aggregation structure formation and maintenance. Bayesian fusion algorithm is adopted to effectively achieve precise event detection on the road. Decision maker makes an optimal decision with minimum Bayesian risk to enhance the information certainty. SLMA improves information accuracy. Cluster-based Accurate Syntactic Compression of Aggregated Data (CASCADE) for aggregation of highway traffic information in VANETs is discussed in [34]. CASCADE is designed to enable both safety (collision warning) and information (congestion notification) applications. CASCADE fails to detect and isolate vehicles that lie about their position or speed or that intentionally mis-aggregate data. Message aggregation and group communication for safety related applications in VANETs is presented in [35]. In this approach, message is securely disseminated to selected vehicles who share a similar view of their environment. With secure aggregation, the information dependability is also increased. Grouping of several safety related messages provide the receiver with more evidence concerned to a given event. Group communication with secure aggregation scheme does not provide a complete solution for aggregation, but concentrates on security aspects. Event Suppression for Safety Message Dissemination (ESSMD) scheme is given in [10]. The ESSMD method works on the principles of data aggregation, i.e. the reduction of redundant transmissions. Rather than aggregating information from several sources at a single point, suppression scheme aims to restrict the number of sources that report about the similar event. ESSMD reduces redundant data transmissions and does not add any extra end-to-end delay. Limited bandwidth and minimal initial deployment challenges for VANET based traffic information are studied in [36]. A domain specific aggregation scheme by means of a multi-layer hierarchy of approximations of the road network is presented in order to minimize the required overall bandwidth. A genetic algorithm is proposed to identify good positions for static roadside units in order to cope with the highly partitioned nature of a VANET in an early deployment stage. Structure-free aggregation mechanism to aggregate data purely based on their correlation is presented in [37]. It shows that efficient and accurate aggregation in VANETs is possible without the need for pre-defined road segments. Fuzzy reasoning is applied to make aggregation decisions. The scheme does not consider secure aggregation against forgery by adversaries or defective sensor VANETs. Trustworthy vehicle-generated announcements on VANETs that relies on a priori measures against internal attackers (vehicles in the VANET sending fake messages) is proposed in [38]. This mechanism is suitable for deployment in both deterministic and nondeterministic scenarios and provide a straightforward deployment

if the communication links between the vehicles are stable. Multilevel conditional privacy preservation authentication protocol in VANETs based on ring signature is proposed in [39]. This protocol offers conditional privacy preservation authentication and multilevel countermeasure. This protocol provides message authentication and verification, cost-effective identity tracking in case of a dispute, and low storage requirements. An adaptive forwarding delay control scheme for VANETs (Catch-Up) is proposed in [40]. The basic idea of the Catch-Up scheme is to insert an adaptive delay before forwarding a report to the next hop. A new paradigm of distributed learning, “Learning-From-Others” is presented in Catch-Up. A fuzzy-rulebased function approximation method is used to speed up the learning process. Traffic matrix (TM) is a key input of traffic engineering and network management. TM estimation in large-scale IP backbone networks based on the generalized regression neural network (GRNN), called GRNN TM estimation (GRNNTME) method is proposed in [41]. A multi-input and multi-output model of largescale TM estimation is presented. The main feature of GRNN is powerful capability of learning and generalizing. The output of the model is efficient to capture the spatio-temporal correlations of TM. Table 1 depicts the comparison of existing aggregation schemes. Limitations of existing aggregation schemes are as follows: lack of intelligence in aggregation, less flexible for dynamic varying critical information parameters, lack of combination of aggregation and dissemination mechanisms, low scalability and moderate data aggregation time. 3. Regression based critical information aggregation and dissemination In this section we discuss regression based critical information and dissemination mechanism. Proposed BDI based cognitive model integrates mobile agents and static agent to deliver a rapid response for aggregation of critical information. Cognitive agency adapts intelligent aggregation revision model that collects critical information arising in the cluster and aggregates with regression mechanism. This section describes the network environment, preliminaries, computation models (models for: position of occurrence of critical information, aggregation and aggregation revision), cognitive aggregation agency, aggregated critical information dissemination scheme and limitations of the proposed work. 3.1. Network environment We consider a VANET in which number of vehicles are separated by distance (between consecutive vehicles). The VANET is purely based on vehicle-to-vehicle (V2V) architecture where both the collection and the restitution of information are done within

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171

Fig. 1. VANET aggregation environment.

the VANET. We assume that vehicles move in an urban road scenario of two lanes by forming clusters as shown in Fig. 1. Clusters 1, 2, 3 and 4 consists of vehicles (V1 to V14) according to cluster pattern. V2, V7, V10, and V12 are the cluster heads for clusters 1, 2, 3 and 4, respectively. All vehicles are equipped with Global Positioning System (GPS) receiver for obtaining location and time, navigation system that can map GPS coordinates to a particular roadway and a communication device using Dedicated Short Range Communications (DSRC) [42]. Vehicles are equipped with number of sensors. Critical information in vehicle is determined by sensors. Thus, a vehicle may produce and receive critical Information. We consider critical information like environmental condition, road condition, accidents, etc. Critical information in each vehicle is represented as a tuple T c = Originator ID, Transit ID, Hops, Time stamp, Position of occurrence. It is assumed that on-board device in each vehicle is equipped with an agent platform and an agency in which cognitive agents reside. We also assumed that agents have protection from hosts on which they execute. Similarly, hosts have protection from agents that can communicate on available platform. The secured and reliable platform consists of protection from denial of execution, masquerading, eavesdropping, etc. Recently developed techniques for mobile agent security have techniques for protecting the agent platform [43,44]. These techniques include software-based fault isolation, safe code interpretation, signed code, authorization and attribute certificates, state appraisal, path histories, and proof carrying code. Techniques for protecting mobile agent include partial result encapsulation, mutual itinerary recording, itinerary recording with replication and voting, execution tracing, environmental key generation, computing with encrypted functions, and obfuscated code (Time Limited Black-box). 3.2. Preliminaries This section presents some of the definitions used in proposed scheme.

• Hops: It is the number of hops critical information has traveled since creation.

• Time stamp: It is the time of critical information generation. • Position of occurrence: It is point of occurrence of critical information.

• Beliefs: Beliefs are agent’s accepted knowledge about collected critical information at cluster head. Example: information from the vehicle sensors/neighboring vehicles. • Desires: States that describe agent’s motivations arising from its nature or type are known as desires. Example: Aggregate the collected information, Disseminate without aggregation, etc. • Intentions: It represents tasks arising from a user that have successfully passed conditional aspects; as far as it knows agent can achieve tasks, and it has committed to doing so. Example: Aggregation, Dissemination, etc. • Relay vehicle: Common cluster member for neighboring clusters is known as a relay vehicle. 3.3. Computational models This section describes the computational models used for finding vehicle current location (i.e., position of occurrence of critical information), aggregation and aggregation revision in the proposed scheme. 3.3.1. Computation of vehicle location A vehicle is required to know the position of occurrence of critical information on a road lane. Let G ( V , E ) be the directed graph describing the road network, where V is the set of vehicles and E is the set of road lanes. Let C be the mid point on the center line of the road lane (a lane without intersections) whose coordinates are ( L c , R c ). Each vehicle position is denoted by the coordinates ( L i , R i ). The distance between a vehicle’s present location (i.e., where the critical information is occurred) and the center line midpoint of road lane is given by the Euclidean distance as given in Eq. (1).



• Critical information: Safety related message of VANET can

d( L c , R c ) =

be considered as critical information. Critical information in each vehicle is represented as a tuple T c = Originator ID, Transit ID, Hops, Time stamp, Position of occurrence. • Originator ID: Originator ID is the identity of the affected/critical information generated vehicle in VANET. • Transit ID: Vehicle ID of neighbor of originator ID.

| L c − L i |2 + | R c − R i |2 .

3.3.2. Regression model for aggregation For all the collected critical information tuple T c , aggregation of the critical information is based on Position of occurrence (Y ), Hops ( X 1 ) and Time stamp ( X 2 ). Hence these three regression parameters are represented as Y , X 1 , X 2 .

(1)

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Fig. 2. Cognitive agency.

In general, linear regression function [45] for any number of independent variables is given as in Eq. (2).

Y = β0 + β1 X 1 + β2 X 2 + ... + βk X k + μ

(2)

where Y is the estimate value of position of occurrence, X k ( j = 1, 2, ..., k) are the factors related to the value Y , βk ( j = 0, 1, 2, ..., k) are k + 1 unknown regression parameters and μ is random error item. For Y , X 1 and X 2 of critical information linear sample regression model is given in Eq. (3). A linear regression line has an equation of the form Y = a + b X , where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

Y´ = β0 + β1 X 1 + β2 X 2

(3)

where Y´ is aggregated critical information value of Y . As parameters β0 , β1 and β2 are unknown, we can estimate them using the sample observed value ( X 1 , X 2 ; Y ) in time window tn to tn+m , where n and m are integers. β0 with two independent variables is given in Eq. (4).

β0 = Y − β 1 X 1 − β 2 X 2

(4)

For collected values of Y , X 1 , and X 2 , two variables β1 and β2 are computed as shown in Eqs. (5) and (6), respectively.

    ( x22 )( x1 y ) − ( x1 x2 )( x2 y ) β1 =    ( x21 )( x22 ) − ( x1 x2 )2     ( x21 )( x2 y ) − ( x1 x2 )( x1 y ) β2 =  2  2  ( x1 )( x2 ) − ( x1 x2 )2

(5)

(6)

where β´0 , β´1 and β´2 are aggregated unknown regression parameters. The residual e between observed critical information value Y and the aggregated critical information value Y´ is given by Eq. (8).

e = Y − Y´ = Y − (β´0 + β´1 X 1 + β´2 X 2 )

(8)

3.3.3. Desire revision model To attain higher correlation between collected critical information and aggregated critical information, we need to revise Y´ . Revision of Y´ is done by minimizing residual e using least square method as given in Eq. (9).

Q (β´0 + β´1 + β´2 ) =



e 2i =



(Y − Y´ )2

(9)

According to least square method, the condition for Q to be a minimum is given as in Eq. (10).

∂Q = 0 ( j = 1, 2) ∂ β´ j

(10)

After simplifying Eq. (10), we get Eq. (11).

X t Y = X t X β´

(11)

where β´ = [β´0 , β´1 , β´2 ]t . Let R ( X ) = K + 1, X t X is (k + 1) step

square formation, then X t X is non-singular, therefore the smallest square estimate vector of β is as given in Eq. (12).

− 1 t  X Y β´ = X t X

(12)

Substituting the modified aggregated critical information value

β´ in Eq. (8), we can minimize the residual e. In this way, the aggregated critical information Y´ is revised.

Substituting unknown parameters β0 , β1 and β2 of the regression model, the linear sample regression equation is as given in Eq. (7).

3.4. Cognitive aggregation agency

Y´ = β´0 + β´1 X 1 + β´2 X 2

In this section, we present cognitive aggregation agency employed in the proposed scheme. Fig. 2 shows a cognitive agency.

(7)

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The agency is located in the cluster head and consists of knowledge base, BDI based static Vehicle Manager Agent (VMA), Critical Information Collection Agent (CICA) and Aggregated Information Dissemination Agent (AIDA) as mobile agents. 3.4.1. Knowledge base (KB) It comprises of collected critical information of cluster members i.e., T c = Originator ID, Transit ID, Hops, Time stamp, Position of occurrence, cluster members vehicle ID, cluster members updated list, available bandwidth for communication, individual cluster member status (connected/disconnected to network), total number of critical information points available for aggregation and aggregated critical information list (old/new). KB is read or updated by VMA, CICA and AIDA. 3.4.2. Critical information collection agent (CICA) It is a mobile agent. VMA of cluster head triggers periodically CICA to cluster members for retrieval of critical information like accidents, weather condition, road conditions, etc. VMA finds relative positions of cluster members using stored road maps by mapping vehicle’s latitude and longitude coordinates to points on road in which vehicle is driving. CICA provides the collected critical information upon request from VMA. 3.4.3. Aggregated information dissemination agent (AIDA) It is a mobile agent employed to disseminate the aggregated critical information to the relay node of the cluster for further dissemination to neighboring clusters. VMA triggers AIDA. 3.4.4. Vehicle manager agent (VMA) It is a static cognitive agent based on BDI model that runs in a node, creates agents and knowledge base, controls and coordinates activities of cognitive agency. VMA triggers CICA for collecting generated critical information from each cluster member. AIDA is initiated by VMA for aggregated critical information delivery to the relay node of cluster. VMA has knowledge of road length, road width, number of lanes and current position of the vehicle. Critical information collection, validation and filtering, aggregation and dissemination are part of VMA functions. Algorithm 1 depicts the functionality of VMA. The operational sequence of VMA is as follows: 1) collection of critical information, 2) validation of collected critical information and filtering, 3) aggregation, and 4) aggregated critical information dissemination. 1. Collection of critical information: VMA of cluster head periodically triggers CICA. CICA creates clones and selectively floods them through the cluster members to collect the critical information generated in time window tn to tn+m . CICA is programmed to move across cluster. CICA communicates to VMA of cluster head about collected critical information tuple T c . Position of occurrence is computed based on the vehicle location at the time of sensed critical information (as given in Section 3.3.1, computation of vehicle location). Based on critical information type, VMA segregates collected critical information. On each segregated group, VMA does the aggregation. Let us consider an example scenario (shown in Fig. 3) to illustrate the concept. For simplicity, we consider only one cluster on one lane of a road. Vehicles V1, V2, V3 and V4 are the cluster members with vehicle V CH as a cluster head. VMA of V CH triggers CICA to all cluster members to collect critical information tuple. To brief about the collected critical information, consider the collected tuple 2, 1, 50, 10, 9. Where 2 is the critical information originator vehicle ID, 1 is the transit vehicle ID, 50 is the number of hops, 10 seconds is the time stamp

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Algorithm 1 VMA functionality (). 1: Nomenclature: Critical information tuple – T c , cluster head – V cm , modified critical information tuple – T cm , beliefs – σ , modified beliefs – σ´ , desire – Y´ , error – e, VMA threshold – Θ . 2: Input: Collection of critical Information tuple T c of cluster member. 3: Output: Aggregated information of cluster. 4: Begin 5: VMA receives T c of each V cm from CICA; // VMA validates and eliminates duplicates 6: for T c of each cluster member do 7: Apply “validation scheme” to validate T c ; 8: if T c is valid then 9: Filter the valid T c by eliminating duplicates; 10: T cm ← T c ; 11: Generate Beliefs (σ ); 12: else 13: Discard T c ; 14: end if // VMA updates beliefs. 15: if T cm of σ exists in “past beliefs” then 16: Discard T cm ; 17: else 18: Modify σ as σ´ ; 19: end if 20: end for // Desire generation 21: Apply regression computation model on σ´ ; 22: Generate desire ( Y´ ); 23: Compute e = Y − Y´ ; //Finalizing intention 24: if e ≥ Θ then 25: Apply aggregation revision model on e; 26: Obtain modified Y´ as intention; 27: Trigger AIDA with modified Y´ to relay vehicle; 28: else 29: Trigger AIDA with Y´ to relay vehicle; 30: end if 31: End

and 9 meters is the position where the event was occurred. Similarly, VMA collects all generated critical information in the cluster and updates KB. 2. Validation and filtering: VMA does the job of validating collected critical information. For each collected critical information, VMA verifies the following details: authenticated cluster member, outdated information, tampered information, etc. If all these checks are successful, they are considered as valid critical information. VMA validates the collected critical information using validation scheme given in [9], which is based on Proof-of-Relevance (PoR) mechanism. It is used in our work to defend against false collected critical information. PoR is achieved via authentic consensus where a number of signatures are collected from the witnessing vehicles for each critical information, and generate the final validation report. VMA eliminates duplicate valid critical information as follows. VMA creates a table in KB for entry of valid critical information tuple. VMA identifies the similar tuple by comparing the rows and columns of the table and deletes the duplicate. From the critical information tuple T c , VMA considers only the parameters position of occurrence, hops and time stamp since these parameters influence more on criticalness of the information. Hence the critical information C i considered for belief generation can be expressed as a modified tuple T cm = Position of occurrence, Hops, Time stamp. 3. Aggregation: VMA executes BDI model on critical information modified tuple for aggregation as follows. • Beliefs generation: Beliefs of VMA stores all the information that is acquired after the process of validation and filtering. Beliefs stored in VMA are of the type static beliefs. Beliefs that do not change their value during the life cycle of execution of an agent are known as static beliefs. For n number of modified tuple, σ is represented as

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Fig. 3. Critical information collection in cluster.

Fig. 4. Aggregated critical information dissemination.

σ = { T cm1 , T cm2 , ..., T cmn }. The belief analyzer classifies the beliefs (σ ) into past and current belief sets. The past belief set consists of historical information of critical information while the current context environment is stored in the current belief set. After classification, belief analyzer returns the present beliefs as modified beliefs (σ´ ). These modified beliefs (σ´ ) are considered for desire generation. • Desire generation: Aggregation of beliefs lead to desire generation. VMA uses regression computation model (as discussed in Section 3.3.2, regression model for aggregation) on modified beliefs (σ´ ), and generates the desire as aggregated critical information. For the regression computation model, T cm = Position of occurrence, Hops, Time stamp is represented as T cm = Y , X 1 , X 2 . Aggregated critical information based on the parameter position of occurrence Y is the desire Y´ . • Intention generation: VMA maintains a threshold value Θ for deciding the quality of aggregation. Θ depends on the critical information parameters like total number of observations, type, total number of cluster members, relay nodes etc. VMA computes residual e between all observed critical information parameters Y and the aggregated critical information value Y´ as given in Eq. (8). If e > Θ , then the desire

revision is needed. Revision of desire is done (as discussed in Section 3.3.3, aggregation revision model) to obtain the modified aggregation on Y´ . This modified aggregation information is disseminated to the relay vehicle of the cluster. Relay vehicle propagates the aggregated information to the neighboring cluster. 4. Aggregated critical information dissemination: VMA employs AIDA to disseminate aggregated critical information to the relay vehicle of the cluster. In the clustering scheme, cluster head knows the relay vehicle ID. VMA triggers AIDA to the relay vehicle with aggregated critical information Y´ in coordination with VMA of vehicle. Relay vehicle sends Y´ to corresponding cluster head (neighboring cluster). IF VMA does not find relay node, it stores the aggregated critical information in its KB until the suitable relay vehicle is available. Consider an example scenario as shown in Fig. 4. Two clusters are shown on one lane of a road. Vehicles V1 and V2 are the cluster members (cluster 1) with vehicle V CH1 as a cluster head. Vehicles V4 and V5 are the cluster members (cluster 2) with vehicle V CH2 as a cluster head. Aggregation process is done at cluster 1, VMA of V CH1 triggers AIDA to the relay vehicle V3. Vehicle V3 is

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Table 2 Simulation parameters.

Fig. 5. Simulation map.

cluster member for both the clusters. V3 forwards the aggregated safety information to neighboring cluster 2. 3.5. Limitations of the proposed work Some of the limitations of our cognitive agent model for critical information aggregation and dissemination are as follows. (1) The model assumes all vehicles to be smart, i.e., smart vehicle has relatively strong computational resources, typically access to on-board sensors of cars, and executes a number of applications for traffic safety and driving comfort. (2) It is assumed that all the vehicles have capability of collecting critical information for aggregation process, and (3) Urban scenario with very low sparse network connectivity may not be favorable for the proposed scheme. 4. Simulation We have simulated proposed model by considering a Bangalore city map as shown in Fig. 5. Due to the limitations of the simulation, Bangalore city area has been scaled down to 10 : 1 (The total city area of 50 × 50 kms has been scaled down to the 5 × 5 kms). Only dense traffic roads and road junctions (road intersections) are considered for simulation. The proposed scheme has been simulated in various network scenarios using “C” programming language. Each simulation run lasted for 6000 seconds. We collected the data after first 120 seconds of warm up period. We repeated the simulations to obtain 95% confidence interval. An average of 1000 executions were carried out in a scenario to achieve confidence interval. In this section, we discuss network model, traffic model, mobility model, channel model, BDI model and simulation procedure used in simulation. Network model: We consider n number of vehicles moving in a fixed region of length A Km. and breadth B Km. We consider vehicles to move in a road of type R t ype , with number of lanes L. Communication coverage area for each vehicle is considered as a V com meters. Total number of clusters considered are C n . Traffic model: Constant bit rate model is used to transmit certain number of fixed size packets, P pkts . Coverage area around each vehicle has a bandwidth, BW, shared among its neighbors. Arrival rate of critical information into the vehicle follows Poisson distribution with mean λ. Reason to use Poisson distribution is that it is an efficient method for arrival process of events to a queuing system. Mobility model: At the beginning of the simulation, vehicles are uniformly distributed in lanes [46]. This setting holds under assumption that there is a free flow movement of vehicles, i.e. we do not account for congestion that may arise in roads. It is assumed that all vehicles are equipped with a communication device and knows start position, start time of vehicle, route that it selects, and speed at which it travels.

Sl. No.

Parameters

Specifications

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.

Simulation duration Simulation area Number of vehicles Transmission range Speed (minimum) Speed (maximum) Mobility model Channel type Antenna model Road type Total number of roads Number of lanes in a road Road junctions Length of road Breadth of road Safety distance between vehicles Number of clusters Critical information

6000 seconds 5000 m × 5000 m 10, 20, 30, 40, 50 250 m, 500 m 10 kmph 40 kmph, 60 kmph, 80 kmph Manhattan model Wireless channel Omnidirectional Free way 21 2 9 4000 m 100 m 2 mts 2, 3, 4, 5 5 to 50/vehicle

Algorithm 2 Simulation procedure. 1: 2: 3: 4: 5: 6: 7: 8: 9:

Begin Create the clusters. Deploy proposed cognitive agency in cluster head. VMA triggers CICA to collect critical information in cluster for time window tn to tn+m (as shown in Fig. 3). CICA submits collected critical information to cognitive agency. VMA executes BDI model for aggregation. VMA triggers AIDA for aggregated information dissemination to the relay vehicle of cluster (as shown in Fig. 4). Compute performance parameters. End

Manhattan mobility model is used for Bangalore city map [47]. Manhattan is generated-map-based model, introduced to simulate an urban environment. Before starting a simulation, a map containing vertical and horizontal roads is generated. Each of these later includes two lanes, allowing the motion in the two directions. At the beginning of a simulation, vehicles are randomly put on the roads. They then move continuously according to history-based speeds. When reaching crossroads, the vehicle randomly chooses a direction to follow. That is, continuing straightforward, turning left, or turning right. Safety distance of R meters is maintained from preceding vehicle for a certain tolerance time, and then change lane if possible. Changing lane allows vehicle to move to an adjacent lane if there is space (safety distance) in that lane. At every lane intersection, we assume that each vehicle can choose to make either a left or right (if not a one-way road) or no turn. Mobility factor for each node is in between the range of I to J kmph. Border effect of bounded simulation region on vehicle mobility is accounted for by making vehicle reappear in the region. Channel model: Communication medium access protocol considered for simulation is Enhanced Distributed Channel Access (EDCA) based Distributed Coordination Function (DCF) of IEEE 802.11 which is responsible for medium access based on CSMA with Collision Avoidance (CSMA/CA) [48,49]. Rician fading with the phase-noise channel model [50] is assumed for channel model. BDI model: Finite beliefs σ1 , ..., σn are considered for VMA. A desire for set of beliefs is generated. Intention is associated with VMA. 4.1. Simulation procedure The simulation input parameters considered are summarized in Table 2. The simulation procedure at abstract level for proposed cognitive agent model is described in Algorithm 2.

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Fig. 6. No. of critical information points collected/second vs. vehicles in lane length of 1 km.

Fig. 7. Critical information acquisition delay vs. vehicles in lane length of 1 km.

4.2. Performance metrics

• Bandwidth utilized: It is defined as the amount of bandwidth

Some of performance metrics evaluated are acquisition delay, aggregation delay, dissemination delay, end-to-end delay, bandwidth utilized and control overhead.

utilized out of the total bandwidth available. It is expressed in terms of Kbps. • Control overhead: It is defined as the ratio of the total number of control messages or agents to the total number of packets generated to perform aggregation.

• Critical information collected: It is defined as the total num•

• • •

• •

ber of critical information points collected at cluster head for particular time window. Critical information acquisition delay: It is defined as the total time taken by the VMA of cluster head to collect critical information generated/sensed in the cluster from CICA. It is expressed in terms of seconds. Aggregation delay: The total time taken by the VMA of cluster head to perform collected critical information aggregation. It is expressed in terms of milli-seconds. Aggregated information dissemination delay: It is the time taken for dissemination of aggregated information to the relay node of the cluster. It is expressed in terms of seconds. End-to-end delay: It measures difference between time of receiving critical information at cluster head and cluster relay node that receives aggregated critical information. It is expressed in terms of seconds. Filtered critical information: It is the total number of selected critical information points out of collected critical information points for aggregation. Aggregated critical information: It is defined as the total number of aggregated critical information points generated/second.

5. Result analysis This section presents the results obtained during simulation. We compare results of proposed work with event suppression scheme for safety message dissemination in VANETs (referred as ESSMD scheme in graphs). The reasons to consider ESSMD scheme for comparison are as follows: (1) aggregation and dissemination mechanism for safety messages, (2) number of broadcasting vehicles reporting the similar event are analyzed, and (3) redundant data transmission is considered for aggregation. Number of critical information (C.I.) points collected for varying vehicle density on lane length of 1 Km under different mobility scenarios is shown in Fig. 6. As the number of vehicles increase, critical information collection increases gradually. For lower vehicle speed (i.e., at 40 kmph), CICA efficiently collects critical information generated/sensed in the cluster. Increase in vehicle speed (i.e., for 60 kmph and 80 kmph) leads to reception of less number critical information points. Critical information acquisition delay for different number of vehicles is illustrated in Fig. 7. As the number of vehicles increase in the lane, critical information acquisition delay increases regularly. For large number of vehicles, acquisition delay almost

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Fig. 8. Aggregation delay vs. No. of critical information points.

Fig. 9. Aggregated information dissemination delay vs. vehicles in lane length of 1 km.

remains constant. This is because of the efficiency of CICA in collection of critical events from the cluster members. Compared to the ESSMD scheme, for all mobility constraints, the acquisition delay of the proposed scheme is less. This is because of the limitations of the ESSMD scheme like, buffering of the data packets and time taken to find the neighbor vehicles for broadcasting the packets. Aggregation delay for different number of critical information points under varying speed conditions of vehicles is illustrated in Fig. 8. As the number of critical information points increase, aggregation delay increases gradually. The increased aggregation delay is tolerable, as the aggregation mechanism executes in the dynamic clustering. In dynamic clustering, the vehicles in a cluster remain in the transmission range up to 150 seconds [8]. Aggregation delay in our proposed scheme is less as compared to ESSMD scheme. This shows the efficiency of VMA in selecting the relevant critical information for aggregation out of the total collected information. Frequent referring and searching for events in the event table of ESSMD leads to more aggregation delay. The comparison of aggregated information dissemination delay for different number of vehicles in a lane under varying mobility scenarios is shown in Fig. 9. As the number of vehicles increase, aggregated information dissemination delay increases constantly. The aggregated information dissemination delay is less as compared to ESSMD scheme. Dissemination of aggregated information over AIDA to the relay vehicle of cluster is the reason to have minimum delay for proposed scheme. For ESSMD scheme more packet buffering is required while forwarding/broadcasting the packet; therefore, the delay is increased.

End-to-end delay for varying number of vehicles in a lane is depicted in Fig. 10. End-to-end delay represents the total delay for performing critical information acquisition, aggregation and dissemination. As the mobility values increase (from 40 kmph, to 80 kmph), end-to-end delay also increases. This delay is high for ESSMD scheme as compared to the proposed scheme. Some of the reasons for proposed scheme to perform better than ESSMD scheme are as follows: (1) collection and dissemination of the critical information with CICA and AIDA respectively, (2) VMA intelligence (i.e., removing the redundant information before aggregation process) in aggregating the critical information and (3) broadcasting mechanism in the ESSMD scheme leads to more number of unnecessary retransmissions. Finalized or filtered critical information for aggregation with respect to the collected critical information for mobility values 40, 60, and 80 kmph are shown in Fig. 11. As the number of critical information points increases, filtered critical information increases gradually. Proposed scheme efficiently eliminates the redundant information before aggregation i.e., VMA does validation, duplication elimination, and segregating as past and present events on all the collected critical information. Aggregated critical information (C. I.) generated for varying number of critical information points and mobility values (i.e., 40 kmph to 80 kmph) is reported in Fig. 12. Generation of aggregated critical information for ESSMD scheme is more as compared to the proposed scheme. In ESSMD scheme sensed/generated events are searched in the event table of every vehicle. If the event time stamp is not matched with anyone of the event in the event table, then the event is broadcasted or forwarded. This leads to more

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Fig. 10. End-to-end delay vs. vehicles in lane length of 1 km.

Fig. 11. Filtered critical information/second vs. No. of critical information points.

Fig. 12. Aggregated C. I./second vs. No. of C. I.

number of event transmissions. In the case of proposed scheme, cluster head VMA aggregates with regression for all filtered critical events. Hence the aggregated critical information are less. This result shows the efficiency of aggregation with cognitive (BDI) model. Fig. 13 outlines the comparison of bandwidth utilization for the proposed scheme and ESSMD scheme. As the mobility (from 40 to 80 kmph) and number of vehicles increase, bandwidth utilization is increased linearly for proposed aggregation scheme. In the proposed scheme, bandwidth utilization is initially low for lower mobility vehicles and increases linearly as the number of vehicles

increase. This behavior results from the network connectivity in the VANET. For optimum number of vehicles, i.e., vehicles between 20 to 30/km, bandwidth utilization is almost constant. For all mobility values the bandwidth utilization for the proposed scheme is less as compared to the ESSMD scheme. Some of the reasons for better performance of the proposed scheme are collection, aggregation and dissemination of critical events with cognitive agent and more number of aggregated frames transmission in the ESSMD scheme. Fig. 14 outlines the control overheads for varying mobility values and number of vehicles. Control overheads are more for in-

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Fig. 13. Bandwidth utilized vs. vehicles in lane length of 1 km.

Fig. 14. Control overhead vs. vehicle speed.

creased number of vehicles and mobility. Because of the network connectivity, as the vehicle mobility increases control overheads are also increased. This is due to the fact that more number of mobile agents are generated for critical information collection and dissemination to the cluster members and relay vehicles, respectively. 6. Conclusions In this paper, we have proposed a BDI based cognitive model integrating mobile agents and static agent to deliver a rapid response for aggregation of critical information. The scheme employs cognitive agency consisting of a static vehicle manager agent (VMA) and two mobile agents called critical information collection agent (CICA) and aggregated information dissemination agent (AIDA). CICA collects critical information arising in the cluster. VMA employs BDI model for aggregation with regression mechanism. To improve the quality of aggregation, VMA adapts aggregation revision model. The proposed work is simulated for various VANET network environments to validate its performance. From the simulations we observed that the proposed scheme performs better than ESSMD scheme in terms critical information acquisition, aggregation, dissemination, end-to-end delay and bandwidth utilization. Cognitive agent systems have a great potential to influence design of future VANET and their services. Cognitive agent systems should be regarded as an “add on” to existing service platforms, providing more flexibility, adaptability, and personalization for realization of services within next generation VANET environments.

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