Security of Supply Chains by Automatic Multi-Agents Collaboration

Proceedings of the 14th IFAC Symposium on Information Control Problems in Manufacturing Bucharest, Romania, May 23-25, 2012

Security of Supply Chains by Automatic Multi-Agents Collaboration Itshak Tkach, Yael Edan* and Shimon Y. Nof ** * Dept. of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel ** School of Industrial Engineering, Purdue University, West Lafayette, USA Abstract: Protocols for real-time collaboration of multiple agents for supply chain security tasks utilizing RFID information with real-time sensor tracking and monitoring are presented. The proposed system provides a structured framework and a set of interaction services by controlling a network of agents; each of the agents has a set of skills and resources that they contribute to the system. Optimal control through task administration protocols of agents’ collaboration is developed. Simulation analysis indicated improved performance of 11% by optimal task assignment and agent’s collaboration, with improved time management flexibility. 1. INTRODUCTION There is an increasing need to increase security at all points along global supply chains, as supply chains are often a subject to hijack, theft, or tampering (Lara and Nof, 2009). Radio frequency identification (RFID) provides a common way to obtain information on individual items (e.g., cars and trucks, containers, pallets; Qiu, 2007) and is typically used for supply chain management (Eyers et al., 2011). RFID is an auto-ID technology which harnesses electromagnetic fields to use radio waves to assist in data exchange between tags and readers. Other auto-ID technologies include barcodes, biometrics, and smart cards (Angeles, 2005). RFID provides unique product identification and can give continuous location and status updates of items with real-time visibility. RFID also offers a level of accuracy which encourages the reduction of inefficiencies and network optimality (Penttila et al., 2006). RFID has been applied in different logistics areas improving shipping, distribution and manufacturing processes and in industries such as automotive, military, retailing, agriculture, healthcare, pharmaceutical and security (Attaran, 2007; Banks et al., 2007). In supply chain management, RFID tags are used to track products throughout the supply chain—from supplier delivery, to warehouse stock and point of sale. A central database records product movement, which manufacturers or retailers can later query for location, delivery confirmation, or theft prevention (Weinstein, 2005). RFID can improve supply chain management efficiency and ease of use. However, RFID based supply chain is exposed to security and privacy challenges. An organization that implements RFID in its supply chain does not want competitors to track its shipments and inventory (Weinstein, 2005). Therefore, a secure RFID system must avoid eavesdropping, traffic analysis, spoofing and denial of service (Gao et al., 2004).

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Real-time visibility and traceability are key components in ensuring security of the supply chain network (Zhang et al., 2010). As RFIDs have limitations such as limited communication bandwidth, reliability issues, tag memory, range coverage and heterogeneous reader landscape (Floerkemeier and Lampe, 2006), monitoring the supply chain security via distributed sensors and sensor networks additionally to RFIDs can contribute to improved performance and cope with unpredictable security events (Fokum et al., 2009). Combining multiple and different types of sensors (e.g., visible, infrared, microwave and acoustic) can further increase performance and reliability (Bian et al., 2006). The objective of this research is to improve the security of supply chains by combining RFID information with real-time tracking and associated sensor information. Using multiple sensors, the security monitoring network is able to detect events and report those relevant to human supervisors for decision making (Fokum et al., 2009) and eventually decrease security risks. There are many factors influencing monitoring system performance (Fig. 1). By combining the advantages of sensor network with human intelligence skills, a collaborative system can further increase monitoring performance and decrease system limitations even in unpredictable security events with which current autonomous systems are incompetent to deal (Tkach et al., 2011; Parasuraman et al., 2000). Managing the security monitoring can be enhanced with the real-time location data of each item. This information can make security more manageable due to the availability of constant tracking and further data analysis can be conducted by human supervisors to increase the utilization rate (Aggarwal et al., 2011). As sensors are costly and subsequent to failure, it is necessary to coordinate and decide when and where to apply them. Multiple sensors provide redundancy, thereby increasing

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robustness. For coordinated control of the multitude of sensors, each individual sensor in the team is considered as an agent with particular capabilities engaged in executing a portion of the supply chain security monitoring mission (Vachtsevanos et al., 2004). The sensors share local information and cooperate with each other. Recent work on sensor management focused on collaborative sensor network middleware (Jeong and Nof, 2009), sensor scheduling and allocation (Xiong and Svensson, 2002). Coordination protocols have been developed by (Ko and Nof, 2010) to achieve effective control by optimizing the collaboration. A network flow optimization model for allocating sensors to tasks was proposed by (Bian et al., 2006) formulated as a linear programming problem. Environmental conditions Illumination Visibility Terrain type

Sensor parameters Accuracy Response time Sensitivity Range coverage Reliability

Human parameters Stress Fatigue Workload Expertise Adaptation time

This paper presents a framework for automatic collaboration of agents in a multi-agent system for ensuring the security of a supply system. The overall system performance was optimized by automatic best matched agents’ assignment. The coordination and real-time control logic are implemented as task protocols that function at the application level. The developed framework was analyzed in simulations and the numerical analysis results indicated that the framework improved monitoring system performance by coordinating tasks and collaborating agents. 2. COLLABORATIVE SYSTEM MODEL 2.1 System definition The sensor management collaborative system framework establishes cooperative behaviour between sensors with external human supervision (Jeong and Nof, 2009). The system, consisting of multiple sensors and human supervisors that must collaborate, assigns each group member (=agent) an appropriate operation at an appropriate time (Fig. 2). Since in many cases, a monitoring task cannot be accomplished by a single sensor agent, multiple sensors must be employed, raising the selection problem (i.e., deciding which sensor is best-fit for which task (Xiong and Svensson, 2002)). Sensors S1

S2 S1

Fig. 1. Factors influencing monitoring system performance Multi-agent management and collaboration (Xiong and Svensson, 2002) refers to planning and control of system resource usage to enhance multi-agent data fusion performance. Controlling collaboration is necessary to enable effective adaptability to respond to changing conditions. Computer-supported assignment, allocation, and priority logic and negotiation procedures can automatically resolve collaboration conflicts arising from competition for limited agents (Williams et al., 2002). The number of agents may be more than the number of tasks available or the number of tasks available may be more than the number of agents. In either case, an efficient task allocation method to assign the agents to perform the right tasks on the right object at the right time is necessary. The classical solution for the task allocation problem is to have a centralized task allocation system that generates the necessary commands for the agents (Sujit et al., 2006). But, a centralized task allocation system has well known limitations and does not address scalability issues well (Sujit et al., 2006). Hence, the approach taken in this work is to develop a decentralized task allocation algorithm. This algorithm must be suitable for implementation in a multiple agent system. It should also be scalable, and consume low computational overhead. An efficient task allocation strategy should have the ultimate objective to complete the security mission in minimum time by cooperating and coordinating with other agents (Bicho et al., 2000).

Sn

S2 Sm

S1 S2 Sp

Controller

Fig. 2. Multi-Agent system interaction scheme Due to the huge and perplexing space of possible alternatives the framework is implemented by partitioning tasks into activities and processing them by protocols. The system controller follows a top-down policy and executes task administration protocols to assign the right agents to perform the right task on the right object at the right time. The following seven activities are managed by the system controller (Tkach et al., 2011): •Mission acquiring: Receives a security monitoring mission and generates tasks for its execution.

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•Task management: decides how to rank the importance or priorities of the required tasks based on characteristics of each task. •Queue management: manages task queue such that no task will be over delayed. This is an important activity since over delaying a task can cause it to lose its relevance. •Sensor selection: selects the best set of sensors for the task execution by deciding what tasks or actions are expected to be performed by them. It also decides when extra sensors are required and how many. It is the task of the sensor control to determine the sensors appropriate for performing the task in order to optimize the performance of the overall system. •Human collaboration: decides when human collaboration is needed. A controller is required here to provide access to human supervisors when necessary. •Synchronization: sets up a timeline of commands for every agent to carry out tasks based on their availability and capabilities, given task requests from the task manager. It ensures that tasks are not unnecessarily delayed while a current task is experiencing delays, given that there are other tasks waiting in queue. This is a non-trivial problem considering the distinct characteristics of tasks to be performed, such as hard or fluid deadlines, as well as varying task priorities. •Measurement: it executes the task by the assigned agents to monitor the supply chain. 2.2 Objective function The specific assignment of agents to tasks is achieved by applying a system objective function that quantifies system performance. The objective function (1) includes the combined sensor and human performances.

VIS = VS + VH

(1)

Where, VIS is the system objective function, VS is the sensors’ performance and VH is the humans’ performance. The performances of sensors and humans are determined by the quality of the skills each applies (e.g., expertise and response time for the human; range coverage, sensitivity, monitoring and data storage capabilities for the sensors), the strength of the signals and the quality of the processing algorithms of each. From equation (1), we can see that some potential problems may result in failure to provide effective monitoring and recognition with limited assignment abilities. For instance, in multi-sensor multi-target tracking applications, the problem of sensor selection means to decide suitable sensor combinations to be applied to different tasks of measurement of different targets. As application of all types of sensors to all tasks is usually impossible due to constraints in sensing and/or computational resources, the best sensor assignment is to choose a subset of available sensors that maximize information gain while minimizing costs (2). n

VS = ∑ Pj C j j =1

(2)

Where, VS is the sensor combination performance, Pj is sensor’s indexed j information gain, n is the number of sensors involved in the recognition process. C is the relative cost of sensor j. 3. METHODOLOGY Sensor assignment is managed by a controller that uses coordination protocols to select the best sensors ensuring best performance. A controller receives the sensors performance data and decides when to collaborate a human taking into consideration the human’s situation (e.g., response time, adaptation time, fatigue, expertise) and the sensors abilities (e.g., strength of the signal, response time, sensitivity). The human can perceive the needed information on a display and decide on appropriate recommendations and actions. 3.1 Assumptions • • •

Independent human and sensory performance (they do not influence each other). The sensors work asynchronously. Only one human can be assigned to each task.

4. TASK ADMINISTRATION PROTOCOLS Six task administration protocols are considered in the collaboration framework based on the above logic (Tkach et al., 2011): “TRAP” (task requirement analysis protocol): Assigns priority levels to monitoring and detection tasks so that they may be sequenced in queue. “ASAP” (assignment analysis protocol): Decides if the newly arrived task should wait in the queue for the best match of sensors, or be processed on a default assignment. “CLAP” (collaboration analysis protocol): Decides if the task should wait in the queue for the best match of sensors and humans, or be processed on a given state assignment. It includes two options: • Wait in the queue till best match of resources is achieved. • Do not wait in queue and operate without best assignment. “SRAP” (sensors allocation protocol): Its function is to consider the priority level assigned to a given task relative to the availability of sensors and then assign the task to appropriate sensors based on its priority. “HOAP” (human operator allocation protocol): Its function is to consider the priority level assigned to a given task relative to the availability of human operators and then assign the task to the available human. “STOP” (synchronization and time-out protocol): It is activated to monitor if the current task in service needs to be timed-out because of its excessive use of the resources, or pre-empted by another urgent tasks (Liu and Nof, 2004). It stops a dedicated collaboration of sensors and humans after a given period. The objective is to ensure that no sensors are unnecessarily delayed. Control theory is employed to ensure the system accumulates the optimal objective function score over time (Fig. 3). The controller manipulates the inputs to the system to obtain the

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desired effect on the output of the system and manipulates collaboration (i.e., performs necessary assignment) to allow the process to obtain a desired reference.

τ =

System

Controller

u(t)

Sensor selector

Sensor agents

Sensors

Measurement

y(t)

In the system’s block diagram, r(t) is the input, the desired reference (optimal objective function score), y(t) is the output, the current objective function score, u(t) is the input to the process (output from the controller). The controller implements protocols to provide optimal agent collaboration as a reference to the process. The process stands for a supply security monitoring process of the system. The ‘sensors’ subsystem comprises a sensor selector and sensor agents for supply chain monitoring tasks processing. 5. PROTOCOLS IMPLEMENTATION A collaboration objective function that evaluates the total gain from sensor and human supervisors (H) collaboration was developed (3). This gain is calculated as the difference between the gain of the objective function score (1) at the optimal collaboration (VISoptimal) and the objective function score at the default assignment (VISdefault). The default sensor assignment is based on an arbitrary combination of sensors that achieve desirable performance.

V= (VISoptimal − VISdefault ) + Vt × (tresponse + τ ) coll

(3)

VISoptimal = max{VOS , VHS }

(4)

∑

k∈default list

Pj C j

g

+ trestg )

(7)

Where g is the index of tasks in the queue, tctg is the task completion time of task g, and trestg is the rest time of H after task g. The task priority is assigned by (8) and considers the following conditions:

Figure 3. System’s block diagram

VISdefault =

n

∑ (tct g =1

Sensors

r(t)

time stress situations such as frequent task assignments changes. Thus, rest time between different tasks must be considered (7).

1) The relatively higher Tj’s profit (value) is, the higher its priority. 2) If Tj requires the same assignment as the current task, its priority is high since response time is minimized or not needed. 3) The higher the frequency of H assignment requests, the higher its priority. 4) If the current task is not finished yet but is almost close to being completed, it has a relatively higher priority. 5) A task relatively closer to its deadline has higher priority. 6) The earlier the tasks’ arrival time the higher its priority.

T j = W1 × Vassign j + W2 ×η j + W3 × f Ri + W4 × ×mcti + W5 × tct j + W6 × (tc - art j )

Where Tj is the task priority function, j is the task index, i is the mission index, Wn are weights of each factor, η is the assignment type request, fR is the frequency rate (frequency of the collaboration requests), mct is the mission completion time, tc is the current time and art is the task arrival time. 1 if same requested assignment 0.5 otherwise

η ={

NCRi f Ri = tsi - tc

(5)

Where Vt is the cost of one time unit and τ is the waiting time in queue for the current task (7). VOS is objective function score at optimal sensors assignment, VHS is objective function score at optimal sensors with H assignment, k is the index of sensors that were arbitrarily decided to be the default set of sensors.

(8)

(9) (10)

Where NCRi is the number of H assignments requests by task i, ts is mission start time of task i.

The system response time includes the H response time and system’s response time (6).

The decision regarding wait in queue or proceed the task autonomously is performed by calculating the difference between the performance of the system in default assignment and optimal collaboration, and the penalty for the waiting times (11), and the difference between the performance of the system with and without H collaboration, and the penalty for the waiting times (12).

= tresponse th _ response + ts _ response

Q = VISdefault − VOS + Vt × τ S

(6) Where ts_response is the time required for the system to reassign the agents and apply desired collaboration and th_response is the time required for the H to identify and adapt to desired assignment. We assume that H will experience workload characterized by real-time stress (Wilson, 2001). This stress is relevant to H in-the-loop control when there are multiple competing goals and multiple, simultaneous task demands on attentional resources. It is particularly relevant in high workload and

QH = VOS − VHS + Vt ×τ

(11) (12)

QS is assignment analysis function and QH is collaboration analysis function. H collaboration is achieved by prioritizing H with maximal resting time of the available H group (13):

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= it max(trestq > ψ ) when i ∈ q = 1...n S = { resti otherwise 0

(13)

INCOM 2012, May 23-25, 2012 Bucharest, Romania

Where S is H allocation function, i is the index of H out of q available H in the system, tresti is the Hi rest time, trestq is the Hq rest time and ψ is the minimal necessary rest time.

Table 1. Simulation parameter values Parameter Value Number of missions 20 Number of tasks in each 10 mission Tasks completion time Between 1 to 10 minutes Human operator rest time 3 minutes Weights Assigned to 1 Waiting time penalty -2000/3600 [units/sec] Performance measure σ (ς k )

Time-out value is given by (14):

= to µct + 2σ ct

(14) Where µ is task mean completion time, σ is the standard deviation of task completion time. Once a job is timed-out, its arrival time is modified to reflect the time when it re-enters the system, and all the service and waiting times must be updated. For the “STOP” implementation it is necessary to monitor the time a task has been in process. If a task’s service time is higher than its time-out period and there are tasks in the queue, the current job is timed-out and returns to the queue with an identifier indicating its priority level and remaining process time. The exceptions for “STOP” are: 1. Task processing time has not exceeded the time-out value. 2. No other task is waiting in the queue. If queue=0 then continue task processing. 3. The task is close to completion. If tct-tc≤α then continue task processing. 4. The process is close to its due date. If mct-tc≤β then continue task processing.   false STOP=   true 

Fig. 4 presents the results of a representative simulation of the protocols framework operation. The simulation illustrates the performances of TAP1 and TAP2. TAP1 represents the task administration protocols framework that was proposed within this paper, and TAP2 represents the best known task administration protocols from previous research (Ko and Nof, 2010). The relative gain of TAP1 over TAP2 is illustrated in Fig. 4(b). In this graph, the results are positive, which implies that in some cases TAP1 has superior performance over TAP2. Furthermore, in this example the proposed framework resulted in improved overall system performance as indicated in Fig. 4(c).

if (tc -tst ≤ to) or (queue=0) or or (tct − tc ≤ α ) or (mct − t c ≤ β ) otherwise

(15)

Where, α and β are thresholds, tst is task start time. In order to calculate the system performance using the proposed methods a performance measure is defined as (17):

σ (ς k ) = VIS

current

(ς k ) - VIS

default

(ς k )

m

f = ∑ σ (ς k )

(16) (17)

k =1

Where, σ is the gain in objective function score achieved in each task compared to the default collaboration score that used as a reference, m is the total number of tasks, ς k is the task indexed k, f is the cumulative gain of the entire operation of the system.

Fig. 4. Task administration protocols results. Graph (a) represents the dynamic changes in system performance values over time, graph (b) represents the relative gain of TAP1 over TAP2, graph (c) represents the cumulative gain for the entire operation of the system of TAP1 over TAP2. Fig. 5 illustrates the cumulative performance results of TAP1 and TAP2 for each mission. Task administration protocols results

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6. RESULTS

Mission1 Mission2 Mission3 Mission4 Mission5 Mission6 Mission7 Mission8 Mission9 Mission10 Mission11 Mission12 Mission13 Mission14 Mission15 Mission16 Mission17 Mission18 Mission19 Mission20

Cummulative performance

600

Simulation analysis was performed to analyze system performance for different protocols based on the proposed framework and comparing it to previous results (Ko and Nof, 2010). The numerical computations were performed on a PC with Matlab software. The parameters used for the simulation are presented in Table 1.

500 400 300 200 100 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

TAP1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

TAP2

Fig. 5. Task administration protocols results summary.

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9. CONCLUSIONS Methodological considerations and a top-down working procedure were presented to enable optimal assignment and collaboration of multiple agents (humans and sensors) in supply security monitoring tasks. A collection of activities controlling such system performance were discussed. Six task administration protocols were proposed within the framework. The framework enables task management and synchronization for more flexible and time saving system operation. Future research is aimed at (1) extending optimal assignment of humans-sensors, including the number of humans and where they should be assigned, how many humans are related to how many sensors and to which sensors, (2) analyzing the impact of human error and sensor error on the optimal assignment and collaboration control and (3) extending the developed methodology for additional tasks. The impacts of this research were (1) better monitoring performance by optimal sensor assignment for the tasks, (2) human operator collaboration for better performance and quality and (3) time management for better resource assignment. Some limitations of our model should be noted. The developed framework was evaluated and tested only through simulations. Future research will deal with implementation of the framework on an operational monitoring system in order to validate its performance. Human learning and variation and adaptation between users and tasks were not included in this research. When adapting the developed methodology to a new task, user experiments must be performed to derive specific parameters. ACKNOWLEGMENTS This research was partially supported by the Paul Ivanier Center for Robotics Research and Production Management, and by the Rabbi W. Gunther Plaut Chair in Manufacturing Engineering, Ben-Gurion University of the Negev. REFERENCES Aggarwal R., Lim M.K. and Tan K. (2011). A Three-Level Rfid-Based Automation Approach to Enhance Network Competitiveness. Intl. Conf. of Producrion Research, Stuttgart, Germany. Angeles, R., (2005). RFID Technologies: Supply-chain Applications and Implementation Issues. Information Systems Management, 22(1): p. 51. Attaran, M., (2007). RFID: an enabler of supply chain operations. Supply Chain Management: An International Journal, 12(4): p. 249-257. Banks, J., D. Hanny, M. Pachano, A., and L. Thompson, G., RFID Applied. 2007, New Jersey: John Wiley & Sons. Bian F., Kempe D., and Govindan R. (2006). Utility-based sensor selection. In Proc. of the IEEE Conf. on Information Processing in Sensor Network. Bicho E., Mallet P. and Schoner G. (2000). Target representation on an autonomous vehicle with low-level sensors. Intl. Journal of Robotic Research, 19(5), 424447. Eyers D.R., Potter A.T. and Wang Y. (2011). Supply chain implications of e-commerce channels for additive manufacturing. Intl. Conf. of Producrion Research, Stuttgart, Germany.

Fokum D. T., Frost V. S., DePardo D. (2009). Experiences from a Transportation Security Sensor Network Field Trial. Technical report, The University of Kansas, ITTC-FY2009-TR-41420-11. Gao X., Ziang Z., Wang H., Shen J., Huang J. and Song S. (2004). An approach to security and privacy of rfid system for supply chain. Proc. of the IEEE Int. Conf. on E-Commerce Technology for Dynamic E-Business. Jeong W. and Nof S.Y. (2009). A collaborative sensor network middleware for automated production systems. Intl. Journal of Computers and Industrial Engineering, 57, 106-113. Ko H.S. and Nof S.Y. (2010). Design of protocols for task administration in collaborative production systems. Intl. Journal of Computers, Communications & Control, 5(1), 91-105. Lara Gracia M. L., Nof S. Y. (2009). Conflict resolution in supply chain security. Intl. Journal of Value Chain Management, 3(2), 168-186. Liu Y., and Nof S.Y. (2004). Distributed Micro Flow-Sensor Arrays and Networks (DMFSN/A): Design of Architectures and Communication Protocols. Intl. Journal of Production Research, 42(15), 3101-3115. Parasuraman R., Sheridan T.B. and Wickens C.D. (2000). A Model for Types and Levels of Human Interaction with Automation. IEEE Transactions on Systems, Man and Cybernetics, 30(3), 286-197. Penttila, K., M. Keskilammi, L. Sydanheimo, and M. Kivikoski, (2006). Radio frequency technology for automated manufacturing and logistics control. Part 2: RFID antenna utilisation in industrial applications. International Journal of Advanced Manufacturing Technology, 31(1-2): p. 116-124. Qiu R. G. (2007). RFID-enabled automation in support of factory integration. Intl. Journal of Robotics and Computer-Integrated Manufacturing 23, 677–683. Sujit P.B., Sinha A. and Ghose D.(2006). Multiple UAV Task Allocation using Negotiation. Intl. Conf. on Autonomous Agents and Multiagent Systems, Hakodate, Hokkaido, Japan. Tkach I., Edan Y. and Bechar A. (2011). Switching Between Collaboration Levels in a Human–Robot Target Recognition System. IEEE Transactions on Systems, Man and Cybernetics, Part C 41(6), 955-967. Tkach I., Edan Y. and Nof S.Y. (2011). A framework for automatic multi-agents collaboration in target recognition tasks. Intl. Conf. of Producrion Research, Stuttgart, Germany. Vachtsevanos G., Tang L. and Reinmann J. (2004). An Intelligent Approach to Coordinated Control of Multiple Unmanned Aerial Vehicles. American Helicopter Society 60th Annual Forum, Baltimore. Williams N. P., Liu Y. and Nof S. Y. (2002). TestLAN approach and protocols for the integration of distributed assembly and test networks. Intl. Journal of Production Research, 40(17), 4505 — 4522. Wilson, G. (2001). Real-time adaptive aiding using psychological operator state assessment. In Harris, D. ed. Engineering Psychology and Cognitive Ergonomics, Ashgate, Aldershot, UK. Weinstein R. (2005). RFID: A technical overview and its application to the enterprise. IT Professional, 7(3), p. 27-33. Xiong N., Svensson P. (2002). Multi-sensor management for sensor fusion: issues and approaches. Information Fusion, 3, 163-186. Zhang, Y., G. Huang, Q. , T. Qu, and O. Ho, (2010). Agentbased workflow management for RFID-enabled realtime reconfigurable manufacturing. Int. J. Comput. Integr. Manuf., 23(2): p. 101-112.

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