Design of a Sensor Network Based Security System

Design of a Sensor Network Based Security System Gergely Vakulya University of Pannonia Department of Computer Science and Systems Technology Veszprém, Hungary Email: vakulya@dcs.uni-pannon.hu Gyula Simon University of Pannonia Department of Computer Science and Systems Technology Veszprém, Hungary Email: simon@dcs.uni-pannon.hu Abstract In this paper a wireless distributed building safety and security framework will be introduced. The main design goals of the system are completely distributed operation, flexibility, robustness, fast response time, and energy efficiency. The framework uses low duty cycle TDMA-based communication scheme, where the scheduling is the result of optimization, based on network discovery measurements. The hardware and software architecture of a prototype system is introduced. I. INTRODUCTION Building security and safety systems (BSSS) use a wide variety of sensors to detect smoke, fire, intrusion, and also can measure additional environmental properties, e.g. temperature, humidity, lighting conditions. Sensors communicate their measurements and based on the measured properties the system makes decisions and may generate alarms. Most BSSS applications use wired technologies, but due to their ease of deployment and reconfigurability several wireless BSSS solutions have been proposed (e.g. [1], [2], [3], [4]). The sensory aspects and the automatic detection in safety systems have a large literature, see e.g. [5] for a survey. The physical sensors and detection algorithms are out of the scope of this paper, the goal of the proposed system is to provide a general framework, which can incorporate arbitrary sensors or actuators, and also any decision making algorithm. Safety and security systems require real-time response (e.g. alarm after a few seconds of the detection of fire) and robust operation (e.g. fail-safe operation in case of the failure of some nodes). Energy efficiency is also a crucial requirement if the system is operated on batteries. In the proposed system the operation is completely distributed (i.e. no central control or supervisor unit is required except for the system setup), thus the potential single point of failure is avoided. The sensors form a wireless network where the important information is spread throughout the network and each node decides autonomously on its own actions. E.g. an alarm node is not ordered to be activated by another node but rather the alarm node decides itself, based upon the received messages, whether the alarm should be set or not. The system may contain central (optionally mobile) supervision units to monitor the network by the operators. The network propagates not only alert messages but status information as well, thus the intactness of the system is continuously monitored. When a failure is detected (e.g. a node is missing) a system alarm is generated. The energy efficient operation of the network is provided by a low duty cycle TDMA scheduling, computed and optimized when the system is deployed. Robustness is provided by guaranteeing multiple data propagation paths to tolerate single node (or link) failures. The paper is organized as follows. Section II reviews relevant literature. In Section III the proposed system architecture is introduced. In Section IV the hardware and software architecture of a prototype system are described. Section V concludes. II. PREVIOUS WORK The scope of duties in safety and security systems is diversified, covering detection, communication, decision making, and actuation. The specific challenges arising in sensor networkbased applications are discussed in [5] from the viewpoint of fire protection systems. The main design considerations and principles of fire rescue applications, including the hardware and software aspects, are summarized in [6]. In [2] a distributed fire monitoring system is described. It uses MicaZ motes as sensors and their onboard thermometers as fire detectors. The application uses a special network protocol for transporting the critical data. In another interesting solution Bluetooth, primarily designed for computer peripherals, was used in security systems [3]. Due to its small protocol stack and advanced energy saving capabilities ZigBee is often used in wireless sensor networking applications. In [4] a complex fire protection system was presented, using ZigBee for sensor-sensor communication, and a combination of WiFi, 3G and DAB as the communication infrastructure for the fire brigades. TDMA is used in various sensor networking applications to achieve high power efficiency, small end-to-end delay and also to avoid collisions. The scheduling problem is unfortunately NP-complete [7], but approximate solutions are known, e.g. based on graph coloring [7] or nonlinear optimization [8]. The problem of clustered wireless sensor networks was handled in [8], with special emphasis on energy efficiency. In this paper a novel TDMA-based solution will be proposed for energy efficient data dissemination in the whole network (from every node to every node). 978-1-4577-1402-3/11/$26.00 2011 IEEE 15

III. THE PROPOSED SECURITY SYSTEM A. Requirements Since this sensor network is designed for safety and security (e.g. fire monitoring and building surveillance) the primary requirements are low latency, high reliability and fault-tolerance. The main system requirements are summarized as follows: The whole system is decentralized; every node is equivalent. In this way no central operation/decision making is required. Every alert message reaches all nodes in at most 5 seconds, which is satisfactory for most fire alarm and intrusion detection systems. The network tolerates a single point of failure, e.g. a fault of one node, or communication link. In case of a failure instant user intervention is required, while the service is still maintained. Dispatching the alert messages are guaranteed even in case of a single point of failure. The network is scalable up to several hundreds of nodes, allowing services in medium-sized buildings. The lifetime of the nodes is at least 1 year, with one battery pack. The requirements on fault tolerance induce that the graph of the network must be 2-connected. In addition to this the following reasonable assumptions are used: The sensor nodes have fixed positions. The nodes have an unique, 64-bit address. by one, with AddressAssign messages. The nodes send an AddressAssignReply message to acknowledge. After the address assignment the base station broadcasts a NeighborRequest message to the network, and after random delays the nodes start to send NeighborShout messages to their neighbors. These messages are single-hop broadcast messages, and they are repeated multiple times. The nodes store the IDs of the neighbors they have received NeighborShout messages from, and also the quality of the links, represented by the triplet {N rec, avgrssi, avglqi}, where N rec is the number of received messages, avgrssi is the average Received Signal Strength Indicator, and avglqi is the average Link Quality Index. After a safe delay the nodes send back the collected information in NeighborReport messages. In case a NeighborReport message does not reach the base station a NeighborRequest message can be addressed to a particular node, instead of the whole network. In this case the selected node sends back its collected information about the neighborhood, without the discovery procedure. B. Operating modes The system has two operating modes: setup and surveillance. The setup mode is used after the deployment of the sensor nodes and it consists of three phases: discovery, optimization and configuration. In discovery phase the network topology is determined. The optimization phase calculates the TDMA schedule, which is then downloaded to the nodes in the configuration phase. The surveillance mode uses the precomputed TDMA schedule to coordinate inter-node communication. C. Network discovery The operation of the network discovery is shown in Fig. 1. The nodes have a 64-bit unique long address, which is impractical for addressing and routing. Thus at the beginning of the discovery phase the long address is replaced by a unique 10-bit short address as follows. The discovery phase is initiated by the base station, by sending a HelloEverybody message to the network. For the network-wide broadcast flood routing is used. As an answer the nodes send their long address in a HelloEverybodyReply message. The converge-cast protocol is handled by a gradientbased routing. To avoid the collisions every node uses a random delay before the actual sending. After the reception of the HelloeverybodyReply messages the base station assigns a short address to the nodes, one Fig. 1. Messages of the discovery phase From the collected NeighborReport messages the base station builds the preliminary connectivity graph G, which contains also the symmetric link quality information, as follows: The vertices of G are the nodes in the network. There is an edge between two vertices in G if the composite link quality index CLQI, computed from {N rec,avgrssi,avglqi}, 16

between the corresponding nodes is good in both directions. The weight of an edge between nodes i and j is the minimum of the CLQI quantities between (i, j) and (j, i). This step is shown in Fig. 2(b). From G the reliable connectivity matrix G is generated, by omitting vertices with weights less than a threshold (Fig. 2(c)). D. TDMA optimization In the optimization phase a TDMA schedule is generated. With a clustering algorithm k-connected subgraphs are searched (k 2), as shown in Fig. 2(c). Then a derived graph F is generated, where the vertices represent the clusters and there is an edge between two vertices in F if there are at least two vertex-disjoint paths between the corresponding clusters in G. Then a Hamiltonian cycle is searched in F with a traveling salesman approximator. This cycle then projected back to G, where an edge of F is associated by an edge in G, so that the edges are vertex-disjoint in G (Fig. 2(d), thick lines). In the next step all the remaining points of the clusters are connected to the Hamiltonian cycle (Fig. 2(e), thin lines), producing graph H. Because of the small number of nodes in each cluster, an exhaustive search can also be used for the last step. This way we obtain a cycle, with optional hooks on it. The traversal is built as follows: 1 Start from an arbitrary node in H to an arbitrary direction. 2 Continue until a branch is found. 3 Travel all paths, one after the other, until the end of branch is reached. 4 Continue from step 2 until there are no nodes left. 5 Time slices are associated with the edges of H in the order of the traversal (Fig. 2(e)). Fig. 2(e) shows one direction of the message transmission path with length L =28. This path is reversed and another scheduling is added with the reverse direction. Thus the full TDMA schedule contains 2L time slices. Communicating in both directions provides fault-tolerance, i.e. the information will get to all nodes even if one of the nodes dies. E. Network configuration In the configuration phase the TDMA schedule is downloaded to the nodes, and other working parameters can also be set. The configuration download is reliable, using an acknowledgement for each downloaded packet. F. Surveillance mode In the surveillance mode the communication is organized using the TDMA schedule provided by the optimization phase. TDMA requires precise time synchronization and guaranteed availability of the required resources (radio, CPU). The accurate time synchronization is provided by using a modified version of FTSP [9]. There are two modifications to the original algorithm. First, there are no separate time synchronization messages: the synchronization information is embedded in the existing network traffic. Second, periodical beaconing is provided according to the TDMA time slots. (b) (d) (a) Fig. 2. The TDMA schedule optimization. (a) An example building layout with fire sensors, motion detectors and alarms. (b) The measured connection graph. The thickness of the arcs represent the quality of the corresponding links. (c) The network after thresholding the links and clustering. (d) Hamiltonian-cycle between the clusters (thick lines) and paths inside the clusters (thin lines). Links represented by dashed lines do not participate in the communication. (e) The TDMA schedule in one direction. For the guaranteed availability of system resources slotted programming [10] is used as shown in Fig. 3. The communication is assigned to dedicated time slots, separated from the processing, which may require longer and uncertain amount of time. In each communication time slot exactly one node can transmit to exactly one other node, with acknowledgement. With the applied synchronization method the duty cycle can be lowered to the range of 0.1 0.5%. Fig. 3. Slotted communication of four consecutive nodes. Each node have a time slot for transmission and for reception. Processing is separated in time from communication. (c) (e) 17

IV. PROTOTYPE SYSTEM A. Hardware architecture The sensor node is built around the ZigBit integrated radio device. It contains an 8-bit Atmel ATmega1281 microcontroller, and an RF212 radio transceiver, connected to each other using an SPI bus. The microcontroller has 128kByte flash program memory, 8kByte SRAM, and several peripherals, such as I2C, SPI and analog-digital converter. In the prototype system the 900MHz version of the radio was used, with an external whip antenna. The maximum radio communication bandwidth is 1Mbps. The analog and digital IO ports of the processor can be accessed using an external connector, so several kinds of sensors can be connected to the prototype board. For programming and communication purposes the mote has a JTAG connector and a USB interface with a USB- RS232 converter. The sensor node is shown on Fig. 4. Fig. 4. The sensor node B. Software architecture The sensor node s software uses TinyOS [11], which is a component-based embedded real-time operating system designed to wireless sensor network applications. TinyOS provides a large number of reusable modules, e.g. serial port access, periodical and one-shot timers, single or multihop radio communication [12] and time synchronization [9]. Fig. 5. The architecture of the sensor nodes. The architecture of the sensor nodes is shown in Fig. 5. The main low-level services are the drivers for the radio, the timers and the sensors. The security application can access them through two middleware services (routing, time synchronization), and the measurement service. The configuration service handles the settings of the sensor node. Fig. 6. The architecture of the supervisor unit. The architecture of supervisor unit is shown in Fig. 6. It consists of two main components. In left hand side of the figure the architecture of the system configuration, while in the right hand side the system management component are shown. Both components use the WSN interface to communicate with the wireless sensor network. The discovery service of the system management component is responsible to for detecting the nodes and building the connectivity matrix. Using the data provided by the discovery service the optimization service computes the TDMA schedule, which is then downloaded to the nodes by the configuration service. While the system configuration component is mainly offline, the system management component is heavily online. The wireless sensor network is accessed through a system state service. The user can execute commands or handle (e.g. cancel or confirm) alarms with the commands and the alarm handling services. All user interactions are done using the graphical user interface. C. Results The prototype system was tested in a 15-node network, deployed in an office building. The communication framework provided a 96-byte packet for the application, which was used to transmit alarm messages (e.g. movement detected) and user commands (e.g. activate sensor) with low delay, and status information (e.g. node battery level) with higher delay. The cycle time was two seconds, thus an alarm message can be delivered to each node in the network within 4 seconds in the worst case. The delivery time consists of max. 2 seconds on-node delay from the event detection to the message transmission, and another 2 seconds cycle time for the message hopping through the network (this time actually was much smaller, 15 20ms in the 15-node network.) The average synchronization error between subsequent nodes was 16μs with maximum network-wide synchronization error of 150μs. The radios were switched on either at the sheduled transmission time or slightly before the scheduled reception time. The tight syncronization allowed the prereception awake time be minimal. As a result, the radio of every node was alive for less than 8ms in each cycle, providing duty cycle of 0.4%, which cannot be approached by current off-the-shelf technologies (e.g. Zigbee). The microcontroller s duty cycle was even lower in this simple application. The applied 96-byte useful payload of the messages was more than enough for our simple application but for higher node numbers a careful application-level mechanism must be implemented to control the access to the scarce message resources. 18

V. CONCLUSION A sensor network-based massively distributed security framework has been proposed, which can distribute alarm messages in the whole network with low latency and provides high power efficiency. The network needs a central control unit only at the initial phase and later on its operation is completely distributed. The proposed system uses a TDMAbased communication protocol, based on graph-theoretic optimization, in order to provide low duty cycle operation for the nodes. The proposed system is robust and fault-tolerant, as it provides guaranteed system services even in case of a single node failure. The hardware and software architectures of the prototype system were introduced. REFERENCES [1] N. Ikram, S. Durranii, H. Sajid, and H. Saeed, A wireless multimedia sensor network based intelligent safety and security system (IS3), in SENSORCOMM 09: Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications. Washington, DC, USA: IEEE Computer Society, 2009, pp. 388 392. [2] A. Tsertou, R. Upadhyay, D. Laurenson, and S. McLaughlin, Towards a tailored sensor network for fire emergency monitoring in large buildings, in Proceedings of the 1st IEEE International Conference in Wireless Rural and Emergency Communications (WRECOM07), Rome, Italy, Sep. 2007. [3] S.-H. Choi, B.-K. Kim, J. Park, C.-H. Kang, and D.-S. Eom, An implementation of wireless sensor network for security system using bluetooth, IEEE Transactions on Consumer Electronics, vol. 50, no. 1, pp. 236 244, Feb. 2004. [4] S.-H. Yang and P. Frederick, SafetyNET a wireless sensor network for fire protection and emergency responses, Measurement and Control, vol. 39, no. 7, pp. 218 219, Sep. 2006. [5] M. Bahrepour, N. Meratnia, and P. J. M. Havinga, Automatic fire detection: A survey from wireless sensor network perspective, http://eprints.eemcs.utwente.nl/14624/, Centre for Telematics and Information Technology University of Twente, Enschede, Technical Report TR-CTIT-08-73, Dec. 2008. [6] K. Sha, W. Shi, and O. Watkins, Using wireless sensor networks for fire rescue applications: Requirements and challenges, in Proceedings of IEEE International Conference on Electro/information Technology, 2006. [7] S. C. Ergen and P. Varaiya, TDMA scheduling algorithms for wireless sensor networks, Wirel. Netw., vol. 16, no. 4, pp. 985 997, 2010. [8] L. Shi and A. O. Fapojuwo, TDMA scheduling with optimized energy efficiency and minimum delay in clustered wireless sensor networks, IEEE Transactions on Mobile Computing, vol. 9, no. 7, pp. 927 940, 2010. [9] M. Maróti, B. Kusy, G. Simon, and A. Lédeczi, The flooding time synchronization protocol, in Proceedings of the 2nd international conference on Embedded networked sensor systems, ser. SenSys 04. New York, NY, USA: ACM, 2004, pp. 39 49. [Online]. Available: http://doi.acm.org/10.1145/1031495.1031501 [10] R. Flury and R. Wattenhofer, Slotted programming for sensor networks, in IPSN 10: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks. New York, NY, USA: ACM, 2010, pp. 24 34. [11] P. Levis and D. Gay, TinyOS Programming. New York, NY, USA: Cambridge University Press, 2009. [12] M. Maróti, Directed flood-routing framework for wireless sensor networks, in Middleware 04: Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware. New York, NY, USA: Springer-Verlag New York, Inc., 2004, pp. 99 114. 19