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Border Surveillance Using Advanced Wireless Sensor Network Mosad Alkhathami , LubnaAlazzawi Department of Electrical and Computer Engineering, Wayne State University, Detroit. USA
ABSTRACT In the last decade, the usage of wireless sensor network (WSN) has become a powerful tool that connects the physical and digital world. Currently, WSN is applied in numerous applications such as monitoring of buildings, wildlife and habitats, pipelines, and smart electrical grid control. The conventional border patrol systems are highly labour intensive, requiring constant human involvement. In addition, there is the lack of a coordination unit to provide accuracy to the system. Therefore, this study presents the implementation of the method of surveillance and intrusion detection system to measure the sensor nodes.
Keywords:-wireless sensor node, border control, sensor node.
1. INTRODUCTION Wireless Sensor Networks (WSNs) have been emerging in the last decade as powerful tools for connecting physical and digital world. WSN provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. WSN is applied in many applications such as monitoring of buildings, wildlife, pipelines, manufacturing, healthcare, environmental monitoring, and security. One of the most recent monitoring applications of WSNs is the border control application. Surveillance of a given area is one of the major application situations for wireless sensor networks. Wireless sensor nodes help in monitoring a given area, such as private property or a border. They are also helpful in helping track trespassers. In order to help detect moving objects, the nodes should be equipped with a number of sensors [1]. The passive infrared sensors are among the cheapest and simplest to use in this situation. As they communicate with each other, sensor nodes track moving directions and positions of various objects and report them to the base station [2]. In areas demanding extreme safety, the nodes should be able to report the locations of the objects in a given time with great accuracy [3]. This paper will discuss the use of wireless sensor nodes in border surveillance while considering the effectiveness, reliability, and power consumption issues.
Figure 1. Block diagram of WSN [1]
2. RELATED WORK Several works has been done in the past in the field of secure surveillance with WSN for the border control. While my literature review going on, I searched for the studies and the researches that are related with analysis of border control systems. I also searched for how to increase border control security. The border patrol has extensively been based on human involvement. The first study [4] found that many works have addressed border surveillance applications based
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Volume 3, Issue 3, March 2015
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on WSNs. Many solutions using WSNs have organized the network nodes as a line-sensor, where every movement going over a barrier of sensors is detected. In this case, sensor nodes deployment should guarantee barrier coverage. Compared to full coverage, a barrier coverage based on a perfect linear deployment requires fewer sensor nodes and may experience radio disconnection due to sensor failure and depletion. The sensor nodes have various capabilities or functionalities, such as, provision of accurate detection and a larger detection range. In addition, the ground sensors also provide additional data that cannot be detected by the existing border patrol techniques. On the other hand, the underground sensors provide efficient system functionalities where devices that are placed above the ground are not desired. The mobile sensors also offer intrusion tracking capabilities. Therefore, by improving the network processing, these types of sensors are able to detect and relay information to a remote administrator [4]. Second studyLiu et al, investigated the construction of sensor barrier on long strip area of irregular shape when sensors are distributed according to Poisson point process. To ensure that trespassers cannot cross the border undetected, multiple disjoint sensor barriers will be created in distributed manner covering large scale boundaries. Then, a segmentation technique has been proposed to achieve continuous barrier coverage of the whole area. Also Liu et focuses on applying WSN applications for border surveillance globally. Such need emanates from the growing risks near borders that every country should take care of. In the first part of the article, existing studies on the topic are analyzed and several significant drawbacks of modern WSN applications for border surveillance were revealed: insufficient coverage, lack of tracking (for linear deployment), high costs, rough surface, ineffectiveness of random deployment etc. In order to solve these issues, authors offer three new elements: connected coverage (thick line architecture along the border), deployment strategy, and routing mechanism for data transferring between nodes. Authors also made two simulations in order to learn the influence of l-coverage on routing technique and the amount of hops necessary to communicate Data Relay Node (DRN) line. It was revealed that with the larger value of k, the number of uncovered nodes decreased. The definition of the k-barrier coverage is as follows. The definition of k- barrier coverage denotes a region covered by a sensor crossing paths with another k- sensor through the coverage area. Underground and ground sensors are deployed at predetermined positions. To achieve optimal manual deployment of the k- barrier area of the belted region, k rows of sensors are deployed along the shortest path possible[5]. The third study researchers from the University of Virginia and Carnegie-Mellon University developed an energy efficient WSN system for detecting moving vehicles through a passage line in a stealthy manner. They used the MICA2 platform, this MICA2 Mote is a third generation mote module used for enabling low-power, wireless, sensornetworks. The primary goal for using the MICA2 platform with 70 MICA2 motes, is for the wireless sensor networks security, Surveillance, and Force Protection. Also they used it for the environmental monitoring for large scale wireless networks up to (1000+ points). They deployed 70 MICA2 motes, along a 280 feet long perimeter in a grassy field that would typically represent a critical choke point to be monitored. Each of the these motes is equipped with a 433 MHz Chipcon radio with 255 selectable transmission power settings. While this radio is sufficient to allow the motes deployed in the field to communicate with each other, it is not capable of long-range (> 1000 ft) communication when put on the ground [6].
3. USING ALGORITHM TO DISTRIBUTE THE NODES In determining how to connect sensor nodes to each other, the region under consideration is assumed to have a convex shape to help in providing the coverage. In this case, the greedy sensor deployment scheme is used to show the manner in which wireless network nodes can be distributed between each other. The performance of the greedy scheme is close to that of optimal processes[7]. The greedy algorithm is defined using the equation do = D di = min (D, xi : β (∑i-1j=0 djc) xiy = E/T) for I = 1… n - 1. for I = 1… n - 1. In this case, di decreases monotonically, that is, di ≤ dj if i ≥ j. The reasoning here is that the closer the node approaches the sink node (larger index), the heavier the relay node becomes. In order to compensate for this, the relay distance needs to be shortened by defining a constant, C = E/cβT. When d ≥ C (1/y+1), the results will be xi≤ D for all i. In this case, the required lifetime is long or the initial energy for each sensor will be low. Hence, the equation of the greedy algorithm simplifies to do = D di = (c/∑i-1j=0dj)1/y, i = 1, …, n-1 [8]. Since the greedy algorithm tries to push its data away, it is appropriate to incorporate the following equation c (∑i-1j = 0 di ), such that the traffic lode for node i and xremain as the maximum distance to which i can push the available amount of energy constraint. This is referred to as the xi pushing distance. Here, the intuition is that node i should not send
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data directly to node j when j ≥ i + 2. This is because it consumes additional power. In the event of the greedy algorithm, all nodes tend to run out of power simultaneously. This means that in any given instance, all nodes consume the same energy that was offered initially [9]. Conversely, in case D D; for example, node 1. Since the optimum distance di ≤ D has Idi = min (d, xi), some of the nodes are left behind due to lack of energy. This happens when other nodes are out of energy. In this case, the greedy algorithm fails to be efficient and the alternative is to create a heuristic remedy to allow the other nodes to supply the energy so that the data can be sent away. For instance, node 1 should be allowed to send data directly to node 3 as reflected in figure 1. In this case, the algorithm would work efficiently while erecting wireless sensor nodes in border surveillance [10].
4. DISTRIBUTING SENSOR NODES Detecting and tracking a target as it moves through a sensor network has become an increasingly important application for sensor networks. For the sensor nodes to work efficiently, they need to be well arranged. One way to achieve this is by distributing keys to sensor nodes before they are deployed [8]. Therefore, nodes play a major role in helping build a network based on the key used before the deployment process [9]. In determining the ways in which to connect wireless sensor nodes to each other, Figure 1 depicts a wireless network comprising of 24 nodes. Each line evident between two nodes reveals that they are in the same transmission range. Each node is associated with each of the classes, including beacons and unknown nodes. The beacons are illustrated by black nodes 3, 7, 13, 17, and 23. Here, it is assumed that the positions of the beacons are known by either being placed in a known position or through GPS tracking. The GPS-less nodes are classified as unknown [11].
Figure 1. Beacons and unknown nodes (8). It is assumed that a range of measurement mechanism is available. Hence, an unknown node that is receiving a packet from either a beacon or an unknown node determines whether it is positioned within the ring characterized by circles having radii of Ri-1 and Ri, situated at the center of either the beacon or an unknown node. In figure 1, the centric rings correspond to different ranges of beacon nodes. These packets are referred to assist unknown nodes in establishing their estimate positions to the beacon packets [11]. The received signal strength indicator (RSSI) measurement is used to indicate the range of all the virtually available transceivers that support it. Although this method is not accurate as in the case of the acoustic one, it does not demand additional equipment. It is assumed that the nodes are outdoor with no obstructions to make the model uniform in a circular range holds surprisingly well. [12]. To evaluate RSSI measurements’ accuracy, two Lucent Orinoco IEEE 802.11b cards in two laptops are used to measure the strength of the signal as a function of the distance between them. One of the laptops is configured to send beacon packets in a continuous manner, while the other one measures the signal strength for each packet received. Two sets of measurements are taken, one in an open space (Figure 2 (a)) and the second in a thickly wooded area (Figure 2 (b)) [13].
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Figure 2. Outdoor and no obstruction range measurement [13] Once the measurements are performed in this manner, they are used to capture range in an effective manner. For instance, in Figure 2 (b), if the average received packets is 24, then it is possible to conclude that the sender is situated between 7 and 14 meters [13]. The transmission and the signal strength can be combined at different power levels to increase accuracy in terms of range determination. In this case, a table with RSSI on columns and power levels on the rows is used to help determine both the minimum and maximum transmitter range of the beacon packet. A graph is then be used to generate a table as shown in table 1. In the case of real implementation, the table comprises an entry for every RSSI valid value [14]. Table 1. Power level and RSSI of received packets [14] Power Level
RSSI 1
2
3
4
1
4- 20m
1-20m
0-8m
0-5m
2
8- 30m
5-15m
1-12m
0-8m
3
15- 40m
10-30m
5-15m
0-10m
4
40- 70m
25-50m
20-30m
0-20m
The most common performance indicators of wireless sensor nodes include resiliency, as well as global and local connectivity. In terms of local connectivity, there is a probability whereby two linked sensor nodes use a common key to create a secure link for communication. For global connectivity, a certain proportion of nodes establish secure connection over several nodes. Therefore, it is true that when distributing sensor nodes, it is appropriate to exercise caution to ensure that they are not subject to compromise. This would play a vital role in ensuring that sensor nodes work efficiently and provide better results during the border surveillance process [11].
5.RELIABILITY OF USING THE SENSOR NODE IN BORDER SURVEILLANCE Wireless sensor networks used in border surveillance comprise a large number of sensor nodes, characterized by limited storage, processing, and battery capabilities [15]. Therefore, a number of strategies facilitate reduction in power consumption of the sensor nodes through improved sensor quality and network lifetime [16]. Nonetheless, an inherent conflict prevails between reliability and power consumption. With an increase in reliability, power consumption rises. Consequently, it is crucial to devise ways of reducing power consumption to realize benefits from both ends and enhance reliability of sensor nodes in border surveillance. Furthermore, routing algorithm plays a major role in enhancing the reliability of sensor nodes in border surveillance [17]. Although it leads to an increase in consumption of power, it increases reliability since it uses diverse ways of transmitting object information to a monitoring zone [18]. Furthermore, simulation modelling plays a major role of helping evaluate the reliability of wireless sensor nodes in border surveillance. Simulation modelling evaluates the reliability of wireless sensor nodes by expressing the behavior of a network, including receiving, sending, creating, rejected, and forwarding packets. The process uses models that
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analyze interference by other elements, irregular propagation, and environmental conditions that lead to discarding of packets. This process plays a major role in terms of evaluating and defining various communication protocols that govern wireless sensor nodes during the border surveillance process [18]. Therefore, to ensure that wireless sensor nodes are reliable, it is appropriate to implement measures of determining the prevailing problems to establish effective ways of improving their reliability and effectiveness.
6. CONCLUSION Networks of small, densely distributed wireless sensor nodes are capable of solving a variety of collaborative problems such as monitoring and surveillance. Border security is a primary concern of the national security agenda in this period of terrorism and threats of terrorism. Use of wireless sensor nodes in border surveillance play a major role in detecting intruders and allows appropriate actions to be taken, especially in high security areas. The issue of wireless sensor nodes reliability is accorded considerable attention to make sure the sensors operate efficiently. In this case, it is appropriate to ensure that sensor nodes are reliable regardless of power consumption. Nonetheless, as technology evolves, it is crucial to identify the appropriate ways of reducing power consumption while sustaining or improving their reliability.
REFERENCES [1] An, Sunshin. Border Surveillance using Sensor-based Thick Lines. Bangkok: Korea University, 2013. [2] Wu, Jie. Handbook on Theoretical and Algorithmic Aspects of Sensor, Ad Hoc Wireless, and Peer-to-Peer Networks. New York, NY: CRC Press, 2010. [3] Hassani, Amin, Alexander Bertrand, and Marc Moonen. "Cooperative Integrated Noise Reduction and NodeSpecific Direction-of-Arrival Estimation in a Fully Connected Wireless Acoustic Sensor Network." Signal Processing 107.2 (2015): 66-81. [4] Sun, Zhi, et al. "BorderSense: Border patrol through advanced wireless sensor networks." Ad Hoc Networks 9.3 (2011): 468-477. [5] Ramzi, NoureddineBoudriga, and Sunshin An. "Border Surveillance using sensor based thick-lines." Information Networking (ICOIN), 2013 International Conference on. IEEE, 2013. [6] T. He, S. Krishnamurthy, J. A. Stankovic, T. Abdelzaher, L. Luo, R. Stoleru, T. Yan, L. Gu, J. Hui and B. Krogh, “An Energy-Efficient Surveillance System Using Wire-less Sensor Networks,” 2nd International Conference on Mobile Systems, Applications and Services, Boston, 6-9 June 2004. [7] Vecchio, Massimo and Roberto López-Valcarce. "Improving Area Coverage of Wireless Sensor Networks via Controllable Mobile Nodes: A Greedy Approach." Journal of Network and Computer Applications 48.1 (2015): 113. [8] Haas, Christian. Secure and Flexible Border Surveillance Using Wireless Sensor Networks. Telematics,12Mar. 2013. web. 14 Feb. 2015. . [9] Perrig, Adrian and Robert Szewczyk. Sensor Network Key Distribution. Sparrow, 1 May. 2014. web. 14 Feb. 2015. . [10] Arputhavijayaselvi, Susila. "Innovative Energy Resourceful Merged Layer Technique (MLT) of Node Deployment to Enhance the Lifetime of Wireless Sensor Networks." Egyptian Informatics Journal 1.1 (2014): 1-16. [11] Sun, Ning, Youngbuk Chou, and Sangho Lee. Node Classification Based on Functionality in Energy-Efficient and Reliable Wireless Sensor Networks. Hindawi, 16 Dec 2012. web. 14 Feb. 2015. . [12] Guoqiang, Mao. Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking: Monitoring and Surveillance Techniques for Target Tracking. Hershey, PA: IGI Global, 2012. [13] Tezcan, Nurcan. Energy-efficient and Reliable Data Transfer in Wireless Sensor Networks. California, CA: ProQuest, 2013. Print. [14] Tan, Yen Kheng. Energy Harvesting Autonomous Sensor Systems: Design, Analysis, and Practical Implementation. New York, NY: CRC Press, 2013. [15] Li, Wei and Zhang Wei. "Coverage Hole and Boundary Nodes Detection in Wireless Sensor Networks." Journal of Network and Computer Applications 48.2 (2013): 35-43. [16] Kar, Koushik and Suman Banerjee. Node Placement for Connected Coverage in Sensor Networks. IA State University, 15 Jul. 2014. web. 14 Feb. 2015. . [17] Malaki, Morteza. Energy-efficient Strategies for Deployment and Resource Allocation in Wireless Sensor Networks. California, CA: ProQuest, 2009.
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[18] Halder, Subir and Das Bit Sipra. "Enhancement of Wireless Sensor Network Lifetime by Deploying Heterogeneous Nodes." Journal of Network and Computer Applications 38.1 (2014): 106-124.
AUTHOR Mosad Alkhathami is a full-time Ph.D. candidate of Electrical and computer Engineer at Wayne State University. He received his M.S. in Telecommunication system from DePaul University for Graduate Studies/ Chicago in 2011, the B.S in Electrical and Computer Science from Purdue Calumet University/ Hammond, IN in 2009. Mosad Alkhathami, now doing his Ph.D. thesis in the field of embedded systems and wireless sensor networks. Dr. Lubna Alazzawi received her PhD from University of Technology and University of MichiganDearborn, USA joint program. Dr. Alazzawi is currently working in the Department of Electrical and Computer Engineering at Wayne State University, USA. Prior to joining Wayne State University, She was teachingin the Department of Electrical and Computer Engineering at University of Michigan, Dearborn, USA. She also worked there as
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