Wireless communication, identification and sensing technologies enabling integrated logistics: a study in the harbor environment

In the last decade, integrated logistics has become an important challenge in the development of wireless communication, identification and sensing technology, due to the growing complexity of logistics processes and the increasing demand for adapting systems to new requirements. The advancement of wireless technology provides a wide range of options for the maritime container terminals. Electronic devices employed in container terminals reduce the manual effort, facilitating timely information flow and enhancing control and quality of service and decision made. In this paper, we examine the technology that can be used to support integration in harbor's logistics. In the literature, most systems have been developed to address specific needs of particular harbors, but a systematic study is missing. The purpose is to provide an overview to the reader about which technology of integrated logistics can be implemented and what remains to be addressed in the future

Intrusion-aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks

Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm.Comment: 19 pages, 7 figure

Active low intrusion hybrid monitor for wireless sensor networks

Several systems have been proposed to monitor wireless sensor networks (WSN). These systems may be active (causing a high degree of intrusion) or passive (low observability inside the nodes). This paper presents the implementation of an active hybrid (hardware and software) monitor with low intrusion. It is based on the addition to the sensor node of a monitor node (hardware part) which, through a standard interface, is able to receive the monitoring information sent by a piece of software executed in the sensor node. The intrusion on time, code, and energy caused in the sensor nodes by the monitor is evaluated as a function of data size and the interface used. Then different interfaces, commonly available in sensor nodes, are evaluated: serial transmission (USART), serial peripheral interface (SPI), and parallel. The proposed hybrid monitor provides highly detailed information, barely disturbed by the measurement tool (interference), about the behavior of the WSN that may be used to evaluate many properties such as performance, dependability, security, etc. Monitor nodes are self-powered and may be removed after the monitoring campaign to be reused in other campaigns and/or WSNs. No other hardware-independent monitoring platforms with such low interference have been found in the literature.This research was supported by the Valencian Regional Government under Research Project GV/2014/012, the Polytechnic University of Valencia under Research Projects VLC/Campus UPV PAID-06-12, financed by the Ministerio de Educacion, Cultura y Deporte as part of the program Campus de excelencia internacional UPV SP20140730 and UPV SP20150050, and the Spanish government under projects CTM2011-29691-C02-01 and TIN2011-28435-C03-0.Navia, M.; Campelo Rivadulla, JC.; Bonastre Pina, AM.; Ors Carot, R.; Capella Hernández, JV.; Serrano Martín, JJ. (2015). Active low intrusion hybrid monitor for wireless sensor networks. Sensors. 15(9):23927-23952. https://doi.org/10.3390/s150923927S2392723952159Mahapatro, A., & Khilar, P. M. (2013). Fault Diagnosis in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 15(4), 2000-2026. doi:10.1109/surv.2013.030713.00062Rodrigues, A., Camilo, T., Silva, J. S., & Boavida, F. (2012). Diagnostic Tools for Wireless Sensor Networks: A Comparative Survey. Journal of Network and Systems Management, 21(3), 408-452. doi:10.1007/s10922-012-9240-6Schoofs, A., O’Hare, G. M. P., & Ruzzelli, A. G. (2012). Debugging Low-Power and Lossy Wireless Networks: A Survey. IEEE Communications Surveys & Tutorials, 14(2), 311-321. doi:10.1109/surv.2011.021111.00098FAQ—TinyOS Wikihttp://tinyos.stanford.edu/tinyos-wiki/index.php/FAQGarcia, F., Andrade, R., Oliveira, C., & de Souza, J. (2014). EPMOSt: An Energy-Efficient Passive Monitoring System for Wireless Sensor Networks. Sensors, 14(6), 10804-10828. doi:10.3390/s140610804Yunhao Liu, Kebin Liu, & Mo Li. (2010). Passive Diagnosis for Wireless Sensor Networks. IEEE/ACM Transactions on Networking, 18(4), 1132-1144. doi:10.1109/tnet.2009.2037497Information Technology—Open Systems Interconnection—Basic Reference Modelhttp://www.iso.org/iso/home/store/catalogue_tc/catalogue_detail.htm?-csnumber=20269STM32F051R8 ARM Cortex-M0 MCUhttp://www.st.com/web/catalog/mmc/CMSIS-Cortex Microcontroller Software Interface Standardhttp://www.arm.com/products/processors/cortex-m/cortex-microcontroller-software-interface-standard.phpKeil MDK-ARM Version 5http://www2.keil.com/mdk5/34405A Digital Multimeter, 5½ digit | Keysight (Agilent)http://www.keysight.com/en/pd-686884-pn-34405A/Gharghan, S., Nordin, R., & Ismail, M. (2014). Energy-Efficient ZigBee-Based Wireless Sensor Network for Track Bicycle Performance Monitoring. Sensors, 14(8), 15573-15592. doi:10.3390/s140815573Molina-Garcia, A., Fuentes, J. A., Gomez-Lazaro, E., Bonastre, A., Campelo, J. C., & Serrano, J. J. (2012). Development and Assessment of a Wireless Sensor and Actuator Network for Heating and Cooling Loads. IEEE Transactions on Smart Grid, 3(3), 1192-1202. doi:10.1109/tsg.2012.2187542Lee, D.-S., Liu, Y.-H., & Lin, C.-R. (2012). A Wireless Sensor Enabled by Wireless Power. Sensors, 12(12), 16116-16143. doi:10.3390/s12121611

Determination of RF source power in WPSN using modulated backscattering

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. During RF transmission energy consumed by critically energy-constrained sensor nodes in a WSN is related to the life time system, but the life time of the system is inversely proportional to the energy consumed by sensor nodes. In that regard, modulated backscattering (MB) is a promising design choice, in which sensor nodes send their data just by switching their antenna impedance and reflecting the incident signal coming from an RF source. Hence wireless passive sensor networks (WPSN) designed to operate using MB do not have the lifetime constraints. In this we are going to investigate the system analytically. To obtain interference-free communication connectivity with the WPSN nodes number of RF sources is determined and analyzed in terms of output power and the transmission frequency of RF sources, network size, RF source and WPSN node characteristics. The results of this paper reveal that communication coverage and RF Source Power can be practically maintained in WPSN through careful selection of design parametersComment: 10 pages; International Journal on Soft Computing (IJSC) Vol.3, No.1 (2012). arXiv admin note: text overlap with arXiv:1001.5339 by other author

Resilient networking in wireless sensor networks

This report deals with security in wireless sensor networks (WSNs), especially in network layer. Multiple secure routing protocols have been proposed in the literature. However, they often use the cryptography to secure routing functionalities. The cryptography alone is not enough to defend against multiple attacks due to the node compromise. Therefore, we need more algorithmic solutions. In this report, we focus on the behavior of routing protocols to determine which properties make them more resilient to attacks. Our aim is to find some answers to the following questions. Are there any existing protocols, not designed initially for security, but which already contain some inherently resilient properties against attacks under which some portion of the network nodes is compromised? If yes, which specific behaviors are making these protocols more resilient? We propose in this report an overview of security strategies for WSNs in general, including existing attacks and defensive measures. In this report we focus at the network layer in particular, and an analysis of the behavior of four particular routing protocols is provided to determine their inherent resiliency to insider attacks. The protocols considered are: Dynamic Source Routing (DSR), Gradient-Based Routing (GBR), Greedy Forwarding (GF) and Random Walk Routing (RWR)