Human-mobility-based sensor context-aware routing protocol for delay-tolerant data gathering in multi-sink cell-phone-based sensor networks

Ubiquitous use of cell phones encourages development of novel applications with sensors embedded in cell phones. The collection of information generated by these devices is a challenging task considering volatile topologies and energy-based scarce resources. Further, the data delivery to the sink is delay tolerant. Mobility of cell phones is opportunistically exploited for forwarding sensor generated data towards the sink. Human mobility model shows truncated power law distribution of flight length, pause time, and intercontact time. The power law behavior of inter-contact time often discourages routing of data using naive forwarding schemes. This work exploits the flight length and the pause time distributions of human mobility to design a better and efficient routing strategy. We propose a Human-Mobility-based Sensor Context-Aware Routing protocol (HMSCAR), which exploits human mobility patterns to smartly forward data towards the sink basically comprised of wi-fi hot spots or cellular base stations. The simulation results show that HMSCAR significantly outperforms the SCAR, SFR, and GRAD-MOB on the aspects of delivery ratio and time delay. A multi-sink scenario and single-copy replication scheme is assumed

Socio-economic aware data forwarding in mobile sensing networks and systems

The vision for smart sustainable cities is one whereby urban sensing is core to optimising city operation which in turn improves citizen contentment. Wireless Sensor Networks are envisioned to become pervasive form of data collection and analysis for smart cities but deployment of millions of inter-connected sensors in a city can be cost-prohibitive. Given the ubiquity and ever-increasing capabilities of sensor-rich mobile devices, Wireless Sensor Networks with Mobile Phones (WSN-MP) provide a highly flexible and ready-made wireless infrastructure for future smart cities. In a WSN-MP, mobile phones not only generate the sensing data but also relay the data using cellular communication or short range opportunistic communication. The largest challenge here is the efficient transmission of potentially huge volumes of sensor data over sometimes meagre or faulty communications networks in a cost-effective way. This thesis investigates distributed data forwarding schemes in three types of WSN-MP: WSN with mobile sinks (WSN-MS), WSN with mobile relays (WSN-HR) and Mobile Phone Sensing Systems (MPSS). For these dynamic WSN-MP, realistic models are established and distributed algorithms are developed for efficient network performance including data routing and forwarding, sensing rate control and and pricing. This thesis also considered realistic urban sensing issues such as economic incentivisation and demonstrates how social network and mobility awareness improves data transmission. Through simulations and real testbed experiments, it is shown that proposed algorithms perform better than state-of-the-art schemes.Open Acces

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies

[[abstract]]Over the last few years, we have witnessed a growing interest in Cyber Physical Systems (CPSs) that rely on a strong synergy between computational and physical components. CPSs are expected to have a tremendous impact on many critical sectors (such as energy, manufacturing, healthcare, transportation, aerospace, etc) of the economy. CPSs have the ability to transform the way human-to-human, human-toobject, and object-to-object interactions take place in the physical and virtual worlds. The increasing pervasiveness of Wireless Sensor Networking (WSN) technologies in many applications make them an important component of emerging CPS designs. We present some of the most important design requirements of CPS architectures. We discuss key sensor network characteristics that can be leveraged in CPS designs. In addition, we also review a few well-known CPS application domains that depend on WSNs in their design architectures and implementations. Finally, we present some of the challenges that still need to be addressed to enable seamless integration of WSN with CPS designs.[[incitationindex]]SCI[[booktype]]紙

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Unified Role Assignment Framework For Wireless Sensor Networks

Wireless sensor networks are made possible by the continuing improvements in embedded sensor, VLSI, and wireless radio technologies. Currently, one of the important challenges in sensor networks is the design of a systematic network management framework that allows localized and collaborative resource control uniformly across all application services such as sensing, monitoring, tracking, data aggregation, and routing. The research in wireless sensor networks is currently oriented toward a cross-layer network abstraction that supports appropriate fine or course grained resource controls for energy efficiency. In that regard, we have designed a unified role-based service paradigm for wireless sensor networks. We pursue this by first developing a Role-based Hierarchical Self-Organization (RBSHO) protocol that organizes a connected dominating set (CDS) of nodes called dominators. This is done by hierarchically selecting nodes that possess cumulatively high energy, connectivity, and sensing capabilities in their local neighborhood. The RBHSO protocol then assigns specific tasks such as sensing, coordination, and routing to appropriate dominators that end up playing a certain role in the network. Roles, though abstract and implicit, expose role-specific resource controls by way of role assignment and scheduling. Based on this concept, we have designed a Unified Role-Assignment Framework (URAF) to model application services as roles played by local in-network sensor nodes with sensor capabilities used as rules for role identification. The URAF abstracts domain specific role attributes by three models: the role energy model, the role execution time model, and the role service utility model. The framework then generalizes resource management for services by providing abstractions for controlling the composition of a service in terms of roles, its assignment, reassignment, and scheduling. To the best of our knowledge, a generic role-based framework that provides a simple and unified network management solution for wireless sensor networks has not been proposed previously

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

[EN] Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.Kumar, K.; Kumar, S.; Kaiwartya, O.; Cao, Y.; Lloret, J.; Aslam, N. (2017). Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities. Energies. 10(12):1-40. https://doi.org/10.3390/en10122073S1401012Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. IEEE Internet of Things Journal, 1(1), 22-32. doi:10.1109/jiot.2014.2306328Kamalinejad, P., Mahapatra, C., Sheng, Z., Mirabbasi, S., M. Leung, V. C., & Guan, Y. L. (2015). Wireless energy harvesting for the Internet of Things. IEEE Communications Magazine, 53(6), 102-108. doi:10.1109/mcom.2015.7120024Kaiwartya, O., Abdullah, A. H., Cao, Y., Altameem, A., Prasad, M., Lin, C.-T., & Liu, X. (2016). Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects. IEEE Access, 4, 5356-5373. doi:10.1109/access.2016.2603219Grieco, L. A., Rizzo, A., Colucci, S., Sicari, S., Piro, G., Di Paola, D., & Boggia, G. (2014). IoT-aided robotics applications: Technological implications, target domains and open issues. Computer Communications, 54, 32-47. doi:10.1016/j.comcom.2014.07.013Aijaz, A., & Aghvami, A. H. (2015). Cognitive Machine-to-Machine Communications for Internet-of-Things: A Protocol Stack Perspective. IEEE Internet of Things Journal, 2(2), 103-112. doi:10.1109/jiot.2015.2390775Lin, Y.-B., Lin, Y.-W., Chih, C.-Y., Li, T.-Y., Tai, C.-C., Wang, Y.-C., … Hsu, S.-C. (2015). EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design and Art. IEEE Internet of Things Journal, 2(6), 551-561. doi:10.1109/jiot.2015.2423286Bello, O., & Zeadally, S. (2016). Intelligent Device-to-Device Communication in the Internet of Things. IEEE Systems Journal, 10(3), 1172-1182. doi:10.1109/jsyst.2014.2298837Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Kaur, N., & Sood, S. K. (2017). An Energy-Efficient Architecture for the Internet of Things (IoT). IEEE Systems Journal, 11(2), 796-805. doi:10.1109/jsyst.2015.2469676Erol-Kantarci, M., & Mouftah, H. T. (2015). Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues. IEEE Communications Surveys & Tutorials, 17(1), 179-197. doi:10.1109/comst.2014.2341600Machine-to-Machine Communications (M2M). M2M Service Requirementshttp://www.etsi.org/deliver/etsi_ts/102600_102699/102689/01.01.01_60/ts_102689v010101p.pdfKhan, M., Silva, B. N., & Han, K. (2016). Internet of Things Based Energy Aware Smart Home Control System. IEEE Access, 4, 7556-7566. doi:10.1109/access.2016.2621752Huang, S.-C., Chen, B.-H., Chou, S.-K., Hwang, J.-N., & Lee, K.-H. (2016). Smart Car [Application Notes]. IEEE Computational Intelligence Magazine, 11(4), 46-58. doi:10.1109/mci.2016.2601758Kant, K., & Pal, A. (2017). Internet of Perishable Logistics. IEEE Internet Computing, 21(1), 22-31. doi:10.1109/mic.2017.19Roopaei, M., Rad, P., & Choo, K.-K. R. (2017). Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging. IEEE Cloud Computing, 4(1), 10-15. doi:10.1109/mcc.2017.5Tröster, G. (2011). Smart Clothes—The Unfulfilled Pledge? IEEE Pervasive Computing, 10(2), 87-89. doi:10.1109/mprv.2011.32Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376. doi:10.1109/comst.2015.2444095Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet of Things Journal, 4(5), 1125-1142. doi:10.1109/jiot.2017.2683200Perera, C., Liu, C. H., Jayawardena, S., & Min Chen. (2014). A Survey on Internet of Things From Industrial Market Perspective. IEEE Access, 2, 1660-1679. doi:10.1109/access.2015.2389854Kamilaris, A., & Pitsillides, A. (2016). Mobile Phone Computing and the Internet of Things: A Survey. IEEE Internet of Things Journal, 3(6), 885-898. doi:10.1109/jiot.2016.2600569Arcadius Tokognon, C., Gao, B., Tian, G. Y., & Yan, Y. (2017). Structural Health Monitoring Framework Based on Internet of Things: A Survey. IEEE Internet of Things Journal, 4(3), 619-635. doi:10.1109/jiot.2017.2664072Razzaque, M. A., Milojevic-Jevric, M., Palade, A., & Clarke, S. (2016). Middleware for Internet of Things: A Survey. IEEE Internet of Things Journal, 3(1), 70-95. doi:10.1109/jiot.2015.2498900Luong, N. C., Hoang, D. T., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2016). Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey. IEEE Communications Surveys & Tutorials, 18(4), 2546-2590. doi:10.1109/comst.2016.2582841Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, 16(1), 414-454. doi:10.1109/surv.2013.042313.00197Khan, A. A., Rehmani, M. H., & Rachedi, A. (2017). Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions. IEEE Wireless Communications, 24(3), 17-25. doi:10.1109/mwc.2017.1600404Ahmed, E., Yaqoob, I., Gani, A., Imran, M., & Guizani, M. (2016). Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wireless Communications, 23(5), 10-16. doi:10.1109/mwc.2016.7721736Cao, Y., Jiang, T., & Han, Z. (2016). A Survey of Emerging M2M Systems: Context, Task, and Objective. IEEE Internet of Things Journal, 3(6), 1246-1258. doi:10.1109/jiot.2016.2582540Rajandekar, A., & Sikdar, B. (2015). A Survey of MAC Layer Issues and Protocols for Machine-to-Machine Communications. IEEE Internet of Things Journal, 2(2), 175-186. doi:10.1109/jiot.2015.2394438Botta, A., de Donato, W., Persico, V., & Pescapé, A. (2016). Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684-700. doi:10.1016/j.future.2015.09.021Risteska Stojkoska, B. L., & Trivodaliev, K. V. (2017). A review of Internet of Things for smart home: Challenges and solutions. Journal of Cleaner Production, 140, 1454-1464. doi:10.1016/j.jclepro.2016.10.006Liu, C. H., Fan, J., Branch, J. W., & Leung, K. K. (2014). Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments. IEEE Transactions on Emerging Topics in Computing, 2(4), 473-487. doi:10.1109/tetc.2014.2364915Du, R., Gkatzikis, L., Fischione, C., & Xiao, M. (2015). Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing. IEEE Journal on Selected Areas in Communications, 33(12), 2997-3010. doi:10.1109/jsac.2015.2481199Chen, Y., Chiotellis, N., Chuo, L.-X., Pfeiffer, C., Shi, Y., Dreslinski, R. G., … Kim, H. S. (2016). Energy-Autonomous Wireless Communication for Millimeter-Scale Internet-of-Things Sensor Nodes. IEEE Journal on Selected Areas in Communications, 34(12), 3962-3977. doi:10.1109/jsac.2016.2612041Akgül, Ö. U., & Canberk, B. (2016). Self-Organized Things (SoT): An energy efficient next generation network management. Computer Communications, 74, 52-62. doi:10.1016/j.comcom.2014.07.004Ahn, J. H., & Lee, T.-J. (2018). ALLYS: All You can Send for Energy Harvesting Networks. IEEE Transactions on Mobile Computing, 17(4), 775-788. doi:10.1109/tmc.2017.2740929Mondal, S., & Paily, R. (2017). Efficient Solar Power Management System for Self-Powered IoT Node. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(9), 2359-2369. doi:10.1109/tcsi.2017.2707566Qureshi, F. F., Iqbal, R., & Asghar, M. N. (2017). Energy efficient wireless communication technique based on Cognitive Radio for Internet of Things. Journal of Network and Computer Applications, 89, 14-25. doi:10.1016/j.jnca.2017.01.003Nguyen, T. D., Khan, J. Y., & Ngo, D. T. (2017). Energy harvested roadside IEEE 802.15.4 wireless sensor networks for IoT applications. Ad Hoc Networks, 56, 109-121. doi:10.1016/j.adhoc.2016.12.003Khanouche, M. E., Amirat, Y., Chibani, A., Kerkar, M., & Yachir, A. (2016). Energy-Centered and QoS-Aware Services Selection for Internet of Things. IEEE Transactions on Automation Science and Engineering, 13(3), 1256-1269. doi:10.1109/tase.2016.2539240Afzal, B., Alvi, S. A., Shah, G. A., & Mahmood, W. (2017). Energy efficient context aware traffic scheduling for IoT applications. Ad Hoc Networks, 62, 101-115. doi:10.1016/j.adhoc.2017.05.001Song, L., Chai, K. K., Chen, Y., Schormans, J., Loo, J., & Vinel, A. (2017). QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems. IEEE Systems Journal, 11(3), 1447-1455. doi:10.1109/jsyst.2015.2465292Energy-Efficient Probabilistic Routing Algorithm for Internet of Thingshttp://www.ietf.org/rfc/rfc3561.txtMachado, K., Rosário, D., Cerqueira, E., Loureiro, A., Neto, A., & de Souza, J. (2013). A Routing Protocol Based on Energy and Link Quality for Internet of Things Applications. Sensors, 13(2), 1942-1964. doi:10.3390/s130201942Chelloug, S. A. (2015). Energy-Efficient Content-Based Routing in Internet of Things. Journal of Computer and Communications, 03(12), 9-20. doi:10.4236/jcc.2015.312002Zhao, M., Ho, I. W.-H., & Chong, P. H. J. (2016). An Energy-Efficient Region-Based RPL Routing Protocol for Low-Power and Lossy Networks. IEEE Internet of Things Journal, 3(6), 1319-1333. doi:10.1109/jiot.2016.2593438Qiu, T., Lv, Y., Xia, F., Chen, N., Wan, J., & Tolba, A. (2016). ERGID: An efficient routing protocol for emergency response Internet of Things. Journal of Network and Computer Applications, 72, 104-112. doi:10.1016/j.jnca.2016.06.009Liu, Y., Liu, A., Hu, Y., Li, Z., Choi, Y.-J., Sekiya, H., & Li, J. (2016). FFSC: An Energy Efficiency Communications Ap-proach for Delay Minimizing in Internet of Things. IEEE Access, 1-1. doi:10.1109/access.2016.2588278Qiu, S., Haselmayr, W., Li, B., Zhao, C., & Guo, W. (2017). Bacterial Relay for Energy-Efficient Molecular Communications. IEEE Transactions on NanoBioscience, 16(7), 555-562. doi:10.1109/tnb.2017.2741669Biason, A., Pielli, C., Rossi, M., Zanella, A., Zordan, D., Kelly, M., & Zorzi, M. (2017). EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design. IEEE Access, 5, 6894-6908. doi:10.1109/access.2017.2692522Huang, Z., Lin, K.-J., Yu, S.-Y., & Hsu, J. Y. (2014). Co-locating services in IoT systems to minimize the communication energy cost. Journal of Innovation in Digital Ecosystems, 1(1-2), 47-57. doi:10.1016/j.jides.2015.02.005Kwak, J., Kim, Y., Lee, J., & Chong, S. (2015). DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems. IEEE Journal on Selected Areas in Communications, 33(12), 2510-2523. doi:10.1109/jsac.2015.2478718Abu Sharkh, M., & Shami, A. (2017). An evergreen cloud: Optimizing energy efficiency in heterogeneous cloud computing architectures. Vehicular Communications, 9, 199-210. doi:10.1016/j.vehcom.2017.02.004Bui, D.-M., Yoon, Y., Huh, E.-N., Jun, S., & Lee, S. (2017). Energy efficiency for cloud computing system based on predictive optimization. Journal of Parallel and Distributed Computing, 102, 103-114. doi:10.1016/j.jpdc.2016.11.011Liu, A., Zhang, Q., Li, Z., Choi, Y., Li, J., & Komuro, N. (2017). A green and reliable communication modeling for industrial internet of things. Computers & Electrical Engineering, 58, 364-381. doi:10.1016/j.compeleceng.2016.09.005Kim, J. (2015). Energy-Efficient Dynamic Packet Downloading for Medical IoT Platforms. IEEE Transactions on Industrial Informatics, 11(6), 1653-1659. doi:10.1109/tii.2015.2434773Chiu, T.-C., Shih, Y.-Y., Pang, A.-C., & Pai, C.-W. (2017). Optimized Day-Ahead Pricing With Renewable Energy Demand-Side Management for Smart Grids. IEEE Internet of Things Journal, 4(2), 374-383. doi:10.1109/jiot.2016.2556006Gandotra, P., Jha, R. K., & Jain, S. (2017). Green Communication in Next Generation Cellular Networks: A Survey. IEEE Access, 5, 11727-11758. doi:10.1109/access.2017.2711784Li, J., Peng, M., Yu, Y., & Ding, Z. (2016). Energy-Efficient Joint Congestion Control and Resource Optimization in Heterogeneous Cloud Radio Access Networks. IEEE Transactions on Vehicular Technology, 65(12), 9873-9887. doi:10.1109/tvt.2016.2531184Kaiwartya, O., Abdullah, A. H., Cao, Y., Lloret, J., Kumar, S., Shah, R. R., … Prakash, S. (2018). Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things. IEEE Internet of Things Journal, 5(2), 571-580. doi:10.1109/jiot.2017.2717704Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3kaiwartya, omprakash, Abdullah, A., Cao, Y., Rao, R. S., Kumar, S., Lobiyal, D. K., … Shah, R. R. (2016). T-MQM: Testbed based Multi-metric Quality Measurement of Sensor Deployment for Precision Agriculture-A Case Study. IEEE Sensors Journal, 1-1. doi:10.1109/jsen.2016.2614748Alrajeh, N. A., Khan, S., Lloret, J., & Loo, J. (2013). Secure Routing Protocol Using Cross-Layer Design and Energy Harvesting in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(1), 374796. doi:10.1155/2013/374796Mehmood, A., Khan, S., Shams, B., & Lloret, J. (2013). Energy-efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems, 28(5), 972-989. doi:10.1002/dac.272

Collision Free Communication for Energy Saving in Wireless Sensor Networks

International audienceA Wireless Sensor Network (WSN) distinguishes from other wireless or wired networks through its capability of interaction with the environment. Such networks have been proposed for various applications including search and rescue, disaster relief, smart environments, and localization systems. These applications require a large amount of battery-powered wireless sensors, and are generally designed for long-term deployments with no human intervention. Consequently, energy efficiency is one of the main design objectives for these sensor networks

Routing, Localization And Positioning Protocols For Wireless Sensor And Actor Networks

Wireless sensor and actor networks (WSANs) are distributed systems of sensor nodes and actors that are interconnected over the wireless medium. Sensor nodes collect information about the physical world and transmit the data to actors by using one-hop or multi-hop communications. Actors collect information from the sensor nodes, process the information, take decisions and react to the events. This dissertation presents contributions to the methods of routing, localization and positioning in WSANs for practical applications. We first propose a routing protocol with service differentiation for WSANs with stationary nodes. In this setting, we also adapt a sports ranking algorithm to dynamically prioritize the events in the environment depending on the collected data. We extend this routing protocol for an application, in which sensor nodes float in a river to gather observations and actors are deployed at accessible points on the coastline. We develop a method with locally acting adaptive overlay network formation to organize the network with actor areas and to collect data by using locality-preserving communication. We also present a multi-hop localization approach for enriching the information collected from the river with the estimated locations of mobile sensor nodes without using positioning adapters. As an extension to this application, we model the movements of sensor nodes by a subsurface meandering current mobility model with random surface motion. Then we adapt the introduced routing and network organization methods to model a complete primate monitoring system. A novel spatial cut-off preferential attachment model and iii center of mass concept are developed according to the characteristics of the primate groups. We also present a role determination algorithm for primates, which uses the collection of spatial-temporal relationships. We apply a similar approach to human social networks to tackle the problem of automatic generation and organization of social networks by analyzing and assessing interaction data. The introduced routing and localization protocols in this dissertation are also extended with a novel three dimensional actor positioning strategy inspired by the molecular geometry. Extensive simulations are conducted in OPNET simulation tool for the performance evaluation of the proposed protocol

Energy aware and privacy preserving protocols for ad hoc networks with applications to disaster management

Disasters can have a serious impact on the functioning of communities and societies. Disaster management aims at providing efficient utilization of resources during pre-disaster (e.g. preparedness and prevention) and post-disaster (e.g. recovery and relief) scenarios to reduce the impact of disasters. Wireless sensors have been extensively used for early detection and prevention of disasters. However, the sensor\u27s operating environment may not always be congenial to these applications. Attackers can observe the traffic flow in the network to determine the location of the sensors and exploit it. For example, in intrusion detection systems, the information can be used to identify coverage gaps and avoid detection. Data source location privacy preservation protocols were designed in this work to address this problem. Using wireless sensors for disaster preparedness, recovery and relief operations can have high deployment costs. Making use of wireless devices (e.g. smartphones and tablets) widely available among people in the affected region is a more practical approach. Disaster preparedness involves dissemination of information among the people to make them aware of the risks they will face in the event of a disaster and how to actively prepare for them. The content is downloaded by the people on their smartphones and tablets for ubiquitous access. As these devices are primarily constrained by their available energy, this work introduces an energy-aware peer-to-peer file sharing protocol for efficient distribution of the content and maximizing the lifetime of the devices. Finally, the ability of the wireless devices to build an ad hoc network for capturing and collecting data for disaster relief and recovery operations was investigated. Specifically, novel energy-adaptive mechanisms were designed for autonomous creation of the ad hoc network, distribution of data capturing task among the devices, and collection of data with minimum delay --Abstract, page iii