Location based services in wireless ad hoc networks

In this dissertation, we investigate location based services in wireless ad hoc networks from four different aspects - i) location privacy in wireless sensor networks (privacy), ii) end-to-end secure communication in randomly deployed wireless sensor networks (security), iii) quality versus latency trade-off in content retrieval under ad hoc node mobility (performance) and iv) location clustering based Sybil attack detection in vehicular ad hoc networks (trust). The first contribution of this dissertation is in addressing location privacy in wireless sensor networks. We propose a non-cooperative sensor localization algorithm showing how an external entity can stealthily invade into the location privacy of sensors in a network. We then design a location privacy preserving tracking algorithm for defending against such adversarial localization attacks. Next we investigate secure end-to-end communication in randomly deployed wireless sensor networks. Here, due to lack of control on sensors\u27 locations post deployment, pre-fixing pairwise keys between sensors is not feasible especially under larger scale random deployments. Towards this premise, we propose differentiated key pre-distribution for secure end-to-end secure communication, and show how it improves existing routing algorithms. Our next contribution is in addressing quality versus latency trade-off in content retrieval under ad hoc node mobility. We propose a two-tiered architecture for efficient content retrieval in such environment. Finally we investigate Sybil attack detection in vehicular ad hoc networks. A Sybil attacker can create and use multiple counterfeit identities risking trust of a vehicular ad hoc network, and then easily escape the location of the attack avoiding detection. We propose a location based clustering of nodes leveraging vehicle platoon dispersion for detection of Sybil attacks in vehicular ad hoc networks --Abstract, page iii

Energy efficient security and privacy management in sensor clouds

Sensor Cloud is a new model of computing for Wireless Sensor Networks, which facilitates resource sharing and enables large scale sensor networks. A multi-user distributed system, however, where resources are shared, has inherent challenges in security and privacy. The data being generated by the wireless sensors in a sensor cloud need to be protected against adversaries, which may be outsiders as well as insiders. Similarly the code which is disseminated to the sensors by the sensor cloud needs to be protected against inside and outside adversaries. Moreover, since the wireless sensors cannot support complex, energy intensive measures, the security and privacy of the data and the code have to be attained by way of lightweight algorithms. In this work, we first present two data aggregation algorithms, one based on an Elliptic Curve Cryptosystem (ECC) and the other based on symmetric key system, which provide confidentiality and integrity of data against an outside adversary and privacy against an in network adversary. A fine grained access control scheme which works on the securely aggregated data is presented next. This scheme uses Attribute Based Encryption (ABE) to achieve this objective. Finally, to securely and efficiently disseminate code in the sensor cloud, we present a code dissemination algorithm which first reduces the amount of code to be transmitted from the base station. It then uses Symmetric Proxy Re-encryption along with Bloom filters and HMACs to protect the code against eavesdropping and false code injection attacks. --Abstract, page iv

Distributed Database Management Techniques for Wireless Sensor Networks

Authors and/or their employers shall have the right to post the accepted version of IEEE-copyrighted articles on their own personal servers or the servers of their institutions or employers without permission from IEEE, provided that the posted version includes a prominently displayed IEEE copyright notice and, when published, a full citation to the original IEEE publication, including a link to the article abstract in IEEE Xplore. Authors shall not post the final, published versions of their papers.In sensor networks, the large amount of data generated by sensors greatly influences the lifetime of the network. In order to manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.This work was partially supported by the Instituto de Telcomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Ministerio de Ciencia e Innovacion, through the Plan Nacional de I+D+i 2008-2011 in the Subprograma de Proyectos de Investigacion Fundamental, project TEC2011-27516, by the Polytechnic University of Valencia, though the PAID-05-12 multidisciplinary projects, by Government of Russian Federation, Grant 074-U01, and by National Funding from the FCT-Fundacao para a Ciencia e a Tecnologia through the Pest-OE/EEI/LA0008/2013 Project.Diallo, O.; Rodrigues, JJPC.; Sene, M.; Lloret, J. (2013). Distributed Database Management Techniques for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems. PP(99):1-17. https://doi.org/10.1109/TPDS.2013.207S117PP9

Hardware-Aware Algorithm Designs for Efficient Parallel and Distributed Processing

The introduction and widespread adoption of the Internet of Things, together with emerging new industrial applications, bring new requirements in data processing. Specifically, the need for timely processing of data that arrives at high rates creates a challenge for the traditional cloud computing paradigm, where data collected at various sources is sent to the cloud for processing. As an approach to this challenge, processing algorithms and infrastructure are distributed from the cloud to multiple tiers of computing, closer to the sources of data. This creates a wide range of devices for algorithms to be deployed on and software designs to adapt to.In this thesis, we investigate how hardware-aware algorithm designs on a variety of platforms lead to algorithm implementations that efficiently utilize the underlying resources. We design, implement and evaluate new techniques for representative applications that involve the whole spectrum of devices, from resource-constrained sensors in the field, to highly parallel servers. At each tier of processing capability, we identify key architectural features that are relevant for applications and propose designs that make use of these features to achieve high-rate, timely and energy-efficient processing.In the first part of the thesis, we focus on high-end servers and utilize two main approaches to achieve high throughput processing: vectorization and thread parallelism. We employ vectorization for the case of pattern matching algorithms used in security applications. We show that re-thinking the design of algorithms to better utilize the resources available in the platforms they are deployed on, such as vector processing units, can bring significant speedups in processing throughout. We then show how thread-aware data distribution and proper inter-thread synchronization allow scalability, especially for the problem of high-rate network traffic monitoring. We design a parallelization scheme for sketch-based algorithms that summarize traffic information, which allows them to handle incoming data at high rates and be able to answer queries on that data efficiently, without overheads.In the second part of the thesis, we target the intermediate tier of computing devices and focus on the typical examples of hardware that is found there. We show how single-board computers with embedded accelerators can be used to handle the computationally heavy part of applications and showcase it specifically for pattern matching for security-related processing. We further identify key hardware features that affect the performance of pattern matching algorithms on such devices, present a co-evaluation framework to compare algorithms, and design a new algorithm that efficiently utilizes the hardware features.In the last part of the thesis, we shift the focus to the low-power, resource-constrained tier of processing devices. We target wireless sensor networks and study distributed data processing algorithms where the processing happens on the same devices that generate the data. Specifically, we focus on a continuous monitoring algorithm (geometric monitoring) that aims to minimize communication between nodes. By deploying that algorithm in action, under realistic environments, we demonstrate that the interplay between the network protocol and the application plays an important role in this layer of devices. Based on that observation, we co-design a continuous monitoring application with a modern network stack and augment it further with an in-network aggregation technique. In this way, we show that awareness of the underlying network stack is important to realize the full potential of the continuous monitoring algorithm.The techniques and solutions presented in this thesis contribute to better utilization of hardware characteristics, across a wide spectrum of platforms. We employ these techniques on problems that are representative examples of current and upcoming applications and contribute with an outlook of emerging possibilities that can build on the results of the thesis

A novel approach for energy- and memory-efficient data loss prevention to support Internet of Things networks

Internet of Things integrates various technologies, including wireless sensor networks, edge computing, and cloud computing, to support a wide range of applications such as environmental monitoring and disaster surveillance. In these types of applications, IoT devices operate using limited resources in terms of battery, communication bandwidth, processing, and memory capacities. In this context, load balancing, fault tolerance, and energy and memory efficiency are among the most important issues related to data dissemination in IoT networks. In order to successfully cope with the abovementioned issues, two main approaches—data-centric storage and distributed data storage—have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the abovementioned problems, the proposed solutions typically focus on one issue at a time. In this article, we propose a cross-layer optimization approach to increase memory and energy efficiency as well as support load balancing. The optimization problem is a mixed-integer nonlinear programming problem, and we solve it using a genetic algorithm. Moreover, we integrate the data-centric storage features into distributed data storage mechanisms and present a novel heuristic approach, denoted as Collaborative Memory and Energy Management, to solve the underlying optimization problem. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms the existing approaches in various IoT scenarios

A Survey on Wireless Sensor Network Security

Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.Comment: 24 pages, 4 figures, 2 table

A privacy preserving framework for cyber-physical systems and its integration in real world applications

A cyber-physical system (CPS) comprises of a network of processing and communication capable sensors and actuators that are pervasively embedded in the physical world. These intelligent computing elements achieve the tight combination and coordination between the logic processing and physical resources. It is envisioned that CPS will have great economic and societal impact, and alter the qualify of life like what Internet has done. This dissertation focuses on the privacy issues in current and future CPS applications. as thousands of the intelligent devices are deeply embedded in human societies, the system operations may potentially disclose the sensitive information if no privacy preserving mechanism is designed. This dissertation identifies data privacy and location privacy as the representatives to investigate the privacy problems in CPS. The data content privacy infringement occurs if the adversary can determine or partially determine the meaning of the transmitted data or the data stored in the storage. The location privacy, on the other hand, is the secrecy that a certain sensed object is associated to a specific location, the disclosure of which may endanger the sensed object. The location privacy may be compromised by the adversary through hop-by-hop traceback along the reverse direction of the message routing path. This dissertation proposes a public key based access control scheme to protect the data content privacy. Recent advances in efficient public key schemes, such as ECC, have already shown the feasibility to use public key schemes on low power devices including sensor motes. In this dissertation, an efficient public key security primitives, WM-ECC, has been implemented for TelosB and MICAz, the two major hardware platform in current sensor networks. WM-ECC achieves the best performance among the academic implementations. Based on WM-ECC, this dissertation has designed various security schemes, including pairwise key establishment, user access control and false data filtering mechanism, to protect the data content privacy. The experiments presented in this dissertation have shown that the proposed schemes are practical for real world applications. to protect the location privacy, this dissertation has considered two adversary models. For the first model in which an adversary has limited radio detection capability, the privacy-aware routing schemes are designed to slow down the adversary\u27s traceback progress. Through theoretical analysis, this dissertation shows how to maximize the adversary\u27s traceback time given a power consumption budget for message routing. Based on the theoretical results, this dissertation also proposes a simple and practical weighted random stride (WRS) routing scheme. The second model assumes a more powerful adversary that is able to monitor all radio communications in the network. This dissertation proposes a random schedule scheme in which each node transmits at a certain time slot in a period so that the adversary would not be able to profile the difference in communication patterns among all the nodes. Finally, this dissertation integrates the proposed privacy preserving framework into Snoogle, a sensor nodes based search engine for the physical world. Snoogle allows people to search for the physical objects in their vicinity. The previously proposed privacy preserving schemes are applied in the application to achieve the flexible and resilient privacy preserving capabilities. In addition to security and privacy, Snoogle also incorporates a number of energy saving and communication compression techniques that are carefully designed for systems composed of low-cost, low-power embedded devices. The evaluation study comprises of the real world experiments on a prototype Snoogle system and the scalability simulations

Building efficient wireless infrastructures for pervasive computing environments

Pervasive computing is an emerging concept that thoroughly brings computing devices and the consequent technology into people\u27s daily life and activities. Most of these computing devices are very small, sometimes even invisible , and often embedded into the objects surrounding people. In addition, these devices usually are not isolated, but networked with each other through wireless channels so that people can easily control and access them. In the architecture of pervasive computing systems, these small and networked computing devices form a wireless infrastructure layer to support various functionalities in the upper application layer.;In practical applications, the wireless infrastructure often plays a role of data provider in a query/reply model, i.e., applications issue a query requesting certain data and the underlying wireless infrastructure is responsible for replying to the query. This dissertation has focused on the most critical issue of efficiency in designing such a wireless infrastructure. In particular, our problem resides in two domains depending on different definitions of efficiency. The first definition is time efficiency, i.e., how quickly a query can be replied. Many applications, especially real-time applications, require prompt response to a query as the consequent operations may be affected by the prior delay. The second definition is energy efficiency which is extremely important for the pervasive computing devices powered by batteries. Above all, our design goal is to reply to a query from applications quickly and with low energy cost.;This dissertation has investigated two representative wireless infrastructures, sensor networks and RFID systems, both of which can serve applications with useful information about the environments. We have comprehensively explored various important and representative problems from both algorithmic and experimental perspectives including efficient network architecture design and efficient protocols for basic queries and complicated data mining queries. The major design challenges of achieving efficiency are the massive amount of data involved in a query and the extremely limited resources and capability each small device possesses. We have proposed novel and efficient solutions with intensive evaluation. Compared to the prior work, this dissertation has identified a few important new problems and the proposed solutions significantly improve the performance in terms of time efficiency and energy efficiency. Our work also provides referrable insights and appropriate methodology to other similar problems in the research community