On Security and Reliability using Cooperative Transmissions in Sensor Networks

Recent work on cooperative communications has demonstrated benefits in terms of improving the reliability of links through diversity and/or increasing the reach of a link compared to a single transmitter transmitting to a single receiver (single-input single-output or SISO). In one form of cooperative transmissions, multiple nodes can act as virtual antenna elements and provide such benefits using space-time coding. In a multi-hop sensor network, a source node can make use of its neighbors as relays with itself to reach an intermediate node, which will use its neighbors and so on to reach the destination. For the same reliability of a link as SISO, the number of hops between a source and destination may be reduced using cooperative transmissions. However, the presence of malicious or compromised nodes in the network impacts the use of cooperative transmissions. Using more relays can increase the reach of a link, but if one or more relays are malicious, the transmission may fail. In this paper, we analyze this problem to understand the conditions under which cooperative transmissions may fare better or worse than SISO transmissions

A Framework of Efficient Hybrid Model and Optimal Control for Multihop Wireless Networks

The performance of multihop wireless networks (MWN) is normally studied via simulation over a fixed time horizon using a steady-state type of statistical analysis procedure. However, due to the dynamic nature of network connectivi- ty and nonstationary traffic, such an approach may be inap- propriate as the network may spend most time in a transien- t/nonstationary state. Moreover, the majority of the simu- lators suffer from scalability issues. In this work, we presents a performance modeling framework for analyzing the time varying behavior of MWN. Our framework is a hybrid mod- el of time varying connectivity matrix and nonstationary network queues. Network connectivity is captured using s- tochastic modeling of adjacency matrix by considering both wireless link quality and node mobility. Nonstationary net- work queues behavior are modeled using fluid flow based differential equations. In terms of the computational time, the hybrid fluid-based model is a more scalable tool than the standard simulator. Furthermore, an optimal control strategy is proposed on the basis of the hybrid model

Improving the Performance of Multi-Hop Wireless Networks by Selective Transmission Power Control

In a multi-hop wireless network, connectivity is determined by the link that is established by the receiving signal strength computed by subtracting the path loss from the transmission power. Two path loss models are commonly used in research namely two-ray ground and shadow fading, which determine the receiving signal strength and affect the link quality. Link quality is one of the key factors that affect network performance. In general, network performance improves with better link quality in a wireless network. In this study, we measure the connectivity and performance in a shadow fading path loss model, and our observations shows that both are severely degraded in this path loss model. To improve network performance, we propose power control schemes utilizing link quality to identify the set of nodes required to adjust the transmission power in order to improve the network throughput in both homogeneous and heterogeneous multi-hop wireless networks. Numerical studies to evaluate the proposed schemes are presented and compared.\ud \ud \ud \ud \ud \u

How can polycentric governance of spectrum work?

Spectrum policy in the US (and throughout most of the world) consists generally of a set of nationally determined policies that apply uniformly to all localities. However, it is also true that there is considerable variation in the features (e.g., traffic demand or population density), requirements and constraints of spectrum use on a local basis. Global spectrum policies designed to resolve a situation in New York City could well be overly restrictive for communities in central Wyoming. At the same time, it is necessary to ensure that more permissive policies of central Wyoming would not create problems for NYC (by ensuring, for example, that relocated radios adapt to local policies). Notions of polycentric governance that have been articulated by the late E. Ostrom [17] argue that greater good can be achieved by allowing for local autonomy in resource allocation. Shared access to spectrum is generally mediated through one of several technologies. As Weiss, Altamimi and Liu [22] show, approaches mediated by geolocation databases are the most cost effective in today’s technology. In the database oriented Spectrum Access System, or SAS, proposed by the FCC, users are granted (renewable) usage rights based on their location for a limited period of time. Because this system grants usage rights on a case-by-case basis, it may also allow for greater local autonomy while still maintaining global coordination. For example, it would be technically feasible for the database to include parameters such as transmit power, protocol, and bandwidth. Thus, they may provide the platform by which polycentric governance might come to spectrum management. In this paper, we explore, through some case examples, what polycentric governance of spectrum might look like and how this could be implemented in a database-driven spectrum management system. The approach proposed in this paper aims at approaching spectrum management as an emergent phenomenon rather than a top down system. This paper will describe the key details of this system and present some initial modelling results in comparison with the traditional global model of spectrum regulation. It will also address some of the concerns associated with this approach

Identifying Malicious Nodes in Multihop IoT Networks using Dual Link Technologies and Unsupervised Learning

Packet manipulation attack is one of the challenging threats in cyber-physical systems (CPSs) and Internet of Things (IoT), where information packets are corrupted during transmission by compromised devices. These attacks consume network resources, result in delays in decision making, and could potentially lead to triggering wrong actions that disrupt an overall system's operation. Such malicious attacks as well as unintentional faults are difficult to locate/identify in a large-scale mesh-like multihop network, which is the typical topology suggested by most IoT standards. In this paper, first, we propose a novel network architecture that utilizes powerful nodes that can support two distinct communication link technologies for identification of malicious networked devices (with typical singlelink technology). Such powerful nodes equipped with dual-link technologies can reveal hidden information within meshed connections that is hard to otherwise detect. By applying machine intelligence at the dual-link nodes, malicious networked devices in an IoT network can be accurately identified. Second, we propose two techniques based on unsupervised machine learning, namely hard detection and soft detection, that enable dual-link nodes to identify malicious networked devices. Our techniques exploit network diversity as well as the statistical information computed by dual-link nodes to identify the trustworthiness of resource-constrained devices. Simulation results show that the detection accuracy of our algorithms is superior to the conventional watchdog scheme, where nodes passively listen to neighboring transmissions to detect corrupted packets. The results also show that as the density of the dual-link nodes increases, the detection accuracy improves and the false alarm rate decreases

Hierarchical Data Integrity for IoT Devices in Connected Health Applications

Internet of things devices are increasingly replacing expensive monitoring devices in many environments such as healthcare. People can eventually own their data, collected from smart personal devices, store them in a variety of cloud services, and make them available to service providers of their choice. In such cases, whenever service providers use these data to provide appropriate services, the data owner may become responsible for ensuring the integrity of data retrieved from multiple points. We present a Hierarchical Data Integrity (HDI) approach to verify if the data, sent by monitoring devices to the cloud, remain unchanged. It is hierarchical as follows: there is a quick verification of the integrity of recent health data (in less than 1 ms), followed if necessary by a low overhead secure option for verifying the integrity of both recent and historical data (still only in 6:1 ms). Further, the hierarchy allows granular identification of data units that fail integrity checks, without requiring any key sharing. It is possible for a data owner to periodically (randomly) use a more secure process to verify the integrity of data. This reduces the computation, storage, and time of integrity verification as shown by analysis, simulation, and hardware implementation

Data Credence in IoR: Vision and Challenges

As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things

Data Credence in IoT: Vision and Challenges

As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things

Group Privacy-aware Disclosure of Association Graph Data

In the age of Big Data, we are witnessing a huge proliferation of digital data capturing our lives and our surroundings. Data privacy is a critical barrier to data analytics and privacy-preserving data disclosure becomes a key aspect to leveraging large-scale data analytics due to serious privacy risks. Traditional privacy-preserving data publishing solutions have focused on protecting individual's private information while considering all aggregate information about individuals as safe for disclosure. This paper presents a new privacy-aware data disclosure scheme that considers group privacy requirements of individuals in bipartite association graph datasets (e.g., graphs that represent associations between entities such as customers and products bought from a pharmacy store) where even aggregate information about groups of individuals may be sensitive and need protection. We propose the notion of εg-Group Differential Privacy that protects sensitive information of groups of individuals at various defined group protection levels, enabling data users to obtain the level of information entitled to them. Based on the notion of group privacy, we develop a suite of differentially private mechanisms that protect group privacy in bipartite association graphs at different group privacy levels based on specialization hierarchies. We evaluate our proposed techniques through extensive experiments on three real-world association graph datasets and our results demonstrate that the proposed techniques are effective, efficient and provide the required guarantees on group privacy