Sybil attack detection in a hierarchical sensor network

In a wireless sensor network, a large number of small-size cheap sensor nodes are scattered, and have limited resources such as battery power, data processing capability and memory capacity. They should show fault-tolerance, energy efficiency, and topological adaptation to the change of external environment, therefore, an effective routing algorithm is essential. In general, if a network is large in scale, multi-hop transmission is more energy efficient than direct transmission. In case a large number of nodes are deployed densely within a fixed 1-hop communication range, however, many nodes participate in data transmission, multi-hop transmission consumes much more energy in such a case. Thus, this study proposes a routing algorithm that solves above problems at the same time in a dense and large scale sensor network. The result of experiment showed that routing algorithm proposed in this study has reasonable fault-tolerance, energy efficiency, and offers its adaptability to topological changes in a dense and large scale sensor network.

Sensor networks potentially feature large numbers of nodes that can sense their environment over time, communicate with each other over a wireless network, and process information. They differ from data networks in that the network as a whole may be designed for a specific application. We study the theoretical foundations of such large scale sensor networks, addressing four fundamental issues- connectivity, capacity, clocks and function computation. To begin with, a sensor network must be connected so that information can indeed be exchanged between nodes. The connectivity graph of an ad-hoc network is modeled as a random graph and the critical range for asymptotic connectivity is determined, as well as the critical number of neighbors that a node needs to connect to. Next, given connectivity, we address the issue of how much data can be transported over the sensor network. We present fundamental bounds on capacity under several models, as well as architectural implications for how wireless communication should be organized. Temporal information is important both for the applications of sensor networks as well as their operation.We present fundamental bounds on the synchronizability of clocks in networks, and also present and analyze algorithms for clock synchronization. Finally we turn to the issue of gathering relevant information, that sensor networks are designed to do. One needs to study optimal strategies for in-network aggregation of data, in order to reliably compute a composite function of sensor measurements, as well as the complexity of doing so. We address the issue of how such computation can be performed efficiently in a sensor network and the algorithms for doing so, for some classes of functions.

In wired networks, monitor-based network tomography has been proved to be an effective technology for network internal state measurements. Existing wired network tomography approaches assume that the network topology is relatively static. However, the network topology of sensor networks are usually changing over time due to wireless dynamics. In this paper, we study the problem to assign a number of sensor nodes as monitors in large scale sensor networks, so that the end-to-end measurements among monitors can be used to identify hop-by-hop link metrics. We propose RoMA, a Robust Monitor Assignment algorithm to assign monitors in large scale sensor networks with dynamically changing topology. RoMA includes two components, confidence-based robust topology generation and cost-minimized monitor assignment. We implement RoMA and evaluate its performance based on a deployed large scale sensor network. Results show that RoMA achieves high identifiability with dynamically changing topology and is able to assign monitors with minimum cost.

GOAL: A parsimonious geographic routing protocol for large scale sensor networks

Geographic routing is well suited for large scale sensor networks deployments, because the per node state it maintains is independent of the network size. However, due to the “local minimum” caused by holes/obstacles, the path stretch of geographic routing can grow as O(c2), where c is the length of the optimal path. Recently, VIGOR, a geographic routing protocol based on the visibility graph, shows that a constant path stretch can be achieved. This, however, is possible with increased overhead. To address this issue, we propose GOAL (Geometric Routing using Abstracted Holes), a routing protocol that provably achieves a constant path stretch, with lower message, space and computational overhead. We develop a novel distributed convex hull construction (DCC) algorithm that compactly describes holes. This compact representation of a hole is leveraged by nodes to make locally optimal routing decisions. Our theoretical analysis proves the constant stretch property and average stretch of GOAL. Through extensive simulations and a hardware implementation, we demonstrate the effectiveness of GOAL and its feasibility for large-scale sensor networks. In our network settings, GOAL reduces the energy consumption by up to 32%, routing table size by an order of magnitude, when compared with VIGOR.

One of the most important considerations in designing sensor nodes in a wireless sensor network is to extend the network lifetime by minimizing the energy consumption with limited resources. The most challenging aspects of the studies conducted on this topic have been supporting the mobility of sink nodes in a wireless sensor network, and providing an efficient data dissemination approach.

A fundamental knowledge of the trade-off between sensor cooperation and autonomous vehicles' (AV) trajectory planning is pivotal towards characterizing the sensing capabilities of wireless sensor networks that employ AV for collecting the data and to ensure their successful integration in large scale sensor deployments. We formulate the problem of efficient data gathering as a mixed integer linear programming (MILP) problem that provides a joint optimization of AV trajectory and data-routing. Since MILP formulations are not scalable, we propose an approach to develop heuristics where the joint optimization is decoupled into three sub-problems. The first is to determine clusters of sensors with communication range limitations. The second is to efficiently connect the clusters. The third is to design the route inside the cluster that will minimize the cost of data collection. We characterize performance of the proposed heuristics through Monte-Carlo simulations. Performance is measured in terms of (a) the joint energy cost for cooperation and AV movement for different number of sensor nodes and communication ranges of these sensors, and (b) computational effort of the various heuristics. For small deployments, we compare the heuristics to the MILP global optimization and show that the gap between them can be lower than 2% for deployments as large as 18 nodes and typically below 25% for a wide range of scenarios.

Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in large scale sensor networks; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the aggregation and forwarding load across the network. With CDG, multiple forwarding trees are constructed, each for aggregating a coded or compressed measurement, and these measurements are collected at the sink for recovering the uncoded transmissions from the sensors. This paper studies the problem of constructing forwarding trees for collecting and aggregating sensed data in the network under the realistic physical interference model. The problem of gathering tree construction and link scheduling is addressed jointly, through a mathematical formulation, and its complexity is underlined. Our objective is to collect data at the sink with both minimal latency and fewer transmissions. We show the joint problem is NP-hard and owing to its complexity, we present a decentralized method for solving the tree construction and the link scheduling subproblems. Our link scheduling subproblem relies on defining an interference neighbourhood for each link and co-ordinating transmissions among network links to control the interference. We prove the correctness of our algorithmic method and analyse its performance. Numerical results are presented to compare the performance of the decentralized solution with the joint model as well as prior work from the literature.

To solve the problems of high memory occupation, low connectivity and poor resiliency against node capture, which existing in the random key pre-distribution techniques while applying to the large scale Wireless Sensor Networks (WSNs), an Identity-Based Key Agreement Scheme (IBKAS) is proposed based on identity-based encryption and Elliptic Curve Diffie-Hellman (ECDH). IBKAS can resist man-in-the-middle attacks and node-capture attacks through encrypting the key agreement parameters using identity-based encryption. Theoretical analysis indicates that comparing to the random key pre-distribution techniques, IBKAS achieves significant improvement in key connectivity, communication overhead, memory occupation, and security strength, and also enables efficient secure rekeying and network expansion. Furthermore, we implement IBKAS for TinyOS-2.1.2 based on the MICA2 motes, and the experiment results demonstrate that IBKAS is feasible for infrequent key distribution and rekeying for large scale sensor networks.

In this paper an approach for dynamic sensor selection in large video-based sensor networks for the purpose of multi-camera object tracking is presented. The sensor selection approach is based on computational geometry algorithms and is able to determine task-relevant cameras (camera cluster) by evaluation of geometrical attributes, given the last observed object position, the sensor configurations and the environment model. Hereby, a special goal of this algorithm is to determine the minimum number of sensors needed to relocate an object, even if the object is temporarily out of sight (e.g., by non-overlapping sensor coverage). It will be shown that the algorithm enables self-organizing tracking approaches to perform optimal camera selection in a highly efficient way. In particular, the approach is applicable to very large camera networks and leads to a highly reduced network and processor load for multi-camera tracking.

Detection method to eliminate Sybil attacks in Vehicular Ad-hoc Networks

This paper addresses the distributed state estimation over a large scale sensor network wherein each sensor may have an unknown measurement bias. In order to mitigate the influence of biases to state estimation, a bias estimator only depending on the structure of local sensor is constructed to ensure the algorithm's scalability for sensor networks. It is proved that the proposed bias estimator can effectively realize the bias estimation for the slow time-varying measurement bias. In addition, a distributed Kalman filter embedded with local bias estimator is developed to efficiently correct the biased measurements. Both consistency and stability of the filter are guaranteed under a general collective observability condition for linear time-varying systems. A numerical simulation is provided to illustrate the effectiveness of the developed results.

We consider the problem of minimizing the energy needed for data fusion in a large scale sensor network by varying the transmission times assigned to different sensor nodes. The optimal scheduling protocol is derived, based on which, we develop a low-complexity inverse-log scheduling algorithm that achieves near-optimal energy efficiency. To eliminate the communication overhead required by centralized scheduling protocols, we also develop a distributed inverse-log protocol that is applicable to networks with a large number of nodes. Simulations demonstrate that this distributed scheduling protocol achieves substantial energy savings over uniform time division multiple access protocol.

Target tracking using wireless sensor networks offers multiple challenges because it usually involves intensive computation and requires accurate methods for tracking and energy consumption. Above all, scalability, energy optimization, efficiency, and overhead reduction are some among the key tasks for any protocol designed to perform target tracking using large scale sensor networks. Border surveillance systems, on the other side, need to report border crossings in a real time manner. They should provide large coverage, lower energy consumption, real time crossing detection, and use efficient tools to report crossing information. In this paper, we present a scheme, called Border Cooperative and Predictive Tracking protocol (BCTP), capable of energyaware surveillance and continuous tracking of objects and individuals’ crossing a country border and anticipating target motion within a thick strip along the border and estimating the target exit zone and time.

In-network data aggregation plays an important role to reduce the transmitting energy and data redundancy in large scale sensor networks. However, the security of wireless sensor networks is a challenging problem in the process of data aggregation. An efficient secure data aggregation is proposed to enhance the data security of wireless sensor networks. Firstly, the secure in-network aggregation tree is introduced, then a judgment method based on trust schema was used to detect whether a sensor node has potential misbehavior. After the detection, a local recovery schema is proposed to reduce the possibility to become isolated nodes, which will increase the security level in data aggregation in wireless sensor networks. The performance analysis shows the efficiency of the schema.