Abstract: The ability of sensor nodes to enter a low power sleep mode is very useful for extending network longevity. We show how adversary nodes can exploit clustering algorithms to ensure their selection as cluster heads for the purpose of launching attacks that prevent victim nodes from sleeping. We present two such attacks: the barrage attack and the sleep deprivation attack. The barrage attack bombards victim nodes with legitimate requests, whereas the sleep deprivation attack makes requests of victim nodes only as often as in necessary to keep the victims awake. We show that while the barrage attack causes its victims to spend slightly more energy, it is more easily detected and requires more effort on behalf of the attacker. Thus, we have focused our research on the sleep deprivation attack. Our analysis indicates that this attack can nullify any energy savings obtained by allowing sensor nodes to enter sleep mode. We also analyze three separate methods for mitigating this attack: the random vote scheme, the...

Abstract: In this article we propose several new progress based, localized, power, and cost aware algorithms for routing in ad hoc wireless networks. These algorithms attempt to minimize the total power and/or cost needed to route a message from source to destination. In localized algorithms, each node makes routing decisions solely on the basis of location of itself, its neighbors and destination. The new algorithms are based on the notion of proportional progress. A node currently holding the packet will forward it to a neighbor, closer to destination than itself, which minimizes the ratio of power and/or cost to reach that neighbor, and the progress made, measured as the reduction in distance to destination, or projection along the line to destination. First, we propose Power Progress, Iterative Power Progress, Projection Power Progress, and Iterative Projection Power Progress algorithms, where the proportional progress is defined in terms of power measure. The power metrics are then replaced by cost or power-co...

Abstract: An energy efficient cover of a region using Wireless Sensor Networks (WSNs) is addressed in this paper. Sensor nodes in a WSN are characterized by limited power and computational capabilities, and are expected to function for extended periods of time with minimal human intervention. The life span of such networks depends on the efficient use of the available power for sensing and communication. In this paper, the coverage problem in a three dimensional space is rigorously analyzed and the minimum number of sensor nodes and their placement for complete coverage is determined. Also, given a random distribution of sensor nodes, the problem of selecting a minimum subset of sensor nodes for complete coverage is addressed. A computationally efficient algorithm is developed and implemented in a distributed fashion. Numerical simulations show that the optimized sensor network has better energy efficiency compared to the standard random deployment of sensor nodes. It is demonstrated that the optimized WSN continues to offer better coverage of the region even when the sensor nodes start to fail over time. A localized “self healing” algorithm is implemented that wakes up the inactive neighbors of a failing sensor node. Using the “flooding algorithm” for querying the network, it is shown that the optimized WSN with integrated self healing far outweighs the performance that is obtained by standard random deployment. For the first time, a “measure of optimality” is defined that will enable the comparison of different implementations of a WSN from an energy efficiency stand point.

Abstract: Localization is one of the essential issues in wireless sensor networks. Randomly deployed sensor nodes that do not own any positioning device need to determine their locations for further purposes. Range-based schemes typically utilize extra hardware to provide higher accuracy based on either node-to-node distances or angles. On the other hand, range-free mechanisms support coarse localization without the specific equipment. The paper presents a range-free localization scheme with aerial anchor nodes. Each aerial anchor flies across the sensing field and also periodically broadcasts its location information obtained from its GPS receiver. The sensor nodes use the information and geometry principles to calculate their positions. With the mechanism, the sensor nodes do not need distance-probing hardware or additional communication among them. The localization mechanism has been evaluated and the results show that the mechanism had better performance than other range-free mechanisms.

Abstract: Multi-hop wireless networks (such as ad-hoc or sensor networks) consist of sets of mobile nodes without the support of a pre-existing fixed infrastructure. For scalability purpose, ad-hoc and sensor networks may both need to be organized into clusters and require efficient protocols to perform common global communication patterns like the broadcasting operation. During a broadcasting task, a source node needs to send the same message to all other nodes in the network. Some desired properties of a scalable broadcasting are energy and bandwidth efficiency, i.e., message retransmissions should be minimized. In this article, we present scalable broadcasting schemes that take advantage of the clustering structure. In this way, we only build one structure to perform both self-organization and broadcasting in clusters and in the entire network. It appears that our broadcasting scheme presents the best trade-off between the number of retransmissions and transmitters (for energy saving) and reliability, when compa...

Abstract: Wireless sensor nodes can be mobile within a chosen area and communicate with neighboring nodes in the bounds of protocol limits. Since communications among all network components in sensor networks are wireless, a peer-to-peer protocol is employed between two nodes. Among many concerns about design of sensor networks are growing bandwidth demands, speed of information retrieval, and transporting bytes over the wireless networks to provide a quality service for the diverse requirements of the users, such as signal processing or multimedia applications. Although traditional routing protocols ignore power management issues for sensor networks, design and implementation of an efficient energy based routing is in the core interest. In this paper, we discuss the current power management protocols, and propose an energy restrained information dissemination scheme. Experimental analysis and comparison with related work show that using the proposed scheme we can save substantial energy as compared to the prior methods.

Abstract: In this paper, a distributed approach to radio scene analysis is considered. A Wireless Sensor Network, composed by Software Defined and Cognitive terminals, is used to classify air interfaces present in the radio scene. Two modes, namely Frequency Hopping Code Division Multiple Access and Direct Sequence Code Division Multiple Access, are identified, employing a signal processing technique. Time Frequency analysis, and a distributed decision theory. Advantages given by distributed detection are used to improve the performance of a Mode Identification module. Results in terms of error probability are obtained by modelling the probability density function of considered features as Asymmetric Generalized and Generalized Gaussian functions.

Abstract: We present a rigorous geometric analysis of the computation of the global positions of an airborne video camera and ground based objects using aerial images of known landmarks. This has also been known as the perspective-n-point (PnP) problem. A robust Hough transform-like method, facilitated by a class of CORDIC-structured computations is developed to find the camera position followed by a method of computing the position of a ground object from images of that object and three known landmarks. The results enable fast and effective visual terrain navigation of aerial surveillance systems when the global positioning and inertial navigation sensors become faulty, inaccurate, or dysfunctional. These new hardware implementable algorithms can also be used with MEMS based INS sensors through a multisensory fusion process.

Abstract: We present a novel formulation of the problem of energy misbehavior and develop an analytical framework for quantifying its impact on other nodes. Specifically, we formulate two versions of the power control problem for wireless sensor networks with latency constraints arising from duty cycle allocations. In the first version, strategic power optimization, nodes are modeled as rational agents in a power game, who strategically adjust their powers to minimize their own energy. In the other version, joint power optimization, sensor nodes adjust their transmission powers to minimize the aggregate energy expenditure. Our analysis of these models yields insight into the different energy outcomes of strategic versus joint power optimization. We show that while joint power optimization fits the accepted paradigm of cooperation among sensor nodes (for example large number of sensor nodes cooperating for a task such as target tracking), it comes with both advantages and disadvantages when energy misbehavior is taken into account. One advantage is that it can (sometimes) be energy-dominant, i.e., the optimal energy cost for each node under joint energy minimization is lower than its strategically optimal energy cost. We then develop a model for characterizing energy misbehavior and show that joint optimization is disadvantageous because it is impossible to prevent misbehavior under any channel quality and load constraints, whereas strategic optimization is more resilient. We prove that it is impossible for a node to unilaterally and undetectably follow a different energy optimization strategy than the other nodes and hence the only threat to the network is misbehavior through false advertisement. We then provide sufficient conditions under which misbehavior through false advertisement can be prevented under a strategic optimization regime. Our analytical results reveal optimal strategies for attacking nodes in an enemy network through energy depletion and help develop effective defense mechanisms for protecting our own wireless network against energy attacks by an intelligent adversary.

Abstract: A wireless sensor network (WSNET) can support various types of queries. The energy resource of sensors constrains the total number of query responses, called query capacity, received by the sink. T...

Abstract: Mobile ad hoc networks are self-organizing networks that provide rapid network connectivity in infrastructureless environments. Most routing protocols designed for MANETs assume connected networks....

Abstract: Sensor network research is still in its infancy. There is a large volume of exploratory research. From lack of experimental data and sophisticated models derived from such data, many sensor network publications continue to use data generated from simple models in their algorithm evaluation. It is commonly agreed that data processing algorithms in sensor networks are sensitive to input data. However, no previous efforts have been devoted to quantitatively characterize the range of the algorithm performance when evaluated using different data input.

Abstract: Recent advances in technology have made it possible to build surveillance systems using many low-cost sensor nodes with limited computation and communication capabilities. Due to a potentially large number of nodes deployed, node failures are inevitable and can render a surveillance system that has degraded detection performances. The exposure metric has been proposed earlier to assess the quality of a surveillance system based on the detection performances. In this paper, we characterize the vulnerability of a system in terms of its exposure with respect to the number of faulty nodes and their combinations. Specifically, we assess the exposure of a surveillance network subject to a given number of faulty nodes, and identify the worst-case fault combination for both an idling target and a traversing target. For an idling target, the worst-case fault combination and exposure is analytically identified. For a traversing target, a genetic algorithm based approach is proposed to derive a near worst-case fault...

Abstract: Sensor networks based on the de facto standard Berkeley TinyOS platform are changing the way environmental information is collected in the field. One such network has been designed, deployed, and tested in order to determine where ephemeral streams (small, temporary channels of runoff) form during precipitation events. This small, proof-of-concept test network was designed around a generic nondeterministic finite state machine component, which was built to be re-used in later environmental sensor network applications. A simplistic broadcast mechanism was devised to provide collective sampling interval changes to adapt to environmental conditions. In this paper, the design and testing of the ephemeral stream detection network are discussed, along with design features that can be re-used in later applications. Improvements for a later deployment of a larger, operational ephemeral stream detection network are also described.

Abstract: Energy conservation is an important design consideration for battery powered wireless sensor networks (WSNET) Energy constraint in WSNETs limits the total amount of sensed data (data capacity) received by sinks In the commonly used static model of sensor networks with uniformly distributed homogenous sensors with a stationary sink, sensors close to the sink drain their energy much faster than sensors far away from the sink due to the unevenly distributed forwarding workloads among sensors A major issue, which has not been adequately addressed so far, is the question of how sensor deployment governs the data capacity, and how to improve data capacity of WSNETs In our previous work, we provided a simple analytical model to address this issue for one specific type of WSNETs In this paper, we extend our previous work to address this issue for general WSNETs In the extended static models, for large networks, we find that after the lifetime of a sensor network is over, there is a great amount of energy le