A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We concentrate on performance of respective genes; genes are necessary to measure a network's performance from a sensor's viewpoint. We conclude that the choice of genes has a profound influence on the performance of the AIS. We identified a specific MAC layer based gene that showed to be especially useful for detection. We also discuss implementation details of AIS when used with sensor networks.
The main discerning factor between our work and works shortly discussed above is that we carefully considered hardware parameters of current sensor devices, the set of input parameters was designed in order to target specifically sensor networks and our simulation setup reflects structural qualities of such networks with regards to existence of multiple independent routing paths. In comparison to @cite_20 @cite_7 we showed that in case of static sensor networks it is reasonable to expect the detection rate to be above 80
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We consider the problem of deploying or repairing a sensor network to guarantee a specified level of multi-path connectivity (k-connectivity) between all nodes. Such a guarantee simultaneously provides fault tolerance against node failures and high capacity through multi-path routing. We design and analyze the first algorithms that place an almost-minimum number of additional sensors to augment an existing network into a k-connected network, for any desired parameter k. Our algorithms have provable guarantees on the quality of the solution. Specifically, we prove that the number of additional sensors is within a constant factor of the absolute minimum, for any fixed k. We have implemented greedy and distributed versions of this algorithm, and demonstrate in simulation that they produce high-quality placements for the additional sensors. We are also in the process of using our algorithms to deploy nodes in a physical sensor network using a mobile robot. This paper presents an indoor positioning system called LOSNUS (LOcalization of Sensor Nodes by Ultra-Sound). It offers high accuracy of ∼10 mm, a locating rate up to ∼10 cycles s and is applicable for both tracking mobile and locating static devices. LOSNUS is mainly designed to localize static devices especially in a wireless sensor network (WSN) with numerously deployed sensor actuator devices which enables substantially improving a lot of aspects of applications, e.g. network integration of nodes, supplying node locations to application programs, supervising locations with respect to accidentally dislocating, automatic setup and detecting faking of node locations. In order to deal with the demand of locating static devices, the system is optimized for cheap implementation and on the other hand for a high resolution of locations. Concept and basic operation, realization of system components and low-cost receiver principles, improved system performance and setup of a test system will be discussed in this paper. This paper presents and evaluates a method for simultaneously tracking a target while localizing the sensor nodes of a passive device-free tracking system. The system uses received signal strength (RSS) measurements taken on the links connecting many nodes in a wireless sensor network, with nodes deployed such that the links overlap across the region. A target moving through the region attenuates links intersecting or nearby its path. At the same time, RSS measurements provide information about the relative locations of sensor nodes. We utilize the Sequential Monte Carlo (particle filtering) framework for tracking, and we use an online EM algorithm to simultaneously estimate static parameters (including the sensor locations, as well as model parameters including noise variance and attenuation strength of the target). Simultaneous tracking, online calibration and parameter estimation enable rapid deployment of a RSS-based device free localization system, e.g., in emergency response scenarios. Simulation results and experiments with a wireless sensor network testbed illustrate that the proposed tracking method performs well in a variety of settings. Previous work on the coverage of mobile sensor networks focuses on algorithms to reposition sensors in order to achieve a static configuration with an enlarged covered area. In this paper, we study the dynamic aspects of the coverage of a mobile sensor network that depend on the process of sensor movement. As time goes by, a position is more likely to be covered; targets that might never be detected in a stationary sensor network can now be detected by moving sensors. We characterize the area coverage at specific time instants and during time intervals, as well as the time it takes to detect a randomly located stationary target. Our results show that sensor mobility can be exploited to compensate for the lack of sensors and improve network coverage. For mobile targets, we take a game theoretic approach and derive optimal mobility strategies for sensors and targets from their own perspectives.