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
@cite_7 @cite_20
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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. Recent years have witnessed the deployments of wireless sensor networks in a class of mission-critical applications such as object detection and tracking. These applications often impose stringent Quality-of-Service requirements including high detection probability, low false alarm rate, and bounded detection delay. Although a dense all-static network may initially meet these Quality-of-Service requirements, it does not adapt to unpredictable dynamics in network conditions (e.g., coverage holes caused by death of nodes) or physical environments (e.g., changed spatial distribution of events). This paper exploits reactive mobility to improve the target detection performance of wireless sensor networks. In our approach, mobile sensors collaborate with static sensors and move reactively to achieve the required detection performance. Specifically, mobile sensors initially remain stationary and are directed to move toward a possible target only when a detection consensus is reached by a group of sensors. The accuracy of final detection result is then improved as the measurements of mobile sensors have higher Signal-to-Noise Ratios after the movement. We develop a sensor movement scheduling algorithm that achieves near-optimal system detection performance under a given detection delay bound. The effectiveness of our approach is validated by extensive simulations using the real data traces collected by 23 sensor nodes. Inter-vehicle communication is regarded as one of the major applications of mobile ad hoc networks (MANETs). Compared to MANETs or wireless sensor networks (WSNs), these so-called vehicular ad hoc networks (VANETs) have unique requirements on network protocols. The requirements result mainly from node mobility and the demands of position-dependent applications. On the routing layer, those requirements are well met by geographic routing protocols. Functional research on geographic routing has already reached a considerable level, whereas security aspects have only been recently taken into account. Position information dissemination has been identified as being crucial for geographic routing since forged position information has severe impact regarding both performance and security. In this work, we first summarize the problems that arise from falsified position data. We then propose a framework that contains different detection mechanisms in order to mitigate or lessen these problems. Our developed mechanisms are capable of recognizing nodes cheating about their position in beacons (periodic position dissemination in most single-path geographic routing protocols, e.g., GPSR). Unlike other proposals described in the literature, our detection system does not rely on additional hardware or special nodes, which would contradict the ad hoc approach. Instead, we use a number of different independent sensors to quickly give an estimation of the trustworthiness of other nodes' position claims. The different sensors run either autonomously on every single node, or they require cooperation between neighboring nodes. The simulation evaluation proves that the combination of autonomous and cooperative position verification mechanisms successfully discloses most nodes disseminating false position information, and thereby widely prevents attacks using position cheating. In the recent past, wireless sensor networks have found their way into a wide variety of applications and systems with vastly varying requirements and characteristics. As a consequence, it is becoming increasingly difficult to discuss typical requirements regarding hardware issues and software support. This is particularly problematic in a multidisciplinary research area such as wireless sensor networks, where close collaboration between users, application domain experts, hardware designers, and software developers is needed to implement efficient systems. In this article we discuss the consequences of this fact with regard to the design space of wireless sensor networks by considering its various dimensions. We justify our view by demonstrating that specific existing applications occupy different points in the design space. A routing problem in static wireless ad hoc networks is considered as it arises in a rapidly deployed, sensor based, monitoring system known as the wireless sensor network. Information obtained by the monitoring nodes needs to be routed to a set of designated gateway nodes. In these networks, every node is capable of sensing, data processing, and communication, and operates on its limited amount of battery energy consumed mostly in transmission and reception at its radio transceiver. If we assume that the transmitter power level can be adjusted to use the minimum energy required to reach the intended next hop receiver then the energy consumption rate per unit information transmission depends on the choice of the next hop node, i.e., the routing decision. We formulate the routing problem as a linear programming problem, where the objective is to maximize the network lifetime, which is equivalent to the time until the network partition due to battery outage. Two different models are considered for the information-generation processes. One assumes constant rates and the other assumes an arbitrary process. A shortest cost path routing algorithm is proposed which uses link costs that reflect both the communication energy consumption rates and the residual energy levels at the two end nodes. The algorithm is amenable to distributed implementation. Simulation results with both information-generation process models show that the proposed algorithm can achieve network lifetime that is very close to the optimal network lifetime obtained by solving the linear programming problem. At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are normally preplanned by operators using ladder or zig-zag paths. Such surveys are conducted with side-looking sonar sensors whose performance is dependent on environmental, target, sensor, and AUV platform parameters. It is difficult to obtain precise knowledge of all of these parameters to be able to design optimal mission plans offline. This research represents the first known sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned using a multiobjective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that not only produces shorter paths than conventional means, but is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method. Geographic routing is one of the most widely-accepted techniques to route information in large-scale wireless sensor networks. It is based on a greedy forwarding strategy by which a sensor node selects as next hop relay the most promising neighbor (according to some metric) among those being closer to the destination than itself. This decision is based solely on the position of its neighbors and the destination. Given that sensor nodes are usually operated by batteries, energy-efficiency is a very important metric to be considered by the routing protocol. In this paper we present Locally-Optimal Source Routing (LOSR), a new localized and energy-efficient geographic routing algorithm for wireless sensor networks. Unlike existing energy-efficient geographic routing algorithms, in which current node routing the packet only considers nodes closer to destination than itself, LOSR uses all nodes in the neighborhood to compute a local energy-optimal path formed only by neighbors of the current node towards the selected next hop. Then, source routing is used to force data packets to follow that locally optimal path until next hop is reached. Our simulation results show that the proposed algorithm outperforms the best existing solution, over a variety of network densities and scenarios. in micro-sensor and radio technology will enable small but smart sensors to be deployed for a wide range of environmental monitoring applications. In order to constrain communication overhead, dense sensor networks call for new and highly efficient methods for distributing queries to nodes that have observed interesting events in the network. A highly efficient data-centric routing mechanism will offer significant power cost reductions (17), and improve network longevity. Moreover, because of the large amount of system and data redundancy possible, data becomes disassociated from specific node and resides in regions of the network (10)(7)(8). This paper describes and evaluates through simulation a scheme we call Rumor Routing, which allows for queries to be delivered to events in the network. Rumor Routing is tunable, and allows for tradeoffs between setup overhead and delivery reliability. It's intended for contexts in which geographic routing criteria are not applicable because a coordinate system is not available or the phenomenon of interest is not geographically correlated. We consider the use of binary proximity sensors for tracking targets. Such sensors provide only 1-bit information regarding a target's presence or absence in their vicinity, albeit with less than 100 reliability. A novel tracking method employing such binary sensors is proposed and its performance in different deployment scenarios evaluated. For a given target, the method utilizes the sensor outputs to estimate individual positions in the path of the target in the near past and finds the line which best fits the path points. This line is then used to estimate the target's current position. A performance study has been conducted through comprehensive simulation using parameters collected from a prototype deployment consisting of wireless micro-sensors with binary acoustic detectors. Although many energy efficient conserving routing protocols have been proposed for wireless sensor networks, the concentration of data traffic towards a small number of base stations remains a major threat to the network lifetime. The main reason is that the sensor nodes located near a base station have to relay data for a large part of the network and thus deplete their batteries very quickly. The solution we propose in this paper suggests that the base station be mobile; in this way, the nodes located close to it change over time. Data collection protocols can then be optimized by taking both base station mobility and multi-hop routing into account. We first study the former, and conclude that the best mobility strategy consists in following the periphery of the network (we assume that the sensors are deployed within a circle). We then consider jointly mobility and routing algorithms in this case, and show that a better routing strategy uses a combination of round routes and short paths. We provide a detailed analytical model for each of our statements, and corroborate it with simulation results. We show that the obtained improvement in terms of network lifetime is in the order of 500 . Underwater acoustic sensor networks (UASNs) are often used for environmental and industrial sensing in undersea ocean space, therefore, these networks are also named underwater wireless sensor networks (UWSNs). Underwater sensor networks are different from other sensor networks due to the acoustic channel used in their physical layer, thus we should discuss about the specific features of these underwater networks such as acoustic channel modeling and protocol design for different layers of open system interconnection (OSI) model. Each node of these networks as a sensor needs to exchange data with other nodes; however, complexity of the acoustic channel makes some challenges in practice, especially when we are designing the network protocols. Therefore based on the mentioned cases, we are going to review general issues of the design of an UASN in this paper. In this regard, we firstly describe the network architecture for a typical 3D UASN, then we review the characteristics of the acoustic channel and the corresponding challenges of it and finally, we discuss about the different layers e.g. MAC protocols, routing protocols, and signal processing for the application layer of UASNs. In the recent past, search in sensor systems focused on node hardware constraints and very limited energy resources. But nowadays, that new applications need data processing with temporal constraints in their tasks; then one of the new challenges faced by wireless sensor networks (WSNs) is handling real-time storage and querying the data they process. Two main approaches to storage and querying data are generally considered warehousing and distributed. The warehousing approach stores data in a central database and then queries may be performed to it. In a distributed approach, sensor devices are considered as local databases and data are managed locally. The data collected by sensors must represent the current state of the environment; for this reason they are subject to logic and time constraints. Then, this paper identifies the main specifications of real-time data management and presents the available real-time data management solutions for WSNs, in order to discuss them and identify some open issues and provide guidelines for further contributions. Unlike traditional multihop forwarding among homogeneous static sensor nodes, use of mobile devices for data collection in wireless sensor networks has recently been gathering more attention. It is known that the use of mobility significantly reduces the energy consumption at each sensor, elongating the functional lifetime of the network, in exchange for increased data delivery latency. However, in previous work, mobility and communication capabilities are often underutilized, resulting in suboptimal solutions incurring unnecessarily large latency. In this paper, we focus on the problem of finding an optimal path of a mobile device, which we call "data mule," to achieve the smallest data delivery latency in the case of minimum energy consumption at each sensor, i.e., each sensor only sends its data directly to the data mule. We formally define the path selection problem and show the problem is @math -hard. Then we present an approximation algorithm and analyze its approximation factor. Numerical experiments demonstrate that our approximation algorithm successfully finds the paths that result in 10 -50 shorter latency compared to previously proposed methods, suggesting that controlled mobility can be exploited much more effectively. As a crucial operation, routing plays an important role in various communication networks. In the context of data and sensor networks, routing strategies such as shortest-path, multi-path and potential-based (“all-path”) routing have been developed. Existing results in the literature show that the shortest path and all-path routing can be obtained from @math and @math flow optimization, respectively. Based on this connection between routing and flow optimization in a network, in this paper we develop a unifying theoretical framework by considering flow optimization with mixed (weighted) @math -norms. We obtain a surprising result: as we vary the trade-off parameter @math , the routing graphs induced by the optimal flow solutions span from shortest-path to multi-path to all-path routing—this entire sequence of routing graphs is referred to as the routing continuum. We also develop an efficient iterative algorithm for computing the entire routing continuum. Several generalizations are also considered, with applications to traffic engineering, wireless sensor networks, and network robustness analysis. Sensor network lifetime is largely affected by the energy consumption for data transmission from sensor nodes to a base station. We generalize and solve the problems of deploying multiple base stations in sensor networks using one-hop and multi-hop communication models to maximize network lifetime. Under the one-hop communication model, the sensors far away from base stations always deplete their energy much faster than others. We propose an optimal solution for small-scale networks and a heuristic approach for large-scale ones based on the smallest enclosing circle algorithm that deploys a base station at the geometric center of each cluster. Under the multi-hop communication model, both the base station locations and the data routing scheme need to be considered in maximizing network lifetime. We propose an iterative algorithm based on rigorous mathematical derivations and use linear programming to compute the optimal routing path for benchmark purposes. Extensive simulation results show superior network lifetime performance of the proposed deployment algorithms in comparison with existing ones. Localization is crucial for wireless ad hoc and sensor networks. As the distance-measurement ranges are often less than that of the communication range for many ranging systems, most communication-dense wireless networks are often localization-sparse. Consequently, most existing algorithms fail to provide accurate localization supports. In order to address this issue, by introducing a concept of component, we propose to group nodes into components, so that nodes are able to better share their ranging and anchor knowledge. This design, CALL, relaxes two essential restrictions in localization: node ordering and anchor distribution. We evaluate the effectiveness of CALL through extensive simulations. The results show that CALL locates 80 nodes in a network with average degree 7.5 and 5 percent anchors, which outperforms the state of the art design Sweeps about 20 . Synchronicity is a useful abstraction in many sensor network applications. Communication scheduling, coordinated duty cycling, and time synchronization can make use of a synchronicity primitive that achieves a tight alignment of individual nodes' firing phases. In this paper we present the Reachback Firefly Algorithm (RFA), a decentralized synchronicity algorithm implemented on TinyOS-based motes. Our algorithm is based on a mathematical model that describes how fireflies and neurons spontaneously synchronize. Previous work has assumed idealized nodes and not considered realistic effects of sensor network communication, such as message delays and loss. Our algorithm accounts for these effects by allowing nodes to use delayed information from the past to adjust the future firing phase. We present an evaluation of RFA that proceeds on three fronts. First, we prove the convergence of our algorithm in simple cases and predict the effect of parameter choices. Second, we leverage the TinyOS simulator to investigate the effects of varying parameter choice and network topology. Finally, we present results obtained on an indoor sensor network testbed demonstrating that our algorithm can synchronize sensor network devices to within 100 μsec on a real multi-hop topology with links of varying quality.