Although most research in lattice wireless sensor networks are focus on determining the optimal deployment pattern to provide full sensing coverage while maximizing the deployment efficiency, we study the partial sensing coverage problem and the corresponding node saving rate in 3D lattice WSNs. Two popular 3D deployment patterns including cube and triangular prism are considered. The partial sensing coverage and the note saving rate with respect to full sensing coverage are derived through mathematically modeling and theoretical analysis. Research results show that partial sensing coverage is of paramount significance to 3D lattice WSN design and implementation as a large amount of expensive 3D sensors can be saved by sacrificing a small amount of sensing coverage. For example, 38.43% and 22.14% sensors can be saved when providing 0.9864 and 0.9898 sensing coverage with respect to full sensing coverage in a cubic pattern and a triangular prism pattern based lattice WSN respectively. Computer-based simulations results validate the modeling and analysis.

In a Wireless Sensor Network (WSN), intrusion detection is of significant importance in many applications in detecting malicious or unexpected intruder(s). The intruder can be an enemy in a battlefield, or a malicious moving object in the area of interest. With uniform sensor deployment, the detection probability is the same for any point in a WSN. However, some applications may require different degrees of detection probability at different locations. For example, an intrusion detection application may need improved detection probability around important entities. Gaussian-distributed WSNs can provide differentiated detection capabilities at different locations but related work is limited. This paper analyzes the problem of intrusion detection in a Gaussian-distributed WSN by characterizing the detection probability with respect to the application requirements and the network parameters under both single-sensing detection and multiple-sensing detection scenarios. Effects of different network parameters on the detection probability are examined in detail. Furthermore, performance of Gaussian-distributed WSNs is compared with uniformly distributed WSNs. This work allows us to analytically formulate detection probability in a random WSN and provides guidelines in selecting an appropriate deployment strategy and determining critical network parameters.

ABSTRACT Intrusion detection is prominently important for civil and military applications in wireless sensor networks (WSNs). To date, related works address the problem by assuming a straight‐line intrusion path and a Boolean sensing model. However, a straight‐line intrusion path is often not the case in reality, and the Boolean sensing model cannot resemble a real‐world sensor precisely. Results based on these assumptions are therefore not applicable with desirable accuracy in practice. In view of this, we propose a novel sine‐curve mobility model that can simulate different intrusion paths by adjusting its features (amplitude, frequency, and phase) and can be integrated into the random WSN model for intrusion detection analysis. It can also be applied to different sensor models and makes influencing factors tractable. With the model, we examine the effects of different intrusion paths on the intrusion detection probability in a random WSN, considering both Boolean and realistic Elfes sensing models. Further, we investigate the interplays between network settings and intruder's mobility patterns and identify the benefits and side effects of the model theoretically and experimentally. Simulation outcomes are shown to match well with the theoretical results, validating the modeling, analysis, and conclusions. Copyright © 2011 John Wiley & Sons, Ltd.

Intrusion detection is prominently important for civil and military applications in wireless sensor networks (WSNs). It is defined as the mechanism to detect moving intruder(s) by single-sensing or jointly-sensing detection. To date, related works focus on the problem of network configuration for detecting intruder(s) within a pre-specified time/distance threshold by assuming a straight line intrusion path of the intruder. However, straight line intrusion path is often not the case in reality. An intruder can invade the network following a curved path or even a random walk in order to improve its attacking probability. Therefore, the results based on the assumption of a straight line intrusion path may not hold. In view of this, we are to address the problem from another angle, i.e., the effects of different intrusion paths on the intrusion detection probability in an arbitrary wireless sensor network. First, we propose a novel Sine Curve mobility model that can simulate different intrusion paths by adjusting its features such as amplitude, frequency, and phase. Next we are to explore the effects of different intrusion paths of the intruder on the intrusion detection probability in an arbitrary wireless sensor network. In other words, we are to investigate the following question theoretically and experimentally: Is straight-line path always optimal for intruder(s) to adopt for minimizing the detection probability of an arbitrary WSN, so as to maximize its attacking probability? Simulation outcomes are shown to match well with the analytical results, and therefore validate our analysis and conclusion.

Intrusion detection in a Wireless Sensor Network (WSN) is of significant importance in many applications to detect malicious or unexpected intruder(s). The intruder can be an enemy in a battlefield, or a unusual environmental change in a chemical industry etc. With uniform distribution, the detection probability is the same for any point in a WSN. However, some applications may require different degrees of detection probability at different locations in the deployment area. Gaussian distributed WSNs (i.e., normal distribution) can provide differentiated detection capabilities at different locations and are widely deployed in practice. In view of this, this paper analyzes the problem of intrusion detection in a Gaussian distributed WSN, by characterizing intrusion detection probability with respect to intrusion distance and network deployment parameters. Two detection models are considered: single-sensing detection and multiple-sensing detection. Effects of different network parameters on the intrusion detection probability are examined in details. This work allows us to analytically formulate the intrusion detection probability within a certain intrusion distance under various application scenarios, therefore provides insight for directing the application-specific WSN deployment such as intrusion detection.

Localization algorithm continues to be an important and challenging topic in today's wireless sensor networks (WSNs). In this paper, a novel range-free localization algorithm using expected hop progress (LAEP) to predict the location of any sensor in a WSN is proposed. This algorithm is based on an accurate analysis of hop progress in a WSN with randomly deployed sensors and arbitrary node density. By deriving the expected hop progress from a network model for WSNs in terms of network parameters, the distance between any pair of sensors can be accurately computed. Since the distance estimation is a key issue in localization systems for WSNs, the proposed range-free LAEP achieves better performance and less communication overhead as compared to some existent schemes like DV-Hop and RAW. In addition, we study the effect of anchor placement on the algorithm performance by deriving the corresponding mean position error range. Extensive simulations are performed and the results are observed to be in good agreement with the theoretical analysis.

The technology of wireless mesh network (WMN) provides a flexible Internet access service in a wide region by employing multi-hop wireless routing and enhances the network performance by utilizing multi-radio and multi-channel. Delay analysis is a fundamental issue for delay-sensitive application such as VoIP and video conference, and delay constrained capacity is essential for a network to provide satisfied service. This paper models such a network and obtains the packet delay and network capacity by considering a queueing network formed by multi-radio mesh routers (MRs) and carrier sense multiple access (CSMA) mechanism is assumed for packet transmissions over links between MRs. We investigate the problems of intercluster dominated traffic, study the impact of the number of radios and channels to the delay and the capacity, and optimize the network performance by adjusting the MR deployment and the transmission power.

Intrusion detection in Wireless Sensor Network (WSN) is of practical interest in many applications such as detecting an intruder in a battlefield. The intrusion detection is defined as a mechanism for a WSN to detect the existence of inappropriate, incorrect, or anomalous moving attackers. For this purpose, it is a fundamental issue to characterize the WSN parameters such as node density and sensing range in terms of a desirable detection probability. In this paper, we consider this issue according to two WSN models: homogeneous and heterogeneous WSN. Furthermore, we derive the detection probability by considering two sensing models: single-singing detection and multiple-sensing detection. In addition, we discuss the network connectivity and broadcast reachability, which are necessary conditions to ensure the corresponding detection probability in a WSN. Our simulation results validate the analytical values for both homogeneous and heterogeneous WSNs.

The problems of energy efficient sensor network configuration and local fusion of sensed data have been addressed separately in the previous works. In this paper, we propose to analyze the sensing accuracy with the consideration of the node distribution and fusion overhead in terms of the energy consumption. By extending existing sensing model in the literature, we quantify how the sensing ranges and node densities of sensor nodes impact the sensing accuracy, and how it affects the fusion overhead and the sensing lifetime of the network. We identify the tradeoff between sensing accuracy and energy consumption in an analytical framework. It can be used to choose optimal parameters of sensor networks to meet the specified sensing or lifetime requirements.

As wireless channel is bursty in nature, a two-state Markov channel model that considers the impact of bursty characteristics of channel errors is introduced into the analysis of operation and performance of saturated IEEE 802.11 DCF in this paper. A new scheme IDCF to exploit the information on the channel burstiness is further proposed to enhance the performance of DCF. The main idea of IDCF is to differentiate the handling of collision and data packet corruption at the sender. By adopting a persistent re-transmission approach according to the instantaneousness of the channel error, the performance of IEEE 802.11 DCF can be improved. Simulations are conducted and simulation results show that our analysis is accurate, and our proposed IDCF scheme does improve the performance in terms of throughput soundly.

While most existing research efforts in the area of wireless sensor networks have focused on networks with identical nodes, deploying sensors with different capabilities has become a feasible choice. In this paper, we focus on sensor networks with two types of nodes that differ in their capabilities, and discuss the effects of heterogeneity of sensing and transmission ranges on the network coverage and broadcast reachability. Our work characterizes how the introduction of a few sensor nodes with better capabilities can reduce the number of total required sensors without sacrificing the coverage and the broadcast reachability. Analytical results are validated via simulations. This work can serve as a guideline for designing large-scale sensor networks cost-effectively. It can also be extended to more complicated heterogeneous wireless sensor networks with more than two types of sensors.

Wireless sensor networks present a feasible and economic solution to some of the most challenging problems such as intrusion detection. In this paper, we establish relationship between node density, sensing range, and the possible intrusion distance before the intruder is detected by any of the sensors. We also extend our model to the multi-sensor joint detection case where an event can only be detected by k (k > 1) sensors simultaneously. Furthermore, we consider the effect of node heterogeneity on the intrusion detection in wireless sensor network, where two types of sensors with different sensing ranges are deployed. All the analytical results are validated via simulations. Our analysis can provide very useful insights in choosing network design parameters of sensor networks so that specified performance requirements can be met, thereby enhancing the acceptance of sensor networks.

A wireless sensor network (WSN) is composed of wireless sensors using batteries with energy constraints, which limits the network lifetime. How to maximize the network lifetime is an important issue in the design of WSNs. We establish a relationship between node density and network lifetime in a periodically data delivery scenario of WSNs with sensors transmitting data to the sink node periodically. Based on our analysis, we propose a sensor node density deployment method that could implement a differential node density in WSNs so that the lifetime of the network could be maximized. Both theoretic analysis and simulation results show that our method outperforms the uniform node distribution method in terms of the network lifetime

In this paper, we study the node sleep scheduling scheme in the commonly used static disk model for sensor networks, where homogenous sensors are uniformly distributed in a circular area with a stationary sink located at the center. Then, we introduce and analyze a hops-based sleeping scheduling algorithm (HSS) for enhancing the lifetime and energy efficiency of the sensor networks. The HSS divides the circular sensor network into several levels according to the average hop distance based on certain network parameters. By assigning hops-based sleeping probabilities to each sensor, and the network lifetime as well as energy efficiency can be greatly improved. Simulations are performed to compare our proposed HSS scheme with the traditional randomized sleeping scheduling (RSS) scheme, where all the nodes have the uniform sleeping probability. The results demonstrate that our HSS scheme can enhance network lifetime greatly, provide desired QoS requirement effectively, and drastically outperform the RSS scheme

In this paper, we propose a novel localization algorithm using expected hop progress (LAEP) to predict the positions of sensor nodes in WSNs. Our algorithm is based on a hop progress analytical model for a given network topology. By deriving relationship between node connectivity and expected-hop progress from the model, the distance between any two nodes can be accurately computed. As the distance estimation is a key issue in positioning system of WSNs, our expected-hop progress based localization algorithm also achieves reasonable performance in anchor-based WSNs with very few anchors. In addition, we considered the impact of deterministic anchor placement on the algorithm performance when the sensors are random deployed. Both grid and triangular placement of anchor nodes have been examined in this paper. Simulation results are seen to be in good agreement with the theoretical analysis and substantiate the validity of our proposed scheme LAEP