Node localization using mobile robots in delay-tolerant sensor networks

We present a novel scheme for node localization in a Delay-Tolerant Sensor Network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a Robust Extended Kalman Filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1m in a large indoor setting.

Using mobile agents to detect node compromise in path-based DoS attacks on wireless sensor networks

Wireless sensor networks represent a new generation of real-time  embedded systems with significantly different communication constraints from the traditional networked systems. With their development, a new attack called a path-based DoS (PDoS) attack has appeared. In a PDoS attack, an adversary, either inside or outside the network, overwhelms sensor nodes by flooding a multi-hop endto- end communication path with either replayed packets or injected spurious packets. In this article, we propose a solution using mobile agents which can detect PDoS attacks easily.

Using mobile agents to detect and recover from node compromise in path-based DoS attacks in wireless sensor networks

Wireless sensor networks represent a new generation of real-time embedded systems with significantly different communication constraints from the traditional networked systems. With their development, a new attack called a path-based DoS (PDoS) attack has appeared. In a PDoS attack, an adversary, either inside or outside the network, overwhelms sensor nodes by flooding a multi-hop end-to end communication path with either replayed packets or injected spurious packets. Detection and recovery from PDoS attacks have not been given much attention in the literature. In this article, we propose a solution using mobile agents which can detect PDoS attacks easily and efficiently and recover the compromised nodes.

Sensor node activity scheduling approach

Sensor networks, once deployed, are expected to continue to function unmanned. However, energy of the sensors is typically unrenewable thus making it a very scarce resource. Therefore, in order to extend the life of the sensor networks for the duration of a particular mission, energy has to be managed wisely. In this paper, we consider the problem of energy conservation for grid-based sensor networks. A wireless sensor network architecture that is based on mobile base station is presented. Based on this architecture, we propose an asynchronous duty-cycle scheduling scheme for extending the lifetime of the grid-based wireless sensor networks.

Lymphedema after complete axillary node dissection for melanoma: assessment using a new, objective definition

Objectives: The objectives of this study were to define appropriate criteria for assessing the presence of lymphedema, and to report the prevalence and risk factors for development of upper limb lymphedema after level I-III axillary dissection for melanoma.
Summary Background Data: The lack of a consistent and reliable objective definition for lymphedema remains a significant barrier to appreciating its prevalence after axillary dissection for melanoma (or breast carcinoma).
Methods: Lymphedema was assessed in 107 patients (82 male, 25 female) who had previously undergone complete level I-III axillary dissection. Of the 107 patients, 17 had also received postoperative axillary radiotherapy. Arm volume was measured using a water displacement technique. Change in volume of the arm on the side of the dissection was referenced to the volume of the other (control) arm. Volume measurements were corrected for the effect of handedness using corrections derived from a control group. Classification and regression tree (CART) analysis was used to determine a threshold fractional arm volume increase above which volume changes were considered to indicate lymphedema.
Results: Based on the CART analysis results, lymphedema was defined as an increase in arm volume greater than 16% of the volume of the control arm. Using this definition, lymphedema prevalence for patients in the present study was 10% after complete level I-III axillary dissection for melanoma and 53% after additional axillary radiotherapy. Radiotherapy and wound complications were independent risk factors for the development of lymphedema.
Conclusions: A suggested objective definition for arm lymphedema after axillary dissection is an arm volume increase of greater than 16% of the volume of the control arm.

Using mobile agents to recover from node and database compromise in path-based DoS attacks in wireless sensor networks

Wireless sensor networks represent a new generation of real-time embedded systems with significantly different communication constraints from the traditional networked systems. With their development, a new attack called a path-based DoS (PDoS) attack has appeared. In a PDoS attack, an adversary, either inside or outside the network, overwhelms sensor nodes by flooding a multi-hop end-to-end communication path with either replayed packets or injected spurious packets. Detection and recovery from PDoS attacks have not been given much attention in the literature. In this article, we consider wireless sensor networks designed to collect and store data. In a path-based attack, both sensor nodes and the database containing collected data can be compromised. We propose a recovery method using mobile agents which can detect PDoS attacks easily and efficiently and recover the compromised nodes along with the database.

Fast recovery from node compromise in wireless sensor networks

Wireless Sensor Networks (WSNs) are susceptible to a wide range of security attacks in hostile environments due to the limited processing and energy capabilities of sensor nodes. Consequently, the use of WSNs in mission critical applications requires reliable detection and fast recovery from these attacks. While much research has been devoted to detecting security attacks, very little attention has been paid yet to the recovery task. In this paper, we present a novel mechanism that is based on dynamic network reclustering and node reprogramming for recovering from node compromise. In response to node compromise, the proposed recovery approach reclusters the network excluding compromised nodes; thus allowing normal network operation while initiating node recovery procedures. We propose a novel reclustering algorithm that uses 2-hop neighbourhood information for this purpose. For node reprogramming we propose the modified Deluge protocol. The proposed node recovery mechanism is both decentralized and scalable. Moreover, we demonstrate through its implementation on a TelosB-based sensor network testbed that the proposed recovery method performs well in a low-resource WSN.

Recover fault services via complex service-to-node mappings in wireless sensor networks

With the motivation of seamlessly extending wireless sensor networks to the external environment, service-oriented architecture comes up as a promising solution. However, as sensor nodes are failure prone, this consequently renders the whole wireless sensor network to seriously faulty. When a particular node is faulty, the service on it should be migrated into those substitute sensor nodes that are in a normal status. Currently, two kinds of approaches exist to identify the substitute sensor nodes: the most common approach is to prepare redundancy nodes, though the involved tasks such as maintaining redundancy nodes, i.e., relocating the new node, lead to an extra burden on the wireless sensor networks. More recently, other approaches without using redundancy nodes are emerging, and they merely select the substitute nodes in a sensor node's perspective i.e., migrating the service of faulty node to it's nearest sensor node, though usually neglecting the requirements of the application level. Even a few work consider the need of the application level, they perform at packets granularity and don't fit well at service granularity. In this paper, we aim to remove these limitations in the wireless sensor network with the service-oriented architecture. Instead of deploying redundancy nodes, the proposed mechanism replaces the faulty sensor node with consideration of the similarity on the application level, as well as on the sensor level. On the application level, we apply the Bloom Filter for its high efficiency and low space costs. While on the sensor level, we design an objective solution via the coefficient of a variation as an evaluation for choosing the substitute on the sensor level. © 2014 Springer Science+Business Media New York.

An alternative node deployment scheme for WSNs

© 2001-2012 IEEE. Sensing coverage is a fundamental design problem in wireless sensor networks (WSNs). This is because there is always a possibility that the sensor nodes may function incorrectly due to a number of reasons, such as failure, power, or noise instability, which negatively influences the coverage of the WSNs. In order to address this problem, we propose a fuzzy-based self-healing coverage scheme for randomly deployed mobile sensor nodes. The proposed scheme determines the uncovered sensing areas and then select the best mobile nodes to be moved to minimize the coverage hole. In addition, it distributes the sensor nodes uniformly considering Euclidean distance and coverage redundancy among the mobile nodes. We have performed an extensive performance analysis of the proposed scheme. The results of the experiment show that the proposed scheme outperforms the existing approaches.

Malicious node traceback in opportunistic networks using merkle trees

Security is a major challenge in Opportunistic Networks because of its characteristics, such as open medium, dynamic topology, no centralized management and absent clear lines of defense. A packet dropping attack is one of the major security threats in OppNets since neither source nodes nor destination nodes have the knowledge of where or when the packet will be dropped. In this paper, we present a malicious nodes detection mechanism against a special type of packet dropping attack where the malicious node drops one or more packets and then injects new fake packets instead. Our novel detection and traceback mechanism is very powerful and has very high accuracy. Each node can detect and then traceback the malicious nodes based on a solid and powerful idea that is, Merkle tree hashing technique. In our defense techniques we have two stages. The first stage is to detect the attack, and the second stage is to find the malicious nodes. We have compared our approach with the acknowledgement based mechanisms and the networks coding based mechanism which are well known approaches in the literature. Simulation results show this robust mechanism achieves a very high accuracy and detection rate.

Node-coupling clustering approaches for link prediction

Due to the potential important information in real world networks, link prediction has become an interesting focus of different branches of science. Nevertheless, in "big data" era, link prediction faces significant challenges, such as how to predict the massive data efficiently and accurately. In this paper, we propose two novel node-coupling clustering approaches and their extensions for link prediction, which combine the coupling degrees of the common neighbor nodes of a predicted node-pair with cluster geometries of nodes. We then present an experimental evaluation to compare the prediction accuracy and effectiveness between our approaches and the representative existing methods on two synthetic datasets and six real world datasets. The experimental results show our approaches outperform the existing methods.