The widespread adoption of the Wireless Sensor Networks (WSNs) in various applications in the terrestrial environment and the rapid advancement of the WSN technology have motivated the development of Underwater Acoustic Sensor Networks (UASNs). UASNs and terrestrial WSNs have several common properties while there are several challenges particular to UASNs that are mostly due to acoustic communications, and inherent mobility. These challenges call for novel architectures and protocols to ensure successful operation of the UASN. Localization is one of the fundamental tasks for UASNs which is required for data tagging, node tracking, target detection, and it can be used for improving the performance of medium access and network protocols. Recently, various UASN architectures and a large number of localization techniques have been proposed. In this paper, we present a comprehensive survey of these architectures and localization methods. To familiarize the reader with the UASNs and localization concepts, we start our paper by providing background information on localization, state-of-the-art oceanographic systems, and the challenges of underwater communications. We then present our detailed survey, followed by a discussion on the performance of the localization techniques and open research issues.

A Survey of Architectures and Localization Techniques for Underwater Acoustic Sensor Networks

Due to harsh aqueous environments, non-negligible node mobility and large network scale, localization for large-scale mobile underwater sensor networks is very challenging. In this paper, by utilizing the predictable mobility patterns of underwater objects, we propose a scheme, called Scalable Localization scheme with Mobility Prediction (SLMP), for underwater sensor networks. In SLMP, localization is performed in a hierarchical way, and the whole localization process is divided into two parts: anchor node localization and ordinary node localization. During the localization process, every node predicts its future mobility pattern according to its past known location information, and it can estimate its future location based on the predicted mobility pattern. Anchor nodes with known locations in the network will control the localization process in order to balance the trade-off between localization accuracy, localization coverage, and communication cost. We conduct extensive simulations, and our results show that SLMP can greatly reduce localization communication cost while maintaining relatively high localization coverage and localization accuracy.

Scalable Localization with Mobility Prediction for Underwater Sensor Networks

Underwater wireless sensor networks (UWSNs) have been showed as a promising technology to monitor and explore the oceans in lieu of traditional undersea wireline instruments. Nevertheless, the data gathering of UWSNs is still severely limited because of the acoustic channel communication characteristics. One way to improve the data collection in UWSNs is through the design of routing protocols considering the unique characteristics of the underwater acoustic communication and the highly dynamic network topology. In this paper, we propose the GEDAR routing protocol for UWSNs. GEDAR is an anycast, geographic and opportunistic routing protocol that routes data packets from sensor nodes to multiple sonobuoys (sinks) at the sea's surface. When the node is in a communication void region, GEDAR switches to the recovery mode procedure which is based on topology control through the depth adjustment of the void nodes, instead of the traditional approaches using control messages to discover and maintain routing paths along void regions. Simulation results show that GEDAR significantly improves the network performance when compared with the baseline solutions, even in hard and difficult mobile scenarios of very sparse and very dense networks and for high network traffic loads.

Geographic and Opportunistic Routing for Underwater Sensor Networks

Distributed denial of service (DDoS) resilience in cloud

Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks

http://staff.ustc.edu.cn/~kxing/Publications/BookChapters/attack-NetworkSecurity.pdf

Attacks and Countermeasures in Sensor Networks: A Survey

In this paper, we study the performance of location-centric storage (LCS) theoretically. Our results indicate that LCS utilizes network resource efficiently. In particular, the storage load of sensors is independent of the network size, and is evenly distributed across the network. Moreover, the communication distance for getting event information is small. Therefore, the protocol has great scalability. In addition, we also propose algorithms for data retrieval in LCS. Our analysis shows that both the number of queries and the response cost involved in the algorithms could approach to the theoretical lower bound.

/pdf/on-the-performance-of-location-centric-storage-in-sensor-52s7gzufzo.pdf

On the Performance of Location-Centric Storage in Sensor Networks

Data falsification attack in Vehicular Ad hoc Networks (VANET) for the Internet of Vehicles (IoV) is achieved by corrupting the data exchanged between nodes with false information. Data is the most valuable asset these days from which many analyses and results can be drawn out. But the privacy concern raised by users has become the greatest hindrance in performing data analysis. In IoV, misbehavior detection can be performed by creating a machine learning model from basic safety message (BSM) dataset of vehicles. We propose a privacy-preserving misbehavior detecting system for IoV using Federated Machine Learning. Vehicles in VANET for IoV are given the initial dull model to locally train using their own local data. On doing this we get a collective smart model that can classify Position Falsification attack in VANET using the data generated by each vehicle. All this is done without actually sharing the data with any third party to perform analysis. In this paper, we compare the performance of the attack detection model trained by using a federated and central approach. This training method trains the model on a different kind of position falsification attack by using local BSM data generated on each vehicle.

Privacy Preserving Misbehavior Detection in IoV Using Federated Machine Learning

Cloud service availability has been one of the major concerns of cloud service providers (CSP), while hosting different cloud based information technology services by managing different resources on the internet. The vulnerability of internet, the distribute nature of cloud computing, various security issues related to cloud computing service models, and cloud's main attributes contribute to its susceptibility of security threats associated with cloud service availability. One of the major sophisticated threats that happen to be very difficult and challenging to counter due to its distributed nature and resulted in cloud service disruption is Distributed Denial of Service (DDoS) attacks. Even though there are number of intrusion detection solutions proposed by different research groups, and cloud service providers (CSP) are currently using different detection solutions by promising that their product is well secured, there is no such a perfect solution that prevents the DDoS attack. The characteristics of DDoS attack, i.e., Having different appearance with different scenarios, make it difficult to detect. This paper will review and analyze different existing DDoS detecting techniques against different parameters, discusses their advantage and disadvantages, and propose a hybrid statistical model that could significantly mitigate these attacks and be a better alternative solution for current detection problems.

Analysis of DDoS Attacks and an Introduction of a Hybrid Statistical Model to Detect DDoS Attacks on Cloud Computing Environment

The development of delay-tolerant networks (DTNs) has attracted significant attention in the network research community In this paper, we propose a new type of DTN - a homing-pigeon-based DTN (HoP-DTN) In the HoP-DTN, each node owns a dedicated messenger (called a pigeon) The only form of internode communication is for a pigeon to periodically carry a batch of messages originated at the home node, deliver them to the corresponding destination nodes, and return home Clearly, given message-expiration times, some messages may not reach their destinations by the deadline Through analysis and simulations, we study the relationship between key parameters of the system, including average message delay, expiration time, delivery ratio, arrival rate, and batch size of messages Various simulations have been conducted to demonstrate the accuracy of our analysis Our theoretical study and simulations present a deep insight into the characteristics of the proposed system Some interesting observations from simulations are discussed in detail in this paper, and the results demonstrate that our proposed scheme performs better in terms of average message delay, message-delivery ratio, and cost-effectiveness

Jiang Li

Papers

Attacks and Countermeasures in Sensor Networks: A Survey

On the Performance of Location-Centric Storage in Sensor Networks

Privacy Preserving Misbehavior Detection in IoV Using Federated Machine Learning

Analysis of DDoS Attacks and an Introduction of a Hybrid Statistical Model to Detect DDoS Attacks on Cloud Computing Environment

HoP-DTN: Modeling and Evaluation of Homing-Pigeon-Based Delay-Tolerant Networks