Traffic locality oriented route discovery algorithms for mobile ad hoc networks

There has been a growing interest in Mobile Ad hoc Networks (MANETs) motivated by the advances in wireless technology and the range of potential applications that might be realised with such technology. Due to the lack of an infrastructure and their dynamic nature, MANETs demand a new set of networking protocols to harness the full benefits of these versatile communication systems. Great deals of research activities have been devoted to develop on-demand routing algorithms for MANETs. The route discovery processes used in most on-demand routing algorithms, such as the Dynamic Source Routing (DSR) and Ad hoc On-demand Distance Vector (AODV), rely on simple flooding as a broadcasting technique for route discovery. Although simple flooding is simple to implement, it dominates the routing overhead, leading to the well-known broadcast storm problem that results in packet congestion and excessive collisions. A number of routing techniques have been proposed to alleviate this problem, some of which aim to improve the route discovery process by restricting the broadcast of route request packets to only the essential part of the network. Ideally, a route discovery should stop when a receiving node reports a route to the required destination. However, this cannot be achieved efficiently without the use of external resources; such as GPS location devices. In this thesis, a new locality-oriented route discovery approach is proposed and exploited to develop three new algorithms to improve the route discovery process in on-demand routing protocols. The proposal of our algorithms is motivated by the fact that various patterns of traffic locality occur quite naturally in MANETs since groups of nodes communicate frequently with each other to accomplish common tasks. Some of these algorithms manage to reduce end-to-end delay while incurring lower routing overhead compared to some of the existing algorithms such as simple flooding used in AODV. The three algorithms are based on a revised concept of traffic locality in MANETs which relies on identifying a dynamic zone around a source node where the zone radius depends on the distribution of the nodes with which that the source is “mostly” communicating. The traffic locality concept developed in this research form the basis of our Traffic Locality Route Discovery Approach (TLRDA) that aims to improve the routing discovery process in on-demand routing protocols. A neighbourhood region is generated for each active source node, containing “most” of its destinations, thus the whole network being divided into two non-overlapping regions, neighbourhood and beyond-neighbourhood, centred at the source node from that source node prospective. Route requests are processed normally in the neighbourhood region according to the routing algorithm used. However, outside this region various measures are taken to impede such broadcasts and, ultimately, stop them when they have outlived their usefulness. The approach is adaptive where the boundary of each source node’s neighbourhood is continuously updated to reflect the communication behaviour of the source node. TLRDA is the basis for the new three route discovery algorithms; notably: Traffic Locality Route Discovery Algorithm with Delay (TLRDA D), Traffic Locality Route Discovery Algorithm with Chase (TLRDA-C), and Traffic Locality Expanding Ring Search (TL-ERS). In TLRDA-D, any route request that is currently travelling in its source node’s beyond-neighbourhood region is deliberately delayed to give priority to unfulfilled route requests. In TLRDA-C, this approach is augmented by using chase packets to target the route requests associated with them after the requested route has been discovered. In TL-ERS, the search is conducted by covering three successive rings. The first ring covers the source node neighbourhood region and unsatisfied route requests in this ring trigger the generation of the second ring which is double that of the first. Otherwise, the third ring covers the whole network and the algorithm finally resorts to flooding. Detailed performance evaluations are provided using both mathematical and simulation modelling to investigate the performance behaviour of the TLRDA D, TLRDA-C, and TL-ERS algorithms and demonstrate their relative effectiveness against the existing approaches. Our results reveal that TLRDA D and TLRDA C manage to minimize end-to-end packet delays while TLRDA-C and TL-ERS exhibit low routing overhead. Moreover, the results indicate that equipping AODV with our new route discovery algorithms greatly enhance the performance of AODV in terms of end to end delay, routing overhead, and packet loss

Improving Vehicular Authentication in VANET Using

In the last several years, many types of research are focusing on Vehicular Ad-hoc Networks (VANETs) field due to the lifesaving factor. VANETs are defined as a set of vehicles in the road interact with other vehicles or with the Road Side Unit (RSU) through wireless Local Area Network (WLAN) technologies. The fundamental advantages of VANETs are enhancing the road and driver's safety and improving the vehicle security against adversaries’ attacks. Security is the most difficult issue belonging to VANETs since messages are exchanged through open wireless environments. Especially in the authentication process, the vehicles need to be authenticated before accessing or sending messages through the network. Any violation of the authentication process will open the whole network for the attack. In this paper, we applied security algorithms to improve authentication in VANETs with four stages of cryptography techniques: challenge-response authentication, digital signature, timestamping, and encryption/decryption respectively. Also, we also proposed an algorithm model and framework. Finally, we implemented the challenge-response authentication technique, and then measured and evaluated the result from the implementatio

Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test

A Randomized Watermarking Technique for Detecting Malicious Data Injection Attacks in Heterogeneous Wireless Sensor Networks for Internet of Things Applications

Using Internet of Things (IoT) applications has been a growing trend in the last few years. They have been deployed in several areas of life, including secure and sensitive sectors, such as the military and health. In these sectors, sensory data is the main factor in any decision-making process. This introduces the need to ensure the integrity of data. Secure techniques are needed to detect any data injection attempt before catastrophic effects happen. Sensors have limited computational and power resources. This limitation creates a challenge to design a security mechanism that is both secure and energy-efficient. This work presents a Randomized Watermarking Filtering Scheme (RWFS) for IoT applications that provides en-route filtering to remove any injected data at an early stage of the communication. Filtering injected data is based on a watermark that is generated from the original data and embedded directly in random places throughout the packet’s payload. The scheme uses homomorphic encryption techniques to conceal the report’s measurement from any adversary. The advantage of homomorphic encryption is that it allows the data to be aggregated and, thus, decreases the packet’s size. The results of our proposed scheme prove that it improves the security and energy consumption of the system as it mitigates some of the limitations in the existing works

Distributed Energy-Efficient Approaches for Connected Dominating Set Construction in Wireless Sensor Networks

Energy efficiency is one of the major issues in wireless sensor networks (WSNs) and their applications. Distributed techniques with low message and time complexities are expected in WSNs. Connected dominating sets (CDSs) have been widely used for virtual backbone construction in WSNs to control topology, facilitate routing, and extend network lifetime. Most of the existing CDS approaches suffer from a very poor approximation ratio, high time, and message complexities. This paper proposes two novel approaches for CDS distributed construction in WSNs. The proposed approaches are intended to construct a small CDS as well as allowing energy-efficient CDS construction and maintenance in WSNs. Simulation shows that our distributed approaches have an approximation factor of 7.5 to the optimal CDS. This approximation outperforms the existing distributed CDS construction algorithms