Malicious Node Detection in Delay Tolerance Network

Delay tolerant networks (DTNs), such as sensor networks with scheduled intermittent connectivity, vehicular DTNs that disseminate location-dependent information, and pocket-switched networks that allow humans to communicate without network infrastructure, are highly partitioned networks that may suffer from frequent disconnectivity. In DTNs, the in-transit messages, also named bundles, can be sent over an existing link and buffered at the next hop until the next link in the path appears. This message propagation process is usually referred to as the “store-carry-and-forward” strategy, and the routing is decided in an “opportunistic” fashion. We aim to evaluate the added effect of the presence of malicious nodes on ad hoc network performance, and determine appropriate measures to detect malicious nodes. A malicious node advertising itself as having a valid route to the destination. With this intension the attacker consumes or intercepts the packet without any forwarding. An attacker can completely modify the packet and generate fake information, this cause the network traffic diverted or dropped. Let H be a malicious node. When H receives a Route Request, it sends back a Route Reply immediately, which constructs the data and can be transmitted by itself with the shortest path. So S receives Route Reply and it is replaced by H->S. then H receives all the data from S. In this research we propose a new assesment based scheme for detection of Malicious Nodes in DTN. And examine different strategies for prevention to malicious nodes as well as Compare out come proposed scheme with the earliest established schemes.

Timed protocol analysis of interconnected mobile IoT devices

With the emergence of the Internet of Things (IoT), application developers can rely on a variety of protocols and Application Programming Interfaces (APIs) to support data exchange between IoT devices. However, this may result in highly heterogeneous IoT interactions in terms of both functional and non-functional semantics. To map between heterogeneous functional semantics, middleware connectors can be utilized to interconnect IoT devices via bridging mechanisms. In this paper, we make use of the Data eXchange (DeX) connector model that enables interoperability among heterogeneous IoT devices. DeX interactions, including synchronous, asynchronous and streaming, rely on generic post and get primitives to represent IoT device behaviors with varying space/time coupling. Nevertheless, non-functional time semantics of IoT interactions such as data availability/validity, intermittent connectivity and application processing time, can severely affect response times and success rates of DeX interactions. We introduce timing parameters for time semantics to enhance the DeX API. The new DeX API enables the mapping of both functional and time semantics of DeX interactions. By precisely studying these timing parameters using timed automata models, we verify conditions for successful interactions with DeX connectors. Furthermore, we statistically analyze through simulations the effect of varying timing parameters to ensure higher probabilities of successful interactions. Simulation experiments are compared with experiments run on the DeX Mediators (DeXM) framework to evaluate the accuracy of the results. This work can provide application developers with precise design time information when setting these timing parameters in order to ensure accurate runtime behavior.

Feature Extraction of Real-World Traces Using K Means Clustering In OppNet

Natural calamities leave people helpless by arising several situations such as network breakdown, zero communication, intermittent connectivity, dynamic network topology. In such situation an application of dynamic and intermittent routing scheme is essential to make further communication possible during likewise scenarios. An application of TCP/IP becomes futile in mentioned circumstances as it best works for static nodes and pre-defined network topology wherein source and destination nodes are first establishing the communication link with each other. An alternative measure of such hitches is to encounter an application of DTN protocol which possess all characteristics to withstand in such scenarios such as; dynamic network topology, intermittent connectivity, frequent path breaks, store – carry – forward fashion. In this paper we did thorough investigation of forest fire dataset (Uttarakhand) after exploring its implementation in ONE with Epidemic, Prophet, Spray and Wait, HBPR, GAER respectively. An extensive and thorough investigation for real world traces implementation has been done with OppNet routing protocols against mobility models namely; Shortest path map – based, Random Direction, Random Walk, Random Waypoint, Cluster Movement respectively for network performance metrics namely packet delivery ratio, packet overhead ratio and average latency ratio respectively with the application of K means clustering machine learning algorithm. With the help of this analysis, we explore the real-world traces characteristics and study the areas on which network performance can be improved.

Capturing Beneficial Changes to Racehorse Veterinary Care Implemented during the COVID-19 Pandemic

In March 2020, the World Health Organisation called for countries to take urgent and aggressive action against a global pandemic caused by COVID-19. Restrictions were introduced in many countries to reduce transmission of COVID-19 and ultimately deaths. Such restrictions have been colloquially referred to as “lockdown”. Anecdotal evidence of the beneficial practices that facilitated safe veterinary treatment and equine care had been reported together with an increase in the use of electronic communication and information technologies during the first “lockdown”. Thus, the aim of this qualitative study was to capture any beneficial changes to racehorse veterinary care that were implemented during the first “lockdown” period in the UK that lasted from 23 March to 12 May 2020. Ten equine veterinary surgeons who primarily treat racehorses and 10 racehorse trainers were interviewed either by telephone or by videoconferencing. After using thematic analysis from a critical realist social constructionist perspective, four themes were identified. These were, firstly, according to our participants, the trainer–vet relationship is predicated upon a good working relationship, secondly, there had been little or no change in the vet–trainer relationship during the first “lockdown” period. Thirdly, when COVID-19 restrictions were in force, more remote consultations took place using images or videos as well as telephone consults, viewed favourably by both trainers and vets, and finally, intermittent connectivity and poor-quality images and videos limited their effectiveness. In order to fully benefit from the positive changes employed by some vets and trainers in their working relationships, we recommend that rural connectivity is prioritised.

UAVs Trajectory Optimization for Data Pick Up and Delivery with Time Window

Unmanned Aerial Vehicles (UAVs), also known as drones, are a class of aircraft without the presence of pilots on board. UAVs have the ability to reduce the time and cost of deliveries and to respond to emergency situations. Currently, UAVs are extensively used for data delivery and/or collection to/from dangerous or inaccessible sites. However, trajectory planning is one of the major UAV issues that needs to be solved. To address this question, we focus in this paper on determining the optimized routes to be followed by the drones for data pickup and delivery with a time window with an intermittent connectivity network, while also having the possibility to recharge the drones’ batteries on the way to their destinations. To do so, we formulated the problem as a multi-objective optimization problem, and we showed how to use the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve this problem. Several experiments were conducted to validate the proposed algorithm by considering different scenarios.

Performance Comparison of Fresh and Spray & Wait Protocol through ONE Simulator

Opportunistic Networks (OppNets) are becoming the prime interest for researchers day-by-day due to the large scope of further research into it. An opportunistic network is used to transmit data in an environment of intermittent connectivity. OppNet offers a variety of routing protocols based on different strategies. Each protocol has some pros and cons. Among the available ones, Fresh Routing Protocol and Spray-and-wait Routing Protocol are the most efficient routing protocols in terms of performance during data transmission. This paper aims to compare these two different routing protocols through simulation on the ground of standard performance metrics. It is believed that this simulation comparison will help upcoming researchers in the selection of appropriate routing protocol as per their requirement

Distributed probability density based multi-objective routing for Opp-IoT networks enabled by machine learning

Opportunistic IoT networks operate in an intermittent, mobile communication topology, employing peer-to-peer transmission hops on a store-carry-forward basis. Such a network suffers from intermittent connectivity, lack of end-to-end route definition, resource constraints and uncertainties arising from a dynamic topology, given the mobility of participating nodes. Machine learning is an instrumental tool for learning and many histories-based machine learning paradigms like MLPROPH, KNNR and GMMR have been proposed for digital transformations in the field with varying degrees of success. This paper explores the dynamic topology with a plethora of characteristics guiding the node interactions, and consequently, the routing decisions. Further, the study ascertains the need for better representation of the versatility of node characteristics that guide their behavior. The proposed scheme Opportunistic Fuzzy Clustering Routing (OFCR) protocol employs a three-tiered intelligent fuzzy clustering-based paradigm that allows representation of multiple properties of a single entity and the degree of association of the entity with each property group that it is represented by. Such quantification of the extent of association allows OFCR a proper representation of multiple node characteristics, allowing a better judgement for message routing decisions based on these characteristics. OFCR performed 33.77%, 6.07%, 3.69%, 6.88% and 78.14% better than KNNR, GMMR, CAML, MLPRoPH and HBPR respectively across Message Delivery probability. OFCR, not only shows improved performance from the compared protocols but also shows relatively more consistency across the change in simulation time, message TTL and message generation interval across performance metrics.

BiRep: A Reputation Scheme to Mitigate the Effects of Black-Hole Nodes in Delay-Tolerant Internet of Vehicles

Delay-tolerant networking (DTN) enables communication in disruptive scenarios where issues such as sparse and intermittent connectivity, long and variable delays, high latency, high error rates, or no end-to-end connectivity exist. Internet of Vehicles (IoV) is a network of the future in which integration between devices, vehicles, and users will be unlimited and universal, overcoming the heterogeneity of systems, services, applications, and devices. Delay-tolerant internet of vehicles (DT-IoV) is emerging and becoming a popular research topic due to the critical applications that can be realized, such as software or map update dissemination. For an IoV to work efficiently, a degree of cooperation between nodes is necessary to deliver messages to their destinations. However, nodes might misbehave and silently drop messages, also known as a black-hole attack, degrading network performance. Various solutions have been proposed to deal with black-hole nodes, but most are centralized or require each node to meet every other node. This paper proposes a decentralized reputation scheme called BiRep that identifies and punishes black-hole nodes in DT-IoV. BiRep is tested on the Prophet routing protocol. Simulation results show excellent performance in all scenarios, comparable or better to other reputation schemes, significantly increasing the delivery ratio of messages.

A Fuzzy-Logic-Based Double Q -Learning Routing in Delay-Tolerant Networks

Delay-tolerant networks (DTNs) are wireless mobile networks, which suffer from frequent disruption, high latency, and lack of a complete path from source to destination. The intermittent connectivity in DTNs makes it difficult to efficiently deliver messages. Research results have shown that the routing protocol based on reinforcement learning can achieve a reasonable balance between routing performance and cost. However, due to the complexity, dynamics, and uncertainty of the characteristics of nodes in DTNs, providing a reliable multihop routing in DTNs is still a particular challenge. In this paper, we propose a Fuzzy-logic-based Double Q -Learning Routing (FDQLR) protocol that can learn the optimal route by combining fuzzy logic with the Double Q -Learning algorithm. In this protocol, a fuzzy dynamic reward mechanism is proposed, and it uses fuzzy logic to comprehensively evaluate the characteristics of nodes including node activity, contact interval, and movement speed. Furthermore, a hot zone drop mechanism and a drop mechanism are proposed, which can improve the efficiency of message forwarding and buffer management of the node. The simulation results show that the fuzzy logic can improve the performance of the FDQLR protocol in terms of delivery ratio, delivery delay, and overhead. In particular, compared with other related routing protocols of DTNs, the FDQLR protocol can achieve the highest delivery ratio and the lowest overhead.

Operation of VANET Communications

This work proposed an integrated novel architecture of UAV System, LTE/4G, and WAVE technologies with its forwarding schemes in highway scenario to enhance the VANET communications and achieve the requirements of its basic applications, particularly safety and traffic. Algorithms for UAV sensing, tagging (based on the proposed safety and traffic info model), and broadcasting operations, and forwarding of safety or traffic info to respective infrastructures, and then smart ground vehicles are designed, particularly to minimize intermittent connectivity and bandwidth usage as well as to satisfy the requirements of VANET applications. The authors have evaluated the performance of the integrated novel architecture with its forwarding schemes/algorithms through integrated and simulated VANETs and wireless access technologies (LTE/4G and UAV system) environment.