Enhancing route request broadcasting protocols constitutes a substantial part of research in mobile ad hoc network routing. In the thesis, enhancements of ad hoc routing protocols, energy efficiency metrics and clustered topology generators are discussed. The contributions include the followings. First, a node caching enhancement of Ad-hoc On-demand Distance Vector (AODV) routing protocol is introduced. Extensive simulation studies of the enhanced AODV in NS2 shows up to 9-fold reduction in the routing overhead, up to 20% improvement in the packet delivery ratio and up to 60% reduction in the end-to-end delay. The largest improvement happens to highly stressed situations. Secondly, new metrics for evaluating energy efficiency of routing protocols are suggested. New node cached AODV protocols employing non-adaptive and adaptive load balancing techniques were proposed for extending network lifetime and increasing network throughput. Finally, the impact of node clustered topology on ad hoc network is explored. A novel method for generating clustered layout in NS2 is introduced and experiments indicate performance degradation of AODV protocols for the case of two clusters.

Deer Vehicle Collisions (DVCs) are a global problem that is not only resulting in seriousinjuries to humans but also results in loss of human and deer lives. Deer are more active and less attentive during the mating and hunting seasons. Roadside deer activity such as feeding and strolling along the roadside has a significant correlation with DVCs. To mitigate DVCs, several strategies were used that include vegetation management, fences, underpasses and overpasses, population reduction, warning signs and animal detection systems (ADS). These strategies vary in their efficacy. These strategies may help to reduce DVCs. However, they are not always easily feasible due to false alarms, high cost, unsuitable terrain, land ownership, and other factors. Thus, DVCs are increasing due to the increase in number of vehicles and the absence of intelligent highway safety and alert systems. Detecting deer in real-time on our roads is a challenging problem. Thus, this research work proposed a deer detection and movement DDM technique to automate DVCs mitigation system. The DDM combines computer vision, artificial intelligent methods with deep learning techniques. DDM includes two main deep learning algorithms 1)onestage deep learning algorithm based on Yolov5 that generates a detection model(DM) to detect deer and 2) deep learning algorithm developed by python toolkit DeepLabCut to generate movement model(MM) for detecting the movement of the deer. The proposed method can detect deer with 99.7% precision and succeeds to ascertain if the deer is moving or static with an inference speed of 0.29s. The proposed method can detect deer with 99.7% precision and using DeepLabCut toolkit on the detected deer we can ascertain if the deer is moving or static with an inference speed of 0.29s.

A Disruption Tolerant Network (DTN) is a sparse network where connectivity is regulated by the proximity of mobile nodes. Connections are sporadic and the delivery rate is closely related to node movement. As network resources often are limited in such settings, it is useful to monitor the network in order to make more efficient communication decisions. This study investigates existing routing protocols and monitoring tools for DTN that best cope with the requirements of a tactical military network. A model is proposed to estimate source to destination delay in DTN. This model is evaluated in a Java-based software simulator called The ONE. In order to match the tactical military environment, two scenarios are constructed. The squad scenario simulates the formation movement pattern of several squads and the hierarchical communication scheme that is maintained in a military context. The other scenario simulates a convoy line movement of a military group during transportation. The results of this study show that the proposed mechanism can improve delivery rate and reduce network overhead in settings with strict buffer limitations. The estimation worked best in scenarios that contained some patterns of movement or communication. These patterns are resembled in the model's collected data and the model can provide the user with rough estimates of end-to-end delays in the network. Primary use of this model has been to reduce number of old messages in the network, but other applications like anomaly detection are also discussed in this work. / Ett avbrottstolerant nätverk (DTN) är ett glest nät där konnektiviteten avgörs av närheten bland de rörliga noderna i nätverket. Avbrotten i ett sådant nät förekommer ofta och sporadiskt. Eftersom nätverksresurserna oftast är begränsade i sådana sammanhang, så är det lämpligt att övervaka nätverket för att göra det möjligt att fatta mer effektiva kommunikationsbeslut. Det här arbetet undersöker olika routingalgoritmer och övervakningsvektyg för DTN med hänsyn till de krav som ställs av ett taktiskt nät. En modell för att uppskatta fördröjningen från källa till destination är framtagen i arbetet. Modellen är utvärderad med hjälp av en Javabaserad mjukvarusimulator som heter The ONE. För att bäst representera den miljö som uppstår i militära sammanhang är två scenarion framtagna. Det första är ett truppscenario där nodernar rör sig i fromationer och nättrafiken följer den hierarkiska modellen som används i militär kommunikation. Det andra scenariot är ett konvojscenario där enheter marcherar på led. Resultaten från denna studie visar att den föreslagna modellen kan öka andelen levererade meddelanden och minska nätverksbelastningen i en miljö där bufferstorleken hos noderna är begränsad. Uppskattningen visade sig fungera bäst i scenarion som innehöll någon form av mönster bland nodernas rörelse eller deras kommunikation. Dessa mönster återspeglas i modellens insamlade data och modellen kan förse användaren med en grov estimering av slutfördröjningen till alla destinationer i nätet. Modellen har i huvudsak använts till att minska antalet gamla meddelanden i nätet, men arbetet berör även andra användningsområden som anomalidetektion.