Data aggregation is an important technique in wireless sensor networks. The data are gathered together by data fusion routines along the routing path, which is called data-centralized routing. We propose a localized, Delay-bounded and Energy-efficient Data Aggregation framework(DEDA) based on the novel concept of DEsired Progress (DEP). This framework works under request-driven networks with realistic MAC layer protocols. It is based on localized minimal spanning tree (LMST) which is an energy-efficient structure. Besides the energy consideration, delay reliability is also considered by means of the DEP. A node’s DEP reflects its desired progress in LMST which should be largely satisfied. Hence, the LMST edges might be replaced by unit disk graph (UDG) edges which can progress further in LMST. The DEP metric is rooted on realistic degree-based delay model so that DEDA increases the delay reliability to a large extent compared to other hop-based algorithms. We also combine our DEDA framework with area coverage and localized connected dominating set algorithms to achieve two more resilient DEDA implementations: A-DEDA and AC-DEDA. The simulation results confirm that our original DEDA and its two enhanced variants save more energy and attain a higher delay reliability ratio than existing protocols.

Data aggregation is an important technique in wireless sensor networks. The data are gathered together by data fusion routines along the routing path, which is called data-centralized routing. We propose a localized, Delay-bounded and Energy-efficient Data Aggregation framework(DEDA) based on the novel concept of DEsired Progress (DEP). This framework works under request-driven networks with realistic MAC layer protocols. It is based on localized minimal spanning tree (LMST) which is an energy-efficient structure. Besides the energy consideration, delay reliability is also considered by means of the DEP. A node’s DEP reflects its desired progress in LMST which should be largely satisfied. Hence, the LMST edges might be replaced by unit disk graph (UDG) edges which can progress further in LMST. The DEP metric is rooted on realistic degree-based delay model so that DEDA increases the delay reliability to a large extent compared to other hop-based algorithms. We also combine our DEDA framework with area coverage and localized connected dominating set algorithms to achieve two more resilient DEDA implementations: A-DEDA and AC-DEDA. The simulation results confirm that our original DEDA and its two enhanced variants save more energy and attain a higher delay reliability ratio than existing protocols.

Data aggregation is an important technique in wireless sensor networks. The data are gathered together by data fusion routines along the routing path, which is called data-centralized routing. We propose a localized, Delay-bounded and Energy-efficient Data Aggregation framework(DEDA) based on the novel concept of DEsired Progress (DEP). This framework works under request-driven networks with realistic MAC layer protocols. It is based on localized minimal spanning tree (LMST) which is an energy-efficient structure. Besides the energy consideration, delay reliability is also considered by means of the DEP. A node’s DEP reflects its desired progress in LMST which should be largely satisfied. Hence, the LMST edges might be replaced by unit disk graph (UDG) edges which can progress further in LMST. The DEP metric is rooted on realistic degree-based delay model so that DEDA increases the delay reliability to a large extent compared to other hop-based algorithms. We also combine our DEDA framework with area coverage and localized connected dominating set algorithms to achieve two more resilient DEDA implementations: A-DEDA and AC-DEDA. The simulation results confirm that our original DEDA and its two enhanced variants save more energy and attain a higher delay reliability ratio than existing protocols.

Data aggregation is an important technique in wireless sensor networks. The data are gathered together by data fusion routines along the routing path, which is called data-centralized routing. We propose a localized, Delay-bounded and Energy-efficient Data Aggregation framework(DEDA) based on the novel concept of DEsired Progress (DEP). This framework works under request-driven networks with realistic MAC layer protocols. It is based on localized minimal spanning tree (LMST) which is an energy-efficient structure. Besides the energy consideration, delay reliability is also considered by means of the DEP. A node’s DEP reflects its desired progress in LMST which should be largely satisfied. Hence, the LMST edges might be replaced by unit disk graph (UDG) edges which can progress further in LMST. The DEP metric is rooted on realistic degree-based delay model so that DEDA increases the delay reliability to a large extent compared to other hop-based algorithms. We also combine our DEDA framework with area coverage and localized connected dominating set algorithms to achieve two more resilient DEDA implementations: A-DEDA and AC-DEDA. The simulation results confirm that our original DEDA and its two enhanced variants save more energy and attain a higher delay reliability ratio than existing protocols.

Providing end-to-end quality-assured services implies many challenges, which go beyond technical ones, involving as well economic and even cultural or political issues. In this thesis we first focus on a technical problem and then intent a more holistic regard to the whole problem, considering at the same time Network Service Providers (NSPs), stakeholders and buyers' behaviour and satisfaction. One of the most important problems when deploying interdomain path selection with Quality of Service (QoS) requirements is being able to rely the computations on metrics that hold for a long period of time. Our proposal for solving that problem is to compute bounds on the metrics, taking into account the uncertainty on the traffic demands. We then move to a NSP-alliance scenario, where we propose a complete framework for selling interdomain quality-assured services, and subsequently distributing revenues. At the end of the thesis we adopt a more holistic approach and consider the interactions with the monitoring plane and the buyers' behaviour. We propose a simple pricing scheme and study it in detail, in order to use QoS monitoring information as feedback to the business plane, with the ultimate objective of improving the seller's revenue.

Network Calculus views a network system as a queuing framework and provides a series of mathematical functions for finding an upper bound of an end-to-end delay. It is crucial for the design of networks and applications with a hard delay guarantee, such as the emerging Time Sensitive Network. Even though several approaches in Network Calculus can be used directly to find bounds on the worst-case delay, these bounds are usually not tight, and making them tight is a hard problem due to the extremely intensive computing requirements. This problem has also been proven as NP-Hard. One newly introduced solution to tighten the delay bound is the so-called Flow Prolongation. It extends the paths of cross flows to new sink servers, which naturally increases the worst-case delay, but might at the same time decrease the delay bound. The most straightforward and the most rigorous solution to find the optimal Flow Prolongation combinations is by doing exhaustive searches. However, this approach is not scalable with the network size. Thus, a machine learning model, Graph Neural Network (GNN), has been introduced for the prediction of the optimal Flow Prolongation combinations, mitigating the scalability issue. However, early research also found out that machine learning models consistently misclassify adversarial examples. In this thesis, Fast Gradient Sign Method (FGSM) is used to benchmark how adversarial attacks will influence the delay bound achieved by the Flow Prolongation method. It is performed by slightly modifying the input network features based on their gradients. To achieve this, we first learned the usage of NetCal DNC, an Free and Open Source Software, to calculate the Pay Multiplexing Only Once (PMOO), one of the Network Calculus methods for the delay bound calculation. Then we reproduced the GNN model based on PMOO, and achieved an accuracy of 65%. Finally, the FGSM is implemented on a newly created dataset with a large number of servers and flows inside. Our results demonstrate that with at most 14% changes on the network features input, the accuracy of GNN drastically decreases to an average 9.45%, and some prominent examples are found whose delay bounds are largely loosened by the GNN Flow Prolongation prediction after the FGSM attack. / Nätverkskalkylen behandlar ett nätverkssystem som ett system av köer och tillhandahåller ett antal matematiska funktioner som används för att hitta en övre gräns för end-to-end förseningar. Det är mycket viktigt för designen av nätverk och applikationer med strikta begränsningar för förseningar, så som det framväxande Time Sensitive Network. Även om ett flertal tillvägagångssätt i nätverkskalkylen kan användas direkt för att finna gränsen för förseningar i det värsta fallet så är dessa vanligtvis inte snäva. Att göra gränserna snäva är svårt då det är ett NP-svårt problem som kräver extremt mycket beräkningar. En lösning för att strama åt förseningsgränserna som nyligen introducerats kallas Flow Prolongation. Den utökar vägarna av korsflöden till nya sink servrar, vilket naturligt ökar förseningen i värsta fallet, men kan eventuellt också sänka förseningsgränsen. Den enklaste och mest rigorösa lösningen för att hitta de optimala Flow Prolongation kombinationerna är att göra uttömmande sökningar. Detta tillvägagångssätt är dock inte skalbart för stora nätverk. Därför har en maskininlärningsmodell, ett Graph Neural Network (GNN), introducerats för att förutspå de optimala Flow Prolongation kombinationerna och samtidigt mildra problemen med skalbarhet. Dock så visar de tidiga fynden att maskininlärningsmodeller ofta felaktigt klassificerar motstridiga exempel. I detta projekt används Fast Gradient Sign Method (FGSM) för att undersöka hur motståndarattacker kan påverka förseningsgränsen som hittas med hjälp av Flow Prolongation metoden. Detta görs genom att modifiera indata-nätverksfunktionerna en aning baserat på dess gradienter. För att uppnå detta lärde vi oss först att använda NetCal DNC, en mjukvara som är gratis och Open Source, för att kunna beräkna Pay Multiplexinng Only Once (PMOO), en metod inom nätverkskalkylen för att beräkna förseningsgränser. Sedan reproducerade GNN modellen baserat på PMOO, och uppnådde en träffsäkerhet på 65%. Slutligen implementerades FGSM på ett nytt dataset med ett stort antal servrar och flöden. Våra resultat visar att förändringar på upp till 14% på indata-nätverksfunktionerna resulterar i att träffsäkerheten hos GNN minskar drastiskt till ett genomsnitt på 9.45%. Vissa exempel identifierades där förseningsgränsen utvidgas kraftfullt i GNN Flow Prolongation förutsägelsen efter FGSM attacken.

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