Efficient Routing Primitives for Low-power and Lossy Networks in Internet of Things

At the heart of the Internet of Things (IoTs) are the Low-power and Lossy networks (LLNs), a collection of interconnected battery-operated and resource-constrained tiny devices that enable the realization of a wide range of applications in multiple domains. For an efficient operation, such networks require the design of efficient protocols especially at the network layer of their communication stack. In this regards, the Routing Protocol for LLNs (RPL) has been developed and standardised by the IETF to fulfil the routing requirements in such networks. Proven efficient in tackling some major issues, RPL is still far from being optimal in addressing several other routing gaps in the context of LLNs. For instance, the RPL standard lacks in a scalable routing mechanism in the applications that require bidirectional communication. In addition, its routing maintenance mechanism suffers from relatively slow convergence time, limiting the applicability of the protocol in time-critical applications, and a high risk of incorrect configurations of its parameters, risking the creation of sub-optimal routes. Furthermore, RPL lacks in a fair load-distribution mechanism which may harm both energy and reliability of its networks. Motivated by the above-mentioned issues, this thesis aimed at overcoming the RPL’s weaknesses by developing more efficient routing solutions, paving the way towards successful deployments and operations of the LLNs at different scales. Hence, to tackle the inefficiency of RPL’s routing maintenance operations, a new routing maintenance algorithm, namely, Drizzle, has been developed characterized by an adaptive, robust and configurable nature that boosts the applicability of RPL in several applications. To address the scalability problem, a new downward routing solution has been developed rendering RPL more efficient in large-scale networks. Finally, a load-balancing objective function for RPL has been proposed that enhances both the energy efficiency and reliability of LLNs. The efficiency of the proposed solutions has been validated through extensive simulation experiments under different scenarios and operation conditions demonstrating significant performance enhancements in terms of convergence time, scalability, reliability, and power consumption

SklCoin: Toward a Scalable Proof-of-Stake and Collective Signature Based Consensus Protocol for Strong Consistency in Blockchain

The proof-of-work consensus protocol suffers from two main limitations: waste of energy and offering only probabilistic guarantees about the status of the blockchain. This paper introduces SklCoin, a new Byzantine consensus protocol and its corresponding software architecture. This protocol leverages two ideas: 1) the proof-of-stake concept to dynamically form stake proportionate consensus groups that represent block miners (stakeholders), and 2) scalable collective signing to efficiently commit transactions irreversibly. SklCoin has immediate finality characteristic where all miners instantly agree on the validity of blocks. In addition, SklCoin supports high transaction rate because of its fast miner election mechanis

Scalable Multi-domain Trust Infrastructures for Segmented Networks

Within a trust infrastructure, a private key is often used to digitally sign a transaction, which can be verified with an associated public key. Using PKI (Public Key Infrastructure), a trusted entity can produce a digital signature, verifying the authenticity of the public key. However, what happens when external entities are not trusted to verify the public key or in cases where there is no Internet connection within an isolated or autonomously acting collection of devices? For this, a trusted entity can be elected to generate a key pair and then split the private key amongst trusted devices. Each node can then sign part of the transaction using their split of the shared secret. The aggregated signature can then define agreement on a consensus within the infrastructure. Unfortunately, this process has two significant problems. The first is when no trusted node can act as a dealer of the shares. The second is the difficulty of scaling the digital signature scheme. This paper outlines a method of creating a leaderless approach to defining trust domains to overcome weaknesses in the scaling of the elliptic curve digital signature algorithm. Instead, it proposes the usage of the Edwards curve digital signature algorithm for the definition of multiple trust zones. The paper shows that the computational overhead of the distributed key generation phase increases with the number of nodes in the trust domain but that the distributed signing has a relatively constant computational overhead

A Student Advising System Using Association Rule Mining

Academic advising is a time-consuming activity that takes a considerable effort in guiding students to improve student performance. Traditional advising systems depend greatly on the effort of the advisor to find the best selection of courses to improve student performance in the next semester. There is a need to know the associations and patterns among course registration. Finding associations among courses can guide and direct students in selecting the appropriate courses that leads to performance improvement. In this paper, the authors propose to use association rule mining to help both students and advisors in selecting and prioritizing courses. Association rules find dependences among courses that help students in selecting courses based on their performance in previous courses. The association rule mining is conducted on thousands of student records to find associations between courses that have been registered by students in many previous semesters. The system has successfully generated a list of association rules that guide a particular student to select courses. The system was validated on the registration of 100 students, and the precision and recall showed acceptable prediction of courses

The Impact of Mobile DIS and Rank-Decreased Attacks in Internet of Things Networks

With a predicted 50 billion devices by the end of 2020, the Internet of things has grown exponentially in the last few years. This growth has seen an increasing demand for mobility support in low power and lossy sensor networks, a type of network characterized by several limitations in terms of their resources including CPU, memory and batter, causing manufactures to push products out to the market faster, without the necessary security features. IoT networks rely on the Routing Protocol for Low Power and Lossy Network (RPL) for communication, designed by the Internet Engineering Task Force (IETF). This protocol has been proven to be efficient in relation to the handling of routing in such constrained networks, However, research studies revealed that RPL was inherently designed for static networks, indicating poor handling of mobile or dynamic topologies which is worsen when introducing mobile attacker. In this paper, two IoT routing attacks are evaluated under a mobile attacker with the aim of providing a critical evaluation of the impact the attacks have on the network in comparison to the case with static attacker. The first attack is the Rank attack in which the attacker announces false routing information to its neighbour attracting them to forward their data via the attacker. The second attack is the DIS attack in which the attacker floods the network with DIS messages triggering them to reset their transmission timers and sending messages more frequently. The comparison were conducted in terms of average power consumption and also the packet delivery ratio (PDR). Based on the results collected from the simulations, it was established that when an attacking node is mobile, there’s an average increase of 36.6 in power consumption and a decrease of 14 for packet delivery ratios when compared to a static attacking node

A New Load-Balancing Aware Objective Function for RPL’s IoT Networks

The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized as the de facto solution for routing in the context of the emerging Internet of Things (IoT) paradigm. RPL, along with other standards, has provided a baseline framework for IoT that has helped advance communications in the world of embedded resource-constrained networks. However, RPL still suffers from issues that may limit its efficiency such as the absence of an efficient load-balancing primitive. In this study, we show how RPL suffers from a load-balancing problem that may harm both the reliability of the protocol and its network lifetime. To address this problem, a novel load-balancing scheme is introduced that significantly enhances the reliability of RPL and fosters the protocol’s efficiency in terms of power consumption

An adaptive warning message scheme for emergency vehicles using vehicular ad hoc communication

Nowadays, traffic management has been a challenging task due to the growing number of vehicles. More specifically, operation management of Emergency Vehicles (EVs) such as ambulances, police force and fire fighting vehicles require extensive industrial and academic studies. The research community has been placing a great deal of emphasis for reducing the travelling time of the EV between the starting point and the destination point. In the foreseeable future, all vehicles are assumed to be fully equipped with wireless technology. This facilitates communication and coordination between vehicles and traffic lights, and shortens the time needed for EVs to reach their destinations. This paper focuses on developing an efficient broadcast algorithm, namely, Adaptive Warning Message Scheme (AWMS), using Vehicle-to-Vehicle (V2V) communication, to deliver a Warning Message (WM) as quickly as possible to a target traffic light. In the AWMS, a high priority message dissemination is given to WMs, which are responsible for informing the traffic light about any approaching EVs, while a low dissemination priority is assigned to normal Information Messages (IMs), (i.e. messages that carry general information about a vehicle). In addition, the EV direction toward a traffic light is considered in our scheme when broadcasting the WM to reduce the broadcast storm problem. Time delay between two consecutive WMs is calculated based on the EV speed and traffic density. The simulation results have shown that the AWMS has the capacity and ability to disseminate WMs with minimum number of re-transmissions, collision rate and end-to-end delay

The RPL load balancing in IoT network with burst traffic scenarios

In Low Power and Lossy Networks (LLNs) sensor nodes are deployed in various traffic load conditions such as, regular and heavy traffic load. The adoption of Internet-of-Things enabled devices in the form of wearables and ubiquitous sensors and actuators has demanded LLNs to handle burst traffic load, which is an event required by myriad IoT devices in a shared LLN. In the large events, burst traffic load requires a new radical approach of load balancing, this scenario causes congestion increases and packet drops relatively when frequent traffic burst load rises in comparison with regular and heavy loads. In this paper, we introduced a new efficient load balance mechanism for traffic congestion in IPv6 Routing Protocol for Low Power and Lossy Network (RPL). To measure the communication quality and optimize the lifetime of the network, we have chosen packet delivery ratio (PDR) and power consumption (PC) as our metrics. We proposed a traffic-aware metric that utilizes ETX and parent count metrics (ETXPC), where communication quality for LLNs with RPL routing protocol are playing an important role in traffic engineering. In addition, we provided analytical results to quantify the impact of Minimum Rank with Hysteresis Objective on Function (MRHOF) and Objective Function zero (OF0) to the packet delivery, reliability and power consumption in LLNs. The simulation results pragmatically show that the proposed load balancing approach has increased packet delivery ratio with less power consumption