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Blockchain (BC) and Information for Operational and Tactical Analysis (IOTA) are distributed ledgers that record a huge number of transactions in multiple places at the same time using decentralized databases. Both BC and IOTA facilitate Internet-of-Things (IoT) by overcoming the issues related to traditional centralized systems, such as privacy, security, resources cost, performance, and transparency. Still, IoT faces the potential challenges of real-time processing, resource management, and storage services. Edge computing (EC) has been introduced to tackle the underlying challenges of IoT by providing real-time processing, resource management, and storage services nearer to IoT devices on the network's edge. To make EC more efficient and effective, solutions using BC and IOTA have been devoted to this area. However, BC and IOTA came with their pitfalls. This survey outlines the pitfalls of BC and IOTA in EC and provides research directions to be investigated further.

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.

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.

Destination Advertisement Objects (DAOs) are sent upward by RPL nodes toward the DODAG root, to build the downward routing paths carrying traffic from the root to its associated nodes. This routing mechanism can be exploited by a malicious node periodically transmitting a large volume of DAO messages towards its parent, which in turn will forward such messages to its own parent and so on, until they arrive at the Direction-Oriented Directed Acyclic Graph (DODAG) root. This ultimately results in a negative effect on network performance in terms of energy consumption, latency and reliability. The first objective of this paper is to evaluate the effect of such a DAO attack in the context of an RPL IoT network. In particular, identifying the particular performance metrics and network resources affected most greatly. The second objective is the proposal of mitigating security mechanisms in relation to DAO attacks and to evaluate their effectiveness. The simulation results have shown how the attack can damage the network performance by significantly increasing the DAO overhead and power consumption. It also demonstrated that the DAO attack affect the reliability of the downward traffic under specific conditions. The proposed mechanisms showed a good capacity in restoring the optimal performance of the network by up to 205%, 181%, 87% and 6%, in terms of overhead, latency, power consumption and packet delivery ratio respectively.

In RPL routing protocol, the destination advertisement object (DAO) control messages are announced by the child nodes to their parents to build downward routes. A malicious insider node can exploit this feature to send fake DAOs to its parents periodically, triggering those parents, in turn, to forward the fake messages upward to the root node. In this letter, we show how this behavior can have a detrimental side effect on the performance of the network, increasing power consumption, latency, and reducing reliability. To address this problem, a new scheme is introduced to mitigate significantly the effect of the DAO attack on network performance.

Driven by the special requirements of the Lowpower and Lossy Networks (LLNs), the IPv6 Routing Protocol for LLNs (RPL) was standardized by the IETF some six years ago to tackle the routing issue in such networks. Since its introduction, however, numerous studies have pointed out that, in its current form, RPL suffers from issues that limit its efficiency and domain of applicability. Thus, several solutions have been proposed in the literature in an attempt to overcome these identified limitations. In this survey, we aim mainly to provide a comprehensive review of these research proposals assessing whether such proposals have succeeded in overcoming the standard reported limitations related to its core operations. Although some of RPL weaknesses have been addressed successfully, the study found that the proposed solutions remain deficient in overcoming several others. Hence, the study investigates where such proposals still fall short, the challenges and pitfalls to avoid, thus would help researchers formulate a clear foundation for the development of further successful extensions in future allowing the protocol to be applied more widely., 31 pages

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.

In this paper, we introduce Drizzle, a new algorithm for maintaining routing information in the Low-power and Lossy Networks (LLNs). The aim is to address the limitations of the currently standardized routing maintenance (i.e. Trickle algorithm) in such networks. Unlike Trickle, Drizzle has an adaptive suppression mechanism that assigns the nodes different transmission probabilities based on their transmission history so to boost the fairness in the network. In addition, Drizzle removes the listen-only period presented in Trickle intervals leading to faster convergence time. Furthermore, a new scheme for setting the redundancy counter has been introduced with the goal to mitigate the negative side effect of the short-listen problem presented when removing the listen-only period and boost further the fairness in the network. The performance of the proposed algorithm is validated through extensive simulation experiments under different scenarios and operation conditions. In particular, Drizzle is compared to four routing maintenance algorithms in terms of control-plane overhead, power consumption, convergence time and packet delivery ratio (PDR) under uniform and random distributions and with lossless and lossy links. The results indicated that Drizzle reduces the control-plane overhead, power consumption and the convergence time by up to 76%, 20% and 34% respectively while maintaining approximately the same PDR rates.

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

Low-power and Lossy Networks (LLNs) have been a key component in the Internet of Things (IoT) paradigm. Recently, a standardized algorithm, namely Trickle algorithm, is adopted for routing information maintenance in such networks. This algorithm is originally designed for disseminating code updates through a wireless sensor network. Thus, when it comes to routing maintenance in LLNs, Trickle suffers from some issues related to power, convergence time, network overhead and load-distribution. In this paper, a new algorithm for maintaining the network topology in LLNs is developed motivated by Trickle weaknesses, namely, Drizzle algorithm. Unlike Trickle, Drizzle uses an adaptive suppression mechanism that permits the nodes to have different transmission probabilities consistent with their transmission history. Another distinctive feature of Drizzle in comparison with Trickle, is the absence of the listen-only period from Drizzle's intervals, thus, leading to faster convergence time. Furthermore, a new policy for setting the redundancy coefficient has been used to mitigate the negative effect of the short-listen problem presented when removing the listen-only period and to further boost the fairness in the network. Our extensive simulation experiments confirm the superiority of the proposed algorithm over Trickle under different operating conditions.

This paper proposes a novel model called Space Time Features-based Recurrent Neural Network (STF-RNN) for predicting people next movement based on mobility patterns obtained from GPS devices logs. Two main features are involved in model operations, namely, the space which is extracted from the collected GPS data and also the time which is extracted from the associated timestamps. The internal representation of space and time features is extracted automatically in the proposed model rather than relying on handcraft representation. This enables the model to discover the useful knowledge about people behaviour in more efficient way. Due to the ability of RNN structure to represent the sequences, it is utilized in the proposed model in order to keep track of user movement history. These tracks help the model to discover more meaningful dependencies and as consequence, enhancing the model performance. The results show that STF-RNN model provides good improvements in predicting people's next location compared with the state-of-the-art models when applied on a large real life dataset from Geo-life project.

Low Power and Lossy Networks (LLNs) have a crucial role in the Internet of Things (IoT) paradigm. For the IPv6 Routing Protocol for LLNs (RPL), two objective functions (OF) have been standardized so far, namely (Objective Function zero (OF0), and the Minimum Rank with Hysteresis Objective Function (MRHOF). However, these particular objective functions are used to build a topology where bottleneck nodes may suffer from an excessive unbalanced traffic load. As a result, a part of the network may be disconnected as the energy of the overburdened preferred parent node will drain much faster than other candidate parent nodes. Thus, in this paper, we propose a new objective function that balances the number of children nodes for the overburdened nodes to ensure node lifetime maximization. To implement this OF, we amended the DIO message format. By using a new utilization technique, we also mitigated any potential extra overhead. Finally, a new RPL metric has been introduced to balance the traffic load over the network. Simulation experiments have been conducted to examine the performance of our new approach. Collected results corroborate the superiority of our objective function over the existing ones in terms of lifetime, power consumption and packet delivery ratio.

The main component of Routing Protocol for Low Power and Lossy Networks (RPL) is the trickle timer algorithm. Such algorithm is used to control the flow of messages through the Internet of Things (IoT) network. However, the trickle algorithm is suffering from a short listen problem that causes some nodes to starve for long latency, and delay in propagating transmissions. This article attempts to propose a new Dynamic trickle timer algorithm in order to deal with the listen only period problems. Consequently, this mainly affects the convergence time and power consumption. The simulation results showed a significant enhancement in terms of convergence time using random topology, performance and energy consumption.

Constrained Low-power and Lossy networks (LLNs) represent the building block for the ever-growing Internet of Things (IoT) that deploy the Routing Protocol for Low Power and Lossy networks (RPL) as a key routing standard. RPL, along with other routing protocols, relies on Trickle algorithm as a mechanism for controlling and maintaining the routing traffic frequency. The efficiency of Trickle has been approved in terms of power consumption and scalability. However, different settings of Trickle parameters affect differently the routing behavior, energy consumption and network convergence time. In particular, a network could be only configured to have either lower convergence time with high power consumption or vice versa using Trickle. Thus, motivated by this observation, the paper presents Trickle-Plus as an extended version of the Trickle algorithm. Trickle-Plus increases the elasticity of the protocol, enabling the network configuration within guaranteed optimality for both convergence time and energy consumption. Our simulation experiments confirm the gains of the proposed Trickle-Plus under different operating conditions.

Routing Protocol for Low Power and Lossy networks (RPL) is the de-facto standard for routing in resource-constrained low-power and lossy networks (LLNs) which represent the building block for the ever-growing Internet of Things (IoT). RPL along with other routing protocols make the deployment of Trickle algorithm as a mechanism for controlling and maintaining the routing traffic frequency. Trickle employs two different techniques to manage the routing traffic efficiently. Firstly, the suppression phase where a node suppresses its control data transmission if it is redundant. Secondly, the traffic frequency adaptation, where the nodes increase their sending rate when discovering inconsistency while slow down their rate as the network becomes stable. The efficiency of Trickle has been approved in terms of power consumption and scalability, however, imposing the so-called listen-only period as a solution for the short-listen problem comes at the expense of increased convergence time. Thus, in this paper, an enhanced version of Trickle is proposed, namely, E-Trickle which solves the short-listen problem without imposing the listen-only period. Simulation results show that E-Trickle have decreased the convergence time by up to 43% while preserving the same efficiency of Trickle in terms of power consumption, scalability and reliability.