Pervasive service discovery in low-power and lossy networks

Pervasive Service Discovery (SD) in Low-power and Lossy Networks (LLNs) is expected to play a major role in realising the Internet of Things (IoT) vision. Such a vision aims to expand the current Internet to interconnect billions of miniature smart objects that sense and act on our surroundings in a way that will revolutionise the future. The pervasiveness and heterogeneity of such low-power devices requires robust, automatic, interoperable and scalable deployment and operability solutions. At the same time, the limitations of such constrained devices impose strict challenges regarding complexity, energy consumption, time-efficiency and mobility. This research contributes new lightweight solutions to facilitate automatic deployment and operability of LLNs. It mainly tackles the aforementioned challenges through the proposition of novel component-based, automatic and efficient SD solutions that ensure extensibility and adaptability to various LLN environments. Building upon such architecture, a first fully-distributed, hybrid pushpull SD solution dubbed EADP (Extensible Adaptable Discovery Protocol) is proposed based on the well-known Trickle algorithm. Motivated by EADPs’ achievements, new methods to optimise Trickle are introduced. Such methods allow Trickle to encompass a wide range of algorithms and extend its usage to new application domains. One of the new applications is concretized in the TrickleSD protocol aiming to build automatic, reliable, scalable, and time-efficient SD. To optimise the energy efficiency of TrickleSD, two mechanisms improving broadcast communication in LLNs are proposed. Finally, interoperable standards-based SD in the IoT is demonstrated, and methods combining zero-configuration operations with infrastructure-based solutions are proposed. Experimental evaluations of the above contributions reveal that it is possible to achieve automatic, cost-effective, time-efficient, lightweight, and interoperable SD in LLNs. These achievements open novel perspectives for zero-configuration capabilities in the IoT and promise to bring the ‘things’ to all people everywhere

A reinforcement learning-based link quality estimation strategy for RPL and its impact on topology management

Over the last few years, standardisation efforts are consolidating the role of the Routing Protocol for Low-Power and Lossy Networks (RPL) as the standard routing protocol for IPv6-based Wireless Sensor Networks (WSNs). Although many core functionalities are well defined, others are left implementation dependent. Among them, the definition of an efficient link-quality estimation (LQE) strategy is of paramount importance, as it influences significantly both the quality of the selected network routes and nodesâ\u80\u99 energy consumption. In this paper, we present RL-Probe, a novel strategy for link quality monitoring in RPL, which accurately measures link quality with minimal overhead and energy waste. To achieve this goal, RL-Probe leverages both synchronous and asynchronous monitoring schemes to maintain up-to-date information on link quality and to promptly react to sudden topology changes, e.g. due to mobility. Our solution relies on a reinforcement learning model to drive the monitoring procedures in order to minimise the overhead caused by active probing operations. The performance of the proposed solution is assessed by means of simulations and real experiments. Results demonstrated that RL-Probe helps in effectively improving packet loss rates, allowing nodes to promptly react to link quality variations as well as to link failures due to node mobility

Secure-Rpl: Approach To Prevent Resource-Based Attacks In Wireless Sensor Networks Using Balanced Clustering

Internet of Things (IoT) is an evolving computing technology that enables an interconnection amongst physical devices, which offers many advantages, such as easy access to information, cost effectiveness, automation, efficient resource utilisation, reduced human effort and high productivity, all of which have attracted many industry players and researchers. However, the involvement of a vast number of devices and IoT users introduces many issues, including those related to quality of service and security. In IoT, routing amongst resource-constrained devices and nodes is realised by using the routing protocol for a low-power and lossy network (RPL), which selects an optimal route according to the specific objective function

Energy-efficient and lifetime aware routing in WSNs

Network lifetime is an important performance metric in Wireless Sensor Networks (WSNs). Transmission Power Control (TPC) is a well-established method to minimise energy consumption in transmission in order to extend node lifetime and, consequently, lead to solutions that help extend network lifetime. The accurate lifetime estimation of sensor nodes is useful for routing to make more energy-efficient decisions and prolong lifetime. This research proposes an Energy-Efficient TPC (EETPC) mechanism using the measured Received Signal Strength (RSS) to calculate the ideal transmission power. This includes the investigation of the impact factors on RSS, such as distance, height above ground, multipath environment, the capability of node, noise and interference, and temperature. Furthermore, a Dynamic Node Lifetime Estimation (DNLE) technique for WSNs is also presented, including the impact factors on node lifetime, such as battery type, model, brand, self-discharge, discharge rate, age, charge cycles, and temperature. In addition, an Energy-Efficient and Lifetime Aware Routing (EELAR) algorithm is designed and developed for prolonging network lifetime in multihop WSNs. The proposed routing algorithm includes transmission power and lifetime metrics for path selection in addition to the Expected Transmission Count (ETX) metric. Both simulation and real hardware testbed experiments are used to verify the effectiveness of the proposed schemes. The simulation experiments run on the AVRORA simulator for two hardware platforms: Mica2 and MicaZ. The testbed experiments run on two real hardware platforms: the N740 NanoSensor and Mica2. The corresponding implementations are on two operating systems: Contiki and TinyOS. The proposed TPC mechanism covers those investigated factors and gives an overall performance better than the existing techniques, i.e. it gives lower packet loss and power consumption rates, while delays do not significantly increase. It can be applied for single-hop with multihoming and multihop networks. Using the DNLE technique, node lifetime can be predicted more accurately, which can be applied for both static and dynamic loads. EELAR gives the best performance on packet loss rate, average node lifetime and network lifetime compared to the other algorithms and no significant difference is found between each algorithm with the packet delay

Congestion and medium access control in 6LoWPAN WSN

In computer networks, congestion is a condition in which one or more egressinterfaces are offered more packets than are forwarded at any given instant [1]. In wireless sensor networks, congestion can cause a number of problems including packet loss, lower throughput and poor energy efficiency. These problems can potentially result in a reduced deployment lifetime and underperforming applications. Moreover, idle radio listening is a major source of energy consumption therefore low-power wireless devices must keep their radio transceivers off to maximise their battery lifetime. In order to minimise energy consumption and thus maximise the lifetime of wireless sensor networks, the research community has made significant efforts towards power saving medium access control protocols with Radio Duty Cycling. However, careful study of previous work reveals that radio duty cycle schemes are often neglected during the design and evaluation of congestion control algorithms. This thesis argues that the presence (or lack) of radio duty cycle can drastically influence the performance of congestion control mechanisms. To investigate if previous findings regarding congestion control are still applicable in IPv6 over low power wireless personal area and duty cycling networks; some of the most commonly used congestion detection algorithms are evaluated through simulations. The research aims to develop duty cycle aware congestion control schemes for IPv6 over low power wireless personal area networks. The proposed schemes must be able to maximise the networks goodput, while minimising packet loss, energy consumption and packet delay. Two congestion control schemes, namely DCCC6 (Duty Cycle-Aware Congestion Control for 6LoWPAN Networks) and CADC (Congestion Aware Duty Cycle MAC) are proposed to realise this claim. DCCC6 performs congestion detection based on a dynamic buffer. When congestion occurs, parent nodes will inform the nodes contributing to congestion and rates will be readjusted based on a new rate adaptation scheme aiming for local fairness. The child notification procedure is decided by DCCC6 and will be different when the network is duty cycling. When the network is duty cycling the child notification will be made through unicast frames. On the contrary broadcast frames will be used for congestion notification when the network is not duty cycling. Simulation and test-bed experiments have shown that DCCC6 achieved higher goodput and lower packet loss than previous works. Moreover, simulations show that DCCC6 maintained low energy consumption, with average delay times while it achieved a high degree of fairness. CADC, uses a new mechanism for duty cycle adaptation that reacts quickly to changing traffic loads and patterns. CADC is the first dynamic duty cycle pro- tocol implemented in Contiki Operating system (OS) as well as one of the first schemes designed based on the arbitrary traffic characteristics of IPv6 wireless sensor networks. Furthermore, CADC is designed as a stand alone medium access control scheme and thus it can easily be transfered to any wireless sensor network architecture. Additionally, CADC does not require any time synchronisation algorithms to operate at the nodes and does not use any additional packets for the exchange of information between the nodes (For example no overhead). In this research, 10000 simulation experiments and 700 test-bed experiments have been conducted for the evaluation of CADC. These experiments demonstrate that CADC can successfully adapt its cycle based on traffic patterns in every traffic scenario. Moreover, CADC consistently achieved the lowest energy consumption, very low packet delay times and packet loss, while its goodput performance was better than other dynamic duty cycle protocols and similar to the highest goodput observed among static duty cycle configurations

Energy Efficiency in Green Internet of Things (IoT) Networks

Internet of Things (IoT) is having a major impact on the digital world and how we interact with the internet. The wireless sensor network (WSN) is a promising wireless communication system for enabling IoT networks. But these networks have limited energy (battery) resources and energy-saving has become a pressing need in such networks and there have been increasing efforts to minimise energy consumption via message scheduling, optimal routing, clustering formation, aggregation techniques, etc. However, significant improvement is still required and this study has produced algorithms which have been shown to reduce energy consumption and prolong network life. Increasing the number of neighbour nodes around a node has a negative impact on the network lifetime of WSNs. This is due to the adverse effects caused by overhearing and interference. This thesis presents a new routing technique that considers the transmission distances from one node to all neighbouring nodes within its transmission range. The interference measurement approach is adopted to select the next-hop node. The cluster head (CH) node selection is based on transmission distances to the base station (BS) with the nearest node to the BS in a sub-cluster elected as CH node for that sub-cluster. The thesis also introduces a novel scheduling algorithm called the “long hop” (LH) which assigns high priority to messages coming from sensor nodes that are located farthest away and have accessed a high number of hops, to be served first at CH nodes. This minimised energy consumption caused by the retransmission process. Redundant data increases the unnecessary/unwanted processing and transmission of data. Thus, the thesis introduces a new method that reduces redundant data transmission and lowers the communication costs related to sending unnecessary data. The study also provides a remote monitoring system for the end-user that can check and track the performance of the sensors/IoT devices during real-time communication. Extensive simulation tests on randomly situated WSNs show the potential of the solutions proposed in this thesis to reduce energy consumption and extend network lifetime

Key pre distribution in the context of IoT: the RPL new objective function SISLO

The purpose of this thesis is to develop a novel objective function that ensures secure links between all nodes in an Internet of Things network when using the Routing Protocol for Low-Power and Lossy Networks (RPL) and only allow nodes in the network that share a key to join the network. We propose the Shared Identifier Secure Link Objective Function (SISLOF) to allow only nodes that share a key to join the network and therefore ensuring that all links between the nodes in the network are secure. SISLOF will look at a route that includes all nodes in the network and if a node shares a key with more than one node, it will then choose the node that has a shorter pathway to the root. We evaluate the overhead of the security keys on the Internet of Things nodes and the routing metrics by measuring the overhead when using first ETX and OF0 objective functions when using either the probabilistic scheme or the deterministic scheme. We then identified that the use of ETX or OF0 with both schemes is not appropriate because of the large overhead it adds on the devices and the link. We show that both ETX and OF0 add a large overhead and they are not suitable to be used with the security schemes. The secure objective function was needed as the existing objective functions add a large overhead on the Internet of Things devices when using two different key distribution schemes to distribute and provide keys between nodes and to create a link. We develop an objective function that only adds nodes that share a key to the routing table without the overhead cost the other objective functions added. We also identify that the probabilistic key distribution scheme outperforms the deterministic key distribution scheme for all objective functions. The significance of this study is that it has identified the need for an objective function that incorporates the security key distributions for the Routing Protocol for Low-Power and Lossy Networks (RPL) in the Internet of Things networks and the Shared Identifier Secure Link Objective Function (SISLOF) was developed to solve this problem

Game theory for dynamic spectrum sharing cognitive radio

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University on 21 June 2010.‘Game Theory’ is the formal study of conflict and cooperation. The theory is based on a set of tools that have been developed in order to assist with the modelling and analysis of individual, independent decision makers. These actions potentially affect any decisions, which are made by other competitors. Therefore, it is well suited and capable of addressing the various issues linked to wireless communications. This work presents a Green Game-Based Hybrid Vertical Handover Model. The model is used for heterogeneous wireless networks, which combines both dynamic (Received Signal Strength and Node Mobility) and static (Cost, Power Consumption and Bandwidth) factors. These factors control the handover decision process; whereby the mechanism successfully eliminates any unnecessary handovers, reduces delay and overall number of handovers to 50% less and 70% less dropped packets and saves 50% more energy in comparison to other mechanisms. A novel Game-Based Multi-Interface Fast-Handover MIPv6 protocol is introduced in this thesis as an extension to the Multi-Interface Fast-handover MIPv6 protocol. The protocol works when the mobile node has more than one wireless interface. The protocol controls the handover decision process by deciding whether a handover is necessary and helps the node to choose the right access point at the right time. In addition, the protocol switches the mobile nodes interfaces ‘ON’ and ‘OFF’ when needed to control the mobile node’s energy consumption and eliminate power lost of adding another interface. The protocol successfully reduces the number of handovers to 70%, 90% less dropped packets, 40% more received packets and acknowledgments and 85% less end-to-end delay in comparison to other Protocols. Furthermore, the thesis adapts a novel combination of both game and auction theory in dynamic resource allocation and price-power-based routing in wireless Ad-Hoc networks. Under auction schemes, destinations nodes bid the information data to access to the data stored in the server node. The server will allocate the data to the winner who values it most. Once the data has been allocated to the winner, another mechanism for dynamic routing is adopted. The routing mechanism is based on the source-destination cooperation, power consumption and source-compensation to the intermediate nodes. The mechanism dramatically increases the seller’s revenue to 50% more when compared to random allocation scheme and briefly evaluates the reliability of predefined route with respect to data prices, source and destination cooperation for different network settings. Last but not least, this thesis adjusts an adaptive competitive second-price pay-to-bid sealed auction game and a reputation-based game. This solves the fairness problems associated with spectrum sharing amongst one primary user and a large number of secondary users in a cognitive radio environment. The proposed games create a competition between the bidders and offers better revenue to the players in terms of fairness to more than 60% in certain scenarios. The proposed game could reach the maximum total profit for both primary and secondary users with better fairness; this is illustrated through numerical results

IoT and Smart Cities: Modelling and Experimentation

Internet of Things (IoT) is a recent paradigm that envisions a near future, in which the objects of everyday life will communicate with one another and with the users, becoming an integral part of the Internet. The application of the IoT paradigm to an urban context is of particular interest, as it responds to the need to adopt ICT solutions in the city management, thus realizing the Smart City concept. Creating IoT and Smart City platforms poses many issues and challenges. Building suitable solutions that guarantee an interoperability of platform nodes and easy access, requires appropriate tools and approaches that allow to timely understand the effectiveness of solutions. This thesis investigates the above mentioned issues through two methodological approaches: mathematical modelling and experimenta- tion. On one hand, a mathematical model for multi-hop networks based on semi- Markov chains is presented, allowing to properly capture the behaviour of each node in the network while accounting for the dependencies among all links. On the other hand, a methodology for spatial downscaling of testbeds is proposed, implemented, and then exploited for experimental performance evaluation of proprietary but also standardised protocol solutions, considering smart lighting and smart building scenarios. The proposed downscaling procedure allows to create an indoor well-accessible testbed, such that experimentation conditions and performance on this testbed closely match the typical operating conditions and performance where the final solutions are expected to be deployed