Distributed mobility management (DMM) solution is proposed to address the downsides of centralized mobility management protocols. The standard DMM is proposed for flat architectures and always selects the anchor point from access layer. Numerical analysis is used in this paper to show that dynamic anchor point selection can improve the performance of standard DMM in terms of packet signalling and delivery cost. In next step, an SDN-based DMM solution that we refer to as SD-DMM is presented to provide dynamic anchor point selection for hierarchical mobile network architecture. In SD-DMM, the anchor point is dynamically selected for each mobile node by a virtual function implemented as an application on top of the SDN controller which has a global view of the network. The main advantages of SD-DMM is to decrease packet delivery cost.

Software-defined networking (SDN) enables centralized control of a network of programmable switches by dynamically updating flow rules. This paves the way for dynamic and autonomous control of the network. In order to be able to apply a suitable set of policies to the correct set of traffic flows, SDN needs input from traffic classification mechanisms. Today, there is a variety of classification algorithms in machine learning. However, recent studies have found that using an arbitrary algorithm does not necessarily provide the best classification outcome on a dataset, and therefore a framework called ensemble which combines individual algorithms to improve classification results has gained attraction. In this paper, we propose the application of the ensemble algorithm as a machine learning pre-processing tool, which classifies ingress network traffic for SDN to pick the right set of traffic policies. Performance evaluation results show that this ensemble classifier can achieve robust performance in all tested traffic types.

Distributed Mobility Management (DMM) protocol is proposed to address the shortcomings of centralized mobility management protocols. In DMM, unlike centralized protocols, flows can be routed optimally in the network by dynamic IP address allocation, which can yield lower signaling and packet delivery costs. In this paper, we introduce an SDN based mobility management service with multiple controllers called Hierarchical Software Defined Distributed Mobility Management (HSD-DMM). In contrast to standard DMM which is proposed for flat architectures, HSD-DMM uses a dynamic anchor point selection method for each flow in a hierarchical mobile network architecture. The main condition in selecting an anchor point is packet delivery cost reduction based on collaboration of multiple controllers responsible for different tiers of the hierarchy. Numerical analysis results reveal that HSD-DMM can decrease signaling and packet delivery cost compared to an SDN based standard DMM solution.

Software-defined networking (SDN) is the key technology to enable network softwarization by offering a programable and flexible network control capabilities. In order to dynamically manage and reconfigure the underlying network through SDN, network-based monitoring functionality needs to be in place. However, existing network monitoring schemes are normally heavyweight which can cause substantial monitoring overhead when dealing with entire network infrastructure and complex policies. Such a limitation can be critical in a software-based network system that enables the construction of multiple networks with various network policies designed by a network operator. In this article, we propose a new lightweight monitoring mechanism referred to as Active-port Aware Monitoring (APAM) in order to support the monitoring of complex networks with substantially reduced overhead. APAM typically monitors active ports which are the switch ports utilized by current flow rules. These active ports are dynamically monitored with reconfigurable monitoring intervals according to their port utilization. The measurement results show that APAM adapts varying traffic route due to a change of flow rules and also adjusts its monitoring performance according to network traffic dynamicity, which reduces the monitoring overhead and also improves monitoring accuracy.

Network function virtualization (NFV) is one of key features envisioned for the upcoming 5G core networks in order to support high flexibility in network deployment and management. However, potential performance degradations that could be caused by virtualization of network functions is still a controversial issue, especially in regards to virtualization of core network components. In this paper, we evaluate the effect of NFV on an end-to-end mobile network testbed, which is deployed in the 5G Innovation Centre (5GIC) in University of Surrey. The testbed consists of indoor and outdoor LTE Radio Access Networks (RAN) equipment, as well as an enhanced Evolved Packet Core (EPC) following LTE Release 14 specifications, such as control and user plane separation (CUPS). The paper compares the performance of the softwarised core network and that of its virtualized counterpart. Measurement results show that the virtualized core network has adequately similar network performance in terms of throughput and latency, compared with the non-virtual core.

Software-defined networking (SDN) is a promising paradigm of computer networks, offering a programmable and centralized network architecture. However, although such a technology supports the ability to dynamically handle network traffic based on real-time and flexible traffic control, SDN-based networks can be vulnerable to dynamic change of flow control rules, which causes transmission disruption and packet loss in SDN hardware switches. This problem can be critical because the interruption and packet loss in SDN switches can bring additional performance degradation for SDN-controlled traffic flows in the data plane. In this paper, we propose a novel robust flow control mechanism referred to as priority-based flow control (PFC) for dynamic but disruption-free flow management when it is necessary to change flow control rules on the fly. PFC minimizes the complexity of flow modification process in SDN switches by temporarily adapting the priority of flow rules in order to substantially reduce the time spent on control-plane processing during run-time. Measurement results show that PFC is able to successfully prevent transmission disruption and packet loss events caused by traffic path changes, thus offering dynamic and lossless traffic control for SDN switches.

Dynamic network conditions are inevitable in wireless sensor networks (WSNs). In multihop WSN data collection scenarios, data traffic load becomes heavier closer to the data sink. Therefore, extensive research work has been devoted to duty cycle adaptation mechanisms to meet the time-varying and/or spatially non-uniform traffic loads. In this paper, we propose a new algorithm, called Control-theoretic approach for Duty Cycle Adaptation (CDCA), based on the non-beacon-enable mode of the IEEE 802.15.4 protocol. We consider a distributed controller to be run at each node which adapts its duty cycle to achieve energy efficiency and maximise packet delivery performance under variable traffic conditions. CDCA adjusts a sensor's duty cycle using a traffic estimation mechanism based on a novel concept called a virtual queue. This is determined based on the sensor's local queue length as well as explicit packet-drop indications collected from its child nodes in a multihop tree structure rooted at the network's data sink. Based on control theory, the proposed controller is analysed to derive system stability. Performance results obtained using Network Simulator 3 demonstrate that CDCA outperforms the most recent and relevant scheme by saving more energy while also significantly improving packet delivery ratio to the data sink.

In this paper, the design, construction, and testing of a fully-functional virtualised mobile core network is outlined. Lessons learned and recommendations for future improvements are provided. The presented work uses open-source software for virtual network function infrastructure control (OpenStack), network flow programming (OpenDaylight), and network orchestration (OpenBaton) to virtualise a commercial software evolved packet core solution deployed on common off-the-shelf hardware. The findings presented in this paper prove the concept of function virtualisation for mobile networks, and paves the way towards future mobile core network function flexibility as required for 5G networks. The paper provides researchers and network operators with first-hand experience to help build similar virtual mobile network infrastructures, and highlights the challenges to tackle and the issues to address to harness the power of virtualisation in 5G networks.

The Internet-of-Things (IoT) paradigm envisions billions of devices all connected to the Internet, generating low-rate monitoring and measurement data to be delivered to application servers or end-users. Recently, the possibility of applying in-network data caching techniques to IoT traffic flows has been discussed in research forums. The main challenge as opposed to the typically cached content at routers, e.g., multimedia files, is that IoT data are transient and therefore require different caching policies. In fact, the emerging location-based services can also benefit from new caching techniques that are specifically designed for small transient data. This paper studies in-network caching of transient data at content routers, considering a key temporal data property: data item lifetime. An analytical model that captures the trade-off between multihop communication costs and data item freshness is proposed. Simulation results demonstrate that caching transient data are a promising information-centric networking technique that can reduce the distance between content requesters and the location in the network where the content is fetched from. To the best of our knowledge, this is a pioneering research work aiming to systematically analyze the feasibility and benefit of using Internet routers to cache transient data generated by IoT applications.

Node clustering has been widely studied in recent years for Wireless Sensor Networks (WSN) as a technique to form a hierarchical structure and prolong network lifetime by reducing the number of packet transmissions. Cluster Heads (CH) are elected in a distributed way among sensors, but are often highly overloaded, and therefore re-clustering operations should be performed to share the resource intensive CH-role. Existing protocols involve periodic network-wide re-clustering operations that are simultaneously performed, which requires global time synchronisation. To address this issue, some recent studies have proposed asynchronous node clustering for networks with direct links from CHs to the data sink. However, for large-scale WSNs, multihop packet delivery to the sink is required since longrange transmissions are costly for sensor nodes. In this paper, we present an asynchronous node clustering protocol designed for multihop WSNs, considering dynamic conditions such as residual node energy levels and unbalanced data traffic loads caused by packet forwarding. Simulation results demonstrate that it is possible to achieve similar levels of lifetime extension by re-clustering a multihop WSN via independently made decisions at CHs, without a need for time synchronisation required by existing synchronous protocols.

The recent forecast of billions of devices, all connected to the Internet and generating low-rate monitoring, measurement, or automation data that many end-users/applications frequently request, signifies the need for applying in-network caching techniques to Internet-of-Things (IoT) traffic. Although time delay is not critically important for small-sized IoT content, the expected total traffic load on the Internet from a large number of devices is significant. However, the main challenge as opposed to the typically cached content at content routers, e.g. multimedia files, is that IoT data are transient and therefore require different caching policies. This paper studies in-network caching of IoT data at content routers in the Internet. An IoT data item is uniquely defined not only by its time and location tags, but also a time-range value set by end-users/applications. We provide a model for the trade-off between multihop communication costs and the freshness of a transient data item. Results show that the model can successfully capture the effect of data transiency and can accurately represent the expected gains of a caching system: considerable savings in terms of reduction of network load, especially for highly requested data items.

Nodes in Wireless Sensor Networks (WSNs) are becoming more and more complex systems with the capabilities to run distributed structured applications. Which single task should be implemented by each WSN node needs to be decided by the application deployment strategy by taking into account both network lifetime and execution time requirements. In this paper, we propose an adaptive decentralised algorithm based on noncooperative game theory, where neighbouring nodes negotiate among each other to maximize their utility function. We then prove that an increment of the nodes utility corresponds to the same increment of the utility for the whole network. Simulation results show significant performance improvement with respect to existing algorithms.

Network coding has been proposed as a technique that can potentially increase the transport capacity of a wireless network via mixing data packets at intermediate routers. However, most previous studies either assume a fixed transmission rate or do not consider the impact of using diverse rates on the network coding gain. Since in many cases, network coding implicitly relies on overhearing, the choice of the transmission rate has a big impact on the achievable gains. The use of higher rates works in favor of increasing the native throughput. However, it may in many cases work against effective overhearing. In other words, there is a tension between the achievable network coding gain and the inherent rate gain possible on a link. In this paper, our goal is to drive the network toward achieving the best tradeoff between these two contradictory effects. We design a distributed framework that: facilitates the choice of the best rate on each link while considering the need for overhearing; and dictates the choice of which decoding recipient will acknowledge the reception of an encoded packet. We demonstrate that both of these features contribute significantly toward gains in throughput. We extensively simulate our framework in a variety of topological settings. We also fully implement it on real hardware and demonstrate its applicability and performance gains via proof-of-concept experiments on our wireless testbed. We show that our framework yields throughput gains of up to 390% as compared to what is achieved in a rate-unaware network coding framework.

Clustering algorithms are widely used in Wireless Sensor Networks (WSNs), which however incurs significant energy consumption at Cluster Headers(CHs). Therefore, a re-clustering operation is typically used to balance the workload, where different CHs are selected and clusters are reorganized. However, a considerable number of control messages is initiated during this process which inevitably consumes on-board node energy. Hence, the question of how often the network should perform the re-clustering operation needs to be addressed. In this paper, a distributed re-clustering solution is proposed, which provides an energy-efficient re-clustering rate to conserve node energy while also equalizing the node energy consumption across the network. The proposed algorithm calculates the approximate amount of energy required to reorganize the clusters and to deliver the sensory data. By properly predicting the levels of the energy consumptions values, the appropriate frequency of performing the re-clustering operation can be determined, which reduces control message overhead. To the best of our knowledge, this is the first work that analytically analyzes the overhead in re-clustering a WSN, groups re-clustering rounds to reduce this overhead, and simultaneously equalizes node lifetimes. Performance results show that the proposed algorithm outperforms two other popular clustering algorithms in node energy conservation and node lifetime equalization.

Hot spots in a wireless sensor network emerge as locations under heavy traffic load. Nodes in such areas quickly deplete energy resources, leading to disruption in network services. This problem is common for data collection scenarios in which Cluster Heads (CH) have a heavy burden of gathering and relaying information. The relay load on CHs especially intensifies as the distance to the sink decreases. To balance the traffic load and the energy consumption in the network, the CH role should be rotated among all nodes and the cluster sizes should be carefully determined at different parts of the network. This paper proposes a distributed clustering algorithm, Energy-efficient Clustering (EC), that determines suitable cluster sizes depending on the hop distance to the data sink, while achieving approximate equalization of node lifetimes and reduced energy consumption levels. We additionally propose a simple energy-efficient multihop data collection protocol to evaluate the effectiveness of EC and calculate the end-to-end energy consumption of this protocol; yet EC is suitable for any data collection protocol that focuses on energy conservation. Performance results demonstrate that EC extends network lifetime and achieves energy equalization more effectively than two well-known clustering algorithms, HEED and UCR.

Network coding has been proposed as a technique that can potentially increase the transport capacity of a wireless network via processing and mixing of data packets at intermediate routers. However, most previous studies either assume a fixed transmission rate or do not consider the impact of using diverse rates on the network coding gain. Since in many cases, network coding implicitly relies on overhearing, the choice of the transmission rate has a big impact on the achievable gains. The use of higher rates works in favor of increasing the native throughput; however, it may in many cases work against effective overhearing. In other words, there is a tension between the achievable network coding gain and the inherent rate gain possible on a link. In this paper our goal is to drive the network towards achieving the best trade-off between these two contradictory effects. Towards this, we design a distributed framework that (a) facilitates the choice of the best rate on each link while considering the need for overhearing and (b) dictates the choice of which decoding recipient will acknowledge the reception of an encoded packet. We demonstrate that both of these features contribute significantly towards gains in throughput. We extensively simulate our framework in a variety of topological settings. We also fully implement it on real hardware and demonstrate its applicability and performance gains via proof-of-concept experiments on our wireless testbed. We show that our framework yields throughput gains of up to 390% as compared to what is achieved in a rate-unaware network coding framework.

Towards increasing spatial reuse, cellular networks may be augmented with ad hoc connectivity. In the resulting hybrid network, the coverage area of the base station (BS) is reduced and the users within this area relay packets from/to the users outside. With this approach, shorter range, higher-rate links are used; this favors an increase in spatial reuse and thus, the achievable capacity. However, multi-hop relaying overhead can hurt capacity. In this paper, we analytically compute the uplink capacity, defined as an upper bound on the achievable throughput under max-min fairness. To gage the tightness of the bound, we seek to find the optimal transmission schedule for delivering the packets from the nodes in a cell to the BS. In general, constructing the optimal uplink schedule is NP-hard. We develop a heuristic approach and show via simulations that the resulting performance is close to the derived capacity bound. Our results suggest that (a) the hybrid network can achieve up to a 200 % increase in the uplink capacity compared to a pure cellular network, and (b) the simulated throughput is close to the analytically computed capacity showing that our bound is tight.

Wireless sensor networks have been used to gather data and information in many diverse application settings. The capacity of such networks remains a fundamental obstacle toward the adaptation of sensor network systems for advanced applications that require higher data rates and throughput. In this paper, we explore potential benefits of integrating directional antennas into wireless sensor networks. While the usage of directional antennas has been investigated in the past for ad hoc networks, their usage in sensor networks bring both opportunities as well as challenges. In this paper, Sectored-Antenna Medium Access Control (SAMAC), an integrated cross-layer protocol that provides the communication mechanisms for sensor network to fully utilize sectored antennas, is introduced. Simulation studies show that SAMAC delivers high energy efficiency and predictable delay performance with graceful degradation in performance with increased load.

Location and intersensor distance estimations are important functions for the operation of wireless sensor networks, especially when protocols can benefit from the distance information prior to network deployment. The maximum multihop distance that can be covered in a given number of hops in a sensor network is one such parameter related with coverage area, delay, and minimal multihop transmission energy consumption estimations. In randomly deployed sensor networks, intersensor distances are random variables. Hence, their evaluations require probabilistic methods, and distance models should involve investigation of distance distribution functions. Current literature on analytical modeling of the maximum distance distribution is limited to 1D networks using the Gaussian pdf. However, determination of the maximum multihop distance distribution in 2D networks is a quite complex problem. Furthermore, distance distributions in 2D networks are not accurately modeled by the Gaussian pdf. Hence, we propose a greedy method of distance maximization and evaluate the distribution of the obtained multihop distance through analytical approximations and simulations.

In-network data storage techniques can provide robustness against failure of dedicated sink nodes in sensor networks. For storing data in the network it is critical to ensure that the data can be accessed from other nodes for a long period of time. As replicating the data will increase consumption of resources, we propose a technique for data relocation that optimizes the accessibility of the data in the network. The two challenging constraints for solving this problem are "limited sensor lifetime" and "cost of data relocation". We study both centralized and distributed approaches for computing accessibility of each node. Using extensive simulations, we evaluate the performance of our techniques.

In this paper, we present a novel packet delivery mechanism called multi-path and multi-speed routing protocol (MMSPEED) for probabilistic QoS guarantee in wireless sensor networks. The QoS provisioning is performed in two quality domains, namely, timeliness and reliability. Multiple QoS levels are provided in the timeliness domain by guaranteeing multiple packet delivery speed options. In the reliability domain, various reliability requirements are supported by probabilistic multipath forwarding. All these for QoS provisioning are realized in a localized way without global network information by employing localized geographic packet forwarding augmented with dynamic compensation, which compensates the local decision inaccuracy as a packet travels towards its destination. This way, MMSPEED can guarantee end-to-end requirements in a localized way, which is desirable for scalability and adaptability to large scale dynamic sensor networks. Simulation results show that MMSPEED provides QoS differentiation in both reliability and timeliness domains and, as a result, significantly improves the effective capacity of a sensor network in terms of number of flows that meet both reliability and timeliness requirements.

In spatially random sensor networks, estimating the Euclidean distance covered by a packet in a given number of hops carries a high importance for various other methods such as localization and distance estimations. The inaccuracies in such estimations motivate this study on the distribution of the Euclidean distance covered by a packet in spatially random sensor networks in a given number of hops. Although a closed-form expression of distance distribution cannot be obtained, highly accurate approximations are derived for this distribution in one dimensional spatially random sensor networks. Using statistical measures and numerical examples, it is also shown that the presented distribution approximation yields very high accuracy even for small number of hops. A discussion on how these principles can be extended to the analysis of the same problem in two dimensional networks is also provided.