The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g., WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions (assigning one controller per partition) and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g., 67.21 percent throughput improvement, 72.99 percent reduced delay, and 69.59 percent jitter improvements, compared with alternative SDN systems.

Estimating communication latency is a challenging task in the applications of industrial Internet of things (IIoT). Mobile convergecast, as a many-to-one communication pattern, has been recently explored in mobile sensor networks for IIoT, where sensor nodes are usually in mobile status, and report the sensed data regularly or randomly to one or more stationary sinks through the multihop routing path. As convergecast becomes increasingly relevant for industrial sensing and monitoring, a critical part of empowering information aggregation is to maintain consistent transmission. Path duration is one important component of end-to-end delay for communications along the path. In this paper, a probabilistic model for mobile convergecast has been proposed and evaluated to capture path duration times, by considering parameters including network models, sensor network scope, and mobility patterns of network elements. Through experiments, it has been verified that the proposed model can provide a feasible analysis of end-to-end delays in industrial networks implementing convergecast.

In this paper, we develop effective solutions for enabling mobile sensing/data collection in community IoT deployments where sensing/communication coverage is intermittent and varying. Specifically, we address the optimized upload planning problem, i.e. determine optimal schedules for upload of gathered information to enable timely data collection in dynamic settings. We develop a two-phase approach and associated policies, where an initial upload plan is generated with prior knowledge of upload opportunities and data needs, and a subsequent runtime adaptation phase alters the plan based on dynamic network and data conditions. To validate our approach, we designed and built SCALECycle, a prototype mobile data collection platform and deployed it in real world community settings; measurements from testbeds in Rockville, MD and Irvine, CA are used to drive extensive simulations. Experimental results indicate that a judicious combination of policies in the two phases of upload planning (a balanced delay-opportunity-priority method with Lyapunov-inspired upload adaptation) can result in a 30-60% improvement in overall utility of collected data compared with opportunistic operation along with 30% reduction in collection delays /overheads.

The growing of Internet of Things (IoT) devices has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist. Managing the multinetworks for mobile IoT access is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick configuration of network devices, but its application in multinetworks with frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for ubiquitous flow control and mobility management in multinetworks. UbiFlow adopts distributed controllers to divide urban-scale SDN into different geographic partitions. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on the per-device requirement and whole-partition capability. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance in each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks.

Wireless mesh networks (WMNs) suffer from scalability, performance degradation and service disruption issues due to overwhelming multi-hop co-channel interference, unscrupulous channel utilization and inherent network mobility. We propose a set of efficient distributed dynamic channel access scheduling protocols, called DDCS, based on distributed clustering and dynamic Latin squares for WMN systems with multi-radio multi-channel (MRMC) communication capabilities. DDCS uses nodal interference information to form cliques for inter-cluster and intra-cluster structures in WMNs, and then applies Latin squares to map the clique-based clustering results to radios and channels for wireless communication purposes. Afterwards, DDCS again applies Latin squares to schedule the channel access amongst nodes within each cluster in a collision-free manner. A coexistence mechanism for DDCS and widely used IEEE 802.11 DCF is also addressed in this paper, so that our MRMC access protocol DDCS can be effectively applied in large-scale WMNs. The evaluation results show that DDCS achieves much better performance than existing IEEE 802.11 standards and other multi-channel access control protocols.

The growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a software-defined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the IoT multinetwork capabilities.

The networking landscape of today is characterized by diverse access technologies including cellular, WiFi, Ethernet, MANETs, and ZigBee, and properly managing this heterogeneous networking infrastructure is a key challenge to take full advantage of its many opportunities. In this paper, we propose MINA (Multinetwork INformation Architecture), a reflective (self-observing and adapting) middleware approach to realize and manage dynamic and heterogeneous multi-networks in pervasive environments. A novel aspect of MINA is that it embodies an Observe-Analyze-Adapt (OAA) loop to i) achieve a reasonably accurate, centralized global view of the multi-network through the design of novel techniques for overlay structuring, network state collection and formal methods-based analysis, and ii) take advantage of the global view for adapting multi-network structure by reallocating application flows across networks and proactively planning and deploying additional network resources.

Estimating communication latency is challenging in dynamic settings, e.g mobile applications where the source and/or destination of the communication can move arbitrarily. In this paper, we explore a many-to-one communication pattern, called convergecast, where mobile sensors report their sensed values to one or more servers (data sinks) in a periodic or queried manner. Such ”convergecast” communication is becoming increasingly relevant in sensor monitoring and pervasive sensing applications. An important aspect of enabling accurate collection is to ensure small delays in the collection process. The path duration, i.e time between when a path from sensor to source is established and when it gets disrupted, is a key factor that affects end-to-end delay; much of the existing work on this topic is in the context of MANETs. We propose and evaluate a probabilistic model in convergecast to capture path duration times given models of the network/transmission, the scale of the network and mobility of network elements. We validate our model through simulations and verify that the proposed model can provide a reasonable representation for end-to-end delays in the convergecast network. Pervasive sensor networks of the future are likely to encompass multiple access technologies with varying network properties and transmission characteristics. The ability to model end-to-end network properties (such as path duration) can assist in improved utilization in such pervasive multinetworks.