Reliable Dynamic Packet Scheduling over Lossy Real-Time Wireless Networks

Along with the rapid development and deployment of real-time wireless network (RTWN) technologies in a wide range of applications, effective packet scheduling algorithms have been playing a critical role in RTWNs for achieving desired Quality of Service (QoS) for real-time sensing and control, especially in the presence of unexpected disturbances. Most existing solutions in the literature focus either on static or dynamic schedule construction to meet the desired QoS requirements, but have a common assumption that all wireless links are reliable. Although this assumption simplifies the algorithm design and analysis, it is not realistic in real-life settings. To address this drawback, this paper introduces a novel reliable dynamic packet scheduling framework, called RD-PaS. RD-PaS can not only construct static schedules to meet both the timing and reliability requirements of end-to-end packet transmissions in RTWNs for a given periodic network traffic pattern, but also construct new schedules rapidly to handle abruptly increased network traffic induced by unexpected disturbances while minimizing the impact on existing network flows. The functional correctness of the RD-PaS framework has been validated through its implementation and deployment on a real-life RTWN testbed. Extensive simulation-based experiments have also been performed to evaluate the effectiveness of RD-PaS, especially in large-scale network settings

Isolating SDN Control Traffic with Layer-2 Slicing in 6TiSCH Industrial IoT Networks

Recent standardization efforts in IEEE 802.15.4-2015 Time Scheduled Channel Hopping (TSCH) and the IETF 6TiSCH Working Group (WG), aim to provide deterministic communications and efficient allocation of resources across constrained Internet of Things (IoT) networks, particularly in Industrial IoT (IIoT) scenarios. Within 6TiSCH, Software Defined Networking (SDN) has been identified as means of providing centralized control in a number of key situations. However, implementing a centralized SDN architecture in a Low Power and Lossy Network (LLN) faces considerable challenges: not only is controller traffic subject to jitter due to unreliable links and network contention, but the overhead generated by SDN can severely affect the performance of other traffic. This paper proposes using 6TiSCH tracks, a Layer-2 slicing mechanism for creating dedicated forwarding paths across TSCH networks, in order to isolate the SDN control overhead. Not only does this prevent control traffic from affecting the performance of other data flows, but the properties of 6TiSCH tracks allows deterministic, low-latency SDN controller communication. Using our own lightweight SDN implementation for Contiki OS, we firstly demonstrate the effect of SDN control traffic on application data flows across a 6TiSCH network. We then show that by slicing the network through the allocation of dedicated resources along a SDN control path, tracks provide an effective means of mitigating the cost of SDN control overhead in IEEE 802.15.4-2015 TSCH networks

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application