While mesh networking for edge settings (e.g., smart buildings, farms, battlefields, etc.) has received much attention, the layer of control over such meshes remains largely centralized and cloud-based. This paper focuses on applications with commonplace sense-trigger-actuate (STA) workloads—like the abstraction of routines popular now in smart homes, but applied to larger-scale edge IoT deployments. We present CoMesh, which tackles the challenge of building a decentralized mesh-based control plane for local, non-cloud, and hubless management of sense-trigger-actuate applications. CoMesh builds atop an abstraction called the coterie, which spreads STA load in a finegrained way both across space and across time. A coterie uses a novel combination of techniques such as zero-message-exchange protocols (for fast proactive member selection), quorum-based agreement, and locality-sensitive hashing. We analyze and theoretically prove safety and liveness properties of CoMesh. Our evaluation with both a Raspberry Pi-4 deployment and largerscale simulations, using real building maps and real routine workloads, shows that CoMesh is load-balanced, fast, faulttolerant, and scalable.

Hardware transports such as RoCE deliver high performance with minimal host CPU, but are best suited to special-purpose deployments that limit their use, e.g., backend networks or Ethernet with Priority Flow Control (PFC). We introduce Falcon, the first hardware transport that supports multiple Upper Layer Protocols (ULPs) and heterogeneous application workloads in general-purpose Ethernet datacenter environments (with losses and without special switch support). Key design elements include: delay-based congestion control with multipath load balancing; a layered design with a simple request-response transaction interface for multi-ULP support; hardware-based retransmissions and error-handling for scalability; and a programmable engine for flexibility. The first Falcon hardware implementation delivers a peak performance of 200 Gbps, 120 Mops/sec, with near-optimal operation completion times that are up to 8× lower than CX-7 RoCE under network congestion, and up to 65% higher goodput under lossy conditions.

Efficient and correct operation of an IoT network requires the presence of a failure detector and membership protocol amongst the IoT nodes. This paper presents a new failure detector for IoT settings wherein nodes are connected via a wireless ad-hoc network. Our failure detector, named Medley, is fully decentralized, allows IoT nodes to maintain a local membership list of other alive nodes, detects failures quickly (and updates the membership list), and incurs low communication overhead. We adapt a failure detector originally proposed for datacenters (SWIM), for the IoT environment. This adaptation is non-trivial. In Medley each node picks a medley of ping targets in a randomized and skewed manner, preferring nearer nodes. We also provide optimizations to achieve time-bounded detection, as well as to reduce tail detection times. Via analysis, simulation, and Raspberry Pi deployments, we show that Medley can simultaneously optimize detection time and communication traffic.

Smart environments (homes, factories, hospitals, buildings) contain an increasing number of IoT devices, making them complex to manage. Today, in smart homes when users or triggers initiate routines (i.e., a sequence of commands), concurrent routines and device failures can cause incongruent outcomes. We describe SafeHome, a system that provides notions of atomicity and serial equivalence for smart homes. Due to the human-facing nature of smart homes, SafeHome offers a spectrum of visibility models which trade off between responsiveness vs. isolation of the smart home. We implemented SafeHome and performed workload-driven experiments. We find that a weak visibility model, called eventual visibility, is almost as fast as today's status quo (up to 23% slower) and yet guarantees serially-equivalent end states.

Efficient and correct operation of an IoT network requires the presence of a failure detector and membership protocol amongst the IoT nodes. This paper presents a new failure detector for IoT settings where nodes are connected via a wireless ad-hoc network. This failure detector, which we name Medley, is fully decentralized, allows IoT nodes to maintain a local membership list of other alive nodes, detects failures quickly (and updates the membership list), and incurs low communication overhead in the underlying ad-hoc network. In order to minimize detection time and communication, we adapt a failure detector originally proposed for datacenters (SWIM), for the IoT environment. In Medley each node picks a medley of ping targets in a randomized and skewed manner, preferring nearer nodes. Via analysis and NS-3 simulation we show the right mix of pinging probabilities that simultaneously optimize detection time and communication traffic. We have also implemented Medley for Raspberry Pis, and present deployment results.