With the increasing popularity of small computing devices and applications of IoT, the need for platform integrity grows both in scale and scope. In particular, the detection of successful attempts to inject a malicious software module or modify an existing one is of utmost importance. This paper promotes LiSB, a novel approach for validating software/firmware integrity and ensuring secure boot-up for resource-constrained embedded devices. LiSB is lightweight, yet very robust. A hardware primitive is used as a Root-of-Trust to support the confidentiality of generated digests and the security of the attestation protocol. Specifically, LiSB employs Physically Unclonable Functions (PUFs) to make the digest device-specific without storing any secrets in the device memory. The performance and robustness of LiSB are validated using a prototype implementation on an FPGA. The results demonstrate that LiSB outperforms recently-published and prominent commercial attestation schemes like TPM, and consumes 25 times less power than SHA-256, which serves as the core component of most existing attestation schemes. The security properties of LiSB are formally analyzed.

Incorporating smart technology into critical infras-tructure (CI) and smart cities promises substantial efficiency improvements as networks of machines communicate and make rapid decisions autonomously. Yet the promise of greater effi-ciency that such cyber-physical systems (CPS) bring is tempered by increased fragility unless machine-to-machine (M2M) trust is enhanced, particularly in Internet of Things (IoT) networks. This work makes two contributions toward improving M2M trust. First, it proposes a multifaceted trust framework comprised of identity verification, experience, context, and recommendation scores to enable high-integrity M2M interactions. Second, this trust framework is implemented via an IoT-friendly distributed ledger on a physical testbed, where it is shown to identify and mitigate errors due to a compromised system component. This implementation mirrors real-world IoT systems in which resource-constrained endpoint devices pose trust score compu-tation challenges and the number of devices raises scalability obstacles for information sharing among nodes.

Homes are rich with information about people's energy consumption, medical health, and personal or family functions. In this paper, we present our experiences deploying large-scale residential sensing systems in over 20 homes. Deploying small-scale systems in homes can be deceptively easy, but in our deployments we encountered a phase transition in which deployment effort increases dramatically as residential deployments scale up in terms of 1) the number of nodes, 2) the length of time, and 3) the number of houses. In this paper, we distill our experiences down to a set of guidelines and design principles to help future deployments avoid the potential pitfalls of large-scale sensing in homes.

Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption. In this paper, we demonstrate how to use cheap and simple sensing technology to automatically sense occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning off the home's HVAC system. We call this approach the smart thermostat. We evaluate this approach by deploying sensors in 8 homes and comparing the expected energy usage of our algorithm against existing approaches. We demonstrate that our approach will achieve a 28% energy saving on average, at a cost of approximately $25 in sensors. In comparison, a commercially-available baseline approach that uses similar sensors saves only 6.8% energy on average, and actually increases energy consumption in 4 of the 8 households.

Macroprogramming abstractions provide abstract distributed data structures to simplify the programming of wireless embedded networks. However, none of the current macroprogramming systems provide debugging support for application development. We have developed MDB, a GDB-like post-mortem debugger for the MacroLab macroprogramming abstraction. In this demonstration, we show how MDB enables application development and debugging at a single level of abstraction. MDB eliminates the need for a programmer to reason about low-level event traces and message passing protocols, instead allowing debugging in terms of abstract data types. We expect MDB to fill a crucial link in the development cycle as a macroprogram progresses from the drawing board to real deployment.

Creating and debugging programs for wireless embedded networks (WENs) is notoriously difficult. Macroprogramming is an emerging technology that aims to address this problem by providing high-level programming abstractions. We present MDB, the first system to support the debugging of macroprograms. MDB allows the user to set break-points and step through a macroprogram using a source-level debugging interface similar to GDB, a process we call macrodebugging. A key challenge of MDB is to step through a macroprogram in sequential order even though it executes on the network in a distributed, asynchronous manner. Besides allowing the user to view distributed state, MDB also provides the ability to search for bugs over the entire history of distributed states. Finally, MDB allows the user to make hypothetical changes to a macroprogram and to see the effect on distributed state without the need to redeploy, execute, and test the new code. We show that macrodebugging is both easy and efficient: MDB consumes few system resources and requires few user commands to find the cause of bugs. We also provide a lightweight version of MDB called MDB Lite that can be used during the deployment phase to reduce resource consumption while still eliminating the possibility of heisenbugs: changes in the manifestation of bugs caused by enabling or disabling the debugger.

We present a macroprogramming framework called MacroLab that offers a vector programming abstraction similar to Matlab for Cyber-Physical Systems (CPSs). The user writes a single program for the entire network using Matlab-like operations such as addition, find, and max. The framework executes these operations across the network in a distributed fashion, a centralized fashion, or something between the two - whichever is most efficient for the target deployment. We call this approach deployment-specific code decomposition (DSCD). MacroLab programs can be executed on mote-class hardware such as the Telos [24] motes. Our results indicate that MacroLab introduces almost no additional overhead in terms of message cost, power consumption, memory footprint, or CPU cycles over TinyOS

We demonstrate MacroLab, which is a macroprogramming framework that offers a vector programming abstraction similar to Matlab for cyber-physical systems (CPSs). The user writes a single program for an entire network using Matlab like operations such as addition, find, and max. The framework executes these operations across the network in a distributed fashion, a centralized fashion, or something between the two - whichever is most efficient for the target deployment. We call this approach deployment-specific code decomposition (DSCD). The MacroLab programming framework will facilitate the easy development of applications for CPSs by domain experts such as scientists and engineers with almost no additional overhead to the nodes in terms of message cost, power consumption, memory footprint, or CPU cycles over TinyOS programs.

Environmental wireless sensor network (EWSN) systems are deployed in potentially harsh and remote environments where inevitable node and communication failures must be tolerated. LUSTER---Light Under Shrub Thicket for Environmental Research---is a system that meets the challenges of EWSNs using a hierarchical architecture that includes distributed reliable storage, delay-tolerant networking, and deployment time validation techniques.