More Like this

1. REBOUND: Defending Distributed Systems Against Attacks with Bounded-Time Recovery

   https://doi.org/10.1145/3447786.3456257  

   Gandhi, Neeraj; Roth, Edo; Sandler, Brian; Haeberlen, Andreas; Phan, Linh Thi (April 2021, Proceedings of the 16th European Conference on Computer Systems (EuroSys'21))

   null (Ed.)

   This paper shows how to use bounded-time recovery (BTR) to defend distributed systems against non-crash faults and attacks. Unlike many existing fault-tolerance techniques, BTR does not attempt to completely mask all symptoms of a fault; instead, it ensures that the system returns to the correct behavior within a bounded amount of time. This weaker guarantee is sufficient, e.g., for many cyber-physical systems, where physical properties - such as inertia and thermal capacity - prevent quick state changes and thus limit the damage that can result from a brief period of undefined behavior. We present an algorithm called REBOUND that can provide BTR for the Byzantine fault model. REBOUND works by detecting faults and then reconfiguring the system to exclude the faulty nodes. This supports very fine-grained responses to faults: for instance, the system can move or replace existing tasks, or drop less critical tasks entirely to conserve resources. REBOUND can take useful actions even when a majority of the nodes is compromised, and it requires less redundancy than full fault-tolerance. 

   more » « less
2. Energy-Aware Resource Management in Vehicular Edge Computing Systems

   https://doi.org/10.1109/IC2E48712.2020.00012  

   Bahreini, Tayebeh; Brocanelli, Marco; Grosu, Daniel (April 2020, Proc. of the IEEE International Conference on Cloud Engineering (IC2E 2020))

   The low-latency requirements of connected electric vehicles and their increasing computing needs have led to the necessity to move computational nodes from the cloud data centers to edge nodes such as road-side units (RSU). However, offloading the workload of all the vehicles to RSUs may not scale well to an increasing number of vehicles and workloads. To solve this problem, computing nodes can be installed directly on the smart vehicles, so that each vehicle can execute the heavy workload locally, thus forming a vehicular edge computing system. On the other hand, these computational nodes may drain a considerable amount of energy in electric vehicles. It is therefore important to manage the resources of connected electric vehicles to minimize their energy consumption. In this paper, we propose an algorithm that manages the computing nodes of connected electric vehicles for minimized energy consumption. The algorithm achieves energy savings for connected electric vehicles by exploiting the discrete settings of computational power for various performance levels. We evaluate the proposed algorithm and show that it considerably reduces the vehicles' computational energy consumption compared to state-of-the-art baselines. Specifically, our algorithm achieves 15-85% energy savings compared to a baseline that executes workload locally and an average of 51% energy savings compared to a baseline that offloads vehicles' workloads only to RSUs. 

   more » « less
3. Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

   https://doi.org/10.1145/3606263  

   Yu, Yuning; Hsu, Shanglin; Chen, Andre; Chen, Yutian; Tang, Bin (October 2023, ACM Transactions on Sensor Networks)

   We aim to preserve a large amount of data generated insidebase station-less sensor networks(BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP:datapreservationproblem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit. In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN. 

   more » « less
4. AD ² : Improving Quality of IoT Data through Compressive Anomaly Detection

   https://doi.org/10.1109/BigData47090.2019.9005533  

   Moon, Aekyeung; Zhuo, Xiaoyan; Zhang, Jialing; Son, Seung Woo (December 2019, IEEE International Conference on Big Data (Big Data))

   With recent technological advances in sensor nodes, IoT enabled applications have great potential in many domains. However, sensing data may be inaccurate due to not only faults or failures in the sensor and network but also the limited resources and transmission capability available in sensor nodes. In this paper, we first model streams of IoT data as a handful of sampled data in the transformed domain while assuming the information attained by those sampled data reveal different sparsity profiles between normal and abnormal. We then present a novel approach called AD2 (Anomaly Detection using Approximated Data) that applies a transformation on the original data, samples top k-dominant components, and detects data anomalies based on the disparity in k values. To demonstrate the effectiveness of AD2 , we use IoT datasets (temperature, humidity, and CO) collected from real-world wireless sensor nodes. Our experimental evaluation demonstrates that AD2 can approximate and successfully detect 64%-94% of anomalies using only 1.9% of the original data and minimize false positive rates, which would otherwise require the entire dataset to achieve the same level of accuracy. 

   more » « less
5. Bioelectronic Sensor Nodes for the Internet of Bodies

   https://doi.org/10.1146/annurev-bioeng-110220-112448  

   Chatterjee, Baibhab; Mohseni, Pedram; Sen, Shreyas (June 2023, Annual Review of Biomedical Engineering)

   Energy-efficient sensing with physically secure communication for biosensors on, around, and within the human body is a major area of research for the development of low-cost health care devices, enabling continuous monitoring and/or secure perpetual operation. When used as a network of nodes, these devices form the Internet of Bodies, which poses challenges including stringent resource constraints, simultaneous sensing and communication, and security vulnerabilities. Another major challenge is to find an efficient on-body energy-harvesting method to support the sensing, communication, and security submodules. Due to limitations in the amount of energy harvested, we require a reduction in energy consumed per unit information, making the use of in-sensor analytics and processing imperative. In this article, we review the challenges and opportunities of low-power sensing, processing, and communication with possible powering modalities for future biosensor nodes. Specifically, we analyze, compare, and contrast ( a) different sensing mechanisms such as voltage/current domain versus time domain, ( b) low-power, secure communication modalities including wireless techniques and human body communication, and ( c) different powering techniques for wearable devices and implants. 

   more » « less