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1. Stochastic Computing-based Baseband Processing for Resource Constraint IoT Devices

   https://doi.org/10.1007/978-3-031-14627-5_3  

   Kazi Ahmed, Yang Kim ( January 2022 , INCoS-2022)

   With the recent deployment of 5G network, the ever increasing IoT has got a tremendous boost in its expansion and already has penetrated well into the government, commercial and private sectors. With the countless IoT devices and myriad of applications, many of them are resource constrained and have limited energy budget. These IoT devices demand low-energy technique for their computing and communication tasks to stay active for longer period. The two main baseband processes that dissipate bulk of CPU power from the IoT device are synchronization and Finite Impulse Response (FIR) filtering. In this circumstance, hardware-based baseband processing can take these tasks off of the CPU and may significantly reduce energy consumption. While conventional Binary Radix Computing (BC)-based hardware modules can improve power dissipation, Stochastic Computing (SC)-based hardware will certainly cut down much more both the power as well as silicon space in comparison. With this motivation, we propose novel SC-based hardware designs in regards to synchronization and Finite Impulse Response (FIR) filter for resource constraint IoT devices. Comparative analysis shows that our proposed SC-based design can reduce significantly more power and silicon area compared to the BC as well as other proposed SC designs. 

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2. Wake-up radio-based data forwarding for green wireless networks

   https://doi.org/10.1016/j.comcom.2020.05.046  

   Koutsandria, G. ; DiValerio, V. ; Spenza, D. ; Basagni, S. ; Petrioli, C. ( April 2020 , Computer communications)

   Green wireless networks Wake-up radio Energy harvesting Routing Markov decision process Reinforcement learning 1. Introduction With 14.2 billions of connected things in 2019, over 41.6 billions expected by 2025, and a total spending on endpoints and services that will reach well over $1.1 trillion by the end of 2026, the Internet of Things (IoT) is poised to have a transformative impact on the way we live and on the way we work [1–3]. The vision of this ‘‘connected continuum’’ of objects and people, however, comes with a wide variety of challenges, especially for those IoT networks whose devices rely on some forms of depletable energy support. This has prompted research on hardware and software solutions aimed at decreasing the depen- dence of devices from ‘‘pre-packaged’’ energy provision (e.g., batteries), leading to devices capable of harvesting energy from the environment, and to networks – often called green wireless networks – whose lifetime is virtually infinite. Despite the promising advances of energy harvesting technologies, IoT devices are still doomed to run out of energy due to their inherent constraints on resources such as storage, processing and communica- tion, whose energy requirements often exceed what harvesting can provide. The communication circuitry of prevailing radio technology, especially, consumes relevant amount of energy even when in idle state, i.e., even when no transmissions or receptions occur. Even duty cycling, namely, operating with the radio in low energy consumption ∗ Corresponding author. E-mail address: koutsandria@di.uniroma1.it (G. Koutsandria). https://doi.org/10.1016/j.comcom.2020.05.046 (sleep) mode for pre-set amounts of time, has been shown to only mildly alleviate the problem of making IoT devices durable [4]. An effective answer to eliminate all possible forms of energy consumption that are not directly related to communication (e.g., idle listening) is provided by ultra low power radio triggering techniques, also known as wake-up radios [5,6]. Wake-up radio-based networks allow devices to remain in sleep mode by turning off their main radio when no communication is taking place. Devices continuously listen for a trigger on their wake-up radio, namely, for a wake-up sequence, to activate their main radio and participate to communication tasks. Therefore, devices wake up and turn their main radio on only when data communication is requested by a neighboring device. Further energy savings can be obtained by restricting the number of neighboring devices that wake up when triggered. This is obtained by allowing devices to wake up only when they receive specific wake-up sequences, which correspond to particular protocol requirements, including distance from the destina- tion, current energy status, residual energy, etc. This form of selective awakenings is called semantic addressing [7]. Use of low-power wake-up radio with semantic addressing has been shown to remarkably reduce the dominating energy costs of communication and idle listening of traditional radio networking [7–12]. This paper contributes to the research on enabling green wireless networks for long lasting IoT applications. Specifically, we introduce a ABSTRACT This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively. 

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3. A PUF-based Approach for Sustainable Cybersecurity in Smart Agriculture

   https://doi.org/10.1109/OCIT53463.2021.00080  

   Bathalapalli, Venkata K. ; Mohanty, Saraju P. ; Kougianos, Elias ; Yanambaka, Venkata P. ; Baniya, Babu K. ; Rout, Bibhudutta ( December 2021 , 2021 19th OITS International Conference on Information Technology (OCIT))

   Padhy, Sudarsan ; Oria, Vincent (Ed.)

   The simplicity, low cost, and scalability of Internet of Things (IoT) devices have led researchers to study their applications in a wide range of areas such as Healthcare, Transportation, and Agriculture. IoT devices help farmers to monitor the conditions in a field. These are connected to edge devices for real-time analysis. The edge servers send commands to actuators in the farm directly, without human intervention. At the same time, security vulnerabilities are a big concern, concomitant with the increasing utilization of IoT devices. If the duplication of an IoT device occurs and attackers gain access to the system, then the integrity of the entire ecosystem will be at stake, regardless of the application domain. This paper presents a Physical Unclonable Function (PUF) based hardware security primitive for the authentication of Internet of Agro-Things (IoAT) devices. The proposed security scheme has been prototyped with a testbed evaluation. An arbiter PUF module has been used for the validation of the proposed scheme. The PUF based security primitive is lightweight, scalable, and robust as it mainly depends on inherent manufacturing variations, thereby ensuring no chance for the duplication of IoT devices. 

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4. SIA: Secure Intermittent Architecture for Off-the-Shelf Resource-Constrained Microcontrollers

   Dinu, Daniel ; Krishnan, Archanaa ; Schaumont, Patrick ( May 2019 , IEEE International Symposium on Hardware Oriented Security and Trust (HOST))

   Recent advancements in energy-harvesting techniques provide an alternative to batteries for resource constrained IoT devices and lead to a new computing paradigm, the intermittent computing model. In this model, a software module continues its execution from where it left off when an energy shortage occurred. Enforcing security of an intermittent software module is challenging because its power-off state has to be protected from a malicious adversary in addition to its power-on state, while the security mechanisms put in place must have a low overhead on the performance, resource consumption, and cost of a device. In this paper, we propose SIA (Secure Intermittent Architecture), a security architecture for resource-constrained IoT devices. SIA leverages low-cost security features available in commercial off-the-shelf microcontrollers to protect both the power-on and power-off state of an intermittent software module. Therefore, SIA enables a host of secure intermittent computing applications such as self-attestation, remote attestation, and secure communication. Moreover, our architecture provides confidentiality and integrity guarantees to an intermittent computing module at no cost compared to previous approaches in the literature that impose significant overheads. The salient characteristic of SIA is that it does not require any hardware modifications, and hence, it can be directly applied to existing IoT devices. We implemented and evaluated SIA on a resource-constrained IoT device based on an MSP430 processor. Besides being secure, SIA is simple and efficient. We confirm the feasibility of SIA for resource-constrained IoT devices with experimental results of several intermittent computing applications. Our prototype implementation outperforms by two to three orders of magnitude the secure intermittent computing solution of Suslowicz et al. presented at IGSC 2018. 

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5. SIA: Secure Intermittent Architecture for Off-the-Shelf Resource-Constrained Microcontrollers

   Dinu, Daniel ; Krishan, Archanaa ; Schaumont, Patrick ( May 2019 , IEEE International Symposium on Hardware Oriented Security and Trust (HOST))

   Recent advancements in energy-harvesting techniques provide an alternative to batteries for resource-constrained IoT devices and lead to a new computing paradigm, the intermittent computing model. In this model, a software module continues its execution from where it left off when an energy shortage occurred. Enforcing security of an intermittent software module is challenging because its power-off state has to be protected from a malicious adversary in addition to its power-on state, while the security mechanisms put in place must have a low overhead on the performance, resource consumption, and cost of a device. In this paper, we propose SIA (Secure Intermittent Architecture), a security architecture for resource-constrained IoT devices. SIA leverages low-cost security features available in commercial off-the-shelf microcontrollers to protect both the power-on and power-off state of an intermittent software module. Therefore, SIA enables a host of secure intermittent computing applications such as self-attestation, remote attestation, and secure communication. Moreover, our architecture provides confidentiality and integrity guarantees to an intermittent computing module at no cost compared to previous approaches in the literature that impose significant overheads. The salient characteristic of SIA is that it does not require any hardware modifications, and hence, it can be directly applied to existing IoT devices. We implemented and evaluated SIA on a resource-constrained IoT device based on an MSP430 processor. Besides being secure, SIA is simple and efficient. We confirm the feasibility of SIA for resource-constrained IoT devices with experimental results of several intermittent computing applications. Our prototype implementation outperforms by two to three orders of magnitude the secure intermittent computing solution of Suslowicz et al. presented at IGSC 2018. 

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