While cryptographic implementations provide computational security in circuits and systems, hardware attack techniques, e.g., electromagnetic (EM) side-channel analysis (SCA) attack can still break through. The commonplace countermeasures for EM SCA attack require significant overheads in terms of power consumption. This article explores an on-chip capacitive sensing technique for the purpose of detection of an approaching EM probe even before an attack is performed, thereby alleviating the overheads incurred by any countermeasure against such attacks. Different type of capacitive structures are considered in regards to sensitivity and area. The proposed method of coplanar capacitive asymmetry sensing (CEASE) consists of a grid of four metal plates of the same size and dimensions determined through design space exploration. A comparison between the capacitive and inductive sensing technique is also performed in terms of detection range through theoretical arguments and EM simulation. A$>$17% change in capacitance is shown at a distance of 1 mm, implying a$>10\times $improvement in the detection range over inductive sensing methods. Furthermore, at 0.1-mm distance, a$>$45% change in capacitance is observed, leading to a$>3\times $and$>11\times $sensitivity improvement over capacitive parallel plate sensing and inductive sensing, respectively.

Intensive research on energy-harvested sensor nodes has been driven by the challenges in achieving stringent design goals of battery lifetime, information accuracy, transmission distance, and cost. This challenge is further amplified by the inherent power-intensive nature of long-range communication when sensor networks are required to span vast areas, such as agricultural fields and remote terrain. Solar power is a common energy source in wireless sensor nodes, however, it is not reliable due to fluctuations in available power stemming from the changing seasons and weather conditions. This article tackles these issues by presenting a perpetually powered, energy-harvesting sensor node which utilizes a minimally sized solar cell and is capable of long-range communication by dynamically co-optimizing energy consumption and information transfer, termed as energy-information dynamic co-optimization (EICO). This energy-information intelligence is achieved by adaptive dutycycling of information transfer based on the total amount of energy available from the harvester and charge storage element to optimize the energy consumption of the sensor node, while employing in-sensor analytics (ISA) to minimize loss of information. This is the first reported sensor node1 km at continuous information transfer rates of up to 1 packet/second which is enabled by EICO and ISA.

Electromagnetic (EM) side-channel analysis (SCA) attack, which breaks cryptographic implementations, has become a major concern in the design of circuits and systems. This paper focuses on EM SCA and proposes the detection of an approaching EM probe even before an attack is performed. The proposed method of co-planar capacitive asymmetry sensing consists of a grid of four metal plates of the same size and dimension. As an EM probe approaches the sensing metal plates, the symmetry of the sensing metal plate system breaks, and the capacitance between each pair diverge from their baseline capacitances. Using Ansys Maxwell Finite Element Method (FEM) simulations, we demonstrate that the co-planar capacitive asymmetry sensing has an enhanced detection range compared to other sensing methods. At a distance of 1 mm between the sensing metal plates and the approaching EM probe, it shows >17 % change in capacitance, leading to a > 10 × improvement in detection range over the existing inductive sensing methods. At a distance of 0.1 mm, a > 45% change in capacitance is observed, leading to a > 3 × and > 11 × sensitivity improvement over capacitive parallel sensing and inductive sensing respectively. Finally, we show that the co-planar capacitive asymmetry sensing is sensitive to both E-field and H-field probes, unlike inductive sensing which cannot detect an E-field probe.

Electromagnetic (EM) side-channel analysis (SCA) attack, which breaks cryptographic implementations, has become a major concern in the design of circuits and systems. This paper presents the design and analysis of the EM side-channel attack detection system utilizing patterned-ground co-planar capacitive asymmetry sensing (PG-CAS) for approaching probe, targeting to improve sensitivity, detection range, and power consumption compared to LC oscillator utilizing inductive sensing. The PG-CAS consists of a grid of four metal plates of the same size at the top metal layer and a patterned ground plane at a lower metal. As an EM probe approaches, electric field lines between the plates and plate-ground get distorted, thereby breaking the symmetry of the inter-plate and the plate-ground capacitance system and this change in capacitance is sensed. The PG-CAS circuit consists of two LC oscillators, mixer, low pass filter (LPF), resistive feedback amplifier (RFA) and a digital logic. By down-converting sensing signal to low-frequency using mixer, LPF, RFA and digital logic, the detection range is significantly improved. At a distance of 1 mm between the sensing metal plates and the approaching EM probe, system-level simulation results using TSMC 65nm technology and Ansys Maxwell show a > 10% change in the output frequency from the baseline frequency, leading to a > 10× improvement in the detection range and a ~ 3× improvement in power consumption over existing inductive sensing methods.

Decades of continuous scaling has reduced the energy of unit computing to virtually zero, while energy-efficient communication has remained the primary bottleneck in achieving fully energy-autonomous Internet-of-Things (IoT) nodes. This article presents and analyzes the tradeoffs between the energies required for communication and computation in a wireless sensor network, deployed in a mesh architecture over a 2400-acre university campus, and is targeted toward multisensor measurement of temperature, humidity and water nitrate concentration for smart agriculture. Several scenarios involving in-sensor analytics (ISA), collaborative intelligence (CI), and context-aware switching (CAS) of the cluster head during CI has been considered. A real-time co-optimization algorithm has been developed for minimizing the energy consumption in the network, hence maximizing the overall battery lifetime. Measurement results show that the proposed ISA consumes ≈ 467× lower energy as compared to traditional Bluetooth low energy (BLE) communication, and ≈ 69500× lower energy as compared with long-range (LoRa) communication. When the ISA is implemented in conjunction with LoRa, the lifetime of the node increases from a mere 4.3 h to 66.6 days with a 230-mAh coin cell battery, while preserving >99% of the total information. The CI and CAS algorithms help in extending the worst case node lifetime by an additional 50%, thereby exhibiting an overall network lifetime of ≈ 104 days, which is >90% of the theoretical limits as posed by the leakage current present in the system, while effectively transferring information sampled every second. A Web-based monitoring system was developed to continuously archive the measured data, and for reporting real-time anomalies.