We propose a new approach for lossless data compression to reduce the amount of data transmitted by Cyber-Physical Systems (CPS) by several-fold. Our approach uses an indexing technique inspired in the concept of generalized deduplication to compress data by a) finding matches of similar (not necessarily equal) data chunks, and b) exploiting data chunks previously stored in the Edge (or Cloud) by the same or even other CPS devices. We propose a mathematical model to predict the gains based on the number of previously received chunks and validate it using simulations. We show that compression factors of 23-fold are possible even with a limited number of previously stored chunks in the Edge. Gains of 2.8-fold over DEFLATE compression on differential samples are possible even for small data chunks (16 B) for synthetic data. Using real-world CPS data sets, we show that our technique can provide gains of up to 2.9. We also show our solution's processing speed in a Raspberry Pi 3 can be as high as 163 MB/s.

Revolving Codes (ReC) are introduced as an alternative to other network codes to reduce per packet and total overhead, and reduce the probability of linearly dependent coded packets. Furthermore, Revolving Codes reduce the costs to intermediate nodes by introducing a recoding scheme based on XOR operations. Revolving Codes are well suited for new applications transmitting small payloads, e.g., IoT, Industry 4.0. Our numerical results show that ReC outperforms RLNC in total overhead by as much as two orders of magnitude and it outperforms Fulcrum codes by as much as two orders of magnitude in terms of the overhead caused by linearly dependent packets.

The amount of data generated worldwide is expected to grow from 33 to 175 ZB by 2025 [1] in part driven by the growth of Internet of Things (IoT) and cyber-physical systems (CPS). To cope with this enormous amount of data, new edge (and cloud) storage techniques must be developed. Generalised Data Deduplication (GDD) is a new paradigm for reducing the cost of storage by systematically identifying near identical data chunks, storing their common component once, and a compact representation of the deviation to the original chunk for each chunk. This paper presents a system architecture for GDD and a proof-of-concept implementation. We evaluated the compression gain of Generalised Data Deduplication using three data sets of varying size and content and compared to the performance of the EXT4 and ZFS file systems, where the latter employs classic deduplication. We show that Generalised Data Deduplication provide up to 16.75% compression gain compared to both EXT4 and ZFS with data sets with less than 5 GB of data.

This paper proposes a high-efficiency charge pump circuit with small integrated capacitors, dedicated to high-density microstimulators. The proposed circuit offers improvement of about 35% in the charge pump efficiency over the conventional cross-coupled charge pumps. This is achieved through proper employment of two techniques: (a) omitting the undesired conductive paths that discharge the output capacitor, and (b) discounting the dynamic switching power losses by half. Moreover, a straightforward physical layout is proposed to prevent the latchup phenomenon. Occupying 0.5 mm 2 of silicon area, circuits for a 4-stage (1 positive stage and 3 negative stages) charge pump were designed and simulated in transistor level in a standard 0.18-μm CMOS technology. Designed for an implantable visual prosthesis, the charge pump generates output voltages of 3.48V,-1.69V,-3.38V, and-5.05V out of a 1.8V input voltage and exhibits average power efficiency of 92.8% and 86.8% for 1-and 3-stage circuits respectively, all in the case of a 100μA current load. An output per stage with current sinking/sourcing ability allows different stimulation channels to be independently connected to different supply levels according their operational needs.

High frame-corruption is widely observed in Long Range Wide Area Networks (LoRaWAN) due to the coexistence with other networks in ISM bands and an Aloha-like MAC layer. LoRa's Forward Error Correction (FEC) mechanism is often insufficient to retrieve corrupted data. In fact, reallife measurements show that at least one-fourth of received transmissions are corrupted. When more frames are dropped, LoRa nodes usually switch over to higher spreading factors (SF), thus increasing transmission times and increasing the required energy. This paper introduces ReDCoS, a novel coding technique at the application layer that improves recovery of corrupted LoRa frames, thus reducing the overall transmission time and energy invested by LoRa nodes by several-fold. ReDCoS utilizes lightweight coding techniques to pre-encode the transmitted data. Therefore, the inbuilt Cyclic Redundancy Check (CRC) that follows is computed based on an already encoded data. At the receiver, we use both the CRC and the coded data to recover data from a corrupted frame beyond the built-in Error Correcting Code (ECC). We compare the performance of ReDCoS to (i) the standard FEC of vanilla-LoRaWAN, and to (ii) Reed Solomon (RS) coding applied as ECC to the data of LoRaWAN. The results indicated a 54x and 13.5x improvement of decoding ratio, respectively, when 20 data symbols were sent. Furthermore, we evaluated ReDCoS on-field using LoRa SX1261 transceivers showing that it outperformed RS-coding by factor of at least 2x (and up to 6x) in terms of the decoding ratio while consuming 38.5% less energy per correctly received transmission.