Due to power limitations and coexistence in ISM bands, up to 50% of the Long Range (LoRa)-frames are corrupted at low signal strengths (≈ -115dBm) and the built-in redundancy schemes in LoRa-Wide Area Network (LoRaWAN) cannot correct the corrupted bytes. To address this, higher Spreading Factors (SF) are used resulting in wasted energy, increased traffic load, and highly compromised effective data rate. Our on-field experiments showed a high correlation in the corruption of close-by frames. We propose a novel Divide & Code (DC) scheme for LoRaWANs as an alternative to using higher SF. DC pre-encodes LoRa payloads using lightweight and memoryless encoding. After receiving a corrupted frame, DC uses a combination of most probable patterns of errors, Time Thresholds (TT), and splitting of payloads into subgroups for batch processing to recover frames effectively and maintain low complexity and timely operation. By implementing DC on our LoRa-testbed, we show it outperforms vanilla-LoRaWAN and Reed-Solomon codes in decoding and energy consumption. Our schemes decode up to 80.5% of corrupted payloads on SF10 by trying only 0.03% of all patterns of error combinations. TT keeps processing times below 2 ms with only minor reductions in the decoding ratio of corrupted payloads. Finally, we showcase that introducing 30% redundancy with DC results in minimum energy consumption and high decoding ratio at low SNRs.

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, real-life 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.

Long Range Wide Area Networks (LoRaWAN) offer ubiquitous communications for The Internet of Things (IoT). However, there are many challenges in rolling out LoRaWAN - mainly scalability, energy efficiency, Packet Reception Ratio (PRR), and keeping the channel access as simple as unslotted ALOHA. To this end, we design non-persistent Capture Effect Channel Activity Detection Algorithm (np-CECADA), which is a novel, distributed protocol for the MAC layer of LoRaWAN. It utilizes Channel Activity Detection (CAD), which is a built-in imperfect mechanism for channel sensing and minimal feedback from the gateways. In np-CECADA each device independently adapts backoff times based on the traffic in its vicinity and the transmission power based on the heuristically inferred probability of capturing the channel. To achieve this, first, we carried out an extensive on-field evaluation to measure the effectiveness of CAD and capture effect in LoRa. Using them we designed np CECADA and developed $ns-3$ modules. Packet Reception Ratio of np-CECADA is $ 15.74\times$ and $ 5.13\times$ higher than vanilla LoRaWAN and p-CARMA, respectively. Channel utilization is $ 11.24\times$ higher compared to LMAC. Further, on a testbed of 30 LoRa devices np-CECADA outperforms LoRaWAN up to 5 times.

Timely delivery of sensor data is crucial for a wide array of Internet-of-Things (IoT) applications. Due to the large space and time correlation of sensor data, there is a high potential for compression. However, conventional wisdom dictates that compression is at odds with information freshness and timely delivery of data. The reason is that sufficient data needs to be accumulated in order to achieve reasonable compression rates, which introduces additional delays on data transmission. This article studies a novel approach to perform online compression of data across multiple data sources which achieves significantly better performance in both Age of Information (AoI) and compression for sensor applications. More specifically, we show that our approach can remove the tradeoff between these two metrics, particularly, when considering an instantly decodable variant of our approach. We also propose and study techniques to further improve both these metrics by using preset and dynamically created dictionaries at the source nodes. Using real-world data sets, we show that our solution reduces the AoI (by up to a factor of 2.3) and compression ratio (by up to an order of magnitude) with respect to DEFLATE and LZW. Finally, we show that using multiple sources benefits results in an improvement of AoI and compression for each involved source compared to compressing individually.

Network appliances continue to offer novel opportunities to offload processing from computing nodes directly into the data plane. One popular concern of network operators and their customers is to move data increasingly faster. A common technique to increase data throughput is to compress it before its transmission. However, this requires compression of the data---a time and energy demanding preprocessing phase---and decompression upon reception---a similarly resource consuming operation. Moreover, if multiple nodes transfer similar data chunks across the network hop (e.g., a given pair of switches), each node effectively wastes resources by executing similar steps. This paper proposes ZipLine, an approach to design and implement (de)compression at line speed leveraging the Tofino hardware platform which is programmable using the P416 language. We report on lessons learned while building the system and show throughput, latency and compression measurements on synthetic and real-world traces, showcasing the benefits and trade-offs of our design.

Utility providers are relying more often on smart, wirelessly connected smart meters to collect consumption information of their customers. The sheer amount of connected smart meters and the growing requirements to provide more frequent reports from each device are putting a large strain on existing systems and protocols. In this paper, we propose three novel lossless compression schemes that significantly reduce the size of standard DLMS data messages uploaded by smart electricity meters. Using real life data sets, we show that these methods can achieve compression rates of over 90% while being transparent to the DLMS protocol.

Vehicles generate a large amount of data from their internal sensors. This data is not only useful for a vehicle's proper operation, but it provides car manufacturers with the ability to optimize the performance of individual vehicles and companies with fleets of vehicles (e.g., trucks, taxis, tractors) to optimize their operations to reduce fuel costs and plan repairs. This paper proposes algorithms to compress CAN bus data, specifically, packaged as MDF4 files. In particular, we propose lightweight, online and configurable compression algorithms that allow limited devices to choose the amount of RAM and flash memory allocated to them. We show that our proposals can outperform LZW for the same RAM footprint, and can even deliver comparable or better performance to DEFLATE under the same RAM limitations.

Generalized deduplication (GD) has been proposed as a new approach for reducing the cost of storage. Recent work has adapted this technique to provide distributed, multisource lossless compression to reduce the total number of bits transmitted in sensor networks. In this paper, we characterize its performance and advantages from an age of information perspective. For simplicity, we analyze the case of one source node receiving one symbol/sample per unit time and transmitting bits to the sink node. We show the potential for GD to also deliver instant decoding of the data to further reduce the average age of information. Using real-world data sets, our solution reduces the information age by 25% and 36% when considering the standard and the instantly decodable versions, respectively compared to the use of the DEFLATE algorithm for compression.