We present radio-frequency (RF) information harvesting, a chan-nel sensing technique that takes advantage of the energy in the wireless medium to detect channel activity at essentially no en-ergy cost. RF information harvesting is essential for event-driven wireless sensing applications using battery-less devices that har-vest tiny amounts of energy from impromptu events, such as op-erating a switch, and then transmit the event notification to a one-hop gateway. As multiple such devices may concurrently de-tect events, coordinating access to the channel is key. RF infor-mation harvesting allows devices to break the symmetry between concurrently-transmitting devices based on the harvested energy from the ongoing transmissions. To demonstrate the benefits of RF information harvesting, we integrate it in a tailor-made ultra low-power hardware MAC protocol we call Radio Frequency-Distance Packet Queuing (RF-DiPaQ). We build a hardware/software proto-type of RF-DiPaQ and use an established Markov framework to study its performance at scale. Comparing RF-DiPaQ against sta-ple contention-based MAC protocols, we show that it outperforms pure Aloha and 1-CSMA by factors of 3.55 and 1.21 respectively in throughput, while it saturates at more than double the offered load compared to 1-CSMA. As traffic increases, the energy saving of RF-DiPaQ against CSMA protocols increases, consuming 36% less energy than np-CSMA at typical offered loads.@en

Long-range wide-area network (LoRaWAN) is an energy-efficient and inexpensive networking technology that is rapidly being adopted for many Internet-of-Things applications. In this study, we perform extensive measurements on a new LoRaWAN deployment to characterise the spatio-temporal properties of the LoRaWAN channel. Our experiments reveal that LoRaWAN frames are mostly lost due to the channel effects, which are adverse when the end-devices are mobile. The frame losses are up to 70 percent, which can be bursty for both mobile and stationary scenarios. Frame losses result in data losses since the frames are transmitted only once in the basic configuration. To reduce data losses in LoRaWAN, we design a novel coding scheme for data recovery called DaRe that works on the application layer. DaRe combines techniques from convolutional and fountain codes. By implementing DaRe, we show that 99 percent of the data can be recovered with a code rate of 1/2 when the frame loss is up to 40 percent. Compared to the repetition coding scheme, DaRe provides 21 percent higher data recovery and can save up to 42 percent of the energy consumed on a transmission for 10-byte data units. We also show that DaRe provides better resilience to bursty frame losses.

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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.

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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× and 5.13× higher than vanilla LoRaWAN and p-CARMA, respectively. Channel utilization is 11.24× higher compared to LMAC. Further, on a testbed of 30 LoRa devices np-CECADA outperforms LoRaWAN up to 5 times.

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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.@en

The electricity grid, using Information and Communication Technology, is transformed into Smart Grid (SG), which is highly efficient and responsive, promoting two-way energy and information flow between energy-distributors and consumers. Many consumers are becoming prosumers by also harvesting energy. The trend is to form small communities of consumers/prosumers, leading to Micro-grids (MG) to manage energy locally. MGs are parts of SG that decentralize the energy flow, allocating the excess of harvested energy within the community. Energy allocation amongst them must solve certain issues viz., 1) balancing supply/demand within MGs; 2) how allocating energy to a user affects his/her community; and 3) what are the economic benefits for users. To address these issues, we propose six Energy Allocation Strategies (EASs) for MGs - ranging from simple to optimal and their combinations. We maximize the usage of harvested energy within the MG. We form household-groups sharing similar characteristics to apply EASs by analyzing energy and socioeconomic data thoroughly. We propose four evaluation metrics and evaluate our EASs on data acquired from 443 households over a year. By prioritizing specific households, we increase the number of fully served households to 81% compared to random sharing. By combining EASs, we boost the social welfare parameter by 49%.

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Long Range Wide Area Network (LoRaWAN) covers the needs of energy-constrained IoT-devices for operational longevity and extended communication range in a best-effort fashion. However, Lo- RaWAN’s minimalist design cannot handle the traffic from dense deployments with more than a few hundred devices connected to a single gateway, since each LoRa-device transmits data-packets without any information regarding the availability of the medium. In this paper, we try to improve the scalability of LoRaWAN by manifolds, serving thousands of devices per gateway. We present a novel protocol called p persistent-Channel Activity Recognition Multiple Access (p-CARMA) that exploits LoRaWAN’s Channel Activity Detection (CAD) as a crude mechanism to assess if the channel is free. Due to CAD’s imperfections (it only scans for preambles, not for any channel activity) p-CARMA operates probabilistically with each device deciding on a p value based upon local estimation. At the beginning of operation, this estimate is derived from pure local information, that is without involvement of the gateway, and devices automatically adapt to changes in the environment. Then, the adaptation of p-value is assisted by critical information on the cumulative device-delays, multicasted by the gateway at regular, large timespans. To evaluate the performance of p-CARMA, we implemented it in ns-3 based upon a detailed characterization of LoRaWAN’s CAD mechanism involving an extensive set of real-world experiments. We compared p-CARMA to vanilla LoRaWAN as well as a variant using the theoretically optimal p = 1=N (N being the total number of devices). The simulation results show that p-CARMA achieves from three-fold, up to a twenty-fold higher Packet Reception Ratio than LoRaWAN while handling thousands of devices. Further, its adaptivity outperforms the fixed p-value by a factor of 5.25 when scaling up. Moreover, p-CARMA does so while consuming 37.31%-58.17% less energy on average per device compared to vanilla LoRaWAN. @en

Internet of Things (IoT) has created a niche in the last decade. We are in the midst of an unprecedented growth of automation and smart systems, driven by the miniaturization of sensing and computing & communication technologies. Even though battery technology has grown to a large extent, it is still not possible to power these sensors for long, especially in situations where sensors need to provide data at a higher frequency, which drains the batteries fast. Thus, recently researchers are looking into various means of self-powered sensing systems that harvest energy from the ambiance. In this paper, we characterize and optimize a piezoelectric energy harvesting device consisting of a cantilever beam, which is suitable for self-powered Wireless Sensor Network (WSN) nodes in the rail transport networks. The integrated unimorph-piezoelectric sensor harvests energy from ambient vibration. We attune the harvester parameters to the low range of ambient vibration frequencies and demonstrate an experimental model to validate the results of our setup. Vibration frequency and amplitude are measured by performing real experiments inside trains on the routes of the Dutch and German railway network. Each harvester provides 0.72µJ to 0.19mJ per hour depending on the vibration. Multiple of them can be utilized as secondary energy sources inside trains to measure the ambient vibration while harvesting to make it a perpetually powered sensor@en

Information and Communication Technology (ICT) is now touching various aspects of our lives. The electricity grid with the help of ICT is transformed into Smart Grid (SG) which is highly efficient and responsive. It promotes twoway energy and information flow between energy distributors and consumers. Many consumers are becoming prosumers by also producing energy. The trend is to form small communities of consumers and prosumers leading to Micro-grids (MG) to manage energy locally. MGs are parts of SG that decentralize the energy flow by allocating the produced energy within the community. Energy allocation amongst them needs to solve issues viz., (i) how to balance supply/demand within micro-grids; (ii) how allocating energy to a user affects his/her community. To address these issues we propose six Energy Allocation Strategies (EASs) for MGs - ranging from simple to optimal. We maximize the usage of the energy generated by prosumers within MG. We form household-groups sharing similar characteristics to apply EASs by analyzing thoroughly energy and socioeconomic data of households. We propose four metrics to evaluate EASs. We test our EASs on the data from 443 households over a year. By prioritizing specific households, we increase the number of fully served households up to 81 compared to random sharing.

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