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.

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.

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.

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%.

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.

The Internet of Things (IoT) is an enabler of the digital transformation dictating new needs and trends in the domains of business and technology. Ecosystems of IoT devices are often organized in networks, using wireless technology and sharing access infrastructure. These networks are used to monitor a wide range of systems, from simple household activities to fully-interconnected smart cities. In many usage scenarios, the IoT devices are resource-constrained. Thus, energy scavenging is utilized to meet their expanding longevity requirements. In this paper, we study the local resource dynamics of IoT devices in an ecosystem, i.e., a set of different IoT devices that co-exist in spatiotemporal level to coordinate the use of available common resources for their individual goals. To this end, we model an ecosystem of IoT devices as a time-varying graph and provide a theoretical foundation for resource distribution using Graph Theory. We show that simple graph-theoretic metrics, such as, the clustering coefficient and degree distribution, can provide rich information about the priority policy that is followed for the distribution of resources among different IoT devices. We take the case of micro grids; with some nodes having harvesting potential and smart meters measuring the current consumption/generation and being connected to the control unit. We use this notion in our example use-case, appropriating this to micro-grids with enough harvested energy. Even one link per node can describe an ecosystem as a connected component with more than 60% of its total energy needs covered. Additionally, the nodes presenting harvesting potential are formed into unipartite graphs of affiliation networks. Studying their clustering coefficient we infer the priority policy that ia applied when excess energy is shared within their ecosystem.