Many challenges continue to hinder digital technologies' adoption by small and medium scale enterprises (SMEs) in developing economies. Comparatively, there are more success stories by SMEs in emerging markets. However, most SMEs operating in the informal sector in the emerging markets and developing economies (EMDEs) face similar challenges that inhibit the adoption of advanced technologies and innovations needed to improve business operations and re-engineer processes. This article evaluates the implementation and use of state-of-the-art technologies by SMEs in EMDEs to improve operations performance and create sustainable competitive advantages. Further, the papers in this Special Issue identify FinTech and analytical algorithms as some of the current technologies employed by SMEs in EMDEs to improve operations and processes in the manufacturing and service industries. The recognized technologies and technical innovations that seem novel in EMDEs have long existed in the advanced economies. Most state-of-the-art technologies, including cloud computing, 'big data', and predictive analytics that can improve operations and strategic decisions, are yet to make inroads in most EMDEs. Also, disruptive computing technologies, data analytics, and the Internet of Things (IoT) required to engineer new business models, reduce overheads, enhance competitive advantages, and digitize SMEs' business operations remain untapped. The absence and non-adoption of digital technologies in EMDEs explain why business activities in most EMDEs remain shut during the outbreak of SARS-CoV-2 and the community lockdown to contain the COVID-19 pandemic. The strategies to survive the 'new normal' imposed by COVID-19 and fierce global competition includes a successful adoption of advanced technologies. RÉSUMÉ De nombreux d efis continuent a entraver l'adoption des technologies num eriques par les petites et les moyennes entreprises (PME) dans les economies en d eveloppement. En comparaison, les PME ont plus de succ es dans les march es emergents. Cependant, la plupart de celles qui op erent dans le secteur informel des march es emergents et des economies en d eveloppement (MEED)

The rapid growth of Internet-of-Things (IoT) in the current decade has led to the development of a multitude of new access technologies targeted at low-power, wide area networks (LP-WANs). However, this has also created another challenge pertaining to technology selection. This paper reviews the performance of LP-WAN technologies for IoT, including design choices and their implications. We consider Sigfox, LoRaWAN, WavIoT, random phase multiple access (RPMA), narrow band IoT (NB-IoT) as well as LTE-M and assess their performance in terms of signal propagation, coverage and energy conservation. The comparative analyses presented in this paper are based on available data sheets and simulation results. A sensitivity analysis is also conducted to evaluate network performance in response to variations in system design parameters. Results show that each of RPMA, NB-IoT and LTE-M incurs at least 9 dB additional path loss relative to Sigfox and LoRaWAN. This study further reveals that with a 10% improvement in receiver sensitivity, NB-IoT 882 MHz and LoRaWAN can increase coverage by up to 398% and 142% respectively, without adverse effects on the energy requirements. Finally, extreme weather conditions can significantly reduce the active network life of LP-WANs. In particular, the results indicate that operating an IoT device in a temperature of-20 • C can shorten its life by about half; 53% (WavIoT, LoRaWAN, Sigfox, NB-IoT, RPMA) and 48% in LTE-M compared with environmental temperature of 40 • C.

In the UK, non-commodity charges account for 55-65% of energy bills [1] and network charges alone account for about 25% of that figure [2]. Peer-to-peer (P2P) energy trading offers a unique approach to produce and sell energy at the edge of the network and can help in reducing such charges. When these prosumers are coordinated using communication systems [3], [4], significant power network values could be achieved including reduced pollution and, increased energy network efficiency and security [5].Reliable communication systems play vital roles in smart grids [3], [6]. For example, communication infrastructure can be leveraged to regroup prosumers into logical clusters called virtual microgrids (VMGs) in order to improve performance and aid network management cost reduction [3], [4], [7].

Abstract:Transaction-based energy (TE) management and control has become an increasingly relevant topic, attracting considerable attention from industry and the research community alike. As a result, new techniques are emerging for its development and actualization. This paper presents a comprehensive review of TE involving peer-to-peer (P2P) energy trading and also covering the concept, enabling technologies, frameworks, active research efforts and the prospects of TE. The formulation of a common approach for TE management modelling is challenging given the diversity of circumstances of prosumers in terms of capacity, profiles and objectives. This has resulted in divergent opinions in the literature. The idea of this paper is therefore to explore these viewpoints and provide some perspectives on this burgeoning topic on P2P TE systems. This study identified that most of the techniques in the literature exclusively formulate energy trade problems as a game, an optimization problem or a variational inequality problem. It was also observed that none of the existing works has considered a unified messaging framework. This is a potential area for further investigation.

Abstract-Recently, energy efficiency in sensor enabled wire-

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Abstract-Energy-harvesting (EH) and wireless power transfer in cooperative relaying networks have recently attracted a considerable amount of research attention. Most of the existing work on this topic however focuses on Rayleigh fading channels, which represent outdoor environments. In contrast, this paper is dedicated to analyze the performance of dual-hop relaying systems with EH over indoor channels characterized by lognormal fading. Both half-duplex (HD) and full-duplex (FD) relaying mechanisms are studied in this work with decode-andforward (DF) and amplify-and-forward (AF) relaying protocols. In addition, three EH schemes are investigated, namely, time switching relaying, power splitting relaying and ideal relaying receiver which serves as a lower bound. The system performance is evaluated in terms of the ergodic outage probability for which we derive accurate analytical expressions. Monte Carlo simulations are provided throughout to validate the accuracy of our analysis. Results reveal that, in both HD and FD scenarios, AF relaying performs only slightly worse than DF relaying which can make the former a more efficient solution when the processing energy cost at the DF relay is taken into account. It is also shown that FD relaying systems can generally outperform HD relaying schemes as long as the loop-back interference in FD is relatively small. Furthermore, increasing the variance of the log-normal channel has shown to deteriorate the performance in all the relaying and EH protocols considered.Index Terms-Amplify-and-forward relay, decode-and-forward relay, ergodic outage probability, full-duplex, half-duplex, energyharvesting protocols, log-normal fading, wireless power transfer.

In Wireless Sensor Networks (WSNs), routing data towards the sink leads to unbalanced energy consumption among intermediate nodes resulting in high data loss rate. The use of multiple Mobile Data Collectors (MDCs) has been proposed in the literature to mitigate such problems. MDCs help to achieve uniform energy-consumption across the network, fill coverage gaps, and reduce end-to-end communication delays, amongst others. However, mechanisms to support MDCs such as location advertisement and route maintenance introduce significant overhead in terms of energy consumption and packet delays. In this paper, we propose a self-organizing and adaptive Dynamic Clustering (DCMDC) solution to maintain MDCrelay networks. This solution is based on dividing the network into well-delimited clusters called Service Zones (SZs). Localizing mobility management traffic to a SZ reduces signaling overhead, route setup delay and bandwidth utilization. Network clustering also helps to achieve scalability and load balancing. Smaller network clusters make buffer overflows and energy depletion less of a problem. These performance gains are expected to support achieving higher information completeness and availability as well as maximizing the network lifetime. Moreover, maintaining continuous connectivity between the MDC and sensor nodes increases information availability and validity. Performance experiments show that DCMDC outperforms its rival in the literature. Besides the improved quality of information, the proposed approach improves the packet delivery ratio by up to 10%, end-to-end delay by up to 15%, energy consumption by up to 53%, energy balancing by up to 51%, and prolongs the network lifetime by up to 53%.