Abstract:The role of Demand Side Management (DSM) with Distributed Energy Storage (DES) has been gaining attention in recent studies due to the impact of the latter on energy management in the smart grid. In this work, an Energy Scheduling and Distributed Storage (ESDS) algorithm is proposed to be installed into the smart meters of Time-of-Use (TOU) pricing consumers possessing in-home energy storage devices. Source of energy supply to the smart home appliances was optimized between the utility grid and the DES device depending on energy tariff and consumer demand satisfaction information. This is to minimize consumer energy expenditure and maximize demand satisfaction simultaneously. The ESDS algorithm was found to offer consumer-friendly and utility-friendly enhancements to the DSM program such as energy, financial, and investment savings, reduced/eliminated consumer dissatisfaction even at peak periods, Peak-to-Average-Ratio (PAR) demand reduction, grid energy sustainability, socio-economic benefits, and other associated benefits such as environmental-friendliness.

The contributions of Distributed Energy Generation (DEG) and Distributed Energy Storage (DES) for Demand Side Management (DSM) purposes in a smart macrogrid or microgrid cannot be over-emphasised. However, standalone DEG and DES can lead to under-utilisation of energy generation by consumers and financial investments; in grid-connection mode, though, DEG and DES can offer arbitrage opportunities for consumers and utility provider(s). A grid-connected smart microgrid comprising heterogeneous (active and passive) smart consumers, electric vehicles and a large-scale centralised energy storage is considered in this paper. Efficient energy management by each smart entity is carried out by the proposed Microgrid Energy Management Distributed Optimisation Algorithm (MEM-DOA) installed distributively within the network according to consumer type. Each smart consumer optimises its energy consumption and trading for comfort (demand satisfaction) and profit. The proposed model was observed to yield better consumer satisfaction, higher financial savings, and reduced Peak-to-Average-Ratio (PAR) demand on the utility grid. Other associated benefits of the model include reduced investment on peaker plants, grid reliability and environmental benefits. The MEM-DOA also offered participating smart consumers energy and tariff incentives so that passive smart consumers do not benefit more than active smart consumers, as was the case with some previous energy management algorithms.

Low power wide area network (LPWAN) is among the fastest growing networks in Internet of Things (IoT) technologies. Owing to varieties of outstanding features which include long range communication and low power consumption, LPWANs are fast becoming the most widely deployed connectivity standards in IoT domain. However, this promising network are exposed to various security and privacy threats and challenges. For reliable connectivity within networks, the security and privacy challenges need to be effectively addressed with proper mitigation protocol in place. In this paper, a comprehensive review on the security feature of LPWAN is presented. The paper mainly focuses on analyzing LPWAN’s key cybersecurity architecture and it present a significant emphasis on how the LPWAN is highly attractive to intruders and attackers. This paper aims at summarizing recent research works on key LPWAN security challenges such as replay attack, denial-of-service attack, worm hole attack, and eavesdropping attack, the effect of the attacks, and most importantly the various approaches proposed in the literature for the attacks’ mitigation. The paper concludes by highlighting major research gaps and future directions for the successful deployment of LPWAN.

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The Internet of Things (IoT) is a promising technology that allows numerous devices to be connected for ease of communication. The heterogeneity and ubiquity of the various connected devices, openness to devices in the network, and, importantly, the increasing number of connected smart objects (or devices) have exposed the IoT network to various security challenges and vulnerabilities which include manipulative data injection and cyberattacks such as a denial of service (DoS) attack. Any form of intrusive data injection or attacks on the IoT networks can create devastating consequences on the individual connected device or the entire network. Hence, there is a crucial need to employ modern security measures that can protect the network from various forms of attacks and other security challenges. Intrusion detection systems (IDS) and intrusion prevention systems have been identified globally as viable security solutions. Several traditional machine learning methods have been deployed as IoT IDS. However, the methods have been heavily criticized for poor performances in handling voluminous datasets, as they rely on domain expertise for feature extraction among other reasons. Thus, there is a need to devise better IDS models that can handle the IoT voluminous datasets efficiently, cater to feature extraction, and perform reasonably well in terms of overall performance. In this paper, an IDS based on redefined long short-term memory deep learning approach is proposed for detecting DoS attacks in IoT networks. The model was tested on benchmark datasets; CICIDS-2017 and NSL-KDS datasets. Three pre-processing procedures, which include encoding, dimensionality reduction, and normalization were deployed for the datasets. Using key classification metrics, experimental results obtained show that the proposed model can effectively detect DoS attacks in IoT networks as it performs better compared to other methods including models from related works.